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Transcription factors are crucial regulators of gene expression . Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function . High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors , yet algorithms for analyzing such data have not yet fully matured . Here , we present a general framework ( FeatureREDUCE ) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding . When training on protein binding microarray ( PBM ) data , we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision . We provide quantitative validation of our results by comparing to gold-standard data when available .
Transcription factors ( TFs ) play a central role in the regulation of gene expression . To be able to understand and predict the behavior of the gene regulatory circuitry in any given organism , we need to know the in vivo DNA binding preferences of the TFs that its genome encodes . In recent years , a number of high-throughput in vitro technologies have been introduced that can provide such information ( Berger and Bulyk , 2009; Warren et al . , 2006; Maerkl and Quake , 2007; Zhao et al . , 2009; Slattery et al . , 2011; Berger et al . , 2006 ) . However , while the volume of the data generated using these assays dwarfs that of more traditional measurements of protein-DNA interaction strength , the available computational methodology for analyzing them has not fully matured ( Weirauch , 2013 ) . The number of base pairs that constitute the DNA 'footprint' within which base identity can influence binding affinity depends strongly on the three-dimensional structure of the DNA-binding domain ( DBD ) of the TF . In theory , as long as thermodynamic equilibrium can be assumed , sequence specificity is completely defined by a table containing the ( relative ) affinity with which the DBD binds to each possible oligonucleotide within the footprint . This tabular approach has been widely used to analyze Protein Binding Microarray ( PBM ) data ( Berger and Bulyk , 2009 ) . It comes with significant challenges , however . First , the size of the oligomer table grows exponentially with footprint size , which in practice limits it to eight base pairs , shorter than the footprint of most TFs . Even for octamer tables , the number of affinity parameters to be estimated is on the same order as the number of PBM data points . This limits precision and necessitates the use of non-parametric methods ( as opposed to parameterized biophysical methods ) , resulting in an associated loss of quantitative information . A long-standing alternative has been to assume that each nucleotide position within the footprint contributes independently to the overall binding affinity . The most commonly used representation of sequence specificity that makes this independence assumption is the position weight matrix ( PWM ) ( Berg and von Hippel , 1987; Stormo and Fields , 1998; Stormo , 2000; Djordjevic et al . , 2003 ) , which defines position-specific base frequencies . Algorithms for inferring the PWM coefficients traditionally aim to maximize its information content relative to a random background model ( Lawrence et al . , 1993; Frech et al . , 1997; Bailey , 1995; Roth et al . , 1998 ) . In an alternative approach , sequence specificity is represented in terms of the relative affinity ( or , equivalently , the difference , △△G , in binding free energy ) associated with each possible point mutation of the optimal sequence ( Stormo and Yoshioka , 1991; Stormo et al . , 1993 ) , and summarized in the form of a position-specific affinity matrix ( PSAM ) ( Foat et al . , 2006; Bussemaker et al . , 2007 ) . The difference in philosophy between the PWM and PSAM representations also leads to a different approach to estimating their coefficients . It is no longer the information content ( i . e . the height of the letters in the standard sequence logo ) that is being optimized , but rather the ability of the PSAM to quantitatively explain the variation in a measurable quantity in terms of variation in the nucleotide sequence associated with each quantity ( for instance , the expression level of a gene in terms of its upstream promoter sequence ) . In the case of PBM data , the PSAM parameters are inferred by performing a nonlinear fit of a sequence-based model that predicts the signal intensity for each probe . The first implementations of this idea were the MatrixREDUCE ( Foat et al . , 2006; Foat et al . , 2005 ) and PREGO ( Tanay , 2006 ) algorithms; a more recent extension is BEEML-PBM ( Zhao and Stormo , 2011 ) . Whether dependencies between nucleotide positions can be accurately estimated from PBM data and used to refine models of binding specificity remains an open question ( Weirauch , 2013; Zhao and Stormo , 2011; Benos et al . , 2002 ) . Furthermore , while the existence of alternative binding modes is now widely recognized , accurate quantification of their relative usage has not yet been attempted . To address these needs , we developed our FeatureREDUCE software . It provides a flexible framework for building sequence-to-affinity models from PBM data ( Figure 1 ) . 10 . 7554/eLife . 06397 . 003Figure 1 . The FeatureREDUCE workflow for analyzing PBM intensities . ( 1 ) A robust method is used to estimate relative affinities for each K-mer of a given length . The K-mer with the highest affinity is chosen as the seed . ( 2 ) Using the seed as a reference , robust linear regression is used to estimate the relative affinity parameters in each column of the position-specific affinity matrix ( PSAM ) . ( 3 ) With the current affinity model , linear regression is used to estimate the positional bias profile across the probe . ( 4 ) An optional step uses nonlinear regression to solve for the free protein concentration . ( 5 ) Robust regression is used to estimate free energy contributions associated with higher-order sequence features such as dinucleotides . ( 6 ) Steps 2 through 5 are repeated until convergence . ( 7 ) The procedure results in a feature-specific affinity model ( FSAM ) that can be used to predict the relative affinity for any DNA sequence . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 003
FeatureREDUCE is based on an extensible biophysical model in which the binding free energy difference △△G ( Sref→S ) between an arbitrary nucleotide sequence S and a reference sequence Sref ( usually taken as the highest-affinity sequence ) is defined as a sum of parameters △△Gφ over all nucleotide sequence 'features' φ that distinguish S from Sref ( see Materials and methods ) . The full set of possible features in which S and Sref differ includes all possible single-base substitutions by default , but can be supplemented with dinucleotides that model dependencies and/or insertions at specific positions within the binding site that model variation in binding mode . Such a feature-based approach has been used previously ( Sharon et al . , 2008; Gordân et al . , 2013; Zhou et al . , 2015 ) , but , as we will argue below , our approach to estimating the coefficients of the model is different and optimal . The contribution of a binding site to the PBM intensity depends on its position within the probe , as was previously demonstrated by planting the same motif at different offsets ( Berger et al . , 2006 ) . This may preclude accurate model estimation unless it is dealt with explicitly . Moreover , whenever the TF binds near the free end of a probe , loss of contacts with the DNA backbone can reduce binding affinity . FeatureREDUCE captures such spatial bias by introducing an independent multiplicative correction factor for the ratio [TF]/Kd at each offset within the probe ( Figure 2a ) . These coefficients are estimated from the PBM intensities in parallel with the △△G parameters ( see Materials and methods ) . The positional bias profile inferred by FeatureREDUCE for the homo-dimer Cbf1p is shown in Figure 2b . It indicates that the magnitude of the contribution of an individual binding site to the PBM intensity can vary by an order of magnitude depending on its offset within the probe , and that there is preference for Cbf1p binding away from the substrate . For Pho4p , binding near the free end of the probe shows an opposite trend ( Figure 2—figure supplement 1 ) . The fraction of the variance ( R2 ) in PBM intensity explained by a 10-bp independent-nucleotide model increases dramatically , from 48% to 71% , after accounting for the positional bias . FeatureREDUCE can also detect any preference for monomeric TFs to bind in one of the two possible orientations on the dsDNA probes . For example , Zif268 exhibits a strong bias for binding to the negative strand over the positive strand ( Figure 2c ) . Indeed , it is known that Zif268 requires non-specific contacts with the DNA backbone on the 5'-end of the motif ( Berger et al . , 2006 ) . BEEML-PBM ( Zhao and Stormo , 2011 ) also models positional biases along the probes , but it does not account for orientation preference , or for overhang binding at the free end of the probe . 10 . 7554/eLife . 06397 . 004Figure 2 . Quantifying PBM-specific positional and orientational bias . ( a ) Accounting for biases related to the position of the binding site within the probe . The effective protein concentration is lower closer to the substrate , presumably due to steric hindrance . Furthermore , binding near the free end of probe is associated with loss of contacts with the DNA backbone . ( b ) Positional bias profile for the homo-dimeric bHLH transcription factor Cbf1p , as inferred by a model fit to the PBM intensities . ( c ) Idem , for the monomeric zinc finger transcription factor Zif268 . Figure 2—figure supplement 1 shows how positional bias can be used as an indicator of data quality . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 00410 . 7554/eLife . 06397 . 005Figure 2—figure supplement 1 . Using positional bias profiles as an indicator of data quality . ( a ) The positional bias profile for Cbf1p exhibits a strong preference for binding far from the substrate . The negative slope of the dashed red line is an indicator of high data quality . ( b ) The same plot for Pho4p . Here , the quality of the data is questionable . The sign of the slope from a linear regression over the range denoted by the solid red line is a useful data quality indicator . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 005 The readout of base identity at different nucleotide positions is only approximately independent . Indeed , various studies have analyzed whether representations of sequence specificity that account for nucleotide dependencies are more accurate than those that do not ( Sharon et al . , 2008; Agius et al . , 2010; Lee et al . , 2002; Stormo et al . , 1986; Zhou and Liu , 2004 ) . Controversy , however , remains about whether the additional parameters associated with such dependencies reflect structural mechanisms or technology-specific biases ( Weirauch , 2013 ) . In the biophysical model that underlies FeatureREDUCE , we model dependencies by simply including additional DNA sequence features φ that define base identity at two ( or more ) nucleotide positions , and estimating the corresponding free energy parameters △△Gφ along with those for single nucleotides ( see Materials and methods ) . The nucleotide dependencies discovered by FeatureREDUCE for Cbf1p are shown in Figure 3a . As expected for a model with additional parameters , accounting for dependencies significantly increased the fraction of the variance that could be explained when training on PBM intensities ( R2 improved from 71% to 96% ) . The real question , however , is how well the inferred model parameters perform on independent validation data . 10 . 7554/eLife . 06397 . 006Figure 3 . Robust estimation of dependencies between nucleotide positions . ( a ) Overview of the dependencies between pairs of neighboring nucleotides positions identified by FeatureREDUCE for homodimers of the basic helix-loop-helix ( bHLH ) factor Cbf1p . ( b ) Including dinucleotide dependencies in the sequence-to-affinity model , in combination with the use of robust regression , improves the ability to delineate Gene Ontology associations with Cbf1p targets predicted from the genome sequence . Figure 3—figure supplement 1 shows the crucial importance of using robust inference methods for estimating the binding free energy correction terms associated with dinucleotide features . Figure 3—figure supplement 2 shows the underlying cumulative distributions of yeast promoter affinities for 'sulfur compound metabolic process' , the GO category with the most statistically significant association with Cbf1p . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 00610 . 7554/eLife . 06397 . 007Figure 3—figure supplement 1 . The crucial importance of using robust inference methods for estimating the binding free energy correction terms associated with dinucleotide features . Shown is comparison between relative affinities inferred by FeatureREDUCE from PBM data for the transcription factor Cbf1p ( horizontal axis ) and the gold-standard measurement of the same affinity obtained using MITOMI ( vertical axis ) . When the independent-nucleotide model ( PSAM ) is augmented with nearest-neighbor dinucleotide dependencies ( FSAM ) , the agreement with the gold standard improves significantly , as indicated by the root-mean-square error ( RMSE ) and corresponding 100 , 000-iteration permutation p-value of 2 . 7e-5 . The lower plots show that using robust regression techniques is essential for capturing the effect of dinucleotide features . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 00710 . 7554/eLife . 06397 . 008Figure 3—figure supplement 2 . Cumulative distributions of yeast promoter affinities for Cbf1 using four different affinity models and the GO category with the highest association p-value ( 'sulfur compound metabolic process' ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 008 A unique aspect of FeatureREDUCE is the use of robust inference techniques ( Huber and Ronchetti , 2009 ) , which , as it turns out , is crucial for obtaining accurate estimates of the various contributions to the binding free energy . To demonstrate this , we compared our results to measurements of binding affinity for Cbf1p obtained using the orginal version of the MITOMI technology ( Maerkl and Quake , 2007 ) , which are in excellent ( R2 = 0 . 95 ) agreement with similar measurements obtained using surface plasmon resonance ( Berger et al . , 2006; Teh et al . , 2007 ) . We used these 'gold-standard' binding affinity measurements to assess the quality of the sequence-to-affinity models inferred from PBM data by FeatureREDUCE . When we fit a position-specific affinity matrix ( PSAM ) based model that ignores dependencies between nucleotides , the root-mean-square error ( RMSE ) between FeatureREDUCE model predictions and gold-standard MITOMI 1 . 0 measurements improved from 0 . 071 to 0 . 035 when standard least-squares fitting was replaced by robust iteratively re-weighted least-squares ( Figure 3—figure supplement 1 , left panels ) . When positional dependencies were added to the model ( feature-specific affinity model; FSAM ) , it actually performed worse than the model assuming independence between nucleotides ( position-specific affinity matrix; PSAM ) when we used standard least-squares fitting , indicative of over-fitting to noise in the training data . When we used robust regression , however , the RMSE for the FSAM-based model was significantly better than for the PSAM-based model ( Figure 3—figure supplement 1 , right panels ) . These results indicate that dependencies within the binding interface indeed exist , and that they can be modeled incorrectly when non-robust regression techniques are used . We also assessed the ability of our models to make predictions regarding in vivo TF function . First , we found that the inclusion of nucleotide dependencies when predicting aggregated yeast promoter affinities improves the ability to delineate Gene Ontology categories associated with regulation by Cbf1p ( see Materials and methods ) , but again only when robust inference techniques are used ( Figure 3B; see also Figure 3—figure supplement 2 ) . Second , to assess the extent to which we can quantitatively predict in vivo binding , we considered the degree of occupancy by Cbf1p at 955 potential genomic binding sites of type NNCACGTGNN ( E-box ) in yeast cells growing in rich media as measured by ChIP-seq ( Zhou and O'Shea , 2011 ) . Using a simple thermodynamic equilibrium model with a single free protein concentration parameter to account for binding saturation in the ChIP-seq experiment , we found that by this in vivo measure , FeatureREDUCE performs well ( RMSE = 0 . 075 ) , and significantly better than BEEML-PBM ( Zhao and Stormo , 2011 ) ( RMSE = 0 . 156 ) ; full data are shown in Figure 4 . 10 . 7554/eLife . 06397 . 009Figure 4 . ChIP-seq based validation of position-specific affinity matrix ( PSAM ) inferred for Cbf1p . ( a ) Direct comparison between relative affinities for 10-mers inferred from PBM intensities by FeatureREDUCE and relative in vivo occupancy at 955 genomic locations of type NNCACGTGNN ( E-box with flanks ) as measured by ChIP-seq ( Zhou and O'Shea , 2011 ) . Trimmed-mean ( trim = 10% ) ChIP-seq fold-enrichments were computed for all unique 10-mer sequences that occur at least three times in the genome . To account for saturation of higher-affinity binding sites , a basic equilibrium model ( green curve ) was fit with a single free-protein parameter . Red lines indicate the error between the observed and predicted relative ChIP enrichments . ( b ) The same plot for the BEEML-PBM algorithm ( Zhao and Stormo , 2011 ) . The same equilibrium model ( green curve ) was fit , but the optimal free protein concentration parameter was much lower than in ( a ) , so the saturation is not apparent in this case . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 009 The accuracy of FeatureREDUCE was demonstrated more broadly in a recent benchmark comparison between 26 different PBM data analysis algorithms ( Weirauch , 2013 ) . This study employed two distinct performance metrics . The in vitro metric quantified the accuracy with which probe intensities were predicted in a test PBM experiment using a model trained on independent training data from a PBM experiment for the same TF but with a different probe design; this was done for 66 different TFs from various structural families . The in vivo metric used a non-parametric score to quantify accuracy in ranking sequences in terms of their local ChIP-seq enrichment , for nine different TFs . FeatureREDUCE emerged as the top-performing algorithm according to both criteria . Weirauch et al . ( Weirauch , 2013 ) first assessed algorithm performance across a large dataset by using cross-validation between two different PBM designs . However , one of their surprising findings was that some algorithms generate models that perform well across different PBM designs but poorly when predicting in vivo binding , presumably due to over-fitting to PBM-specific biases . For example , the performance of one of the algorithms went from second-best in the PBM cross-validation test to worst in the ChIP-seq prediction test ( Weirauch , 2013 ) . Likewise , an extension of BEEML-PBM that adds dinucleotide parameters ( Zhao et al . , 2012 ) did not perform well on ChIP-seq data ( Weirauch , 2013 ) , presumably because it does not employ robust inference techniques . We conclude that FeatureREDUCE is currently the only algorithm that succeeds in parameterizing dependencies within the binding site . Another current debate in the field is whether or not PBM data can provide evidence of alternative DNA binding modes employed by the same TF ( Zhao and Stormo , 2011; Badis et al . , 2009; Morris et al . , 2011; Gordân et al . , 2011 ) . Oligomeric TF complexes can often bind DNA using different relative orientations of and/or spacing between their subunits ( Gordân et al . , 2011 ) . The modeling framework employed by FeatureREDUCE provides a natural opportunity to perform forward selection of multiple binding modes , represented by distinct PSAMs , which can subsequently be combined into a single predictive model and refined in an iterative manner ( see Materials and methods ) . We applied this procedure for the basic leucine zipper ( bZIP ) proteins Yap1p and Gcn4p . FeatureREDUCE accurately captures the known intrinsic differences in half-site preference of Yap1p ( which prefers TTAC , typical for the C/EBP subfamily ( Warren et al . , 2012 ) ; Figure 5a ) and Gcn4p ( which prefers TGAC , typical for the CREB subfamily; Figure 5b ) . It is well known that each protein , when binding DNA as a homo-dimer , can do so with or without a 1-bp overlap between the half-sites . What is unique about our approach is that the multi-PSAM model captures fold-differences in thermodynamic stability between the binding modes by estimating an overall relative affinity coefficient for the secondary PSAM . For instance , the binding affinity of Gcn4p for TGACGTCA is predicted to be 24% of that of TGASTCA ( cf . Figure 5b ) . This is in good agreement with recent high-throughput measurements of Gcn4 binding constants for all 12-mers using HiTS-FLIP ( Nutiu et al . , 2011 ) , specifically , Kd = 65 nM and 15 nM for the respective sequences . In addition , while the accuracy of the dominant TGASTCA motif essentially does not change , the accuracy of the weaker TGACGTCA motif increases significantly when using our multi-PSAM model compared to the single-fit model ( R2 improved from 69% to 80% for sites with relative affinity > 0 . 1 ) . We note that the results of our analysis can be used in a straightforward manner to predict which binding mode is dominant for a particular DNA sequence . 10 . 7554/eLife . 06397 . 010Figure 5 . Quantifying the differential usage of alternative binding modes . The transcription factors Yap1p ( a ) and Gcn4p ( b ) can each bind in two distinct modes , in which the two half-sites respectively do ( top ) and do not ( bottom ) overlap . Not only is the sequence of preferred half-site different between the two factors , the preferred binding mode is different too , as indicated by the relative association constant ( Ka ) inferred from the PBM data by FeatureREDUCE . DOI: http://dx . doi . org/10 . 7554/eLife . 06397 . 010 In conclusion , FeatureREDUCE analysis yields an accurate and interpretable biophysical affinity model that can provide detailed clues about the structural mechanisms that underlie protein-DNA recognition ( Roider et al . , 2007; Kinney et al . , 2007 ) , such as dependencies between nucleotide positions and the modulation of binding mode by variation in the underlying DNA sequence . Our algorithm allows one to make optimal use of the large volume of data that has been generated using microarray-based protein-nucleotide interaction profiling technology .
FeatureREDUCE builds upon the biophysical model for protein-DNA interaction on which Matrix-REDUCE ( Foat et al . , 2006 ) is based . A transcription factor P binds to DNA sequence S to form a TF-DNA complex PS , with forward and backward rate constants kon and koff , respectively: ( 1 ) P+S ⇄koffkon PS The affinity of P for S can be expressed in terms of an equilibrium association constant Ka ( S ) or dissociation constant Kd ( S ) : ( 2 ) Ka ( S ) =1Kd ( S ) =konkoff=[PS][P][S] The Gibbs free energy of binding per mole ( relative to a 1M reference concentration ) is given by ∆G = RT ln ( Kd /1M ) is , where R is the universal gas constant and T the absolute temperature . The fractional occupancy , N ( S ) , defined as the probability that S is bound by P , can be expressed as: ( 3 ) N ( S ) =[PS][PS]+[S]=[P][P]+Kd ( S ) =[P]Ka ( S ) [P]Ka ( S ) +1 If we assume a low-concentration regime where [P]≪Kd ( S ) where no saturation occurs , the expression for the occupancy simplifies to: ( 4 ) N ( S ) ≈ [P]Kd ( S ) =[P]Ka ( S ) Relative to a reference sequence Sref ( typically chosen to be the highest-affinity sequence ) , there will be multiplicative change in the affinity Ka , or , equivalently , an additive change ∆∆G in the free energy of binding , for any other sequence S: ( 5 ) Ka ( S ) Ka ( Sref ) = exp-∆∆G ( S ) RT where ( 6 ) ∆∆G ( S ) =∆G ( S ) -∆G ( Sref ) FeatureREDUCE models the relative binding free energy for sequence S as a sum of parameters associated with the DNA sequence features φ∈Φ ( S ) that characterize S: ( 7 ) ∆∆G ( S ) =∑φ∈Φ ( S ) ∆∆Gφ In this study , we considered both single-nucleotide features ( e . g . φ = A1 , denoting the presence of an A at position 1 within the binding site window ) and adjacent-dinucleotide ones ( e . g . , φ = C3G4 , denoting the presence of a CpG dinucleotide starting at position 3 ) . At a given position , exactly one of a set of 4 single-nucleotide features ( A , C , G , or T ) will be present in any particular sequence . We refer to such a set of mutually exclusive and jointly exhaustive features as a 'block' . Each sequence has exactly one feature from each block . A binding window of length L contains L mononucleotide blocks . There is a one-to-one correspondence between the ( exponentiated ) ∆∆Gφ/RT values in a mononucleotide block and a column in a position-specific affinity matrix ( PSAM ) . Together , the dinucleotide features constitute L − 1 dinucleotide blocks , each consisting of 16 features . Within each block , the 4 or 16 ∆∆Gφ values are only defined up to a common additive constant . The convention we use for mononucleotide blocks is that ∆∆Gφ = 0 for the feature that occurs in the reference sequence . For dinucleotide blocks , however , we use a different convention intended to minimize the number of ∆∆Gφ values that are significantly different from zero ( see below for details ) . The model on which MatrixREDUCE ( Foat et al . , 2006 ) was based assumes that the measured fluorescence intensity y ( S ) for probe S is given by a sum over all possible ways in which the TF can bind to the probe ( all possible offsets in either the forward or the reverse direction ) , which we will here refer to as partial 'views' Sv on the full probe sequence S: ( 8 ) y ( S ) = β0+β1∑ve−ΔΔG ( Sv ) /RT In FeatureREDUCE , to account for positional and directional biases in the extent to which binding affinity in a particular view contributes to the probe intensity , we introduce coefficients γν that are shared across all probes: ( 9 ) y ( S ) = β0+β1∑vγve−ΔΔG ( Sv ) /RT We do not necessarily assume a low TF concentration ( cf . Equation 4 ) . Moreover , we include a term ∆∆Gns that accounts for non-specific binding , which helps capture the DNA binding characteristics of the protein succinctly and has a positive effect on numerical convergence when estimating the model parameters . Together , this leads to the following model: ( 10 ) y ( S ) = β0+β1∑ν11+γve−ΔΔG ( Sv ) /RT+e−ΔΔGns/RT−1 FeatureREDUCE estimates the parameters in Equation 9 using iteratively reweighted least squares ( IRLS ) . This procedure down-weights data points that have high residuals compared to the fitted model . However , the weights depend on the residuals and the residuals depend on the weights . This dependency is broken by first choosing uniform initial weights , then iteratively refitting the model and re-calculating new residuals and weights , until convergence . IRLS prevents over-fitting and thereby allows for improved parameter estimation . FeatureREDUCE uses the 'rlm' function in the MASS package for R to perform the robust regression . We set the trimmed probes hyperparameter to 20% , as we previously found this value to be optimal during cross-validation on the DREAM5 dataset ( Weirauch , 2013 ) . Repeated rounds of parameter re-estimation that cycle over feature 'blocks' are performed until convergence , first for mononucleotide blocks ( resulting in a converged PSAM ) , and subsequently for dinucleotide blocks . Specifically , when estimating the free energy parameters for a given block B , the following model is fit: ( 11 ) y ( S ) = β0+∑φ∈Bβφ∑v∈Vφ ( S ) e- ( ∆∆G ( Sv ) -∆∆Gφ ) /RT Here Vφ ( S ) denotes the subset of views on S that contain feature φ . Note that only the βφ coefficients are treated as fit parameters here . The ∆∆Gφ values come from the previous iteration . However , they are re-estimated as: ( 12 ) ΔΔGφRT=−logβφβPSAM Here βPSAM stands for the value of β1 in Equation 12 when only mononucleotide features are fit . With this normalization , ∆∆Gφ is no longer equal to zero for dinucleotide features occurring in the reference sequence . However , most of the ∆∆Gφ values for dinucleotide features now tend to be close to zero , which is desirable . In each round , the spatial bias parameters γν are also re-estimated using robust regression . After iteration and convergence over multiple such rounds , FeatureREDUCE fits the additional parameters in the non-linear model in Equation 10 using the Levenberg-Marquardt nonlinear least-squares algorithm . Finding a good seed for the feature-based regression procedure is a crucial first step in our algorithm . To select the seed from the set of all oligomers of length K , we developed a dedicated robust iterative algorithm based on trimmed means designed to deal with the sparseness of the design matrix . We fit probe intensities as a weighted sum of the number of occurrences of each of the 4K oligomers: ( 13 ) y ( S ) =∑mβmXm ( S ) Here Xm ( S ) denotes the number of occurrences of oligomer m in sequence S . Regression coefficients βm were initialized to a small value ( 10−4 ) representative of non-specific binding . Next , for each individual probe S , we determined the coefficient value β’m ( S ) that exactly predicts the intensity: ( 14 ) βm' ( S ) = 1Xmy ( S ) −∑m'≠m βm'Xm' ( S ) We then computed the trimmed mean βm' ( removing the top and bottom 15% values ) of all β’m ( S ) values across all probes for which Xm > 0 . Finally , we updated the coefficient according to: ( 15 ) βm→ ( 1−α ) βm+αβm' using a step size α = 0 . 1 . This was done for all oligomers in parallel . After iteration and convergence , the oligomer with the highest regression coefficient value was chosen as the seed . This step in the algorithm starts by separately fitting and then comparing positive and negative strand PSAMs . If these are similar according to the L1-norm ( within a small tolerance ) then the motif is flagged as symmetric . The PSAM is then rebuilt using the highest-affinity symmetric seed . The symmetric version of the binding motif tends to be more accurate while using half the number of parameters . We determine the length of the binding site by adding columns to either side of the PSAM until the coefficient of determination ( R2 ) decreases . An increase indicates that there are direct or indirect protein-DNA contacts being made at the additional positions , while a decrease indicates that we have increased the length of the motif past the effective range of specificity and inadvertently excluded valid binding sites at the end of the PBM probe . Following ( Ward and Bussemaker , 2008 ) , we first predict the total affinity of each gene’s upstream region , as a sum over a sliding window of binding affinities computed using our model . Next , all genes were ranked by this total promoter affinity and the Wilcoxon-Mann-Whitney rank-sum test was used to score association with each Gene Ontology category ( Gene Ontology and Consortium , 2015 ) . P-values were corrected for multiple testing using a Bonferroni correction based on the total number of GO categories tested in parallel . To infer multiple-binding models for a single TF , the following procedure was used: ( i ) Fit a single-binding-model using the standard algorithm . ( ii ) Fit additional binding-mode model ( s ) to the residuals of the previous model . ( iii ) Iteratively update each binding mode as a weighted mean between a newly fit model and the model from the previous iteration , until convergence . ( iv ) Perform a final multiple regression to determine the relative preference for ( and statistical significance of ) each binding mode . Stretches of four or more guanines affect the efficiency of PBM probe synthesis and were replaced by their reverse complement in some PBM designs ( Berger et al . , 2008 ) . Therefore , if the highest-affinity motif contains four or more consecutive cytosines , FeatureREDUCE uses only the positive strand to generate the biophysical model; conversely , if the highest-affinity motif contains a poly-G stretch , only the negative strand is used . http://bussemakerlab . org/software/FeatureREDUCE/ | Transcription is the process by which the information contained within DNA is copied to a short-lived molecule called RNA so that it can be transported to other areas of the cell for various purposes . Transcription factors are key components in this process . These proteins recognise and gather at specific sequences of DNA near genes , and then assist the enzymes that copy the information in the gene into a molecule of RNA . This means that transcription factors essentially control which genes are expressed , and when and where these genes are expressed . Recent technological advances have made it possible to identify where transcription factors can bind within DNA sequences . Yet , while a lot of data has been generated in this area , the computational tools needed to make sense of it have not kept pace . Now , Riley et al . have developed software called FeatureREDUCE that will allow researchers to build computer predictions of how strongly a transcription factor will interact with specific short sections of DNA sequence . The software can be applied to experimental data collected in so-called ‘protein binding microarray’ experiments . FeatureREDUCE can also be used to investigate questions in the field of transcription factor research that had previously remained unanswered . First , to what level of detail can data obtained from recent technological advances be understood ? Second , can transcription factors bind to DNA in more than one way , and can data from protein binding microarrays be used to uncover this ? Riley et al . show that FeatureREDUCE can produce accurate and interpretable clues about the biology behind how transcription factors recognize DNA sequences . This includes how mutations as small as a change to single DNA letter can affect recognition . The next step will be to use the software to make sense of the existing volume of experimental data regarding protein-DNA interactions and data that will be generated in future experiments . | [
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] | 2015 | Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE |
The lateral amygdala ( LA ) acquires differential coding of predictive and non-predictive fear stimuli that is critical for proper fear memory assignment . The neurotransmitter dopamine is an important modulator of LA activity and facilitates fear memory formation , but whether dopamine neurons aid in the establishment of discriminative fear coding by the LA is unknown . NMDA-type glutamate receptors in dopamine neurons are critical for the prevention of generalized fear following an aversive experience , suggesting a potential link between a cell autonomous function of NMDAR in dopamine neurons and fear coding by the LA . Here , we utilized mice with a selective genetic inactivation functional NMDARs in dopamine neurons ( DAT-NR1 KO mice ) combined with behavior , in vivo electrophysiology , and ex vivo electrophysiology in LA neurons to demonstrate that plasticity underlying differential fear coding in the LA is regulated by NMDAR signaling in dopamine neurons and alterations in this plasticity is associated non-discriminative cued-fear responses .
Across taxa the amygdala is a central locus for fear processing ( Weiskrantz , 1956; Goldstein , 1965; Slotnick , 1973; Adolphs et al . , 1995 ) . Comprised of several interconnected subdivisions that are populated by different types of neurons , the prevailing view is that substructures within the amygdala have specific roles for the acquisition , expression , and extinction of fear-related memories ( LeDoux , 2000; Maren and Quirk , 2004; Ehrlich et al . , 2009 ) . Within the amygdala the LA is a major site of convergence for information that arrives from cortical and thalamic nuclei and represents an early processing point for emotionally salient information ( LeDoux , 2000 ) . Consistent with an early role of the LA in fear memory formation , inactivation of this area prior to , but not after , fear conditioning prevents cued fear behavior ( LeDoux et al . , 1990; Wilensky et al . , 1999 ) . Optogenetic activation ( Johansen et al . , 2010 ) or suppression ( Johansen et al . , 2014 ) of principal neurons in the LA facilitates and impairs cued fear memory formation , respectively . Direct stimulation of principal neuron cell bodies in the basolateral amygdala ( BLA ) can also increase anxiety-like behavior ( Tye et al . , 2011 ) , thus activation of the LA/BLA is a key neural substrate of fear and anxiety . In addition to principal projection neurons , distinct interneuron populations within the BLA can also potently regulate fear memory formation and fear coding ( Wolff et al . , 2014 ) , indicating that both inhibitory and excitatory synaptic transmission within this region regulates fear-related behavior and learning . Fear generalization has been proposed to occur through a failure in an animal's ability to define specific outcome contingencies ( Grillon , 2002; Lovibond and Shanks , 2002 ) . Thus , aberrant fear coding in the LA may be an early site of generalized fear manifestation . Consistent with this hypothesis , exposure to fear-inducing stimuli has been demonstrated to increase activity in the amygdala of human subjects ( Breiter et al . , 1996; Morris et al . , 1996; Rauch et al . , 2000 ) , and hyper-amygdala activation is observed in numerous disorders , including post-traumatic stress disorder ( Rauch et al . , 2000 ) . In rodents , increasing the intensity of an unconditioned fear stimulus increases fear generalization that correlates with enhanced activation of LA neurons to both conditioned ( CS+ ) and non-conditioned ( CS− ) stimuli ( Ghosh and Chattarji , 2015 ) . In addition , suppression of GABAB-mediated signaling in the LA facilitates non-associative long-term potentiation ( LTP ) of excitatory synapses that correlates with generalized fear responses ( Shaban et al . , 2006 ) . Collectively , these data suggest that aberrant plasticity in the LA that facilitates non-selective potentiation of excitatory drive or suppression of inhibitory tone is an important neural substrate of generalized fear . LA neurons demonstrate short latency responses to auditory , visual , and somatosensory stimuli ( Ben-Ari and Le Gal la Salle , 1971 ) . Following fear conditioning the latency of responses to predictive auditory stimuli decreases ( Quirk et al . , 1995; Maren , 2000 ) and the response amplitude increases selectively to predictive , but not non-predictive , stimuli ( Collins and Pare , 2000 ) , thus demonstrating acquired selectivity in responding to CS+ and CS− stimuli . Acquisition of differential coding of predictive and non-predictive fear-related information is correlated with studies investigating changes in synaptic strength in the LA after fear conditioning . Auditory fear conditioning elicits LTP-like effects in LA neurons ( Rogan and LeDoux , 1995; Rogan et al . , 1997 ) and elicits presynaptic enhancement of inputs arriving in the LA from the medial geniculate nucleus of the thalamus and cortex ( McKernan and Shinnick-Gallagher , 1997; Tsvetkov et al . , 2002; Zinebi et al . , 2002 ) . Dopamine is a potent modulator of plasticity in the LA . In the anesthetized rat , odor-evoked potentials in the LA are potentiated following pairing with a footshock that is dependent on dopamine receptor activation ( Rosenkranz and Grace , 2002 ) . In addition , dopamine signaling modulates local inhibitory networks in the LA ( Loretan et al . , 2004 ) , facilitates LTP induction through suppression of feedforward inhibition ( Bissiere et al . , 2003 ) , and regulates intrinsic excitability of principal neurons ( Yamamoto et al . , 2007 ) . Consistent with the modulation of fear-evoked plasticity in the LA , suppression of dopamine signaling attenuates acquisition of conditioned fear memory ( Borowski and Kokkinidis , 1996; Lamont and Kokkinidis , 1998; Guarraci et al . , 1999 , 2000; Greba et al . , 2001 ) and fear memory expression ( Nader and LeDoux , 1999 ) . Subsets of dopamine neurons are activated by aversive stimuli ( Schultz and Romo , 1987; Guarraci and Kapp , 1999; Brischoux et al . , 2009 ) and undergo plasticity following an aversive experience ( Lammel et al . , 2011 ) or fear conditioning ( Guarraci and Kapp , 1999; Brischoux et al . , 2009; Gore et al . , 2014 ) . NMDAR signaling in dopamine neurons regulates both synaptic plasticity ( Bonci and Malenka , 1999 ) and phasic activation of these cells ( Overton and Clark , 1997 ) , and disruption of NMDARs in dopamine neurons of mice results in the development of behavioral correlates of generalized fear and anxiety following an aversive experience ( Zweifel et al . , 2011 ) . These results suggest a potential link between cell autonomous functions of this receptor in dopamine neurons and the disruption of fear coding in the amygdala that may be an underlying cause of fear generalization . Here we demonstrate that mice lacking NMDARs in dopamine neurons have deficits in cued fear discrimination and that this deficit is associated with altered fear coding in the LA . Contrary to our initial hypothesis , fear generalization in DAT-NR1 KO mice was not associated with a non-specific increased activation of LA neurons to a non-US predictive conditioned stimulus ( CS− ) , but rather a lack of enhanced LA activation to a US predictive conditioned stimulus ( CS+ ) . These findings demonstrate that erroneous enhancement of LA activity alone is not necessary for fear generalization , but rather cued fear discrimination coding in general is an essential component for proper fear assignment .
We have shown previously that DAT-NR1 KO mice develop non-selective enhancement of acoustic startle reflex and increased anxiety-like behavior following an aversive experience ( Zweifel et al . , 2011 ) . To determine whether DAT-NR1 KO mice fail to discriminate between fear predictive and non-predictive stimuli we assayed their performance in a delayed Pavlovian cued fear discrimination task ( Figure 1A ) . Mice were presented with 10 trials each of a 10 s light ( constant illumination ) cue ( CS+ ) that co-terminated with delivery of an unconditioned ( US ) fear stimulus ( 0 . 3 mA , 0 . 5 s footshock ) , randomly interspersed with delivery of a distinct light ( pulsing illumination ) cue ( CS− ) that did not co-terminate with delivery of the US . 24 hours following conditioning mice were given interspersed presentations of the CS+ and CS− in a novel context . Both DAT-NR1 KO mice and littermate controls displayed enhanced fear ( freezing behavior ) following both one and two days of conditioning . Control mice demonstrated cue discrimination on both days , a behavior that was significantly impaired in DAT-NR1 KO mice ( Figure 1B ) . 10 . 7554/eLife . 08969 . 003Figure 1 . Impaired fear discrimination and c-Fos activation in the LA of DAT-NR1 KO mice . ( A ) Fear conditioning paradigm . Mice were probed for freezing in context B ( top right ) to the CS+ and CS− with three presentations of each stimulus ( middle ) prior to fear conditioning ( Pre ) and 24 hr after each conditioning session ( Test 1 and Test 2 ) . Mice were conditioned in context A ( top left ) with 10 presentations of a CS− or CS + co-terminating with US delivery ( bottom ) . ( B ) Freezing behavior ( % Time Immobile ) during presentation of the CS+ and CS− during pre-conditioning and Test 1 and Test 2 . ( C ) Brain atlas image ( left ) ( Paxinos and Franklin , 2013 ) illustrating LA subdivisions ( gray shading ) analyzed for c-Fos induction following fear conditioning . Representative c-Fos immunoreactive cells in the LA of control ( control , left ) and DAT-NR1KO ( KO , right ) mice following a single fear conditioning session . Scale bar = 250 μm ( D ) Average c-Fos positive cells in the LA of Ctrl and KO mice following fear conditioning ( n = 3 mice each group , 8 sections/mouse ) . p < 0 . 01 , unpaired Student's T-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 003 The LA is known to participate in cued-fear processing ( LeDoux , 2000 ) and altered fear coding in this region correlates with generalized fear responses ( Ghosh and Chattarji , 2015 ) . Fos expression in the amygdala has been previously shown to be induced following unconditioned footshock and is proposed as an early marker of plasticity ( Campeau et al . , 1991 ) . To determine whether activity-dependent processes in the LA are altered in DAT-NR1 KO mice , we analyzed the induction of the immediately early gene Fos 90 min following one conditioning session in context A ( Figure 1A ) . Fos expression was significantly reduced in DAT-NR1 KO mice relative to controls ( Figure 1C , D ) indicating a potential reduced activation of the LA . To determine whether an aversive US results in a general increase in LA activation in a manner that is dependent on NMDAR signaling in dopamine neurons , we recorded the activity of amygdala neurons during three days of fear conditioning following bilateral implantation of tetrodes into the LA of DAT-NR1 KO and control mice ( an independent cohort from those used for behavioral analysis ) . The majority of tetrodes were localized to the ventral lateral and ventral medial subdivisions of the LA ( Figure 2A ) . Average waveforms of isolated neurons were similar between groups and across days ( Figure 2B ) ; similarly , average baseline firing rate and the range of firing rates did not differ between groups or across days of conditioning ( Figure 2C ) . 10 . 7554/eLife . 08969 . 004Figure 2 . Population activity in the LA is not enhanced in DAT-NR1 KO mice following footshock . ( A ) Brain atlas image ( Paxinos and Franklin , 2013 ) illustrating bilateral tetrode implantation ( top ) and location of recording electrodes in Ctrl and KO mice . ( B ) Average waveform of recorded units in Ctrl and KO mice across days of conditioning . ( C ) Baseline firing rate of individual units in Ctrl and KO mice across days of conditioning ( Control: n = 55 , 52 , and 54 Days 1–3 , respectively; DAT-NR1 KO: n = 48 , 57 , and 58 Days 1–3 , respectively ) . ( D ) Heat plot of normalized activity in concatenated CS + trials from Ctrl ( top ) and KO ( bottom ) mice . ( E ) Percent change from baseline activity during CS + trials following presentation of the US across days of conditioning . ( F ) Heat plot of normalized activity in concatenated CS− trials from Ctrl ( top ) and KO ( bottom ) mice . ( G ) Percent change from baseline activity during CS− trials following presentation of the US across days of conditioning . ( E , G ) Data are presented as the mean ± S . E . M . Repeated measures ANOVA , p < 0 . 001 and p < 0 . 05 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 00410 . 7554/eLife . 08969 . 005Figure 2—figure supplement 1 . Population activity during fear conditioning . ( A ) Histogram of firing rate of an example neuron on day one of fear conditioning illustrating increased activity following presentation of the US . Arrow indicates onset of first footshock . ( B ) Proportion of activated neurons is highest in control mice on day 1 and decreases with subsequent conditioning . p < 0 . 001 , Chi-square . ( C , D ) Heat plots of normalized firing rate for individual cells in control and DAT-NR1 KO mice during CS+/US ( C ) and CS− ( D ) presentation and subsequent ITI . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 005 In a number of control neurons we detected an increase in overall firing rate of the cell following presentation of the first US ( Figure 2—figure supplement 1 ) , with the highest proportion of activated neurons observed in control mice on the first day of conditioning ( Figure 2—figure supplement 1 ) . To determine whether increased activity occurs more prominently following CS + trials , or represents a general increase , we concatenated all CS + trials with subsequent inter-trial intervals ( ITIs; Figure 2D ) and CS− trials with subsequent ITIs ( Figure 2F ) and compared them to baseline activity . In control mice , population activity during both CS+ and CS− trials and their subsequent ITIs was significantly enhanced relative to baseline on the first day of conditioning , but this elevated activity diminished across conditioning days ( Figure 2D–G ) . This change in activation was reflected in the proportion of cells activated across days ( Figure 2—figure supplement 1 ) . We did not detect a significant increase in population activity in DAT-NR1 KO mice ( Figure 2D–G ) . Visual inspection of our population data revealed discrete phasic events time-locked to the presentation of the US; these results are consistent with previous reports of LA neurons responding to footshock ( Ben-Ari and Le Gal la Salle , 1971; Romanski et al . , 1993 ) . To determine whether these phasic events undergo plasticity across days of conditioning that is dependent on NMDAR signaling in dopamine neurons , we analyzed neurons with US responsiveness . Action potential waveforms , and baseline firing rates of neurons activated by the US did not differ between genotypes ( Figure 3A , B ) and the proportion of cells responding did not differ between groups or across days ( Figure 3C ) . In control mice we found a transient potentiation of the US response , with day 2 responses significantly higher than day 1 ( Figure 3D–F ) . This effect was not present on the third day of conditioning . Moreover , we did not detect this effect in DAT-NR1 KO mice ( Figure 3D–F ) . The response to the US on the second day was significantly higher in controls than in DAT-NR1 KO mice ( Figure 3H ) , but did not differ on the other days ( Figure 3G , I ) . 10 . 7554/eLife . 08969 . 006Figure 3 . Transient plasticity in US-activated LA neurons is absent in DAT-NR1 KO mice . ( A ) Average waveform of recorded units in Ctrl and KO mice that were activated by the US . ( B ) Baseline firing rate of individual units in Ctrl and KO mice that were activated by the US ( Control: n = 32 , 26 , and 28 Days 1–3 , respectively; DAT-NR1 KO: n = 25 , 34 , and 35 Days 1–3 , respectively ) . ( C ) Proportion of neurons from Ctrl and KO mice that were activated or inhibited by the US . ( D ) Average normalized firing rate of US activated neurons in Ctrl mice across days of conditioning . ( E ) Average normalized firing rate of US-activated neurons in KO mice across days of conditioning . ( F ) Average area under the curve ( AUC ) of activated response for Ctrl and KO mice across days . ( G–I ) Comparison of activated responses of Ctrl and KO mice during day 1 ( G ) , day 2 ( H ) , and day 3 of conditioning ( I ) . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , p < 0 . 001 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 00610 . 7554/eLife . 08969 . 007Figure 3—figure supplement 1 . US-inhibited LA neurons do not change across days of conditioning . ( A ) Average normalized firing rate of US-inhibited neurons in control mice across days of conditioning ( Control: n = 8 , 16 , and 14 Days 1–3 , respectively; DAT-NR1 KO: n = 8 , 8 , and 9 Days 1–3 , respectively ) . ( B ) Average normalized firing rate of US- inhibited neurons in DAT-NR1 KO mice across days of conditioning . ( C ) Average area under the curve ( AUC ) of inhibited response for control and DAT-NR1 KO mice across days . ( D–F ) Comparison of inhibited responses of control and DAT-NR1 KO mice during day 1 ( D ) , day 2 ( E ) , and day 3 of conditioning ( F ) . Data are presented as the mean ± S . E . M . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 007 While the majority of responsive cells increased their firing rates during the US , a small number of neurons showed decreased activity relative to baseline during US presentation ( Figure 3C and Figure 3—figure supplement 1 ) , similar to previous reports ( Ben-Ari and Le Gal la Salle , 1971 ) . Neurons displaying transient inhibition to the US showed little change across days of conditioning and were not significantly different between genotypes ( Figure 3—figure supplement 1 ) . Previous studies have shown that LA neurons undergo plasticity in CS−evoked responses , demonstrating increased response magnitude ( Quirk et al . , 1995 , 1997; Maren , 2000 ) and enhanced discrimination between CS+ and CS− stimuli ( Collins and Pare , 2000; Ghosh and Chattarji , 2015 ) . Neurons were detected in both control and DAT-NR1 KO mice that responded to the CS+ and CS− stimuli . Average waveforms , firing rate , and the proportion of neurons responding to these stimuli did not differ between groups ( Figure 4A–C ) . In control mice we observed a progressive enhancement of the CS + response in neurons activated by the stimulus across days ( Figure 4—figure supplement 1 ) . In contrast , DAT-NR1 KO mice did not show enhancement of CS + responses ( Figure 4—figure supplement 1 ) . Neither control nor DAT-NR1 KO mice showed changes in CS− responses ( Figure 4—figure supplement 1 ) . Enhanced responding to CS + across days of conditioning in control mice was not due to repeated presentations of the cue , as this effect was not observed in control mice that received three consecutive days of cue ( CS+ and CS− ) presentation delivered without footshock ( unpaired , Figure 4—figure supplement 1 ) . Increased CS + responses in control mice across days of conditioning resulted in significant cue discrimination that was absent in DAT-NR1 KO mice ( Figure 4D , E ) . This change was not associated with differences in the proportion of neurons responding to both CS+ and CS− stimuli ( Control: Day 1 , 84%; Day 2 , 78%; Day 3 , 87% vs DAT-NR1 KO: Day 1 , 88%; Day 2 , 80%; Day 3 , 80% ) . To determine whether discriminative fear coding emerges early in conditioning , we analyzed the responses of neurons on day 1 of conditioning during the first and last cue presentation . Although there was a trend towards discrimination in both control and DAT-NR1 KO mice by the last conditioning trial this effect did not reach statistical significance ( Figure 4—figure supplement 2 ) . These findings indicate that early cue discrimination may occur in both groups of mice , but is only maintained in control mice . 10 . 7554/eLife . 08969 . 008Figure 4 . Plasticity in CS activated LA neurons is absent in DAT-NR1 KO mice . ( A ) Average waveform of recorded units in Ctrl and KO mice that were activated by the CS+ and CS− . ( B ) Baseline firing rate of individual units in Ctrl and KO mice that were activated by the CS+ and CS− ( Control: n = 21 , 22 , and 19 Days 1–3 , respectively; DAT-NR1 KO: n = 14 , 23 , and 23 Days 1–3 , respectively ) . ( C ) Proportion of neurons from Ctrl and KO mice that were activated by the CS+ and CS− . ( D ) Average normalized firing rate of CS+ and CS− activated neurons in Ctrl and KO mice on day 1 of conditioning . ( E ) Average normalized firing rate of CS+ and CS− activated neurons in Ctrl and KO mice on day 3 of conditioning . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , p < 0 . 001 and p < 0 . 01 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 00810 . 7554/eLife . 08969 . 009Figure 4—figure supplement 1 . Plasticity in CS activated LA neurons is absent in DAT-NR1 KO mice . ( A ) Average normalized firing rate of CS + activated neurons in control mice across days of conditioning . ( B ) Average normalized firing rate of CS + activated neurons in in DAT-NR1 KO mice across days of conditioning . ( C ) Average normalized firing rate of CS− activated neurons in control mice across days of conditioning . ( D ) Average normalized firing rate of CS− activated neurons in in DAT-NR1 KO mice across days of conditioning . ( E ) Average normalized firing rate of activated neurons during presentation of unpaired CS+ in control mice across days of conditioning . ( F ) Average normalized firing rate of activated neurons during presentation of unpaired CS− in control mice across days of conditioning . ( G ) Average normalized firing rate of CS+ and CS− activated neurons in unpaired Ctrl mice on day 1 of conditioning . ( H ) Average normalized firing rate of CS+ and CS− activated neurons in unpaired Ctrl mice on day 3 of conditioning . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , ****p < 0 . 0001 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 00910 . 7554/eLife . 08969 . 010Figure 4—figure supplement 2 . CS activated LA neurons are not different at the start of conditioning . ( A ) Average normalized firing rate of CS+ and CS− activated neurons in Ctrl and KO mice during trial 1 of the first day of conditioning . ( B ) Average normalized firing rate of CS+ and CS− activated neurons in Ctrl and KO mice during trial 10 of the first day of conditioning . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 01010 . 7554/eLife . 08969 . 011Figure 4—figure supplement 3 . Differential response latencies in CS activated neurons between control and DAT-NR1 KO mice . A ) Average cumulative distribution of latency to increase activity in response to CS + presentation in control mice does not change across days . ( B ) Average cumulative distribution of latency to increase activity in response to CS + presentation in DAT-NR1 KO mice decrease across days . ( C ) Average cumulative distribution of latency to increase activity in response to CS− presentation in control mice decrease across days . ( D ) Average cumulative distribution of latency to increase activity in response to CS− presentation in DAT-NR1 KO mice decrease across days . ( E ) Average cumulative distribution of latency to increase activity in response to CS + presentation in control vs DAT-NR1 KO mice on day 1 . ( F ) Average cumulative distribution of latency to increase activity in response to CS + presentation in control vs DAT-NR1 KO mice on day 3 . ( G ) Average cumulative distribution of latency to increase activity in response to CS− presentation in control vs DAT-NR1 KO mice on day 1 . ( H ) Average cumulative distribution of latency to increase activity in response to CS− presentation in control vs DAT-NR1 KO mice on day 3 . Average normalized firing rate of CS+ and CS− activated neurons in unpaired Ctrl mice on day 3 of conditioning . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , *p < 0 . 05 , ****p < 0 . 0001 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 011 Following auditory fear conditioning the latency of responses to a conditioned stimulus decreases in the LA ( Quirk et al . , 1995; Maren , 2000 ) . To determine whether the latency of responding to a visually paired stimulus also decreases with fear conditioning we assessed cumulative frequency distributions in the latency to fire across days . We did not observe a significant change in frequency distribution of CS + responses in control mice across days of conditioning ( Figure 4—figure supplement 3 ) . In contrast , we did observe a significant difference in the distribution in DAT-NR1 KO mice , with increasing response latencies on the third day of conditioning ( Figure 4—figure supplement 3 ) . Intriguingly , we observed a similar increase in latency across days of conditioning in response to the CS− in both control and DAT-NR1 KO mice ( Figure 4—figure supplement 3 ) . In addition to neurons activated by the CS+ and CS− we also observed a number of neurons that displayed transient inhibitions in response to these stimuli . Average waveforms , firing rate , and the proportion of neurons inhibited by these stimuli were similar across groups ( Figure 5A–C ) . Repeated days of conditioning did not alter inhibitory responses to the CS+ in either group ( Figure 5—figure supplement 1 ) . However , we did observe a progressive decrease in the magnitude of the inhibitory response to the CS− in control mice , with the largest inhibition observed on day 1 and significant reductions in this inhibition on subsequent days ( Figure 5—figure supplement 1 ) . We did not observe this plasticity in DAT-NR1 KO mice ( Figure 5—figure supplement 1 ) . Similar to the acquisition of cue discrimination in neurons activated by the CS+ and CS− , control , but not DAT-NR1 KO mice , acquired cue discrimination in neurons inhibited by these stimuli ( Figure 5D , E ) . This change was also not associated with differences in the proportion of neurons responding to both CS+ and CS− stimuli ( Control: Day 1 , 81%; Day 2 , 79%; Day 3 , 80% vs DAT-NR1 KO: Day 1 , 84%; Day 2 , 87%; Day 3 , 77% ) . Similar to neurons activated by the CS+ and CS− , neurons inhibited by these stimuli did not differ during the first or last presentation on the first day of conditioning ( Figure 5—figure supplement 1 ) . 10 . 7554/eLife . 08969 . 012Figure 5 . Plasticity in CS inhibited LA neurons is absent in DAT-NR1 KO mice . ( A ) Average waveform of recorded units in Ctrl and KO mice that were inhibited by the CS+ and CS− . ( B ) Baseline firing rate of individual units in Ctrl and KO mice that were inhibited by the CS+ and CS− ( Control: n = 21 , 15 , and 22 Days 1–3 , respectively; DAT-NR1 KO: n = 13 , 20 , and 18 Days 1–3 , respectively ) . ( C ) Proportion of neurons from Ctrl and KO mice that were inhibited by the CS+ and CS− . ( D ) Average normalized firing rate of CS+ and CS− inhibited neurons in Ctrl and KO mice on day 1 of conditioning . ( E ) Average normalized firing rate of CS+ and CS− inhibited neurons in Ctrl and KO mice on day 3 of conditioning . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , p < 0 . 05 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 01210 . 7554/eLife . 08969 . 013Figure 5—figure supplement 1 . Plasticity in CS inhibited LA neurons is absent in DAT-NR1 KO mice . ( A ) Average normalized firing rate of CS + inhibited neurons in control mice across days of conditioning . ( B ) Average normalized firing rate of CS + inhibited neurons in in DAT-NR1 KO mice across days of conditioning . ( C ) Average normalized firing rate of CS− inhibited neurons in control mice across days of conditioning . ( D ) Average normalized firing rate of CS− inhibited neurons in in DAT-NR1 KO mice across days of conditioning . ( E ) Average normalized firing rate of CS+ and CS− inhibited neurons in Ctrl and KO mice during trial 1 of the first day of conditioning . ( F ) Average normalized firing rate of CS+ and CS− inhibited neurons in Ctrl and KO mice during trial 10 of the first day of conditioning . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , **p < 0 . 01 and , ***p < 0 . 001 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 013 Synaptic plasticity in the LA following fear conditioning has been previously reported , indicating presynaptic enhancement of excitatory synapses from both thalamic and cortical inputs ( McKernan and Shinnick-Gallagher , 1997; Tsvetkov et al . , 2002; Zinebi et al . , 2002 ) . Dopamine receptor signaling has also been demonstrated to influence plasticity in both excitatory and inhibitory pathways in the LA ( Bissiere et al . , 2003; Loretan et al . , 2004 ) . Our in vivo recordings indicate complex , bidirectional changes in LA cellular activity following multiple days of fear conditioning , including enhanced excitation to the CS+ and reduced inhibition to the CS− in control mice . To determine whether fear generalization associated with disruption of dopamine neuron physiology alters conditioning-evoked plasticity in the LA , we performed whole-cell patch clamp recordings of principal neurons in the LA in acute slices from naïve mice and from mice 24 hr following the second conditioning session . To test whether fear conditioning elicits pathway-specific changes in the balance of excitatory and inhibitory inputs we measured evoked post-synaptic potentials ( PSPs ) . A stimulating electrode placed in the internal capsule ( IC , thalamic ) , or the external capsule ( EC , cortical; Figure 6A ) reliably evoked a compound PSP in LA neurons ( Figure 6B ) . The excitatory PSP ( EPSP ) was isolated using bath application of the GABAA receptor antagonist picrotoxin ( 100 µM ) , and the inhibitory PSP ( IPSP ) was determined through digital subtraction of the EPSP from the compound PSP ( Figure 6B ) . We next calculated the ratio of EPSP:IPSP amplitudes , allowing for the normalized assessment of select changes in either excitatory or inhibitory input . When we compared the ratio of EPSP to IPSP amplitudes in both pathways in both naïve and fear conditioned control and DAT-NR1 KO mice , we saw no significant changes in the balance of excitatory to inhibitory inputs in any group in either the thalamic or cortical pathway ( Figure 6C , D ) indicating a lack of selective change in either inhibitory or excitatory inputs . 10 . 7554/eLife . 08969 . 014Figure 6 . Synaptic plasticity in LA neurons is impaired in DAT-NR1 KO mice . ( A ) Brain atlas image52 illustrating placement of stimulating electrodes in the IC and EC and recording electrode in the LA . ( B ) Representative compound PSP following thalamic stimulation ( left ) and isolated EPSP and IPSP from Ctrl ( middle ) and KO ( right ) mice . ( C , D ) Excitation/inhibition ratios of EPSP/IPSPs of individual neurons from Ctrl and KO mice following cortical ( C , Control: n = 7 naïve , n = 8 shock; DAT-NR1: KO n = 9 naïve , n = 11 shock ) and thalamic ( D , Control: n = 6 naïve , n = 10 shock; DAT-NR1: KO n = 11 naïve , n = 7 shock ) stimulations . ( E , F ) Representative mEPSCs ( E ) and mIPSCs ( F ) from naïve ( black ) and fear conditioned ( gray ) Ctrl and KO mice . ( G , H ) Cumulative distribution of mEPSC frequency from naïve and fear conditioned control ( G , n = 18 naïve , n = 14 shock ) and KO mice ( H , n = 14 naïve , n = 13 shock ) . ( I , J ) Cumulative distribution of mIPSC frequency from naïve and fear conditioned control ( I ) and KO mice ( J ) . Data are presented as the mean ± S . E . M . Repeated measures ANOVA , p < 0 . 0001 , Bonferroni post-test . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 01410 . 7554/eLife . 08969 . 015Figure 6—figure supplement 1 . mEPSC and mIPSC amplitude does not change in LA neurons following fear conditioning . ( A ) Average amplitude of mEPSCs in naïve and fear conditioned ( shock ) control and DAT-NR1 KO mice . ( B ) Average amplitude of mIPSCs in naïve and fear conditioned ( shock ) control and DAT-NR1 KO mice . DOI: http://dx . doi . org/10 . 7554/eLife . 08969 . 015 To further probe for possible changes in synaptic strength evoked by fear conditioning , we recorded miniature excitatory and inhibitory postsynaptic currents ( mEPSCs and mIPSCs ) in the LA of both naïve and fear-conditioned mice ( Figure 6E , F ) . Fear conditioning evoked a significant increase in the frequency of mEPSCs in control mice , which was not observed in DAT-NR1 KO mice ( Figure 6G , H ) . We did not detect significant changes in the amplitude of mEPSCs in either group ( Figure 6—figure supplement 1 ) . Fear conditioning also elicited a significant enhancement of mIPSC frequency in control , but not DAT-NR1 KO mice ( Figure 6I , J ) . We did not detect significant changes in the amplitude of mIPSCs in either group ( Figure 6—figure supplement 1 ) . These results are consistent with an enhancement of both excitatory and inhibitory inputs to LA neurons .
Our observations that mice lacking NMDARs in dopamine neurons fail to undergo plasticity at multiple levels is consistent with previous reports linking dopamine to plasticity and fear coding in the LA ( Rosenkranz and Grace , 2001 , 2002; Bissiere et al . , 2003 ) and to the regulation of intrinsic excitability of LA neurons ( Yamamoto et al . , 2007 ) . Previous studies have demonstrated that failure of LA neurons to discriminate between fear predictive and non-predictive stimuli is highly correlated with fear generalization ( Ghosh and Chattarji , 2015 ) . More specifically these authors demonstrate that increased intensity of the US results in non-selective enhancement of LA excitatory responses to a CS− , thus impairing the discrimination between CS+ and CS− ( Ghosh and Chattarji , 2015 ) . Consistent with these findings it has also been shown that disruption of GABAB signaling in the LA results in non-associative plasticity , and mice lacking the GABAB receptor subtype GABAB ( 1a ) develop generalized fear responses ( Shaban et al . , 2006 ) . In contrast to previous reports of generalized fear being associated with an over-exuberance of plasticity and activation of the LA ( Shaban et al . , 2006; Ghosh and Chattarji , 2015 ) , in DAT-NR1 KO mice with generalized fear the LA neurons appear as though no CS/US association has been made . More specifically , LA neurons in DAT-NR1 KO mice do not potentiate to the CS+ , fail to reduce inhibition to the CS− , and demonstrate longer response latencies to the CS+ and CS− , similar to longer response latencies to the CS− in control mice . Although the manner in which discrimination occurs between activated and inhibited neurons differs , the end result is an acquired differential response that is dependent upon NMDAR signaling in dopamine neurons . These results indicate that generalized freezing behavior to the CS− is not dependent upon excessive activation of the LA , but rather suggest that discriminative coding in the LA reflects the proper assignment of cue-outcome contingencies . Collectively , these findings suggest that disruption of plasticity underlying discriminative fear coding in the LA is associated with generalized fear responses . Our results extend previous findings and confirm that plasticity in the LA is associated with discriminative fear; however , it is not an over-exuberance of plasticity per se that underlies generalization but rather the lack of appropriate plasticity . If a lack of synaptic strengthening occurs in the LA in the absence of NMDA receptor signaling in dopamine neurons , then where is the site of heightened threat responses following fear conditioning ? It is well established that the LA is an early site of convergent sensory information processing that is essential for fear memory acquisition and expression ( LeDoux , 2000 ) ; however , fear coding and plasticity are broadly distributed across multiple brain regions including the thalamus , cortex ( sensory and prefrontal ) , and hippocampus ( Tovote et al . , 2015 ) . In addition , plasticity also occurs within other subdivision of the amygdala including the BLA and lateral subdivision of the central nucleus ( CeAl ) ( Tovote et al . , 2015 ) . More specifically , generalized defensive responses to threat have been correlated with non-specific coding of cue information in the CeAl ( Ciocchi et al . , 2010 ) , and activation of a specific population of CeAl neurons during discriminative fear conditioning results in response generalization ( Botta et al . , 2015 ) . Thus , it is possible that the generalized threat responses reported here are mediated by exaggerated responses in the CeA or other areas of the brain implicated in fear processing , many of which are innervated by midbrain dopamine neurons . Consistent with plasticity in the activity of LA neurons in freely moving mice during fear conditioning , we observed synaptic plasticity in LA neurons in acute slice following fear conditioning in control mice that was absent in DAT-NR1 KO mice . Numerous studies have identified pathway-specific plasticity in the LA ( Rogan and LeDoux , 1995; McKernan and Shinnick-Gallagher , 1997; Rogan et al . , 1997; Zinebi et al . , 2002; Schroeder and Shinnick-Gallagher , 2005; Shin et al . , 2010; Cho et al . , 2012 ) . Examination of the ratio of excitatory and inhibitory postsynaptic potentials in response to stimulation of either thalamic or cortical input to LA neurons did not reveal a pathway-specific change in either excitatory or inhibitory drive . These findings indicate that either one or both pathways are enhanced with proportional increases in excitatory and inhibitory drive , or that neither pathway is altered specifically . Our observations of increased frequency of both mIPSCs and mEPSCs suggest a scenario in which one or both pathways are potentiated with proportional changes in excitatory and inhibitory inputs . There are numerous mechanisms that have been identified that underlie plasticity in LA neurons ( Rodrigues et al . , 2004 ) with both pre- and postsynaptic sites of action ( Tsvetkov et al . , 2002; Zinebi et al . , 2002; Apergis-Schoute et al . , 2005; Schroeder and Shinnick-Gallagher , 2005; Fourcaudot et al . , 2008; Shin et al . , 2010; Cho et al . , 2012 ) . Our observed increase in the frequency of both mEPSCs and mIPSCs without a concomitant change in amplitude , is consistent with a presynaptic increase in synaptic transmission , similar to previous descriptions of facilitated synaptic transmission from cortical and thalamic inputs to LA neurons ( McKernan and Shinnick-Gallagher , 1997; Tsvetkov et al . , 2002; Zinebi et al . , 2002 ) . Although we did not observe an increase in mEPSC amplitude this does not exclude postsynaptic changes in glutamate receptors . It was previously demonstrated that trafficking of postsynaptic AMPA receptors in the LA is critical for associative memory formation ( Rumpel et al . , 2005 ) . One potential explanation for these apparent discrepancies is the unmasking of previously silent synapses ( Kerchner and Nicoll , 2008 ) . Indeed , silent synapses have been reported in the amygdala ( Lee et al . , 2013 ) and stress has been shown to increase the formation of nascent silent synapses in the LA ( Suvrathan et al . , 2014 ) . Alternatively , it has been proposed that different synaptic sites of LTP in LA neurons ( pre- vs postsynaptic ) may occur depending on the strength the US , with low US intensities favoring presynaptic LTP and high US intensities favoring postsynaptic LTP ( Shin et al . , 2010 ) . Because we used a low intensity footshock ( 0 . 3 mA ) it is possible that our protocol favored the induction of a presynaptic form of synaptic strengthening . The mechanism by which NMDAR signaling in dopamine neurons influences plasticity in fear coding in the LA remains to be elucidated . We have previously demonstrated that NMDARs in dopamine neurons regulate phasic activation of these neurons in response to an aversive stimulus , thus suggesting that phasic dopamine release may facilitate plasticity at excitatory and inhibitory synapses within the LA . Consistent with this hypothesis , previous studies have demonstrated that dopamine signaling within the LA modulates local inhibitory networks ( Loretan et al . , 2004 ) and gates LTP induction in LA neurons through a suppression of feedforward inhibition ( Bissiere et al . , 2003 ) . In addition to local inhibitory neurons , dopamine neurons innervate paracapsular intercalated cell clusters ( Marcellino et al . , 2012 ) and potently modulate intercalated neurons through a D1 receptor dependent inhibitory mechanism ( Marowsky et al . , 2005 ) . More specifically , dopamine signaling in lateral paracapsular intercalated neurons suppresses feedforward inhibition from cortical inputs to the BLA ( Marowsky et al . , 2005 ) . Thus , phasic dopamine release facilitated by NMDARs in dopamine neurons is likely to regulate excitability and plasticity of LA neurons through both local inhibitory networks and feedforward inhibition from lateral paracapsular intercalated neurons . In addition to suppression of inhibition , dopamine increases the excitability of LA neurons through induction of a slow afterhyperpolarization ( Yamamoto et al . , 2007 ) . Collectively , these effects would significantly increase the spike firing of LA neurons . We find that the activity of a significant proportion of LA neurons increases after the first presentation of the US in control mice that parallels our finding of increased Fos levels in the LA following the first conditioning session . Footshock has been demonstrated to increase firing of midbrain dopamine neurons ( Brischoux et al . , 2009 ) , thus increased dopamine release facilitated by NMDAR signaling in dopamine neurons could explain the observed increase in LA activity in control mice that is absent in DAT-NR1 KO mice . Such an increase in the activity of LA neurons would then facilitate the induction of LTP ( Bissiere et al . , 2003 ) and lasting changes in synaptic strength . NMDAR signaling in dopamine neurons regulates phasic activation of these neurons as well as synaptic plasticity . Thus , alterations in fear-evoked plasticity within dopamine neurons could also be a major contributor to the disruption of fear coding in the LA of DAT-NR1 KO mice . Using a similar fear conditioning paradigm we have previously shown that dopamine neurons undergo plasticity in fear-evoked increases in calcium signals in dopamine neurons ( Gore et al . , 2014 ) , which occurs on a similar time course to the results described here . Others have also demonstrated that mesocortical projecting dopamine neurons undergo synaptic plasticity following a painful experience ( Lammel et al . , 2011 ) , indicating pathway specific activation of dopamine neurons . In support of an interdependent plasticity between dopamine neurons and target structures , it has been demonstrated that NMDAR-dependent cocaine-evoked plasticity in dopamine neurons occurs prior to plasticity in dopaminergic targets of the nucleus accumbens and that plasticity within the accumbens is dependent on NMDAR signaling in dopamine neurons ( Mameli et al . , 2009 ) . The interrelationship between plasticity and phasic activation of dopamine neurons and the relationship between phasic dopamine and plasticity in dopamine neurons that modulates plasticity in the LA remains to be determined . However , our results provide an important first step in linking NMDAR signaling in dopamine neurons with fear discrimination coding in the LA and demonstrate that fear generalization can occur in the absence of hyperexcitation of the LA .
All Materials and methods were approved by the University of Washington Institutional Animal Care and Use Committee . Control ( Grin1Δ/+; Slc6a3Cre/+ ) and DAT-NR1 KO ( Grin1Δ/lox; Slc6a3Cre/+ ) mice were generated as previously described ( Zweifel et al . , 2008 ) . 8- to 12-week old male mice were used for behavior and electrophysiology . 5- to 6-week old mice were used for slice electrophysiology . All mice were housed on a 12 hr light/dark cycle in a temperature controlled environment with ad libitum access to food and water for the duration of the study . Behavioral conditioning was performed in a sound attenuating cabinet with a mouse extra wide modular test chamber outfitted with a shock grid and stimulus lights ( Med Associates Inc . , St . Albans , VT , United States ) . Mice were habituated to handling each day one week prior to conditioning . For baseline cue responding mice were placeplaced in the box with a white corrugated plastic insert placed in the chamber to cover the walls and shock grid . Mice were assessed for freezing in response to three randomly interspersed presentations of the CS+ and CS− following a two minute baseline period . Each session of the 2-day fear conditioning paradigm included a 2 min baseline period followed by 20 randomly interleaved trials , comprised of 10 CS + trials and 10 CS− trials , each followed by a 110 s inter-trial interval ( ITI ) . The CS + trials consisted of a constant light cue presentation for 9 . 5 s , terminating with a 0 . 5 s 0 . 3 mA footshock ( US ) and the CS− trials used a different light cue that flashed 5 times for 200 ms every 2 s , ending with the last light flash and the absence of the US . For assessment of cue-specific freezing behavior 24 hr following conditioning mice were placed in the chamber with the white corrugated plastic insert and freezing was assessed in response to three randomly interspersed presentations of the CS+ and CS− following a 2 min baseline period . Freezing responses were scored as immobility , except for movement associated with breathing , by two independent investigators blind to genotype . Data were analyzed for statistical significance by two-way repeated measures ANOVA . Electrophysiology in freely moving mice was performed using microdrives fabricated in house , utilizing 16-channel electrode interface boards ( EIB-16; Neuralynx ) and tetrodes made from 0 . 00099" diameter tungsten wire ( Tungsten 99 . 95% CS SFV NATRL; California Fine Wire Company ) . Microdrive implantations in anesthetized mice were stereotaxically targeted at the LA ( −1 . 65 mm A-P , ±2 . 85→3 . 25 mm M-L , −4 . 3 mm D-V; Paxinos ) ; Bilateral targeting was achieved using coupled polyimide guide tubes ( 200 µm OD; source ) spaced at 6 . 5 mm . One week after surgery , mice were connected to a 16-channel Medusa Preamplifier and filtered signals ( 300-5000 Hz ) were acquired using a RZ5 Signal Processor ( Tucker–Davis Technologies ) . Tetrodes were lowered daily in ∼40 μm increments until unit activity was observed , at which point the animals began the fear conditioning paradigm; tetrodes were not lowered on subsequent days unless cell activity was absent . For conditioning , mice were treated as above , except they were not exposed to the testing chamber ( white corrugated plastic insert ) and did not receive cue presentations prior to conditioning . Conditioning proceeded each day following a 10 min baseline period to establish basal neural activity . Tetrode placement was histologically confirmed postmortem using cresyl violet histochemical stain and the presence of implant-induced tissue damage . Neurons were isolated by cluster analysis using Offline Sorter ( Plexon ) and subsequently formatted and analyzed with MATLAB ( Mathworks ) and Prism ( Graphpad Software ) . Data were acquired from 13 control and 12 DAT-NR1 KO mice . For unpaired recordings in control mice ( N = 5 ) , animals were treated exactly as above , except during the CS + presentation that normally co-terminated with footshock the shock was omitted . Reponses for each cell were normalized to baseline firing rate by calculating a Z-score ( Z = ( Ri-Rm ) /S . D . ) , where Ri = firing rate at an individual time point and Rm = mean firing rate , S . D . = standard deviation of the mean firing rate . Units were characterized as increasing to a stimulus if the Z-score was greater than 1 within the first 500 msec following stimulus presentation . Units were characterized as decreasing to a stimulus if the Z-score was less than −0 . 5 within the first 500 msec following stimulus presentation . Data were analyzed for statistical significance by two-way repeated measures ANOVA and repeated measures ANOVA , where appropriate . For fear conditioned mice , animals were handled for one week prior to conditioning . Mice were conditioned as above with two consecutive days of 10 CS+/US and CS− presentations . Mice were not pre-exposed to cues prior to conditioning . 24 hours following the second conditioning session mice were euthanized for brain slice preparation . Whole-cell recordings were made using an Axopatch 700B amplifier ( Molecular Devices ) with filtering at 1 KHz using 4–6 MΩ electrodes . Coronal brain slices ( 250 μm ) were prepared in an ice slush solution containing ( in mM ) : 250 sucrose , 3 KCl , 2 MgSO4 , 1 . 2 NaH2PO4 , 10 D-glucose , 25 NaHCO3 , 0 . 1 CaCl2 . Slices recovered for 1 hr at 34°C in artificial cerebral spinal fluid ( ACSF ) continually bubbled with O2/CO2 and containing ( in mM ) : 126 NaCl , 2 . 5 KCl , 1 . 2 NaH2PO4 , 1 . 2 MgCl2 11 D-glucose , 18 NaHCO3 , 2 . 4 CaCl2 . For evoked PSP and mEPSC recordings patch electrodes were filled with an internal solution containing ( in mM ) : 130 K-Gluconate , 10 KCl , 10 HEPES , 1 EGTA , 5 NaCl , 5 Mg-ATP , 0 . 5 Na-GTP , 5 QX-314 , pH 7 . 2–7 . 4 , 290 mOsm . For mIPSC recordings patch electrodes were filled with an internal solution containing ( in mM ) : 145 KCl , 10 HEPES , 1 EGTA , 5 Mg-ATP , 0 . 5 Na-GTP , pH 7 . 2–7 . 4 , 290 mOsm . ACSF at 32°C was continually perfused over slices at a rate of ∼2 ml/min during recording . For PSP recordings a concentric bipolar electrode was placed in either the internal ( thalamic ) or external ( cortical ) lateral capsule . 1 ms stimuli were delivered at 0 . 1 Hz , and compound PSPs were recorded in current clamp mode adjusting stimulus intensity until half maximal responses were detected . 15–30 traces were averaged per cell , followed by bath application of picrotoxin ( 100 μM ) to isolate the EPSP . 15–30 EPSP traces were averaged and digitally subtracted from the averaged compound PSP to isolate the IPSP . mEPSC and mIPSC recordings were made in voltage clamp mode at a holding potential of −60 mV . mEPSCs were recorded in the presence of picrotoxin ( 100 μM ) and tetrodotoxin ( 500 nM ) ; mIPSCs were recorded in the presence of kynurenic acid ( 2 mM ) and tetrodotoxin ( 500 nM ) . Events were detected automatically and were visually inspected and confirmed using Mini Analysis Program ( Synaptosoft ) . Data were analyzed for significance by Student's t-test . For Fos protein analysis , mice were handled and conditioned as above . Mice were euthanized and perfused 90 min following the start of a single conditioning session , which consisted of 10 CS + presentations randomly interspersed with 10 CS− presentations . The CS + co-terminated with a 0 . 3 mA , 0 . 5 s footshock ( US ) . Following perfusion , 30 µm frozen sections were collected and incubated overnight at 4° in primary antibody ( rabbit anti c-Fos , 1:2000 , CalBiochem ) , then washed and incubated for 1 hr at room temperature in a fluorescently labeled secondary antibody ( AlexaFluor488 donkey anti rabbit , 1:250 , JacksonImmuno ) . Sections were imaged using a Nikon Diaphot 200 inverted microscope . c-fos positive cells in the lateral amygdala were counted manually by an investigator blind to genotype from −1 . 0 mm posterior to bregma to −2 . 3 mm posterior to bregma . Cells were counted bilaterally from every fifth section . Statistical analysis was performed using Prism software ( GraphPad ) . | When we experience a situation that causes us to feel fearful , the brain processes information about the events that led up to it . This information is encoded by groups of nerve cells called neurons in a region of the brain called the lateral amygdala . The nerve cells communicate with each other through chemicals called neurotransmitters . At a junction between two neurons—called a synapse—neurotransmitters are released from one cell and influence the activity of the other cell . Long-term changes in the strength of these communications in response to specific cues underlie the formation of memories about fearful events . When these changes occur incorrectly they can lead to memories about particular events becoming inaccurate , which can lead to fear being associated with related , but non-threatening , situations . This ‘generalization’ of fear can lead to generalized anxiety disorder and post-traumatic stress disorder . Dopamine is a neurotransmitter that plays an important role in forming memories of fearful events . However , it is not clear whether neurons that release dopamine are also involved in correctly discriminating fearful events from non-fearful ones . ‘Receptor’ proteins called NMDARs on the surface of neurons that release dopamine are critical for preventing generalized fear . These receptors detect another neurotransmitter called glutamate . Jones et al . used genetics and ‘electrophysiology’ techniques to study these receptors in mice . The experiments show that a gene that encodes part of an NMDAR in dopamine neurons plays a key role in how fear memories are formed . When this gene is selectively switched off in the dopamine neurons , mice are more likely to develop generalized fear and anxiety behaviors after a threatening experience . The experiments also demonstrate that these generalized threat responses are associated with differences in the way the synaptic connections in the lateral amygdala are strengthened . The next major challenge will be to find out which specific synaptic connections are strengthened and to establish how dopamine neuron activity patterns influences this connectivity . | [
"Abstract",
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"neuroscience"
] | 2015 | A genetic link between discriminative fear coding by the lateral amygdala, dopamine, and fear generalization |
We associate small numbers with the left and large numbers with the right side of space . Recent evidence from human newborns and non-human animals has challenged the primary role assigned to culture , in determining this spatial numerical association ( SNA ) . Nevertheless , the effect of individual spatial biases has not been considered in previous research . Here , we tested the effect of numerical magnitude in SNA and we controlled for itablendividual biases . We trained 3-day-old chicks ( Gallus gallus ) on a given numerical magnitude ( 5 ) . Then chicks could choose between two identical , left or right , stimuli both representing either 2 , 8 , or 5 elements . We computed the percentage of Left-sided Choice ( LC ) . Numerical magnitude , but not individual lateral bias , explained LC: LC2 vs . 2>LC5 vs . 5>LC8 vs . 8 . These findings suggest that SNA originates from pre-linguistic precursors , and pave the way to the investigation of the neural correlates of the number space association .
Number knowledge and processing is fundamental for everyday living . A peculiar characteristic of numbers concerns their strong association with space . Galton , 1880 first reported that humans , in many cases , describe and think of numbers as being represented on a mental number line ( MNL ) oriented from left to right . Dehaene et al . , 1993 provided seminal evidence for a left-to-right oriented MNL . Healthy humans are faster in processing small numbers through left-sided responses , and large numbers through right-sided responses . Traditionally , this effect , deemed SNARC ( Spatial-Numerical Association of Response Codes ) , has been attributed to exposure to formal instruction , and therefore considered a by-product of culture , based on reading/writing conventions . Cultural aspects can in fact influence the orientation of the MNL . Arabs , who read from right to left , show an inverted SNARC effect ( Zebian , 2005 ) ; people with mixed reading habits ( e . g . , Israelis ) show no SNARC at all ( Shaki et al . , 2009 ) . The placement of numbers along a left-to-right oriented MNL can be also modulated by the experimental context and adjusted by various forms of experience . Participants instructed to conceive numbers as distances along a ruler , showed a left-to-right oriented SNARC effect , whereas conceiving numbers as hours on a clock face elicited an inverted SNARC effect ( Bächtold et al . , 1998 ) . Increasing evidence , however , has highlighted the importance of pre-linguistic and biologically-determined precursors of spatial-numerical associations ( SNA ) . Evidence from infants rules out a primary influence of verbal counting in SNA orientation . Seven-month olds , habituated to left-to-right sequences of numerical magnitudes either increasing ( e . g . 1-2-3 ) or decreasing ( e . g . 3-2-1 ) , at test looked longer at new increasing , but not decreasing , sequences . Whenever during habituation , the sequences were instead displayed from right-to-left , such bias was not reported for both increasing and decreasing sequences ( de Hevia et al . , 2014 ) . The presentation of a small numerosity ( two dots ) or of a large numerosity ( nine dots ) oriented spatial attention of 8-month-old infants , respectively towards the left or the right side of space ( Bulf et al . , 2015 ) . Nevertheless , these results could be determined by the interactions that few-months olds entertained with adults or their environment ( Patro et al . , 2016 ) . SNA has been described even in 3-day-old newborns , strongly reducing the possible influence of the interaction with caregivers ( de Hevia et al . , 2017 ) . Recently , Di Giorgio et al . , 2019 reported that newborns habituated to a numerical value ( a group of 12 items ) , spontaneously associated a smaller number ( four items ) with the left side of space and a larger number ( 36 items ) with the right side . Interestingly enough , the same number , for instance ‘12’ , was associated with the left side after habituation with a large number ( 36 ) , but with the right side after habituation with a smaller number ( 4 ) . The studies that cast most doubts on the importance of language and symbolic thought for the origin of the SNA come from comparative research ( Brugger , 2015; Vallortigara , 2018 ) . Adult Clark’s nutcrackers ( Rugani et al . , 2010 ) and rhesus monkeys Drucker and Brannon , 2014 have shown unilateral , left-to-right oriented bias in associating numerosity with space . A spatial representation of magnitude has been found also in gorillas and orangutans ( Gazes et al . , 2017 ) . Though present in most apes , SNA is either left-to-right or right-to-left oriented , depending on the individual . Idiosyncratic experiences , such as the interactions with caregivers , rather than differences related to species or handedness , are reported among the main factors that might determine the orientation of the SNA ( Gazes et al . , 2017 ) . This interpretation makes it even clearer that the only way to rule out the role of culture as well as of caregiving experience is to test day-old ( almost ) naive animals . For instance , baby chicks ( Gallus gallus ) trained to respond to a target numerosity , spontaneously associated a number smaller than the target with the left side of space , and a number larger than the target with the right side ( Rugani et al . , 2015a ) . This study strongly renovated the interest on the origin of the SNA ( Drucker and Brannon , 2014; Rugani and de Hevia , 2017 ) and provided insights on testing SNA in non-verbal subjects . The paradigm of Rugani et al . , 2015a has been applied onto different species , with mixed results . On one side , studies that merely applied the chicks’ paradigm failed in finding a SNA ( Triki and Bshary , 2018; Beran et al . , 2019 ) . On the other hand , studies that extrapolated the core idea and tuned the paradigm to the test situation did successfully find a SNA ( de Hevia et al . , 2017; Di Giorgio et al . , 2019 ) . An alternative explanation of the original study by Rugani et al . , 2015a pointed at the importance of any , however subtle , individual biases either toward the left or the right which would be magnified over the course of the test ( Mangalam and Karve , 2015 ) , but see Rugani et al . , 2015b ) . Furthermore , Núñez and Fias , 2017 sustained that SNA in chicks could critically depend on having used novel stimuli at test with respect to the training . Chicks’ responses might be triggered by novelty , rather than by numerosity . Therefore , Núñez and Fias , 2017 highlighted the importance of testing the chicks on the same numerosity as that of the training ( 5 vs . 5 ) . The aim of the present study was to directly investigate the role of number magnitude versus individual spatial bias in 3-day-old domestic chicks . At test , chicks were presented with the same numerosity already experienced at training ( 5 ) . In different experiments this test was administered either as first or as last , to ascertain any role of experience in determining the orientation of the SNA . Data from the 5 vs . 5 control test were meant to be very insightful as no preference was expected according to the hypothesis that the bias is solely determined by the differences in numerical magnitude experienced by the animals . Hence , this test could unveil the presence of any idiosyncratic spatial bias . The performance of each chick in the 5 vs . 5 test was to this purpose employed to normalize performance scored in the other tests ( 2 vs . 2 and 8 vs . 8 ) . This allowed to control for any individual bias as well as for responses guided by non-numerical variables ( e . g . , novelty ) . To test for lateralization in processing numerical magnitude , we scored the side from which chicks circumnavigated the panels . Chicks can use their eyes independently to process different visual stimuli; generally , they approached a stimulus from the side which allows an analysis with the eye connected to the most specialized hemisphere ( Daisley et al . , 2009 ) . A left circumnavigation , which implies to look with the right eye , would indicate a preferential processing with the left hemisphere; a right circumnavigation , which implies to look with the left eye , would indicate a preferential processing with the right hemisphere . Here , we trained 3-day-old chicks ( n = 48 ) to circumnavigate a central panel depicting five elements to get a food reward . Then chicks underwent three consecutive tests , each consisting of five trials , in which two identical panels were presented . Each panel depicted either 2 , 8 or 5 randomly arranged 1-cm-sided red squares . Each chick underwent a smaller number ( 2 vs . 2 ) , a larger number ( 8 vs . 8 ) , and the control ( 5 vs . 5 ) test . On each test trial we scored the first inspected panel ( left or right ) and the side from which chicks circumnavigated the panel . In Experiment 1 , test presentation Order was: 2 vs . 2 , 8 vs . 8 , 5 vs . 5 ( N = 12 chicks ) or 8 vs . 8 , 2 vs . 2 , 5 vs . 5 ( another sample of N = 12 chicks ) ; chicks were randomly assigned to either group . In Experiment 2 , the Order of the three tests was: 5 vs . 5 , 2 vs . 2 , 8 vs . 8 ( N = 12 chicks ) or 5 vs . 5 , 8 vs . 8 , 2 vs . 2 ( another sample of N = 12 chicks ) ; chicks were randomly assigned to either group . Sample size for each group was calculated , as indicated by the Ethical committee for animal welfare of the University of Padova , using the formula for quantitative variables: n= ( 2σ2 ) / ( μ1-μ2 ) 2 x f ( α , β ) ; with the following values α = 0 . 05 e ß = 0 . 80 average = 70% standard deviation = 17% . For each experiment , we calculated the percentage of trials in which the chick chose the left panel ( Left Choices: LC ) . LC ranged from 0 ( left panel never chosen ) to 100 ( left panel always chosen ) . Our main prediction was that Test ( 2 vs . 2; 8 vs . 8; 5 vs . 5 ) would affect LC; in particular we expected the following order restriction on ‘Test’ variable: LC ( 2 vs . 2 ) >LC ( 5 vs . 5 ) >LC ( 8 vs . 8 ) . To assess whether chicks’ behavior was based on number magnitude or on individual bias , for each chick we calculated a Small Number Bias ( SNB ) : LC ( 5 vs . 5 ) – LC ( 2 vs . 2 ) and a Large Number Bias ( LNB ) : LC ( 5 vs . 5 ) – LC ( 8 vs . 8 ) . We expected a SNB <0 , which indicates a left bias in responding to small magnitudes and a LNB >0 , which indicates a right bias in responding to large magnitudes .
We first considered the effect of Order and Test on the percentage of Left-sided Choices ( LC; Figure 1A; Source data 1 ) . Against the ‘Intercept only’ model , the Bayesian ANOVA ( BfANOVA ) produced an extreme evidence in favor of a Test effect ( BF >100 ) , ( repeated measures Anova: F ( 2 , 44 ) =36 . 375; p<0 . 01 , η2 = 0 . 423 ) ; but no effect of Order ( BF = 0 . 263 ) , ( repeated measures Anova: F ( 1 , 44 ) = 0 . 336; p=0 . 714 , η2 = 0 . 004 ) , ( Source data 2 ) . We then tested the equality constraints of our model by comparing the unconstrained model ( LC_2 vs . 2 ≠ LC_8 vs . 8 ≠ LC_5 vs . 5 ) with every other possible somewhat constrained model ( e . g . : LC_2 vs . 2 ≠ LC_8 vs . 8 = LC_5 vs . 5 ) . The unconstrained model was preferred to all the possible constrained models by a factor ranging from 12 to >100 . This is an evidence , ranging from strong to extreme , in favor of a differential performance in the three tests . To compare the order restrictions model ( 2 vs . 2 > 5 vs . 5 > 8 vs . 8 ) with the unconstrained full model ( LC_2 vs . 2 ≠ LC_8 vs . 8 ≠ LC_5 vs . 5 ) we firstly ran a Markov Chain Monte Carlo ( MCMC ) which showed 9983/10000 cases consistent with our hypothesis . We found a moderate evidence in favor of the order restriction: LC_2 vs . 2>LC_5 vs . 5>LC_8 vs . 8 ( BF = 5 . 990 ) . Frequentist analyses confirmed that: LC_2 vs . 2 was significantly larger than chance ( 50% ) : mean = 71 . 667 , SD = 19 . 486 , t ( 23 ) =5 . 447 , p<0 . 001 , Cohen's d = 1 . 572; LC_8 vs . 8 was significantly smaller than chance: mean = 28 . 333 , SD = 20 . 359 , t ( 23 ) = −5 . 214 , p<0 . 001 , Cohen's d = 1 . 505; the LC_5 vs . 5 was not statistically different from chance: mean = 52 . 500 , SD = 23 . 452 , t ( 23 ) = 0 . 522 , p=0 . 604 , Cohen's d = 0 . 151 , ( Source data 2 ) . For what concerns the Number Bias , we firstly computed the SNB and the LNB , then we compared each Number Bias with the null = 0 . T-test Bayes factor analysis yielded a very strong evidence in favor of the Number Bias for SNB , ( BF = 49 . 037; One sample t test: t ( 23 ) =-3 . 922; p<0 . 001 , Cohen’s d = −0 . 801 ) , ( Source data 3 ) and an extreme evidence in favor of the Number Bias for LNB ( BF >100; One sample t test: t ( 23 ) =4 . 872; p<0 . 001 , Cohen’s d = 0 . 994 ) ( Source data 4 ) . For what concerns the side of circumnavigation , we did not find any consistent evidence for each numerical magnitude: 2 vs . 2 ( BF = 0 . 301; X2 = 0 . 403; p=0 . 525 , Phi = 0 . 058 ) ; 5 vs . 5 ( BF = 3 . 314; X2 = 5 . 444; p=0 . 020 , Phi = 0 . 213 ) ; 8 vs . 8 ( BF = 0 . 249; X2 = 0 . 062; p=0 . 804 , Phi = 0 . 023 ) , see Table 1 , Experiment 1; ( Source data 5 ) . On the basis of these analyses we concluded that chicks’ performance is affected by the number magnitude of elements faced at test . We first considered the percentage of Left Choices ( LC ) , separately for each test ( Figure 1B; Source data 6 ) . The BFANOVA against the ‘Intercept only’ model produced an extreme evidence in favor of a Test effect ( BF = 1470 . 58; repeated measures Anova: F ( 2 , 44 ) = 16 . 736; p<0 . 001 , η2 = 0 . 277 ) ; but no effect of Order ( BF = 0 . 249; repeated measures Anova: F ( 2 , 44 ) =4 . 388; p=0 . 018 , η2 = 0 . 073 ) ( Source data 7 ) . Frequentist analyses confirmed that: LC_5 vs . 5 did not statistically differ from chance: mean = 50 . 833 , SD = 23 . 575 , t ( 23 ) = 0 . 173 , p=0 . 863 , Cohen's d = 0 . 05; LC_2 vs . 2 was significantly larger than chance: mean = 70 . 000 , SD = 26 . 375 , t ( 23 ) = 3 . 715 , p<0 . 001 , Cohen's d = 1 . 072 ) , and that LC_8 vs . 8 was significantly smaller than chance: mean = 33 . 333 , SD = 24 . 077 , t ( 23 ) = −3 . 391 , p<0 . 001 , Cohen's d = 0 . 979 . We then tested the equality constraints of our model by comparing the unconstrained model ( LC_2 vs . 2 ≠ LC_8 vs . 8 ≠ LC_5 vs . 5 ) with every other possible somewhat constrained model ( e . g . : LC_2 vs . 2 ≠ LC_8 vs . 8=LC_5 vs . 5 ) . The unconstrained model was preferred to all the possible constrained models by a factor ranging from three to >100 . This is an evidence ranging from moderate to extreme in favor of a differential performance in the three tests . To compare the order restrictions model ( LC_2 vs . 2>LC_5 vs . 5>LC_8 vs . 8 ) with the unconstrained full model ( 2 vs . 2 ≠ 8 vs . 8 ≠ 5 vs . 5 ) we first ran a Markov Chain Monte Carlo ( MCMC ) which showed 9798/10000 cases consistent with our hypothesis . The BF of our order restriction model was 5 . 879 in favor of the restricted model against the full model , showing , thus , a moderate evidence in favor of the order restriction: LC2vs . 2>LC5vs . 5>LC8vs . 8 ( Source data 7 ) . For what concerns the Number Bias , we first computed the SNB and the LNB , then we compared each Number Bias with the null = 0 . T-test Bayes factor analysis produce a moderate evidence for SNB ( BF = 4 . 350 ± 0; One sample t test: t ( 23 ) =-2 . 752; p=0 . 011 , Cohen’s d = −0 . 562 ) ( Source data 8 ) and a moderate evidence for LNB ( BF = 3 . 525 ± 0; One sample t test: t ( 23 ) =2 . 64; p=0 . 015 , Cohen’s d = 0 . 539 ) ( Source data 9 ) . We did not find any consistent effect of the side of circumnavigation for 2 vs . 2 ( BF = 1 . 512; X2 = 3 . 880; p=0 . 049 , Phi = 0 . 180 ) and 5 vs . 5 ( BF = 0 . 224; X2 = 0 . 024; p=0 . 877 , Phi = 0 . 014 ) . Nevertheless , there was an evidence for 8 vs . 8 ( BF = 49 . 104; X2 = 10 . 930; p<0 . 001 , Phi = 0 . 302 ) , see Table 1 , Experiment 2 ( Source data 5 ) .
The results of Experiments 1 and 2 support the hypothesis that number magnitude affects chicks’ performance . Interestingly , the numerical bias reported in Experiment two seems to be less strong than that reported in Experiment 1: the evidence in favor of the Number Bias was very strong-to-extreme in Experiment 1 , but moderate in Experiment 2 . Plausibly this reduced strength of the SNA is related to the first test , in which chicks experienced a magnitude identical to the training one . A recent paper showed that the effect of spatial-motor experience could modulate the SNA in pre-literate children . A short-term ( ≈15 min ) spatial –and not numerical– training ( i . e . playing a video game which required either left-to-right or right-to-left oriented movements ) is sufficient to modulate the orientation of the spatial numerical association in 3- and 4-year-old children ( Patro et al . , 2016 ) . Even if we did not find any consistent bias on the side of circumnavigation , side differences in circumnavigation seemed stronger in Experiment 2 , where evidence of spatial numerical association were found to be somewhat weaker than in Experiment 1 . This allows to speculate that experience may modulate the processing underlying numerical perception and its association with space . On the contrary in Experiment 1 , chicks experienced an appreciable variation in numerical magnitude in the very first test , in either direction: smaller ( 2 ) or larger ( 8 ) . Such difference in the strength of the SNA opens new challenging opportunities to study the role of experience in modulating the SNA . Taken together , our two experiments showed that number magnitude and space are strongly associated in young and naïve chicks . In presence of very limited numerical and environmental/spatial experience this association is left-to-right oriented . But is this effect sensitive to experience ? Future studies are needed to assess whether and how the association of number and space , however predisposed , can be modulated by experience . Chicks showed a left bias in the 2 vs . 2 , a right bias in the 8 vs . 8 and no bias in the control test 5 vs . 5 , irrespectively of testing order ( Figure 1A and B ) . Our results cannot be explained by individual orienting biases or by preference for novelty ( Núñez and Fias , 2017 ) . Our results show , instead , that it is the relative numerical magnitude between the training and the testing values , which determines the direction of the bias . Moreover , the presence of a linear trend with three points of reference - LC ( 2 vs . 2 ) >LC ( 5 vs . 5 ) >LC ( 8 vs . 8 ) - poses further restrictions in favor of a spatial-numerical mapping guided by the mental number line ( see Núñez and Fias , 2017 ) . One important issue is the adaptive significance , if any , of a directional number-space association for non-human ( and human ) animals . One could argue that the basic phenomena is the mapping of number to space , and that the ordering of such mapping is simply the outcome of chance processes . However , if this were the case , then one would expect different individuals showing different directional biases , for there would be no specific reason for an alignment in the direction of the space-number association in different individuals . Some more basic biological phenomenon may , however , be at work here . One hypothesis has been put forward by Vallortigara , 2018 . It is based on evidence that the two sides of the brain provide qualitatively different contributions to the control of functions related to motivation and emotion - sometimes referred to as the ‘valence hypothesis’ ( Davidson , 2004 ) - with the left and right sides of the brain specialized for positive ( approach ) or negative ( withdrawal ) aspects in the control of behavior . It is plausible that changes towards larger or smaller magnitudes are associated with prevalent activation of , respectively , the left ( positive valence ) and the right ( negative valence ) hemisphere , and , thus , that attention to contralateral hemispace arises from it . Even if not explicitly trained to such association , animals can establish that for appetitive stimuli like the ones used in our experiments , larger magnitudes are intrinsically better ( and approachable ) than smaller magnitudes . According to this hypothesis , when chicks are faced with either abrupt increase or decrease in numerosities this would evoke preferential activation of , respectively , the left hemisphere ( positive emotion ) or the right hemisphere ( negative emotion ) . In turn , this would promote attending to the contralateral side of the activated hemisphere , i . e . to the left for changes from large to smaller numerosities and to the right for changes from small to larger numerosities . The hypothesis can be tested in future experiments by e . g . establishing specific associations between certain magnitudes and an aversive ( rather than an appetitive ) stimulus , thus inducing a reduction if not an inversion of the direction of the space–number association .
Subjects were forty-eight domestic chicks ( Gallus gallus ) , Ross 308 Broiler ( Aviagen ) . Twenty-four subjects took part in Experiment 1 , and the other 24 took part in Experiment 2 . We obtained chicks weekly from a local commercial hatchery ( Agricola Berica , Montegalda , Vicenza , Italy ) . All procedures chicks underwent are summarized in Table 2 . On arrival , the chicks were a few-hours old . They were immediately housed in standard metal cages ( 28 cm × 32 cm × 40 cm ) , with food and water available ad libitum in transparent glass jars ( 5 cm in diameter , 5 cm high ) , and placed at the corners of the cages . Food and water were placed randomly , one jar per corner , and their position was changed daily . Each cage was illuminated by fluorescent lamps ( 36 W ) located 45 cm above it . Temperature and humidity were constantly monitored and maintained respectively at 28–31°C and 68% . Twice a day we fed chicks with some mealworms ( Tenebrio molitor larvae ) as these were used as food reinforcement during training . We reared chicks in these conditions from the morning of arrival ( from 11 a . m . ) to the morning ( 8 a . m . ) of Day 3 , when the food jars were removed while water was left available . Two hours later ( 10 a . m . ) birds underwent shaping , in which they learnt to circumnavigate a panel located in the center of the experimental apparatus . At the end of shaping each chick was placed back in its rearing cage and , two hours later , it underwent training . Immediately after the end of training the chick underwent Test 1 . When the first test was over , the chick was placed back in its rearing cage for one hour , before entering a second session of training and , immediately after it , Test 2 . At the end of Test 2 , each chick was placed back in its rearing cage for about an hour , then the third training started , and , immediately thereafter , Test 3 . At the end of all tests , chicks were caged in social groups of three birds , with food and water available ad libitum . A few hours later they were donated to local farmers . During training and test sessions we used the same experimental apparatus . This was located in a room adjacent to the rearing room . In the experimental room , temperature and humidity were controlled and maintained , respectively , at 25°C and 70% . Lighting was provided by four 58 W lamps , placed on the ceiling , 194 cm above the basement of the experimental apparatus . The experimental apparatus consisted of a diamond-shaped arena ( see Figure 2 ) made of uniformly white plastic panels . The external wall consisted of a 20 cm high plastic panel; the floor consisted of a white plastic sheet . A transparent removable partition ( 10 cm ×20 cm ) positioned at about 10 cm from the main vertex of the apparatus delimited the chick starting area . The transparent partition was used to confine the bird before the beginning of each training or testing trial . The chick was gently positioned and maintained in the starting area for five seconds , before being released within the arena . During this time the chick could access visually the inside of the arena and the panel ( s ) . During the inter-trial period each chick was moved to a separate opaque box ( 20 cm ×40 cm × 40 cm ) adjacent to the experimental apparatus , to prevent it from seeing the experimenter while cleaning the apparatus and changing the setup of the training/test stimuli . The stimuli were presented on panels ( 16 cm ×8 cm ) provided with 3 cm sides bent back to prevent the chicks from spotting behind the panel ( where the mealworm was hidden during training ) before having walked around of it . During training we used a single panel , located in the center of the apparatus , directly in front of the starting area and 40 cm away from it ( see Figure 2A ) . During testing , we used two identical panels , spaced 30 cm apart , and located symmetrically one on the right side and one on the left side with respect to the main vertex ( see Figure 2B ) . A partition , on the opposite side of the starting area separated the far side of the apparatus in two symmetrical sectors , facilitating the scoring of chicks’ choices . Training and test stimuli consisted of static 2D images . The stimuli depicted a number of red squares , printed on identical white rectangular boards ( 11 . 5 cm ×9 cm ) . On each trial a stimulus ( during training ) and a pair of stimuli ( during testing ) were placed on the panel ( s ) . The training stimuli depicted five red squares ( 1 cm ×1 cm ) . To prevent the chicks from learning to identify the stimuli on the basis of the spatial disposition of the squares , we created 20 different training stimuli ( one for each training trials ) differing one another for the spatial disposition of the squares on the board , which was randomly determined so that the distance between squares varied from 0 . 3 cm to 3 . 8 cm . The test stimuli depicted either of 2 , 5 or 8 identical red squares . Five different test stimuli , differing from one another in the spatial arrangement of the squares , were produced for the 2 vs . 2 , 5 vs . 5 , and 8 vs . 8 test . For each test stimulus we printed two identical copies . To the specific purpose of this study we designed the stimuli on the basis of those used in Experiment 1 of our previous study ( Rugani et al . , 2015a ) . On the morning of Day 3 ( i . e . , the testing day ) each chick underwent shaping , in which it was acquainted with feeding in the apparatus . A single panel was in place , and a mealworm was placed between the starting area and the panel . The chick was at first placed within the arena , in the starting area , without the confining partition , for a couple of minutes . During this time the chick was free to move around and get accustomed to the novel environment . In five subsequent trials we offered the chick a small mealworm ( or a piece of a mealworm ) . In the first shaping trial , the mealworm was positioned closer to the starting area , while in the fifth shaping trial the mealworm was closer to the panel . Then chicks had to learn to search for food behind the panel . In this phase the chick was confined within the starting area . A plastic mealworm , which looked similar to a real one , was placed in front of the panel and then it was progressively moved ( by a very fine thread handled and slowly dragged by the experimenter ) behind the panel . Then the chick was released in the arena and could search for food , located behind the panel where an edible mealworm had been positioned . At the end of shaping , the chick confidently moved from the starting area and walked behind the screen to eat the reward . Then chicks underwent training . On each training trial a stimulus was placed on the panel . The chick was confined in the starting area for five seconds and then it was released in the arena . The chick had one minute to circumnavigate the panel and to reach the reward . The training was over once the chick had circumnavigated the panel on 20 consecutive trials . In all training trials chicks received a food reinforcement . Previous studies , in which we used a procedure similar to this , have shown that after having found the food behind a panel depicting a certain number stimulus for a few times , the chicks learn to identify the panel by the number depicted on it ( Rugani et al . , 2013; Rugani et al . , 2014 ) . Overall , depending on the chick behavior , the training phase lasted from 10 to 20 min . Chicks that showed little interest in the food reinforcement ( i . e . , poor mealworm-following or eating behavior ) in this phase , were discarded from the study: this occurred in about 25% of cases; these chicks are not included in the final sample . All chicks that completed the training phase moved on to the test session . Before the second and the third test , each chick underwent a training session identical to the first one . This phase comprised three tests ( 2 vs . 2; 8 vs . 8; 5 vs . 5 ) , each of them consisted of five trials . Test trials were never reinforced ( i . e . , chicks did not find any food reward behind the panels ) . On each test trial , chicks were firstly placed into the starting area , behind the transparent partition for about five seconds . Inside the arena , the two identical stimuli had been already positioned on the two new ( left and right ) panels , and were fully visible to the confined chick . Then the chick was released by lifting the transparent partition , and it was free to walk within the arena . As soon as the chick had circumnavigated one of the two panels , the trial was considered over . Only one choice was allowed and was scored per trial . A choice was defined as when the head and at least ¾ of the chick’s body had entered the area behind one of the two panels ( beyond the side edges ) . At the end of each trial , the chick was moved in the opaque box outside of the apparatus , where it remained for about 15 s , during which time , the experimenter prepared the experimental setting for the following trial . On each trial the panels were shifted and the stimuli were changed . As soon as the new stimuli were in place , the chick was positioned back into the starting area , and the whole procedure was repeated . If during a trial the chick did not choose one of the two panels within the available time ( one minute ) , that trial was immediately repeated . The procedure continued until each chick had undergone three complete testing sessions of 5 valid trials each . During the tests , subjects’ behavior was observed from a screen connected to a video camera so as not to disturb the animals by direct observation , all trials were video-recorded . For each test , we computed the number of trials in which each chick circumnavigated the left panel , and the percentages were computed as: ( number of left choices/5 ) ×100 . | Most of the world modern-day languages are written from left to right – but what about numbers ? As it turns out , the majority of people also represent numbers using a ‘mental line’ , with smaller numbers on the left and larger numbers on the right . Some researchers argue that this phenomenon results from the way humans learn to read and write: in other words , that it is a by-product of culture , rather than an innate property of the brain . Recent evidence suggests that newborn infants , as well as certain species of monkeys and birds , also associate smaller numbers with the left and larger numbers with the right side of space . This raises the possibility that human mental number line may stem from an ability that evolved before language , in a common ancestor of humans and other animals . Yet , critics claim that findings in infants and non-human species result from a failure to account for individual biases in responding . To resolve this controversy , Rugani et al . trained three-day-old domestic chicks to approach a target board sporting five red squares . Chicks were then given the choice to approach two identical boards , which would both show two , five or eight red squares . Rugani et al . showed that when both boards had two red squares , the chicks tended to approach the left-hand board more often than the right . By contrast , when both boards had eight red squares , the birds approached the right-hand board more often than the left . Importantly , no left-right bias was observed when the number of red squared remained unchanged ( five ) . These results also could not be explained by individual chicks favoring the left or right side . Instead , the findings suggest that even newborn animals tend to associate numbers with positions on a mental number line . Additional research is needed to determine the role of experience – or culture – in shaping this tendency , and future studies should also examine which brain regions support the association between number and space . | [
"Abstract",
"Introduction",
"Results",
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"and",
"methods"
] | [
"neuroscience"
] | 2020 | Numerical magnitude, rather than individual bias, explains spatial numerical association in newborn chicks |
Toxoplasma gondii , a protozoan parasite , undergoes a complex and poorly understood developmental process that is critical for establishing a chronic infection in its intermediate hosts . Here , we applied single-cell RNA-sequencing ( scRNA-seq ) on >5 , 400 Toxoplasma in both tachyzoite and bradyzoite stages using three widely studied strains to construct a comprehensive atlas of cell-cycle and asexual development , revealing hidden states and transcriptional factors associated with each developmental stage . Analysis of SAG1-related sequence ( SRS ) antigenic repertoire reveals a highly heterogeneous , sporadic expression pattern unexplained by measurement noise , cell cycle , or asexual development . Furthermore , we identified AP2IX-1 as a transcription factor that controls the switching from the ubiquitous SAG1 to rare surface antigens not previously observed in tachyzoites . In addition , comparative analysis between Toxoplasma and Plasmodium scRNA-seq results reveals concerted expression of gene sets , despite fundamental differences in cell division . Lastly , we built an interactive data-browser for visualization of our atlas resource .
Toxoplasma gondii is an intracellular protozoan parasite that is thought to infect over a quarter of the world’s population ( Pappas et al . , 2009 ) . Like some of its Apicomplexan cousins , Toxoplasma undergoes a complex developmental transition inside the host . In intermediate hosts , including humans and virtually all other non-feline , warm-blooded animals , Toxoplasma parasites remain haploid and transition from a replicative , virulent tachyzoite to an encysted , quasi-dormant bradyzoite . This asexual developmental transition is tightly coupled to the clinical progression of Toxoplasma infection . Although acute infection with tachyzoites produces few if any symptoms in healthy human children and adults , infected individuals , if left untreated , progress to a chronic stage wherein tachyzoites transition to bradyzoites that can persist for life in neurons and muscle cells . When infected individuals become immunocompromised , such as in chemotherapy , HIV infection , or organ transplantation ( Rabaud et al . , 1994; Robert-Gangneux et al . , 2015 ) , bradyzoites can reactivate to become tachyzoites , causing severe neurological damage and even death . While no causal link has been established , a population-wide study has uncovered significant association of Toxoplasma infection with schizophrenia in chronically infected humans ( Sutterland et al . , 2015 ) . Chronic infection in mice has been observed to induce behavioral changes such as loss of aversion to cat urine , which is hypothesized to increase the transmission rate of Toxoplasma to its definitive feline host where sexual reproduction occurs ( Vyas et al . , 2007 ) . As there are no therapeutic interventions to prevent or clear cysts in infected individuals , understanding how Toxoplasma transitions through its life stages remains of critical importance . The development of in vitro methods to induce Toxoplasma differentiation have facilitated investigation of several aspects of chronic infection , including transition of tachyzoites to bradyzoites ( Soête et al . , 1994; Jeffers et al . , 2018 ) . Bulk transcriptomic analyses of Toxoplasma gondii at distinct asexual stages reveal genetic modules that are expressed in each stage ( Buchholz et al . , 2011; Manger et al . , 1998a; Pittman et al . , 2014; Yip , 2007; Cleary et al . , 2002; Radke et al . , 2005; Chen et al . , 2018; Fouts and Boothroyd , 2007 ) , including AP2 transcription factors that are thought to play a role in differentiation ( Hong et al . , 2017; White et al . , 2014 ) ; however , transitioning parasites convert to the bradyzoite stage asynchronously and display a high degree of heterogeneity along the developmental pathway and in gene expression ( Soete et al . , 1993; Watts et al . , 2015 ) . Furthermore , parasites within the same tissue cysts have been shown to display heterogeneity in the expression of bradyzoite marker proteins ( Ferguson et al . , 1994 ) . The transition of tachyzoites to the bradyzoite stage results in an overwhelming majority of mature bradyzoites in the G1 phase of the cell cycle that divide slowly , if at all ( Radke et al . , 2003; Sinai et al . , 2016 ) . Furthermore , tachyzoites exhibit slower growth kinetics immediately prior to the bradyzoite transition ( Radke et al . , 2003; Jerome et al . , 1998 ) . This suggests that parasites exit the cell cycle to differentiate into bradyzoites , a pattern consistent with developmental processes in several other eukaryotic organisms ( Ali et al . , 2011; Kim et al . , 2010 ) . Dissecting these cell cycle aspects of stage conversion requires a more detailed analysis than has been possible with bulk measurement of tachyzoite or bradyzoite populations , or with the use of genetically modified parasites coupled with chemical synchronization of cell cycle progression ( Radke et al . , 2005; Radke and White , 1998; Conde de Felipe et al . , 2008; Behnke et al . , 2010 ) . This is because the latter approaches require large quantities of synchronized parasites and can potentially introduce artificial perturbations . Furthermore , bulk measurement fails to distinguish parasite-to-parasite variation that is independent of cell cycle or known developmental processes , potentially missing the phenotypic diversity intrinsic to a population of cells . Single-cell RNA sequencing ( scRNA-seq ) offers a powerful and unbiased approach to reveal the underlying heterogeneity in an asynchronous population of cells . Droplet and FACS-based approaches have already been applied towards multicellular parasites such as Schistosoma to reveal developmental changes within different hosts ( Wang et al . , 2018 ) . Recently , scRNA-seq has revealed a surprising degree of heterogeneity in another apicomplexan parasite , Plasmodium ( Reid et al . , 2018; Ngara et al . , 2018; Poran et al . , 2017; Howick et al . , 2019 ) . Analyses derived from these single-parasite measurements uncovered rare and critical transition events in parasite development that were undetectable in bulk measurements . Combined with novel analytical tools and increase in measurement throughput , scRNA-seq can help facilitate the discovery of regulatory factors that mediate these developmental transitions in a system-wide fashion . Here , we performed scRNA-seq to reconstruct transcriptional dynamics of asynchronous Toxoplasma parasites in the course of cell cycle and asexual development in vitro . Our analysis reveals the existence of hidden cell states and rare parasites that show highly unusual patterns of gene expression associated with specific transcription factors . We also discover that individual parasites vary substantially in the expression of surface antigen genes , suggesting the possibility of a novel form of antigenic variation that may play a crucial role in host immunity evasion . Importantly , we identified a single parasite from our scRNA-seq dataset that displayed an unusual expression pattern of surface antigens , leading us to identify and validate the regulatory role of a previously uncharacterized AP2 transcription factor typically associated with parasites in sexual development . Lastly , we show that despite fundamental differences in their modes of cell division , there are conserved transcriptional programs between the asexual life cycles of Toxoplasma gondii and Plasmodium berghei . Our results combined provide the first comprehensive single-cell atlas of Toxoplasma in the course of asexual development and help reveal that the antigenic repertoire of this parasite is much more heterogeneous than previously appreciated .
There are more than a dozen approaches available for single-cell isolation and transcriptome amplification . Based on benchmark comparisons , Smart-seq2 generally has higher sensitivity than competing droplet-based approaches ( Svensson et al . , 2017; Ziegenhain et al . , 2017 ) . We reasoned that sensitive measurement is crucial in our study , given that single Toxoplasma gondii parasites are at least 50-fold smaller in volume than a typical mammalian cell , and thus the average parasite gene is likely expressed with much lower copy number per cell than a typical mammalian gene . For our initial studies , we used the common Type I lab strain of Toxoplasma , RH , grown in vitro in human foreskin fibroblasts ( HFFs ) . Following such growth , individual tachyzoites were released by passage through a narrow-gauge needle and then purified by fluorescence activated cell sorting ( FACS ) into 384-well or 96-well plates . We then synthesized , amplified , and barcoded cDNA using Smart-seq2 and Illumina Nextera protocols . We reduced the reagent cost in 384-well plates effectively by ten-fold compared to the 96-well format . The sequenced reads were bioinformatically deconvolved and grouped into individual parasites for analysis using modified bcl2fastq and custom python scripts ( Materials and methods ) . A schematic to illustrate our experimental workflow is shown in Figure 1 . To ensure that our workflow efficiently captures single Toxoplasma parasites , we mixed equal numbers of two transgenic lines of RH , one expressing GFP and the other expressing mCherry , and sorted individual parasites into a 384 well plate based on the presence of either green or red fluorescence . After Smart-seq2 amplification , we quantified the expression of GFP and mCherry mRNAs using quantitative polymerase chain reaction ( qPCR ) . Across all 301 wells that we measured , we observed the presence of both GFP and mCherry mRNA in only one well , indicating that the rate of doublet events is below 1% ( Figure 1—figure supplement 1a ) . To address the possibility that the reduced reagent volume in the 384-well format could potentially saturate the reaction chemistry and thus limit quantification range , we sorted varying numbers of RH and quantified with qPCR the mRNA of a gene encoding the abundantly expressed surface protein , SRS29B ( SAG1 ) ( Figure 1—figure supplement 1b ) . The detected amount of SAG1 mRNA present in wells containing single , eight , or fifty RH fell into the expected distributions based on the number of parasites sorted , without signs of saturation , indicating that the assay is capable of quantitative measurement at the single Toxoplasma level . We then proceeded to sort parasites into 384-well based on live/dead staining and sequence 729 RH ( 612 passed quality control ) strain single Toxoplasma parasites from asynchronous populations grown under tachyzoite conditions . We also sorted and sequenced 287 RH parasites ( 237 passed quality control ) into 96-well plate for comparison , which we will discuss further in another section . For Pru and ME49 strains , we collected parasites at several time points post alkaline treatment which induces differentiation from tachyzoites to bradyzoites to follow changes in their expression profiles during in vitro development ( Materials and methods ) , yielding 2655 Pru ( 2198 passed quality control ) and 1828 ME49 ( 1552 passed quality control ) single parasites . RH reads were aligned to the GT1 strain genome , which is the most complete reference for Type I parasites , while Pru and ME49 were aligned to the ME49 strain Type II genome reference . Because many genes encoding Toxoplasma secretion factors and surface proteins are evolutionary products of gene duplication events ( Reid , 2015 ) , we expected high sequence similarity amongst a substantial portion of the parasite genes . Thus , we modified our gene counting pipeline to account for duplicated genes by distributing reads across all regions with equal alignment score that passed thresholds ( Materials and methods ) . The reason why we adopted a correction scheme for multiply-mapped reads is because the analysis of co-occurrence , or lack thereof , of pathogenic factors ( e . g . surface antigens ) hinges on sensitive detection of their expression . We thus faced a choice of increasing the false positive in gene alignment by correcting for multiply mapped reads or increasing its false negative by counting only uniquely aligned reads . We chose to account for multiply mapped genes , which would otherwise be obscured , as we hypothesized that surface antigen expression may vary between individual parasites , which we further discuss in another section . A comparison of counting methods does not reveal significant differences in the observed counts ( Figure 1—figure supplement 1c ) . Further analysis reveals that our modified pipeline recovered the detection of more parasite genes than default parameters ( Figure 1—figure supplement 1d ) . To ensure that poorly amplified or sequenced parasites did not confound our downstream analysis , we filtered samples based on several quality metrics including percent reads mapping to ERCC spike-in sequences , number of genes detected , and sequencing depth ( Materials and methods; Figure 1—figure supplement 2a ) . On average , each sequenced parasite contains 30–50% reads that mapped to Toxoplasma genes encoding proteins ( top panel in Figure 1—figure supplement 2b ) . Most of the unmapped reads are from Toxoplasma’s 28 s ribosomal RNA . The relatively high rate of rRNA contamination was also observed in single-parasite RNA sequencing of Plasmodium ( Reid et al . , 2018 ) . We suspect this occurred due to non-specific priming as protozoan cells have low RNA input . We normalized for sequencing depth across cells by dividing each read count by the read sum of each corresponding cell and then multiplied by the median of read sum within each dataset to yield ‘count per median’ ( CPM ) . After filtering ERCC spike-in and rRNA genes , we detected on average 862 , 1290 , and 970 genes per parasite with greater than or equal to two read counts ( Materials and methods ) in the RH , Pru , and ME49 datasets , respectively ( bottom panel in Figure 1—figure supplement 2b ) . Characterization of our measurement sensitivity based on logistic regression modeling of ERCC spike-in standards ( Materials and methods ) ( Lönnberg et al . , 2017 ) reveals a 50% detection rate of 17 , 17 , and 26 molecules for RH , Pru , and ME49 datasets , respectively ( top panels in Figure 1—figure supplement 2c ) . The sensitivity of our 384-well Smart-seq2 measurement is comparable to the previously reported range for the 96-well format ( Ziegenhain et al . , 2017 ) . As expected from our qPCR titration experiment , scRNA-seq measurement of gene expression is quantitative at single parasite resolution based on ERCC standards . We determined that the linear dynamic range of our scRNA-seq measurement spans over three orders of magnitude ( bottom panels in Figure 1—figure supplement 2c ) . Taken together , we demonstrate a scalable and cost-effective approach to measure the transcriptomic changes of individual parasites with high sensitivity and accuracy . Previous work posited a potential link between bradyzoite development and cell cycle , which poses a significant challenge to the bioinformatic analysis of either process ( Radke et al . , 2003 ) . To characterize cell cycling changes without confounding contributions from developmental processes , we first analyzed an asynchronous population of Type I RH strain parasites grown under tachyzoite conditions; this extensively passaged lab strain is known to have little propensity to switch to bradyzoites under such conditions ( Soête et al . , 1994 ) ( Materials and methods ) . After filtering out genes whose expression levels did not vary significantly between individual parasites , we projected the data with principal component analysis ( PCA ) ( Materials and methods ) . Interestingly , the first two principal components ( PCs ) reveal a circular trajectory that coincides with relative DNA content , determined using a cell permeable DNA content stain ( top panel in Figure 2a ) . Unsupervised neighborhood clustering identified five distinct clusters of parasites based on their transcriptional profiles ( middle panel in Figure 2a ) ( Materials and methods ) . We computed RNA velocity to infer transcriptional dynamics ( La Manno et al . , 2018; Wolf et al . , 2018 ) and the velocity vector field indicates a net ‘counter-clockwise’ flow of transcriptional changes ( bottom panel in Figure 2a ) ( Materials and methods ) . We assigned cell cycle phase to the clusters based primarily on change in DNA content ( Figure 2—figure supplement 1a ) but also considering previous bulk transcriptomic characterization ( Behnke et al . , 2010; Figure 2—figure supplement 1b ) . Unsupervised clustering identified two distinct clusters in G1 state , which we have designated as G1a and G1b . We found a list of differentially expressed genes between the two G1 clusters . The G1a cluster is highly enriched for the expression of metabolic genes such as phenylalanine hydroxylase ( TGGT1_411100 ) and pyrroline-5-carboxylate reductase ( TGGT1_236070 ) , as well as invasion-related secreted factors such as MIC2 ( TGGT1_201780 ) , MIC3 ( TGGT1_319560 ) , and MIC11 ( TGGT1_204530 ) . On the other hand , G1b cluster is enriched for the expression of 3-ketoacyl reductase ( TGGT1_217740 ) and cytidine and deoxycytidylate deaminase ( TGGT1_200430 ) , as well as numerous uncharacterized proteins ( Supplementary file 2 ) . The relative abundance of G1a , G1b , S , M , and C states were determined to be 18% , 32% , 28% , 15% , and 7% , respectively . Without chemical synchronization , the correlation between the scRNA-seq data of asynchronous parasites and previously published bulk transcriptomic measurement suggests strong agreement in cluster assignment and cell cycle state identification ( Figure 2—figure supplement 1b ) . This highlights a key advantage of scRNA-seq , as it enables identification of cell cycle status of a parasite without reliance on chemical induction , which may lead to unnatural cellular behavior . To verify the cyclical nature of gene expression through the lytic cycle , we reconstructed a biological pseudotime of RH using Monocle 2 ( Materials and methods ) . The results show a clear oscillatory expression pattern for the variably expressed genes along the pseudotime axis ( Figure 2b ) . To further characterize cell cycle expression patterns , we clustered genes based on pseudotime interpolation and hierarchical clustering ( Materials and methods ) . Some of the key organelles in tachyzoites are made at different times in the cell cycle ( Behnke et al . , 2010 ) . To confirm and refine this finding , we calculated the mean expression values for each set of organelle-specific genes based on their annotation in ToxoDB ( Gajria et al . , 2008; Supplementary file 4 ) . This showed the expected , strong oscillation with pseudotime ( bottom panel in Figure 2—figure supplement 1c ) , which also strongly correlates with the oscillation of DNA and total mRNA content ( top panels in Figure 2—figure supplement 1c ) . On the other hand , we also observed instances where a given gene’s expression was discordant to the dominant trend of its nominal organelle set ( arrows in Figure 2—figure supplement 1d ) . For example , 63 . 5% of genes annotated as rhoptry ( ROP ) or rhoptry neck ( RON ) are assigned pseudotime cluster 3 , while the remaining 36 . 5% rhoptry genes are assigned pseudotime clusters 1 or 2 ( Figure 2—figure supplement 1e ) . Specifically , genes annotated as ROP33 and ROP34 , based on their homology to genes encoding known rhoptry proteins , are assigned to cluster 2 instead of cluster 3 ( left panel in Figure 2—figure supplement 1f ) . Recent reports have experimentally determined these two to be non-rhoptry-localizing proteins . This is consistent with our observation of discordance between their and known ROPs' expression profiles along the pseudotime ( Beraki et al . , 2019 ) . Through analysis of pseudotime clustering , we also identified genes not annotated as ROPs within the ROP-dominated cluster 3 , such as TGGT1_218270 and TGGT1_230350 , that have recently been shown to encode bona fide rhoptry and rhoptry neck proteins , now designated as ROP48 and RON11 , respectively ( Camejo et al . , 2014; Beck et al . , 2013 ) ( left panel in Figure 2—figure supplement 1f ) . As another example , IMC2a peaks in expression level in G1 , while the majority of inner-membrane complex ( IMC ) genes are expressed towards the M/C phase of the cell cycle ( right panel in Figure 2—figure supplement 1f ) . A recent report has proposed reannotation of IMC2a as a dense granule ( GRA ) protein ( GRA44 ) based on subcellular localization ( Coffey et al . , 2018 ) , which is consistent with our unsupervised group assignment of IMC2a as falling in cluster one where GRA genes dominate . A list of 8590 RH genes with their corresponding pseudotime clustering assignment is provided ( Supplementary file 5 ) . We observe high discordance of pseudotime expression for several genes in each annotated organelle sets , suggesting that the current Toxoplasma annotation may need significant revision . Our scRNA-seq data provide an important resource to help identify mis-annotated genes and infer putative functions of uncharacterized proteins . Toxoplasma has one of the most complicated developmental programs of any single-celled organism; however , it is unknown how synchronized the transition is between developmental states . To address this , we assessed the inherent heterogeneity within asexually developing Pru , a type II strain that is capable of forming tissue cysts with characteristics that resemble early ‘bradyzoites’ from in vivo source upon growth in in vitro alkaline conditions ( Soete et al . , 1993; Jones et al . , 2017 ) . We applied scRNA-seq to measure and analyze Pru parasites grown in HFFs as tachyzoites ( ‘uninduced’ ) and after inducing the switch to bradyzoites by growth in alkaline media for 3 , 5 , and 7 days . Projection of the first two PCs of uninduced Pru tachyzoites ( Day 0 ) reveals the expected circular projection ( Figure 3—figure supplement 1a ) , presumably reflecting cell cycle progression as seen for the RH tachyzoites , described above . To validate this , we developed a random forest classifier model based on our cell cycle assignment in RH ( Materials and methods ) . Comparable to what we observed in RH , cell cycle prediction reveals that the uninduced population of Pru is composed of 28% , 41% , 21% , 7% , and 3% parasites in G1a , G1b , S , M , and C states , respectively . Consistent with previous observation ( Jerome et al . , 1998 ) , our data show most induced Pru parasites ( Day 3–7 ) are in the G1 state with a predominance of G1b ( Figure 3—figure supplement 1b ) . To identify transcriptomic changes associated with the tachyzoite-bradyzoite transition , we next projected data from both induced and uninduced Pru parasites onto two dimensions using UMAP , a nonlinear dimensionality reduction method ( Materials and methods ) ( McInnes et al . , 2018 ) . Unsupervised clustering revealed six distinct clusters of parasites , which we label P1-6 ( Figure 3a ) . Cluster formations partially correlate with treatment time points and cell cycle states ( Figure 3b; Figure 3—figure supplement 1c ) , suggesting that the asexual differentiation program overlaps with cell cycle regulation in Toxoplasma , as proposed previously ( Radke et al . , 2003 ) . We stratified the datasets by days post alkaline induction ( dpi ) and observed elevated expression of previously described ‘early’ bradyzoite marker genes in induced parasites , including SRS44 ( CST1 ) and BAG1 , with a concomitant reduction in expression of SRS29B ( SAG1 ) , a tachyzoite-specific surface marker gene ( Figure 3—figure supplement 2 ) . The abundance of SAG1+ parasites ( 72% ) in the induced population suggests two possible interpretations: ( 1 ) the depletion of SAG1 mRNA is relatively slow and we are measuring SAG1 transcripts made when the parasites were still tachyzoites , or ( 2 ) the asexual transition induced by alkaline treatment is highly asynchronous . Interestingly , RNA velocity analysis suggests that P3 may be a fate decision point as the trajectory trifurcates into either P4 ( cell cycle ) , P1 , or P2 as evident by the net transcriptional flow ( compare Figure 3a to right panel in Figure 3b ) . To determine the gene modules specific to a given cluster , we conducted differential gene expression for each cluster ( Figure 3c and Supplementary file 5 ) . P1 cluster cells are enriched for expression of bradyzoite-specific genes while P2-5 are enriched for that of tachyzoite-specific or cell cycle-associated genes ( Figure 3c–d ) . In our scRNA-seq data , we also observe a small portion of BAG1+ bradyzoites ( 7 . 1% ) annotated as either S , M , or C states , indicating that they are replicating ( Figure 3—figure supplement 1d ) . Our data supports the notion that bradyzoites can undergo cell cycle progression ( Watts et al . , 2015 ) . We observe a family of transcription factors known as Apetala 2 ( AP2 ) that are differentially expressed across different clusters , some of which are implicated in Toxoplasma development ( De Silva et al . , 2008; Radke et al . , 2013; Walker et al . , 2013; Figure 3e ) . In particular , we identify AP2Ib-1 , AP2IX-1 , AP2IX-6 , and AP2VI-2 as over-expressed in P1 , suggesting their potential roles in the regulation of developmental transition , while AP2IX-9 , AP2X-8 , AP2VIIa-6 , AP2XI-1 , AP2IX-3 , AP2VIII-7 , and AP2-domain protein TGME49_215895 ( not yet assigned a formal AP2 number ) , are highly expressed in P6 , hinting at their possible roles in defining this distinct cluster of parasites . The most highly expressed genes in P6 include genes enriched in P2 as well as bradyzoite-specific genes found in P1 ( Figure 3c ) . To identify genes that are specifically expressed in P6 , we used Wilcoxon’s test ( Figure 3—figure supplement 3a ) ( Materials and methods ) between P6 and P2 or P1 . Comparison of our data to previous bulk transcriptomic measurement in tachyzoites , tissue cyst , or isolates at the beginning or the end of sexual cycle showed no specific enrichment in known developmental stages ( Figure 3—figure supplement 3b; Ramakrishnan et al . , 2019 ) . Instead , we show that based on their expression , P6 forms a distinct sub-population of parasites which suggests that alkaline induced Toxoplasma may be more heterogeneous than previously thought . Thus , scRNA-seq resolves a transcriptomic landscape of asexual development and suggests the existence of an otherwise hidden state . To determine the reproducibility of the phenomena we observed in the differentiating Pru strain parasites , we repeated the analysis with another widely used Type II strain , ME49 , examining 1828 single ME49 parasites exposed to alkaline conditions to induce switching to bradyzoites . Data from the two experiments were computationally aligned using Scanorama to remove technical batch effects while retaining sample-specific differences ( Hie et al . , 2019 ) . Unsupervised clustering revealed five distinct clusters in ME49 which share significant overlap in expression patterns with Pru ( Figure 3—figure supplement 4a ) . Matrix correlation of batch-corrected expression across the two strains demonstrate analogous mapping for most , but not all cluster identities ( Figure 3—figure supplement 4b ) . To simplify the visualization and comparison across the two datasets , we next applied Partition-Based Graph Abstraction ( PAGA ) to present clusters of cells as nodes with connectivity based on similarity of the transcriptional profiles between clusters ( Wolf et al . , 2019 ) . A side-by-side comparison of expression of tachyzoite , bradyzoite , and sexual stage specific genes reveals some key similarities and dissimilarities ( Figure 3—figure supplement 4c ) . Clusters P1 and M1 are both enriched for the expression of bradyzoite marker genes , while clusters M4-5 and P4-5 are both predicted to be S/M/C phases of the cell cycle . Curiously , P6-specific genes ( green panels in Figure 3—figure supplement 4c ) are not enriched in any cluster in ME49 , suggesting that P6 state is not conserved across the two type II strains . Such differences may not be surprising , however , as Pru and ME49 have entirely distinct passage histories . A unique advantage of scRNA-seq over bulk RNA-seq is its ability to measure cell-to-cell variation that is independent of known biological processes . The Toxoplasma gondii genome encodes a family of over 120 SAG1-related sequence ( SRS ) proteins that fall into distinct subfamilies; most or all of these are presumed to be surface antigens based on their sequence similarities , including the presence of a predicted GPI-addition signal ( Manger et al . , 1998b ) . Whether SRSs constitute an antigenic repertoire that contribute to evasion of host adaptive immunity response is unclear; however , existing data on developmentally regulated expression of SRSs including SAG1 ( SRS29B ) and SRS16B lend support to that hypothesis ( Kim and Boothroyd , 2005; Kim et al . , 2007 ) . In the experiments using 384-well plates , we noted that most SRS genes were detected in only a few sporadic cells ( Figure 4—figure supplement 1a ) ; however , biological variation was difficult to distinguish from measurement dropout ( failure to capture a mRNA molecule ) given the limited sensitivity of 384-well assay and the relatively low abundance of SRS transcripts . To increase our sensitivity , therefore , we performed scRNA-seq of extracellular Toxoplasma in 96-well plates which achieved ~40% sensitivity of detection for single molecules of ERCC spike-ins , compared to 14% sensitivity that we obtained in the 384-well experiments , at roughly equivalent sequencing depth ( Figure 4a ) . To determine the extent of transcriptional variation in SRS genes independent of cell cycle or asexual development , we isolated RH parasites in G1 state using DNA content stain and FACS ( Figure 4—figure supplement 1b ) . We noticed that the distributions of DNA content in single cells varied across the two experiments ( compare Figure 4—figure supplement 1b to Figure 3—figure supplement 1a ) , suggesting that the distribution may depend on external factors such as tissue culture confluence , parasite load , or the amount of time passed since infection . We also measured on average 984 genes with read counts equal to or greater than two in 96-well plates , up from 862 genes in 384-well plates ( Figure 4—figure supplement 1c ) . Analysis of the resulting 96-well plates data show that the vast majority of SRS genes are detected in only a small fraction of the population ( Figure 4b ) , similar to what we observed in 384-well experiments . To control for measurement dropout as a cause of such variation , we first determine the non-zero median for those genes , that is , the median expression level in cells where any transcript for that gene is detected . We then assess how often we fail to detect an ERCC spike-ins that had a similar non-zero median expression level . If the failure to detect a SRS was due to measurement dropout , then the fraction of cells without detection for its transcript should be about the same as for the ERCC spike-in with similar non-zero median expression . If , on the other hand , the failure to detect the SRS transcript is due to underlying biological variation between cells , then we will find a lower frequency of cells with detectable transcript for that gene than the ERCC spike-in with similar expression . The results show that SRSs are indeed detected at a substantially lower rate when compared to ERCC spike-ins ( Figure 4c ) , indicating that the variation of SRS detection cannot be explained by measurement noise . Compared to most other genes , SRSs are expressed in a significantly smaller fraction of the population and at a relatively lower abundance ( Figure 4—figure supplement 1d-e ) . To determine whether variation of SRS expression is due to cell cycle or asexual development , we developed a bootstrapping approach to quantify the dependence of expression on a topological network that represents either process ( Materials and methods ) . We show that apart from a few known SRSs , most SRSs do not co-vary with cell cycle or asexual development , which suggests that the sporadic nature of SRS expression may hint at a different biological role for their variation , beyond cell cycle and asexual development , as discussed further below ( Figure 4d ) . These results reveal new insights into the antigenic repertoire of Toxoplasma , which appears far more heterogeneous , on a parasite-to-parasite basis , than previously known . In examining our data , we were struck by a lone cell in the 384-well dataset of RH that had no detectable level of SRS29B ( SAG1 ) , which was otherwise ubiquitously and abundantly expressed in all tachyzoites ( Figure 5a ) . We wondered if this cell was damaged or unhealthy but its gene count and read depth were similar to its cohort ( Figure 5b ) . Further analysis of this SAG1- cell’s transcriptome revealed that it lacks expression of the most abundant tachyzoite-specific and bradyzoite-specific genes; instead , its gene expression most closely resembles the sexual stages from cat intestinal isolates , including abundant expression of a cat-stage SRS , SRS22C ( Figure 5c ) . Within the set of genes uniquely expressed in the SAG1- cell are AP2IX-1 and AP2III-4 which suggest a possible role for one or both of these transcription factors in regulating the transcript differences observed in this outlier . To test this hypothesis , we transiently expressed AP2IX-1 under the control of a strong promoter in RH parasites ( Figure 5d ) . Consistent with our hypothesis , immunofluorescence assay ( IFA ) revealed a reduction of SAG1 surface protein expression within ~18–20 hr after transfection ( Figure 5e–f ) , while quantitative RT-PCR showed significantly higher mRNA expression for several of the genes that were upregulated in the SAG1- cell: namely , 207965 , 222305 , 205210 , and SRS22C , the latter being over 1000-fold higher in the transfected population than control ( Figure 5g ) . Note that the SAG1 transcript levels in the transfected population were not substantially lower , as expected because there remains a large number of untransfected cells in the population which still express high levels of this gene . As SRS22C is predicted to be a surface antigen like the rest of SRS ( Gajria et al . , 2008 ) , this suggests that AP2IX-1 induction can control the switching of surface antigens . We also attempted to express AP2III-4 that is upregulated in the SAG1- cell but were unable to obtain an epitope-tagged version of the gene ( which is over nine kbp , including the promoter ) . Overall , these results demonstrate the ability to infer transcriptional regulation from a single parasite cell that has an unusual co-expression pattern , revealing AP2IX-1 as a novel transcriptional factor that can alter antigen expression in Toxoplasma . Plasmodium and Toxoplasma are both unusual for their prevalence in humans and their complex developmental transition , despite the fact that humans are non-definitive hosts for both pathogens . Toxoplasma has been used as an experimental model for other apicomplexans including Plasmodium , yet the replication modes amongst apicomplexans can be very different . For example , asexual replication of Toxoplasma involves endodyogeny , which is similar to canonical binary fission , in order to replicate and divide . Asexual division of Plasmodium , on the other hand , involves schizogony , a process that entails multiple rounds of DNA replication and nuclear division followed by a mass cytokinesis ( Aly et al . , 2009; Cowman et al . , 2016 ) . Despite their fundamental differences in cell cycle progression , we wondered if our Toxoplasma dataset can be combined with the Malaria Atlas ( Howick et al . , 2019 ) to provide insights into apicomplexan biology . Toward this end , we first identified 1830 one-to-one orthologous genes between Plasmodium berghei and Toxoplasma gondii Pru ( Figure 6—figure supplement 1a ) . As expected , the vast majority of orthologous genes are not surface adhesion factors or effector molecules in Toxoplasma ( Figure 6—figure supplement 1b ) . After removing non-orthologous genes , we combined the two species’ datasets to produce an integrated UMAP projection with Scanorama ( Hie et al . , 2018 ) . In this integrated projection space , mutually similar clusters of cells in the two datasets are brought close together , while organism-specific cell types are not ( Figure 6a ) . Transitional dynamics of the parasitic development are preserved in the integrated projection , as separation of the original cluster assignment indicates ( Figure 6b ) . We highlight comparative similarity across the two organisms by calculating the fraction of cells that share the same topological neighborhood in the integrated network of Plasmodium and Toxoplasma ( Figure 6c ) , revealing striking similarity of expression pattern between the two organisms . We discover the concerted transcriptional expression of several conserved orthologous gene sets in the life cycles of these two parasites ( Figure 6d ) . For example , the pre-erythrocytic stages of Plasmodium ( sporozoite and merozoites ) most closely match the ‘G1 a’ stage of Toxoplasma . Both parasites at these stages are not actively replicating DNA and express high levels of ribosomal and mitochondrial genes . Meanwhile , the ring stage of Plasmodium most closely matches the ‘G1 b’ phase of Toxoplasma because they both express high levels of ribosomal genes with minimal expression of IMC-related and microtubule-related genes . Exo-erythrocytic form ( EEF ) , microgametes ( male ) , and trophozoite stages of Plasmodium berghei most closely match the ‘S’ phase of Toxoplasma cell cycle . This is because they express DNA-replication factors and centrosome components , which are required for condensation and segregation of chromosomes . Intriguingly , the schizont stage of Plasmodium most closely resembles the ‘M’ and ‘C’ phases due to the expression of microtubule , centrosome , and IMC genes . A more detailed illustration of these gene sets expression is shown in Figure 6—figure supplement 1c . Our results indicate that while the cellular morphology and replication strategies may differ drastically between the two parasites , the cellular state , as defined by the transcriptomic profiles , can bear striking analogy and resemblance in the course of asexual replication . Toward enabling the larger scientific community to take advantage of the scRNA-seq data we collected on the three Toxoplasma strains , we have constructed an interactive single-cell atlas for Toxoplasma gondii ( http://st-atlas . org ) using Bokeh and Javascript . Our atlas resource allows users to visualize the expression pattern of individual cells by providing a gene ID of interest and using the built-in graphical interface toolset . A factor plot displays expression levels within the parasite population based on cell cycle status , cluster identities , days post induction , or other categories . A sub-panel on the bottom left shows additional information for each input gene including translated product , mean , and standard deviation of the expression . Users can selectively highlight a subset of parasites by using a simple click-and-drag interface for further analysis . Example use cases are provided in Figure 6e–f . We have also made the raw data available through the atlas website . We hope this will help make our data and results readily accessible for others interested in exploring Toxoplasma parasitology .
We describe here single-cell RNA sequencing ( scRNA-seq ) for measurement of mRNA transcripts from individual extracellular in vitro Toxoplasma gondii , an obligate intracellular protozoan parasite . The results show that scRNA-seq can reveal intrinsic biological variation within an asynchronous population of parasites . Two types of biological variation could be seen in our asynchronous populations: cell cycle progression and asexual differentiation . We found the existence of two distinct 1N transcriptional states in cycling parasites which we call G1a and G1b , concurring with what was previously reported in bulk analyses of Toxoplasma ( Behnke et al . , 2010 ) . Interestingly , bradyzoites are found predominantly in G1b but not in G1a , suggesting the possibility of a putative checkpoint between these two phases that may also play a role in regulating the developmental transition . Our data further shows a small fraction of bradyzoites to be cycling which supports the hypothesis that bradyzoites can in fact divide ( Sinai et al . , 2016 ) . Our results showed a very strong correlation between cell cycle and expression of genes encoding proteins in various subcellular organelles , as noted previously using synchronized bulk populations ( Behnke et al . , 2010 ) . The results here , however , show an even more dramatic and extreme dependence on cell cycle , allowing refinement of approaches that use such timing to predict a given protein’s ultimate organellar destination in the cell ( Camejo et al . , 2014 ) . They also extend such analyses to the Type II strains , Pru and ME49 , which have not previously been examined in this way . In addition to the above , we observed some striking and unexpected heterogeneity within asexually developing parasites . We discovered a cluster of cells , labeled P6 , in the differentiating Pru parasites that is distinct from the rest of the alkaline-induced population of cells . Constituting 21% of the alkaline-induced population , the P6 cluster is marked by a set of genes that were previously detected by bulk transcriptomics in bradyzoites of tissue cysts ( Ramakrishnan et al . , 2019 ) . Remarkably , while most of these genes have unknown functions , we identified an enriched gene with a predicted AP2 domain , which may contribute to the unique expression pattern observed in this group of parasites . We found that the P6 expression profile is intermediate to P2 tachyzoites and P1 bradyzoite clusters . Interestingly , the genes enriched in P6 overlap with a subset of canonical bradyzoite marker genes including LDH2 and SRS35A , albeit expressed at a lower level than in P1 ( Figure 3c ) . In addition , we observed a gradual increase in the proportion of P6 cells as induction proceeded from day 3 to day 7 . Taken together , one possible explanation for the emergence of P6 cluster is a reverted conversion from bradyzoites to tachyzoites in which alkaline stress fails to maintain the bradyzoite state . Our data and previous reports are consistent with this interpretation ( Weiss et al . , 1998 ) . On the other hand , we cannot rule out the possibility that this cluster is developmentally ‘confused’ by the presence of a general stressor such as alkaline . RNA velocity analysis in the Pru data does not reveal a strong transcriptional flow between P1 and P6 . Rather , P6 appears to transcriptionally transition from P2 tachyzoites . Thus , the P1 bradyzoites and P6 parasites are either distinct and separate developmental trajectories , or the transition from P1 to P6 is a rapid and rare event . Regardless , our results reflect a surprising diversity in an asexually transitioning population of Toxoplasma . Future measurement of single parasites isolated from in vivo sources coupled with genetic manipulation of the parasite genome , will further clarify the underlying developmental states that we identified here . To quantify the variation of SRSs , which are generally expressed at low copy number , we performed 96-well Smart-seq2 , which greatly improved measurement sensitivity over the 384-well format , likely due to changes in the input mRNA concentration . For scRNA-seq of pathogens , which tend to have smaller size and lower mRNA content than mammalian cells , we think a careful selection of the measurement approach is necessary based on consideration of throughput and measurement sensitivity , between which there is often a tradeoff . Combined with a novel approach that we developed based on random permutation and K-nearest neighbor ( KNN ) averaging , we were able to quantify the association of gene expression variation to known biological processes , like cell cycle and development . We discovered that Toxoplasma exhibits unexplained , sporadic variation in the expression of most SRSs , which may have biological implications . For example , it could expand the mode of interactions with the host and be the result of strong selective pressure to maximize invasion efficiency and transmission in a variety of different host species of cell types . Maintaining a large phenotypic diversity can be beneficial in ensuring at least some members will be able to invade the cells it encounters and/or evade adaptive immune response , enabling propagation in whatever the host environment encountered . Very surprisingly , our scRNA-seq analysis identified an atypical co-expression pattern in an in vitro RH ‘tachyzoite’ that is indicative of sexual development , which has not been previously observed in these culture conditions . This suggests that at least the beginnings of sexual developmental can spontaneously occur even in the absence of the cat intestinal environment or other chemical cues . Combined with transient expression experiments , the data from this cell enabled us to show that AP2IX-1 is sufficient to drive a switching of surface antigen expression toward that resembling the sexual stages of the parasite . Assuming this change in mRNA abundance translates into a change in protein levels of TGGT1_222305 , which is predicted to contain a transmembrane domain , and SRS22C , which is strongly indicated to be a surface antigen like the rest of SRS , our data indicate that AP2IX-1 contributes to remodeling of the surface antigen repertoire during differentiation . Furthermore , this suggests AP2IX-1 may play a causal role in controlling the sexual differentiation of Toxoplasma . Considering that the family of AP2 transcription factors was originally found to regulate stress response and floral sexual differentiation in plants , our finding suggests the biological role of AP2 family in Toxoplasma is evolutionarily conserved . Switching of surface antigens in parasites may be particularly favorable to parasites under stressful conditions , perhaps including the stress of an immune response , thereby enabling evasion of host immunity . Regardless , these results show that scRNA-seq can reveal rare parasite variants that are , presumably , a result of spontaneous epigenetic changes similar to what has been described in cancer cells ( Litzenburger et al . , 2017 ) or transcriptional noise which was previously characterized in Escherichia coli ( Elowitz et al . , 2002 ) . An important difference from the situation with cancer cells , however , is that these individual variants may be non-viable and so impossible to obtain as a stable line; thus scRNA-seq may be uniquely able to provide a detailed understanding of their very interesting and informative gene expression . In this case , a previously uncharacterized AP2 factor was revealed and shown to be responsible for regulating at least some of the genes that were uniquely expressed in this variant , relative to the remainder of the tachyzoites in this population . Expanding the number of AP2 transcription factors ( totaling over 68 of them ) analyzed in this way could enable the deduction of the sets of ‘regulons’ in this parasite . Thus , even though these rare variants may be non-viable ‘biological freaks’ , they can be highly informative and would be completely undetectable in bulk measurement . Recently , cross-species analysis of scRNA-seq datasets has attracted considerable interest ( Butler et al . , 2018; Ding et al . , 2019 ) . Our study provides the first comparative analysis of developmental processes between two apicomplexans , both of which cause prevalent and potentially devastating diseases . While Plasmodium and Toxoplasma undergo distinct modes of cell replication and asexual development , we identified cross-species clusters that share significant similarity in the expression of orthologous genes . We discovered that the timing of expression in gene sets involved in cell cycle are conserved in the erythrocytic cycle of Plasmodium berghei . Lastly , we have made the datasets of our study available by creating an interactive and easily accessible web-browser . We hope this encourages other individuals interested in single-cell parasitology to actively explore our dataset without having expertise in programming or bioinformatics . Building on the work described here , which lays a foundation for a detailed understanding of the parasite itself , we anticipate future , single-cell co-transcriptomic sequencing of both the host cell and the parasite as a potentially powerful approach to further deconstruct the complexity of parasite-host interactions .
All Toxoplasma gondii strains were maintained by serial passage in human foreskin fibroblasts ( HFFs ) cultured at 37 C in 5% CO2 in complete Dulbeco’s Modified Eagle Medium ( cDMEM ) supplemented with 10% heat-inactivated fetal bovine serum ( FBS ) , 2 mM L-glutamine , 100 U/ml penicillin , and 100 ug/ml streptomycin . T . gondii strains used in this study were RH , Pru-GFP , and ME49-GFP-luc . Differentiation to bradyzoite was induced by growth under low-serum , alkaline conditions in ambient ( low ) CO2 as previously described ( Weiss et al . , 1995 ) . Briefly , confluent monolayers of HFFs were infected with tachyzoites at a multiplicity of infection ( MOI ) of 0 . 025 in RPMI 1640 medium ( Invitrogen ) lacking sodium bicarbonate and with 1% FBS , 10 mg/ml HEPES , 100 U/ml penicillin , and 100 g/ml streptomycin at pH 8 . 2 . The infected HFFs were cultured at 37°C without supplemented CO2 . HFF monolayers infected with parasites overnight were scraped , and the detached host cells were lysed by passing them through a 25-gauge needle three times or a 27-gauge needle six times . The released parasites were spun down at 800 rpm for 5 min to pellet out host cell debris , and the supernatant was spun down at 1500 rpm for 5 min to pellet the parasites . The parasites were then resuspended in 500 µL of FACS buffer ( 1x phosphate-buffered saline , PBS , supplemented with 2% FBS , 50 ug/ml DNAse I , and 5 mM MgCl2*6H2O ) , passed through both a 5 µm filter and a filter cap into FACS tubes , and stored on wet ice until it was time to sort . In samples stained for DNA content , the parasites were resuspended in 500 µL of FACS buffer plus 1 . 5 µL of Vybrant DyeCycle Violet ( from ThermoFisher , catalog number V35003 ) and incubated at 37 C and 5% CO2 for 30 min . The parasites were also stained with either propidium iodide ( PI ) , Sytox Green , or the live/dead fixable blue dead cell stain kit ( catalog number L34962 ) prior to sorting in order to distinguish live cells from dead cells . To stain with PI , 10 µL of 0 . 5 mg/ml PI was added to every 500 µL of parasite suspension in FACS buffer , and the parasites were incubated covered on ice for at least 15 min . To stain with Sytox Green , 1 drop of Sytox Green per ml was added to the parasite suspension in FACS buffer , and the parasites were incubated at room temperature for at least 15 min . To stain with the live/dead fixable blue dead cell stain kit , 1 . 5 µL of the kit’s viability dye was added to every 500 µL of parasites along with the secondary antibody , and parasites were washed and resuspended in FACS buffer as usual . Eight mL of lysis buffer was prepared by mixing together: 5 . 888 mL of water , 160 µL recombinant RNase inhibitor ( Takara Clonetech ) , 1 . 6 mL of 10 mM dNTP ( ThermoFisher ) , 160 µL of 100 uM oligo-dT ( iDT; see attached Supplementary file 1 for oligos ) , 1:600 , 000 diluted ERCC spike-in ( ThermoFisher ) , and 32 µL of 10% Triton X-100 . All reagents are declared RNase free . Lysis plates were prepared by dispensing 0 . 4 µL of lysis buffer into each well of a 384 well hard-shell low profile PCR plate ( Bio-rad ) using liquid handler Mantis ( Formulatrix ) . Single parasites were sorted using the Stanford FACS Facility’s SONY SH800s sorter or BD Influx Special Order sorter into the 384-well plates loaded with lysis buffer . Single color and colorless controls were used for compensation and adjustment of channel voltages . The data were collected with FACSDiva software and analyzed with FlowJo software . RH parasites were index sorted with fluorescence signal of cell permeable DNA stain , DyeCycle Violet . Smart-seq2 protocol was carried out as previously described ( Picelli et al . , 2014 ) using liquid handlers Mantis and Mosquito ( TTP Labtech ) with slight modifications . 384-well Smart-seq2 was performed with a 2 µL final reaction volume , while 96-well format was carried out in 25 µL final reaction volume as recommended by the original protocol . For 384-well Smart-seq2 , we performed 19 rounds of cDNA pre-amplification after reverse transcription with oligo-dT primers . Each well is then diluted with 1 to 4 v:v in RNAse free elution buffer ( QIAgen ) to a total volume of 8 µL . For 96-well Smart-seq2 , we performed 30 rounds of cDNA pre-amplification after reverse transcription . PCR is performed with ‘IS_PCR primers’ . Each well is then purified with Ampure XP beads at 0 . 8X volume ratio and resuspended in 20 µL RNAse free elution buffer . We measured the size distribution and concentration of each well in 96-well plate using Fragment Analyzer High-sensitivity NGS kit ( Agilent ) . We normalized the concentration of cDNA of each well to a concentration of 0 . 4 ng/µL . Finally , for both 384-well and 96-well Smart-seq2 measurements , we conducted library preparation with in-house Tn5 tagmentation using custom cell barcode and submitted for 2 × 150 bp paired-end sequencing on NovaSeq 6000 at the Chan Zuckerberg Biohub Genomics core . All primer sequences are provided as a supplementary file . The pGRA-AP2I × 1-V5 plasmid for AP2I × 1 transient expression was created using Gibson assembly ( NEB ) from the pGRA-V5 ( Panas et al . , 2019 ) . RH parasites were transfected with pGRA-AP2I × 1-V5 using the Amaxa 4D Nucleofector ( Lonza ) . Tachyzoites were mechanically released in PBS , pelleted , and resuspended in 20 µL P3 Primary Cell Nucleofector Solution ( Lonza ) with 7 or 15 µg DNA for transfection . After transfection , parasites were allowed to infect HFFs in DMEM . After 18–20 hr of infection , parasites were prepared for Immunofluorescence Assay ( IFA ) or qRT-PCR . To compute statistical independence between the transfected and control samples , we applied Student’s t-test by assuming unequal variance . One sigma ( * ) indicates one standard deviation in mean difference assuming null hypothesis . Monolayers of infected cell on glass coverslips were fixed with cold methanol for 12 min . Samples were washed with PBS and blocked using 3% bovine serum albumin ( BSA ) in PBS for at least 30 min . SAG1 was detected with rabbit anti-SAG1 polyclonal antibody and V5 was detected with mouse anti-V5 tag monoclonal antibody ( Invitrogen ) . Primary antibodies were detected with goat polyclonal Alexa Fluor-conjugated secondary antibodies ( Invitrogen ) . Primary and secondary antibodies were both diluted in 3% BSA in PBS . Coverslips were incubated with primary antibodies for 30 min , washed , and incubated with secondary antibodies for 30 min . Vectashield with DAPI stain ( Vector Laboratories ) was used to mount the coverslips on slides . Fluorescence was detected using wide-field epifluorescence microscopy and images were analyzed using ImageJ . All images shown for any given condition/staining in any given comparison/dataset were obtained using identical parameters . To quantify the purity of single parasite sort and to ensure the cDNA synthesis reaction was not saturated , GFP , mCherry , or SAG1 mRNA expression were measured using commercial qPCR mastermix , SsoAdvanced Universal SYBR Green mastermix ( Bio-rad ) . Briefly , 0 . 1 µL of diluted cDNA was added in a total of 2 . 1 µL reaction volume per well on a 384 well plate with qPCR mastermix and 200 nM PCR primers . The reaction was incubated on a Bio-rad qPCR thermal cycler with the following programs: 5 min of 95°C , 45 cycles of 95°C for 5 s and 56°C for 1 min , and imaging . To quantify the transcriptional effects of AP2IX-1 transient expression in RH parasites , infected cell monolayers were first lysed with Trizol ( Invitrogen ) and RNA was extracted using standard molecular biology technique . cDNA from each sample was generated with Smart-seq2 protocol using 20 µL total reaction volume and roughly 200 ng RNA input followed by 0 . 8X AMPure XP beads ( Beckman Coulter ) purification . For qPCR , 1 ng of cDNA was added in a total of 2 . 1 µL reaction volume per well on a 384 well plate with qPCR mastermix and 200 nM PCR primers as described above . The reaction was incubated on a Bio-rad qPCR thermal cycler with the following programs: 5 min of 95°C , 60 cycles of 95°C for 5 s and 61°C for 30 s , and imaging . Each gene was measured four times with samples collected from at least two separate wells . The transfection and qRT-PCR experiments were performed twice in separate experiments . Fold change of gene expression was calculated as shown previously ( Livak and Schmittgen , 2001 ) using ACT1 expression as an internal control for samples . All primer sequences are provided in Supplementary file 1 . BCL output files from sequencing were converted into gzip compressed FastQs via a modified bcl2fastq demultiplexer which is designed to handle the higher throughput per sequencing run . To generate genome references with spike-in sequences , we concatenated T . gondii ME49 NCBI genome assembly version 11/1/2013 or T . gondii GT1 T NCBI Genome assembly version 7/19/2013 genome references with ERCC sequences ( obtained from ThermoFisher website ) . The raw fastq files from sequencing are aligned to the concatenated genomes with STAR aligner ( version 2 . 6 . 0c ) using the following settings: '--readFilesCommand zcat --outFilterType BySJout --outFilterMutlimapNmax 20 --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterMismatchNmax 999 --outFilterMismatchNoverLmax 0 . 04 --alignIntronMin 20 --alignIntronMax 1000000 --alignMatesGapMax 1000000 --outSAMstrandField intronMotif --outSAMtype BAM Unsorted --outSAMattributes NH HI AS NM MD --outFilterMatchNminOverLread 0 . 4 --outFilterScoreMinOverLread 0 . 4 --clip3pAdapterSeq CTGTCTCTTATACACATCT --outReadsUnmapped Fastx' . Transcripts were counted with a custom htseq-count script ( version 0 . 10 . 0 , https://github . com/simon-anders/htseq ) using ME49 or RH GFF3 annotations ( version 36 on ToxoDB ) concatenated with ERCC annotation . Instead of discarding reads that mapped to multiple locations , we modified htseq-count to add transcript counts divided by the number of genomic locations with equal alignment score , thus rescuing measurement of duplicated genes in the Toxoplasma genome . Parallel jobs of STAR alignment and htseq-count were requested automatically by Bag of Stars ( https://github . com/iosonofabio/bag_of_stars ) and computed on Stanford high-performance computing cluster Sherlock 2 . 0 . Estimation of reads containing exonic and intronic regions is computed with Velocyto estimation on the BAM output files and requested automatically by Bag of Velocyto ( https://github . com/xuesoso/bag_of_velocyto ) on Sherlock 2 . 0 . Gene count matrix is obtained by summing up transcripts into genes using a custom python script . Scanpy velocyto package is then used to estimate transcriptional velocity on a given reduced dimension . Parameters used for generating the results are supplied as supplementary python scripts . Sample code to generate the analysis figures are provided in supplementary jupyter notebooks . To filter out cells with poor amplification or sequencing reaction and doublet cells , we discarded cells based on gene counts ( >0 reads ) , total reads sum , percent reads mapped to Toxoplasma genome , percent ERCC reads , and percent ribosomal RNA reads . We reported ‘% mapped’ based on the meta-alignment output from STAR aligner . We checked for some of the unmapped reads on BLASTn and found the majority of them to map to Toxoplasma 28S ribosomal RNA . Next , we filtered ‘ribosomal RNA’ genes from the gene count matrix . Gene count matrices are normalized as counts per median ( CPM ) : ( 1 ) Xnorm=x∑ ( x ) ⋅medium ( ∑ ( x ) ) where X is the gene count matrix , sum ( X ) is the read sum for each cell , and median ( sum ( X ) ) is the median of read sums . Normalized data are added with a pseudocount of 1 and log transformed ( e . g . log2 ( Xnorm+1 ) ) . To determine the detection limit ( e . g . 50% detection rate ) , we modeled the detection probability of ERCC standards with a logistic regression as a function of spike-in amount ( Svensson et al . , 2017 ) . We calculated an estimate of absolute molecular abundance for all genes by fitting a linear regression to ERCC spike-ins: ( 2 ) log2 ( y ) =m⋅log2 ( Xnorm+1 ) +bwhere Xnorm , ERCC>0 . 5 is the observed CPM value for ERCC spike-ins above the detection limit , Y is the amount of ERCC spike-in , m is the regression coefficient , and b is the intercept . To reduce the influence of measurement noise , we fit the model only to ERCC spike-ins with mean expression above the detection limit . To determine the transcriptional variation associated with cell cycle , we applied Self-Assembling Manifolds ( SAM ) ( Tarashansky et al . , 2019 ) to filter for highly dispersed gene sets ( >0 . 35 SAM weights ) in asynchronous RH population . Principal components analysis ( PCA ) is then applied to the filtered and normalized RH data , and the nearest neighbor graph ( K = 50 ) is computed using ‘correlation’ as a similarity metric . We identified the putative ‘G1’ clusters with 1N based on DNA content stain . Parasites in ‘G1’ cluster are further sub-clustered with Louvain Clustering , in which we identified ‘G1a’ and ‘G1b’ clusters with distinct transcriptional profiles . Pearson correlation between single-cell and bulk transcriptomic data is computed between bulk assignment ( Behnke et al . , 2010 ) and the scRNA-seq cluster assignment through which each cluster is uniquely assigned with a cell cycle state . To quantify genes that are differentially expressed across cell cycle clusters , we applied Kruskal-Wallis test . Genes are considered differentially expressed if their p-values are less than 0 . 05 and they are at least 2-fold over-expressed in a cluster compared to the average expression level of other clusters . We computed differential expression across all cell cycle clusters as well as between the ‘G1a’ and ‘G1b’ clusters; the results are uploaded as Supplementary files 2 and 3 , respectively . To enable cell cycle assignment transfer from RH to Pru and ME49 data , we implemented a random forest classification model trained on RH data . Briefly , this is done by training a model with 1000 estimators on L2-normalized RH expression data containing only cell cycle associated genes in a 60–40 split scheme . Then the model is applied to predict cell cycle labels of L2-normalized Pru or ME49 data containing the homologous cell cycle associated genes . The testing accuracy was over 95% . Pseudotime analysis is conducted with Monocle two package in R on preprocessed dataset with highly dispersive genes as described previously . A cell in ‘G1a’ is designated as the root cell , and all other cells are placed after this cell in order of their inferred pseudotime . To cluster genes based on their pseudotime expression pattern , high frequency patterns are removed through a double spline smoothing operation . The interpolated expression matrix is then normalized by maximum expression along pseudotime such that the maximum value of gene expression along pseudotime is bound by 1 . We then applied agglomerative clustering on this interpolated and normalized expression matrix using ‘correlation affinity’ as similarity metric and ‘average linkage’ method to predict three distinct clusters of genes . To quantify the dependence of expression variation on a two-dimensional projection , we developed a novel approach based on k-nearest neighbor ( KNN ) averaging . First , a KNN graph is computed by locating nearest neighborhood in a projection using euclidean distance . We then generated a null expression matrix by shuffling the gene expression matrix along each cell column , such that its correlation with respect to the coordinate on projection is completely lost . Next , we compute an updated gene expression value by taking the average of expression values across the KNN . This is equivalent to: ( 3 ) XKNN=Mk⋅Xnormwhere XKNN is the updated KNN averaged expression , M is the nearest-neighbor graph with k being the number of nearest neighbor , and X is the log-transformed CPM of observed or null expression matrices . We chose a k of 5 for all our analysis as varying k did not have a large effect on the results ( data not shown ) . In our experiments , we have shown that the first two principal components ( PCs ) of PCA on RH correspond to the projection of cell cycle progression , and a two-dimensional UMAP projection of Pru corresponds to asexual development and cell cycle progression . We thus computed XKNN for both the original , observed expression matrix and the shuffled , null matrix on either projection to reflect dependence on cell cycle progression and/or asexual development . XKNN is further normalized to have identical sum as the original expression values . A Kolmogorov-Smirnoff two sample test is then computed between the normalized XKNN of the observed matrix and that of the shuffled matrix based on 100 random permutations . The projection-dependence score for each gene is then computed as: ( 4 ) Sg=−log ( p¯g ) where Sg is the projection-dependence score for gene g and p¯g is the average p-values of 100 tests . We present Sg normalized by the maximum score within each respective data set . To integrate scRNA-seq data of Plasmodium berghei from Malaria Atlas ( Howick et al . , 2019 ) with our Toxoplasma Pru dataset ( measured both induced and induced population in 384-well ) , we first identified one-to-one orthologous genes obtained from PlasmoDB ( https://plasmodb . org/ ) and ToxoDB ( https://toxodb . org/toxo/ ) . Next , we filtered each dataset with the ortholog genes . Using scanpy library , we filtered for the intersect of genes that are within the top 800 most dispersed genes in each dataset , resulting in a list of 403 genes . Finally , we used Scanorama ( Hie et al . , 2019 ) with default parameter settings to integrate the two datasets . We calculated co-clustering similarity as follows . We first computed a Leiden ( Traag et al . , 2019 ) clustering on the integrated graph and returned a cluster co-occurrence matrix to the original cluster assignment in Plasmodium berghei ( ‘ShortenedLifeStage4’ ) or Toxoplasma Pru ( ‘cell_cylcle’ ) . Then , the dot product between the two matrices was calculated and normalized such that it has a maximum of one . | Toxoplasma gondii is a single-celled parasite that can infect most warm-blooded animals , but only reproduces sexually in domestic and wild cats . Distantly related to the malaria agent , it currently infects over a quarter of the world’s human population . Although it is benign in most cases , the condition can still be dangerous for foetuses and people whose immune system is compromised . In the human body , Toxoplasma cells infiltrate muscle and nerve cells; there it undergoes a complex transformation that helps the parasites to stop dividing quickly and instead hide from the immune system in a dormant state . It is still unclear how this transition unfolds , and in particular which genes are switched on and off at any given time . To understand this transformation , scientists often measure which genes are active across a group of parasites . However , this approach gives only an ‘average’ picture and does not allow each parasite to be profiled , missing out on the diversity that may exist between individuals . One area of particular interest , for example , is a set of genes called SAG1-related sequences . They code for the ‘molecular overcoat’ of the parasite , an array of proteins that sit on the surface of Toxoplasma cells . More than 120 SAG1-related genes exist in the genome of each Toxoplasma parasite , creating a whole wardrobe of proteins that potentially hide the parasites from the immune system . Here , Xue et al . harnessed a technique called single-cell RNA sequencing , which allowed them to screen which genes were active in 5 , 400 individual Toxoplasma parasites from different strains . The analysis included both the rapidly dividing form of the parasite ( present in the initial stage of an infection ) , and the slowly dividing form found in people who carry Toxoplasma without any symptoms . The resulting ‘atlas’ contains previously hidden information about the genes used at each stage of parasite development: this included unexpected similarities between Toxoplasma and the malaria agent , as well as subtle differences between two of the Toxoplasma strains . The atlas also sheds light on how individual parasites turns on SAG1-related sequences . It reveals a surprising diversity in the composition of the protein coats sported by Toxoplasma cells at the same developmental stage , a strategy that may help to thwart the immune system . One individual parasite in particular had an unusual combination of coat and other proteins found in both the fast and slow-dividing human forms . This parasite had been grown in human cells , yet a closer analysis revealed that it had activated several genes ( including ones encoding the protein coat ) that are normally only ‘on’ in the parasites going through sexual reproduction in domestic and wild cats . This new data atlas helps to understand how Toxoplasma are transmitted to and grow within humans , which could aid the development of treatments . Ultimately , a better knowledge of these parasites could also bring new information about the agent that causes malaria . | [
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] | 2020 | A single-parasite transcriptional atlas of Toxoplasma Gondii reveals novel control of antigen expression |
The canonical action of the p85α regulatory subunit of phosphatidylinositol 3-kinase ( PI3K ) is to associate with the p110α catalytic subunit to allow stimuli-dependent activation of the PI3K pathway . We elucidate a p110α-independent role of homodimerized p85α in the positive regulation of PTEN stability and activity . p110α-free p85α homodimerizes via two intermolecular interactions ( SH3:proline-rich region and BH:BH ) to selectively bind unphosphorylated activated PTEN . As a consequence , homodimeric but not monomeric p85α suppresses the PI3K pathway by protecting PTEN from E3 ligase WWP2-mediated proteasomal degradation . Further , the p85α homodimer enhances the lipid phosphatase activity and membrane association of PTEN . Strikingly , we identified cancer patient-derived oncogenic p85α mutations that target the homodimerization or PTEN interaction surface . Collectively , our data suggest the equilibrium of p85α monomer–dimers regulates the PI3K pathway and disrupting this equilibrium could lead to disease development .
The phosphatidylinositol 3-kinase ( PI3K ) pathway is a master regulator of many cellular processes . Activation of the class 1A PI3K is gated , in part , by the p85α regulatory subunit , which contains a Src homology 3 ( SH3 ) domain , two proline-rich ( PR ) regions ( PR1 and PR2 ) separated by a Rho-GAP/BCR-homology ( BH ) domain , and two Src homology 2 ( SH2 ) domains ( nSH2 , cSH2 ) flanking an inter-SH2 ( iSH2 ) domain . The interaction of p85α nSH2-iSH2-cSH2 fragment with the p110 catalytic subunit represses p110 activity allosterically and also stabilizes p110 against degradation , creating a pool of stable but quiescent p110 ( Yu et al . , 1998 ) . Upon receptor tyrosine kinase activation , p85α undergoes phosphorylation/dephosphorylation events that alleviate p110 inhibition resulting in p110-mediated production of PI ( 3 , 4 , 5 ) P3 ( Cuevas et al . , 2001 ) . Strikingly , PIK3R1 , the gene coding for p85α , is the twelfth most frequently mutated gene across all cancers . Indeed , somatic mutations of the p85α:p110α interface disrupt the inhibitory action of p85α on bound p110α leading to PI3K pathway activation in cancers ( Miled et al . , 2007; Jaiswal et al . , 2009 ) . However , PIK3R1 mutations outside the nSH2-iSH2-cSH2 fragment are relatively common in cancers , particularly endometrial cancer . The mechanisms underlying the transforming activity of these mutations remain to be elucidated and may provide novel biomarkers or therapeutic opportunities . Although the best-characterized role of p85α is p110-dependent , interaction of p85α with other proteins via its SH3 and BH domains has been proposed to mediate p110-independent functions ( Jimenez et al . , 2000; Chamberlain et al . , 2010 ) . We and others have also shown that the BH domain binds to the tumor suppressor PTEN to promote PTEN protein stability and that at least one PIK3R1 somatic mutation interferes with this process ( Chagpar et al . , 2010; Cheung et al . , 2011 ) . Moreover , intermolecular interactions between the SH3 domain and the PR region of p85α contribute to p85α homodimerization ( Harpur et al . , 1999; Cheung et al . , 2011 ) . However , the function of p85α homodimers remains to be elucidated . In this study , we demonstrate that p110α-free p85α homodimers positively regulate PTEN and that this regulatory mechanism is disrupted by mutations in a subset of endometrial cancers . Together , our findings suggest that the relative abundance of p110α-bound p85α monomer and p110α-free p85α homodimer is critical in PI3K pathway regulation .
Previous analyses suggested that the region encompassing SH3-PR1-BH of p85α mediates p85α homodimerization and binding to PTEN ( Harpur et al . , 1999; Chagpar et al . , 2010; Cheung et al . , 2011 ) . However , how each motif contributes to homodimerization and the orientation of the homodimer remain unknown . Using analytical ultracentrifugation ( AUC ) and microscale thermophoresis ( MST ) , we demonstrated that purified recombinant full-length p85α homodimerized with a micromolar dissociation constant Kd in absence of other proteins under reducing conditions ( Kd was 7 ± 0 . 7 μM and 3 . 9 ± 0 . 2 μM for AUC and MST , respectively ) ( Figure 1—figure supplements 1 , 2 ) . AUC and MST also showed that the SH3-PR1-BH-PR2 fragment retained the full capacity to dimerize ( Kd was 0 . 53 ± 0 . 03 μM and 0 . 44 ± 0 . 03 μM for AUC and MST , respectively ) . The difference in dimerization Kd between the N-terminal fragment and full-length p85α might indicate an additional entropic penalty arising upon full-length dimerization and/or weak intramolecular interactions occurring between the SH3-PR1-BH-PR2 and the nSH2-iSH2-cSH2 fragments in the monomer , which have to be displaced to allow dimerization . The isolated SH3 domain does not form stable dimers in size-exclusion chromatography ( Harpur et al . , 1999 ) and none of the available p85α SH3 crystal structures ( [PDB 3I5S ( Batra-Safferling et al . , 2010 ) , 3I5R ( Batra-Safferling et al . , 2010 ) , 1PHT ( Liang et al . , 1996] ) contains quaternary assemblies predicted to be stable in solution by the PISA algorithm ( Krissinel and Henrick , 2007 ) , suggesting that SH3:SH3 contacts do not have a major role in p85α homodimerization . We therefore asked if the p85α homodimer could be stabilized by SH3:PR1 interactions in trans . p85α PR1 ( residues 79–99 ) contains a canonical class I PXXP SH3-interacting motif ( [R/K]XXPXXP; R93PLP96VAP99 ) ( Ladbury and Arold , 2011 ) . Indeed , our isothermal titration calorimetry ( ITC ) analyses showed that the synthetic p85α PR1 peptide PKPRPPRPLPVAP bound purified recombinant p85α SH3 with a Kd of 24 μM ( Figure 1—figure supplement 3M ) . p85α SH3 bound p85αΔSH3 ( residues 86–742 , comprising PR1 to cSH2 ) with a similar Kd of 17 μM ( Figure 1—figure supplement 3N ) , demonstrating that the p85α SH3 domain binds PR1 also in the context of an almost complete p85α , in agreement with previously published qualitative data ( Harpur et al . , 1999 ) . The linker between SH3 and PR1 in p85α is too short for an SH3:PR1 interaction to occur in cis ( Figure 1—figure supplement 3O ) , indicating that the SH3:PR1 interaction would need to form in trans in the p85α homodimer . Two published experimental structures provide comparable templates for this interaction: ( i ) p85α SH3 in a complex with a class I PXXP peptide that is similar to PR1 ( HSKRPLPPLPSL; Kd of 40 μM; [Batra-Safferling et al . , 2010] ) and ( ii ) p85α PR1 in a complex with Fyn SH3 ( Kd 16 μM ) ( Renzoni et al . , 1996 ) . We used this structural information to build a theoretical homology model for the SH3:PR1 interaction to guide studies aimed at identifying the amino acids involved in the molecular interactions ( Figure 1A ) . 10 . 7554/eLife . 06866 . 003Figure 1 . Intermolecular interactions contribute to p85α homodimerization and PTEN binding . ( A ) Theoretical molecular model of the p85α SH3:PR1 interaction . The Qualitative Model Energy Analysis ( QMEAN ) Z-score of the model is −0 . 8 [the QMEAN Z-score ranges from −4 ( worse ) to +4 ( best ) with the average for high-resolution X-ray structures being 0 ( Benkert et al . , 2009 ) ] . Key residues on the Src homology 3 ( SH3 ) domain ( green ) are highlighted . The RXXPXXP motif of the PR1 region ( carbons colored in amber ) is indicated . ( B ) Schematic showing the residues mutated in the PR1 and PR2 mutants . ( C ) KLE cells co-transfected with Flag-tagged wild-type ( WT ) p85α ( Flag-WT ) and HA-tagged WT p85α or proline-rich ( PR ) mutants for 72 hr were harvested for immunoprecipitation ( IP ) with anti-HA and Western blotting ( WB ) . ( D , E ) In other sets of the experiment , cells co-transfected with Flag-WT and mutants in SH3 domain ( D ) or both SH3 and PR domains ( E ) were harvested for IP using the same Materials and methods . ( F ) The molecular structure of BH:BH domain dimer , taken from the crystal structure of this domain ( 1PBW ) . Individual monomers are color-coded . P309 , the last PR2 residue modeled in the crystal structure , is indicated . The zoom-in window shows details of the BH:BH interaction , with residues discussed in this study highlighted . ( G ) KLE cells co-transfected with Flag-WT and HA-WT or mutants in BH domain were collected for IP with anti-HA . LacZ was used as control . Numerical values below each lane of the immunoblots represent quantification of the relative protein levels by densitometry ( normalized to HA levels ) . ( H ) Schematic model of the p85α SH3-PR1-BH homodimer . The nSH2-iSH2-cSH2 fragment is not included the model . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00310 . 7554/eLife . 06866 . 004Figure 1—figure supplement 1 . ( A–F ) Sedimentation equilibrium data for full-length p85α , p85α PR1-SH3-BH-PR2 ( residues 1–333 ) , and p85α PR1-BH ( residues 79–301 ) . The data of final equilibrium profiles ( black symbols ) were fitted ( red line ) to the monomer–dimer–tertramer model ( p85α and p85α PR1-SH3-BH-PR2 ) or a monomer–dimer model ( PR1-BH ) with reduced χ2 of 1 . 253 , 1 . 14 , and 1 . 18 and root mean squared differences of 0 . 0026–0 . 0090 , 0 . 0021–0 . 0073 , 0 . 0017–0 . 0018; yielding the dimerization Kd of 7 . 0 ± 0 . 7 μM , 0 . 53 ± 0 . 03 μM , and 162 . 9 ± 41 . 4 μM for p85α , p85α PR1-SH3-BH-PR2 , and p85α PR1-BH , respectively . The errors represent standard errors . ( G–I ) Sedimentation coefficient distribution profiles of each protein at 1 . 5 mg/ml . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00410 . 7554/eLife . 06866 . 005Figure 1—figure supplement 2 . ( J–L ) Microscale thermophoresis ( MST ) data on dimerization of p85α ( full length , SH3-PR1-BH-PR2 and PR1-BH ) . Affinities for individual constructs were determined at Kd p85α = 3 . 9 ± 0 . 2 μM , Kd p85α1-333 = 441 ± 30 nM , and Kd p85α PR1-BH = 22 . 3 ± 1 . 5 μM . The errors are standard deviation calculated from three independent measurements . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00510 . 7554/eLife . 06866 . 006Figure 1—figure supplement 3 . ( M–N ) Isothermal titration calorimetry ( ITC ) data on p85α SH3:PR1 and p85αΔSH3 interactions . Kd = 23 . 3 ± 6 μM , ΔG = −26 . 5 kJ/mol , ΔH = −4 . 9 kJ/mol , TΔS = 21 . 5 kJ/mol and the stoichiometry N = 1 . 06 ± 0 . 3 ( M ) and Kd = 16 . 8 ± 4 . 2 μM ΔG = −27 . 7 kJ/mol , ΔH = −13 . 4 kJ/mol , TΔS = 14 . 4 kJ/mol and the stoichiometry N = 0 . 8 ± 0 . 1 ( N ) . Measurement errors are less than 10% for ΔH and TΔS , as judged from independent analysis of repeat experiments . The errors are standard deviation . ( O ) Graphical illustration showing that SH3:PR1 interactions in cis are not possible because the RXXPXXP motif ( color-coded ) on PR1 ( white ) is located too close to the SH3 domain C-terminus to be able to reach the RXXPXXP binding site on the same SH3 ( green surface ) . RXXPXXP bound to SH3 is shown as color-coded spheres . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00610 . 7554/eLife . 06866 . 007Figure 1—figure supplement 4 . ( A , B ) p85α knockout mouse embryonic fibroblast ( MEF ) cells ( A ) or PTEN knockout MEF cells ( B ) co-transfected with Flag-tagged WT p85α ( Flag-WT ) and HA-tagged WT p85α or PR mutants for 72 hr were collected for IP with anti-HA and WB . ( C ) KLE cells co-transfected with Flag-WT and HA-tagged WT p85α or truncated mutant A360* were collected for IP with anti-HA and WB . LacZ was used as control . Numerical values below the immunoblots represent quantification of the relative protein levels by densitometry . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00710 . 7554/eLife . 06866 . 008Figure 1—figure supplement 5 . ( A ) KLE cells co-transfected with HA-tagged WT p85α ( or p85β ) and Flag-tagged WT p85α ( or p85β ) for 72 hr were harvested for IP with anti-HA and WB . ( B ) The crystallographic BH domain dimers from p85α ( PDB id . 1PBW; light and dark red ) and p85β ( PDB 2XS6; yellow and pale yellow ) are superimposed on one of the BH domains . ( C ) KLE cells transfected with WT p85α or WT p85β for 72 hr were collected for WB . LacZ was used as control . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 008 We mutated either the key prolines or basic residues in the PR1 and PR2 regions proposed to bind p85α SH3 ( Figure 1B ) . These mutants were expressed in an endometrial cancer cell line KLE , which expresses low level of endogenous p85α and does not have mutations in major members of the PI3K pathway . Co-immunoprecipitation analysis showed that mutations in prolines ( denoted as PR1mut ) or basic residues ( PR1mut' ) in PR1 decreased p85α homodimerization ( Figure 1C ) . Consistent with our hypothesis that homodimerized p85α binds PTEN , PR1mut , and PR1mut' decreased the association of p85α with PTEN ( Figure 1C ) . Interestingly , both PR2mut ( proline residues mutated ) and PR2mut' ( basic residues mutated ) decreased binding to PTEN without affecting p85α dimerization ( Figure 1C ) , suggesting that PR2 contributes directly to PTEN binding rather than to p85α homodimerization . Accordingly , PTEN binding was further decreased by combined mutations of PR1 and PR2 ( PR1+PR2mut and PR1+PR2mut' ) , which however did not decrease p85α homodimerization compared to PR1 mutations alone . These observations were replicated in p85α knockout mouse embryonic fibroblasts ( MEFs ) ( Figure 1—figure supplement 4A ) and more importantly in PTEN knockout MEFs ( Figure 1—figure supplement 4B ) , indicating that the p85α homodimer is able to form in the absence of PTEN . We next mutated p85α SH3 residues predicted to mediate binding to PR1 ( Figure 1A ) . The D21A/W55A double mutant , which targets the canonical RXXPXXP binding site , decreased p85α homodimerization and PTEN interaction comparably to PR1mut or PR1+PR2mut ( Figure 1D ) . Mutations of E19 and E20 , which situate on the SH3 RT loop and may contribute to long-range electrostatic interactions with PR1 ( e . g . , K88 and R90 ) , also decreased p85α homodimerization and PTEN binding ( Figure 1D ) . The D21A/W55A and E19A/E20A mutations further decreased p85α homodimerization and hence PTEN binding when combined with PR1+PR2mut ( Figure 1E ) . In the p85α SH3:HSKRPLPPLPSL crystal structure ( 3I5R ) ( Batra-Safferling et al . , 2010 ) and our p85α SH3:PR1 model ( Figure 1A ) , R18 weakly interacts with R93 of the PR1 RXXPXXP motif via a planar stacking interaction . Accordingly , R18A modestly decreased p85α homodimerization ( Figure 1D ) . None of these mutants altered the interaction of p85α with p110α ( Figure 1C–E , Figure 1—figure supplement 4A , B ) , consistent with C-terminus but not N-terminus of p85α contributing to p110α binding . The spatial separation of p85α homodimerization from p110α interaction sites is further supported by our observations that a truncation p85α mutant , A360* , which lacks the nSH2-iSH2-cSH2 fragment to bind p110α , displayed comparable p85α homodimerization and PTEN binding to wild-type ( WT ) p85α ( Figure 1—figure supplement 4C ) . It is noteworthy that none of the mutations in PR1 disrupted binding to PTEN without affecting p85α homodimerization , consistent with a causal link between homodimerized p85α and PTEN binding . Together , these results suggest that a canonical SH3:PR1 interaction in trans contributes to the formation of the homodimeric p85α platform for PTEN binding , whereas PR2 contributes to binding to PTEN through an alternative mechanism and potentially by direct PTEN binding . The p85α BH-PR2 fragment ( residues 105–319 ) forms BH:BH domain dimers in two different crystal forms , suggesting that this dimer arrangement is not an artifact of a particular crystallization condition ( Musacchio et al . , 1996 ) . The crystallographic dimer is stabilized by M176 that inserts into a hydrophobic pocket formed mainly by L164' , I177' , V181' ( Figure 1F ) . The BH:BH interface in the crystal structure is highly conserved in vertebrates . As experimental support for this BH dimer , we found that a M176A mutation decreased p85α homodimerization and PTEN binding ( Figure 1G ) . In contrast , mutation of D178 , which was predicted to contribute to BH:BH dimer formation only minimally in our model ( Figure 1F ) , did not diminish p85α homodimerization . In each monomer , the BH:BH association buries only 527 Å2 of the total solvent-accessible area of 9000 Å2 , suggesting that the BH:BH interaction alone is insufficient for stable p85α homodimerization . Indeed , we measured a much lower affinity for p85α PR1-BH ( AUC: 163 μM; MST: 23 μM ) than for constructs containing the SH3-PR1-BH region ( Figure 1—figure supplements 1C and 2L ) . Together with the decrease in p85α homodimerization that occurred with combined mutations of PR1 and M176 ( Figure 1G ) , these data support a model wherein the p85α homodimer is stabilized by both SH3:PR1 and BH:BH interactions . The length of the SH3-BH linker is sufficient for SH3:PR1 interactions in trans within the context of the crystal-derived BH:BH dimer ( Appendix figure 3 in Appendix 1 ) . Collectively , our data are consistent with a molecular model for the p85α homodimer in which the SH3 domain binds PR1 in trans through a canonical class I interaction , while simultaneously the two BH domains associate through an interface centered on M176 ( Figure 1H ) . The contribution of the BH domain in the homodimerization of p85 appears to be important , because p85β , which has a completely conserved SH3 ligand-binding site , an almost identical sequence in the PR1 motif and an 80% identity in the nSH2-iSH2-cSH2 fragment , homodimerized and interacted with PTEN to a lesser degree than p85α ( Figure 1—figure supplement 5A ) . Indeed , the p85α and p85β BH domains share only 30% identity , and in particular the region that mediates BH dimerization in p85α is not conserved in p85β , neither in sequence nor in the published crystallographic structure ( p85α: PDB 1PBW and p85β: PDB 2XS6 ) ( Figure 1—figure supplement 5B ) . Our previous data suggested that p85α homodimerization and PTEN binding contribute to increased PTEN stability by inhibiting ubiquitination ( Cheung et al . , 2011 ) . We therefore investigated whether p85α mutants that alter formation of p85α homodimers and/or PTEN binding affect PTEN ubiquitination and stability . WT p85α markedly decreased PTEN ubiquitination compared with LacZ control and more importantly with each of the homodimer-disrupting mutants ( R18A , E19A/E20A , D21A/W55A , and R66A in SH3; PR1mut , PR1mut’ in PR1; M176A in BH ) and PTEN binding-disrupting mutants ( PR2mut and PR2mut' ) ( Figure 2A–C ) . Ubiquitinated PTEN in cells expressing the mutants was less than that in cells expressing LacZ because there was more PTEN-bound homodimer present in the mutant cells . PR1+PR2mut and PR1+PR2mut' increased ubiquitinated PTEN compared with single mutations , suggesting cooperativity between PR1 and PR2 in preventing PTEN ubiquitination . Further , the increased PTEN ubiquitination in the presence of these mutants was associated with decreased PTEN protein levels and hence activation of the PI3K pathway as indicated by increased AKT phosphorylation ( Figure 2D–F ) . Conversely , p85β failed to stabilize PTEN , as expected from the reduced dimerization and altered BH domain structure of this isoform ( Figure 1—figure supplement 5C ) . 10 . 7554/eLife . 06866 . 009Figure 2 . p85α homodimer increases protein stability , lipid phosphatase activity , and membrane association of PTEN . ( A–F ) KLE cells transfected with WT p85α or PR1 and PR2 mutants ( A , D ) , SH3 domain mutants ( B , E ) , or BH domain mutants ( C , F ) for 72 hr were collected for IP with anti-PTEN and WB with anti-ubiquitin ( A–C ) or directly for WB ( D–F ) . PTEN protein levels were normalized prior to IP by using proportionally different amounts of lysates . ( G ) In vitro lipid phosphatase activity of recombinant PTEN in the presence or absence of recombinant p85α was determined . ( H ) Endogenous PTEN proteins were immunoprecipitated indirectly using anti-p85α antibody and phosphatase activity was measured . The activity was normalized to the levels of immunoprecipitated PTEN protein in each sample . ( I ) Transfected KLE cells were harvested for subcellular fractionation and WB ( C , cytosol; M , membrane; N , nuclear ) . ( J ) Binding of recombinant PTEN to large multilamellar vesicles in the presence or absence of recombinant p85α was assayed . Pellets ( P ) and supernatants ( S ) corresponding to phospholipid-bound fraction and phospholipid-unbound fraction , respectively , were subjected to SDS-PAGE followed by top , Coomassie blue staining or bottom , WB . Numerical values below the immunoblots represent relative protein levels by densitometry . *p < 0 . 05 , compared with LacZ control . #p < 0 . 05 , compared with WT . The error bars represent S . D . of triplicates from two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 00910 . 7554/eLife . 06866 . 010Figure 2—figure supplement 1 . ( A ) Negative feedback of the phosphatidylinositol 3-kinase ( PI3K ) pathway mediated by S6K . ( B ) The efficiency of siRNAs targeting S6K was confirmed by WB . Non-specific ( NS ) siRNA was used as control . ( C , D ) KLE cells co-transfected with WT p85α or PR1+PR2mut and 10 nM siRNA targeting S6K or NS control for 72 hr were harvested for WB directly ( C ) or IP with anti-HA and WB ( D ) . ( E ) Positive feedback of the PI3K pathway mediated by mTORC2 to AKT . ( F ) The efficiency of siRNAs targeting Rictor was confirmed by WB . ( G , H ) Cells co-transfected with WT p85α or PR1+PR2mut and 10 nM siRNA targeting Rictor or NS control for 72 hr were harvested for WB directly ( G ) or IP with anti-HA ( H ) . ( I ) Feedforward activation of mTOR by AKT . ( J ) Cells were treated with rapamycin ( 500 nM ) or AKT inhibitor ( MK2206; 1 μM ) for 48 hr and were harvested for WB . DMSO was used as vehicle control . ( K ) Cells transfected with WT p85α or PR1+PR2mut were treated with rapamycin or MK2206 for 48 hr before being harvested for IP with anti-HA . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01010 . 7554/eLife . 06866 . 011Figure 2—figure supplement 2 . PTEN in vitro lipid phosphatase activity was determined using a malachite green phosphatase assay with soluble PIP3 as the substrate . Endogenous PTEN proteins were immunoprecipitated in lysates using anti-PTEN antibody extracted from transfected cells and phosphatase activity was measured in triplicate . The activity was normalized to the levels of immunoprecipitated PTEN protein in each sample . *p < 0 . 05 , compared with LacZ control . #p < 0 . 05 , compared with WT . The error bars represent S . D . of triplicates from two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 011 A number of feedback and feedforward regulations exist along the PI3K pathway , including negative feedback by S6K , positive feedback by mTORC2 , and feedforward activation of mTOR by AKT ( Sekulic et al . , 2000; Harrington et al . , 2004; Hahn-Windgassen et al . , 2005; Sarbassov et al . , 2005; Humphrey et al . , 2013 ) ( Figure 2—figure supplement 1A , E , I ) . The differences in AKT phosphorylation in presence of mutant or WT p85α raised the possibility that feedback or feedforward signaling mechanisms contributed to the observed changes in PTEN interactions and stability . We therefore examined whether these regulatory mechanisms affect the interaction between PTEN and WT p85α or the PR1+PR2mut . S6K but not Rictor siRNA altered AKT phosphorylation suggesting the existence of S6K-mediated but not mTORC2-mediated feedback in the KLE cell line , although the siRNAs decreased the expression of the proteins by 80% ( Figure 2—figure supplement 1B , C and 1F , G ) . Importantly , the S6K and Rictor siRNAs had no effect on the PTEN interactions ( Figure 2—figure supplement 1D , H ) . Interference with mTOR or AKT signaling by inhibitors also did not alter the interaction ( Figure 2—figure supplement 1J , K ) . These data supported that the observed effects on PTEN stability result from the direct interaction with p85α , rather than from indirect feedback or feedforward-signaling mechanisms . Phosphorylation of the PTEN C-terminal tail residues S380/T382/T383 ( hereafter referred to as phosphorylated PTEN ) regulates PTEN activity and stability , with the unphosphorylated counterpart ( denoted hereafter as unphosphorylated PTEN ) being more active ( Vazquez et al . , 2000; Odriozola et al . , 2007 ) . Consistent with a previous study showing that p85α preferentially binds unphosphorylated PTEN ( Rabinovsky et al . , 2009 ) , we found that p85α increased levels of unphosphorylated PTEN to a greater extent than that of phosphorylated PTEN ( Figure 2D–F ) . Intriguingly , p85α also increased PTEN phosphatase activity towards soluble PIP3 substrate in a recombinant cell-free system ( Figure 2G ) . This increase in phosphatase activity was recapitulated when PTEN was immunoprecipitated from WT p85α-transfected cell lysates indirectly using anti-p85α antibodies ( Figure 2H ) or directly using anti-PTEN antibodies ( Figure 2—figure supplement 2 ) . Importantly , the increased phosphatase activity was reversed by mutations in PR1 and PR2 ( Figure 2H and Figure 2—figure supplement 2 ) . The PIP3 substrate of PTEN is located on the cell membrane with unphosphorylated PTEN exhibiting stronger membrane association than phosphorylated PTEN ( Odriozola et al . , 2007 ) . While p85α globally increased PTEN levels in the cytosol , membrane , and nucleus , p85α markedly increased unphosphorylated PTEN , but not phosphorylated PTEN , in the membrane ( Figure 2I ) . Moreover , binding of recombinant PTEN to large multilamellar vesicles ( LMVs ) , which represent model membranes used to assess protein-membrane association ( Fukuda et al . , 1996; Davletov et al . , 1998; Lee et al . , 1999 ) , was increased in the presence of p85α ( Figure 2J ) . Of note , the recombinant PTEN was predominantly in the unphosphorylated form . Together , these data suggest that p85α homodimers likely increase PTEN activity and membrane association of PTEN , in particular unphosphorylated active PTEN . The ubiquitin-proteasome pathway consists of a cascade of reactions with the substrate specificity being largely defined by E3 ubiquitin ligases . Therefore , we attempted to identify the PTEN E3 ligase that is regulated by the p85α homodimer . Consistent with previous studies ( Wang et al . , 2007; Maddika et al . , 2011 ) , PTEN interacted with WWP2 and to a lesser extent with NEDD4 ( Figure 3A ) . The E3 ligases c-cbl and cbl-b , which bind p85α ( Fang et al . , 2001 ) , did not bind PTEN . Importantly , WT p85α decreased the binding of PTEN to WWP2 without a demonstrable effect on NEDD4 binding ( Figure 3B ) . Mutations in PR1 or PR2 increased interactions between PTEN and WWP2 , suggesting that homodimerized p85α decreases PTEN ubiquitination by preventing WWP2 from binding PTEN . Again , siRNA and/or inhibitor-based interference with S6K , mTORC2 , and AKT signaling had no effect on the interaction of PTEN with WWP2 , suggesting that the increase in PTEN:WWP2 association is directly linked to the failure of the p85α mutants to bind PTEN , and not to indirect effects on other PI3K pathway components ( Figure 3—figure supplement 1 ) . Indeed , both p85α ( Figure 3C ) and WWP2 ( Maddika et al . , 2011 ) bound to the PTEN phosphatase domain ( residues 14–187 ) , suggesting that p85α and WWP2 may compete for PTEN binding . A competition model is supported by the observation that WWP2 dose-dependently decreased the interaction between p85α and PTEN ( Left , Figure 3D ) and p85α-induced PTEN stabilization ( Right , Figure 3D ) . Reciprocally , p85α inhibited binding of WWP2 to PTEN ( Left , Figure 3E ) and reversed the decrease in PTEN levels induced by WWP2 ( Right , Figure 3E ) . In contrast , p85α PR1+PR2mut ( which does not bind PTEN efficiently ) failed to reverse the effect of WWP2 ( Figure 3F ) . As an additional support of the competition model , siRNAs that target endogenous p85α or WWP2 decreased the interaction of PTEN with WWP2 or p85α , respectively , in an endometrial cancer cell line HEC1A that expresses high levels of p85α and WWP2 ( Figure 3—figure supplement 2 ) . Together , these data indicate that p85α homodimers compete with WWP2 for binding to an overlapping site on the PTEN phosphatase domain and thereby inhibit PTEN ubiquitination . 10 . 7554/eLife . 06866 . 012Figure 3 . p85α homodimer competes with E3 ligase WWP2 for PTEN binding . ( A ) KLE cells were harvested for IP with anti-PTEN and WB . Normal IgG was used as a negative control . ( B ) Cells transfected with WT p85α or PR mutants were harvested for IP after 72 hr . ( C ) Cells were co-transfected with p85α and full-length PTEN ( FL ) or deletion mutants ( Left ) . PTEN proteins were immunoprecipitated by anti-Flag antibody and the immunoprecipitate was analyzed by WB ( Right ) . ( D ) Cells transfected with HA-tagged p85α in the absence or presence of an increasing amount of WWP2 were collected for IP with HA ( Left ) or WB ( Right ) . ( E , F ) Cells transfected with Myc-tagged WWP2 in the absence or presence of an increasing amount of WT p85α ( E ) or PR mutant ( F ) were collected for IP with Flag for PTEN ( Left ) or WB ( Right ) . LacZ was used as control . Numerical values below each lane of the immunoblots represent quantification of the relative protein levels by densitometry . PBM , phosphoinositide-binding motif . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01210 . 7554/eLife . 06866 . 013Figure 3—figure supplement 1 . ( A , B ) KLE cells co-transfected with WWP2 and 10 nM siRNA targeting S6K or NS control for 72 hr were harvested for WB directly ( A ) or IP with anti-Flag and WB ( B ) . ( C , D ) Cells co-transfected with WWP2 and 10 nM siRNA targeting Rictor or NS control for 72 hr were harvested for WB ( C ) or IP with anti-Flag ( D ) . ( E ) Cells transfected with WWP2 were treated with rapamycin ( 500 nM ) or MK2206 ( 1 μM ) for 48 hr before being harvested for IP with anti-Flag . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01310 . 7554/eLife . 06866 . 014Figure 3—figure supplement 2 . ( A , B ) HEC1A cells were transfected with 10 nM siRNA targeting WWP2 ( A ) or p85α ( B ) for 72 hr . The efficiency of the siRNAs was confirmed by WB . NS siRNA was used as control . ( C , D ) Cells transfected with 10 nM siRNA targeting WWP2 ( C ) or p85α ( D ) or NS control for 72 hr were harvested for IP with anti-PTEN and WB . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 014 To validate this model , analytical gel filtration was performed to characterize the association and competition between p85α , p110α , PTEN , and WWP2 . PTEN was recovered in fractions that were free of p110α ( Figure 4A ) and binding of p85α to p110α and to PTEN was mutually exclusive ( Figure 4B ) . Coomassie blue staining of p85α immune complex in p110α-containing fractions indicated the existence of other proteins ( Figure 4—figure supplement 1A ) including several known p85α:p110α heterodimer-binding proteins ( Wang et al . , 1995; Rodriguez-Viciana et al . , 1996; Lamothe et al . , 2004; Asano et al . , 2005 ) ( Figure 4—figure supplement 1B ) . Likewise , consistent with a previous study ( Rabinovsky et al . , 2009 ) , p85α-PTEN existed in a high-molecular weight multi-protein complex ( Figure 4—figure supplement 1A ) . The majority of unphosphorylated PTEN eluted in fractions containing high levels of p85α ( Figure 4A ) . Immunoprecipitation ( IP ) confirmed that unphosphorylated PTEN but not phosphorylated PTEN was associated with p85α ( Figure 4B ) . Strikingly , no interaction between WWP2 and PTEN was detected in fractions that contained p85α ( Figure 4C ) , indicating that WWP2 only binds PTEN that is not bound to p85α in the cell-derived complex . 10 . 7554/eLife . 06866 . 015Figure 4 . Binding of PTEN to WWP2 and to p85α homodimer is mutually exclusive . ( A–F ) Cell lysates from KLE cells transfected with WT p85α ( A–C ) or combined PR1 and PR2 mutant ( PR1+PR2 ) ( D–F ) were fractionated using a gel filtration column and the indicated fractions were analyzed by WB ( A , D ) or pooled for IP with anti-p85α antibody ( B , E ) or anti-PTEN antibody ( C , F ) . Input , total lysates before being subjected to gel filtration; F , fraction; MW , molecular weight . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01510 . 7554/eLife . 06866 . 016Figure 4—figure supplement 1 . ( A-B ) KLE cells were transfected with WT p85α for 72 hr . Whole cell lysates were then fractionated using a gel filtration column and the indicated fractions were pooled for IP with anti-p85α antibody . The immunocomplexes were subjected to SDS-PAGE followed by Coomassie Blue staining ( A ) or WB ( B ) with indicated antibodies . F , fraction . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 016 Next , we determined whether complex formation is altered when p85α homodimerization is disrupted by the PR1+PR2mut . Intriguingly , while distribution of p110α was unchanged , there was a shift in the distribution of unphosphorylated PTEN to fractions lacking p85α ( Figure 4D ) , suggesting that the PR mutations decreased the interaction between PTEN and p85α . Moreover , we observed a marked accumulation of PR1+PR2mut in fraction 39 which was absent in WT p85α-transfected cells ( Figure 4A ) that most likely represents monomeric p85α ( Figure 4D ) . Importantly , PTEN did not bind PR1+PR2mut in fraction 39 ( Figure 4E–F ) . p110α was not detected in fraction 39 likely because p85α was in excess of p110α . Together , these results confirm that p110α binds monomeric p85α , while unphosphorylated PTEN binds homodimeric p85α and further that the binding of PTEN to WWP2 and to homodimeric p85α is mutually exclusive . Mutations in PIK3R1 can contribute to tumorigenesis ( Jaiswal et al . , 2009; Cheung et al . , 2011 ) . Given p85α homodimerization is a key regulator of PTEN and PI3K pathway activation status , mutations that affect p85α homodimerization or association with PTEN would be expected to increase PI3K pathway activation and tumorigenesis . We therefore searched our in-house endometrial cancer data set ( Cheung et al . , 2011 ) and The Cancer Genome Atlas ( TCGA ) ( Figure 5—figure supplement 1 ) ( Cerami et al . , 2012 ) for cancer patient-derived PIK3R1 mutations that could target p85α homodimerization . Among mutations in the BH domain , E175K ( in skin cutaneous melanoma; TCGA ) and I177N ( in endometrial cancer ( Cheung et al . , 2011 ) ) localize to the BH:BH dimer interface ( Figure 1F ) and occur at sites that are highly conserved across species . I177 is a key residue of the BH:BH hydrophobic core . Substitution of the bulky hydrophobic isoleucine by a smaller and partly polar residue would be predicted to weaken homodimerization . Indeed , I177N decreased homodimer formation , decreased PTEN binding , increased ubiquitinated PTEN , and increased phosphorylated AKT consistent with PI3K pathway activation ( Figure 5A–C ) . I177N also promoted interleukin-3 ( IL3 ) -independent survival of the IL3-dependent BaF3 cells ( Figure 5D ) ( Jaiswal et al . , 2009; Cheung et al . , 2011 ) , consistent with the oncogenic potential of the mutant . These results indicate that I177N could act as an oncogenic mutation at least in part by perturbing p85α homodimerization . Of note , I177N has biochemical phenotypes similar to E160* , which is a patient-derived p85α mutant that disrupts p85α homodimerization and destabilizes PTEN protein ( Cheung et al . , 2011 ) . In contrast , E175 localizes to the polar and charged rim of the BH:BH interface with our model predicting that the E175K mutation would only have minimal influence on the BH:BH interaction ( Figure 1F ) . Indeed , biochemical analysis failed to detect effects of E175K on PTEN levels , ubiquitination , p85α binding , and pAKT as well as on proliferation of BaF3 ( Figure 5A–D ) . 10 . 7554/eLife . 06866 . 017Figure 5 . Oncogenic cancer patient-derived PIK3R1 mutation perturbs p85α homodimerization leading to PI3K pathway activation . ( A ) KLE cells co-transfected with Flag-tagged WT p85α ( Flag-WT ) and HA-tagged WT p85α or patient-derived p85α BH domain mutants were collected for IP with anti-HA and WB . ( B , C ) Cells transfected with WT p85α or mutants were collected for IP with anti-PTEN and WB with anti-ubiquitin ( B ) or directly for WB ( C ) . PTEN protein levels were normalized prior to IP by using proportionally different amounts of lysates . Numerical values below each lane of the immunoblots represent quantification of the relative protein levels by densitometry . ( D ) Ba/F3 cells transfected with WT p85α or mutants were cultured without interleukin-3 for 4 weeks and harvested for viability assays . *p < 0 . 05 , compared with WT . The error bars represent S . D . of triplicates from three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01710 . 7554/eLife . 06866 . 018Figure 5—figure supplement 1 . PIK3R1 mutations from The Cancer Genome Atlas ( TCGA ) data sets across tumor lineages are represented by lollipops ( green , missense; red , nonsense , frameshift , or splice; black , in-frame deletion/insertion; purple , different types of mutations at the same site ) . The diagram was adopted from the cBioPortal ( http://www . cbioportal . org/public-portal/ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 018 Given the pathophysiological relevance of the p85α:PTEN interaction , we next investigated whether the PTEN-interacting interface on p85α would be mutated in cancer patients . To this end , we compiled a total of 16 experimental , bioinformatics , structural and functional constraints ( see Appendix 1 ) , including our observation that PTEN and small GTPases bind the p85α BH domain ( Figure 6—figure supplement 1A , B ) non-competitively , to compute the most likely structural model for the homodimerized p85α:PTEN complex ( Figure 6A and Appendix 1 ) . In this model , the hydrophobic and charged residues I127/I133/E137 of the p85α BH domain are within the proposed PTEN-binding surface . We therefore engineered a I127A/I133A/E137A triple p85α mutant . The mutant decreased PTEN binding without altering p85α homodimerization ( Figure 6B ) . Combined mutations of I127A/I133A/E137A and PR2 , which also contributes to interaction with PTEN ( Figure 1C ) , resulted in further inhibition of PTEN binding ( Figure 6—figure supplement 1C ) , indicating that these residues are cooperative in binding PTEN . 10 . 7554/eLife . 06866 . 019Figure 6 . Molecular model of the p85α homodimer:PTEN complex reveals cancer patient-derived p85α mutant with decreased PTEN binding . ( A ) Schematic theoretical molecular working model of the homodimerized p85α:PTEN . This speculative model has been constructed by integrating experimental data , physical constraints , and computational scoring functions ( see Appendix 1 for details ) . For simplicity , only one PTEN molecule is shown . The PTEN molecular structure is taken from PDB 1D5R ( Lee et al . , 1999 ) . The model orientation corresponds to the view from the membrane toward the cytosol . Encircled numbers indicate locations of: 1 , PTEN phosphatase active site; 2 , the side chain of p85α W298 , located at the start of the flexible PR2 sequence; 3 , approximate position of the PTEN K13 and of the p85α triple mutation I127/I133/E137 . ( B ) KLE cells co-transfected with Flag-tagged WT p85α ( Flag-WT ) and HA-tagged WT p85α or mutants were collected for IP and WB . ( C ) Cells transfected with WT p85α or mutants were harvested for IP and WB . PTEN protein levels were normalized prior to IP by using proportionally different amounts of lysates . ( D ) Cells transfected with WT p85α or mutants were harvested for WB . Numerical values below the immunoblots represent relative protein levels by densitometry . ( E ) Ba/F3 cells transfected with WT p85α or mutants were cultured without interleukin-3 for 4 weeks and harvested for viability assays . *p < 0 . 05 , compared with WT . The error bars represent S . D . of triplicates from three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 01910 . 7554/eLife . 06866 . 020Figure 6—figure supplement 1 . ( A ) KLE cells co-transfected with Flag-tagged WT p85α ( Flag-WT ) and HA-tagged WT p85α or mutants were harvested for IP with anti-HA and then subjected to WB . The mutated amino acids are highly conserved residues potentially for small GTPase binding . ( B ) Cells were co-transfected with HA-tagged p85α in the absence or presence of increasing amounts of PTEN ( Left ) or Cdc42 ( Right ) . The lysates were collected for IP with anti-HA antibody . ( C ) Cells transfected with HA-tagged WT p85α or mutants were harvested for IP with anti-HA and WB . Numerical values below the immunoblots represent quantification of the relative protein levels by densitometry . ( D ) 62 patient-derived PTEN phosphatase domain missense mutations from our in-house endometrial cancer data set and TCGA were mapped onto the surface of PTEN . These mutations form three clusters . Red: mutations in the active site and membrane anchoring regions ( 27 mutations out of 62 ) ; magenta: mutations of the predicted p85α binding site ( 19/62 ) ; orange: mutations of the phosphatase-C2 interface ( 5/62 ) ; yellow: mutations of a site with unknown function ( 11/62 ) . Blue spheres represent a bound L ( + ) -tartrate molecule found in the PTEN crystal structure ( 1D5R ) , which is thought to mimic interactions of PTEN with substrate phosphates . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 020 Strikingly , in an extension of our previous study to additional tumor samples ( Cheung et al . , 2011 ) , we detected a p85α I133N mutant in an endometrial cancer patient . I133N led to a decrease in PTEN binding but not p85α homodimer formation ( Figure 6B ) . Comparable to I177N , I133N increased PTEN ubiquitination , induced PI3K pathway activation , and enhanced survival of BaF3 cells ( Figure 6C–E ) . Together , these data are consistent with I133N being another oncogenic mutation that blocks stabilization of PTEN . Further , 19 of 62 cancer patient-derived PTEN missense mutations in the phosphatase domain mapped to the proposed p85α interaction surface ( Figure 6—figure supplement 1D ) , suggesting that additional PTEN mutations may function through disrupting the association of PTEN with p85α .
The PI3K pathway is tightly regulated at multiple levels , for example , through PTEN , INPP4B and negative feedback loops such as that mediated by activated S6K ( Harrington et al . , 2004 ) . In this study , we provide a mechanistic model of how the p85α regulatory component of PI3K itself plays a dual role in regulating the PI3K pathway . A molecular excess of p85 over p110 has been observed ( Ueki et al . , 2002 ) . It has been suggested that p110-free p85 sequesters the adaptor protein insulin receptor substrate ( IRS ) through an nSH2-iSH2-cSH2 fragment-mediated interaction , thereby competing with the p85:p110 heterodimer for IRS binding and limiting the extent of insulin-induced PI3K signaling ( Luo et al . , 2005 ) . Our results herein suggest an additional but compatible mechanistic model of how free p85α negatively regulates the PI3K pathway . Our data support a model in which stable p85α homodimerization requires a simultaneous SH3:PR1 interaction in trans and a BH:BH interaction around residue M176 . These homodimers can form in absence of other proteins . Collectively , our results illustrate opposing functions of the p85α monomer and homodimer . As a homodimer , p85α competes with WWP2 for binding to the PTEN phosphatase domain and protects PTEN against WWP2-mediated degradation ( Figure 7 ) . The p85α homodimer also enhances the activity and membrane association of PTEN . Thus , homodimeric p85α indirectly downregulates PI3K signaling . Although the p85α homodimer does not bind p110α , it remains to be ascertained whether the nSH2-iSH2-cSH2 fragment of p85α homodimer can bind phosphotyrosines in activated cell surface molecules and thereby recruit PTEN to the activation nidus . As a monomer , p85α binds p110α in a 1:1 ratio ( Layton et al . , 1998 ) . This interaction is intrinsically inhibitory but it stabilizes p110α and allows activation of the p85α:p110α complex upon stimulation , promoting propagation of ligand-dependent PI3K pathway signaling . We therefore propose that the p85α monomer–dimer equilibrium is a gatekeeper for PI3K pathway activation . 10 . 7554/eLife . 06866 . 021Figure 7 . Schematic working model of how homodimerized p85α promotes PTEN stabilization and lipid phosphatase activity . Our data support a p85α homodimer model that includes intermolecular interactions between SH3:PR1 in trans and BH:BH interactions between protomers . Key contact residues at the interfaces are shown as blue lollipops . The homodimerized p85α binds PTEN at least partly through the PR2 domain and the indicated residues ( red lollipops ) in the BH domain . This interaction prevents PTEN from binding to the E3 ligase WWP2 , thereby inhibiting PTEN ubiquitination . The homodimerized p85α preferentially binds unphosphorylated PTEN , which has an open conformation and is more active . Independent of stabilizing PTEN protein , homodimerized p85α also enhances PTEN lipid phosphatase activity and association of unphosphorylated PTEN with the membrane where the PIP3 substrates localize . It remains unknown whether homodimerized p85α induces conversion of unphosphorylated PTEN from its close , inactive phosphorylated counterpart . DOI: http://dx . doi . org/10 . 7554/eLife . 06866 . 021 Homodimeric p85α preferentially binds unphosphorylated PTEN , which is less stable yet more active than its phosphorylated counterpart ( Vazquez et al . , 2000; Odriozola et al . , 2007; Rabinovsky et al . , 2009 ) . Phosphorylated PTEN is in a ‘closed’ conformation in which the phosphatase domain is auto-inhibited by the C-terminal tail . In contrast , unphosphorylated PTEN is in an ‘open’ conformation with the catalytic domain exposed , leading to increased membrane association and activity . The observation that p85α homodimers selectively target the unphosphorylated and less stable form of PTEN fits into a mechanistic model wherein in the absence of excess p85α , and thus , in the absence of p85α homodimers , the unphosphorylated and active PTEN is destabilized , allowing PI3K pathway activation initiated by p110α-bound p85α monomers . Further , our data showed that p85α can bind PTEN , promote PTEN activity and membrane binding in the absence of other proteins . Therefore , the interaction between p85α and PTEN appears to be direct and does not require the presence of other proteins , although p85α and PTEN are part of a high-molecular weight complex containing multiple other proteins . Based on our data , we speculate that the dynamic formation of p85α homodimers plays a role in the termination of signals initiated at the activation nidus by monomeric p85α-bound p110α . There are several possible scenarios in which the dynamics of p85α homodimer formation could be altered . For example , our data in this and previous studies ( Cheung et al . , 2011 ) suggested that p110α can disrupt p85α homodimerization by decreasing the abundance of p110α-free p85α . An increase in p110α levels , such as through PIK3CA gene amplification , might therefore decrease p85α homodimerization leading to PTEN destabilization . Secondly and intriguingly , PIK3R1 also encodes for two functional splice variants , p55α and p50α , which lack the domains required for homodimerization . By sequestering p110α , p55α , and p50α could increase the amount of free p85α . Moreover , naturally occurring somatic PIK3R1 mutations ( I177N and E160* ) and likely others disrupt p85α homodimerization , including mutations at P99 in PR1 , R162* , frame shifts at Q153 , D168 , H180 , V181 , and L182 ( Cheung et al . , 2011; Cerami et al . , 2012 ) . Further , a patient-derived p85α mutant I133N that targets the PTEN interaction interface was identified using our speculative theoretical p85α:PTEN model . The PIK3R1 mutations may hence be selected , at least in part , by effects on p85α homodimer formation and PTEN destabilization . Intriguingly , the proposed p85α-binding site on PTEN also overlaps with a PTEN surface that is frequently mutated in cancers ( in 30% of the missense mutations of the phosphatase domain ) , suggesting that some of these PTEN mutants may disrupt association with p85α therefore decreasing PTEN protein stability and activity . In addition to predicting mutations that disrupt homodimerization or PTEN interaction interfaces , the homodimerized p85α:PTEN model also offers possible mechanistic explanations for experimental observations . For example , the region outlined by I127/I133/E137 is close to in space to PR2 and both regions could thus together constitute a binding site for PTEN , possibly explaining the cooperativity between these residues in PTEN binding ( Figure 6—figure supplement 1C ) . The model also proposes that PTEN:BH interaction renders a site of PTEN ubiquitination K13 inaccessible , suggesting a mechanism for how p85α could protect PTEN from ubiquitination ( Figure 6A ) . Moreover , the model predicts that the phosphorylated C-terminal tail of the ‘closed’ inactive PTEN competes with the p85α PR2 for binding to the phosphatase domain of PTEN , providing a plausible explanation for why p85α selectively binds unphosphorylated PTEN . One key insight from our efforts to establish a speculative theoretical p85α:PTEN model was that our experimental constraints ( derived from our mutational analysis , and from our observation that the PTEN:p85α association is compatible with the p85α:GTPase interaction and with PTEN catalytic activity ) cannot be satisfied by a structural model in which one PTEN phosphatase domain binds simultaneously to both BH domains of the p85α homodimer . Instead , our theoretical model proposes that the PTEN phosphatase domain binds to the BH domain of only one p85α protomer , opposite to the GTPase-binding site ( Figure 6A and Appendix 1 ) . To explain the requirement of a stable p85α homodimer for PTEN binding and protection , our model suggests that ( i ) although the PTEN phosphatase domain only binds to one BH molecule , the flexible N-terminal 13 residues of PTEN may interact with the second BH molecule of the dimer ( in our model , PTEN residue 14 is close to and its main chain orients towards the BH:BH domain interface ) and/or ( ii ) homodimerization allosterically affects the PTEN-binding site on BH ( p85α residues I127/I133/E137 are close to the BH:BH homodimerization interface ) . Although our molecular model is supported by experimental data and showed strong predictive power , it remains hypothetical and we cannot exclude that other structural complexes could satisfy our constraints , or that some of the constraints are wrong . For instance , our model assumes that the PTEN:p85α ( :GTPase ) assembly is symmetric and homogenous . Yet , there could be other possible scenarios , for example , where only one of two p85α homodimer–bound PTEN molecules is able to reach the membrane with its catalytic site , or where only one p85α molecule of the homodimer binds to a GTPase , allowing a PTEN molecule to bind to both p85α molecules of the dimer's other side . Moreover , the contribution of flexible regions in protein interactions makes the structural modeling of these interactions necessarily more uncertain . Accordingly , uncertainty in our speculative PTEN:p85α complex model arises from the lack of precise information concerning the structure and possible interactions of the flexible regions of PTEN ( the first 13 residues ) and p85α ( the PR2 region , and the loop between residues 123 and 130 ) . Thus , while currently representing a useful working model for guiding and informing experimental analysis , our theoretical PTEN:p85α structure requires confirmation by an experimental structure . In conclusion , our study suggests that the PI3K pathway activation status can be influenced by the relative levels of p110 , p85α , and PTEN . These findings lend new insight to how changes in p110 levels through amplification or PIK3R1 mutations could lead to PI3K pathway hyperactivation and thus contribute to diseases such as cancer . By decreasing PTEN expression and activity , these non-nSH2-iSH2-cSH2 PIK3R1 mutations may affect sensitivity to particular targeted therapeutics . Indeed , p110β rather than p110α has been proposed as the primary target in cells with PTEN protein loss ( Jia et al . , 2008 ) . Targeting p85α homodimerization or the p85α:PTEN interaction may represent a new avenue for cancer treatment .
The complex between p85α SH3 and p85α PR1 peptide P87KPRPPRPLPVAP was constructed based on the crystal structures of p85α SH3:HSKRPLPPLPSL ( 3I5R ) and Fyn SH3: PPRPLPVAPGSSKT ( 1A0N ) . This structure was refined using the Rosetta energy function as implemented in FlexPepDock ( London et al . , 2011 ) . Construction of the PTEN:p85α molecular interaction model is detailed in the Appendix 1 . The p85α SH3 ( residues 1–81 ) and PR1-BH domains ( residues 79–301 ) were produced in E . coli BL21 cells as a 6His-fusion protein using the pET28b expression vector ( Novagen ) . Protein production was induced by 0 . 4 mM Isopropyl β-D-1-thiogalactopyranoside ( IPTG ) at 37°C . Cells were lysed using mild sonication , and proteins were purified to homogeneity by immobilized nickel affinity column using standard procedures . Eluted protein was dialyzed into 20 mM Tris pH 8 . 0 , 150 mM NaCl , 2 mM EDTA , 5 mM DTT and applied to S75 ( Pharmacia ) size-exclusion chromatography . Fractions containing pure SH3 were pooled and concentrated . The p85α SH3-PR1-BH-PR2 fragment ( residues 1–333 ) and WT p85α were produced in E . coli BL21 cells as GST-fusion proteins using a pGex6P-2 expression vector ( GE Healthcare , Pittsburgh , PA ) . Protein expression was induced by 0 . 4 mM IPTG at 18°C overnight , and proteins were purified by GST affinity column . Eluted proteins were dialyzed into 20 mM Tris pH 8 . 0 , 150 mM NaCl , 5 mM DTT . WT p85α was further purified using MonoQ 10/100 column and eluted by a linear NaCl gradient ( 20 mM Tris pH 8 . 0 , 1M NaCl , 5 mM DTT ) . Finally , p85α1-333 C147S and WT p85α were applied to a Superdex200 16/600 ( GE Healthcare ) size-exclusion chromatography . Fractions containing pure proteins were pooled and concentrated . Proteins were fluorescently labeled with Alexa647 according to the manufacture’s protocol ( L001 , Nanotemper technologies , Germany ) . Labeled proteins were kept at 20 nM . The unlabeled protein was titrated starting at 50 , 000 nM and serially diluted in 1:1 ratio in reaction buffer ( 20 mM sodium phosphate pH 8 , 150 mM NaCl , 2 mM EDTA , 5 mM DTT ) . The measurements were performed at 40% LED and 20 , 40 , and 60% MST power on NanoTemper Monolith NT . 115 . Data were analyzed using NT Analysis Software ( Nanotemper technologies , Germany ) . p85α SH3 and GST-ΔSH3 p85α were dialyzed in degassed ITC buffer ( 10 mM sodium phosphate pH 7 . 5 , 150 mM NaCl , 5 mM DTT ) . Titrations for p85α SH3:PR1 peptide interaction were carried out on a MicroCal ITC200 at 25°C by serially injecting 2 μl of peptide P87KPRPPRPLPVAP into the measurement cell that contained p85α SH3 at a concentration of 100 μM . GST-ΔSH3 p85α and p85α SH3 titrations were carried out similarly , with SH3 in the syringe ( 400 μM ) and ΔSH3 p85α in the cell . Ligand concentrations were 10× the cell concentration . Titrations were analyzed using Origin software . The experiments were conducted on a Beckman Coulter XL-A analytical ultracentrifuge at 20°C using absorption optics . Samples were loaded at 17 , 9 , 5 , 3 , and 0 . 7 µM in a standard cell ( 400 μl ) . Data were acquired ( 120 scans ) in intensity mode at 280 nm at 6-min intervals with a rotor speed of 48 krpm for p85α PR1-BH , 45 krpm for p85α SH3-PR1-BH-PR1 , and 42 krpm for WT p85α . Data were analyzed in SEDFIT 14 . 3e ( Schuck , 2000 ) using the continuous c ( s ) distribution model . Solution densities ρ and viscosities η were calculated using the program SEDNTERP 1 . 09 ( Cole et al . , 2008 ) . Analyses of sedimentation coefficients were carried out using s range of 0 . 05–10 with linear resolution of 100 and confidence levels of 0 . 95 . Fits were achieved with root mean square deviations ranging from 0 . 0030 to 0 . 0055 absorbance units . Sedimentation coefficients were corrected to standard conditions at 20°C in water , so20 , w using SEDNTERP 1 . 09 . The experiments were performed on a Beckman Optima XL-A analytical ultracentrifuge at 20°C . Samples were loaded at 1 . 0 , 0 . 5 , and 0 . 25 mg/ml into a 2-channel , 12-mm path-length cells ( 100 μl ) . Data were acquired at 11 . 8 , 12 . 8 , 13 . 8 krpm , as an average of 5 absorbance measurements at 280 nm using a radial spacing of 0 . 001 cm . Experiments were started from zero to the lowest rotor speed by taking scans at 2-hr intervals and testing for equilibrium by determining the differences between successive scans in SEDFIT . The rotor speed was then increased and the process repeated at each rotor speed studied and sedimentation equilibrium was achieved within 48 hr . Data for the individual proteins collected at different speeds and loading concentrations at 280 nm were sorted and assembled in SEDFIT and analyzed globally in terms of monomer–dimer–tetramer ( monomer-m-mer-n-mer ) or monomer–dimer model in SEDPHAT 10 . 58e ( Schuck , 2003 ) with the implementation of mass conservation . The error analysis was performed with 500 ( 1000 for p85α PR1-BH ) Monte Carlo iterations at the 95% confidence level . The KLE cell line was provided by Dr . Russell Broaddus ( M . D . Anderson Cancer Center ) . The p85α knockout MEFs were obtained from Dr Lewis C . Cantley's lab ( Weill Cornell Medical College , New York , NY ) . The PIK3R1 cDNA has been described ( Cheung et al . , 2011 ) . Specific mutations were generated using QuikChange Lightning Site-Directed Mutagenesis Kit ( Agilent Technology , Santa Clara , CA ) . Transfection of plasmids was performed using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) . All ON-TARGET plus siRNAs and control siRNA were obtained from Dharmacon ( Lafayette , CO ) and introduced into the cells using Lipofectamine RNAiMAX ( Invitrogen ) according to the manufacturer's instructions . We utilized two independent siRNA sequences per target . The sequences of the siRNAs are p85α-CCAACAACGGUAUGAAUAA and UAUUGAAGCUGUAGGGAAA; WWP2- AGACACGUCCGUUGGGCAG and GCUUCACCCUCCCUUUCUA; S6K- CAUGGAACAUUGUGAGAAA and GGAAUGGGCAUAAGUUGUA; Rictor- GACACAAGCACUUCGAUUA and GAAGAUUUAUUGAGUCCUA . Whole cell lysates ( 25 µg ) extracted with radioimmunoprecipitation assay buffer ( RIPA ) lysis buffer were loaded onto sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) . Primary antibodies specific to PTEN , p110α , total Akt , phospho-Akt ( Thr308 or Ser473 ) and total p110α ( Cell Signaling Technology , Danvers , MA ) , HA ( Covance , Princeton , NJ ) , Myc and FLAG ( Sigma–Aldrich , St . Louis , MO ) , ubiquitin ( Enzo Life Sciences , Farmingdale , NY ) were used . For IP , cell lysates ( 1 mg ) were immunoprecipitated with antibodies against HA or Flag ( 1 μg ) or PTEN ( 1:500 ) overnight at 4°C . The immune complexes were collected by incubation with protein A/G agarose ( Santa Cruz ) for 4 hr before being resolved by SDS-PAGE . Cells were lysed in a hypotonic lysis buffer containing 10 mM HEPES ( pH 7 . 9 ) , 1 . 5 mM MgCl2 , 10 mM KCl , 1 M EDTA , 0 . 1% NP-40 and protease inhibitors . Lysates ( 5 mg ) clarified by ultracentrifugation ( 30 , 000×g , 45 min , 4°C ) were applied to Superose 6 , 10/300 GL column ( GE Healthcare ) run at 4°C in binding buffer ( 0 . 1% NP-40 ) on a BioLogic HR system ( BioRad , Hercules , CA ) . Elution was performed at 0 . 1 ml/min , and 0 . 5-ml fractions were collected . Fractions were analyzed by Western blotting ( WB ) or pooled for IP with anti-p85α ( 1:50 ) ( Abcam , Cambridge , MA ) or PTEN ( 1:500 ) antibody . The assay was described previously ( Cheung et al . , 2011 ) . In brief , Ba/F3 cells transfected with WT PIK3R1 or mutants were cultured in medium without IL-3 for 4 weeks . Cell viability was evaluated using PrestoBlue ( Invitrogen ) for mitochondrial dehydrogenase activity . In vitro PTEN lipid phosphatase activity was determined using malachite green phosphatase assay kit ( Echelon Biosciences , Inc . , Salt Lake City , UT ) . Briefly , protein lysates ( 1 mg ) were subjected to IP of endogenous PTEN using anti-PTEN or anti-p85α antibody and the bead complex was resuspended in PTEN reaction buffer before PIP3 substrate was added to initiate the reaction . Reaction mixture with PIP3 but not lysate was used for background correction . The reaction was stopped after 4-hr incubation at 37°C and the supernatant was separated from the beads for activity detection . The beads were used for WB to quantify immunoprecipitated PTEN . The activity was normalized to immunoprecipitated PTEN level in each sample . To assess the activity of recombinant PTEN , 1 μg of PTEN was incubated with 1 μg of recombinant p85α in buffer containing 50 mM Tris–HCl pH 8 . 0 , 50 mM NaCl , and 10 mM MgCl2 ( PTEN reaction buffer without DTT ) at room temperature for 1 hr before being subjected to the reaction . The assay was performed in reference to previous studies ( Fukuda et al . , 1996; Davletov et al . , 1998; Lee et al . , 1999 ) . Formation of LMVs composing of brain phosphatidylserine ( PS ) , phosphatidylethanolamine ( PE ) , and phosphatidylcholine ( PC ) ( molar ratio 40:10:50 ) resuspended in buffer containing 50 mM Tris-Cl , 150 mM NaCl , 10 mM DTT , 0 . 001% Triton X-100 ( pH 8 . 0 ) was obtained from Avanti Polar Lipids , Inc . ( Alabaster , Al ) . PTEN recombinant protein ( 5 µg ) was incubated in 300 µl of buffer containing 50 mM Tris-Cl , 150 mM NaCl , and 0 . 001% Triton X-100 ( pH 8 . 0 ) in the presence or absence of 5 µg of recombinant p85α for 1 hr at 25°C . LMVs ( 150 μg ) were then added and the mixture was incubated for another 15 min at 25°C . After centrifugation at 12 , 000×g for 10 min , the phospholipid pellets were dissolved in SDS sample buffer . The proteins in the supernatants corresponding to the lipid-unbound population were precipitated with 20% trichloroacetic acid and the precipitates were dissolved in SDS sample buffer . Equal amounts of samples from the supernatants and pellets were analyzed by SDS-PAGE and Coomassie Brilliant Blue R-250 staining . All experiments were independently repeated at least twice , and data are presented as mean values ± SD . The significance of differences was analyzed by a Student’s t test . Significance was accepted at the 0 . 05 level of probability ( p < 0 . 05 ) . | Many cancers arise due to genetic mutations that allow cells to proliferate uncontrollably . Cell proliferation and many other cell processes can be regulated through a signaling pathway that involves an enzyme called PI3K . This ‘heterodimeric’ enzyme is made up of two protein subunits , one of which is called p85α and inhibits the other subunit of the enzyme ( known as p110 ) to prevent uncontrolled cell proliferation . At the same time , p85α stabilizes p110 and allows the PI3K pathway to be briefly activated when appropriate . Many cancer cells contain mutations in the gene that encodes p85α that prevent the protein from inhibiting p110 . This results in the activation of PI3K and promotes cancer formation . A protein called PTEN is a key inhibitor of the PI3K pathway . Common mutations to the PTEN gene in cancer cells stop the PTEN protein working efficiently , or prevent PTEN production . Recent research has revealed that two molecules of p85α that are free from p110 can bind to each other to form a ‘homodimer’ . Cheung et al . have now used biochemical , cell biological and computational methods to investigate the role of these p85α homodimers . This revealed that p85α homodimers stop PTEN being broken down by binding to it . As a consequence , there is enough PTEN in the cell to inhibit the PI3K pathway . By examining the mutations present in cancer patients , Cheung et al . next identified mutations that prevent the p85α protein from forming homodimers , or that prevent the homodimers from interacting with PTEN . PTEN therefore degrades and cannot inhibit the PI3K pathway , which allows the cells to proliferate . Methods that increase p85α homodimer formation or enhance the ability of p85α homodimers to bind to PTEN may therefore provide new approaches for developing cancer treatments . More generally , it appears that maintaining the correct balance between the amount of p85α in the form of p110-bound heterodimers and p110-free homodimers in a cell may be important for preventing diseases involving the PI3K pathway . | [
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] | 2015 | Regulation of the PI3K pathway through a p85α monomer–homodimer equilibrium |
The bacterial Min protein system provides a major model system for studying reaction-diffusion processes in biology . Here we present the first in vitro study of the Min system in fully confined three-dimensional chambers that are lithography-defined , lipid-bilayer coated and isolated through pressure valves . We identify three typical dynamical behaviors that occur dependent on the geometrical chamber parameters: pole-to-pole oscillations , spiral rotations , and traveling waves . We establish the geometrical selection rules and show that , surprisingly , Min-protein spiral rotations govern the larger part of the geometrical phase diagram . Confinement as well as an elevated temperature reduce the characteristic wavelength of the Min patterns , although even for confined chambers with a bacterial-level viscosity , the patterns retain a ~5 times larger wavelength than in vivo . Our results provide an essential experimental base for modeling of intracellular Min gradients in bacterial cell division as well as , more generally , for understanding pattern formation in reaction-diffusion systems .
The Min protein system determines the localization of the division site in a wide range of bacterial cells ( Loose et al . , 2011b; Lutkenhaus , 2012; Shih and Zheng , 2013; Rowlett and Margolin , 2015 ) . In Escherichia coli ( E . coli ) , Min proteins dynamically oscillate from pole-to-pole on a typical time scale of about 1 min ( Raskin and de Boer , 1999; Hu and Lutkenhaus , 1999; Hale et al . , 2001 ) . Reaction-diffusion mechanisms were invoked in order to explain these oscillations ( Kruse et al . , 2007 ) , and as of today , the Min system is one of the most prominent examples of intracellular pattern formation in biology ( Soh et al . , 2010 ) . Three proteins participate in the E . coli Min oscillations: ( i ) MinD , an ATPase that can bind the plasma membrane through a short amphipathic peptide ( Hu and Lutkenhaus , 2003; Szeto et al . , 2003 ) in a cooperative manner ( Mileykovskaya et al . , 2003; Renner and Weibel , 2012 ) . Though it was commonly assumed that it binds the membrane only in its ATP-bound form ( de Boer et al . , 1991; Hu and Lutkenhaus , 2001; Lackner et al . , 2003 ) , a recent work showed that MinD can also bind the membrane in the ADP-bound form ( Zheng et al . , 2014 ) . ( ii ) MinE , a protein that is recruited to the membrane by MinD ( Ma et al . , 2003 ) , upon which it induces MinD’s ATPase activity causing MinD to be released from the membrane ( Hu et al . , 2002 ) . Subsequently , while diffusing in the cytosol , an exchange of ADP to ATP occurs , and the MinD proteins re-enter the cycle by rebinding the membrane . Several authors showed that MinE can persist on the membrane after MinD detachment ( Loose et al . , 2011a; Park et al . , 2011 ) or can even interact with the membrane by itself ( Hsieh et al . , 2010; Shih et al . , 2011; Zheng et al . , 2014 ) . The exact contribution of this process to the overall Min dynamics remains unclear . Finally , ( iii ) MinC protein is also recruited to the membrane by MinD , but , due to overlap binding with MinE , is released from MinD after MinE binding ( Hu et al . , 2003; Ma et al . , 2004; Wu et al . , 2011 ) . While MinC is the sole member of the system that directly interacts with the division apparatus ( Hu et al . , 1999; Cordell et al . , 2001; Dajkovic et al . , 2008 ) , it is believed to be only a passive hitchhiker that does not determine the dynamical behavior of the system . Thus , only MinD and MinE are needed in order to form dynamical pole-to-pole oscillations in E . coli cells . A number of relevant properties of the Min system were identified when E . coli bacteria were perturbed from their native rod-shape form . When cells were grown as filaments , a dynamic series of Min bands with a characteristic length of ∼8 µm was observed ( Raskin and de Boer , 1999 ) . When the filamentous cells were grown at different temperatures , this length scale did not change but the temporal period of the oscillations decreased according to an Arrhenius law ( Touhami et al . , 2006 ) . Likewise , in oval-shape cells , the Min system preferentially oscillated along the longest axis ( Corbin et al . , 2002; Shih et al . , 2005 ) . Interestingly , while in round ΔMreB cells , the Min oscillation occurred , in the majority of the cases , from one end of the cell to the other in a well-defined manner , in rounded rodA-amber-mutation cells , the oscillation direction moved chaotically from one spot along the membrane to another . A similar mode of chaotic oscillations was also observed when cells adopted aberrant shapes upon getting squeezed into slits smaller than their natural width ( Männik et al . , 2012 ) . In addition , when E . coli cells were mutated to form a Y shape , a sequence of oscillation nodes along the cell arms was observed that depended on the relative length of the Y shape arms ( Varma et al . , 2008 ) . These results show that the Min system can adapt to the cell geometry and modify its dynamical behavior accordingly . Indeed , in a recent example , Wu et al . sculpted E . coli cells into various rectangular and square shapes . They found that the Min system behavior was characterized by a typical length scale of 3–6 µm . In addition , they showed that the choice for a particular Min pattern was governed by the symmetries of the cell shape , its aspect ratio , and its size . Consequentially , a variety of Min patterns were observed , including rotational , transversal , and longitudinal modes ( Wu et al . , 2015 ) . In parallel with these cell-biology studies , in vitro reconstitution of the Min system on supported lipid membranes ( SLB ) significantly advanced the understanding of its dynamical mechanism ( Loose et al . , 2008 ) . On two-dimensional ( 2D ) SLBs that are much larger than the typical length scale of the Min patterns , instead of oscillations , the Min proteins exhibit patterns that can be grouped into two classes: rotating spirals and traveling waves . In general , traveling waves were formed when bands that emanated from two counter-rotating spirals collided . Importantly , the wavelength of the traveling waves was very large , between 65–100 µm , and depended on the ratio of MinD to MinE concentration . Thus , when the Min system is reconstituted in vitro , it exhibits a pattern-formation behavior with a typical spatial dimension that is about an order of magnitude larger than the one observed in vivo . Interestingly , when the Min system was reconstituted under limiting concentration conditions , unique patterns , in particular a bursting type , were observed ( Vecchiarelli et al . , 2016 ) . It was suggested that these patterns might be more closely related to the behavior of the Min system in vivo . One of the intriguing properties of the in vitro behavior of the Min system is its ability to adapt to geometrical patterns that are embedded in the SLB . For example , when rectangular patches of flat surface SLB were separated one from the other with gold barriers that were much larger than the characteristic size of the Min dynamics in vitro ( ∼100 μm ) , Min waves propagated in a direction that depended on the aspect ratio of the patch ( Schweizer et al . , 2012 ) . Similarly , Min waves can be oriented within an SLB if parallel grooves are molded into the surface ( Zieske et al . , 2014 ) . However , the geometry-selection rules that were found in these in vitro experiments do not correspond to the ones that were observed in vivo ( Wu et al . , 2015 ) . Recently , utilizing partly confined fabricated reaction chambers , Zieske and Schwille were able to reproduce Min pole-to-pole oscillations as well as double and triple-band standing waves , similar to the patterns observed in filamentous E . coli ( Zieske and Schwille , 2013 , 2014 ) . Similarly , Zieske et al . showed that when grooves had a shape similar to that of dividing bacteria at the last division stage , the Min proteins stochastically distributed between the two sides of the grooves . This observation is similar to the way that Min proteins are distributed between the two progenies of an E . coli mother cell ( Di Ventura and Sourjik , 2011 ) . However , these in vitro Min oscillations were stable only when the groove width was much smaller than the in vitro wavelength of the waves . Since the geometrical selection rules of the Min patterns that were established in sculpted cells ( Wu et al . , 2015 ) do not coincide with the ones that were established for the in vitro grooves , the exact relation between the underlying mechanisms of these two phenomena of Min proteins remains unclear . Recalling that the Min dynamics is a reaction-diffusion process , both the reaction and the diffusion parameters may control the behavior of the system in vitro as well as in vivo . Several experimental attempts have been made to study these reaction-diffusion factors . For example , when the overall bulk diffusivity of the proteins was decreased by a factor of 10 , the Min wavelength was , surprisingly , only marginally reduced ( Schweizer et al . , 2012 ) . In contrast , when the Min dynamics was studied on the outer side of giant unilamellar vesicles ( GUVs ) , both the wavelength and wave velocity increased considerably relative to the behavior on an SLB ( Martos et al . , 2013 ) . It was suggested that the 4-fold increase in the diffusion rate of the Min protein on the GUVs membrane was responsible for this phenomenon . Concerning the reaction rates , by increasing the salt concentration or the ratio of anionic to natural phospholipids , Vecchiarelli et al . observed a reduction in the size of the Min bands ( Vecchiarelli et al . , 2014 ) , which they attributed to the different affinity of MinE to the membrane under these conditions . Similarly , Zieske and Schwille observed a decreased wavelength to a value of about 25 µm by increasing the concentration of a negatively charged lipid , cardiolipin , in the SLB to an ( unphysiologically high ) molar ratio of 70% ( Zieske and Schwille , 2014 ) . Note that even this reduced value is still much larger than the characteristic ∼5 µm size scale measured in vivo . Over the years , many mathematical models have been constructed for the Min system ( Meinhardt and de Boer , 2001; Huang and Wingreen , 2004; Meacci and Kruse , 2005; Fischer-Friedrich et al . , 2007; Arjunan and Tomita , 2010; Hoffmann and Schwarz , 2014 ) . Each of them postulated a somewhat different molecular mechanism and was able to reproduce several of the observed Min phenomena for certain values of the model’s parameters . However , the ability to explain all Min behaviors and the robustness to changes in the parameters was less well considered . In particular , the ability to gap the in vitro and in vivo behavior of the system was not given much attention . Recently , Bonny et al . claimed that they were able to bridge this gulf ( Bonny et al . , 2013 ) . However , they were only able to reproduce the in vivo oscillations and in vitro surface waves for reaction parameters that were different in both cases over several orders of magnitudes , and the experimental basis for this variation remained unclear . To date , the best Min model was developed by Halatek and Frey , based on a previous model by Huang and Wingreen ( Huang et al . , 2003; Halatek and Frey , 2012; Thalmeier et al . , 2016 ) . This model is able to reproduce most of the in vivo experimental results over a wide range of geometries and conditions ( Wu et al . , 2015 ) . The ability of this model to reproduce the Min behavior in vitro was , however , not yet reported . One reason for the lack of theoretical ability to reproduce all observed Min behaviors using a single set of parameters is the lack of comprehensive experimental results for the in vitro behavior of Min proteins under a wide range of geometrical confinements . First steps in this direction were taken by Zieske and Schwille ( Zieske and Schwille , 2013 , 2014 ) . However , their compartment were only semi-confined and they probed the system behavior mainly for compartments that were much narrower than the typical Min wavelength , and hence , a full description of the geometrical selection rules for the Min system in confined chambers , and especially the relation between in vitro oscillations and in vitro traveling waves , is still missing . Here , we studied the pattern formation behavior of MinD and MinE proteins inside fully 3D confined chambers , under the aim to better understand the relations between different factors that determine the influence of geometry on the dynamics of the Min system in vitro . To obtained these chambers and encapsulate Min proteins inside them , we fabricated PDMS chips and coated all the chamber walls with SLB before injecting the Min proteins and spatially isolating the chambers from the rest of the chip through soft-lithography PDMS valves . We determine the Min patterns as a function of the geometrical factors of the chambers as well as of other factors such as temperature and viscosity , and compared these results to the one that are observed with flat SLB . We show that the rotational Min spirals can evolve to pole-to-pole oscillations if the dimensions of the confinement are reduced or to traveling waves if the dimensions of the confinement are increased . We provide a phase diagram that maps out the Min patterns over a wide range of geometrical parameters . In addition , we show that several parameters , including 3D confinement , medium viscosity and temperature can reduce the wavelength of traveling waves . Yet , all these parameters did not resolve the in vitro/in vivo dichotomy . Our comprehensive set of data , however , provides essential information that is needed in order to understand the molecular mechanism of the Min system , which underlies its essential pattern-formation abilities .
We fabricated microfluidic chips that are composed of two stacked PDMS layers ( see Figure 1a–c and Figure 1—figure supplement 1 to Figure 1—figure supplement 3 ) . The bottom layer had structures of three different heights: ( i ) Rectangular chambers with height of ∼2 . 4 µm and lateral dimensions ranging from 10 × 10 µm to 60 × 80 µm ( blue part in Figure 1b ) ; ( ii ) Reservoir channels with height of ∼30 µm and width of 100 µm ( red part in Figure 1b ) ; and ( iii ) thin connectors channels with height of ∼0 . 9 µm and width of ∼0 . 9 µm ( green part in Figure 1c ) that connect the reservoirs and the chambers . The upper layer of the device consisted of air-filled PDMS channels that were aligned directly above the reservoir channels and connected to a high-pressure argon line thus serving as pneumatic pressure valves ( Unger et al . , 2000 ) . Upon increasing the pressure of the air-filled channels in the upper layer , the ceiling of the lower layer ( thickness ∼20 µm ) deflected downward above the reservoir channels and closed the entrance to the connector lines . Using this design , we were able to spatially isolate the central PDMS chambers ( blue in Figure 1b ) from the rest of the chip and to obtain totally isolated 3D confined volumes for studying the Min patterns . 10 . 7554/eLife . 19271 . 003Figure 1 . Chip structure and basic modes of Min patterns . ( a ) Top view illustration of the microfluidic device structure . We study Min protein pattern formation in totally enclosed microfluidic chambers . Each chamber ( blue ) is 2 . 4 µm high with a width ranging from 10 to 60 µm and a length ranging from 10 to 90 µm . Each chamber is connected through small connector lines ( green , with a cross-section of ∼0 . 9 × 0 . 9 µm ) from both sides to two ∼30 µm deep reservoirs channels ( red ) . A top layer of pressurized microfluidic valves ( gray shading ) are placed above the reservoirs channels . After the walls of the chambers are coated with a lipid bilayer , MinD and MinE proteins are injected into the device . Subsequently , the valves above the reservoir channels are closed . Consequently , the rectangular chambers ( including the connector lines ) are separated from the rest of the device . This unique structure allows studying the 3D geometric selection rules of the Min system in vitro . ( b ) 3D illustration of the microfluidic device structure . Chambers are in blue , connector lines in green , and reservoir channels in red . For clarity , the pressure valves are not shown . ( c ) SEM image of the silicon wafer master that was used in order to replicate the chambers , connector lines and reservoir channels into PDMS . Note that the fabrication was done with a positive resist and , thus , we have used a double replica method in order to recover the right orientation of the chambers ( see Materials and methods ) . ( d–f ) Characterization of dynamical Min patterns observed in the confined fluidic chambers . ( d ) Oscillations - Min proteins periodically move back and forth between two poles of the chamber . ( e ) Traveling waves - Min proteins wave fronts continuously propagate from one side of the chamber to the other . ( f ) Rotations - Min zone circulates around a fixed point in the chamber . Scale bar 40 μm applied to all three examples ( d–f ) . Fluorescence signals represent MinE patterns . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 00310 . 7554/eLife . 19271 . 004Figure 1—figure supplement 1 . Schematics of chip fabrication and operation . ( a ) Illustration of the fabrication process of the microfluidic device . First , a top layer PDMS chip is bound to a thin PDMS membrane via plasma treatment and a high-temperature baking . Next , a lower layer PDMS chip with the chambers is bound to a glass coverslip in the same way . Finally , the two parts are aligned and bound to each other in the same way . ( b ) An illustration of the operation of the chip in order to obtain closed microfluidic chambers . First , the chip is coated with a supported lipid bilayer . Next , Min proteins are infused in the chambers . Finally , the upper layer valves are pressurized with air . As results , one can study the behavior of the Min proteins in confined isolated fluidic volumes . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 00410 . 7554/eLife . 19271 . 005Figure 1—figure supplement 2 . SEM images of two different areas in the lower layer PDMS chip . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 00510 . 7554/eLife . 19271 . 006Figure 1—figure supplement 3 . Schematic representation of the overall lower layer chip structure . A series of three dimensional closed rectangle chambers . Chambers and lines are in blue . Connector lines are in green , and deep reservoirs channels are in red . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 006 We used the traditional method of rupture and spreading of small unilamellar vesicles ( SUVs ) for the formation of an SLB onto all inner surfaces within our microfluidic device ( SUVs composition 67% DOPC , 33% DOPG , supplemented with 0 . 03% TopFluor cardiolipin for fluorescent imaging ) ( Brian and McConnell , 1984; Richter et al . , 2006 ) . After the device was flushed with SUVs and incubated for ∼1 hr , a nice continuous SLB was formed on all the inner walls of the chambers ( data not shown ) . This feature of our microchambers that are covered by an SLB from all sides is what distinguishes our approach from previous studies that reconstituted Min proteins in fabricated structures ( Loose et al . , 2008; Ivanov and Mizuuchi , 2010; Schweizer et al . , 2012; Martos et al . , 2015; Zieske and Schwille , 2013; Vecchiarelli et al . , 2014; Zieske and Schwille , 2014; Vecchiarelli et al . , 2016 ) . Subsequently , the device was extensively washed in order to remove the residual SUVs . For several devices , we checked the formation of a fluid SLB using fluorescence recovery after photobleaching ( data not shown ) ; for other devices , we relied on the homogeneous fluorescence signal of the TopFluor cardiolipin . It was important , for each device , to check that the valves worked properly . As a quality control , we checked the functionality of the valves by operating them during the first stages of the washing process and observing the effect on the flow through the microchambers ( see Video 1 and the Materials and methods section ) . These results corroborated that upon closing the pressure valves , we obtained truly confined chambers in our microfluidic devices . 10 . 7554/eLife . 19271 . 007Video 1 . Isolation of 3D confined chambers from the rest of the chip . After the SLB was formed in the device , as described in the Materials and methods , the chip was connected to a syringe pump containing Min buffer . A flow of 75−150 µl/h was applied ( green traffic light ) . The application of buffer flow resulted in migration of the SUVs that did not splash during the previous incubation period on the chambers walls along the stream lines . After ∼3 min the valves were closed and the flow was stopped ( red traffic light ) . This resulted in an immediate halt of the SUVs flow , showing that the chambers were isolated from the rest of the chip . In order to show reversibility of the valves operation , after additional ∼1 . 5 min , the valves were opened ( second round of green traffic light ) . Immediately upon releasing the pressure in the pressure valves , the SUVs started to flow again , showing that the halting of the flow was the result of the valves operation and did not result from , say , stopping the syringe pump operation . Of course , stopping the operation of the syringe pump will also result in a halt of the flow in the microfluidic device . However , due to pressure-difference equilibration , this process usually lasts couple of minutes . In contrast , upon operating the valves , an immediate halt in the flow was observed . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 007 After the device was washed and the valves were tested for functionality , the chambers were filled with a solution containing MinD and MinE that we purified and labeled beforehand ( MinE 0 . 8 µM: MinE-Cy5 0 . 2 µM: MinD 0 . 9 µM: MinD-Cy3 0 . 2 µM ) , supplemented by ATP ( 5 mM ) and an ATP-regeneration system ( see Materials and methods ) . Subsequently , we closed the pressure valves and , after an incubation period of >1 hr , we recorded movies of the resulting dynamical behavior of the Min system . We observed a rich set of dynamical phenomena for the spatio-temporal behavior of the Min proteins , and studied it in hundreds of confined chambers . In most of the cases , the Min proteins showed a defined pattern in each chamber that was stable for the duration of the movie acquisition ( ∼10 min ) . Three primary dynamical behaviors stood out as the most prominent patterns; ( i ) pole-to-pole oscillations - where the Min proteins periodically move back and forth from one side of a structure to the other ( see Figure 1d ) ; ( ii ) traveling waves - where the proteins move constantly from one side of a structure to the opposite side ( see Figure 1e ) ; and ( iii ) spiral rotations - where the proteins constantly rotate in a spiral fashion within the microchamber ( see Figure 1f ) . As can be seen from Figure 1d–f , the typical time it took to reestablish the Min zone in these microchambers was 1−5 min . For each chamber , we recorded a movie with the dynamical behavior of the Min system . To represent the temporal behavior in a specific chamber in the format of a still image , we adopted a quadrant scheme , see Figure 2a . Each quadrant is composed of: ( top-left ) a single frame from a movie of the dynamical pattern in this chamber; ( bottom-left ) an X-axis kymograph of the Min concentration along the horizontal chamber middle ( red line in top-left image of Figure 2a ) ; ( top-right ) a Y-axis kymograph of the Min concentration along the vertical chamber middle ( blue line in top-left image of Figure 2a ) ; and ( lower-right ) a temporal-standard-deviation ( STD ) image of the movie . In this scheme , each basic type of dynamical behavior such as oscillations , waves or spiral rotations has a distinct signature . 10 . 7554/eLife . 19271 . 008Figure 2 . Atlas of the Min system behavior in 3D confined chambers . We represent the dynamical Min behavior in each case as a quadrant . ( a ) Illustration of the quadrant scheme . Each quadrant is composed of ( upper left ) A single frame from a movie that captures the behavior of the Min system in a specific chamber at a specific point of time; ( lower right ) A temporal standard deviation picture of the movie; ( lower left ) an Xt-kymograph of the middle line cross-section of the chamber along the red line in the upper left movie frame image , and ( upper right ) a Yt-kymograph of a middle line cross-section of the chamber along the blue line in the upper left movie frame image . ( b–d ) Illustrative examples of the three pure genera of Min dynamics that were observed . ( b ) Oscillations . ( c ) Traveling waves , and ( d ) Rotations . ( e ) Table representing real examples of observed Min dynamics in the chambers , organized according to the quadrant scheme . Each image is color coded in a 16-colors look-up table as shown in the legend . Scale bar represents 30 μm in the x and y directions and 600 s for the Xt and Yt kymographs . A detailed explanation for the quadrant representation of the Min pure and non-pure behaviors is found in the main text . Fluorescence signals represent MinE patterns . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 00810 . 7554/eLife . 19271 . 009Figure 2—figure supplement 1 . A zoom-in of the atlas of the Min system behavior in 3D confined chambers . This figure represents a zoom of Figure 2e . Typical examples of the Min protein behavior in smaller chambers are thus shown at larger magnification for clarity . The format of figure follows the same quadrant representation . For more details see the main text . Fluorescence signals represent MinE patterns . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 009 For example , for side-to-side oscillations ( cf . Figure 2b ) , the top-left representation of the quadrant may show a snapshot of the Min concentration at one side of the chamber . The Xt kymograph shows the movement of the Min proteins along the X-axis as they travel from the chamber middle to one side , then to the chamber middle , then to the other side of the chamber , and so on . This repetitive reappearing of Min proteins at the chamber middle results in oblique lines in the Xt kymograph that run from the Kymograph middle to both of its sides . In contrast , the reappearing of Min proteins in the middle of the chamber results in repetitive vertical lines in the Yt kymograph . Finally , the STD image shows the locations where the variations in the Min proteins are the largest , i . e . , at the chamber sides in this example . For a traveling wave ( cf . Figure 2c ) , the top-left single frame image in the quadrant representation can show , for example , two wave fronts propagating from the bottom right corner of the chamber to the opposite corner . The Xt and Yt kymographs for this case show a typical signature of continuous oblique lines that are formed as the Min proteins pass through the mid-lines of the chamber . The STD figure merits a comment . For an infinite-time movie of fully homogeneous waves , the STD figure of a traveling wave should have a uniform flat profile . For a finite-time recording of a wave propagation , the STD figure will , however , show a slightly monotonically decreasing profile . For the rotational behavior ( cf . Figure 2d ) , the repetitive passage of the Min proteins along both the X-axis and the Y-axis chamber midlines results in oblique lines that run from the kymograph middle to its sides , for both the Xt and Yt kymographs . The STD image of Min protein rotation shows the focal point and a symmetric concentric gradient profile around it . Figure 2e represents a typical repertoire of the Min dynamical behaviors for the different geometries of the chambers . A corresponding composite Video 2 is also provided ( very worthwhile to examine this movie , as it illustrates the intrinsic dynamic patterns particularly well ) . In addition , a zoom-in for the smaller chambers sizes is shown in Figure 2—figure supplement 1 . In this atlas of dynamical patterns , one can observe a variety of patterns , e . g . , side-to-side oscillations in the 30 × 10 µm chamber , a traveling wave in the 60 × 50 µm chamber , and a spiral rotation in the 40 × 40 µm chamber . More complex dynamical patterns were also observed . For example , in chambers of size 50 × 10 µm and 60 × 10 µm , instead of the most simple side-to-side oscillation , we observed a striped pattern , where the Min concentration oscillated back and forth from the center of the chamber to both sides ( see Figure 2—figure supplement 1 ) . This is similar to what has been observed in filamentous cells ( Raskin and de Boer , 1999; Touhami et al . , 2006 ) , shaped long cells ( Wu et al . , 2015 ) , and in vitro grooves ( Zieske and Schwille , 2014 ) . Furthermore , in some cases we observed more than one rotational center within a single microchamber , a behavior that can be seen in the 40 × 20 µm , 50 × 20 µm , 60 × 20 µm and 50 × 30 µm chambers . 10 . 7554/eLife . 19271 . 010Video 2 . Movies of Min patterns formation in chambers with different sizes that were used for the construction of Figure 2e of the main text . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 010 We set out to quantify the relation between the dimensions of the chamber and the preferred dynamical behavior of the Min system . Altogether we analyzed the dynamical behavior of the Min proteins in 553 different chambers . For each chamber that was recorded , we identified to what class of dynamical behaviors the observed pattern conform to: ( i ) oscillations . ( ii ) traveling waves , or ( iii ) rotations . Cases with more than one rotational center in the chamber where tagged as rotations and cases of striped or side-to-side oscillations where tagged as oscillations . The large majority of observed patterns ( >95% ) could be readily classified in these three categories . Naturally , there exist some borderline cases between the three types . For example , we observed that a rotational center of the Min proteins could propagate from one side of the chamber to the other . This behavior was grouped under the tag of traveling waves . We thus analyzed the preferred dynamical behavior of the Min system in the chambers in terms of the geometrical parameters of the chambers such as the width ( W ) length ( L ) , aspect ratio ( L/W ) , and area ( L × W ) of the chambers . The results are shown in Figure 3a–c and Figure 3—figure supplement 1a . As can be seen , clear relations exist between the geometry of the chamber and the observed dynamical Min behavior . We note a few particular features: ( i ) Rotational patterns appear as the majority for chambers with small aspect ratio ( Figure 3a ) . As the chamber aspect ratio increases , the probability to obtain rotational behavior decreases , and for chambers with an aspect ratio larger than 2 . 8 , we hardly observed rotational behavior in our chambers . ( ii ) Oscillatory behavior predominantly appears when the chamber width is small ( ∼10 µm ) ( Figure 3b ) . ( iii ) Traveling waves mainly appear if the chamber length is relatively large ( Figure 3c ) , and their prevalence increases as the chamber length increases . Similarly , if the chamber area is relatively large the prevalence of obtaining a traveling waves increases ( see . Figure 3—figure supplement 1a ) . For these large areas , the confined chambers reflect the surface waves that were observed on unbound SLBs ( Loose et al . , 2008; Vecchiarelli et al . , 2014 ) . The clear relation found between the chamber geometry and the dynamical Min patterns unambiguously proves that confinement sets the rules for Min pattern formation . It also supports the notion that the existence of the connector lines in our setup adds only second-order effects . Thus , by using the soft fabricated valves , we were able to extract and study , for the first time , the geometrical selection rules of the Min system in truly confined 3D structures in vitro . 10 . 7554/eLife . 19271 . 011Figure 3 . Preferred dynamical Min behavior as a function of the chamber-geometry parameters . ( a–c ) Geometry selection at room temperature . ( a ) Selection according to the chamber aspect ratio . ( b ) Selection according to chamber width . ( c ) Selection according to chamber length . All measurements were preformed at room temperature on a DOPC:DOPG ( 67:33 ) SLB supplemented with 0 . 03 of TopFluor Cardiolipin . The number of chambers observed with the specific geometrical characteristics are indicated above each bar . ( d–f ) Same as ( a–c ) for measurements that were preformed at 37°C on an E . coli polar lipid extract SLB . The analysis is based on the fluorescence signals of MinE . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 01110 . 7554/eLife . 19271 . 012Figure 3—figure supplement 1 . Min patterns geometry selection rules based on chambers size . Geometry selection of the Min patterns according to the chamber size for measurements at room temperature over DOPC:DOPG SLB membrane ( a ) , and at 37°C with E . coli polar lipids extract SLB ( b ) . Color code , as in Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 012 Having established a relation between the geometry of the chambers and the spatio-temporal behavior of the Min system , we constructed the phase diagram of the Min patterns , see Figure 4a . In this phase diagram , each tile represents , in its color , the most prevalent Min behavior for the chambers with the designated specific dimensions . We restricted ourselves to cases for which we had data from at least 4 different chambers with this specific geometry; the typical number are 20–30 chambers per tile , see Figure 4—figure supplement 1 for the exact numbers . As can be seen from Figure 4a , the phase diagram is nicely separated into three different regions: ( i ) The majority pattern is rotations; ( ii ) oscillations are the most prevalent pattern in narrow chambers , in line with previous observation in semi-3D-confined structures ( Zieske and Schwille , 2013 , 2014 ) ; and ( iii ) as expected ( Loose et al . , 2008 , 2011a ) , the most prevalent behavior for large and long chambers is that of traveling waves . The relative percentage of the oscillations , rotations and traveling waves is presented respectively in Figure 4b–d . The most striking , and unexpected , result of this study is that in fully confined 3D chambers , a large part of the phase diagram is occupied by rotational behavior , i . e . , Min patterns that rotate around a fixed point in a spiral fashion . In other words , confining the Min system in 3D chambers mainly results in the formation of spiral waves . When the chambers becomes too narrow , these rotational centers are less stable , presumably due to reflection of the Min concentration front from the chamber boundaries , and oscillations are formed . Oscillations thus appear to be a derivative phenomenon that results from destabilization of spiral rotating patterns by the chamber walls . In the other extreme , for very large areas , the interaction between multiple rotational centers will equilibrate in such a way that the stable behavior becomes a traveling wave . 10 . 7554/eLife . 19271 . 013Figure 4 . Phase diagram of the Min patterns . ( a ) Phase diagram of the Min behavior at room temperature . Each square represents the most abundant behavior in that geometry . Color code is shown on the right . ( b–d ) Relative abundance of the pole-to-pole oscillations , rotations and traveling waves in each chamber size . Color code for ( b–d ) is shown on the right . ( e ) Phase diagram of Min behavior at 37°C . Color code is the same as in ( a ) . The analysis is based on the fluorescence signals of MinE . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 01310 . 7554/eLife . 19271 . 014Figure 4—figure supplement 1 . Number of chambers that were used per each tile for constructing Figure 4a–d . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 01410 . 7554/eLife . 19271 . 015Figure 4—figure supplement 2 . Detailed analysis of the phase diagram at elevated temperature ( Figure corresponding to Figure 4e ) . ( a ) Relative abundance of the oscillatory behavior for each chamber size . Color code is defined in the legend to the right . ( b ) Same as ( a ) for rotations . ( c ) Same as ( a ) for traveling waves . ( d ) Number of chambers that were used per each tile for constructing Figure 4e and panels ( a–c ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 015 Theoretical models of the Min system rely on the concentration of the MinD and MinE proteins as an important control parameter . Determining the exact concentrations in the microchambers needs some considerations and is less trivial than it may appear to be at first glance . While Min proteins were injected at a well-defined concentration ( 1 µM MinE and 1 . 08 µM MinD ) , the final concentration of the proteins in the chambers are higher than those introduced . The reason is that during the injection process , MinD molecules will bind the membrane , followed by MinE molecules , while proteins continue to flow into the chamber with the fresh bulk solution . This results in larger final concentration in steady state . We therefore measured the concentration of the final Min proteins inside our chambers using a green fluorescence protein ( GFP ) calibration . We measured the relative fluorescence of GFP , MinD-Cy3 , and MinE-Cy5 in bulk using a fluorometer , yielding a calibration curve of intensity versus concentrations . Next , we injected the regular Min proteins mixture , which contains MinD-Cy3 and MinE-Cy5 , together with GFP into the chambers , and measured the resulted fluorescence of the two labeled proteins verses that of GFP on a widefield microscope with a 20X objective . From that , one can infer the actual concentrations ( see Figure 5—figure supplement 1a–c for the fluorometer calibration curves and the Materials and methods section for the detailed derivation of Min proteins concentration in the chambers ) . Note that since GFP does not bind the membrane , its concentration in the microchambers is always known . Thus , it can be used as a calibration reference to estimate the concentration of the Min proteins by comparing their relative fluorescence in the microchamber to the relative fluorescence for the case where there is no membrane binding ( i . e . in the fluorometer cavity ) . The concentration of the Min proteins was measured for 52 different chambers . As can be seen ( Figure 5a ) , the actual concentration of the Min proteins in our chambers was significantly ( ∼factor 5 higher than the value for the injected stock solution . Furthermore , a wide distribution is observed , particularly for MinE . Note that we did not observe a relation between the chamber size and the measured concentration of the Min proteins . From these measurements we concluded that the concentration of MinD in our chambers was 4 . 5 ± 0 . 5 µM ( mean ± SD ) , the concentration of MinE 6 ± 3 µM , and the average ratio of [MinE]/[MinD] amounted to 1 . 3 ± 0 . 5 . 10 . 7554/eLife . 19271 . 016Figure 5 . Concentration of the Min proteins . ( a ) Histogram of the deduced concentration of MinE and MinD in the chambers . ( b ) Idem for the ratio of MinE to MinD . Lines are guides to the eye . Concentration was measured using a GFP protein as a standard as described in the Materials and methods . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 01610 . 7554/eLife . 19271 . 017Figure 5—figure supplement 1 . Calibration curves for the fluorescence intensity of ( a ) MinD , ( b ) MinE , and ( c ) GFP , as collected with a fluorometer . Stock solution of MinD-Cy3 ( 14 μM ) or MinE-Cy5 ( 25 μM ) were diluted at various values and the resulted fluorescence was recorded on a Carry eclipse fluorometer . The same procedure was repeated for GFP . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 017 It is of interest to know if transforming the topology from surface 2D ( Loose et al . , 2008; Ivanov and Mizuuchi , 2010; Loose et al . , 2011a; Vecchiarelli et al . , 2014 ) or semi 3D ( Zieske and Schwille , 2013 , 2014 ) to a fully confined 3D topology will substantially change the wavelength of the traveling waves . This question is interesting since , as mentioned in the introduction , one of the open questions regarding the Min system relates to the differences between its spatial length scale in vitro verses in vivo . Mean and SD of the measured wavelength of 35 traveling-waves in confined chambers are shown in Figure 6a . We measured a wavelength of 43 ± 6 µM at room temperature . This value is significantly lower than the wavelength of 78 ± 12 µM ( n = 30 for traveling waves that we measured on 2D flat SLBs using the exact same protein and lipid composition of the SLB as was used in our microchambers experiments . This shows that the 3D full confinement of the Min system has a clear effect not only on the geometry-selection properties , but also on the characteristic length scale of the system . 10 . 7554/eLife . 19271 . 018Figure 6 . Wavelenght and Min patterns front propagation velocity . ( a ) Statistical box plot representing the wavelength of the Min waves at different conditions: at room temperature and at 37°C , at flat SLB ( blue ) as well as inside the 3D confined chambers ( red ) , and with high viscous medium in the chambers ( at room temperature ) . The lines of each box represent the location of the 25 , 50 , and 75 percentiles . Full squares represent the location of the mathematical mean . Whiskers represent the 5 – 95 percentile range and the diamonds the minimum and maximum values of the data . ( b ) Velocity of Min pattern propagation at the same conditions . Box representation is the same as in ( a ) . The analysis is based on the fluorescence signals of MinE . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 01810 . 7554/eLife . 19271 . 019Figure 6—figure supplement 1 . Examples of traveling waves in the chambers in the presence of a viscous media at 9 . 85 cP . MinD ( 1 . 1 µM ) , MinE ( 1 µM ) , ATP ( 5 µM together with an ATP-regeneration system in a crowding solution ( 4% BSA , 4% PEG8000 and 4% Ficol 400 ) were injected into the chamber device . The panels show the MinE fluorescence signal . ( a ) and ( e ) Montage of the MinE signal in two chambers . Time difference between frames is 60 s . In order to calculate the wavelength , a self-written Matlab code was used ( see Materials and methods section ) . A line was drawn manually along the wave propagation direction and the fluorescence intensity along this line was recorded and normalized for each frame . ( b ) and ( f ) Corresponding kymographs for ( a ) and ( e ) that were used in order to calculate the wavelength and the wave velocity . Wavelength was calculated by fitting a Gaussian function to the distribution of distances between the intensity peaks of these kymographs . Velocity was calculated based on the angle of the intensity peaks along the kymographs temporal direction . Wavelengths are equal to 20 ± 3 µm and 18 ± 0 . 7µm for ( a ) and ( e ) respectively . Velocities are equal to 0 . 024 ± 0 . 001 µm/s and 0 . 024 ± 0 . 001 µm/s for ( a ) and ( e ) respectively . ( c ) and ( g ) Normalized fluorescence intensity along the green line in panels ( b ) and ( f ) respectively . ( d ) and ( h ) Quadrant representation on the Min behavior in these two chambers . Format of the representation is similar to the one in Figure 2e . Scale bars for all panels ( 30 µm and 400 s ) are shown on the bottom right . DOI: http://dx . doi . org/10 . 7554/eLife . 19271 . 019 Similarly , it is of interest to ask whether the confinement has any influence on the propagating velocity of the Min front . We compared the velocity of all different Min patterns in the chambers ( n = 333 chambers ) to the propagation velocity of Min waves on the flat surfaces ( n = 30 , see Figure 6b ) . While on flat SLB surfaces we measured v = 0 . 6 ± 0 . 2 µm/s , similar to previous measured values ( Loose et al . , 2011a ) , inside the chambers we obtained a propagation velocity of 0 . 3 ± 0 . 1 µm/s ( while the propagation velocity was 0 . 4 ± 0 . 1 µm/s for waves only; n = 35 ) . The reduced wavelength inside the chambers was thus accompanied with a reduced velocity . We examined the effects of molecular crowding on the characteristics of the waves in our 3D confined chambers . Crowding the liquid environment can have multiple effects . On the one hand , it increases the viscosity of the solution , thus , by Einstein’s relation , reducing the diffusivity of the proteins . This parameter is particularly important in reaction-diffusion processes , such as the Min dynamics , since the diffusion rate of the fastest species determines the distance between two maxima of the pattern . On the other hand , the use of a high concentration of molecular crowder ( polymers or proteins ) can have unintended side effects and influence the stability and binding properties of the studied proteins themselves ( Kuznetsova et al . , 2014 ) . In order to maximize the viscosity of the solution while minimizing the side effects , we employed a solution of 4% PEG 8000 , 4% Ficol 400 , and 4% BSA ( all three commonly used crowders ) . The measured viscosity of this solution is 9 . 9 ± 0 . 05 cP , i . e . , one order of magnitude larger than that of water . Though the exact viscosity inside E . coli cells is unknown , this value is close to the estimated one ( Trovato and Tozzini , 2014 ) . The results ( n = 17 ) for the wavelength and wave velocity of traveling Min waves in confined 3D chambers under these conditions are shown in Figure 6a and b , see also Figure 6—figure supplement 1 for examples of Min waves under viscous conditions . Increasing the viscosity of the environment resulted in a factor 2 reduction of the wavelength from 43 ± 6 µm to 23 ± 4 µm . This reduction in the wavelength is comparable to the reduction in wavelength that was observed by Martos et al . in the presence of 140 g/l Ficol ( Martos et al . , 2015 ) . Next to the reduction in the wavelength , we observed a very large decrease , of more than one order of magnitude , in the wave velocity from 0 . 3 ± 0 . 1 µm to 0 . 02 ± 0 . 014 µm/s . Note that this substantial decrease in the velocity is much larger than what was observed by Martos et al . and is probably related to the different constellations of 2D surfaces ( used by Martos et al . ) in comparison to 3D confined microchambers ( in this work ) . The previous results showed that there are various ways to modulate the velocity and characteristics length scale of the Min proteins , and prompted us to look for other means to modulate these properties . We therefore studied the Min system properties in our chambers at an elevated temperature of 37°C . As can be seen in Figure 3d–f and Figure 3—figure supplement 1b , the geometry selection properties of the Min system at the elevated temperature of 37°C are very similar to those that were observed at room temperature ( n = 198 ) : ( i ) at low aspect ratio of the chambers ( L/W ) , the prevalence to obtain rotational patterns is high while it decreases as the aspect ratio increases; ( ii ) oscillatory behavior is observed mainly for narrow chambers; ( iii ) traveling waves become dominant for long and large chambers . The phase diagram at elevated temperature is shown in Figure 4e ( a detailed analysis of the phase diagram is shown in Figure 4—figure supplement 2 ) . The overall picture remains the same , although more scatter is apparent in the data . The wavelength at the elevated temperature ( see Figure 6a , n = 34 ) , shows only a small decrease relative to the wavelength at room temperature , that is from 43 ± 6 µm to 37 ± 9 µm ( two sample t-test with different variance = 0 . 001 at β = 0 . 05 ) . This contrasts the behavior of waves on flat 2D SLBs surfaces , for which we observed a much more profound effect ( Figure 6a , n = 27 ) . In this case the wavelength reduced from 78 ± 12 at room temperature to 48 ± 6 µm at 37°C . These data thus show that , in confined chambers , confinement is the major cause of the reduced wavelength , with the elevated temperature adding only a small further reduction in the characteristic spatial scale of the system . Interestingly , we saw an increased , rather than a decreased , velocity of the Min proteins at high temperature . Results are shown in Figure 6b , measured from 162 chambers . At T = 37°C , the velocity v = 0 . 5 ± 0 . 3 µm/s ( velocity for waves only v = 0 . 5 ± 0 . 1 µm/s n = 34 ) . This compares to v = 1 . 4 µm/s ( Figure 6b ) for waves propagating on flat SLBs surfaces . Thus , similar to the conditions in vivo , where the period of the oscillations is inversely correlated with the temperature ( Touhami et al . , 2006 ) , elevated temperature increases the velocity of the Min propagation in vitro , doubling from 23°C to 37°C both on 2D surfaces and in 3D confinement . However , in contrast to the in vivo behavior , the in vitro wavelength is also temperature dependent . Since the viscosity of the bulk media change only by a factor of 1 . 3 from 23°C to 37°C , we have to attribute these results to a change in one of the reaction parameters of the reaction-diffusion Min system . We can conclude that there are different ways to modulate the different dynamical characteristics ( wavelength and velocity ) of the Min system .
In this paper we report a comprehensive experimental data set of Min patterns in fully confined fluidic chambers that are internally coated on all surfaces with a supported lipid bilayer . The ability to obtain a detailed picture of the Min patterns in vitro is important for three reasons . First , the theoretical understanding of Min pattern in vivo is still incomplete , and in vitro studies with their exquisite control will help to resolve existing questions such as the origin of the symmetry-breaking mechanism . Second , the development of a comprehensive theoretical framework for the Min system behavior depends on the ability to experimentally compare the in vivo and the in vitro cases under well-defined conditions such as dimensionality and size . Third , applications that aim at utilizing the Min system for engineering complex behavior in synthetic cells and other man-made systems depend on the ability to fully control its behavior in vitro . Previously , Min proteins were reconstituted in vitro ( Loose et al . , 2008; Ivanov and Mizuuchi , 2010; Loose et al . , 2011a; Schweizer et al . , 2012; Martos et al . , 2013; Vecchiarelli et al . , 2014; Zieske et al . , 2014; Martos et al . , 2015; Vecchiarelli et al . , 2016 ) , and Min oscillations were observed in fabricated microchambers ( Zieske and Schwille , 2013 , 2014 ) . In the latter case , the microchambers had a half-open configuration ( i . e . , with a top surface not coated with SLB ) with a limited width ( ∼10 µm ) , a height of 10 µm , and varying lengths leading to a range of aspect ratios ( 1 . 2–24 ) . In our case , we studied the system for microchambers with a height of 2 . 4 µm , a width of 10–60 µm , and a length of 10–80 µm . We thus considerably expand previous experimental data since we mapped the Min behavior in a well-controlled manner over a section of the geometrical conditions where the microchambers have a much broader range of widths , while tightly restricting the height of the microchambers . The novelty of our approach can particularly be appreciated from the fact that , while previously no ordered patterns were observed in chambers wider that 10−20 µm ( Zieske and Schwille , 2013 , 2014 ) , we observed a well-defined behavior of the system that was mapped into an ordered geometric phase diagram , as discussed below . In this article , we have shown that total confinement of the Min system within 3D chambers leads to three main type of patterns: ( i ) rotational patterns in the form of spiral waves , which are the majority of the patterns found; ( ii ) periodic oscillations that occur mainly if the chamber width is small in comparison to the typical spatial length scale of the system in vitro; ( iii ) traveling waves that occur mainly if one of the dimensions of the chamber is larger than the typical spatial length scale of the system . In our microchambers , rotational behavior in the form of one or multiple adjacent spiral waves thus contributes the largest fraction of the phase diagram . Spiral waves are common in various biological and chemical reaction-diffusion processes such as Dictyostelium discoideum aggregation , calcium variations in Xenopus laevis oocytes , and the famous Belousov-Zhabotinsky ( BZ ) reaction ( see Epstein and Pojman , 1998 and references therein ) . They were also observed for the Min system on flat SLBs ( Loose et al . , 2008; Vecchiarelli et al . , 2014 ) . For reaction-diffusion systems other than the Min system , various symmetry-breaking-mechanisms were invoked in order to explain this spiral behavior . For example , for the BZ reaction , spiral waves were attributed to a Hopf bifurcation mechanism ( Hagan , 1982 ) . In addition , it is well known that in nonlinear reaction-diffusion systems , several symmetry-breaking-mechanisms may coexist in different parts of the parameter phase space and , as result , a plethora of static or dynamic patterns can emerge ( Yang et al . , 2002 ) . Many times these patterns possess similar observable behavior , yet having different underlying symmetry-breaking-mechanisms . In light of this knowledge , it is well possible that in vitro Min patterns , in fact , emanate from a somehow different symmetry-breaking-mechanism than the Min oscillations in live cells despite the fact that the proteins are the same and the patterns share various similar characteristics with the in vivo case . In other words , in spite of the common belief that the main difference between the in vivo behavior of the Min system and its behavior in vitro is merely related to a different wavelength , possibly , the symmetry-breaking-mechanisms are not exactly the same in both cases . The existence of spirals in vitro and their abundance in our confined microchambers , when compared to their absence from in vivo observations where pole-to-pole oscillations dominate ( Wu et al . , 2015 ) , may be related to this . Note that a complete understanding of a biochemical system depends on two complementary sources: a theoretical model that can predict the system behavior and detailed description of its behavior under as wide as possible experimental conditions . For a complex non-linear system , like the Min , it is relatively easy to construct a model that predicts the system behavior under a limited subset of the experimental data . This task becomes much harder , however , when experimental data exist for a wide range of conditions and system geometries . Such data thus help to restrict the class of possible theoretical models . In this report we provide such a detailed description of the in vitro behavior of the Min system in a wide section of the geometrical parameter space . Thus , our data restrict the possible classes of theoretical models that can explain the in vitro behavior of the Min system . In addition , since our microchambers height is relatively close to the actual diameter of a bacterial cell , our data further help to restrict the possible models for symmetry-breaking mechanisms that can describe the system behavior in vitro as well as in vivo . We observed that the spiral patterns were found not to be stable in narrow chambers that are smaller than , or equal to , the characteristic 10 µm length scale of spirals . We observed that pole-to-pole oscillations are established as the chamber walls restrict spirals to form . In other words , in vitro oscillations in fact appear to be a form of truncated spirals . This demonstrates how the interplay between geometrical confinement and the intrinsic spiral symmetry-breaking-mechanism of the Min system may produce distinct patterns . These data are the first to suggest that in vitro oscillations are truncated spirals , a fact that further shows the importance of studying the geometric phase diagram of the Min system , as was done here . Please note also that in vivo , spiral rotations were not observed ( Wu et al . , 2015 ) . Neither were they suggested as the origin of the symmetry-breaking mechanism in theoretical works . This serves as an additional note of caution for drawing a too simple correspondence between oscillatory patterns that are observed in vitro and those observed in vivo . Our findings are further corroborated by comparing the nature of oscillations in vitro to those that are formed in vivo . To study this difference , we collected movies of in vivo oscillations ( see Supplementary file 1 and Supplementary file 2 ) . By comparing the kymographs in both cases ( Figure 2e , Figure 2—figure supplement 1 and Supplementary file 1e–g ) , one can clearly see the distinctly different behavior in these two cases . In vivo , the pole-to-pole oscillations amount to the establishment of polar zone that is replaced by a second polar zone near the opposite pole within a short time scale . In contrast to this , in vitro oscillations are more akin to a traveling wave that propagates toward one pole , whereupon it is then replaced by a wave that is established close to the chamber middle and propagates toward the opposite pole ( we acknowledge the anonymous referee three for pointing out this difference ) . Similar behavior was also observed in in vitro oscillations of other reconstitution work ( Zieske and Schwille , 2014 ) . This may also be the reason why steep in vitro Min gradients that would restrict FtsZ localization to a single ring could not have been established . Note that in vitro we report the MinE signal while in vivo we imaged MinD ( as the MinE signal was low in this strain ) . Essentially this does not alter the conclusion that is drawn here since in this strain the MinE signal merely follows the MinD signal ( see [Wu et al . , 2016] ) . Why do traveling waves form the majority pattern in very large chambers while spirals are less abundant ? In vivo , Min waves are hardly detected . Shih et al . reported the formation of transient Min waves in E . coli cells harboring the MinED45A/V49A mutation that prevents the dimerization of MinE ( Shih et al . , 2002 ) . Recently , Bonny et al . also reported traveling waves in filamentous cells , but the abundance of this phenomenon was not reported ( Bonny et al . , 2013 ) . In this last case , the Min operon was induced at a saturating concentration and thus the concentration of MinDE was probably much higher than in the native case for E . coli ( Sliusarenko et al . , 2011 ) . Finally , in vivo traveling waves were also observed in a small minority of filamentous cells , even if the Min operon is not over-induced and when the Min proteins do not harbor any mutations ( see Supplementary file 1d and Supplementary file 2 ) . Note that in all these cases , the in vivo traveling waves represent a form of abnormal behavior which occurs if the MinE function is impaired or in other anomalous cases . This contrasts to the in vitro case , where traveling waves can be a generic feature of the Min system . In contrast to sporadic reports of Min waves in vivo , waves are almost always detected in in vitro assays on 2D SLBs . Two reasons are responsible for this observation . First , on flat 2D SLBs the spirals waves are not confined and hence waves that emanate from one center can travel long distance and interfere with waves that emanate from a second spiral center to form traveling waves . In our chambers , when their area became large , the intrinsic Min pattern formation mechanism and the reflection from the chambers walls resulted in the annihilation of spiral centers and the establishment of traveling waves , similar to the 2D case where boundaries are absent . The second reason for the formation of traveling waves is probably related to the concentration of the proteins in the in vitro assay . In a typical in vitro assay , one usually controls the bulk concentration of the proteins as the supply material . However , due to the small surface-to-volume ratio in typical in vitro assays , the actual concentration of the proteins on the membrane and the replenished reservoir of new Min proteins is higher than in vivo ( cf . the above section on Min concentration in the microchambers ) . In fact , due to the experimental protocols employed , this observation is also correct for previous reconstitutions of the Min system in grooves ( Zieske and Schwille , 2014 ) , as well as for published in vitro Min system flow assays ( Vecchiarelli et al . , 2014; Ivanov and Mizuuchi , 2010 ) . This situation is qualitatively different from the in vivo case where , during the formation of polar zone or a Min band , most of the MinD proteins are recruited to the membrane and the cytosolic concentration thus drops to a low value . Indeed , in a very recent publication , Vecchiarelli et al . suggested that in vitro spirals waves are related to high concentration of the Min proteins , while reconstituting the Min system under limiting concentration conditions resulted in a dynamical series of Min bursts that are reminiscent of the behavior in cells ( Vecchiarelli et al . , 2016 ) . Vecchiarelli et al . further checked the behavior of the Min proteins when MinE was replaced by a membrane-binding deficient mutant MinE11–88 , and observed that in this case , Min waves were supported at high protein concentration , but bursting dynamics were absent . This observation was consistent with the one of Zieske et al . ( Zieske and Schwille , 2014 ) where oscillations were not observed with a similar mutant MinE ( Δ3–8 ) . Thus , it seems that the interrelations between several factors determine the pattern formation mechanism in vitro: the surface-to-volume ratio , the protein concentration , the confinement , and the binding of MinE to the membrane . Our results , with the relatively high Min protein concentration that we measured , support their conclusions . The fact that we fully confined the Min system in 3D chambers with a relatively small height enabled us to look on the behavior of the system in conditions with a surface-to-volume ratio that is much larger than previously studied . Combined with our measurements of the total Min protein concentration in the chambers , our results set a lower limit for the protein concentration that still reproduces traveling waves , rotational spirals , or oscillations under these geometric conditions . It will be interesting to check if current models of the Min system that reproduce in vivo behavior will also be able to reproduce traveling waves by simply increasing the proteins concentration . Indeed , Bonny et al . were able to show traveling waves in their theoretical system by increasing the Min concentration by 63% or by increasing the MinE to MinD ratio ( Bonny et al . , 2013 ) . In our geometrical analysis of Min patterns selection , we showed that in chambers with high surface-to-volume ratio , and a high Min proteins concentration , the cumulative effect of annihilation of multiple spirals and of the reflection from the walls can result in the formation of traveling waves . A related question regards the Min wavelength . We have observed a reduction in the Min wavelength by a factor of two in our microchambers relative to the case of 2D flat SLB . Taking a reaction-diffusion point-of-view , this observation can be explained in two ways . First , the lateral confinement by itself may be responsible for choosing dynamical modes of the system with a smaller wavelength . Second , the height reduction could change the balance between the reservoir of the free Min proteins in solution relative to the bound ones . Indeed , based on their model , Halatek and Frey recently suggested , that reducing the volume of Min proteins above the surface will drive the typical wavelength of the system to lower values ( personal communication ) . Similarly , it was shown theoretically by Thalmeier et al . ( Thalmeier et al . , 2016 ) that even in the absence of MinE , Arabidopsis thaliana MinD can form an intracellular gradient just based on the relations between the bulk diffusion and nucleotide exchange rates , but only in highly confined spaces . Thus , our observation that reducing the height of the chamber is accompanied by a reduction of both the wavelength and the wave velocity further stresses the value of our approach of studying the Min behavior in 3D confined spaces with a high surface-to-volume ratio . We note , however , that our chambers are 2 . 4 µm high , not too much larger than the 1 µm diameter of an E . coli bacterium , and yet , we measured a wavelength that is an order of magnitude larger than the characteristic length scale observed in vivo . We have shown that two factors can lower the wavelength further . First , the Min wavelength decreased by a factor of 2 when the viscosity of the medium was raised to 10 cP , i . e . , similar to that of cells and ten times larger than a regular buffer . Still , even at this high viscosity , the wavelength was larger by a factor of ∼5 from the one measured in vivo . In addition , the reduction in wavelength was accompanied by a very large reduction in the wave propagation velocity . Note that the measured velocity in the highly viscous media was similar or even smaller than the one measured in vivo ( Unai et al . , 2009 ) ( see also the supporting information in [Bonny et al . , 2013] ) . It is thus unlikely that additional increases in viscosity can bring both the characteristic length scale and the propagation velocity of the system in vitro to the in vivo values . Interestingly , unlike the situation in unconfined 2D reconstitution of the Min proteins ( Martos et al . , 2015 ) , in microchambers with a high surface-to-volume ratio , the period of the Min wave ( i . e . , wavelength/velocity ) was not constant but was reduced by a factor of about 8 . Second , we showed that increasing the temperature also reduced the wavelength . The effect of the temperature on the characteristic wavelength was much larger for the Min waves on flat SLBs than in the 3D confined chambers . Since the diffusion rate does not change substantially between room temperature and 37°C ( only a factor of 1 . 3 , by the Einstein’s relation ) , the temperature effect in the 2D surface case is most likely the result of a change in one of the reaction parameters , most probably the ATP hydrolysis rate . This situation is different in the microchambers . Our observations suggest that the main in vitro determinant of the wavelength at elevated temperature in confined spaces is the geometrical confinement itself . In other word , geometrical confinement forces the system to choose a reaction-diffusion mode with a reduced wavelength which can only marginally be tuned further by increasing the temperature . Thus , in highly confined spaces the effect of temperature on the characteristic scale of the Min system is largely diminished , and is accounted by the small increase in diffusion rate . This fact can explain the previous in vivo results where no temperature dependence for the wavelength was detected ( Touhami et al . , 2006 ) . It should be noted that the reduced length scale was accompanied by an increase , rather than a decrease , of the wave velocity . This observation is in line with the in vivo case where the oscillation period was smaller at high temperature . Our results points out that the dynamical characteristics of the Min system contain several decoupled parameters , such as wavelength and velocity that can be tuned in opposite directions . It is thus essential to study their action in the context of 3D confined spaces with a high surface-to-volume ratio . The symmetry-breaking mechanism of the Min pattern formation depends on a combination of all its dynamical parameters . We have shown that dynamical aspects of the system can be tuned in at least three different ways: by confinement , by changing the temperature , and by reducing the bulk viscosity . Yet , since in our 3D confined structures , even at elevated temperature or with high viscous media , the in vitro Min behavior did not quantitatively reproduce the in vivo one , probably a change of yet another dynamical parameter is necessary . This can be a simple thing like a change in the concentration of one of the Min proteins ( Vecchiarelli et al . , 2016 ) or a change in one of the reaction rates . It is also possible that yet another physical or biological mechanism contributes to the different symmetry breaking in both cases . Biochemical assays have shown intricate effects of the Min system on the membrane organization in the in vivo context . For example , MinD increased the order of the lipids and decreased their mobility in inverted inner E . coli membranes that contained integral proteins more than it does in synthetic vesicles that are purely lipidic ( Mazor et al . , 2008 ) . Similarly , the Min system affects the association of inner-membrane peripheral proteins and interacts with some of them directly ( Lee et al . , 2016 ) . It is thus possible that a yet unidentified protein species is needed in order to reproduce the in vivo geometrical selection rules of the Min system in an in vitro environment . To sum up , we have studied the geometry selection rules of the Min system in 3D fully confined chambers . We found three main patterns in these confined chambers: spiral rotations , oscillations and traveling waves . Spiral behaviors were the most abundant ones in a large part of the phase diagram and we suggest that both traveling waves and oscillations result from interrelation between the spiral symmetry-breaking mechanism and the effects of confinement .
1 , 2-dioleoyl-sn-glycero-3-phosphocholine ( DOPC ) , 1 , 2-dielaidoyl-sn-glycero-3-phospho- ( 1’-rac-glycerol ) ( DOPG ) and E . coli polar lipid extract were purchased from Avanti polar lipids . Tris was purchased from Promega . Potassium chloride and imidazole were purchased from Merck . Silicon wafers were from universitywafers . com . RTV 615 PDMS was purchased from Momentive . Nitric acid was from Merck . Cy3-NHS and Cy5-maleimide dyes were purchased from GE Healthcare . Phosphoenolpyruvic acid ( PEP ) was from Alfa Aesar . All other materials were from Sigma-Aldrich unless otherwise stated . To fabricate the lower layer of the chip , which consists of structures with three different heights , a silicon wafer was processed in three different steps . First , a 4″ wafer was cleaned with a nitric acid for 10 min under sonication , washed with water and then dried . PMMA 495K 8A ( MicroChem ) was spincoated on the wafer at 500 rpm for 5 s and then at 3000 rpm for additional 55 s . Next , the wafer was baked for 1 hr at 180°C and the chambers pattern that was designed using Klayout ( RRID:SCR_014644 ) was written on the PMMA layer using a Vistec EBPG 5000+ ( acceleration voltage 100 kV , aperture 400 µm , dose of 800 µC/cm2 and a resolution of 100 nm ) . Altogether 9 similar chamber structures were written on the wafer . The written PMMA layer was developed in MIBK: isopropanol ( 1:3 ) for 9 min , was washed for 30 s in isopropanol ( IPA ) and the wafer was spin dried . A Bosch deep reactive-ion etching process , with an inductive coupled plasma ( ICP ) reactive-ion etcher ( Adixen AMS 100 I-speeder ) , was used to etch the structures into the silicon wafer . The process consisted of alternate etching ( sulfur hexafluoride , SF6 ) and passivation ( octafluorocyclobutane , C4F8 ) cycles . During the process the pressure was kept around 0 . 04 mbar , the temperature of the wafer was kept at 10°C , while the plasma temperature was 200°C . The sample holder was held at 200 mm from the plasma source . The etching step involved 200 sccm SF6 for 7 s with the ICP power set to 2000 W without a bias on the wafer itself . The passivation step was done with 80 sccm C4F8 for 3 s with the ICP power set to 2000 W and the bias power on the wafer alternate with a low frequency: 80 W , for 10 ms , and 0 W for 90 ms . Total etching time was 34 s . After etching , the wafer was cleaned with Nitric acid for 10 min with sonication . Next , the connector lines were fabricated on the same wafer via similar steps with the following small modifications . Spincoating of PMMA was done at 3000 rmp in the second step . Baking was done for only 45 min . The dose was 742 µC/cm2 and the resolution was 15 nm . Development was done for 3 min . After development the PMMA was descummed on Tepla machine at 0 . 6 mbar at 300 W with 100 sccm for 1 . 5 min . Dry etching was done on the same Adixen AMS 100 I-speeder machine . The first step was the same as previously described only that the SF6 step lasted 2 . 4 s and the passivation step lasted 1 s . The total etching time was 10 s . Next , another dry etching step was applied , this time the ICP power was set to 250 W , the bias power was 20 W , the source-target distance was 240 mm , pressure was kept around 0 . 04 mbar and the temperature was kept at 10°C . The gas combinations was SF6 200 sccm , Ar 100 sccm and O2 100 sccm . Total etching time was 5 min . The wafer was then cleaned in the Adixen AMS 100 I-speeder machine at a pressure of 0 . 04 mbar , ICP power of 2500 W with a biased power of 60 W , a source-target distance of 200 mm and a temperature of 10°C , using a O2 gas at 200 sccm for 5 min . The wafer was finally cleaned in Acetone at 45°C for 10 min and in fuming nitric acid for 10 min with sonication . The reservoirs were fabricated similar to the chambers with the following modifications: the spincoating was done at 1000 rpm , the e-beam writing resolution was 150 nm with a dose of 889 μC/cm2 . Development was done for 12 min . Etching was done for 320 s . Final cleaning was done at 45°C for 10 min in acetone following by cleaning step in nitric acid for 10 min with sonication . To fabricate the upper valves , a 4″ wafer was cleaned with a nitric acid for 10 min under sonication , washed with water and then dried . Next , a thin layer of hexamethyldisilazane ( BASF SE ) was spincoated ( 1000 rpm for 1 min ) and baked at 200°C for 2 min . The negative e-beam resist NEB22A ( Sumitomo Chemical Co . , Ltd ) was spin-coated ( 1000 rpm for 1 min ) and the wafer was baked at 110°C for 3 min . The structures were written similar to the chambers that were described above with a resolution of 100 nm and a dose of 20 μC/cm2 . The structures were immediately developed in MF322 ( Dow Chemical Company ) for 45 s following by a moderate development with MF322:water ( 1:9 ) for 15 s and a washing step in water for additional 15 s . Etching was done using the same reactive ion etching process that is described above for 310 s . Finally , the wafer was cleaned with nitric acid for 10 min under sonication . After fabrication of both wafers ( the one with the lower layer and the one with the upper layer ) , they both were rendered hydrophobic by placing the wafers for at least 12 hr in a desiccator at a pressure of ∼0 . 6 mbar together with 30 μl of ( tridecafluoro-1 , 1 , 2 , 2-tetrahydrooctyl ) trichlorosilane ( abcr GmbH and Co . ) . This treatment forms a hydrophobic monolayer on the wafers surface . To study Min patterns on 2D surfaces we fabricated PDMS flow cells with lateral dimension of 3 . 325 × 2 mm2 . First , a silicon wafer was cleaned for 10 min in nitric acid under sonication . Next , a thin layer of hexamethyldisilazane was spincoated ( 1000 rpm for 1 min ) and baked at 200°C for 2 min , following by spincoating of AZ 5214 resist ( Microchemicals GmbH ) at 1000 rpm and baking step at 105°C for 4 min . The wafer was then exposed on EVG 620 mask aligner ( EVG ) through a polyester film photomask ( JD photo ) and was developed with MF321 ( Dow Chemical Company ) for 4 min following by a washing step in water for 30 s . Next , the wafer was baked for 10 min at 180°C and was etched similar to the chambers pattern with the slight modification that the etching time was 60 min . The depth of the flow channels was 137 °μm . Finally , the wafer was cleaned using nitric acid for 10 min under sonication and was rendered hydrophobic as described above . Since PMMA is a positive resist , the surface of the lower wafer contained grooves that are identical to the ones we like to have on the PDMS chip . In order to get a PDMS layer where the reservoirs , chambers and side channels are grooved inwards we have used the method of double replication of the structures with PDMS . First , 30 g of RTV615 at a volume ratio of 5:1 base:crosslinker was poured on the lower layer wafer and was baked for 4 hr at 60°C following by a baking step for another 4 hr at 120°C . Next , 9 different chips ( 20 × 20 mm ) were cut from the PDMS mold , and served as masters for the second step replication . These masters were treated with ( tridecafluoro-1 , 1 , 2 , 2-tetrahydrooctyl ) trichlorosilane in the same way as is described above . To form a thin PDMS lower layer containing all the desired structures , RTV615 at a mass ratio of 5:1 base:crosslinker was spincoated separately on each master ( 500 rpm for 30 s followed by a step at 1400 rpm for 60 s ) . The PDMS masters with the spincoated PDMS layer were then baked at 60°C for 45–60 min . Glass coverslips ( 22 × 22 mm , VWR , thickness no . 1 . 5 ) were cleaned by sonication in acetone for 30 min following by a sonication step in isopropanol ( IPA ) for 30 min and a final wash in MiliQ water . The coverslips and the top layer of the spincoated PDMS were plasma activated for 12 s in a plasma machine ( Plasma PREEN I , plasmatic system Inc . ) with a flow of 1 SCFH O2 . A coverslip was bound on top of each spincoated PDMS layer and was baked for 10 min at 60°C . The coverslip-bound chips were immersed in methanol overnight . Finally , the lower PDMS layers bound to the corresponding coverslips were peeled off the PDMS masters , dried and kept separately in a plastic box . To form the PDMS layer that separates the upper and lower layers , 5–10 g of RTV615 at a volume ratio of 15:1 base:crosslinkerwas poured on a flat 4″ wafer that was treated with ( tridecafluoro-1 , 1 , 2 , 2-tetrahydrooctyl ) trichlorosilane as was described above , and the PDMS was spincoated ( 500 rpm for 30 s followed by a step at 2000 rpm for 120 s ) to form a thin layer that was baked at 60°C for 1 hr . To form the upper PDMS layer , 30 g of RTV615 at a mass ratio of 5:1 base:crosslinker were poured on the upper layer wafer baked at 60°C for 45–60 min , and 20 × 20 mm pieces were cut from the PDMS mass . Next , the upper PDMS layer pieces and the PDMS separation layer were plasma activated for 12 s at Plasma PREEN I machine with 1 SCFH O2 , bound one to the other and were baked at 60°C for 10 min . Before each experiment , the upper PDMS layer bound to the separation PDMS layer was peeled off the flat silicon wafer , holes were punched and both the peeled piece and a lower layer coverslip-bound chip were plasma activated for 12 s with a Plasma PREEN I machine supplemented with a flow of 1 SCFH O2 , and aligned manually one on top of the other using a home-build alignment machine build on a IX71 Olympus microscope that was equipped with a 4X objective ( UPIanFLN , N . A . 0 . 13 ) . Finally , the two parts were bound to each other and were baked at 80°C for 10 min . For preparing the PDMS chips of the large flow cells that were used for studying the Min dynamics on 2D surfaces , RTV615 at a mass ratio of 10:1 base:crosslinker , was mixed , poured on the silicon wafer master and baked for 1–2 hr at 80°C . Subsequently , the PDMS was peeled from the silicon wafer , a PDMS chip ( 20 × 20 mm ) was cut , holes were punched and both the chip and a coverslip ( that was previously cleaned in acetone and isopropanol as described before ) were activated for 12 s with a Plasma PREEN I machine supplemented with a flow of 1 SCFH O2 . Finally , the PDMS chip was bound to the coverslip and was baked for 10 min at 80°C . Purification of MinD and MinE was done as described before ( Loose et al . , 2008 ) with slight modification . Briefly , E . coli BL21 ( DE3 ) cells containing pET28a with either His-MinD or His-MinE were grown in the presence of 50 μM kanamycin in LB media to an O . D . of ∼0 . 6–0 . 8 at 37°C and 180 rpm shaking . Next , the expression of the Min proteins was induced with 1 mM of IPTG and the cells were grown overnight at 18°C with 180 rpm shaking . The cell were then harvested by centrifugation at 4500 g for 30 min , washed with buffer A ( 50 mM sodium phosphate pH 8 . 0 at 4°C , 300 mM NaCl ) , and then were resuspended in a lysis Buffer ( buffer A supplemented with 10 mM imidazole , 5 mM TCEP ( tris ( 2-carboxyethyl ) phosphine ) , a complete protease inhibitor ( Roche ) ( and 100 mM ADP for the MinD case only ) . Cells were lysed in a cell disrupter machine at 15 , 000 PSI and the lysate was cleared by centrifugation at 37 , 500 g for 1 hr . The supernatant was loaded on a 5 ml HisTrap column ( GE Healthcare ) on an AKTA machine ( GE Healthcare ) , and the column was washed once with lysis buffer supplemented with 10% glycerol . Next , the column was washed with the same buffer +20 mM imidazole +10% glycerol and with the same buffer +50 mM imidazole +10% glycerol . MinE was eluted with 250 mM imidazole and MinD with 160 mM imidazole . Min proteins fractions were collected , concentrated with amicon ultra 10 kDa ( Merck Millipore ) and further purified on a Sephacryl S – 300 HR 16/60 column on an AKTA machine ( GE Healthcare ) using a storage buffer ( 50 mM Hepes pH 7 . 25 at 4°C , 150 mM KCl , 10% V/V glycerol , 0 . 1 mM EDTA pH 7 . 4 and 80 μM of ADP for the MinD case ) . Protein concentration was measured using a QuantiProTM BCA assay kit ( Sigma-Aldrich ) . The ATPase activity of MinD was measured by detecting the reduction in the NADH absorption line at 340 nm . For the activity assay , 100 μl of solution , containing MinD ( 1–5 μM ) , was incubated together with MinE ( 1–5 μM ) , E . coli polar-lipid SUVs ( 1 mg/ml ) , Pyruvate kinase ( PK ) ( 0 . 02 mg/ml ) , ATP ( 5 μM ) , Phospho ( enol ) pyruvic acid ( PEP , 5 μM ) at 37°C . Negative control assays without MinE , without the liposomes , or without the MinD , were similarly prepared and handled . At specific times ( every 40 to 60 min ) , 4 μl fractions of the activity assay or the control reactions were removed and added to 36 μl containing PEP ( 2 . 1 mM ) , NADH ( 0 . 22 µM ) and a solution of Lactate dehydrogenase/PK ( Sigma-Aldrich , 22 U of each component ) . Next , the mixed solutions were incubated at 37 degrees for 10 min and then moved to ice . Finely , the absorption at 340 nm was measured using a nanodrop machine ( Data not shown ) . We used NHS-Cy3 to label MinD and Maleimide-Cy5 to label MinE according to the manufacturer procedure ( GE Healthcare ) . The degree of labeling was Cy3MinD-Lysine=0 . 88 , Cy5MinE-Cysteine=0 . 45 . Small unilamellar vesicles ( SUVs ) were prepared through the common method of thin film hydration . Briefly , lipids in the selected molar ratio dissolved in chloroform were mixed in a round shaped flask ( either E . coli polar lipid extract or 67:33 DOPC:DOPG supplemented with 0 . 03 of TopFluor Cardiolipin , except for measuring proteins concentration where the TopFluor Cardiolipin was not added ) . The chloroform was evaporated using a nitrogen stream and further by incubation in a desiccator for at least 2 hr at a pressure of ∼1 mbar . Next SUV buffer ( 10 mM Tris pH 7 . 45 at 21°C , 150 mM KCl ) was added to a final concentration of 5 mg/ml and the flask was shaken at 250 rpm until all the lipid film completely hydrated . Next , the solution was sonicated at 36°C for ∼30 min and was extruded through a 30 nm filter 21 times . Finally , the SUVs were frozen in liquid nitrogen and stored at −80°C . Min protein were observed on a commercial Olympus IX81 microscope equipped with a 60X objective ( PlanApoN TIRFM UIS 2 , NA 1 . 45 , oil immersion ) or with a 20X objective ( UPlansApo , NA 0 . 85 , oil immersion ) , a USHIO USH-1030L mercury lamp , a Mad City lab Micro-drive stage , an Uniblitz VMM-T1 shutter drive and a Hamamatsu C4742-95 12 ERG camera . Microscope was controlled via the Micro-Manager program ( Edelstein et al . , 2010 ) , and the stage was controlled via a self-written program in Labview . For high-temperature experiments , we used a Julabo F12 water-circulating bath that was connected to a custom-designed heating chamber and an objective heater ( Live cell instruments , chamlide . com ) . In a typical chamber experiment , we first flushed SUVs into the device at a concentration of 2 . 5 mg/ml in SUV buffer . The vesicles were incubated inside the device at 37°C for 1 hr . Next , the chip was washed with a Harvard apparatus 11 plus pump using a Min buffer ( 25 mM Tris pH 7 . 45 at 21°C , 150 mM KCl , 5 mM MgCl2 ) for about 1 hr at a flow rate of 75–150 µl/h . For room-temperature experiments in the chambers , we used a lipid composition of DOPC:DOPG ( 67:33 ) supplanted with 0 . 03% TopFluor Cardiolipin . For the elevated temperature experiments in the chambers , we used E . coli total lipids extract supplemented with 0 . 03% TopFluor Cardiolipin . After the chambers were extensively washed , 10–50 µl of Min buffer containing ( unless otherwise mentioned ) : 1 . 1 µM MinD ( either alone or in most of the cases in total divided between 0 . 9 µM MinD plus 0 . 2 µM MinD-Cy3 ) , 0 . 8 µM MinE , 0 . 2 µM MinE-Cy5 , 5 mM ATP ( magnesium salt ) , 5 mM PEP , and 10 µg/ml PK , was injected into the device and the inlet and outlet were blocked using a sellotape . Next , the device was placed under the microscope . After assuring that patterns started to form in the chambers ( ∼10 min after injection ) , the valves were closed by applying a pressure of 2–3 bars from an argon gas cylinder and the device was incubated for additional of ∼1 hr . Finely , the device was scanned and movies lasting ∼10 min were recorded . Typically , the frame rate was 0 . 1 Hz . For studying Min patterns on 2D supported lipid bilayer we used a similar protocol for preparation and observing the Min patterns to the one that was used for the chamber device with a slight modification that SUVs washing was done manually . In all these experiments we have used SUVs with lipid composition of DOPC:DOPG ( 67:33 ) supplemented with 0 . 03% TopFluor Cardiolipin . Analysis of the patterns was done using a self-written Matlab script . For all cases we analyzed the MinE signals , since the MinD signal gave less contrast and it makes no essential difference since both MinD and MinE signal the same qualitative and quantitative behavior of the patterns . First , for each movie , the field of view ( FOV ) was rotated automatically to make chambers fully vertical by calculating the standard deviation of each line along the average intensity of the movie and finding the angle at which this value is minimized . Next , the frames of the movies were thresholded using one of the built-in thresholding methods of Matlab and segmented automatically into chambers by finding intensity steps along the horizontal and vertical dimensions of an average or STD frame of the movie . We implemented a human-controlled correction step for both the rotation and segmentation steps in order to bypass program imperfection . The intensity of the Min proteins in each chamber was then recorded and the average intensity in each chamber was calculated separately and subtracted in order to produce separate movies for each chamber . The velocity of propagation was calculated by incorporating codes for the U-track 2 . 1 . 3 Matlab package that was developed in the Danuser lab ( Jaqaman et al . , 2008 ) . For calculating the wavelength , a line , featuring the propagating direction was generated automatically based on the U-track results . Next , the intensity along this line was recorded for each frame of the background subtracted movie and a kymograph was generated . A peak finder Matlab routine ( written by Jacob Kerssemakers ) was used in order to locate the peaks distance along each line of the kymograph , and a histogram of the peak distances was generated . A Gaussian fit to the histogram was then used in order to find the mean value of the wavelength in each chamber . For calculating wavelengths on flat supported lipid bilayer ( SLB ) , we used the same algorithm with the slight modification that the line along the waves’ propagation direction was drawn manually . Velocities of the wave propagation on 2D flat SLB were calculated by rotating the peaks’ kymograph and minimizing the average standard deviation along each line of the rotated kymograph . From the angle that minimizes the standard deviation and the error in the angle one can easily infer back the propagation velocity . Figures and graphs for this article were prepared using the programs: ImageJ ( RRID:SCR_001935 ) , SciDAVis ( RRID:SCR_014643 ) , Inkscape ( RRID:SCR_014479 ) and GIMP ( RRID:SCR_003182 ) . Our method of measuring the concentration of the Min proteins is based on the fact that , whereas MinD and MinE bind the membrane and thus one cannot know their concentration inside the microchambers a priori , the concentration of a cytosolic protein will always be equal to the concentration that is injected into the device . This means that the known concentration of a cytosolic fluorescent protein , such as GFP , can be used as a calibration tool in order to infer the actual concentration of the Min proteins in the microchambers by comparing the relative fluorescence , given that the relation between the relative fluorescence of GFP and either MinD or MinE is known for conditions where the Min proteins cannot bind the membrane . Thus , in order to measure the concentrations of the Min proteins in our chambers , we used a combination of a fluorescent measurement of the Min proteins signal in the chambers relative to their fluorescence at different concentrations in a cuvette measured with a fluorometer , and compared these values to the fluorescence of purified green fluorescence protein ( deGFP - a variant of GFP see Shin and Noireaux , 2012 ) , which was a kind gift of the Christophe Danelon lab ( [CGFPStock]=15±0 . 9μM as measured by the Pierce 660 assay from Thermo Fisher scientific ) . The concentration of a fluorescence species is related to the signal of a detector ( S ) according to the formula: ( 1 ) S=[C]⋅B⋅Qeff⋅I⋅τ , where [C] is the concentration of the fluorescent species , B is the brightness of the species , Qeff is the quantum efficiency of the detector at the emission wavelength of the fluorescent species , I is the intensity of the light source at the excitation wavelength of the fluorescent species , and τ is the acquisition time of the detector . Upon defining F≡Qeff⋅I⋅τ , we obtain SMin=FMin⋅BMin⋅[CMin] for the Min proteins , and similarly SGFP=FGFP⋅BGFP⋅[CGFP] for GFP . Since this equation can be written for both the bulk fluorometer ( SFlu ) and for the microscopic imaging of the chambers ( SMic ) one obtains the following two equations: ( 2 ) SMinFluSGFPFlu=[CMinFlu]⋅FMinFlu⋅BMin[CGFPFlu]⋅FGFPFlu⋅BGFP , ( 3 ) SMinMicSGFPMic=[CMinMic]⋅FMinMic⋅BMin[CGFPMic]⋅FGFPMic⋅BGFP , where , for each variable , the superscripts Mic or Flu represent the case for the microchamber or the fluorometer cuvette , respectively . By defining FMic≡FMinMicFGFPMic and FFlu≡FMinFluFGFPFlu and rearranging Equation 2 and 3 , one obtains: ( 4 ) [CGFPFlu]=SGFPFluSMinFlu⋅FFlu⋅BMinBGFP⋅[CMinFlu] , ( 5 ) [CGFPMic]=SGFPMicSMinMic⋅FMic⋅BMinBGFP⋅[CMinMic] . Since the GFP concentration is known and the same for the fluorometer and microchambers , [CGFPMic]=[CGFPFlu] and we can equate Equation 4 and 5 to obtain: ( 6 ) [CMinMic]=[CMinFlu]⋅ ( SGFPFluSMinFlu⋅FFluFMic⋅SMinMicSGFPMic ) ≡[CMinFlu]⋅ℱ . Note that FMic and FFlu are pure machine factors , they can be calculated from the known specs of the microscope camera and fluorometer detector , the relative intensities of the microscope and fluorometer light sources , and the acquisition times in both case . To solve Equation 6 and thus deduce the value of [CMinMic] , one can adapt one out of two strategies that are mathematically equivalent . In the first approach , one substitutes all measured S values to Equation 6 , as well as [CMinFlu]=[CMinSyr] , where [CMinSyr] is the concentration of the Min protein in the syringe that was used in order to inject the Min proteins into the microchambers , to thus obtain [CMinMic] . In the second strategy , which was in fact applied in our work , we account explicitly for the possibly nonlinear dependence of fluorescence intensity versus concentration . Since for varying protein concentrations , ℱ is a function of SMinFlu , one can convert the measured SMinFluvalues ( see Figure 5—figure supplement 1a , b ) to values for ℱ and read off the value of [CMinFlu] where ℱ=1 . At this particular point we thus obtain the unknown Min concentration [CMinMic] in the microchambers . Note that for MinE , the calibration curve ( Figure 5—figure supplement 1b ) was linear while it was found to be nonlinear for MinD ( Figure 5—figure supplement 1a ) . We do not fully understand the source of this non-linearity , but we suspect that it is related to some dequenching effect . However , the behavior was clearly reproducible as it was repeated in three independent experiments . Note also that the background signals in the microchambers in all fluorescence channels were measured before the injection of the proteins and were subtracted for the signal in the microchambers in the presence of the proteins . Similarly , the background values in the fluorometer were measured and subtracted from the protein signals . Strain FW1919 ( W3110 [minDE :: exobrs-sfGFP-minD minE-mKate2 :: frt] ) ( Wu et al . , 2016 ) was used in order to view Min oscillations in vivo . Cells were inoculated into LB and were grown overnight at 37°C with 250 rpm shaking . In the morning , the cells were diluted 1:100 into M9 media + 0 . 2% glucose and continued to grow under the same conditions until they reached an OD of 0 . 3 . Next , the cells were divided into two fractions . The first fraction was directly transferred to an M9 ( +0 . 2% glucose ) agar pad and were observed under the microscope to collect movies of Min oscillations in wild type cells . Cephalexin , ( 100 μg/ml , Sigma-Aldrich ) was added to the second fraction and the cells continued to grow under the same conditions until they reached an OD of 0 . 94 . Then , they were diluted 1:3 into fresh M9 +0 . 2% glucose and Cephalexin , placed on a similar agar pad , and were observed under the microscope to collect Min oscillations movies in filamentous cells . In all cases , movies were taken with the same Olympus IX81 setup that was used for the microchambers experiments . The temperature of the microscope was kept at 36 . 7°C and the acquisition was done using an Olympus UplanApo objective ( 100X , NA 1 . 35 ) . Movies were analyzed twice . First , to create Supplementary file 1a–d and the corresponding movie , the cells boundaries were identified by thresholding and the particle detection tool of ImageJ . Second , the midline of the cells was identified using a self-written script in Matlab based on thresholding and dissection of the cell boundaries into two halves ( see Caspi , 2014 ) . The midline was used in order to dissect the cells to separate areas each one with a width of 0 . 2 μm . The fluorescence in each area was summed and was used in order to construct the kymographs ( Supplementary file 1e–h ) . | Some proteins can spontaneously organize themselves into ordered patterns within living cells . One widely studied pattern is made in a rod-shaped bacterium called Escherichia coli by a group of proteins called the Min proteins . The pattern formed by the Min proteins allows an E . coli cell to produce two equally sized daughter cells when it divides by ensuring that the division machinery correctly assembles at the center of the parent cell . These proteins move back and forth between the two ends of the parent cell so that the levels of Min proteins are highest at the ends and lowest in the middle . Since the Min proteins act to inhibit the assembly of the cell division machinery , this machinery only assembles in locations where the level of Min proteins is at its lowest , that is , at the middle of the cell . When Min proteins are purified and placed within an artificial compartment that contains a source of chemical energy and is covered by a membrane similar to the membranes that surround cells , they spontaneously form traveling waves on top of the membrane in many directions along to surface . It is not clear how these waves relate to the oscillations seen in E . coli . Caspi and Dekker now analyze the behavior of purified Min proteins inside chambers of various sizes that are fully enclosed by a membrane . The results show that in narrow chambers , Min proteins move back and forth ( i . e . oscillate ) from one side to the other . However , in wider containers the wave motion is more common . In containers of medium width the Min proteins rotate in a spiral fashion . Caspi and Dekker propose that the spiral rotations are the underlying pattern formed by Min proteins and that the back and forth motion is caused by spirals being cut short . In other words , if a spiral cannot form because the compartment is too small then the back and forth motion emerges . Similarly , Caspi and Dekker propose that the waves emerge in larger containers when multiple spirals come together . These findings suggest that the different patterns that Min proteins form in bacterial cells and artificial compartments arise from different underlying mechanisms . The next step will be to investigate other differences in how the patterns of Min proteins form in E . coli and in artificial compartments . | [
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Little is known about venom in young developmental stages of animals . The appearance of toxins and stinging cells during early embryonic stages in the sea anemone Nematostella vectensis suggests that venom is already expressed in eggs and larvae of this species . Here , we harness transcriptomic , biochemical and transgenic tools to study venom production dynamics in Nematostella . We find that venom composition and arsenal of toxin-producing cells change dramatically between developmental stages of this species . These findings can be explained by the vastly different interspecific interactions of each life stage , as individuals develop from a miniature non-feeding mobile planula to a larger sessile polyp that predates on other animals and interact differently with predators . Indeed , behavioral assays involving prey , predators and Nematostella are consistent with this hypothesis . Further , the results of this work suggest a much wider and dynamic venom landscape than initially appreciated in animals with a complex life cycle .
Venoms and the toxins they include are mostly used by animals for antagonistic interactions , such as prey capture and defense from predators ( Fry et al . , 2009; Casewell et al . , 2013 ) . Pharmacological research has been focused almost exclusively on the venoms of the adult stages despite the fact that many animals display remarkable transformations in body architectures and ecology during their development ( Ruppert et al . , 2004 ) . Such vast differences may dictate different interspecific interactions for distinct life stages ( Wilbur , 1980 ) . As venom is hypothesized to be metabolically expensive , and in many cases highly specific ( Nisani et al . , 2012; Casewell et al . , 2013 ) , it is plausible that its composition might change between different life stages . Indeed , some ontogenetic variation was reported in the venoms of snakes ( Gibbs et al . , 2011 ) , spiders ( Santana et al . , 2017 ) and cone snails ( Safavi-Hemami et al . , 2011 ) , but until now this phenomenon was not studied thoroughly in an animal with a complex life cycle throughout its development . The oldest extant group of venomous animals is the marine phylum Cnidaria , which includes sea anemones , corals , jellyfish and hydroids . Cnidarians are typified by their stinging cell , the nematocyte , that harbors a unique and highly complex organelle , the nematocyst ( Kass-Simon and Scappaticci , 2002; David et al . , 2008 ) . This proteinaceous organelle is utilized as a miniature venom delivery system ( Thomason , 1991; Lotan et al . , 1995 ) . Most cnidarians have a complex life cycle that includes both sessile benthic and mobile pelagic life stages of wide size distribution and ecological interactions . For example , the canonical life cycle of anthozoans ( corals and sea anemones ) includes a swimming larval stage ( planula ) that metamorphoses into a sessile polyp stage that matures into the reproductive adult . The starlet sea anemone , Nematostella vectensis , is becoming a leading cnidarian lab model as unlike many other cnidarian species it can be grown in the lab throughout its life cycle . This makes Nematostella a unique system to study the venom of an animal with a complex life cycle . Another advantage is that the high genetic homogeneity of the common Nematostella lab strain minimizes individual genetic variation , which is far from trivial in most other venomous animals collected from the wild in limited numbers . Further , Nematostella has a sequenced genome and stage-specific transcriptomes and various molecular tools are available for its genetic manipulation ( Wikramanayake et al . , 2003; Putnam et al . , 2007; Renfer et al . , 2010; Helm et al . , 2013; Layden et al . , 2016 ) . The Nematostella experimental toolbox is unique , not only for cnidarians , but also for venomous animals in general . During the Nematostella life cycle , females release a gelatinous egg package and males release sperm into the water ( Hand and Uhlinger , 1992 ) . After fertilization occurs , the zygote cleavage begins , forming a blastula and less than 24 hr post-fertilization ( hpf ) gastrulation is completed . A planula larva emerges from the egg package 48–72 hpf and starts swimming in the water . Six to seven days after fertilization , the planula settles in soft substrate and starts to metamorphose into a primary polyp and sexual maturation takes about 4 months under lab conditions ( Hand and Uhlinger , 1992 ) ( Figure 1A ) . Whereas the egg and planula are roughly spherical and measure only about 250 µm , the morphologically differentiated elongated adult polyp can reach the length of 4 cm in the wild and up to 20 cm in the lab ( Williams , 1975; Hand and Uhlinger , 1992 ) . Venom is produced in Nematostella by ectodermal gland cells and nematocytes ( Moran et al . , 2012b; Moran et al . , 2013 ) . Although prey capture and production of Nv1 , a major sodium channel modulator toxin ( Moran et al . , 2008a; Moran et al . , 2012b ) , begins at the sessile primary polyp stage , nematocysts appear as early as 48 hpf in the swimming planula ( Marlow et al . , 2009 ) . These are strong indications that venom is likely present already in early life stages and its composition might dramatically change across the Nematostella life cycle . Further complexity in the regulation of venom production may be due to Nematostella producing different cell types that are unevenly distributed between various tissues ( Moran et al . , 2013 ) ( Figure 1B ) . Using an integrated and comparative approach , we carefully quantify and characterize the spatiotemporal expression of known and novel Nematostella toxins across different developmental stages and employ transgenesis to understand the dynamics of venom production in a species with a complex life cycle . We correlate the dynamics of toxin expression in life stages with organismal-level behavior experiments that highlight differential responses of prey and predators when exposed to these stages .
To assay the venomous potential of Nematostella larvae we incubated 4 days old swimming planulae with nauplii of the brine shrimp Artemia salina . Strikingly , within 10 min from the start of the incubation 3 out of 8 Artemia were paralyzed or dead , and within 90 min 7 of 8 were dead ( Video 1 ) , whereas in a control group without planulae all Artemia were alive . This experiment revealed that Nematostella planulae are capable of rapidly killing a crustacean that is larger than themselves . The relatively rapid effect and the size difference suggest that venom is involved in the process . Numerous discharged nematocysts were found in the water around the dead nauplii as well as in their cuticle ( Figure 1C–D ) , further suggesting that the stinging capsules are involved in the envenomation process . To accurately measure the expression levels of known toxin genes , putative toxins and genes encoding nematocyst structural proteins we used the medium-throughput nCounter platform ( see materials and methods ) , which was previously shown to exhibit high sensitivity and precision similar to that of real-time quantitative PCR ( Prokopec et al . , 2013 ) . We assayed the RNA expression levels of the genes encoding the sodium channel modulator neurotoxin Nv1 ( Moran et al . , 2008a ) , the putative toxins NvePTx1 , NEP3 , NEP3-like , NEP4 , NEP8 and NEP16 ( Moran et al . , 2013; Orts et al . , 2013 ) , the putative metallopeptidases NEP6 and NEP14 ( Moran et al . , 2013 ) , NvLysin1b , a cytolytic toxin which may also serve for food digestion ( Moran et al . , 2012a ) , and the structural components of the nematocyst capsule Ncol1 , Ncol3 and Ncol4 ( David et al . , 2008; Zenkert et al . , 2011 ) , as well as the putative nematocyst structural component NR2 ( Moran et al . , 2014 ) . The RNA measurements were performed on nine developmental stages ( Figure 1E ) , adults of each sex and four dissected tissues of an adult female ( Figure 1F; Supplementary file 1 ) . The nCounter analysis revealed that many of the genes form informative clusters ( Figure 1E–F ) . It is noticeable that the expression patterns of NEP3 , NEP3-like and NEP4 strongly clustered with those of genes encoding structural nematocyst components in both the developmental and tissue analyses ( Figure 1E–F ) , which is consistent with the finding that these putative toxins are produced in nematocytes and are released from the capsule upon discharge ( Moran et al . , 2013 ) . Other toxins such as Nv1 , which was shown to be expressed in polyp ectodermal gland cells ( Moran et al . , 2012b ) , or Nvlysin1b , produced by large gland cells in the pharynx and mesenteries beginning in early developmental stages ( Moran et al . , 2012a ) , had different expression patterns in the nCounter analysis and did not form large clusters ( Figure 1E–F ) . Expression levels of NEP3 , Nv1 and NvePTx1 were strikingly distinct across development ( Figure 1G ) . The expression of Nv1 is relatively low in early developmental stages and then sharply peaks in the juvenile and adult polyps to extraordinary levels that are higher by almost two orders of magnitude compared to the other toxins ( Figure 1G ) . These expression levels can be explained by the fact that the Nematostella genome contains more than a dozen gene copies encoding Nv1 ( Moran et al . , 2008b ) and the peak in transcriptional expression late in development is consistent with earlier observations at the protein level ( Moran et al . , 2012b ) . In contrast to Nv1 , NEP3 is expressed at high levels already at gastrulation , peaks in the early planula and remains roughly stable throughout the rest of development into adulthood . Unlike the other toxins , the expression of NvePTx1 peaks at the unfertilized egg , drops sharply across development and rises again in the adult female ( Figure 1G ) . As significant variations between toxin expression at the transcriptional level and protein level were recently reported ( Madio et al . , 2017 ) , we complemented the expression of toxin genes in Nematostella using shotgun proteomics ( tandem mass spectrometry MS/MS ) . We performed these quantifications in four replicates on lysates from four distinct developmental stages , as well as the separate sexes of the adult polyps ( Figure 1H; Supplementary file 2 ) . The dynamic expression patterns we observed at the protein level correlated well with the dynamics we observe at the transcriptomic level . NvePTx1 was originally identified as a homolog of the type five potassium channel blocker BCsTx3 from the sea anemone Bunodosoma caissarum ( Orts et al . , 2013 ) . We also identified bioinformatically several homologous sequences in the sea anemones Anthopleura elegantissima and Metridium senile , and the hydrozoan Hydractinia symbiolongicarpus ( Figure 2A ) . This suggests that this peptide family was already present in the last common ancestor of all Cnidaria but was lost multiple times in various cnidarian lineages . First , to test if NvePTx1 is indeed a toxin we expressed it in a recombinant form and incubated 20 zebrafish ( Danio rerio ) larvae with 0 . 5 mg/ml of highly pure recombinant peptide ( assayed by reverse phase chromatography ) for 20 hr . Upon the addition of the toxin , the fish reacted rapidly , with an increase in swimming speed . After 2 hr of incubation 10 fish larvae had died , with the remaining fish dying over the next 18 hr ( at which point the experiment had ended ) ( Figure 2B ) . The control group ( incubated in 5 mg/ml bovine serum albumin ) behaved normally throughout the experiment and all larvae were alive after a 20-hr incubation ( Figure 2C ) . To complement the nCounter experiment , we assayed the spatiotemporal expression pattern of NvePTx1 by in situ hybridization ( ISH ) . We observed that while NvePTx1 is expressed uniformly throughout the unfertilized egg and early post-fertilization stages , in the gastrula the expression becomes spatially-localized and seems to be absent from the oral and aboral poles ( Figure 2D ) . In the planula , the expression is clearly observed in the ectoderm in packed gland cells absent from the two body poles , and upon metamorphosis , the expression diminishes ( Figure 2D–E ) . The ISH reveals two types of gland cells , one large and elongated ( Figure 2E–F ) and another small and round ( Figure 2G ) . The results of the ISH and nCounter experiments indicated that NvePTx1 is maternally deposited at the RNA level . Further , we could detect NvePTx1 peptide hits in our MS/MS analyses data ( Figure 1H ) as well as in supplementary datasets available from previous proteomic studies of Nematostella eggs ( Lotan et al . , 2014; Levitan et al . , 2015 ) , suggesting maternal deposition also at the protein level . To directly test this , we have injected into Nematostella zygotes a transgenesis construct that carries the gene encoding the fluorescent reporter mOrange2 ( Shaner et al . , 2004 ) fused to an NvePTx1 signal peptide downstream of a putative NvePTx1 promoter . Noticeably , several females of the injected first generation ( F0 ) exhibited strong expression of mOrange2 in round structures in their mesenteries , which are most probably the ovaries where the eggs are formed ( Figure 2H–I ) . This observation is congruent with the fact that the mesentery is the only female adult tissue where high NvePTx1 transcript levels are detected at high levels by the nCounter analysis ( Figure 1F ) . Further , upon induction of spawning , the female polyps with the fluorescent mesenterial tissue released egg packages harboring plenty of fluorescent eggs ( Figure 2J ) , strongly supporting maternal deposition of NvePTx1 . Next , by performing an immunostaining assay on F1 transgenic animals we were able to verify the presence of two distinct types of ectodermal gland cells as identified by the ISH results ( Figure 2K–M ) . The NEP3 , NEP4 , and NEP8 gene products were previously detected by MS/MS to be released from Nematostella nematocysts upon discharge and hence were hypothesized to be putative toxins ( Moran et al . , 2013 ) . We detected an additional gene encoding a NEP3 homolog in the Nematostella genome and named it NEP3-like . The four nucleotide sequences were translated in silico and were suggested to encode precursors of secretory proteins equipped with typical signal peptides ( according to SignalP online tool ) ( Petersen et al . , 2011 ) . An additional search in the Pfam database ( Finn et al . , 2010 ) showed that each precursor contains three ShKT sequence motives ( PF01549 , Figure 3A ) typical for several potent cnidarian toxins ( Aneiros et al . , 1993; Castañeda and Harvey , 2009 ) . Based on these common features and their sequence similarity , we designated NEP3 , NEP3-like , NEP4 , and NEP8 as the ‘NEP3 family’ . We searched publicly available transcriptomic shotgun assembly databases and identified several sequences of homologs from the sea anemones Edwardsiella lineata , Aiptasia pallida and Anthopleura elegantissima as well as the stony corals Acropora digitifera and Stylophora pistillata , showing significant sequence similarity and identical domain structure to the NEP3 family members in Nematostella ( Figure 3—figure supplement 1 ) . A phylogenetic analysis revealed that each of NEP3 , NEP4 and NEP8 from Nematostella formed a strongly supported ( bootstrap values > 0 . 5 ) clade with a highly similar protein from Edwardsiella ( Figure 3B ) , indicating orthology . Thus , the new sequences from Edwardsiella were named according to their Nematostella orthologs . As Nematostella and Edwardsiella are members of the basally branching sea anemone family Edwardsiidae ( Rodríguez et al . , 2014; Stefanik et al . , 2014 ) and possess NEP3 , NEP4 and NEP8 , we can infer that those three proteins probably originated in the last common ancestor of the Edwardsiidae . Other cnidarian species bear more distantly related NEP3 family members , and their exact orthologous or paralogous nature could not be determined due to low bootstrap values ( Figure 3B ) . However , their presence in stony corals suggests that those proteins already appeared 500 million years ago in the last common ancestor of stony corals and sea anemones ( Shinzato et al . , 2011 ) , but were lost in multiple hexacorallian lineages . To check whether NEP3 serves as a toxin , we planned to express it in a recombinant form . However , it was initially not clear in what native form this protein is found in the animal as we detected a potential Lys-Arg tandem , which is a prominent cleavage signal in nematocyst proteins ( Anderluh et al . , 2000 ) , between the first and second domains of NEP3 . Hence , we decided to first explore the primary structure of the native mature NEP3 . For this aim , we discharged lyophilized nematocysts and analyzed the molecular weight of NEP3 making part of the ejected protein mixture by western blot with custom polyclonal antibodies against the first NEP3 ShKT domain . However , the western blot resulted in two major bands ( ~10 and 12 kDa ) ( Figure 3C ) . A gradual three-step FPLC procedure of gel filtration , anion exchange and reverse phase chromatography was applied to purify the dominant NEP3 fragment , and at each stage we carried on with the fraction with the strongest western blot signal . This procedure yielded a nearly pure fraction of mature NEP3 fragment with molecular weight ~10 kDa . Comparison of the electrophoretic mobility of the native mature NEP3 and a recombinant peptide corresponding to the first domain showed that the two peptides possess highly similar molecular weights ( Figure 3C ) . Thus , we conclude that a native peptide composed of only the first domain of NEP3 is found in Nematostella and is released from the nematocyst upon discharge . Following this finding we carried out a toxicity test where we incubated zebrafish larvae with 0 . 5 mg/ml recombinant mature NEP3 . While in the control group ( incubated for 20 hr in 5 mg/ml bovine serum albumin ) all the 20 fish survived ( Figure 3D ) , all 17 Danio fish larvae in the NEP3-treated group died within 5 hr and the larvae exhibited pronounced contraction and tail twitching ( Figure 3E ) suggesting that the mature NEP3 peptide might be neurotoxic . To gain improved resolution of the expression of the four NEP3 family members , we employed ISH to localize their expression in five developmental stages . All four genes are expressed in nematocytes on the surface of the early planula ( Figure 4A; Figure 4—figure supplement 1 ) . However , beginning at the late planula stage the expression of NEP8 shifts to only a handful of nematocytes in the lower pharynx ( Figure 4A ) . In contrast to NEP8 , the three other family members , NEP3 , NEP3-like and NEP4 are expressed throughout the ectoderm in nematocytes , with high concentration of expressing cells in the oral pole . In the primary polyp , expression of NEP3 , NEP3-like and NEP4 is noticeable in nematocytes in the body wall and physa ectoderm and in the upper and lower pharynx ( Figure 4A ) . Further , in tentacle tips , which are very rich with nematocytes , there are large numbers of nematocytes expressing the three toxins , fitting well our nCounter results ( Figure 1F ) . A superficial look on these results may give the impression that in Nematostella there is one small population of pharyngeal nematocytes that express NEP8 and another very large population of nematocytes that express NEP3 , NEP3-like and NEP4 in much of the ectoderm . However , we decided to use double fluorescent ISH ( dFISH ) to check if this is truly the case . In the dFISH experiments , we localized the mRNA combinations of NEP3 with Ncol3 , NEP3 with NEP3-like , NEP3 with NEP4 , and NEP3-like with Ncol3 . Unexpectedly , all the combinations showed only limited overlap in their expression , with NEP3-like and Ncol3 showing the highest overlap and NEP3 and NEP4 showing the lowest ( Figure 4B ) . This result can be explained by two non-exclusive explanations: the first is that the nematogenesis ( production nematocysts ) is a highly dynamic process that requires different genes to be expressed at different times along the nematocyst maturation process; the second is that the three family members are mostly expressed in different nematocyte populations and only few nematocytes express all family members . To test the latter hypothesis , we injected Nematostella zygotes with a construct carrying a chimera of the signal peptide of NEP3 with mOrange2 downstream of the putative promoter of NEP3 . Zygotes started expressing mOrange2 in nematocytes about 4 days after injection . We raised the positive F0 animals to adulthood and then crossed them with wildtype polyps to identify founders . We found six female and three male founders that the mOrange2 expression in their F1 progeny imitated the expression of the native gene ( Figure 5A–C ) . To verify this , we performed double ISH on F1 as well as F2 animals ( third generation ) and found nearly perfect overlap between the transcriptional expression of the mOrange2 transgene and that of the NEP3 toxin gene ( Figure 5D–F ) . mOrange2 expression was observed in many nematocytes with especially dense populations in the tentacle tips and the mesenteries ( Figure 5C ) . Strong expression of mOrange2 was also detected in numerous nematocytes within the nematosomes ( Figure 5G–I ) , defensive structures that Nematostella releases to its surroundings and within egg packages ( Babonis et al . , 2016 ) . Next , we dissociated tentacles of transgenic F1 animals by a mixture of commercial proteases into single cells and observed what cells express mOrange2 and hence NEP3 . As expected , we observed that NEP3 is expressed in nematocytes , but not in spirocytes ( Figure 5J–L ) , which are believed to be used for entangling prey and not for venom delivery ( Mariscal et al . , 1977 ) . Moreover , we also observed that NEP3 is expressed only in a subpopulation of nematocytes ( Figure 5M–O ) , suggesting like the ISH and dFISH experiments that different nematocytes express different toxins . However , at that point there was a possibility that the mOrange2 is noticeable only in developing nematocytes and that mature capsules are not glowing due to various technical limitations such as the mature capsule wall obstructing light . In order to test whether there are mature mOrange2-positive capsules in our transgenic line , we challenged the F1 polyps with Artemia nauplii and zebrafish larvae . We then took the attacked prey items and visualized them with fluorescent microscopy . Strikingly , mOrange2-positive capsules with glowing tubules were pinned in the crustacean and fish cuticle or skin , respectively ( Figure 5P–Q ) , indicating that those are mature capsules . However , these capsules were accompanied by other capsules that were mOrange2-negative , strongly suggesting once again that only a certain nematocyte subpopulation in Nematostella is expressing NEP3 . At different developmental stages Nematostella inhabits various ecological niches , and consequently its interaction with predators and prey may change throughout the life cycle . Here , we tested Nematostella interactions with the grass shrimp Palaemonetes sp . and the killifish Fundulus heteroclitus at egg , planula , primary polyp , and adult life stages ( Table 1 ) . Grass shrimps are reportedly predators of Nematostella ( Kneib , 1985; Kneib , 1988 ) , however , in our observations , when encountering the tentacles of adult polyps of Nematostella burrowed in substrate , shrimps immediately ‘jumped’ away from the tentacles ( Video 2 ) . In contrast , in the absence of the typical mud substrate , shrimps in an environment lacking food would consume adult polyps by starting to feed at the side of their body column , avoiding contact with the tentacles . These results are in agreement with a previous study that found that shrimps can generally prey on Nematostella polyps , but not when they are burrowed in the substrate ( Posey and Hines , 1991 ) . The response of grass shrimp to tentacle contact correlates well with the high sensitivity ( LD50 = 1 . 25 ng/100 mg shrimp ) of this species to Nv1 toxin , which is highly expressed in tentacles starting from the juvenile polyp stage . Our results do indicate that Nematostella may be a potential food source for grass shrimp at earlier developmental stages ( disassociated eggs , egg packages , planulae larvae , and primary polyps ) . Grass shrimp kept in an environment without food for more than 2 days consumed disassociated eggs and egg packages , which may be due to young embryonic stages not expressing Nv1 . Further , injections of embryonically-expressed NvePTx1 and NEP3 ( 50 ng/100 mg shrimp ) did not produce noticeable symptoms in grass shrimp . We did not observe any planulae larvae being consumed by grass shrimp . However , it is likely that grass shrimp do not regularly encounter planulae larvae as they have the potential to disperse away from the substrate in which the shrimp usually feed . When grass shrimps were provided with primary polyps they were consumed after 7 days when no other food sources were provided , suggesting that this food source is likely not preferred , but is ingested . Fundulus is omnivorous , and was reported to feed on a large variety of benthic organisms ( James-Pirri et al . , 2001; McMahon et al . , 2005 ) and possibly prey on Nematostella ( Wiltse et al . , 1984 ) . In the lab , adult fish did not attempt to eat adult polyps , nor did they attempt to eat disassociated eggs or planulae larvae , which may be due to their small size relative to the adult fish and may not even be recognized as a potential food source . Surprisingly , the fish larvae did feed on eggs when separated from the gelatinous portion of the egg package by cysteine treatment , but ejected from their mouths eggs that were separated from the package mechanically ( Video 3 ) . This suggests that the eggs might carry defensive compounds that are removed or inactivated by the cysteine . Fish do not eat eggs if encased within the egg package and attempted to remove the gelatinous egg package from their tails following coincidental encounters ( Video 3 ) . When provided planulae as a prey item , Fundulus larvae attempted to swallow the larvae but immediately released them and swam away ( Video 4 ) . In the lab , Fundulus larvae ( 1–3 days post hatching ) were consumed by adult Nematostella without substrate , but managed to avoid predation by Nematostella when substrate was present , however , this is likely due to the added vertical space provided to the fish larvae within the dishes containing substrate and anemones . Given more time , a coincidental encounter may occur , resulting in predation of the Fundulus larvae . Fundulus larvae actively avoid primary polyps , however , adults will consume them when food is limited ( Table 1 ) . To test the significance of this observation we exposed fish larvae to three treatments: dead Artemia ( food source ) , silica beads ( inert treatment ) , and primary polyps . Fundulus larvae spent more time in the bottom of the aquarium than the top when silica beads or Artemia were present and spent more time at the top than the bottom when the primary polyps were present ( Figure 6 ) . Time spent at each location was significantly different in our one way ANOVA analysis for top ( p value = 0 . 035 ) and bottom ( p value = 0 . 01 ) for each location , with the Tukey analysis indicating that the significant difference was found between primary polyps and the other two treatments , but not when comparing silica beads and Artemia at both locations ( Figure 6 ) . Thus , we can conclude that Fundulus larvae tend to avoid interactions with Nematostella primary polyps .
Our finding of different expression levels of toxins in different developmental stages and adult tissues strongly suggests that venom composition changes across development and that each arsenal of toxins might have been shaped by selection for different biotic interactions . As Nematostella develops from a non-predatory , swimming larva to an adult sessile predatory polyp that is 150-fold larger than the larva ( Figure 1A ) , its interspecific interactions vastly change across development . For example , Nematostella egg packages can be consumed by grass shrimp , but it is highly unlikely that adult polyps are part of the grass shrimp diet ( Table 1; Video 2 ) . These observations correlate with the expression dynamics of Nv1 that is highly toxic for shrimps . Unlike the grass shrimp , Fundulus larvae do not consume Nematostella egg packages and actively expel from their mouths planulae , which were erroneously perceived as food ( Videos 3 and 4 ) . They consumed only individual eggs following cysteine washes , which do not represent native conditions . The reduced occurrence of predation can be explained by the presence of various toxins , such as members of NEP3 family , which may protect Nematostella from fish predators even at very early developmental stages . This notion is supported by the toxic effects of NEP3 on zebrafish larvae . We hypothesize that these dynamic interactions , coupled with the potentially high metabolic cost of toxins ( Nisani et al . , 2012 ) , have driven the evolution of a distinct venom composition in each developmental stage . Moreover , in the case of cnidarians , an additional metabolic cost stems from the fact that nematocysts are single-use venom delivery apparatuses that have to be reproduced in very high numbers after each antagonistic interaction . Hence , there is a clear advantage in using a highly adapted venom in each developmental stage . Because venom in planulae is used purely for defense , whereas the venom in polyps is used both for defense and for prey capture , our results also relate to previous results in scorpions ( Inceoglu et al . , 2003 ) and cone snails ( Dutertre et al . , 2014 ) , where venom compositions for defense and prey-capture were shown to differ . Further , some of the toxins we localized can be attributed for specific functions based on their expression patterns . For example , it is very likely that NvePTx1 is a defensive toxin due to its occurrence in the egg and in ectodermal gland cells of the planula ( Figures 2D–H and 7 ) , whereas NEP8 is probably used in the polyp for killing prey after it is swallowed , as it is expressed exclusively in the lower pharynx and mesenteries ( Figures 4A and 7 ) . Chemical protection of the eggs was reported in several animals such as black widow spiders ( Buffkin et al . , 1971; Lei et al . , 2015 ) , snails ( Dreon et al . , 2013 ) , octopuses and some fishes and amphibians ( Bane et al . , 2014 ) . Our data on Nematostella NvePTx1 expressed in eggs and embryonic stages further support the idea of ecological importance of chemical defense in early life stages across the animal kingdom . Our findings that NEP3 family members are expressed in different population of nematocytes reveals that nematocyte diversity in Nematostella exceeds well-beyond the morphology-based assessments , which revealed only two types of nematocysts in Nematostella: basitrichous haplonemas ( also called basitrichs ) and microbasic mastigophores ( Frank and Bleakney , 1976; Zenkert et al . , 2011 ) . A study in Hydra discovered that two members of a single pore-forming toxin family are expressed in two morphologically distinct types of nematocytes ( Hwang et al . , 2007 ) , but to the best of our knowledge , similar complexity of toxin expression patterns in morphologically similar nematocytes was never reported before . Further , mechanisms of venom biosynthesis at exact cellular level resolution have not been reported in more complex venomous organisms as well . However , at lower resolution , some reports suggest that within one venom gland different secretory units are specialized on production of a limited number of toxins ( Dutertre et al . , 2014; Undheim et al . , 2015 ) . Thus , specialized venom secretory cells are probably a common trait among venomous animals . The distinct expression patterns of the NEP3 family also provides important indications regarding toxin evolution . For example , the expression of NEP8 in pharyngeal nematocytes , and its absence from the tentacles and outer body wall , where its paralogs NEP3 , NEP3-like and NEP4 are expressed ( Figures 4A and 7 ) , is an indication for sub- or neo-functionalization . The specialization of the different family members is also supported by their conservation in Edwardsiella ( Figure 3B ) . Variation in expression patterns of the NEP3 family members and the fact that at least four different types of gland cells at distinct developmental stages and tissues express different toxins ( Nv1 , Nvlysin1b , NEP6 and NvePTx1 ) in Nematostella suggests a highly complex venom landscape in this species ( Figure 7 ) . At first glance , such a system might seem relatively inefficient . However , we hypothesize that harboring many different toxin-producing cell types , provides modularity and enables evolutionary plasticity of toxin expression . Indeed , our results as well as results of others , suggest that different sea anemones species express similar toxins in different cell types ( Moran et al . , 2012b ) and different tissues ( Macrander et al . , 2016 ) . This evolutionary plasticity might be one of the factors that made sea anemones such a successful group that inhabits all the world’s oceans for the last 600 million years .
Nematostella embryos , larvae and juveniles were grown in 16‰ sea salt water at 22°C . Adults were grown in the same salinity but at 17°C . Polyps were fed with Artemia nauplii three times a week . Induction of gamete spawning was performed according to a published protocol ( Genikhovich and Technau , 2009b ) . Total RNA from different developmental stages and body parts of adult female Nematostella was extracted with Tri-Reagent ( Sigma-Aldrich , St . Louis , MO ) according to manufacturer’s protocol , treated with Turbo DNAse ( Thermo Fisher Scientific , Waltham , MA ) and then re-extracted with Tri-Reagent . RNA quality was assessed on Bioanalyzer Nanochip ( Agilent , Santa Clara , CA ) and only samples with RNA Integrity Number ( RIN ) ≥8 . 0 were used . Each sample was prepared from dozens of specimens ( adult polyps and their tissues ) or from hundreds of specimens ( all younger developmental stages ) in order to normalize for any individual variation . Those samples were analyzed on the nCounter platform ( NanoString Technologies , Seattle , WA , USA; performed by Agentek Ltd . , Israel ) in technical triplicates , each made from a different batch of specimens following a previously described protocol ( Geiss et al . , 2008 ) . In brief , for each transcript to be tested , two probes were generated and hybridized to the respective mRNA . The mRNAs were immobilized on a cartridge and the barcodes on one of the probes were counted by an automated fluorescent microscope . For normalization we used a geometric mean of the expression levels of 5 reference genes with stable expression across development . The genes were selected as follows: we calculated the Shannon entropy ( as described in [Schug et al . , 2005] ) for each of 23 , 041 Nematostella genes based on normalized transcript abundance estimates for six time-points of Nematostella development ( Helm et al . , 2013 ) . We then ranked the genes by entropy , which indicates minimal temporal change in abundance , and from the top 20 chose five genes ( NCBI Reference Sequences XM_001629766 . 1 , XM_001628650 . 1 , XM_001625670 . 1 , XM_001640487 . 1 and XM_001624235 . 1 ) with complete gene models and mean abundance levels spanning the expected experimental range . Probe sequences , entropy scores and all raw and normalized nCounter read data are available in Supplementary file 1 . Hundreds of unfertilized eggs , 4 days old planulae , 9 days old primary polyps , adult males ( five individuals ) , and adult females ( five individuals ) were lysed in 8M urea , 400 mM ammonium bicarbonate solution and centrifuged ( 22000 × g , 20 min , 4°C ) . Protein concentrations were measured with BCA Protein Assay Kit ( Thermo Fisher Scientific ) . Ten µg of protein were reduced with DTT and alkylated with iodoacetamide . Tryptic digestion ( 0 . 3 µg trypsin/sample ) was performed overnight at 37°C , followed by addition of 0 . 05% ProteaseMAX Surfactant ( Promega Corp . , USA ) and further incubation for 1 hr at 37°C . The tryptic peptides were desalted on self-made C18 StageTips ( Rappsilber et al . , 2007 ) . A total of 1 . 25 µg of peptides from each sample were injected into the mass spectrometer . MS analysis was performed in four technical replicates using a Q Exactive Plus mass spectrometer ( Thermo Fisher Scientific ) coupled on-line to a nanoflow UHPLC instrument ( Ultimate 3000 Dionex , Thermo Fisher Scientific ) . Eluted peptides were separated over a 180 min gradient run at a flow rate of 0 . 2 µl/min on a reverse phase PepMap RSLC C18 column ( 50 cm ×75 µm , 2 µm , 100 Å , Thermo Fisher Scientific ) . The survey scans ( 380–2 , 000 m/z , target value 3E6 charges , maximum ion injection times 50 ms ) were acquired and followed by higher energy collisional dissociation ( HCD ) based fragmentation ( normalized collision energy 25 ) . A resolution of 70 , 000 was used for survey scans and up to 15 dynamically chosen most abundant precursor ions were fragmented ( isolation window 1 . 6 m/z ) . The MS/MS scans were acquired at a resolution of 17 , 500 ( target value 5E4 charges , maximum ion injection times 57 ms ) . Dynamic exclusion was 60 s . Mass spectra data were processed using the MaxQuant computational platform , version 1 . 5 . 3 . 12 ( Cox and Mann , 2008 ) . Peak lists were searched against translated coding sequences of gene models from N . vectensis . The search included cysteine carbamidomethylation as a fixed modification and oxidation of methionine and N-terminal acetylation as variable modifications . Peptides with minimum of seven amino-acid length were considered and the required FDR was set to 1% at the peptide and protein level . Protein identification required at least two unique or razor peptides per protein group . The dependent-peptide and match-between-runs options were used . Relative protein quantification was performed using iBAQ values . MS/MS raw files as well results of MaxQuant analysis were deposited to the ProteomeXchange Consortium via the PRIDE ( Vizcaíno et al . , 2016 ) partner repository with the data identifier PXD008218 . Single and double ISH were performed as previously described ( Genikhovich and Technau , 2009a; Moran et al . , 2013 ) . dFISH was performed also according to published protocols ( Nakanishi et al . , 2012; Wolenski et al . , 2013 ) with tyramide conjugated to Dylight 488 and Dylight 594 fluorescent dyes ( Thermo Fisher Scientific ) . In ISH and FISH , embryos older than 4 days were treated with 2 u/µl T1 RNAse ( Thermo Fisher Scientific ) after probe washing in order to reduce background . Stained embryos and larvae were visualized with an Eclipse Ni-U microscope equipped with a DS-Ri2 camera and an Elements BR software ( Nikon , Tokyo , Japan ) . For each gene at least 20 specimens from each developmental stage were tested . To generate transgenic constructs , we replaced the mCherry gene with mOrange2 ( Shaner et al . , 2004 ) and replaced the promoter sequence of the pNvT-MHC::mCH plasmid ( Renfer et al . , 2010 ) . For the NEP3 gene , we inserted to the plasmid 920 bp upstream of the transcription start site as well as the non-coding first exon , first intron and the part of the second exon that encodes the signal peptide of NEP3 ( scaffold_7:1 , 219 , 288–1 , 221 , 320 of the Nematostella genome ) . For the NvePTX1 gene , we inserted to the plasmid 1033 bp upstream of the transcription start site as well as the non-coding first exon , first intron and the region of the second exon that encodes the signal peptide ( scaffold_14:1 , 246 , 079–1 , 247 , 853 of the Nematostella genome ) . The constructs were injected with the yeast meganuclease I-SceI ( New England Biolabs , Ipswich , MA ) to facilitate genomic integration ( Renfer et al . , 2010 ) . Transgenic animals were visualized under an SMZ18 stereomicroscope equipped with a DS-Qi2 camera ( Nikon ) . Immunostaining was performed according to a previously described protocol ( Moran et al . , 2012b ) , employing a commercially-available rabbit polyclonal antibody against mCherry ( Abcam ) diluted to 1:400 and DAPI ( Thermo Fisher Scientific ) diluted to 1:500 . Sequences of NEP and NvePTx1 protein families were retrieved using BLAST searches ( Altschul et al . , 1990 ) against NCBI’s non-redundant nucleotide sequence database and the EdwardsiellaBase ( Stefanik et al . , 2014 ) . Maximum-likelihood analysis was employed for the reconstruction of the molecular evolutionary histories . Trees were generated using PhyML 3 . 0 ( Guindon et al . , 2010 ) , and node support was evaluated with 1000 bootstrapping replicates . Tentacles of Nematostella were dissociated using a combination of papain ( 2 mg/ml; Sigma-Aldrich: P4762 ) , collagenase ( 2 mg/ml; Sigma-Aldrich: C9407 ) and pronase ( 4 mg/ml; Sigma-Aldrich: P5147 ) in DTT ( 1 . 3 mM ) and PBS solution ( 1 . 8 mM NaH2PO4 , 8 . 41 mM Na2HPO4 , 175 mM NaCl , pH 7 . 4 ) . The tentacles were incubated with the protease mixture at 22°C overnight . The tissues were then dissociated into single cells by flicking the tubes gently and then by centrifugation at 400 × g for 15 min at 4°C , followed by resuspension in PBS . Lyophilized nematocysts were obtained from Monterey Bay Labs ( Caesarea , Israel ) . 2 . 5 g of the nematocysts were discharged by incubation with 80 ml of 1% sodium triphosphate ( Sigma-Aldrich ) . Following centrifugation ( 21 , 000 × g , 20 min ) , the crude extract was concentrated with Amicon centrifugal filters with 3 kDa cut off ( Merck Millipore , Billerica , MA ) to 2 ml volume , filtered through Amicon centrifugal filters with 50 kDa cut off ( Merck Millipore ) and used for further purification . At the first step , the extract was fractionated by size exclusion FPLC on a calibrated Superdex 75 column ( 60 × 1 . 6 cm , GE Healthcare , Little Chalfont , UK ) in PBS buffer . Protein fractions with molecular weight less than 17 . 6 kDa were pooled and the PBS buffer was exchanged to 20 mM ethanolamine pH nine using Amicone centrifugal filters , cut off 3 kDa . At the second step , the SEC fractions were separated by anion exchange FPLC using a HiTrapQ HP column ( 1 ml , GE Healthcare ) and a NaCl concentration gradient ( 0–750 mM NaCl in 30 column volumes , 20 mM ethanolamine pH 9 . 0 ) . Fractions were analyzed by western blot with anti-Nep3 antibodies and positive ones were pooled . At the last step , Nep3 fragment was purified by reverse phase FPLC on a Resource RPC column ( 3 ml , GE Healthcare ) using acetonitrile concentration gradient ( 8–60% CH3CN in 25 column volumes , 0 . 1% trifluoracetic acid ) . Fractions corresponding to individual peaks were collected and analyzed by western blot with anti-NEP3 antibodies . A synthetic DNA fragment encoding the full NEP3 polypeptide was purchased from GeneArt ( Regensburg , Germany ) . The fragment corresponding to the first domain of NEP3 between the Lys-Arg cleavage sites was amplified by PCR , cloned and expressed as a His6-thioredoxin fusion protein in Shuffle T7 Escherichia coli strain ( New England Biolabs ) . Nv1 and NvePTx1 synthetic DNA fragments were purchased from Integrated DNA Technologies ( Coralville , IA ) and cloned into a modified pET40 vector ( fragment encoding DSBC signal peptide was erased from it by Protein Expression and Purification facility of the Hebrew University to allow cytoplasmic expression of DSBC ) . Nv1 and NvePTx1 were expressed in BL21 ( DE3 ) E . coli ( Merck Millipore ) strain as fusions with His6-DSBC . The polyhistidine tag of the fusion proteins was used for purification from the E . coli lysate by nickel affinity FPLC . Purified fusion proteins were cleaved into two fragments by Tobacco Etch Virus ( TEV ) protease ( room temperature , overnight ) at a TEV protease cleavage site upstream the toxin fragments . The recombinant toxins were then purified by reverse phase FPLC on a Resource RPC column ( GE Healthcare ) using an acetonitrile concentration gradient in 0 . 1% trifluoroacetic acid . Custom polyclonal antibodies specific to the first domain of NEP3 were purchased from GenScript ( Piscataway , NJ ) . Synthetic peptide containing the amino acid positions 47–91 was used as an antigen for immunization of two rats . The antibodies were affinity purified on a column coated with the antigen . Proteins were separated by electrophoresis on 10–20% gradient Tris-tricine gels ( Bio-Rad , Hercules , CA ) and consequently transferred to 0 . 2 um PVDF membranes ( Bio-Rad ) . Membranes were blocked by 5% skim milk in TBST buffer ( 50 mM Tris base , 150 mM NaCl , 0 . 1% Tween 20 , pH 7 . 6 ) and incubated with anti-NEP3 antibodies ( 1 ug/ml ) in 5% Bovine serum albumin ( BSA ) in TBST buffer ( 4°C , overnight ) . This was followed by incubation with goat anti-rat IgG antibodies conjugated with horseradish peroxidase ( 0 . 1 ug/ml; Jackson ImmunoResearch , West Grove , PA ) in 5% skim milk in TBST ( room temperature , overnight ) . ECL reagent ( GE Healthcare ) was used for visualization of the protein bands interacting with anti-NEP3 antibodies . Chemiluminescence was recorded with an Odyssey Fc imaging system ( LI-COR Biosciences , Lincoln , NE ) and fluorescent size marker ( Bio-Rad ) was imaged on the same system . Danio rerio larvae younger than 120 hr were generously provided by Dr . Adi Inbal ( The Hebrew University Medical School ) . The usage of such young larvae does not require ethical permits according to the European and Israeli laws . Fertilized Fundulus eggs from Kings Creek , VA ( 37°18 16 . 2"N 76°24 58 . 9"W ) and Scorton Creek , MA ( 41°43'52 . 1"N 70°24'51 . 3"W ) were kindly provided by Dr . Rafael Trevisan ( Duke University ) and Diane Nacci ( Environmental Protection Agency ) , respectively . They were kept in 15 ‰ artificial sea water ( ASW ) at room temperature until hatching ( around 2–3 weeks ) and used immediately for behavioral analyses . Experiments on F . heteroclitus were performed under permit no . 17–018 granted by the Institutional Animal Care and Use Committee ( IACUC ) at the University of North Carolina at Charlotte according to ethical regulations of Office of Laboratory Animal Welfare ( National Institutes of Health , USA ) . The first batch of grass shrimps and adult Fundulus were collected at an estuary near Georgetown , SC ( 33°21'01 . 0"N 79°11'26 . 1"W ) . A second collection of grass shrimps were collected at Sippewissett Marsh , MA ( 41°35'22 . 9"N 70°38'17 . 0"W ) . Animals were transported to the lab and kept in 15 ‰ ASW until use . Interaction experiments were conducted in 15 ‰ ASW in 24-well plates or Pyrex bowls ( ~150 ml water , ~10 cm diameter ) , depending on size of animals and interactions being recorded . Grass shrimps were fed every day with either mussels or TetraMin tropical fish food ( Tetra Holding , USA ) . Fundulus were fed twice daily with Artemia reared in the lab . To assess the toxicity of NEP3 and NvePTx1 on fish , 4 days old D . rerio larvae were incubated with 0 . 5 mg/ml peptides in 500 µl well . Each experiment was conducted in duplicates . 5 mg/ml BSA was used as a negative control . Three replicates were performed per treatment and each replicate included 5–7 larvae . Effect was filmed and monitored under an SMZ18 ( Nikon ) stereomicroscope after 5 min , 15 min , 1 hr , and 15–17 hr of incubation . To assess the toxicity on grass shrimps , Nv1 , NEP3 , and NvePTx1 were dissolved in PBS buffer to 2 . 5–50 ng/µl concentration and 1 µl was injected into the abdomen from the ventral side for every 200 mg of shrimp mass . Ten shrimps were injected at each concentration . For testing the interaction of Fundulus with Nematostella , fish larvae were put in 24 well plates and preliminary screenings involving duplicate experimental observations were conducted for 5–10 isolated eggs , planula larva , and primary polyps of Nematostella . Based on these initial observations , we conducted additional observational experiments in triplicates , with each well containing five Fundulus larvae interacting with several egg packages or dozens of planula larvae . For each assay , the fish were observed with portions of their interaction recorded to document how the fish responded to the egg packages or planula larvae . Additionally , for the isolated eggs , observational experiments were conducted with a single Fundulus larva in each well along with 10–15 eggs . The number of eggs were noted at the start of the experiment and observed every 2 hr over an 8 hr period . Observational experiments involving two Fundulus larvae and two adult Nematostella were conducted in duplicates using small Pyrex dishes ( ~50 ml of ASW ) over a 2-day period , with and without substrate . For the adult Fundulus , observational experiments involving a single adult alongside eggs ( >100 ) , egg packages ( 4-5 ) , larvae ( >100 ) , primary polyps ( >100 ) , and adult Nematostella polyps ( 3 ) were conducted over 48 hr in small Pyrex dishes ( ~200 ml water , ~10 cm diameter ) . Interactions were assayed in triplicates for shrimps , with a single shrimp interacting with different life stages: three egg packages , >100 eggs , >100 planula larvae , >100 primary polyps , and three adult Nematostella ( with and without substrate ) . Across all observational experiments similar behavioral patterns were observed across experimental replicates , however , we were unable to identify exactly how many instances of an adverse reaction occurred during our observations . It was sometimes difficult to discern between the fish exhibiting sporadic swimming patterns , which we could not confidently link with them interacting with Nematostella . For testing the potential effect of Nematostella primary polyps on Fundulus larvae , newly hatched Fundulus larvae ( N = 10 ) were placed in a small glass aquarium ( ~3 ml ) with 15 ‰ ASW . Fish behavior was recorded over 30 min per experiment using the Moticam 580 ( Motic , Hong Kong , China ) , 15 min serving as a control and 15 min with a treatment . Freshly hatched Artemia were sacrificed by freezing and used as a positive control ( N = 10 ) . Silica beads 0 . 5 mm in diameter ( BioSpec Products , Bartlesville , OK ) were used as a negative control ( N = 10 ) . For each video , the recording was split into the following sections at these time scales: 0–5 min – acclimation , 5–15 min – control , 15–20 min – acclimation to treatment , 20–30 min – recorded behavior . The water container was split into three equal parts ( ‘bottom’ , ‘center’ and ‘top’ ) based on the fish length . If the fish was in the ‘center’ with no portion of their body crossing either side time was not recorded as this was considered no preference . One-way ANOVAs were carried out separately for the upper and lower time points for each treatment . Specific treatments that were statistically significant were identified using the Tukey post hoc analysis . The results were plotted in GraphPad Prism version 7 . 00 for Windows ( GraphPad Software , La Jolla , CA ) . | Some animals produce a mixture of toxins , commonly known as venom , to protect themselves from predators and catch prey . Cnidarians – a group of animals that includes sea anemones , jellyfish and corals – have stinging cells on their tentacles that inject venom into the animals they touch . The sea anemone Nematostella goes through a complex life cycle . Nematostella start out life in eggs . They then become swimming larvae , barely visible to the naked eye , that do not feed . Adult Nematostella are cylindrical , stationary ‘polyps’ that are several inches long . They use tentacles at the end of their tube-like bodies to capture small aquatic animals . Sea anemones therefore change how they interact with predators and prey at different stages of their life . Most research on venomous animals focuses on adults , so until now it was not clear whether the venom changes along their maturation . Columbus-Shenkar , Sachkova et al . genetically modified Nematostella so that the cells that produce distinct venom components were labeled with different fluorescent markers . The composition of the venom could then be linked to how the anemones interacted with their fish and shrimp predators at each life stage . The results of the experiments showed that Nematostella mothers pass on a toxin to their eggs that makes them unpalatable to predators . Larvae then produce high levels of other toxins that allow them to incapacitate or kill potential predators . Adults have a different mix of toxins that likely help them capture prey . Venom is often studied because the compounds it contains have the potential to be developed into new drugs . The jellyfish and coral relatives of Nematostella may also produce different venoms at different life stages . This means that there are likely to be many toxins that we have not yet identified in these animals . As some jellyfish venoms are very active on humans and reef corals have a pivotal role in ocean ecology , further research into the venoms produced at different life stages could help us to understand and preserve marine ecosystems , as well as having medical benefits . | [
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The p53 transcription factor is a potent suppressor of tumor growth . We report here an analysis of its direct transcriptional program using Global Run-On sequencing ( GRO-seq ) . Shortly after MDM2 inhibition by Nutlin-3 , low levels of p53 rapidly activate ∼200 genes , most of them not previously established as direct targets . This immediate response involves all canonical p53 effector pathways , including apoptosis . Comparative global analysis of RNA synthesis vs steady state levels revealed that microarray profiling fails to identify low abundance transcripts directly activated by p53 . Interestingly , p53 represses a subset of its activation targets before MDM2 inhibition . GRO-seq uncovered a plethora of gene-specific regulatory features affecting key survival and apoptotic genes within the p53 network . p53 regulates hundreds of enhancer-derived RNAs . Strikingly , direct p53 targets harbor pre-activated enhancers highly transcribed in p53 null cells . Altogether , these results enable the study of many uncharacterized p53 target genes and unexpected regulatory mechanisms .
The p53 transcription factor is activated by potentially oncogenic stimuli such as ribosomal stress , DNA damage , telomere erosion , nutrient deprivation and oncogene hyperactivation ( Vousden and Prives , 2009 ) . In the absence of activating signals , p53 is repressed by the oncoproteins MDM2 and MDM4 . MDM2 masks the transactivation domain of p53 and is also an E3 ligase that targets p53 for degradation ( Momand et al . , 1992; Oliner et al . , 1993; Kubbutat et al . , 1997 ) . MDM4 lacks E3 ligase activity , but represses p53 transactivation potential ( Riemenschneider et al . , 1999 ) . Diverse signaling pathways converge on the p53/MDM2/MDM4 complex to release p53 from its repressors and enable it to regulate transcription of downstream target genes involved in cellular responses such as cell cycle arrest , apoptosis , senescence , autophagy , DNA repair and central metabolism ( Vousden and Prives , 2009 ) . p53 is inactivated in virtually all human cancers , either by mutations in its DNA binding domain or MDM2/MDM4 overexpression . Significant advances have been made to develop p53-based targeted therapies ( Brown et al . , 2009 ) . One class of small molecules targets the interaction between p53 and its repressors , thus bypassing the need of stress signaling to trigger p53 activation . For example , Nutlin-3 , the first-in-class compound , binds to the hydrophobic pocket in MDM2 required for binding to p53 , thus acting as a competitive inhibitor ( Vassilev et al . , 2004 ) . A second class of molecules binds to mutant p53 and partially restores its wild type function ( Brown et al . , 2009 ) . As these compounds enter clinical trials , their efficacy is limited by the fact that p53 activation leads to cancer cell death only in specific scenarios . Thus , there is a clear need to understand how these molecules modulate p53 function and how cell fate choice upon p53 activation is defined . A missing piece in this effort is a definitive elucidation of the direct p53 transcriptome . Despite its unequivocal importance in cancer biology , our understanding of p53 function as a transcription factor is limited . The protein domains required for DNA binding and transactivation are well characterized , as well as its DNA response elements ( p53REs ) ( Laptenko and Prives , 2006 ) . A recent comprehensive survey of the literature identified ∼120 genes for which direct regulation has been established ( Riley et al . , 2008 ) , but a comprehensive analysis of p53-regulated RNAs is still missing . Up to this point , the global p53 transcriptional response has been investigated with techniques that measure steady state RNA levels , mostly microarray profiling . These methods require long time points to observe a significant change in the expression of p53-regulated RNAs , which confounds direct vs indirect effects , and additional experiments are required to ascertain direct transcriptional regulation . A popular approach has been to cross-reference microarray data with p53 binding data derived from ChIP-seq assays . Meta-analysis of four recent papers using this strategy indicates that p53 may directly activate >1200 genes , yet only 26 of these genes were commonly activated in all four studies ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) ( see later , Figure 2—figure supplement 1 ) . It is unclear to what extent this lack of overlap is due to methodological differences and/or cell type-specific differences in direct p53 action vs post-transcriptional regulation . We report here the first genome-wide analysis of p53-regulated RNA synthesis . Using Global Run-On sequencing ( GRO-seq ) ( Core et al . , 2008 ) , we ascertained direct regulation by using a short time point of Nutlin-3 treatment in isogenic cell lines with or without p53 . Strikingly , Nutlin leads to p53-dependent transcriptional activation of hundreds of genomic loci prior to any significant increase in total p53 levels , thus highlighting the critical role of MDM2 in masking the p53 transactivation domain . Comparative global analysis of RNA synthesis by GRO-seq vs RNA steady state levels by microarray revealed that many p53 target genes transcribed at low levels are missed by microarray experiments . Strikingly , p53 represses the basal expression of a subset of its target genes before MDM2 inhibition . GRO-seq uncovered many gene-specific transcriptional events affecting key survival and apoptotic genes within the network , including the occurrence of bidirectional promoters ( both overlapping and non-overlapping ) , clustered transactivation and various forms of antisense transcription . GRO-seq revealed widespread activation of enhancer-derived RNAs ( eRNAs ) arising from p53REs . Interestingly , direct p53 target genes harbor ‘pre-activated’ p53REs , as defined by the strong production of eRNAs in the isogenic p53 null cells . These results elucidate novel p53-regulated RNAs as well as gene-specific regulatory events within the p53 network and pave the road for a myriad of future mechanistic studies .
In order to study the direct transcriptional response upon p53 activation , we performed GRO-seq in isogenic HCT116 p53 +/+ and p53 −/− cell lines . After several hours of treatment with 10 μM Nutlin-3a ( referred hereto as Nutlin ) , only p53 +/+ cells undergo cell cycle arrest and display induction of many known p53 target genes ( Tovar et al . , 2006; Henry et al . , 2012 ) . Using GRO-seq , we investigated the effects of Nutlin vs vehicle ( DMSO ) in both cell lines under conditions of exponential cell proliferation , when p53 levels are low and cell cultures display virtually no signs of cell cycle arrest or apoptosis . To minimize the possibility of measuring indirect effects , we chose a 1 hr time point of Nutlin treatment . Typical GRO-seq results are shown for two well-characterized p53 target genes , CDKN1A ( p21 ) and TP53I3 ( PIG3 ) ( el-Deiry et al . , 1993; Polyak et al . , 1997 ) , which display significant increase in GRO-seq signals upon Nutlin treatment only in HCT116 p53 +/+ cells ( Figure 1A , Figure 1—figure supplement 1A , respectively ) . Not surprisingly , Q-RT-PCR demonstrates that the steady state expression of the CDKN1A and TP53I3 mRNAs does not increase at the 1 hr time point as used for GRO-seq ( Figure 1B ) . In fact , a significant increase in the steady state levels of both mRNAs requires several hours of p53 activation . Furthermore , Western blot analysis shows that 1 hr of Nutlin treatment does not increase total p53 or p21 protein levels to a significant degree ( Figure 1C ) . Analysis of cell cycle progression using BrdU incorporation assays shows no signs of cell cycle arrest at the 1 hr time point , but a clear reduction in S phase cells is evident at 12 hr only in the p53 +/+ cells ( Figure 1C , Figure 1—figure supplement 1B ) . Overall , these observations indicate that our GRO-seq analysis using a 1 hr time point of p53 activation would largely preclude secondary effects driven by well-established direct p53 target genes , such as CDKN1A and TP53I3 , thus enabling the identification of the direct p53 transcriptome . 10 . 7554/eLife . 02200 . 003Figure 1 . GRO-seq analysis of the p53 transcriptional program . ( A ) GRO-seq results for the p53 target locus CDKN1A ( p21 ) . Isogenic p53 −/− and p53 +/+ HCT116 cells were treated for 1 hr with either 10 μM Nutlin-3a ( Nutlin ) or vehicle ( DMSO , Control ) . Fragments per kilobase per million reads ( fpkm ) are shown for the intragenic region . The first kilobase downstream of the transcription start site ( TSS ) was excluded from the fpkm calculation to minimize effects of RNAPII pausing . The total genomic region displayed is indicated in the top left corner . Blue signals are reads mapping to the sense strand , red signals are reads mapping to the antisense strand . See Figure 1—figure supplement 1A for results of the TP53I3 locus . ( B ) GRO-seq detects transactivation of the canonical p53 target genes CDKN1A and TP53I3 at 1 hr of Nutlin treatment , prior to any detectable increase in steady state mRNA levels as measured by Q-RT-PCR . ( C ) A 1 hr time point of Nutlin treatment does not produce significant p53 accumulation , p21 protein induction or a decrease in number of S phase cells as measured by BrdU incorporation assays . * indicates p<0 . 05 . See also Figure 1—figure supplement 1B for quantification data of BrdU assays . ( D ) Genome-wide analysis using the DESeq algorithm identifies 198 annotated gene loci transactivated upon Nutlin treatment only in HCT116 p53 +/+ cells . See Supplementary file 1 for a detailed annotation of these genes . ( E ) Q-RT-PCR validates induction of novel and predicted direct p53 target genes upon 12 hr of Nutlin treatment . mRNA expression was normalized to 18s rRNA values and expressed as fold change Nutlin/DMSO . Data shown are the average of three biological replicates with standard errors from the mean . ( F ) Flow cytometry analysis using the DO-1 antibody recognizing the MDM2-binding surface in the p53 transcactivation domain 1 ( TAD1 ) reveals increased reactivity as early as 1 hr of Nutlin treatment , indicative of unmasking of the TAD1 at this early time point . ( G ) p53 directly activates a multifunctional transcriptional program at 1 hour of Nutlin treatment , including many canonical apoptotic genes . See Supplementary file 1 for a complete list and annotation . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 00310 . 7554/eLife . 02200 . 004Figure 1—figure supplement 1 . GRO-seq reveals the immediate direct p53 transcriptional response . ( A ) GRO-seq results for the well characterized pro-apoptotic p53 target gene TP53I3 ( PIG3 ) . ( B ) . BrdU incorporation assays for HCT116 p53 −/− and p53 +/+ cells upon Nutlin treatment for the 1 hr time point employed for GRO-seq and the 12 hr time point used for microarray experiments . One hour Nutlin does not induce significant cell cycle arrest . ( C ) GRO-seq shows that 176 of the 198 genes transactivated upon 1 hr of Nutlin are already increased above 1x as early as 30 min after addition of the drug to cell cultures . ( D ) Of the 198 genes transactivated by p53 at 1 hr of Nutlin treatment , 55 were known direct targets , 66 were predicted by published ChIP-seq/microarray studies and 77 are novel . See also Supplementary file 1 . ( E ) Q-RT-PCR shows that most novel p53 targets identified by GRO-seq are also induced by doxorubicin , a genotoxic p53-activating agent . mRNA expression was normalized to 18s rRNA values and is expressed as fold change doxorubicin/DMSO . Data shown are the average of three biological replicates with standard errors from the mean . ( F ) Western blot assays show induction of the novel p53 target genes GRIN2C , PTCDH4 and RINL at the protein level upon Nutlin treatment of HCT116 p53 +/+ cells . Nucleolin and α-tubulin serve as loading controls . Asterisk denotes a non-specific band in the RINL blot . ( G–H ) Immunofluroescence staining of p53 shows a similar pattern for control cells ( 0 hr ) and cells treated with Nutlin for 1 hr . Upon long exposure to Nutlin ( 12 hr ) , almost the entire cellular population exhibits strong nuclear signal for p53 staining . In H , 'high p53 staining' was defined as the average signal observed in the 12 hr Nutlin cell culture . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 004 Next , we used the DESeq algorithm ( Anders and Huber , 2010 ) to identify annotated gene loci whose transcription is significantly changed upon Nutlin treatment ( see ‘Materials and methods’ for details ) . Using a cut-off of adjusted p ( a ) <0 . 1 , we identified 198 gene loci whose transcription is significantly induced upon Nutlin treatment in p53 +/+ cells ( Figure 1D; Supplementary file 1 ) . This analysis identified only four gene loci whose transcription was diminished in the p53 +/+ cells ( FLVCR2 , NR4A3 , RELB and EGR1 ) ; however , none of these genes showed reductions in steady state mRNA levels upon prolonged p53 activation ( see later , Figure 2 ) . The specificity of Nutlin is demonstrated by the negligible changes observed in p53 −/− cells , where our analysis identified 5 induced and 2 repressed genes , all of which have less than 1 . 5-fold changes and none of which was among those differentially transcribed in p53 +/+ cells ( Figure 1D ) . From this point forth , we focused on the 198 genes activated in the p53 +/+ cells , which we considered to be the direct p53 transcriptional program in this cell type . The notion that these genes are indeed direct p53 targets is reinforced by the observation that most of them ( 176 out of 198 ) show an increase in transcription as early as 30 min after Nutlin addition to the cell culture ( Figure 1—figure supplement 1C ) . Of these 198 genes , 55 were known validated direct p53 targets , 66 were targets predicted by one or more published microarray/ChIP-seq studies , and 77 are putative novel direct p53 targets ( Figure 1—figure supplement 1D , a comprehensive annotation of these genes is provided in Supplementary file 1 ) . Q-RT-PCR validation showed that novel genes are induced at a 12 hr time point of Nutlin treatment at the mRNA steady state level to a degree comparable to those genes predicted by published microarray/ChIP-seq studies ( Figure 1E ) . Furthermore , 12 out of the 14 novel p53 target genes tested are also induced at the mRNA steady state level when using doxorubicin , a DNA-damaging agent that activates p53 via stress signaling cascades ( Lowe et al . , 1994 ) , thus revealing that transactivation of most novel genes is not unique to pharmacological inhibition of MDM2 ( Figure 1—figure supplement 1E ) . Finally , we investigated whether activation of novel p53 targets can also be observed at the protein level . Indeed , Western blot analysis demonstrates protein induction for the novel genes GRIN2C , PTCDH4 and RINL ( Figure 1—figure supplement 1F ) . Thus , our GRO-seq experiment clearly expands the universe of direct p53 target genes , paving the road for mechanistic studies investigating the function of these genes in the p53 network . 10 . 7554/eLife . 02200 . 005Figure 2 . Global analysis of p53 effects on RNA synthesis vs steady state levels . ( A ) MAplots for GRO-seq and microarray gene profiling experiments in HCT116 p53 +/+ cells after 1 hr and 12 hr of Nutlin treatment , respectively . Colors indicate whether genes scored as statistically different in both platforms ( purple ) , in the GRO-seq only ( red ) or the microarray experiment only ( blue ) . ( B ) Few genes downregulated in the microarray experiment show p53 binding within 25 kb of the gene , suggestive of indirect regulation . ( C ) Bubble plots displaying relative signals derived from the GRO-seq and microarray experiments illustrate how genes with very high basal expression or very low transcription are not significantly affected at the steady state level as measured by microarray . For the CDC42BPG , KLHDC7A , ADAMTS7 , LRP1 and ASTN2 loci , the signals were replotted at 25-fold magnification . ( D ) Scatter plot showing comparative fold induction for p53 target genes transactivated at 1 hr Nutlin treatment between the GRO-seq and microarray experiments . ( E ) Q-RT-PCR indicates that many low abundance transcripts upregulated by GRO-seq are indeed induced at the steady state level . ( F ) Box and whisker plots showing the expression of various gene sets as detected by microarray . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 00510 . 7554/eLife . 02200 . 006Figure 2—figure supplement 1 . Mechanisms of indirect gene repression by p53 . ( A and B ) Meta-analysis of four recent investigations of the p53 transcriptional program using microarray analysis of RNA steady state levels and ChIP-seq measurements of p53 binding reveals little overlap between the experiments . See Supplementary file 2 for the various gene lists used to generate the Venn diagrams using Venny ( http://bioinfogp . cnb . csic . es/tools/venny/ ) . *This study employed SAOS2 cells , which are p53 null , with overexpression of various p53 isoforms . ** This study employed HCT116 p53 −/− cells with overexpression of natural p53 polymorphic variants . ( C ) 72% of genes downregulated upon 12 hr of Nutlin treatment in HCT116 cells were found to be repressed in this same cell type by overexpression of miR-34a , a p53 inducible miRNA . ( D ) Ingenuity Pathway Analysis of genes downregulated upon 12 hr Nutlin treatment indicates that the top three regulators of this gene set are E2F4 , CDKN1A and RB . ( E ) Model of indirect gene repression by p53 via upregulation of CDKN1A and miR-34a , both of which inhibited G1-S CDK complexes . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 00610 . 7554/eLife . 02200 . 007Figure 2—figure supplement 2 . ChIP analysis of novel p53 target genes . Analysis of p53 ChIP-seq datasets indicated high confidence p53 binding events in the vicinity of novel p53 target genes identified by GRO-seq at the genomic positions labeled with a red asterisk . ChIP-Q-PCR assays in HCT116 p53 +/+ cells confirm p53 binding at these locations above background levels ( IgG control ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 007 Although it is known that MDM2 represses p53 by both masking its transactivation domain and also targeting it for degradation ( Momand et al . , 1992; Oliner et al . , 1993; Kubbutat et al . , 1997 ) , it has been difficult to dissect to what extent each mechanism contributes to repression of p53 target genes in diverse functional categories . Studies employing steady state mRNA measurements concluded that prolonged p53 activation and/or higher levels of cellular p53 were required for activation of apoptotic genes , some of which display delayed kinetics of induction at the mRNA steady state level as compared to cell cycle arrest genes ( Chen et al . , 1996; Zhao et al . , 2000; Szak et al . , 2001; Espinosa et al . , 2003; Das et al . , 2007 ) . However , GRO-seq demonstrates that a 1 hr time point of Nutlin treatment induces transcription of genes in every major pathway downstream of p53 ( Supplementary file 1 ) . The observation that key survival and apoptotic genes ( e . g . , CDKN1A , TP53I3 ) show greater than sixfold increase in transcription at a time point preceding a proportional increase in total p53 levels ( Figure 1A , C , Figure 1—figure supplement 1A ) , suggests that the mere unmasking of the p53 transactivation domain suffices to activate a multifaceted transcriptional program . To further test this notion , we performed flow cytometry analyses using a monoclonal antibody ( DO-1 ) that recognizes an epitope in the p53 N-terminal transactivation domain 1 ( TAD1 ) that overlaps with the MDM2-binding surface competed by Nutlin ( Picksley et al . , 1994 ) . In fact , the DO-1 antibody competes the p53-MDM2 interaction in vitro in analogous fashion to Nutlin ( Cohen et al . , 1998 ) . Under the denaturing conditions of a Western Blot assay , where p53-MDM2 complexes are fully disrupted , this antibody shows no significant increase in total p53 levels at the 1 hr time point of Nutlin treatment ( Figure 1C ) . However , we posited that DO-1 reactivity should increase significantly upon Nutlin treatment under the fixed conditions employed in flow cytometry . Expectedly , flow cytometry quantitation shows that , even before Nutlin treatment , p53 +/+ cells have significantly more DO-1 reactivity than p53 −/− cells ( Figure 1F ) . The functional importance of this ‘basal p53 activity’ will be investigated later in this report ( Figure 3 ) . Interestingly , the p53 +/+ cell population shifts to significantly higher DO-1 reactivity at the 1 hr time point , as predicted by epitope unmasking . A further increase is observed at 12 hr of Nutlin treatment , when total p53 levels have risen considerably as measured by Western blots ( Figure 1C , F ) . Finally , since GRO-seq is a population average experiment , we performed immunofluorescence assays to test if our GRO-seq results could be explained by massive p53 accumulation in just a few outlier cells within the population at the 1 hr time point . However , these experiments discarded the notion of outlier cells: although ∼3% cells show high p53 staining at the 1 hr time point , this number is not significantly different than observed in control p53 +/+ cells ( Figure 1—figure supplement 1G , H ) . 10 . 7554/eLife . 02200 . 008Figure 3 . p53 exerts varying activating and repressing effects on its target genes prior to MDM2 inhibition . ( A ) 198 genes activated upon 1 hr Nutlin treatment in HCT116 p53 +/+ cells are ranked from left to right based on their basal transcription in p53 +/+ cells over p53 −/− cells . Green indicates genes whose basal transcription is greater than twofold in p53 +/+ cells , red indicates lesser than twofold . Grey dots display the transcription of the same genes in Nutlin-treated p53 +/+ cells . ( B ) Heatmap displaying relative transcriptional activity of direct p53 target genes identified by GRO-seq relative to control p53 −/− cells . Genes are sorted based on their transcription in control p53 +/+ cells . ( C ) Genome browser views of representative genes whose basal transcription is higher ( GDF15 ) or lower ( PTP4A1 ) in the presence of MDM2-bound p53 . See Figure 3—figure supplement 1A for matching RNAPII ChIP data . ( D ) Q-RT-PCR measurements of genes whose basal transcription was found to be 2x higher ( green ) or lower ( red ) in the presence of MDM2-bound p53 . ( E ) ChIP assays show binding of p53 and MDM2 to the p53REs in the CDKN1A and PTP4A1 gene loci ( −2283 bp and +1789 relative to TSS , respectively ) , prior to inhibition of the p53-MDM2 interaction by Nutlin . Nutlin treatment leads to increased p53 signals with the DO-1 antibody recognizing the p53 TAD1 , concurrently with a decrease in MDM2 signals . MDM2 ChIP was performed in SJSA cells carrying a MDM2 gene amplification F . Oncomine gene expression analysis of 598 cancer cell lines of varied p53 status shows that CDKN1A , DDB2 and GDF15 are more highly expressed in wild type p53 cell lines , whereas GJB5 is more highly expressed in mutant p53 cell lines . The ranking position of these genes is also indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 00810 . 7554/eLife . 02200 . 009Figure 3—figure supplement 1 . Differential effects of p53 on the basal transcription of its target genes . ( A ) ChIP analysis of the ‘basally activated’ GDF15 and ‘basally repressed’ PTP4A1 gene loci using antibodies against the Serine 5- and Serine 2-phosphorylated forms of the C-terminal domain repeats of RBP1 , the largest subunit of RNAPII . ( B ) ChIP assays for p53 and MDM2 at the p53RE in the HES2 locus ( +5160 relative to TSS ) performed as in Figure 3E . ( C ) Western blots for MDM2 using the protein extracts from SJSA cells employed for MDM2 ChIP assays . Long exposures show detectable amounts of MDM2 in both the input and MDM2-ChIP samples . A 12 hr or Nutlin treatment was included as positive control . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 00910 . 7554/eLife . 02200 . 010Figure 3—figure supplement 2 . p53 mutational status affects the basal expression of its target genes . ( A ) Gene set enrichment analysis ( GSEA ) of direct p53 target genes identified by GRO-seq ( left ) or genes upregulated upon Nutlin treatment in the microarray experiment only ( right ) relative to a ranked list of 12 , 624 genes analyzed via Oncomine for their relative expression in WT p53 vs mutant p53 cell lines . ( B ) Scatter plot comparing relative transcription as measured by GRO-seq in HCT116 p53 +/+ over p53 −/− cells vs relative mRNA expression in p53 WT vs p53 mutant cell lines . Red dots are genes whose transcription was lesser than twofold in p53 +/+ cells , green dots are genes whose transcription was greater than twofold in p53 +/+ cells , all other direct p53 targets in HCT116 cells are highlighted in black . ( C ) Oncomine gene expression analysis of 247 breast carcinomas of varied p53 status shows that CDKN1A , DDB2 and GDF15 are more highly expressed in wild type p53 tumors , whereas GJB5 is more highly expressed in mutant p53 tumors . ( D ) Model depicting the differential effects of MDM2-p53 complexes on the basal expression of p53 target genes . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 010 Altogether , these results indicate that the low levels of p53 present in proliferating cancer cells suffice to directly activate a multifunctional transcriptional program , including many canonical apoptotic genes , upon unmasking of the p53 transactivation domain by Nutlin . However , as discussed later in the paper ( Figure 4 ) , this conclusion does not necessarily conflict with previous reports showing differential timing of mRNA accumulation between arrest and apoptotic genes as seen by steady state RNA measurements . 10 . 7554/eLife . 02200 . 011Figure 4 . GRO-seq analysis of key survival and death genes within the p53 network . ( A ) The 10 most transcribed pro-survival and pro-apoptotic genes identified by GRO-seq ranked by decreasing transcriptional output in Nutlin-treated p53 +/+ cells . The surface of the bubbles represents the GRO-seq signal output relative to the CDKN1A locus . ( B ) Transcriptional output of same genes shown in A in p53 −/− cells . ( C ) Fold change analysis showing the overall effect of p53 on the transcription of its survival and apoptotic targets . ( D ) Survival genes within the p53 network tend to carry more proximally bound , transcriptionally engaged RNAPII over their promoter regions than apoptotic genes . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 01110 . 7554/eLife . 02200 . 012Figure 4—figure supplement 1 . p53 target genes display a wide range of RNAPII pausing and promoter divergence . ( A ) RNAPII pausing is not a pre-requisite for rapid activation among p53 target genes . A ranking of pausing indices show no correlation with fold induction among p53 targets . POLH and PHLDA3 are representative examples of genes with high and low pausing indices . ( B ) p53 target genes show a great variation in the amount of promoter divergence , but a ranking of divergence indices shows no correlation with fold induction . DRAM1 and DDB2 are examples of p53 targets with high and low divergence , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 01210 . 7554/eLife . 02200 . 013Figure 4—figure supplement 2 . Examples of gene-specific features affecting key pro-apoptotic and survival p53 target genes . See main text for details . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 013 The global p53 transcriptional response has been previously investigated using measurements of RNA steady state levels ( i . e . , microarray profiling ) and p53 chromatin binding ( e . g . , ChIP-seq ) . Meta-analysis of four recent reports using this approach indicates that >1200 genes are putative direct targets of p53 transactivation , yet only 26 are common between the four studies ( Figure 2—figure supplement 1A , B; Supplementary file 2 ) ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) . Furthermore , these studies suggest 80 genes that could be directly repressed by p53 , yet none are shared between any two studies ( Figure 2—figure supplement 1A , B; Supplementary file 2 ) . In order to investigate how GRO-seq analysis of the immediate p53 transcriptional response would compare to a global analysis of RNA steady state levels , we performed a microarray analysis of HCT116 p53 +/+ cells after 12 hr of Nutlin treatment , a time point similar to that used in the previous studies . Several important observations arise from this comparison . First , there is a clear lack of overlap between the two analyses ( Figure 2A ) . Among the induced genes identified by the two experimental platforms , only 102 are common . 291 genes are called as induced by the microarray experiment only . This group would include genes whose transcription may be stimulated at later time points via indirect mechanisms , but may also include true direct p53 target genes that require higher levels of p53 to be activated . For example , we noted that the canonical p53 target gene GADD45A fell in this group , as its transcription was mildly induced at 1 hr and thus fell below our statistical cut-off . Interestingly , 72 genes were identified as induced by GRO-seq only , despite the fact that the microarrays utilized harbored multiple probes against these mRNAs . The possible explanations for this finding are discussed below . Second , microarrays detect 324 genes repressed upon 12 hr of Nutlin treatment , none of which were called as repressed by GRO-seq . The mechanism of p53-mediated gene repression remains debated in the field . Multiple independent ChIP-seq studies concur in that p53 binds weakly and very distally to those gene loci whose mRNAs are downregulated at the steady state level , and that the p53REs found at these sites match poorly to the consensus DNA sequence ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) . Using seven different available global ChIP datasets derived from HCT116 and two other cell lines , we created a collection of high confidence p53 binding events to analyze p53 binding in the vicinity of the various gene groups ( ‘Materials and methods’ ) . Nearly 40% of the 198 genes induced by GRO-seq harbor a p53 binding event within 25 kb , significantly more than expected from random occurrence ( p=1e−48 , Hypergeometric test ) ( Figure 2B ) . Among the genes induced by microarray only , nearly 15% harbored p53 binding within 25 kb , still significantly more than expected by chance ( p=8e−11 ) , which suggests that some of these genes may be true direct targets activated at later time points . Most importantly , genes considered as repressed by the microarray profiling show little p53 binding within 25 kb , barely above what is expected by chance ( p=3e−2 ) , suggesting that the repression observed is largely indirect . Indirect gene repression downstream of p53 activation could be mediated at the post-transcriptional level by p53-inducible miRNAs , and/or at the transcriptional level by the action of direct p53 targets known to repress transcription . Of note , GRO-seq identified 5 miRNAs directly transactivated by p53 ( miR-1204 , miR-3189 , miR-34a , miR4679-1 and miR-4692 , see Supplementary file 1 ) . Most prominent among these is miR-34a , a well characterized p53-inducible miRNA known to mediate indirect repression by p53 at late time points . In fact , we found that nearly 72% of genes repressed in our microarray by Nutlin were previously shown by others ( Lal et al . , 2011 ) to be downregulated upon overexpression of miR-34a in HCT116 cells ( p<2 . 2e−16 , Hypergeometric test , Figure 2—figure supplement 1C ) . A recent report demonstrated that p21 and E2F4 , a transcriptional repressor of S-phase genes acting coordinately with co-repressors of the RB family , are required for the downregulation of many genes previously characterized as ‘direct’ targets of p53 repression ( Benson et al . , 2013 ) . In agreement with these published findings , Ingenuity Pathway Analysis ( IPA ) of the genes repressed in our microarray experiment revealed that the top three regulators affecting these genes are indeed E2F4 ( p=1 . 02e−81 , Fisher's Exact Test ) , CDKN1A ( p=8 . 21e−62 ) and RB ( p=8 . 12e−60 ) ( Figure 2—figure supplement 1D ) . Altogether , these data indicate that most gene repression observed in our system is likely to be indirect , either via miRNAs , such as miR-34a , and/or the p21>RB>E2F4 axis ( Figure 2—figure supplement 1E ) . Of note , GRO-seq also identified the transcriptional repressor PRDM1 ( BLIMP1 ) as a direct target of p53 ( Yan et al . , 2007 ) , revealing yet another possible mechanism for indirect gene repression downstream of p53 . However , IPA did not identify PRDM1 as a top regulator of genes repressed in the microarray study ( not shown ) . Given that 72 genes were identified as activated only by GRO-seq , we further investigated the possible reasons for this result . Analysis of absolute signals generated by the GRO-seq ( fpkm ) vs microarray ( mean fluorescence intensity , MFI ) experiments generated several important insights ( Figure 2C ) . First of all , robust p53 target genes such as CDKN1A and GDF15 show strong increases in both platforms . When these results are expressed as fold induction , a strong correlation between transcriptional output and steady state levels is evident for these genes ( Figure 2D ) . However , transcriptional output often does not correlate with steady state mRNA levels . For example , while the BTG2 locus has a greater transcriptional output upon p53 activation than the MDM2 , PTP4A1 , AMZ2 and CCNG1 loci , its steady state mRNA levels are much lower , likely due to post-transcriptional repression of BTG2 ( Figure 2C ) . This would explain why a small group of ‘GRO-seq only' genes , including known p53 targets such as PTP4A1 and CCNG1 , are not called by the microarray experiments: they display very high basal steady state levels , which do not increase significantly despite clear transcriptional induction ( Figure 2C ) . However , most ‘GRO-seq only’ genes belong to a different category , as marked by their very low levels of transcription and steady state mRNA expression . Genes like CDC42BPG , ADAMTS7 and LRP1 are clearly induced at the transcriptional level but show no apparent increase in the microarray signals ( Figure 2C , D ) . Remarkably , the induction of these mRNAs upon p53 activation is evident by Q-RT-PCR at the time point of the microarray experiment ( Figure 2E ) . In fact , when microarray-derived signals are displayed for the various gene sets , the ‘GRO-seq only’ group shows very significant lower expression as compared to all other groups ( Figure 2F ) . Altogether , we conclude that microarray profiling is not sensitive enough to detect these low abundance transcripts , which could explain why several published ChIP-seq/microarray studies failed to identify these genes as direct p53 targets . Alternatively , it is possible that p53 binds to these genes from very distal sites outside of the arbitrary window defined during bioinformatics analysis of ChIP-seq data . To discern among these possibilities , we analyzed ChIP-seq data in search of high confidence p53 binding events in the vicinity of several novel genes identified by GRO-seq , and evaluated p53 binding using standard ChIP assays . Indeed , we detected clear p53 binding to all p53REs tested at these novel p53 targets ( Figure 2—figure supplement 2 ) . Of note , p53 binds to proximal regions at the CDC42BPG and LRP1 loci ( +1373 bp and −694 bp relative to transcription start site [TSS] , respectively ) , indicating that these genes could have been missed in previous studies due to the low abundance of their transcripts . In contrast , p53 binds to very distal sites ( i . e . , >30 kb from the TSS ) at the ADAMTS7 , TOB1 , ASS1 and CEP85L loci ( Figure 2—figure supplement 2 ) , suggesting that these genes would have been missed as direct targets when setting an arbitrary <30 kb window during ChIP-seq analysis . In summary , GRO-seq enables the identification of novel direct p53 target genes due both to its increased sensitivity and the fact that it does not require proximal p53 binding to ascertain direct regulation . Others and we have observed that in proliferating cells with minimal p53 activity , p53 increases the basal expression of some of its target genes ( Tang et al . , 1998; Espinosa et al . , 2003 ) . This was first recorded for CDKN1A ( Tang et al . , 1998 ) , and it is confirmed by our GRO-seq analysis ( Figure 1A , compare 2 . 6 to 5 . 7 fpkm in the Control tracks ) . To investigate whether this is a general phenomenon we analyzed the basal transcription of all p53-activated genes in control p53 +/+ vs p53 −/− cells ( Figure 3A , B ) . Interestingly , p53 status exerts differential effects among its target genes prior to MDM2 inhibition with Nutlin . While many genes show the same behavior as CDKN1A ( e . g . , GDF15 , DDB2 , labeled green throughout Figure 3 ) , another group shows decreased transcription in the presence of MDM2-bound p53 ( e . g . , PTP4A1 , HES2 , GJB5 , labeled red throughout Figure 3 ) . Genome browser views illustrating this phenomena are provided for GDF15 and PTP4A1 in Figure 3C . The differential behavior of RNAPII at these gene loci is also observed in ChIP assays using antibodies against the Serine 5- and Serine 2-phosphorylated forms of the RBP1 C-terminal domain repeats , which mark initiating and elongating RNAPII complexes , respectively ( S5P- and S2P-RNAPII , Figure 3—figure supplement 1A ) . Whereas the ‘basally activated’ GDF15 locus displays higher GRO-seq and RNAPII ChIP signals in untreated p53 +/+ cells , the ‘basally repressed’ PTP4A1 locus shows lower signals in the presence of MDM2-p53 complexes . Such differential effects among p53 target genes have a clear impact on their absolute level of transcription upon MDM2 inhibition: whereas those whose basal activity was increased ranked among the most differentially transcribed between Nutlin-treated p53 +/+ and p53 −/− cells ( top of the heatmap in Figure 3B ) ; those basally repressed are virtually no different in expression between Nutlin-treated p53 +/+ and p53 −/− cells ( bottom of the heatmap in Figure 3B ) . Importantly , Q-RT-PCR shows that the differential effects of p53 on the basal transcription of its targets are generally translated into differences in mRNA steady level ( Figure 3D ) . Overall , these results indicate that p53 acts as a repressor at a subset of its targets in a manner that is relieved by Nutlin , suggesting that MDM2 recruitment by basal levels of p53 may repress transcription at specific loci . To test this hypothesis , we performed ChIP experiments for p53 and MDM2 under conditions matching the GRO-seq experiment . For the p53 ChIP , we employed the monoclonal antibody DO-1 that recognizes the p53 TAD1 and whose reactivity should increase upon displacement of MDM2 by Nutlin . Importantly , ChIP assays show a significant amount of chromatin-bound p53 above background levels at the p53REs in the CDKN1A locus ( basally activated ) , and the PTP4A1 and HES2 loci ( basally repressed ) , even before Nutlin treatment ( Figure 3E , Figure 3—figure supplement 1B ) . Of note , the DO-1 ChIP signals increase upon Nutlin treatment , as expected from epitope unmasking . ChIP assays also detect MDM2 chromatin binding above background levels at these three p53REs , with signals decreasing upon Nutlin treatment , as expected by the competitive action of this molecule ( Figure 3E , Figure 3—figure supplement 1B ) . Of note , although Nutlin disrupts the interaction between the p53 N-terminus and the hydrophobic pocket in the N-terminal domain of MDM2 , a second molecular interaction occurs between the p53 C-terminus and the MDM2 N-terminus that is not competed by Nutlin in vitro ( Poyurovsky et al . , 2010 ) , which may explain why the MDM2 signal is not completely abrogated upon a short time point of Nutlin treatment . Western blots demonstrating specific MDM2 immunoprecipitation under the ChIP conditions utilized are shown in Figure 3—figure supplement 1C . Next , we investigated whether the differential effects of basal p53 levels on expression of its direct targets could be revealed in an analysis of hundreds of cell lines expressing wild type vs mutant p53 . More specifically , we hypothesized that genes that are basally transactivated by p53 would be more highly expressed in p53 WT cells than transrepressed genes . Oncomine analysis of 598 cancer cell lines not treated with p53-activating agents revealed that many genes that are ‘basally activated’ in HCT116 cells such as CDKN1A , DDB2 and GDF15 indeed show significantly higher mRNA expression in WT p53 cell lines ( Figure 3F ) . In contrast , the ‘basally repressed’ gene GJB5 shows significantly higher expression in mutant p53 cell lines . When all 12 , 624 genes in the Oncomine analysis are ranked according to their relative expression in WT p53 over mutant p53 cell lines , many genes whose basal transcription is upregulated by p53 in HCT116 cells appear at the top of this ranking ( e . g . , CDKN1A , DDB2 and GDF15 , ranked 2 , 4 and 62 , respectively ) ( Figure 3—figure supplement 2A ) . However , some direct targets ‘basally repressed’ by p53 , such as GJB5 , show an inverse correlation with WT p53 status . Collectivelly , the direct p53 targets identified by GRO-seq are enriched toward the top of the ranking , which is revealed in a Gene set enrichment analysis ( GSEA ) ( Figure 3—figure supplement 2A ) . In contrast , genes induced only in the microarray platform ( i . e . , mostly indirect targets ) do not show strong enrichment in a GSEA analysis . When the relative basal transcription between HCT116 p53 +/+ and p53 −/− cells is plotted against the relative mRNA expression in p53 WT vs p53 mutant cell lines , it is apparent that many ‘basally activated’ genes are more highly expressed in p53 WT cells ( green dots in the upper right quadrant in Figure 3—figure supplement 2B ) . Finally , the differential pattern of basal expression among p53 targets is also observed in an analysis of 256 breast tumors for which p53 status was determined , where CDKN1A , DDB2 and GDF15 ( but not GJB5 ) show higher expression in the p53 WT tumors ( Figure 3—figure supplement 2C ) . Altogether , these results reveal a qualitative difference among p53 target genes in terms of their sensitivity to basal p53-MDM2 complexes as depicted in Figure 3—figure supplement 2D . Although indirect effects can not be fully ruled out , the fact that we can detect p53 and MDM2 binding to the p53REs near these gene loci suggest direct action . Of note , early in vitro transcription studies demonstrated that MDM2 represses transcription when tethered to DNA independently of p53 , which may provide the molecular mechanism behind our observations ( Thut et al . , 1997 ) ( ‘Discussion’ ) . Many research efforts have been devoted to the characterization of molecular mechanisms conferring gene-specific regulation within the p53 network , as these mechanisms could be exploited to manipulate the cellular response to p53 activation . Most research has focused on factors that differentially modulate p53 binding or transactivation of survival vs apoptotic genes ( Vousden and Prives , 2009 ) . GRO-seq identified a plethora of gene-specific regulatory features affecting p53 targets , but our analysis failed to reveal a universal discriminator between survival and death genes within the network . When direct p53 target genes with well-established pro-survival ( i . e . , cell cycle arrest , survival , DNA repair and negative regulation of p53 ) and pro-death ( i . e . , extrinsic and intrinsic apoptotic signaling ) functions are ranked based on their transcriptional output in Nutlin-treated p53 +/+ cells , it is evident that key pro-survival genes such as CDKN1A , GDF15 , BTG2 and MDM2 are transcribed at much higher rates than any apoptotic gene ( Figure 4A ) . For example , ∼20-fold more RNA is produced from the CDKN1A locus than from the BBC3 locus encoding the BH3-only protein PUMA . The most potently transcribed apoptotic gene is TP53I3 ( PIG3 ) , yet its transcriptional output is still 3 . 4-fold lower than CDKN1A . Based on measurements of steady state RNA levels , it was observed that apoptotic genes such as TP53I3 and FAS are induced with a slower kinetics than CDKN1A ( Szak et al . , 2001; Espinosa et al . , 2003 ) . However , GRO-seq suggests that this is not due to a slower kinetics of RNAPII transactivation , but rather to a lower transcriptional output from the apoptotic loci . Although both CDKN1A and TP53I3 display similar transcriptional induction within 1 hr of Nutlin treatment ( 7 . 4 and 6 . 07 fold induction , Supplementary file 1 ) , the TP53I3 mRNA takes longer to display increased accumulation over basal levels ( see Q-RT-PCR in Figure 1B ) ( Szak et al . , 2001 ) . Thus , differences in mRNA induction kinetics between gene classes could be explained from differential amounts of RNA synthesis . To investigate how much of the differential transcriptional output is due to p53 action vs p53-autonomous mechanisms , we analyzed the activity of these gene loci in p53 −/− cells ( Figure 4B ) . Several important observations arise from this analysis . First , the basal activity of the CDKN1A locus is higher than most pro-apoptotic genes even in p53 −/− cells , which agrees with previous studies revealing the action of strong core promoter elements at this locus ( Espinosa and Emerson , 2001; Morachis et al . , 2010 ) . However , the pro-apoptotic genes TRAF4 and AEN are exceptions to this trend , as they display higher basal activity in p53 −/− than CDKN1A and most pro-survival genes . Second , p53 action reinforces the distinction between the two classes by leading to ‘super-activation’ ( i . e . , greater than fivefold ) of the survival genes CDKN1A , GDF15 , BTG2 and MDM2 ( Figure 4C ) . Although select apoptotic genes such as TP53I3 , PHLDA3 and FAS also undergo super-activation , this does not suffice to override their overall lower transcriptional output . In sum , as a group , survival genes tend to be transcribed at a higher extent than apoptotic genes , which is due to a combination of p53-dependent and -independent mechanisms . Next , we investigated whether RNAPII pausing exerted gene-specific effects within the p53 transcriptional program . Using ChIP assays , we previously reported that the CDKN1A promoter carries significantly higher levels of promoter-bound RNAPII than the apoptotic genes TP53I3 , TNFRSF10B , BBC3 and FAS prior to p53 activation ( Espinosa et al . , 2003 ) . GRO-seq confirms that the promoters of survival genes , including CDKN1A , indeed carry more transcriptionally engaged RNAPII than the promoters of apoptotic genes ( Figure 4D ) ; however , there was no obvious correlation between the amount of active RNAPII over the promoter and the degree of transcriptional output or induction . RNAPII pausing was proposed to modulate the timing of signal-induced gene expression , such as during the cellular response to LPS , where primary response genes were found to carry more paused RNAPII than secondary response genes ( Hargreaves et al . , 2009 ) . However , others found that RNAPII pausing is not necessary for rapid gene induction ( Hah et al . , 2011 ) . To investigate this issue more thoroughly within the p53 network , we performed an analysis of pausing indices as previously described ( Core et al . , 2008 ) . This exercise revealed that although p53 target genes vary widely in their pausing indexes , there is no obvious correlation between pausing and the degree of transcriptional induction at the 1 hr time point of Nutlin treatment ( Figure 4—figure supplement 1A ) . For example , the POLH gene , which shows a very high pausing index , displays a lower fold induction and overall lower transcriptional output than the PHLDA3 gene , which shows little signs of RNAPII pausing ( Figure 4—figure supplement 1A ) . Thus , RNAPII pausing is not a pre-requisite for rapid induction within the p53 transcriptional program . The first GRO-seq analysis in human cells revealed widespread divergent transcription at most active promoters ( Core et al . , 2008 ) . We observed that the degree of divergence is highly variable across promoters of p53 target genes . For example , the DRAM1 promoter displays more divergent transcription than sense transcription ( Figure 4—figure supplement 1B ) , yet divergent transcription is minor at gene loci such as CDKN1A or DDB2 ( Figure 1A , Figure 4—figure supplement 1B , respectively ) . Using a ‘divergence index’ , which evaluates the ratio of productive ( sense ) vs divergent transcription , we ranked p53 target genes from high to low promoter divergence , which revealed that there is no correlation between divergence and fold induction ( Figure 4—figure supplement 1B ) . Thus , divergence of RNAPII in the unproductive direction does not obviously affect p53 transactivation of its target genes . Overall , we did not find evidence of an overarching regulatory feature discriminating between survival vs apoptotic genes . Instead , GRO-seq analysis revealed many instances of potential for gene-specific regulation at key loci . For example , the APAF1 gene , encoding a core component of the apoptosome , is activated from a non-overlapping bidirectional promoter with the IKBIP gene , where the two start sites are less than 280 bp apart ( Figure 4—figure supplement 2A ) . The FAS gene , which encodes a death receptor involved in p53-dependent apoptosis , is transcribed from a bidirectional overlapping promoter with the ACTA2 gene , where the start site of one gene resides within the first exon of the other gene , with the two transcription units overlapping for ∼850 bp ( Figure 4—figure supplement 2B ) . Several p53 target genes are organized in clusters , where a group of genes within a chromatin domain are concurrently upregulated . One example is the TNFRS10 cluster encoding the pro-apoptotic TRAIL receptors DR4 ( TNFRSF10A ) and DR5/Killer ( TNFRSF10B ) , as well as the counteracting decoy receptors DCR1 ( TNFRSF10C ) and DCR2 ( TNFRSF10D ) ( Figure 4—figure supplement 2C ) . Another interesting feature revealed by GRO-seq is the presence of significant intragenic antisense transcription at many p53 target genes . In many cases such antisense transcription can be attributed to eRNAs produced from intronic p53 enhancers ( see later , Figure 5 ) ; however , antisense transcription often originates from an overlapping transcriptional unit that is neither derived from a p53 binding event nor induced by p53 . One example of this scenario is the pro-survival p53 target gene GPR87 , which is embedded within an intron of MED12L , which is not a p53 target ( Figure 4—figure supplement 2D ) . Thus , our GRO-seq dataset paves the way for many future mechanistic studies aimed at deciphering the impact of these gene-specific events in the regulation of p53 target genes and the cellular outcome upon p53 activation . 10 . 7554/eLife . 02200 . 014Figure 5 . Direct p53 target genes harbor pre-activated enhancers . ( A ) GRO-seq results for the DDIT4 locus , representative of p53 target genes that display bidirectional eRNA transcription ( arrow ) arising near sites of p53 binding ( indicated by a purple dot ) . ( B ) Analysis of nearest p53 binding events relative to the transcription start site ( TSS ) of direct p53 target genes detected by GRO-seq ( grey bars ) vs all RefSeq genes ( pink ) . ( C ) GRO-seq results for the SYTL1 locus , representative of p53 target genes that display intronic antisense eRNA transcription arising near sites of p53 binding . ( D ) Analysis of eRNA transcription at distal p53 binding sites ( >25 kb of any gene ) , proximal sites associate with a gene not activated by p53 ( <25 kb of non-target ) , and those proximal to a p53 target gene identified by GRO-seq . ( E ) p53 binding sites near target genes have higher transcription levels than sites near other genes even in p53 null cells , indicating the likely action of pioneer factors . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 01410 . 7554/eLife . 02200 . 015Figure 5—figure supplement 1 . p53 stimulates eRNA production at extragenic and intragenic locations . ( A and B ) GRO-seq results for the region upstream of CDKN1A showing eRNAs derived from the p53REs at −1 . 3 and −2 . 4 kb ( A ) and from the region encoding lincRNA-p21 ( B ) . ( C ) GRO-seq results for the DRAM1 locus , a rare example of a p53 target gene whose enhancer is not obviously transcribed in p53 null cells . ( D ) GRO-seq results for the BTG2 locus , representative of p53 target genes that display intronic antisense eRNA transcription arising near sites of p53 binding . ( E ) Percentage of p53 binding sites that are transcribed as defined by Core et al . ( 2008 ) . The distal sites are transcribed less often than the sites proximal to target genes . A higher percentage of sites near p53 target genes are transcribed than sites that are distal or near non-targets . ( F ) The fold change in transcription after addition of Nutlin is similar across p53 binding sites in various locations . Expectedly , in p53 −/− cells , there is no change in transcription after Nutlin treatment . In p53 +/+ cells eRNAs are increased in transcription is approximately fourfold and this is true for distal sites , sites proximal to non-targets and sites proximal to targets . DOI: http://dx . doi . org/10 . 7554/eLife . 02200 . 015 Recently , a novel class of non-coding RNAs has been identified whose transcription originates from active enhancers . These enhancer-derived RNAs ( eRNAs ) have been shown to contribute to gene activation in a variety of systems ( Jiao and Slack , 2013 ) . A recent report characterized eRNAs derived from three distal p53 enhancers and showed that they are required for efficient p53 transactivation of neighboring genes ( Melo et al . , 2013 ) . In order to investigate the prevalence of transcriptionally active enhancers within the p53 transcriptional program , we examined our GRO-seq data with respect to hundreds of p53 binding events as defined by ChIP-seq . Of note , we have not employed here data on histone marks or p300 occupancy to define how many of these p53 binding events reside within regions harboring the accepted hallmarks of enhancers , and thus some of these p53 binding sites should be considered as putative enhancers . GRO-seq readily detects RNAs originating from most p53 binding events , which we refer hereto as eRNAs . A typical example is shown for the DDIT4 locus in Figure 5A , where a distal p53 binding site located downstream of the gene is clearly transcribed in both the sense and antisense directions , with increased signals upon p53 activation . Interestingly , this p53RE is also transcribed in p53 −/− cells ( Figure 5A , top track , arrow ) . Analysis of the CDKN1A locus shows transcription from the well characterized p53REs at −1 . 3 and −2 . 4 kb ( Figure 5—figure supplement 1A ) . Analysis of the distal upstream region in this locus encoding the long intragenic ncRNA known as lincRNA-p21 shows transcription in both strands originating from a p53 binding site , with the antisense strand corresponding to the reported lncRNA-p21 sequence ( Figure 5—figure supplement 1B ) . This suggests that lncRNA-p21 could be classified as an eRNA , as it originates from the vicinity of a p53RE associated to a canonical p53 target gene . Once again , transcripts derived from the lincRNA-p21 region can also be detected in p53 −/− cells ( Figure 5—figure supplement 1B , top track ) . A rare example of a p53RE near a target gene not transcribed in p53 −/− cells is that of the DRAM1 locus , which displays transcription of bidirectional eRNAs in p53 +/+ cells before p53 activation , with signals increasing upon Nutlin treatment ( Figure 5—figure supplement 1C ) . Analysis of the spatial distribution of p53 binding events relative to transcription start sites ( TSSs ) shows that direct p53 target genes display an enrichment in p53 binding close to promoters , but also within genes ( Figure 5B ) . In fact , it has been estimated that ∼40% of p53 enhancers are intragenic ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) . Although eRNAs derived from the sense strands can not be distinguished from the protein coding pre-mRNAs at these locations , the eRNAs arising from the antisense strands are clearly distinguishable , as illustrated for the SYTL and BTG2 loci ( Figure 5C , Figure 5—figure supplement 1D , respectively ) . Thus , p53 activation leads to antisense transcription within a large fraction of its direct target genes concurrently with activation of the protein-coding RNAs , a phenomenon with potential regulatory consequences . Next , we analyzed the production of eRNAs at three different sets of p53 binding events: ( a ) distal binding sites ( >25 kb of any gene ) , ( b ) proximal binding sites associated with a gene not activated by p53 ( <25 kb of non GRO-seq target gene ) , and ( c ) proximal binding sites associated with a p53 target gene ( <25 kb of a p53 target defined by GRO-seq ) . First , we asked what fraction of sites in each category is transcriptionally active in the four experimental conditions . This revealed that ∼40% of distal sites and those not associated with p53 targets are transcriptionally active in p53 −/− cells ( Figure 5—figure supplement 1E ) . The number increases to ∼60% in p53 +/+ cells , likely revealing the action of basal p53 levels , as observed for the DRAM1 locus . Expectedly , Nutlin leads to further increase in the fraction of active enhancers only in p53 +/+ cells . When the analysis is restricted to those p53REs within 25 kb of a direct p53 target , the fraction that are transcribed in p53 −/− cells increases to ∼70% , and nearly all of them are transcriptionally active in Nutlin-treated p53 +/+ cells ( Figure 5—figure supplement 1E ) . Thus , eRNAs are a hallmark of active p53 binding sites . We next investigated whether there are differences across p53 binding events found in the various locations in terms of eRNA activation and overall expression . A fold induction analysis shows that p53 stimulates eRNA transcription to a similar degree regardless of location ( Figure 5—figure supplement 1F ) . This result counters the notion that p53 acts as transcriptional repressor from distal sites by a mechanism described as ‘enhancer interference’ ( Li et al . , 2012 ) . Interestingly , the absolute amount of eRNA produced varies greatly with location ( Figure 5D ) . First , eRNA transcription is much weaker from distal sites relative to proximal sites ( p=1 . 28e−12 for Nutlin-treated p53 +/+ cells ) . This could be explained by more efficient communication between enhancers and promoters when in closer proximity . Second , eRNA transcription is significantly higher from proximal sites associated with direct p53 target genes . Strikingly , this difference is already evident in p53 −/− cells ( p=1e−10 ) . Thus , a distinctive feature between direct p53 target genes and those genes proximal to bound p53REs but not activated is the strength of eRNA production in the absence of p53 . In other words , p53 seems to activate gene expression at genomic locations carrying ‘primed’ , pre-activated enhancers , likely revealing the action of pioneer factors ( Figure 5E ) .
The importance of p53 in cancer biology is undisputed , yet the mechanisms by which this transcription factor suppresses tumor growth remain to be fully elucidated . In particular , it is unclear which p53 target genes contribute to tumor suppression in various contexts . A thorough analysis of the literature up to 2008 revealed ∼120 direct p53 target genes ( Riley et al . , 2008 ) . Since then , genomics experiments using microarrays and ChIP-seq suggest thousands of p53 targets , but very few genes were commonly identified by multiple studies ( Figure 2—figure supplement 1A , B ) ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) . The lack of overlap between these reports could be due to the fact that they employed different cell types and somewhat different experimental platforms . However , measurements of RNA steady state levels may produce a misleading view of direct p53 action , as they confound direct vs indirect effects . Thus , it is possible that cell type-specific secondary effects and post-transcriptional regulatory mechanisms strongly qualify the direct transcriptional response in different cell types . Ascertaining direct p53 action by the mere presence of a p53 binding event within an arbitrary distance to a putative target gene is an imprecise exercise , as p53 may act directly from very distal sites outside of this arbitrary cutoff ( leading to false negatives ) and because many proximal p53 binding events may be unproductive ( leading to false positives ) . Because of these caveats , we investigated direct transcriptional regulation by p53 using GRO-seq . A surprising result from our GRO-seq analysis is that a short time point of MDM2 inhibition suffices for p53 to activate hundreds of genomic loci , even prior to any detectable increase in total p53 levels . Because MDM2 functions as an E3 ligase targeting p53 for degradation ( Kubbutat et al . , 1997 ) , there was no guarantee that the low basal levels of p53 present in a proliferating cell culture would suffice to induce transcription of its target genes . Importantly , ChIP assays demonstrate that p53 and MDM2 occupy p53REs in proliferating cells and that MDM2 binding is decreased upon Nutlin treatment ( Figure 3E ) . These observations emphasize the role of MDM2 as a transcriptional repressor by masking of the p53 transactivation domains ( Oliner et al . , 1993 ) , but do not negate the importance of p53 degradation as a repressive mechanism , as it is possible that increased p53 levels are required for activation of target genes at later time points . Our results contrast the notion that apoptotic genes require higher levels of p53 for transactivation or that they are transcriptionally induced at later time points , highlighting instead the ‘primed’ nature of a multifunctional p53 transcriptional response . Furthermore , this confirms that the failure of many cell types to undergo apoptosis upon Nutlin treatment is not due to a defect in transactivation of key apoptotic genes ( Henry et al . , 2012; Sullivan et al . , 2012 ) . Although p53 action leads to massive gene repression at a global scale over time , it is unclear how much of these effects are direct vs indirect . Previous genomics experiments identified dozens of genes that are both bound by p53 within a certain arbitrary distance and whose steady state RNA levels decrease at late time points ( Nikulenkov et al . , 2012; Menendez et al . , 2013; Schlereth et al . , 2013; Wang et al . , 2013 ) . However , meta-analysis of these reports failed to identify a single gene commonly repressed in more than one study ( Figure 2—figure supplement 1A , B ) . Recent work showed that p21 is both necessary and sufficient to downregulate many genes commonly described as direct targets of p53 repression , mostly acting via E2F4 ( Benson et al . , 2013 ) . Other cell cycle inhibitory pathways may also converge on E2F4 repressive complexes , such as the p53-inducible miRNA miR-34a , which targets the mRNAs encoding G1-S cyclins ( Lal et al . , 2011 ) . Our data supports the notion that most repression downstream of p53 activation is indirect . First , MDM2 inhibition by 1 hr Nutlin treatment identified only four repressed genes , none of which showed repression at the steady state levels . In contrast , a microarray experiment at 12 hr showed hundreds of downregulated genes . Analysis of this gene set strongly supports the notion that E2F4 , p21 , RB and miR-34a largely mediate their repression ( Figure 2—figure supplement 1C–E ) . Interestingly , GRO-seq analysis of p53 null cells revealed that p53-MDM2 complexes might directly repress transcription at a subset of p53 targets . These genes are downregulated in the presence of MDM2-bound p53 but then activated by Nutlin . These results reveal that basal amounts of p53 found in proliferating cells create an uneven landscape among its transactivation targets , pre-activating some and repressing others . Mechanistically , p53-MDM2 complexes may directly repress transcription due to the inhibitory effects of MDM2 on components of the Pre-Initiation Complex ( PIC ) . Early work by Tjian et al . using in vitro transcription assays demonstrated a dual mechanism of transcription inhibition by MDM2 ( Thut et al . , 1997 ) . Their biochemical assays demonstrated that MDM2 not only masks the p53 transactivation domain , but that it also represses transcription when tethered to DNA by a GAL4 DNA binding domain . They identified an inhibitory domain in MDM2 that binds to the PIC components TBP and TFIIE , and hypothesized that MDM2 could repress transcription by targeting the basal transcription machinery . Our GRO-seq results identify specific p53 targets where this mechanism could be taking place and ChIP experiments using p53 and MDM2 antibodies confirm binding of both proteins to the p53REs at these loci . In agreement with these results , others have previously demonstrated that in proliferating cells MDM2 binds to p53REs in a p53-dependent manner , and that MDM2 recruitment to chromatin can be disrupted by Nutlin or DNA damaging agents ( White et al . , 2006 ) . Also , excess MDM2 was shown to exert uneven repressive effects on the expression of p53 target genes , independently of effects on p53 levels or chromatin binding ( Ohkubo et al . , 2006 ) . Altogether , these data support the arising notion that MDM2 works as a gene-specific co-regulator of p53 target genes by mechanisms other than mere p53 inhibition ( Biderman et al . , 2012 ) . Many research efforts in the p53 field have been devoted to the characterization of regulatory mechanisms discriminating between survival and apoptotic genes . Our GRO-seq analysis reinforced the notion that CDKN1A , a key mediator of arrest , differs from key apoptotic genes in several aspects . CDKN1A has outstanding transcriptional output among p53 target genes , which is partly due to the fact that its promoter drives substantial p53-independent transcription , but also due to potent p53-dependent transactivation . In vitro transcription assays demonstrated the CDKN1A core promoter initiates transcription more rapidly and effectively than the FAS core promoter ( Morachis et al . , 2010 ) , and GRO-seq confirms that FAS has weaker transcriptional output than CDKN1A . However , our GRO-seq analysis failed to identify a uniform criterion discriminating between the most well studied survival and apoptotic genes . To the contrary , GRO-seq revealed that each individual p53 target gene is subject to various layers of gene-specific regulatory mechanisms , including but not restricted to differential levels of p53-independent transcription , p53 transactivation potential , RNAPII pausing , promoter divergence , extragenic vs intragenic eRNAs , overlapping promoters , clustered activation and antisense transcription . A key observation arising from our GRO-seq analysis is that p53 target genes often have ‘primed’ p53REs , as denoted by significantly higher levels of eRNA production in p53 null cells . We interpret this result as the action of unknown pioneering factors acting at these putative enhancers prior to p53 signaling , which would establish enhancer-promoter communication and ready these genes for further transactivation by p53 or other stimulus-induced transcription factors . This notion is supported by a recent analysis of eRNAs at three distal p53 binding sites , which were shown to be involved in long range chromatin loops independently of p53 ( Melo et al . , 2013 ) . This model also agrees with a recent report showing that TNF-responsive enhancers are in physical contact with their target promoters prior to TNF signaling ( Jin et al . , 2013 ) . Thus , it is likely that the p53 transcriptional program is qualified by the action of lineage-specific factors that prepare a subset of p53 enhancers in a cell type-specific manner . Altogether , the results presented here provide a significant advance in our understanding of the p53 transcriptional program and pave the way for functional studies of novel p53 target genes and elucidation of unique regulatory mechanisms within this tumor suppressive gene network .
Global run-on and library preparation for sequencing were basically done as described in Hah et al . ( 2011 ) . GRO-seq and microarray datasets are available at Gene Expression Omnibus , data series GSE53966 . Unless otherwise noted all processing was done with python version 2 . 6 . Graphs were created with either Microsoft Excel or the python package matplotlib version 1 . 0 . 1 . Total RNA was isolated using Trizol ( Life Technologies , Frederick , MD ) following the manufacturer's instructions . cDNA was generated using qScript kit ( Quanta Biosciences , Gaithersburg , MD ) with random priming . cDNA was subjected to standard or quantitative PCR ( Q-PCR ) using SYBR green or SYBR select master mix on a Viia 7 instrument ( Life Technologies ) with the primer pairs listed below . The 18S rRNA was used for normalization . Experiments were done in biological triplicate and error bars represent the SEM . Gene , Forward Primer , Reverse PrimerGene , Forward Primer , Reverse Primer18S rRNA , GCCGCTAGAGGTGAAATTCTTG , CTTTCGCTCTGGTCCGTCTTADAMTS7 , GTCGAGCCTCCCCGCTGTG , CAGCGTCTCGCAGAACCCGAALOX5 , AAACGAGCTGTTCCTGGGCATGT , GGCCTCGAGGTTCTTGCGGAAPOBEC3C , AGTATCCATGTTACCAGGAGGGGCT , TTTAAAATCTTCATAGTCCATGATCTCCACAGCGAASCC3 , GCTTGCAGAGAGTCCACTTTGGGT , ACAGCTCTCTGGCTTTCCCTTGTGASS1 , TGAGGAGCTGGTGAGCATGAACG , ACCCGGTGGCATCAGTTGGCASTN2 , TTTTCTGCCGCAGCGAGGAGG , AGAGTCAAATAATATACGTGATTTTGGTGTCCTTGABLOC1S2 , CCGAGGGCGTACTGGCGAC , GGCATCGTCTCGGGCGGGC19ORF82 ( LOC284385 ) , ATTTAGAGGAGGGACCCGGC , AAGCTTTGGAGGAGCATCCCCDC42BPG , TGTCCTGCCCCCAGGGATCG , GGGCCGTTCTGAGACCTGCATTAGCDKN1A , CTGGAGACTCTCAGGGTCGAAA , GATTAGGGCTTCCTCTTGGAGAACEP85L , AGCTCCTTGGCAACAGCAGCAA , TGATCCAGTCAAAACCTGCATTGGTGTCHAC1 , TGAAGATCATGAGGGCTGCACTTGG , CAGGGCCTTGCTTACCTGCTCCDDB2 , TCTACTCGCTGCCGCACAGG , TCGGGACTGAAACAAGCTGCGTEFNB1 , TGGGCAAGATCCCAATGCTGTG , TGCTTGCCATCAGAGTCACCCAFAM210B , TGGCACTGTTGGCGTGTCAT , CAGGCATGTCCACACCACTTGAFAM212B , AATGGGCGCATGACGATGGAGA , TGCAGTGCACCCATCATGCAGTGDF15 , TAACCAGGCTGCGGGCCAAC , CAGCCGCACTTCTGGCGTGAGJB5 , CTGCTGGGAGCCAGGAGAGC , CGCGTCAGCTGCTACTGAGTGAGPR87 , AACCTATGCTGAACCCACGCCT , GCCGTGCAGCTCGTTATTTGGTGRIN2C , ACCGTGACATGCACACCCACAT , TGAAGGCATCCAGCTTCCCCATHES2 , CTGGGCCGGGAGAACTCCAA , GCAGGAAGCGCACGGTCATTKCNN4 , CCGCCATCAACGCGTTCCG , CCCGGAGCTTCCGGTGTTTCALRP1 , ACCCACTGAGGGGGACCATGT , TCCCATCCATCGCTGCCGTCPHLDA3 , TAAACCACCGGGCGCACCAT , CAGAGGGAACAACGAAGCTGCCPTP4A1 , TAAGACAAAAGCGGCGTGGAGC , ACGCAGCCGCATTTTAGGACGPTPRE , CTTTTCCCGGCTCACCTGGTTCAG , GGGCATCTTCTTGTCGCTGGTGRINL , TGGCTTCCTGCAACCTGACGAT , TGCTTTGTTCACCTGTGGTGGGSCL30A1 , TTGGACCCCGCAGACCCAGA , TGGTCAGGTTCTCTGACAAGATTTCCATTSULF2 , CCCCCGGACTCGAAACATGGA , ACTTTCGACGCTGAAACTGCCTGTTM7SF3 , CTTTCACAAATGTGCCTTTTCAAACTAATGACTTC , AGCATGCCCCATACTGCCAGGTOB1 , GAAGCAGCCCCTTCCCAGGAG , GCTTTTCAGGATACCAGTGCCCTTCATTP53INP1 , AATGAGAAAGAAGATGATGAATGGATT , TGCTGAGAAACCAGTGCAAGTATCWRAP73 , TGCCTGGGGAAGGCGACTTTG , GAGGGCCATCGAGTCTCCGC Protein extracts were separated by SDS-PAGE and transferred to PVDF membranes . Blots were probed with the primary antibodies listed below , followed by peroxidase-conjugated secondary antibodies ( Santa Cruz Biotechnologies ) . Detection was by enhanced chemiluminescence or Luminata Crescendo ( Millipore , Tauton , MA ) and images were captured using an ImageQuant LAS4000 digital camera system ( GE Healthcare , Sweden ) . Antibodies: p21 ( sc-817; Santa Cruz ) ; p53 ( DO-1 , OP43; Calbiochem , Tauton , MA ) ; MDM2 ( SMP14 , sc-965; Santa cruz , used in combination with True Blot anti-mouse secondary antibody ( Rockland , Boyertown , PA ) to avoid IgG signal in immunoprecipitated samples ) ; GRIN2C ( LS-C157785; LifeSpan BioSciences , Seattle , WA ) ; PTCDH4 ( sc-139478; Santa Cruz ( C-15 ) ) ; RINL ( OAAB08343; Aviva Systems Biology , San Diego , CA ) ; Nucleolin ( sc-8031; Santa Cruz ) ; α-tubulin ( T9026; Sigma , Israel ) . Cell proliferation was assessed by quantification of cells actively synthesizing DNA . HCT116 p53+/+ and HCT116 p53−/− cells treated with 10 μM Nutlin-3 as indicated . 60 min prior to harvesting cells , 1 mg/ml of BrdU was added to cultivation medium . After trypsinization , cells were fixed with 70% ethanol . DNA denaturation was achieved by incubation with 2M HCl with 0 . 5% Triton X-100 for 10 min at 37°C . Before staining with anti-BrdU antibody ( sc-51514; Santa Cruz Biotechnology ) , samples were neutralized with 0 . 1M Na2B4O7 . Following incubation with secondary antibody ( A21202 , Life Technologies ) . BrdU positivity was analyzed by flow cytometry . Data represent average numbers of BrdU positive cells from three independent experiments ( each run in duplicate ) . T test was used for statistical analysis; an asterisk denotes significance at p<0 . 001 . Chromatin immunoprecipitation ( ChIP ) was carried out as previously described ( Gomes et al . , 2006 ) . Briefly , for quantitative ChIP analysis of the GDF15 and PTP4A1 locus , cells were cross-linked with 1% formaldehyde and whole-cell lysates were prepared in RIPA buffer . 1 mg of protein extract was immunoprecipitated with specific antibodies . Real-time PCR was carried out on ChIP-enriched DNA against a standard curve of genomic DNA , with amplicons tiling across the locus . Enrichment values for each amplicon were calculated as percentage of the amplicon with maximum signal for each antibody or expressed as equivalents of gDNA in nanograms . For MDM2 ChIPs a modified RIPA pH 7 . 4 buffer was used ( 150 mM NaCl , 1% Nonidet P-40 , 0 . 5% sodium deoxycholate , 0 . 025% SDS , 50 mM Tris pH 7 . 6 , 5 mM EDTA , protease/phosphatase inhibitors ) . Location of amplicon center relative to TSS , forward primer , reverse primer ( 5′>3′ ) : HCT116 cells were fixed and stained as described previously ( Andrysik , 2013 ) . Briefly , trypsinized cells were fixed with methanol and incubated with blocking solution ( 1% BSA-V in PBS/0 . 1% Triton X-100 , 0 . 01% sodium azide , pH 7 . 2 ) . p53 level was detected using DO-1 antibody ( Calbiochem OP43 ) and Alexa Fluor A488 secondary antibody . At least 10 , 000 single cells were measured by Accuri C6 flow cytometer to asses signal distribution in cellular population . Following the Nutlin treatment period , HCT116 cells cultivated on glass cover slips were fixed with cold methanol and washed briefly with wash buffer ( PBS with 0 . 1% Triton X-100 ) . After incubation with blocking solution ( 1% BSA-V in wash buffer , 0 . 01% sodium azide , pH 7 . 2 ) cells were labeled with p53 antibody ( sc-6343X; 1:1000; Santa Cruz FL393 ) or isotype control . Three times washed cells were incubated with secondary antibody ( 1:2000; Alexa Fluor A21202; Invitrogen , Frederick , MD ) , nuclei were counterstained with DAPI ( Invitrogen , 0 . 2 μg/ml ) and cover slips were mounted using anti-fade medium ( Fluoromount , Diagnostic Biosystems , Pleasanton , CA ) . Images were taken using Zeiss 510 Confocal Laser Scanning Microscope . | The growth , division and eventual death of the cells in the body are processes that are tightly controlled by hundreds of genes working together . If any of these genes are switched on ( or off ) in the wrong cell or at the wrong time , it can lead to cancer . It has been known for many years that the protein encoded by one gene in particular—called p53—is nearly always switched off in cancer cells . The p53 protein normally acts like a ‘brake’ to slow the uncontrolled division of cells , and some researchers are working to find ways to switch on this protein in cancer cells . However , this approach appears to only work in specific cases of this disease . For better results , we need to understand how p53 is normally switched on , and what other genes this protein controls once it is activated . Allen et al . have now identified the genes that are directly switched on when cancer cells are treated with a drug that artificially activates the p53 protein . Nearly 200 genes were switched on , and almost three quarters of these genes had not previously been identified as direct targets of p53 . Although p53 tends to act as a brake to slow cell division , it is not clear how it distinguishes between its target genes—some of which promote cell survival , while others promote cell death . Allen et al . found that survival genes are switched on more strongly than cell death genes via a range of different mechanisms; this may explain why most cancers can survive drug treatments that reactivate p53 . Also , Allen et al . revealed that some p53 target genes are primed to be switched on , even before the p53 protein is activated , by proteins ( and other molecules ) acting in regions of the DNA outside of the genes . By uncovering many new gene targets for the p53 protein , the findings of Allen et al . could help researchers developing new drugs or treatments for cancer . | [
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] | 2014 | Global analysis of p53-regulated transcription identifies its direct targets and unexpected regulatory mechanisms |
The striatum integrates excitatory inputs from the cortex and the thalamus to control diverse functions . Although the striatum is thought to consist of sensorimotor , associative and limbic domains , their precise demarcations and whether additional functional subdivisions exist remain unclear . How striatal inputs are differentially segregated into each domain is also poorly understood . This study presents a comprehensive map of the excitatory inputs to the mouse striatum . The input patterns reveal boundaries between the known striatal domains . The most posterior striatum likely represents the 4th functional subdivision , and the dorsomedial striatum integrates highly heterogeneous , multimodal inputs . The complete thalamo-cortico-striatal loop is also presented , which reveals that the thalamic subregions innervated by the basal ganglia preferentially interconnect with motor-related cortical areas . Optogenetic experiments show the subregion-specific heterogeneity in the synaptic properties of striatal inputs from both the cortex and the thalamus . This projectome will guide functional studies investigating diverse striatal functions .
The basal ganglia play an essential role in movement control and action selection ( Balleine et al . , 2009; Graybiel et al . , 1994; Jin and Costa , 2010; Packard and Knowlton , 2002; Wilson , 2004; Yin and Knowlton , 2006 ) . Their primary input station — the striatum — sorts contextual , motor , and reward information from its two major excitatory input sources , the cortex and the thalamus , into specific downstream pathways ( Berendse and Groenewegen , 1990; Gerfen , 1992; Gerfen and Wilson , 1996 , Gerfen and Bolam , 2010; Smith et al . , 2011 ) . The thalamus , which extensively interconnects with the cortex ( Sherman and Guillery , 2009 ) , is also a primary output target of the basal ganglia ( Haber and Calzavara , 2009; Parent and Hazrati , 1995 ) . Knowledge of the precise circuits and organizational principles between the cortex , thalamus , and striatum is essential for the mechanistic dissection of how these structures orchestrate function . Neuronal circuits within large brain structures , such as the cortex and the thalamus , are organized around functional subregions . For example , the cortex contains many distinct functional areas , including the sensory subregions , which are defined by specific sensory inputs , and the motor subregions , which are defined by intracortical microstimulation ( Li and Waters , 1991 ) . The thalamic subregions have traditionally been defined by cytoarchitectural boundaries to delineate ~40 nuclei ( Jones , 2007 ) . In contrast , the spatial organization of the striatum is poorly defined , particularly in mice . The striatum is thought to contain three functional domains: the sensorimotor , associative , and limbic domains , which approximately correspond to the dorsolateral , dorsomedial , and ventral striatum , respectively ( Balleine et al . , 2009; Belin et al . , 2009; Gruber and McDonald , 2012; Thorn et al . , 2010; Yin and Knowlton , 2006; Yin et al . , 2005 ) . However , the precise demarcations between these striatal domains remain unclear , and it is not known whether each striatal domain contains finer levels of organization . Notably , although the striatum extends a significant length along the anterior-posterior ( A-P ) axis ( ~4 mm in mice ) , the existence of domain heterogeneity along this axis remains elusive . Although the striatum lacks accepted domain demarcations , it is known to have stereotypic excitatory input patterns ( Averbeck et al . , 2014; Berendse et al . , 1992; Kincaid and Wilson , 1996; Selemon and Goldman-Rakic , 1985 ) . For example , in primates , the motor cortex has been shown to project to the rostral putamen , which corresponds to the rostral dorsolateral striatum , whereas the premotor cortex projects to the rostral caudate , which corresponds to the rostral dorsomedial striatum ( Künzle , 1975 ) . Investigation of the topographic arrangement of corticostriatal inputs from selected cortical subregions , or to isolated parts of the striatum have also been initiated in mice ( Guo et al . , 2015; Pan et al . , 2011; Wall et al . , 2013 ) . However , the precise projection patterns from most cortical subregions to the entire striatum remain to be systematically characterized . Furthermore , the organization of thalamostriatal inputs , which provide ~1/3 of all glutamatergic synapses in the striatum ( Huerta-Ocampo et al . , 2014 ) , has been less studied . In primates , the centromedian/parafascicular ( CM/Pf ) complex of the thalamus has been the main focus in studies of thalamostriatal function ( François et al . , 1991; Smith et al . , 2011 ) , yet less is known about the thalamostriatal projections from other thalamic subregions . The lack of systematic anatomical maps of corticostriatal and thalamostriatal inputs has stymied efforts to dissect the cortico-thalamo-striatal triangular circuits . For example , recent functional studies suggest that corticostriatal and thalamostriatal axons differ in their release probability and plasticity properties ( Ding et al . , 2008; Smeal et al . , 2007 ) , but the precise differences have been controversial ( Ding et al . , 2008; Smeal et al . , 2007 ) . This controversy raises the possibility of heterogeneity within axons originating from different cortical or thalamic subregions in their synaptic properties ( Kreitzer and Malenka , 2008 ) . A comprehensive excitatory input wiring diagram will provide a road map to enable systematic examination of the differential function of individual inputs . In addition , since the excitatory input patterns are thought to be stereotypic in the striatum , we reasoned that the striatal subregions and their boundaries may be revealed by systematic analysis of these input patterns from individual cortical and thalamic subregions . Here , we provide a quantitative and comprehensive description of cortical and thalamic inputs to the mouse striatum . This is achieved by integrating an in-house comprehensive thalamic anterograde projection dataset ( Hunnicutt et al . , 2014 ) and a selected cortical projection dataset from the Allen Institute for Brain Sciences ( AIBS ) ( Oh et al . , 2014 ) . Analyses of this striatal excitatory input wiring diagram revealed clear boundaries separating the three traditional striatal domains and uncovered a fourth subdivision in the posterior striatum . The dorsomedial striatum exhibited the highest degree of cortical input heterogeneity , suggesting that this subdivision serves as an information hub . In addition , thalamic subregions receiving basal ganglia outputs are preferentially interconnected with motor-related cortical subregions . With all the pathways tested , the anatomically described corticostriatal and thalamostriatal projections were confirmed to be functional using optogenetic approaches . Importantly , striatal inputs originating from different cortical or thalamic subregions form synapses in the striatum with distinct plasticity properties . Our findings lay the foundation for understanding the function of the striatum and its interactions with the cortex and the thalamus .
In order to obtain a comprehensive excitatory map of the striatum , two viral-based anterograde fluorescent-tracing datasets ( Hunnicutt et al . , 2014; Oh et al . , 2014 ) were analyzed and combined ( Figure 1 ) . The cortex has large , well-defined subregions . A relatively sparse set of viral injections confined to individual cortical subregions can therefore be used to localize cortical projections ( Figure 1a–b ) . We visually inspected all ( 1029 at the time ) injections from AIBS Mouse Brain Connectivity Atlas ( AMBA , http://connectivity . brain-map . org ) ( Oh et al . , 2014 ) and identified 127 injections that were well confined to individual cortical subregion boundaries ( Figure 1a–c , Supplementary file 1 , and Materials and methods ) . Other injections from original dataset were not included primarily because many of them span two or more cortical subregions ( Oh et al . , 2014 ) . 10 . 7554/eLife . 19103 . 003Figure 1 . Integration of two large-scale anatomical datasets to investigate whole-brain striatal input convergence . ( a ) Coronal atlas sections showing the 15 cortical subregions targeted by cortical injections ( right of each section ) and the cortical classes they encompass ( left of each section , modified from the Paxinos Mouse Brain Atlas ( PMBA ) ( Paxinos and Franklin , 2001 ) . ( b–e ) Overview of corticostriatal connectivity data generation . ( b ) Unilateral injection of virus expressing eGFP ( green ) in the mouse cortex ( left ) . A total of 127 injections were used to sample the entire cortex ( 15 cortical subregions analyzed , right ) from AIBS . ( c ) Representative coronal section showing a cortical injection ( dashed black line ) and segmented striatal projections with three projection density thresholds ( green lines ) . ( d ) Corticostriatal projections localized within the AIBS averaged template brain ( gray ) . ( e ) An example 3D view of corticostriatal projections . ( f–i ) Overview of thalamostriatal connectivity data generation . ( f ) Bilateral injections of virus expressing tdTomato ( red ) and eGFP ( green ) in the mouse thalamus ( left ) . A total of 218 injections were localized and aligned within an average model thalamus ( Hunnicutt et al . , 2014 ) ( right ) . ( g ) Representative coronal section showing thalamostriatal projection localization in high-resolution images ( red and green outlines ) . ( h ) Each striatum is aligned to the AIBS average template brain . ( i ) Example 3D view of thalamostriatal projections . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 00310 . 7554/eLife . 19103 . 004Figure 1—figure supplement 1 . Fasciculated projection axons were excluded from striatal input maps because they do not form functional connections in the striatum . ( a ) An example coronal slice ( top ) depicting the two primary types of fluorescently labeled thalamostriatal axons in the striatum: defasciculated and fasciculated ( zoomed image , bottom ) . ( b–c ) Top: High-magnification images of striatal areas containing ( b ) defasciculated and ( c ) fasciculated thalamostriatal axons expressing fluorescently tagged Channelrhodopsin ( ChR2 ) . Bottom: Current recordings of two example neurons recorded in the field of view shown in each image for ( b ) defasciculated and ( c ) fasciculated axons , showing synaptic currents elicited by light stimulation of thalamic axons . Gray circles indicate the locations of recorded neuronal cell bodies . Each current trace corresponds to the postsynaptic responses of optogenetic activation of the ChR2-expressing thalamostriatal axons using an 8 × 7 grid , 50 µm spacing blue light ( as previously described ( Hunnicutt et al . , 2014; Mao et al . , 2011 ) and briefly summarized in Materials and methods for details ) . ( d ) Summary results of whole cell recordings in portions of the striatum visually determined to possess either fasciculated axons , or defasciculated axons ( mean ± SD , n = 4 slices for each condition , 4–5 cells per slice ) , and ChR2+ thalamic axons were activated with blue light laser stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 00410 . 7554/eLife . 19103 . 005Figure 1—figure supplement 2 . Illustration of method used to subtract traveling corticostriatal axons . ( a–g ) Example of manual processing required to remove aberrantly localized projections in the striatum resulting from traveling axons . ( a ) Fluorescent image of a coronal section showing projections from an injection in AI . ( b ) The striatal projection localization resulting from the image in panel a ( green ) , overlaid on the AIBS average template brain ( gray ) , showing projections outside the striatum ( red ) , and the striatum outline ( black line ) ( c–e ) Axons in the corpus callosum aberrantly localized as striatal projections . The ( c ) fluorescent image , ( d ) segmented projection image , and ( e ) projection mask of voxels with >5% projection density ( i . e . the moderate projection threshold ) for the area indicated by a dashed box in panels a and b . ( f ) Manual mask ( blue ) created to subtract aberrantly localized projections . ( g ) Resulting striatal projection localization after subtraction ( green ) . ( h–k ) Comparison of corticostriatal projection localization before and after subtraction . Example sections and full projection distributions in the striatum from ( h–i ) M1/2 and ( j–k ) AI with and without subtraction of traveling axons . Striatal section positions are the same as Figure 2b . ( l ) The same coronal brain section shown in Figure 1c with a more detailed view of the striatal projections for three projection density thresholds ( inset ) . ( m ) The coronal section corresponding to panel l , showing only the binary segmented projections in the striatum with the approximate outline of the dense , moderate , and diffuse projections ( green lines ) defined for the voxelized projection data . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 00510 . 7554/eLife . 19103 . 006Figure 1—figure supplement 3 . Overview of semi-automated image segmentation method for thalamostriatal projections . ( a ) Schematic overview of image segmentation method , wherein: ( i ) all coronal sections for each experimental brain containing striatum were analyzed ( ~80 sections per brain ) , ( ii ) a subset of four images were selected from the full image sets to train the Advanced WEKA Image Segmentation plugin in ImageJ to identify defasciculated projections , fasciculated projections , and background fluorescence , ( iii ) the trained Advanced WEKA Image Segmentation plugin was applied to the remaining images , ( iv ) segmented images were manually corrected , and ( v ) localized defasciculated projections were aligned within the average template brain ( see Materials and methods ) . ( b–h ) More detailed example of the method described in ii-iv of panel a . ( b ) A fluorescent image of a coronal section through a mouse brain with fluorescent thalamic axons in the striatum ( scale bar , 1 mm ) . ( c ) Image from panel b with background fluorescence subtracted . ( d ) Red and green color channels of the image are separated . ( e ) Advanced WEKA Image Segmentation is trained to identify defasciculated projections , fasciculated projections , and background fluorescence for each color channel separately . ( f ) The result of the segmentation method is a probability image displaying the probability that each pixel contains defasciculated projections; white represents the highest probability of 1 ( note that the bright , fasciculated red axons on the left are excluded ) . ( g ) A probability threshold is manually chosen to encompass all defasciculated projections , and minor errors in the projection localization method are corrected manually . ( h ) The threshold was applied to the probability map and the manual changes were incorporated to determine the full distribution of defasciculated thalamic axons in the striatum for each color . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 00610 . 7554/eLife . 19103 . 007Figure 1—figure supplement 4 . Striatum alignment for thalamic dataset . ( a ) Illustration depicting the rotations to be applied to striatum masks to align each experimental striatum in the thalamic dataset to the striatum of the AIBS average template brain . ( b ) Example coronal sections showing the outline of each experimental striatum after alignment ( magenta ) , the outline of the striatum in the AIBS average template brain ( white ) , and the outline of each experimental anterior commissure ( acc ) after alignment . Section positions indicated in mm relative to bregma . Scale bar 1 mm . ( c ) Coronal sections shown in panel B , showing all experimental striatum outlines . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 007 The localized dataset used in the current study includes a median of seven injections per subregion for 15 cortical subregions ( Figure 1b and Supplementary file 1; see Table 1 for the list of all cortical subregions and their abbreviations ) . The projection distribution datasets for selected injections , which were aligned to the AIBS averaged template brain ( Kuan et al . , 2015 ) , were acquired from AIBS as downsampled projection maps with a voxel size of 100 µm X 100 µm X 100 µm . Fluorescence signal in the striatum derived from fasciculated traveling axons , which did not form synaptic connections ( Figure 1—figure supplement 1 ) , was manually subtracted ( Figure 1—figure supplement 2 ) . The resulting dataset describes the full distribution of axonal projections in the striatum that originate from defined cortical subregions ( Figure 1c–e ) . In addition to neocortical and mesocortical subregions , allocortical areas , including the amygdala ( Amyg ) and the hippocampal subiculum ( Sub ) , were also included to obtain a comprehensive excitatory input map to the striatum ( Figure 1a ) . 10 . 7554/eLife . 19103 . 008Table 1 . Abbreviations . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 008Cortical plate derived subregions AbbreviationExpanded nameAMBA LocationPMBA LocationAI/GI/DIagranular , granular , dysgranularinsular cortexAI ( all subregions ) + GU + VISCAI + DI + GIAudauditory cortexAUD ( all subregions ) Au1 + AuD + AuVAmygamygdalaBLA+ BMABLA + BMPdACCdorsal anterior cingulate cortexACAdCg1FrAfrontal association cortexFRP + MOs ( bregma 2 . 4 to 3 . 1 mm ) FrAILinfralimbic cortexILAILLOlateral orbital cortexORBlLO + DLOM1/2motor cortexMO ( all subregions , excluding FrA ) M1 +M2MOmedial orbital cortexORBmMOPiripiriform cortexPIRPirPrLprelimbic cortexPLPrLPtlparietal association cortexPTL ( all subregions ) MPtA + LPtA + PtPR + PtPDRhiecto-/peri-/ento-rhinal cortexECT + ENT + PERIEct + Ent + PRh + LentRspretrosplenial cortexRSP ( all subregions ) RSA + RSGS1/2somatosensory cortexSS ( all subregions ) S1 ( all subregions ) + S2Subhippocampal subiculumSUB ( all subregions ) + CA1S ( all subregions ) Temtemporal association cortexTEaTeAvACCventral anterior cingulate cortexACAvCg2Visvisual cortexVIS ( all subregions ) V1 + All visual subregionsVOventral orbital cortexORBvlVOThalamic nuclei AbbreviationExpanded nameAMBA LocationPMBA LocationADanterodorsal nucleusADADAManteromedial nucleusAMd + AMvAM + AMVAVanteroventral nucleusAVAV + AVDM + AVVLCLcentrolateral nucleusCLCLCMcentromedial nucleusCMCMIADinteranterodorsal nucleusIADIADIAMinteranteromedial nucleusIAMIAMIMDintermediodorsal nucleusIMDIMDLDlaterodorsal nucleusLDLD + LDVL + LDDMLGlateral geniculate nucleusLG ( LGd + LGv + IGL ) VLG + DLG + VLG + IGLLPlateral posterior nucleusLPLP + LPLR + LPMP + LPMCMDmediodorsal nucleusMD ( MDl + MDm + MDc ) MDC + MDL + MDMMGmedial geniculate nucleusMG ( MGm + MGv + MGd ) MGD + MGV + MGMPCNparacentral nucleusPCNPC + OPCPfparafascicular nucleusPFPfPoposterior nucleusPOPoPRperireuniens nucleusPRvRePTparataenial nucleusPTPTPVTparaventricular nucleusPVTPVA + PVRereuniens nucleusREReRHrhomboid nucleusRHRhRTreticular nucleusRTRtSMsubmedius nucleusSMTSubVALventral anterior-lateral complexVALVA + VLVMventromedial nucleusVMVMVPLventral posterolateral nucleusVPL + VPLpcVPL + VPLpcVPMventral posteromedial nucleusVPM + VPMpcVPM + VPMpcOther AbbreviationExpanded nameAIBSAllen Institute for Brain SciencePMBAPaxinos Mouse Brain AtlasAMBAAIBS Mouse Brain AtlasDMdorsomedialD-Vdorsal to ventralA-Panterior to posteriorM-Lmedial to lateralNAcnucleus accumbensVLlateral ventricleGpiglobus pallidus internal segmentSNrsubstantia nigra pars reticulataGpeglobus pallidus external segmentcccorpus callosumMSNmedium spiny neurons In contrast to the cortical subregions , certain thalamic nuclei are smaller than the typical size of a viral injection ( Hunnicutt et al . , 2014 ) and many of them have complex boundaries ( Jones , 2007 ) . To overcome this problem , we used a whole brain image dataset produced from 218 highly overlapping viral injections that covered >93% of the thalamic volume ( Figure 1f ) ( Hunnicutt et al . , 2014 ) . The overlapping injections allow for the determination of the thalamic origins of projections in the striatum ( Figure 1f–g ) with finer resolution than the viral injection volume ( Hunnicutt et al . , 2014 ) . Strong fluorescent signal derived from fasciculated axons that originate from the thalamus and travel through the striatum to reach their cortical targets was presented in the striatum ( Figure 1g and Figure 1—figure supplement 1a ) . These axons do not form synaptic connections in the striatum , as confirmed by channelrhodopsin ( ChR2 ) -mediated photostimulation experiments ( Figure 1—figure supplement 1b–d ) , and therefore , their fluorescent signal needed to be removed . The fasciculated axons have distinct morphological features compared to the defasciculated axons which do form synaptic connections with medium spiny neurons ( MSNs ) in the striatum ( Figure 1—figure supplement 1b–d ) . We applied a supervised machine learning algorithm to identify these morphological features and remove the fluorescent signal from fasciculated axons ( Figure 1—figure supplement 3 ) . The resulting striatal input maps were aligned to the AIBS averaged template brain ( see Materials and methods and Figure 1—figure supplement 4 ) , and thalamostriatal projections were localized using a semi-automated image segmentation method and custom-developed algorithms ( Figure 1—figure supplement 4 ) . The resulting dataset describes the axonal projection patterns in the striatum that originate from individual thalamic injections ( Figure 1g–i ) . Corticostriatal projections are known to have two functionally distinct types of innervation patterns: a core projection field of densely packed terminals and a larger diffuse ( i . e . sparse ) projection field ( Haber et al . , 2006; Mailly et al . , 2013 ) . To examine these different patterns , cortical projections within each downsampled striatal voxel were classified as one of three graded densities: dense , moderate , and diffuse projections , which were defined as over 20% , 5% , and 0 . 5% , respectively , of original imaging voxels containing fluorescent axons ( Figure 1c–d and Materials and methods ) . For each cortical subregion , a maximum projection density map throughout the striatum was determined by combining projection distributions from all injections within a given cortical subregion ( Figure 2a–c and Figure 2—figure supplement 1 ) . Each projection distribution was quantified in the dorsal-ventral ( D-V ) , anterior-posterior ( A-P ) ( Figure 2c ) , and medial-lateral ( M-L ) ( data not shown ) axes . Each cortical subregion gave rise to a distinct projection pattern in the striatum , forming either one or two contiguous volumes . While no two projection maps were identical , some were similar . For example , the somatosensory and motor subregions , including FrA , M1/2 , and S1/2 , exhibit similar projection patterns , producing dense , highly overlapping projection fields in a large volume of the central portion of the striatum in the A-P axis . These projections were biased toward the lateral striatum , likely including the traditionally-termed dorsolateral domain ( Figure 2b–c ) . In contrast , frontal subregions , including LO/VO , IL , and MO/PrL , have smaller , largely non-overlapping dense projections in the anterior , medial striatum and diffuse projections that span a larger striatal volume ( Figure 2b–c ) . When injections were further grouped according to their locations in either the A-P or M-L axis , we observed a moderate trend for topographic organization in the A-P and M-L axes for the dense projections , but this was not seen for diffuse projections ( Figure 2—figure supplement 2 ) . Nevertheless , even the dense projections from such grouping often resulted in several discrete , non-contiguous projection fields ( Figure 2—figure supplement 2b and e ) , which are not as well defined as the cortical subregion-specific projection fields , as shown in Figure 2b . 10 . 7554/eLife . 19103 . 009Figure 2 . Comprehensive mapping of cortical inputs to the striatum . ( a ) Coronal section outlines for one hemisphere of the striatum ( starting 1 . 8 mm anterior to bregma and continuing posterior in 300 µm steps , AIBS atlas ) . ( b ) Striatal projection distributions for all cortical subregions ( rows ) . The maximum projection densities ( dense ( white ) , moderate ( light grey ) , diffuse ( dark grey ) , or none ( black ) ) are indicated for the sum of all injections within each cortical subregion . ( c ) Projection distribution plots in the dorsal-ventral ( D–V ) and anterior-posterior ( A–P ) axes for each cortical subregion shown in b . Coverage in the striatum by dense ( light gray ) and diffuse ( dark gray ) projections were calculated in 100 µm steps as the fraction of the striatum covered in each step by either dense or diffuse projections , respectively . Striatal volumes were normalized in each 100 µm step . ( d ) Subregion-specific convergence plots for diffuse ( left panel ) and dense ( right panel ) corticostriatal projections . The color scale indicates the fraction of the projection field from a given cortical subregion ( rows ) covered by the projection field from another cortical subregion ( columns ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 00910 . 7554/eLife . 19103 . 010Figure 2—figure supplement 1 . Cumulative corticostriatal projection distributions . ( a–b ) Projection distributions in the striatum from viral injections in two example cortical subregions: ( a ) dv/ACC and ( b ) LO/VO , respectively . ( c–d ) The final cumulative projection distributions for ( c ) d/vACC and ( d ) LO/VO , created by maximum projection of the density at each striatal voxel from all individual injections in each cortical subregion . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01010 . 7554/eLife . 19103 . 011Figure 2—figure supplement 2 . Topographic organization of corticostriatal inputs . ( a ) Coronal sections ( 2 . 7 , 0 . 9 , –0 . 9 , −3 . 3 , and −4 . 5 mm relative to bregma ) through the AIBS averaged template brain showing the relative locations of injections in each of five medial to lateral ( M–L ) groups ( dark to light green ) . ( b ) Coronal sections ( starting 1 . 8 mm anterior to bregma and continuing posterior in 300 µm steps ) through the ipsilateral striatum showing the striatal projection distributions for the M-L injection groups shown in panel a ( rows ) . The maximum projection densities ( dense ( white ) , moderate ( light grey ) , diffuse ( dark grey ) , or none ( black ) ) are indicated for the sum of all injections within each group . ( c ) Distribution plots for the dense ( light gray ) and diffuse ( dark gray ) projections of each M-L cortical group shown in panel b ( calculated as described in Figure 2 ) . ( d ) Coronal sections ( starting at 2 . 7 , 1 . 5 , –0 . 3 , −2 . 1 , and −4 . 5 mm relative to bregma ) of the AIBS averaged template brain showing the relative locations of injections in each of five anterior to posterior ( A–P ) groups ( red to yellow ) . ( e ) Coronal sections ( as described in panel b ) of the ipsilateral ( to the injection side ) striatum showing the striatal projection distributions for the A-P injection groups ( rows , marked in colors corresponding to panel d ) . The maximum projection densities ( dense ( white ) , moderate ( light grey ) , diffuse ( dark grey ) , or none ( black ) ) are indicated for the sum of all injections within each group . ( f ) Distribution plots for the dense ( light gray ) and diffuse ( dark gray ) projections of each cortical group shown in panel e ( calculated as described in Figure 2 , shown in D-V and A-P axes ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 011 Next , to reveal whether information from different cortical subregions may interact in the striatum , we calculated pairwise projection convergence between cortical subregions for diffuse and dense projections ( Figure 2d ) . As expected , the diffuse projections have a higher degree of convergence than the dense projections; however , we identified cortical areas that showed very little convergence , even for diffuse projections . For example , Ptl , Rsp , IL , and Sub have very little projection overlap with the motor areas M1/2 or FrA . In contrast , several cortical subregions , such as the motor ( FrA and M1/2 ) and select sensory ( S1/2 and AI/GI/DI ) subregions , have a high level of convergence for both diffuse and dense projections ( Figure 2d ) . To localize the projection distribution for each thalamic injection , we developed a semi-automated image segmentation method to identify axonal projections in the striatum ( Figure 1f–h , Figure 3c–d , Figure 1—figure supplement 3 , and Materials and methods ) . To compare thalamostriatal projection patterns across animals , as well as to corticostriatal projections , the segmented striatum for each experiment was computationally aligned to the AIBS averaged template brain ( Figure 1d and Figure 1—figure supplement 4 ) . Similar to our previous study of thalamocortical projections ( Hunnicutt et al . , 2014 ) , the thalamic injections were individually categorized based on their projections to a given striatal volume , such as a striatal volume innervated by a specific cortical subregion ( Figure 3g and Figure 3—figure supplement 1a ) . Figure 3 shows a representative example , wherein four thalamic injections are categorized based on their projection convergence with M1/2 projections in the striatum . Injections found to fulfill each category were combined and then used to derive the thalamic confidence map for the striatal subregion innervated by M1/2 ( Figure 3g–k , Figure 4 and Figure 3—figure supplement 1; see Materials and methods for details ) . Each thalamic confidence map describes the likelihood that a given thalamic volume projects to a given striatal volume with a resolution finer than the size of individual injections ( Figure 3—figure supplement 1 ) . This process was repeated for all cortical subregions , producing a complete map of striatal convergence for corticostriatal and thalamostriatal projections ( Figure 4a ) . 10 . 7554/eLife . 19103 . 012Figure 3 . Localization of the origins of thalamostriatal projections that converge with a corticostriatal projection in the striatum . ( a ) Schematic sagittal view of the mouse brain , adapted from ( Watson et al . , 2012 ) , indicating the location of M1/2 . ( b ) Distribution of dense ( dark yellow ) and diffuse ( light yellow ) corticostriatal projections from M1/2 . ( c–d ) Representative images of two coronal sections through the striatum of one example brain ( left panels in c and d ) showing the fluorescent thalamic axons in the striatum from injections described in panel e . Original images are on the left and segmented striatum and axon projection fields are on the right , with traveling axon bundles subtracted ( black in right images ) . ( e ) Two views of a model thalamus ( gray ) showing the four thalamic viral injections that produced projections shown in panels c and d . Note that since thalamic projections do not cross the midline in mouse , a single injection spanning the midline was treated as two independent injections ( injections 2 and 4 ) . A darker center of each injection site represents the eroded ‘core’ of each injection defined previously for the thalamic injection dataset ( Hunnicutt et al . , 2014 ) . ( f ) Projection distributions in the striatum for each of the injections shown in panel e ( red and green ) aligned and overlaid with the outlines of M1/2 projections in the striatum ( yellow ) delineated in panel b . ( g ) Injections were assigned to one or more of four categories based on quantification of the convergent volumes of thalamostriatal and corticostriatal projection fields ( see Materials and methods ) . Inclusion in each category is used , as described in Figure 3—figure supplement 1 , to localize the thalamic origins of convergence . ( h–i ) Fluorescent images of coronal sections through the thalamus , showing injection sites 1 , 2 , and 4 . Insets show the segmented injection sites ( solid white line ) and the injection site core ( dashed white line ) ( Hunnicutt et al . , 2014 ) . The dashed yellow line in panel h insert shows the brain midline . ( j–k ) Two example coronal sections , approximately corresponding to the position of panels h and i , respectively , of the thalamostriatal confidence maps for M1/2 convergence in panels h and i , respectively ( top panels in j and k ) . The segmented injection sites are overlaid on their corresponding confidence maps ( bottom panels in j and k ) . All scale bars , 1 mm . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01210 . 7554/eLife . 19103 . 013Figure 3—figure supplement 1 . Illustration of the method used to generate thalamic confidence maps . ( a ) Illustration of eight hypothetical injection volumes labeled to indicate whether or not they individually satisfy each of three criteria for their projections in the striatum ( right ) . Each injection indicates the borders of the full injection ( 1 , 2 , 3 , and X ) and the injection core ( 1c , 2c , 3c , and Xc ) , which is generated by eroding the full injection volume by 100 µm . ( b ) Injections shown in panel a , indicating the injections that fulfill each of the three criteria ( green ) . ( c ) Binary injection masks representing the area covered by either the full injection ( top ) or injection cores ( bottom ) that satisfy each of the three criteria . ( d ) Binary injection masks representing the area covered by either the full injection ( top ) or injection cores ( bottom ) that do not satisfy each of the three criteria . Binary mask for the full injections that do not meet the hardest to satisfy criteria are not included . ( e ) Sum of binary masks generated in panel c . ( f ) Subtraction of the binary masks generated in panel d . ( g ) Example confidence map , generated by combining the summed binary masks from panel E and the subtracted binary masks from panel f . Values below zero set to zero . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01310 . 7554/eLife . 19103 . 014Figure 4 . Thalamostriatal projections that converge with subregion-specific corticostriatal projections . ( a ) Example coronal sections through our model thalamus from anterior to posterior ( starting at −0 . 155 mm relative to bregma and continuing in 250 µm steps posterior ) . Confidence maps identifying the complete thalamic origins of thalamostriatal projections that converge with subregion-specific corticostriatal projections ( columns ) . Projection origins indicated for six confidence levels ( see Materials and methods , and also see [Hunnicutt et al . , 2014] ) . ( b ) An example coronal section of the thalamostriatal confidence map converging with corticostriatal inputs of Sub ( gray scale ) , overlaid with thalamic nuclear demarcations from the AMBA . The atlas is colored on the left to indicate the fraction of each thalamic nucleus covered by the average of confidence levels 1 , 3 , and 5 . Coverage values were calculated for the PMBA and AMBA , and their average is shown . The color scale minimum is 0% ( blue ) , inflection point is 25% ( white ) , and the peak coverage is 75% ( red ) . ( c ) The fraction of each thalamic nucleus covered by confidence levels 1 , 3 , and 5 ( dark , medium and light gray bars , respectively ) , with their average ( black line ) . ( d ) Aggregate nucleus coverage map indicating the nuclear origins of the thalamostriatal projections that converge with subregion-specific corticostriatal projections . Nuclei ( rows ) and cortical subregions ( columns ) are hierarchically clustered on the basis of output and input similarity , respectively . Color scale is the same as in panel b . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 014 We further determined the thalamic nuclear origins of the thalamostriatal projections by overlaying confidence maps with the two widely used mouse atlases ( the AMBA and the Paxinos Mouse Brain Atlas ( PMBA ) [Paxinos and Franklin , 2001] ) . The coverage of each atlas-outlined nucleus was calculated for each confidence level ( Figure 4b–d ) . Of the thalamic subregions covered in this dataset , all thalamic nuclei , except the anteroventral nucleus ( AV ) , reticular nucleus ( RT ) , ventral posteromedial nucleus ( VPM ) , and ventral posterolateral nucleus ( VPL ) , project to the striatum ( Figure 4c–d ) ( Hunnicutt et al . , 2014 ) . Overall , overlapping , yet distinct , thalamic subregions converge in the striatum with each cortical subregion ( Figure 4a and d ) . To determine if and how different portions of the striatum exhibit heterogeneity in the excitatory inputs they receive , the dense and diffuse corticostriatal projections ( as illustrated in Figure 2a–c ) were summed , respectively , across all cortical subregions ( Figure 5—figure supplement 1 ) . The results indicate that distinct striatal subdivisions receive different numbers of converging cortical inputs and that there are distinct differences between dense and diffuse projection convergence ( Figure 5—figure supplement 1a–b ) . Nearly all striatal voxels receive diffuse projection from at least five cortical subregions , with an average of 8 . 3 cortical inputs converging per voxel . When the striatal voxels were subdivided based on the average convergence level , two distinct subdivisions formed . A large , contiguous subdivision , constituting ~63% of the ipsilateral , is innervated by diffuse projections from a high number of cortical subregions ( 10 . 7 ± 1 . 1 inputs per voxel , mean ± s . d . ) , and a second subdivision receiving diffuse projections from a low number of cortical subregions ( 6 . 6 ± 0 . 84 inputs per voxel , mean ± s . d . ) ( Figure 5—figure supplement 1a–b ) . Interestingly , when we constructed thalamic confidence maps to localize the thalamic subregions innervating the ipsilateral striatum receiving either a high ( >8 . 3 inputs ) or a low ( ≤8 . 3 inputs ) level of cortical convergence , the striatal subdivision with high cortical input convergence was found to receive inputs from every thalamic nucleus shown to project to the striatum ( Figure 5—figure supplement 1c ) . In contrast , the striatal subdivision with low input convergence does not receive any input from the anterior thalamic nuclei ( Figure 5—figure supplement 1c ) . For dense corticostriatal projections , a lower level of convergence was observed ( 2 . 7 ± 0 . 4 inputs per voxel , mean ± s . d . ) , as expected since dense projections cover a smaller volume . However , their convergence exhibits a different distribution pattern from that of the diffuse projections ( Figure 5—figure supplement 1a ) . For example , a higher level of convergence of the diffuse projections is observed in the dorsal striatum , whereas dense projection convergence is biased toward the ventral striatum ( Figure 5—figure supplement 1a ) . To investigate whether the striatal subdivisions with either high or low cortical input convergence ( Figure 5—figure supplement 1a ) could be attributed to evolutionary differences in the cortical inputs , we mapped the projection distributions for the evolutionarily distinct classes of the cortical plate: neocortex , mesocortex , and allocortex ( Figure 5—figure supplement 2 ) , which carry predominantly sensory/motor , associative , and limbic information , respectively ( McGeorge and Faull , 1989 ) . We found that , instead of a single class , the striatal subdivision with high cortical convergence always received input from multiple cortical classes ( Figure 5—figure supplement 2a ) . Additionally , the thalamostriatal inputs that converge with each cortical class ( Figure 5—figure supplement 2b–c ) did not mimic the thalamostriatal inputs that converge with striatal subdivisions based on high/low input convergence ( Figure 5—figure supplement 1b–c ) . These results provide evidence for multimodal input integration throughout the striatum and functional heterogeneity between striatal areas having distinct diffuse and dense input convergence . The striatum is the largest part of the telencephalon without clearly demarcated subdivisions . Since the above analyses indicate heterogeneity in excitatory input integration across the striatum ( Figure 2 and Figure 5—figure supplement 1 ) , and cortical input patterns are thought to be stereotypic across animals , we sought to subdivide the striatum using an objective and functionally relevant approach based on corticostriatal projection patterns . The striatum was downsampled to a voxel size of 150 µm x 150 µm x 150 µm , and the projection density within each voxel was calculated for all cortical inputs ( Figure 5a ) . The voxels , each treated independently , were clustered based on the input density ( none , diffuse , moderate , or dense , as illustrated in Figure 2 ) they received from all cortical subregions ( Figure 5b–c and Materials and methods ) . Cortical subregions were analogously clustered based on their projections to individual striatal voxels ( Figure 5b ) . To identify striatal subdivisions in an unbiased manner , four increasingly lower thresholds were applied to the voxel clustering dendrogram to generate voxel groups ( Figure 5c ) . Each voxel group was then mapped back onto the striatum ( Figure 5d ) . Notably , although no positional information was used in the clustering analysis , the resulting voxel clusters form largely contiguous volumes ( Figure 5d ) , suggesting that these voxel clusters may represent functionally distinct subdivisions . 10 . 7554/eLife . 19103 . 015Figure 5 . Striatal subdivisions based on cortical input convergence . ( a ) Schematic of voxel clustering method . The striatum was downsampled into 150 µm × 150 µm × 150 µm voxels ( top panel ) , the projection density ( dense , moderate , or diffuse ) to each voxel was determined for inputs from each cortical subregion ( middle panel ) , and the sum of this information was used to cluster voxels with common inputs ( bottom panel ) . ( b ) All striatal voxels ( rows ) were hierarchically clustered based on their cortical input patterns , and cortical subregions ( columns ) were clustered based on common innervation patterns to the striatum . The projection densities in each voxel are indicated in gray scale , as determined in Figure 2b . ( c ) Four separate thresholds were applied to the voxel dendrogram to produce 2 , 3 , 4 , and 15 clusters . The cluster boundaries ( dotted color lines ) for the threshold producing four clusters are carried across the clustered voxels in panel b . Clusters containing only one voxel were ignored in our analyses . ( d ) Coronal sections outline the ipsilateral ( according to the injection hemisphere ) striatum , starting 1 . 8 mm anterior to bregma and continuing posterior in 300 µm steps , showing the spatial location of the clusters determined in panel c . ( e ) Thalamic confidence maps indicating the thalamic origins of thalamostriatal projections to the four striatal subdivisions defined by cluster analysis in panel d ( thalamic section positions are the same as in Figure 4a ) . The method used to localize the origin of thalamic projections was similar to that described for Figures 3 and 4 , except that differences in the data resulted in an eight level confidence maps based on the inclusion of each injection in each of four groups ( see Materials and methods ) . ( f ) The fraction of each thalamic nucleus covered by confidence levels 3 , 5 , and 7 ( dark , medium and light gray bars , respectively ) , with their average ( black line ) shown for the confidence maps in panel e ( see Figure 5—figure supplement 3 for full dataset , and Materials and methods for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01510 . 7554/eLife . 19103 . 016Figure 5—figure supplement 1 . Distribution of cortical input convergence in the striatum . ( a ) Coronal sections of the average template brain showing the cumulative bilateral convergence of diffuse ( top ) and dense ( bottom ) projections from all cortical subregions ( heat map ) . The striatal areas with convergent inputs from nine or more cortical subregions are indicated ( black dashed line ) . Sections start 1 . 8 mm anterior to bregma , the second slice is 300 µm posterior , and the rest continue in 600 µm steps . ( b ) Projection distribution plots in the dorsal-ventral ( D–V ) , medial-lateral ( M–L ) and anterior-posterior ( A–P ) axes for diffuse ( left panel ) and dense ( right panel ) input convergence . Coverage of cortical inputs in the striatum by the indicated number of cortical subregions was calculated in 100 µm steps along each axis . The fraction of the striatum covered in each step by each number of converging projections is shown as a heat map , where each plot is collapsed to show only the dimension indicated ( i . e . the D-V plot does not contain any M-L or A-P information ) . Striatal volumes were normalized in each 100 µm step . ( c ) Left panel: summary of thalamic confidence maps for the origins of thalamostriatal projections that target striatal volumes with high- and low-diffuse cortical projection convergence , as determined in panel a ( thalamic section positions are the same as in Figure 4a ) . Right panel: the fraction of each thalamic nucleus covered by confidence levels 3 , 5 , and 7 ( dark , medium and light gray bars , respectively ) , with their average ( black line ) ( see Materials and methods and Figure 5—figure supplement 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01610 . 7554/eLife . 19103 . 017Figure 5—figure supplement 2 . Projection distribution and thalamic input convergence for cortical subtypes . ( a ) Coronal sections of the model brain showing the bilateral distributions of dense , moderate and diffuse projections from all allocortical ( top ) , mesocortical ( middle ) , and neocortical ( bottom ) subregions . The second slice is 300 µm posterior to the first slice , continuing in 600 µm steps . ( b ) Thalamic confidence maps for the origins of thalamostriatal projections that converge in the striatum with projections from each cortical subtype , as determined in panel a ( section positions are the same as in Figure 4a ) . ( c ) The fraction of each thalamic nucleus covered by confidence levels 3 , 5 and 7 ( dark , mid and light gray bars , respectively ) , and their average ( black line ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01710 . 7554/eLife . 19103 . 018Figure 5—figure supplement 3 . Thalamic origins of inputs to striatal clusters . ( a ) Coronal sections through the thalamus from anterior to posterior . Thalamic confidence maps indicating the origins of thalamostriatal projections to the striatal voxel clusters shown in Figure 5 . Confidence maps are shown for the origins of all projections to each of the two clusters ( left ) , three clusters ( middle ) , and the 5 largest of the 15 clusters ( right ) thresholds ( grayscale , section positions are the same as in Figure 4a ) . ( b ) The fraction of each thalamic nucleus covered by confidence levels 3 , 5 , and 7 ( dark , mid , and light gray bars , respectively ) , with their average ( black line ) is shown for the confidence maps in panel a ( see Materials and methods for details ) . ( c ) Coronal sections of the thalamus showing the boundaries of the canonical thalamic nuclear groups ( anterior , midline , medial , intralaminar , ventral , lateral , and posterior ) . In each section , the nuclear boundaries are shown for both the PMBA ( left ) and the AMBA ( right ) , which were previously aligned to the thalamic dataset used there ( Hunnicutt et al . , 2014 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 01810 . 7554/eLife . 19103 . 019Figure 5—figure supplement 4 . Retrograde verification of anterograde projection maps . ( a–b ) Representative injection sites of Lumafluor retrograde beads in the dorsomedial ( DMS , a ) and posterior striatum ( PS , b ) . Left , reference coronal section from PMBA; center , immunostained sections ( gray , inverted lookup table ) with Lumafluor beads ( red ) ; and right , the striatal subdivisions ( DMS , red; PS , green ) based on Figure 5 . ( c–k ) Retrograde-labeled cells in the thalamus , midbrain , and several other subcortical regions , as indicated after injection of DMS ( c , e , f , i , j ) and PS ( d , g , h , k ) . ( c–d ) Left , thalamic confidence maps indicating the thalamic origins of the thalamostriatal projections to DMS and PS , as shown in Figure 5 . Thal1 and Thal2 , red , were used for optogenetic stimulation of thalamostriatal projections to the DMS ( Figure 8 ) ; center , corresponding coronal sections ( gray , inverted lookup table ) of the thalamus with retrograde-labeled cells ( red dots ) and thalamus outline ( grey line ) ; right , enlarged raw images corresponding to the boxed areas in center . ( e–k ) Retrograde labeled observed in the basolateral amygdala ( BLA , e , g ) , dorsal/ventral anterior cingulate cortex ( d/vACC , f ) , primary auditory cortex ( Au1 , h ) , substantia nigra pars compacta ( SNc , i , k ) , and primary visual cortex ( V1 , j ) . Left , reference sections from PMBA , right , immunostaining of retrograde-labeled cells ( gray , inverted lookup table ) . Ai , agranular insular cortex; APT , anterior pretectal nucleus; fr , fasciculus retroflexus; Hip , hippocampus; L1-6 , cortical layer 1–6; Pir , piriform cortex; S1 , primary somatosensory cortex; SNr , substanta nigra reticulare; Str , striatum . Scale bars: a–d , 500 µm; c1–c4 and d1–d3 , 100 µm; e , g , i , k , 100 µm; f , h , j , 250 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 019 The highest dendrogram threshold divided the striatum into two clusters , separating a small dorsomedial subdivision from the rest of the striatal volume ( Figure 5c–d ) . A slightly lower threshold produced three clusters that are highly reminiscent of the three traditional striatal domains: a dorsomedial subdivision with highly convergent inputs , a lateral subdivision receiving dense sensorimotor innervation , and a ventral subdivision receiving several limbic inputs ( Figure 5b–d ) . Notably , the ventral subdivision contains two non-contiguous segments: a ventral segment in the anterior striatum and the most posterior segment of the striatum , suggesting that they may represent two different domains . Indeed , when the threshold was lowered to create a fourth cluster , the posterior segment became a distinct cluster ( Figure 5d ) . Although this posterior subdivision shares similarities with the limbic domain , it also receives strong auditory and visual innervation ( Figure 5b ) . This posterior cluster may constitute a previously unappreciated functional subdivision of the striatum in mice . Further lowering the threshold to produce 13 clusters divided the dorsomedial subdivision , as well as a small portion of the lateral subdivision immediately adjacent to the dorsomedial subdivision , into many smaller clusters without dividing the remaining three subdivisions ( see the small clusters at the dorsomedial striatum in the bottom row of Figure 5d ) , indicating a high degree of input heterogeneity in this region . When the threshold was lowered to produce 15 clusters , the posterior and ventral subdivisions are each separated into two clusters , where one cluster receives motor and somatosensory information and the other cluster does not ( Figure 5b–d ) . Importantly , even with the low threshold generating 15 clusters , the majority of the lateral subdivision , likely corresponding to the traditional sensorimotor domain , remained as a single large cluster , suggesting a highly homogeneous functional role for this region . We also determined the origins of thalamic inputs to each cluster-defined striatal subdivision ( Figure 5e–f , and Figure 5—figure supplement 3 ) . Each striatal cluster , although defined by cortical inputs , receives innervations from distinct thalamic subregions ( Figure 5e and Figure 5—figure supplement 3a ) . The thalamic inputs largely project to striatal clusters in accordance with the known thalamic nuclear groups ( Figure 5f and Figure 5—figure supplement 3b–c ) . For example , when the striatum is divided into four clusters ( Figure 5e–f ) , the dorsomedial subdivision receives input primarily from the anterior nuclear group , the ventral subdivision receives most of its inputs from the midline and medial nuclear groups , the lateral subdivision receives inputs from the ventral , intralaminar , posterior , and medial nuclear groups , while the posterior subdivision receives only weak thalamic input from the lateral posterior nucleus ( LP ) ( Figure 5f and Figure 5—figure supplement 3b–c ) . To verify that the convergent inputs to each subdivision were accurately localized , retrograde bead injections were performed in portions of the dorsomedial and posterior subdivisions ( Figure 5—figure supplement 4a–b ) . All cortical and thalamic subregions labeled by the retrograde injections were predicted by our dataset ( Figure 5—figure supplement 4c–h ) . The unique cortical and thalamic input patterns to different striatal clusters suggest that each cluster may serve distinct functions . In addition to being a major input source to the striatum , the thalamus is also one of the primary output targets of the basal ganglia ( Haber and Calzavara , 2009; Parent and Hazrati , 1995 ) . Furthermore , the thalamus extensively interconnects with the cortex , thereby creating a cortico-thalamo-basal ganglia circuit loop ( Figure 6a ) . To obtain a complete picture of the organization of this circuit , we overlaid the thalamic confidence map for thalamocortical projections to a given cortical subregion ( Hunnicutt et al . , 2014 ) with the thalamic confidence map for thalamostriatal projections that target the striatal field innervated by the same cortical subregion ( Figure 4a ) . Figure 6c shows a representative example of this overlay process corresponding to the somatosensory cortices ( S1/2 ) ( see Figure 6—figure supplement 1 for all cortical subregions ) . When further aligning these confidence maps to the atlases , we observed that projection patterns varied across thalamic nuclei ( Figure 6d ) . Of interest , VPM and VPL target S1/2 without projecting to the corresponding cortical projection field in the striatum ( Figure 6c–d and Figure 6—figure supplement 1a–b , cyan ) ; the intermediodorsal nucleus ( IMD ) , mediodorsal nucleus ( MD ) , rhomboid nucleus ( RH ) , perireuniens nucleus ( PR ) , submedius nucleus ( SM ) , paraventricular nucleus ( PVT ) , and CM send projections to the S1/2 projection field in the striatum without innervating S1/2 directly ( Figure 6c–d and Figure 6—figure supplement 1a–b , magenta ) , whereas the posterior thalamic nucleus ( Po ) , Pf , LP , paracentral nucleus ( PCN ) , and centrolateral nucleus ( CL ) project to both targets ( Figure 6c–d and Figure 6—figure supplement 1a–b , white ) . 10 . 7554/eLife . 19103 . 020Figure 6 . Connectivity of excitatory projections in the cortico-thalamo-basal ganglia circuit . ( a ) Schematic of the excitatory connections between the cortex , thalamus , striatum , and the output nuclei of the basal ganglia , globus pallidus internal segment ( GPi ) and substantia nigra pars reticulata ( SNr ) , which collectively make up the cortico-thalamo-basal ganglia circuit ( gray box indicates the basal ganglia ) . ( b–d ) Example connectivity matrix for one part of the cortico-thalamo-basal ganglia circuit . ( b ) Confidence map showing the origins of thalamostriatal projections that converge with projections from somatosensory cortex ( S1/2 ) ( left ) , and confidence maps for the origins of thalamocortical projections that terminate in S1/2 ( center , previously published data , [Hunnicutt et al . , 2014] ) , with their overlay shown on the right ( thalamostriatal: magenta; thalamocortical: cyan; overlap: white ) . ( c ) Overlaid thalamocortical and thalamostriatal confidence maps , as described in panel b ( thalamic section positions are the same as in Figure 4a ) . ( d ) Thalamic nuclear localization for the confidence maps shown in panel c . Values are represented as the fraction of each thalamic nucleus covered by the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections ( magenta ) , the average of confidence levels 1 , 4 , and 7 for thalamocortical projections ( cyan ) and the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections that lie within the white overlapping volume shown in panel c . The density of subregion-specific corticothalamic projections within each nucleus is shown in green . ( e ) The nuclear localization data , as described in panel d , are grouped by projection type ( thalamocortical , thalamostriatal , overlap , and corticothalamic ) . As examples , only the thalamic targets of basal ganglia output ( MD , Pf , VAL , and VM ) are shown ( see Figure 6—figure supplement 1 for full dataset ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 02010 . 7554/eLife . 19103 . 021Figure 6—figure supplement 1 . Organization of the thalamus in cortico-thalamo-basal ganglia loops . ( a ) Example coronal sections through the thalamus from anterior to posterior . Overlaid thalamocortical and thalamostriatal confidence maps as described in Figure 6b . Each column shows the origin of thalamic efferent projections associated with the 14 analyzed cortical subregions ( section positions are the same as in Figure 4a ) . ( b ) Nuclear localization for the confidence maps shown in panel a . Values are represented as the fraction of each thalamic nucleus covered by the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections ( magenta ) , the average of confidence levels 1 , 4 , and 7 for thalamocortical projections ( cyan ) and the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections that lie within the white overlapping volume shown in panel a . The density of subregion-specific corticostriatal projections within each nucleus is shown in green ( See Materials and methods for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 02110 . 7554/eLife . 19103 . 022Figure 6—figure supplement 2 . Overview of network interactions throughout the cortico-thalamo-basal ganglia circuit by subregion . ( a ) Network diagram of all corticocortical , corticostriatal , thalamostriatal , thalamocortical , and corticothalamic connections ( top to bottom ) associated with each cortical subregion . Corticocortical connections indicate a projection density >15% in the target area with the line color indicating the projection source . Corticostriatal projections are shown for primary convergent subregions , i . e . subregions whose projection fields converge with >50% the target projection field of each other cortical subregion ( see panels b–p for details ) . Thalamostriatal projections ( i . e . those that converge in the striatum with the projection field of each cortical subregion ) are indicated for each thalamic nucleus with >20% of its volume contributing to the convergent projections ( see Figure 4c–d ) . Thalamocortical projections are also shown for each thalamic nucleus with >20% of its volume contributing projections to the indicated cortical subregion ( See Figure 6—figure supplement 1b , and [Hunnicutt et al . , 2014] ) . Corticothalamic projections are indicated for projections where > 20% of the thalamic nucleus received projections from the corresponding cortical subregion ( Figure 6—figure supplement 1b ) . Arrows indicate the thalamic targets of basal ganglia output . All line widths indicate either the relative density in target area ( corticocortical and corticothalamic projections ) , fraction of nucleus covered ( thalamocortical and thalamostriatal projections ) , or fraction convergent ( corticostriatal ) for all source-target characterizations , as described in Figure 6 . ( b–p ) Chord diagrams highlighting the relationships between the cortical subregions that form the primary convergent inputs to the striatal projection fields of each other cortical subregion , as described for corticostriatal projections in panel a . Individual intra-cortical relationship maps shown for ( b ) FrA , ( c ) M1/2 , ( d ) S1/2 , ( e ) AI/GI/DI , ( f ) LO/VO , ( g ) d/vACC , ( h ) Ptl , ( i ) Rsp , ( j ) Aud , ( k ) Vis , ( l ) PrL/MO , ( m ) IL , ( n ) Rhi/Tem , ( o ) Sub , and ( p ) Amyg . The relative projection density at the target is indicated by the width of the arc at the target . Corticocortical connections are shown between two primary inputs ( darker colored arcs ) and for a primary input to or from a secondary cortical subregion that is not a primary input ( lighter colored arcs ) . Corticocortical connections are indicated for projections with a density >15% in the target area . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 02210 . 7554/eLife . 19103 . 023Figure 6—figure supplement 3 . Network interactions throughout the cortico-thalamo-basal ganglia circuit by subregion , organized according to the cortical subregions . ( a ) Order of cortical ( C ) , striatal ( S ) , and thalamic ( T ) subregions depicted in panels b–p , with arrows below MD , Pf , VAL , and VM indicating the thalamic targets of basal ganglia output . ( b–o ) Network diagrams , as described in Figure 6—figure supplement 2 , for individual subregions , indicating all corticocortical , corticostriatal , thalamostriatal , thalamocortical , and corticothalamic connections associated with each cortical subregion . Individual network interaction maps are shown for ( b ) FrA , ( c ) M1/2 , ( d ) S1/2 , ( e ) AI/GI/DI , ( f ) LO/VO , ( g ) d/vACC , ( h ) Ptl , ( i ) Rsp , ( j ) Aud , ( k ) Vis , ( l ) Rhi/Tem , ( m ) Amyg , ( n ) IL , ( o ) Sub , and ( p ) PrL/MO . Letters to the right of each panel indicate whether each row is corresponds to C , S , or T , and arrows indicate the direction of each projection . Corticocortical connections ( projection density >15% ) are shown for projections from all cortical subregions forming primary convergent inputs with the indicated cortical subregion . Corticostriatal projections are shown for the cortical subregions that form primary convergent projections with the projection field of the indicated cortical subregion . Thalamostriatal projections that converge in the striatum with the projection field of the indicated cortical subregion are shown for each thalamic nucleus having >20% of its volume contributing to the convergent projections ( see Figure 4c–d ) . Thalamocortical projections are shown for each thalamic nucleus with >20% of its volume contributing to projections to the indicated cortical subregion ( See Figure 6—figure supplement 1b , and [Hunnicutt et al . , 2014] ) . Corticothalamic projections are shown if >20% of the thalamic nucleus received projections from the corresponding cortical subregion . Since Amyg constituted a primary convergent input to all corticostriatal projection fields , projections from Amyg were not included in corticocortical maps individually . However , the primary input to the striatal projection field is still indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 023 Cortical feedback to the thalamus is an important component of the cortico-thalamo-basal loop ( Figure 6a ) . To include corticothalamic connections , preprocessed data describing the density of projections in the thalamus for each cortical injection examined herein were downloaded from the AIBS application programming interface ( API ) ( http://connectivity . brain-map . org/ , see Materials and methods ) . These corticothalamic data were integrated into our analysis ( Figure 6d–e , Figure 6—figure supplement 1a–b , green ) , providing a crucial feedback pathway necessary to fully understand excitatory connectivity within the cortico-thalamo-basal ganglia circuit . Corticocortical connections provide another possible path for information integration within this circuit . It has been proposed that cortical subregions whose projections converge in the striatum are more strongly interconnected than subregions that do not converge and non-converging cortical subregions are less interconnected ( Yeterian and Van Hoesen , 1978 ) , which may maintain information segregation . To test the hypothesis that cortical subregions that converge in the striatum are more strongly cortically connected , the same preprocessed AIBS API datasets that were used to map the corticothalamic projections were also used to determine the density of projections between each cortical subregion . Cortical subregions whose projections converge >20% within the striatal projection fields of each other are shown in Figure 6—figure supplement 2b–p . The primary convergent subregions are indicated with a darker color , and projections are shown as ribbons between subregions , where dark ribbons indicate connections between two primary convergent subregions ( Figure 6—figure supplement 2b–p ) . As shown , primary convergent inputs with most cortical subregions form distributed cortical networks , for example frontal subregions IL and d/vACC are more interconnected with cortical subregions that they do not converge with in the striatum . However , some areas , such as FrA , M1/2 , S1/2 , and AI form highly recurrent networks with convergent subregions ( Figure 6—figure supplement 2b–p ) . These varied connectivity patterns suggest that different pathways through the cortico-thalamo-basal ganglia circuit may have different levels of information integration , supporting the existence of both open- and closed-loop circuits . To complete the investigation of the cortico-thalamo-basal ganglia circuit , the thalamocortical , thalamostriatal , corticothalamic , and corticocortical data were compared for MD , ventral anterior-lateral complex ( VAL ) , ventromedial nucleus ( VM ) , and Pf ( Figure 6e , Figure 6—figure supplement 2a and Figure 6—figure supplement 3 ) , which are the main thalamic targets of the basal ganglia output ( Deniau and Chevalier , 1992; McFarland and Haber , 2002; Smith et al . , 2014 ) . A full circuit map shows the relative levels of input convergence between cortical and thalamic subregions , as well as with the basal ganglia output targets ( Figure 6—figure supplement 2a ) , and by focusing on connectivity related to specific subregions , information flow can be traced through the circuit ( Figure 6e and Figure 6—figure supplement 3 ) . For example , these comparisons reveal that the motor cortex ( M1/2 ) directly innervates , receives projections from , and converges in the striatum with all of thalamic nuclei that receive basal ganglia output . This extensive interconnectivity of the thalamic nuclei innervated by the basal ganglia with motor-related cortical and striatal subregions , particularly M1/2 and FrA ( Figure 6e and Figure 6—figure supplement 3 ) , suggests the importance of cortical motor information for basal ganglia function . In contrast , the orbital cortices ( LO/VO ) are highly interconnected with MD , VM , and , to a lesser extent , with VAL , but there are no direct corticothalamic or thalamocortical interactions between LO/VO and Pf . Thus , although LO/VO plays an important role in this circuit , it does not display the ubiquitous connectivity pattern seen with M1/2 ( Figure 6e and Figure 6—figure supplement 3 ) . Similarly , whereas S1/2 is interconnected with VM , Pf , and VAL , it does not send or receive MD projections directly , even though both S1/2 and MD send converging axons in the striatum ( Figure 6e and Figure 6—figure supplement 3 ) . Together , these data provide a comprehensive picture of information flow through the cortico-thalamo-basal ganglia circuit . Taking advantage of the extensive cortico-thalamo-basal ganglia circuit data described above , we examined whether the information flow is segregated with respect to the four major striatal subdivisions described in Figure 5 . First , the primary cortical inputs to each striatal subdivision were identified as either ( 1 ) cortical subregions whose dense projection fields occupy >20% of voxels in the striatal subdivision ( Figure 7—figure supplement 1a–b ) , or ( 2 ) cortical subregions with >50% of their dense projections within the striatal subdivision ( Figure 7—figure supplement 1c ) . The amygdala was excluded from this analysis because it met the criteria for primary inputs for all striatal subdivisions . The primary inputs identified for each subdivision were: dorsomedial ( red ) : d/vACC , Ptl , Rsp , Vis , PrL/MO , and LO/VO; posterior ( green ) : Aud , Vis , and Rhi/Tem; dorsolateral ( cyan ) : FrA , M1/2 , S1/2 , and AI; and ventral ( dark blue ) : PrL , Sub , IL , Rhi/Tem , and AI ( Figure 7—figure supplement 1d ) . The above information allows us to further investigate the cortical and thalamic connections with respect to each striatal subdivision ( Figure 7—figure supplement 1 ) . The thalamocortical , thalamostriatal , corticothalamic , and corticocortical data were compared for each striatal subdivision ( Figure 7 ) using an approach analogous to that used to evaluate information flow through the cortico-thalamo-basal ganglia circuit related to individual cortical subregions ( Figure 6 and Figure 6—figure supplement 2 ) . As seen with the cortical subregion-based analysis ( Figure 6—figure supplement 2 ) , the number and strength of corticocortical connections varied across networks ( Figure 7a–d ) . Cortical areas associated with the dorsolateral striatal subdivision are the most recurrently connected having each primary dorsolateral input connected to at least two other primary dorsolateral inputs ( Figure 7b ) . Interestingly , nearly all cortical subregions ( except Sub ) are connected to at least one other primary striatal subdivision input in their respective networks ( Figure 7a–d ) . 10 . 7554/eLife . 19103 . 024Figure 7 . Cortico-thalamo-basal ganglia circuit organization for striatal subdivisions . ( a–d ) Chord diagrams highlighting the relationships between the cortical subregions forming the primary inputs to the ( a ) dorsomedial , ( b ) dorsolateral , ( c ) posterior , and ( d ) ventral striatal subdivisions respectively . The projection density at the target subregion is indicated by the width of the arc at the target . Corticocortical connections are shown for the afferent and efferent projections of subregions that form the primary input to each striatal subdivision . Primary input regions are shown in darker colors . Darker colored ribbons indicate connections between two primary input subregions , and lighter colored ribbons indicate the connections of a primary input subregion with secondary cortical subregions that do not project to the corresponding striatal subdivision . Connections are shown for projections with a density >15% in the target area . ( e ) Example coronal sections through the thalamus from anterior to posterior with overlaid thalamocortical and thalamostriatal confidence maps , as described in Figure 5 . Each column shows the origin of thalamic projections associated with the four striatal subdivisions shown in Figure 5 . Thalamocortical and corticothalamic projections are grouped across the cortical subregions that form the primary inputs of each striatal subdivision , as determined in Figure 7—figure supplement 1 ( section positions are the same as in Figure 4a ) . ( f ) Nuclear localization for the convergence confidence maps shown in panel e . Values are represented as the fraction of each thalamic nucleus covered by the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections ( magenta ) , the average of confidence levels 1 , 4 , and 7 for thalamocortical projections ( cyan ) and the average of confidence levels 1 , 3 , and 5 for thalamostriatal projections that lie within the white overlapping volume shown in panel e . The density of subregion-specific corticostriatal projections within each nucleus is shown in green ( See Materials and methods for details ) . TC: thalamocortical confidence maps; TS: thalamostriatal confidence maps; O: overlay of thalamocortical and thalamostriatal confidence maps; CT: corticothalamic projections . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 02410 . 7554/eLife . 19103 . 025Figure 7—figure supplement 1 . Intracortical interactions across cortical subregions that innervate the four striatal subdivisions . ( a–b ) Coverage plots indicating the fraction of each striatal cluster ( columns ) occupied by either diffuse or dense corticostriatal projections from each cortical subregion ( rows ) , shown here with the striatal voxel dendrogram ( bottom ) for the four cluster threshold , as determined in Figure 5 . ( c ) Coverage plot indicating the fraction of each subregion specific corticostriatal projection ( rows ) in each of the striatal clusters for the four cluster threshold ( columns ) , with the striatal voxel dendrogram ( bottom ) . ( d–h ) Schematic network diagrams indicating the intra-cortical relationships for the cortical subregions that make up the primary inputs to each striatal subdivisions defined by the four cluster threshold . ( d ) Overview schematic showing the relative locations of cortical subregions on a collapsed sagittal view of the mouse brain ( gray ) . Primary inputs were as follows: ventral subdivision ( dark blue ) : PrL , Sub , IL , Rhi/Tem , and AI; posterior subdivision ( green ) : Aud , Vis , and Rhi/Tem; dorsolateral subdivision ( cyan ) : FrA , M1/2 , S1/2 , and AI; dorsomedial subdivision ( red ) : d/vACC , Ptl , Rsp , Vis , PrL/MO , and LO/VO . Cortical subregions with that target multiple striatal targets are indicated with multiple colors . Amyg projections met the criteria for primary inputs to all striatal subdivisions and is thus shown in gray . Lines connecting subregions indicate projections having a density >15% in the target area . ( e–h ) Schematic corticocortical network diagrams highlighting the spatial relationships between the cortical subregions forming the primary inputs to the ( g ) ventral , ( h ) posterior , ( i ) dorsolateral , and ( j ) dorsomedial subdivision , respectively . Intra-cortical connections are shown for the afferent and efferent projections of subregions that form the primary input to each in striatal subdivision . Connections between two primary inputs are indicated with a colored line , and connections between a primary input subregions and other cortical subregion are indicated with gray lines . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 025 Next , the thalamic relationships with the striatal subdivisions in the cortico-thalamo-basal ganglia circuit were examined ( Figure 7e–f ) . The thalamic origins of projections to each striatal subdivision and the thalamocortical projections to the cortical subregions associated with the same striatal subdivision largely overlap . Thus , nearly all thalamic nuclei that target a given striatal subdivision also send projections to at least one of the cortical subregions that forms a primary input to that striatal subdivision ( Figure 7e–f , white ) . This suggests a strong relationship between the thalamus and the cortex for subdivision-specific input integration in the striatum . In contrast , the projection field of corticothalamic feedback within each network at the thalamus only partially overlaps with the thalamocortical or thalamostriatal projecting nuclei ( Figure 7f , cf . green and white/cyan ) . These data provide further evidence that the striatal clusters identified in the present study ( Figure 5 ) represent functionally relevant striatal subdivisions , and give evidence for robust integration of cortical and thalamic information within each subdivision-associated cortico-thalamo-basal ganglia circuit . The striatal subdivisions described here were defined by their excitatory input patterns , leading us to investigate the functional differences between individual cortical and thalamic inputs to the striatum . Guided by our comprehensive striatal input maps , we examined functional properties of inputs to the dorsomedial ( DM ) striatal subdivision ( Figure 8a and Materials and methods ) . The DM striatal subdivision receives robust innervation from two distinct thalamic areas , with the first area ( Thal1 ) primarily encompassing the anteromedial thalamic nucleus ( AM ) , and the second area ( Thal2 ) including mainly the CL , the lateral portion of MD , and a portion of Po ( Figure 5e–f , Figure 8a and Figure 8—figure supplement 1a ) . In addition , although the DM striatal subdivision receives input from many cortical subregions , dense innervations in this area originate primarily from the d/vACC and Vis ( Figure 5b–c , Figure 8a , and Figure 8—figure supplement 1a , and also see [Berendse et al . , 1992; Khibnik et al . , 2014; McGeorge and Faull , 1989] ) . We performed localized injections of recombinant adeno-associated virus ( AAV ) ( serotype 2 ) expressing channelrhodopsin ( CsChR-GFP ) ( Klapoetke et al . , 2014 ) individually into the four cortical and thalamic subregions ( d/vACC , Vis , Thal1 and Thal2 ) , and confirmed the presence of projections in the DM striatal subdivision ( Figure 8—figure supplement 1a–b ) . Photostimulation of the CsChR-positive axons in the DM striatal subdivision triggered excitatory postsynaptic currents ( EPSCs ) recorded from medium spiny neurons ( MSNs ) , confirming functional connectivity between each input source and the DM striatal subdivision ( Figure 8c–d and Figure 8—figure supplement 1b ) . 10 . 7554/eLife . 19103 . 026Figure 8 . Optogenetic stimulation of cortico- and thalamo-striatal inputs converging on the DM striatal subdivision reveals functional heterogeneity . ( a ) Schematic representation of the DM striatal subdivision shown in red , as presented in Figure 5d . ( b ) The DM subdivision was identified by the convergence of thalamostriatal inputs originating from thalamic centers 1 ( Thal1 ) and 2 ( Thal2 ) ( two left panels , respectively ) , based on the thalamostriatal confidence maps with the thalamic nucleus ( white lines ) fully encompassed by each center , shaded red ( gray scale shows the confidence level as determined in Figure 4 ) , and corticostriatal inputs originating from the d/vACC and Vis ( shaded red , two right panels , PMBA ) . The red areas indicate the targets for viral injections . ( c ) Example traces of paired-pulse EPSCs recorded in MSNs within the DM striatal subdivision , elicited by photostimulation of specific corticostriatal inputs . ( d ) Example traces of paired-pulse EPSCs recorded in MSNs within the DM striatal subdivision , elicited by photostimulation of specific thalamostriatal inputs . ( e ) Quantification of paired-pulse ratio ( PPR ) evoked by photostimulation of specific cortico- and thalamo-striatal inputs reveals strong differences in PPR ( n ( d/vACC ) = 34 , n ( Vis ) = 26 , n ( Thal1 ) = 25 , n ( Thal2 ) = 32 cells , Kruskal-Wallis test , H = 60 . 8699 , df = 3 , p<0 . 0001; post-hoc Dunn’s test , Bonferonni-corrected p=0 . 0002 ) between distinct thalamic nuclei and distinct cortical subregions ( post-hoc Dunn’s test , Bonferroni-corrected , ***p<0 . 001 ) . ( f ) Example traces of repetitive photostimulation ( 20 Hz , 10 stimuli represented by blue lines ) of the four cortico- and thalamo-striatal afferents . ( h ) Quantification of the slow current during repetitive photostimulation , relative to EPSC peak evoked by the first stimulus ( ***p<0 . 0001 ) . Thal1 , thalamic center 1; Thal2 , thalamic center 2 . Group data are presented as mean ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 02610 . 7554/eLife . 19103 . 027Figure 8—figure supplement 1 . Characterization of functional differences between and within individual cortico- and thalamostriatal inputs to the dorsomedial ( DM ) striatum . ( a ) Example injection sites ( left column ) in dorsal and ventral anterior cingulate cortex ( d/vACC ) , visual cortex ( Vis ) , thalamic center 1 ( Thal1 ) , and thalamic center 2 ( Thal2 ) ; example projection sites ( right column ) within the DM striatum , shown with corresponding sections from Paxinos Mouse Brain Atlas ( PMBA , [Paxinos and Franklin , 2001] ) . Injection of channelrhodopsin CsChR2-GFP of Thal1 infected the anteromedial thalamic nucleus ( AM ) , whereas injection of Thal2 mainly infected centrolateral thalamic nucleus ( CL ) together with a portion of the posterior thalamic nucleus ( Po ) , and the lateral part of the mediodorsal thalamic nucleus ( MDL ) . The value in parentheses represents the distance from bregma , with positive value anterior to bregma . S1 , primary somatosensory cortex; Str , striatum . Scale bars represent 1 mm . ( b–e ) Quantification of excitatory postsynaptic current ( EPSC ) amplitude ( b , p>0 . 05 ) , slow current relative to EPSC peak ( c , p<0 . 0001 ) , rise time ( d , p<0 . 05 ) , and decay time ( e , p<0 . 0001 ) . ( f ) Quantification of relative EPSC amplitude over 10 consecutive stimuli , normalized to EPSC amplitude evoked by the first stimulus ( p<0 . 0001 , main effect of injection site: Thal1 vs . Thal2 , p<0 . 0001; d/vACC vs . Vis , p>0 . 05 ) . ( g ) Quantification of the EPSC charge transfer evoked by each stimulus , normalized to the charge transfer evoked by the first stimulus ( p<0 . 0001 , main effect of injection site: Thal1 vs . Thal2 , p<0 . 001; vACC vs . Vis , p<0 . 01 ) . ( h ) Schematic representation of the two cortical subregions and two thalamic centers converging to the DM ( arrows with solid lines ) with suggested region specific connectivity based on previous work ( arrows with dotted lines ) . Statistical comparisons between cortical or thalamic afferents are marked *p<0 . 05 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 19103 . 027 Recent studies have identified functional differences between corticostriatal and thalamostriatal inputs with respect to their synaptic properties ( Ding et al . , 2008; Ellender et al . , 2013; Smeal et al . , 2007 ) . However , the precise synaptic properties of corticostriatal and thalamostriatal inputs differed qualitatively across studies . To determine if these discrepancies were due to a lack of subregion specificity when stimulating cortical or thalamic inputs ( Kreitzer and Malenka , 2008 ) , we examined the synaptic properties of Thal1 , Thal2 , d/vACC , and Vis inputs to MSNs in the DM striatal subdivision . By using a paired-pulse ratio ( PPR ) experiment to examine the presynaptic release probability , we found that paired-pulse photostimulation of Thal1 axons resulted in facilitation of synaptic transmission onto MSNs , whereas Thal2 axons showed synaptic depression ( Figure 8d–e ) . Consistently , repetitive photostimulation ( 10 stimuli , 20 Hz ) of the two thalamic inputs resulted in sustained Thal1 EPSCs with larger relative magnitude than those evoked by Thal2 axons ( Figure 8f–g and Figure 8—figure supplement 1f–g ) . Moreover , a sustained slow current , which is evident even in singly evoked EPSCs ( Figure 8—figure supplement 1c ) in Thal1 inputs , but not in Thal2 inputs , contributed to an overall increased charge transfer during the consecutive photostimuli ( Figure 8—figure supplement 1g ) . Similarly , different cortical projections to the DM striatal subdivision also exhibited heterogeneity ( Figure 8c–g and Figure 8—figure supplement 1b–h ) . The PPR of Vis inputs exhibited strong synaptic depression , which was not observed in d/vACC inputs ( Figure 8c and e ) . Repetitive photostimulation of Vis or d/vACC inputs resulted in similar levels of synaptic depression ( Figure 8f , and Figure 8—figure supplement 1f ) . However , repetitive stimulation d/vACC , but not Vis inputs , resulted in a prominent slow sustained current that led to increased charge transfer at d/vACC–DM synapses relative to Vis–DM synapses over consecutive photostimuli ( Figure 8g and Figure 8—figure supplement 1g ) . Thus , the discrepancies observed across previous studies may be due to electrical stimulation of cortical or thalamic inputs to the striatum lacking sufficient subregion specificity . These data provide , to our knowledge , the first examples of intra-thalamic and intra-cortical heterogeneity among striatal excitatory inputs , suggesting subregion-dependent integration in the striatum .
To our knowledge , the present study provides the first comprehensive excitatory input map of the mouse striatum . Given the broad roles of the striatum in action selection , motor execution , and reward , understanding how individual inputs precisely project to the striatum and how such inputs may interact with one another is a step forward in dissecting the circuit mechanisms underlying striatal function . An unbiased cluster analysis of the corticostriatal input patterns reveals that the striatum can be divided into four large subdivisions with clear boundaries ( Figure 5 ) . Three of these subdivisions most likely correspond to the traditional dorsomedial , dorsolateral , and ventral domains thought to play critical roles in goal-directed behaviors , habitual behaviors , and affective control of behaviors , respectively . The fourth subdivision at the posterior end of the striatum may represent a previously unappreciated functional domain , and illustrates the existence of heterogeneity along the A-P axis . Recent evidence has suggested that the posterior part of the striatum receives inputs from anatomically distinct populations of dopamine neurons ( Menegas et al . , 2015 ) , bears a unique MSN subpopulation composition ( Gangarossa et al . , 2013 ) , and has been shown in primates to mediate specific behavioral functions ( Yamamoto et al . , 2013 ) . Our data identify and describe the distinct connectivity of the posterior striatum in mice , showing that this posterior subdivision receives strong inputs from the auditory , visual , and rhinal cortices , as well as from the amygdala , suggesting that this area may process multi-modality sensory inputs in the context of emotional information ( Figure 5b ) . We also found that the associative striatum , consistent with its proposed function , receives extremely heterogeneous inputs ( Figure 5d ) . The comprehensive input map presented here may also guide future experiments aimed at understanding the function of individual cortical , thalamic , or striatal subdivisions by allowing for a systematic evaluation of all locations to perform imaging and recording experiments . An orthogonal approach to spatially subdividing the striatum involves the separation of the patch and the matrix compartments via neurochemical markers ( Gerfen , 1992; Graybiel and Ragsdale , 1978 ) . These subdivisions have been shown , mainly in primates , to receive distinct patterns of cortical inputs ( Gerfen , 1992 ) . In the future , it will be interesting to examine how the patch/matrix subdivisions interact with the subdivisions described herein to orchestrate striatal function . Besides geometric subdivisions , brain circuitry is also organized based on different neuronal cell types . Future studies combining cell-type-specific and subregion-specific circuit analyses to examine how subregion-specific inputs differentially innervate different cell types , for example , the D1 and D2 MSNs , in striatal subdivisions will provide additional insights into the striatal circuitry in normal and diseased brains . The work presented here was achieved by integrating two large-scale viral-tracing datasets and vigorous data analyses . Recent technical advances have made it possible to systematically generate whole brain projection data at mesoscopic resolution ( Hunnicutt et al . , 2014; Oh et al . , 2014; Pinskiy et al . , 2015; Zingg et al . , 2014 ) . However , it remains challenging to integrate such large datasets ( typically >50 terabytes ) obtained from different research teams under different conditions , and with various forms of metadata . To our knowledge , our study represents the first example of combining two different large mesoscopic imaging datasets ( Figure 1 ) . Our efforts were fruitful for several reasons . First , similar viral infection reagents were used , which standardized many properties of the imaging data , including comparable injection sites and high-imaging sensitivity . Second , our analyses utilize the different advantages of each dataset . For the thalamic dataset ( Hunnicutt et al . , 2014 ) , because the thalamic nuclei can be smaller than the size of an individual injection , high-density , overlapping injections are necessary to achieve adequate mapping resolution ( Hunnicutt et al . , 2014 ) . In contrast , the injections in the AIBS Mouse Connectivity Atlas dataset are sparse and mostly non-overlapping , but they are spread across many brain regions ( Kuan et al . , 2015; Oh et al . , 2014 ) , making them suited for mapping projections from cortical areas , which are larger , more widely spread , and better demarcated than the mouse thalamic nuclei . Although most current efforts at mesoscopic circuit mapping focus on illustrating the connections between two macroscopic brain regions ( Mitra , 2014 ) , information processing in the brain often involves several brain regions . We were able to expand our systematic circuit analyses to include three main brain regions that form a complete loop . Specifically , we examined how subregion-specific projections from the thalamus and the cortex converge in the striatum , and how the thalamus is interconnected with the cortex and basal ganglia ( Figure 2 and Figure 6 ) . To do this , we carried out several analyses . First , we mapped the thalamic origins of thalamostriatal projections and identified the converging subregion-specific corticostriatal inputs ( Figure 2 and Figure 5—figure supplement 1 ) . Second , we illustrated the relationships between the thalamic subregions that directly project to a cortical subregion and the thalamic subregions that converge with the same cortical subregion in the striatum ( Figure 6 and Figure 6—figure supplement 1 ) . It is worth noting that the current thalamic dataset does not include the medial and lateral geniculate nuclei ( MGN and LGN , respectively ) ( Hunnicutt et al . , 2014 ) , although reports in rat ( LeDoux et al . , 1984; Veening et al . , 1980 ) , as well as our visual inspection of AIBS thalamic injections ( data not shown ) , suggest that the MGN , but not the LGN , projects to the posterior striatum . Third , since the thalamus is the major target of basal ganglia output , and only specific thalamic subregions receive basal ganglia innervation ( Deniau and Chevalier , 1992; Gerfen and Bolam , 2010; McFarland and Haber , 2002; Smith et al . , 2014 ) , we examined how these basal ganglia-innervated thalamic subregions differ in connectivity patterns as compared to other thalamic subregions ( Figure 6 ) . We found that these thalamic subregions have strong ties with motor cortical subregions , and converge in the same striatal subdivisions with corticostriatal projections from those motor cortical subregions , consistent with the notion that the basal ganglia play a critical role in movement controls and are in close coordination with the cortical motor processing . Regarding the corticostriatal inputs , the results of the present study are largely consistent with related literature in rat and primate , although several sources could potentially contribute to any discrepancy in isolated cases . First , the relative small size of mouse brain allows the systematic tracing coverage of all cortical subregions and >93% volume of the thalamus with individual injections of small ( 500–600 µm ) sizes , and the imaging of the entire projections of every injection . The level of completeness has not been previously achieved in any mammalian species . The comprehensiveness of the datasets allows us to perform quantitative analyses that are difficult with a few example images . On the other hand , because of the relative small size of mouse brain , and the lack of anatomical landmarks for demarcating certain subregions , accurately assigning cortical subregions can be challenging ( e . g . , for M1 and M2 , see [Mao et al . , 2011] ) . For cortical injections , we applied stringent criteria ( see Materials and methods ) in the selection process and , as a result , only <10% of AIBS injections was included . Even with great care , the lack of clear landmarks for certain mouse cortical subregion definition may still be a source of variability . Second , it is important to note that there are two distinct projection patterns of corticostriatal axons , a localized dense core projection and a diffuse projection that generally spans a wider area than the dense projections ( Mailly et al . , 2013 ) . Many previous mapping studies preferentially focused on the dense projections , particularly when reporting a summary result of several tracing experiments . In our data , we mapped both the dense and diffuse projections , and this revealed some previously underappreciated convergence patterns , such as the diffuse somatosensory-motor inputs to a portion of the limbic striatum ( mid-dark blue , Figures 5 , 15 clusters ) ( Draganski et al . , 2008 ) , and the widespread diffuse projections of LO/VO to nearly the entire striatal volume ( Figure 2 ) . Our dense projection results are highly consistent with the corticostriatal projection distributions reported in the literature ( Gruber and McDonald , 2012 ) , and studies that separate the dense and diffuse projections describe similarly widespread diffuse projections ( Haber et al . , 2006; Mahan and Ressler , 2012 ) . Finally , there might be circuit differences at mesoscopic resolution across species due to parallel evolution and it will be interesting to systematically compare them in the future when similar type of data become available in other mammalian species . Recent work from Hintiryan and colleagues uses an anterograde tracing dataset from cortical injections to illustrate the corticostriatal circuits and demonstrate the usefulness of large scale mesoscopic projection mapping to study ‘circuitry-specific connectopathies’ ( Hintiryan et al . , 2016 ) . Although Hintiryan et al . and our studies both use comprehensive mesoscopic cortical projections in the striatum to understand striatal circuit logic , these two studies are also complementary . In addition to cortico-dorsal striatal projections , our study also includes cortico-ventral striatal projections , thalamostriatal projections , as well as corticocortical and thalamocortical connectivity . Our thalamostriatal dataset is of particular interest because thalamostriatal data for mouse is scarce in the literature and the circuits are much less understood compared to the corticostriatal pathways . The completeness of our dataset allows us to illustrate the features of the cortico-thalamo-basal ganglia loop ( Figure 6 , Figure 7 , Figure 6—figure supplement 2 , Figure 6—figure supplement 3 , and Figure 7—figure supplement 1 ) . The anatomical axonal projection map suggests , but does not guarantee , synaptic connections ( e . g . , see [Dantzker and Callaway , 2000; Mao et al . , 2011; Shepherd and Svoboda , 2005] ) , especially in the striatum where many fasciculated axons pass through without forming synapses . Therefore , we examined the existence of synaptic connections using optogenetic stimulation and physiological recording for the anatomically described corticostriatal and thalamostriatal projections ( Figure 8 and Figure 8—figure supplement 1 ) . Our results indicate that , for the projections identified after computer-assisted exclusion of passing fasciculated axons , the mapped axonal projections do form functional synapses in the striatum ( Figure 8 and data not shown ) . Furthermore , taking advantage of our comprehensive anatomical input map , we examined functional heterogeneity of synaptic connections in the striatum . A series of recent studies have shown that cortical and thalamic inputs form functionally unique synapses in the striatum , although their synaptic properties remain controversial . In addition , little was known about whether different subregions within the cortex or the thalamus form functionally unique synapses in the striatum . We found that distinct cortical and thalamic subregions each give rise to synapses in the striatum with unique synaptic properties ( Figure 8 ) , providing a potential explanation for the discrepancies reported previously ( Ding et al . , 2008; Smeal et al . , 2007 ) when the thalamic or cortical inputs were stimulated in a non-subregion-specific manner . Taken together , these results presented here demonstrate the value of creating comprehensive input maps , and their utility in guiding the effective design of functional studies .
Thalamic injection and imaging data were generated as described previously ( Hunnicutt et al . , 2014 ) . In brief , viral injections were performed in male and female wild-type C57BL/6J mice at postnatal days 14–18 using a hydraulic apparatus to stereotaxically inject ~10 nl of rAAV ( serotype 2/1 ) encoding either eGFP or tdTomato . Two weeks post-injection , each brain was fixed , cryostat-sectioned at 50 µm , and imaged using a Hamamatsu Nanozoomer imaging system ( Japan ) , resulting in 0 . 5 µm/pixel lateral resolution for the full-brain fluorescence images of all injections and their cortical and striatal projections . Injection sites were then re-imaged at a lower exposure time on either the Nanozoomer or a Zeiss Axio Imager to avoid overexposure . Injection site images were matched to their corresponding full brain Nanozoomer images through rigid translation and rotation using manually selected anatomical landmarks visible in both images . The thalamus was manually segmented from the full brain images , and injection sites were segmented from background fluorescence in the green and red channels using a supervised custom MATLAB routine . The alignment of injection sites and thalami , and the generation of the model thalamus were described previously ( Hunnicutt et al . , 2014 ) . Each injection and image was manually inspected for quality control . The raw data for cortical viral injection and projection were obtained from the AIBS Mouse Connectivity Atlas ( http://connectivity . brain-map . org/ ) ( Research Resource Identifier ( RRID ) : SCR_008848 ) ( Oh et al . , 2014 ) . The data generation pipeline was analogous to that used in the thalamostriatal projectome dataset , with a few differences . Briefly , a single iontophoretic injection of AAV2/1 encoding eGFP was performed per animal at postnatal day 56 ( Oh et al . , 2014 ) . Both male and female wild-type and Cre-expressing C57BL/6J mice were used . At two weeks post-infection , the animals were fixed and imaged using a TissueCyte 1000 serial two-photon tomography system , with a lateral resolution of 0 . 35 µm/pixel and a z-resolution of 100 µm . The AIBS Mouse Connectivity Atlas contains >1000 cortical injections . We manually inspected each injection , and selected 127 injections specifically targeting 15 cortical subregions ( See Supplementary file 1 for selection details ) . Specifically , at the time our analyses were performed , the AIBS Mouse Brain Connectivity Atlas contained 1029 cerebral cortex injections ( Oh et al . , 2014 ) which sampled the telencephalon . Here subregions of the isocortex , hippocampus , and amygdala are all broadly defined as telencephalic cortical areas that originate developmentally from the cortical plate , and separated into neocortex ( FrA , M1/2 , S1/2 , Vis , Ptl , and Aud ) , mesocortex ( AI/GI/DI , Rhi/Tem , LO/VO , PrL/MO , IL , dACC/vACC , and Rsp ) and allocortex ( Sub and Amyg ) classes . Neocortex is primarily six-layered and comprised of the primary sensory and motor cortices . Mesocortex , also called the paralimbic cortex , is generally three-layered and is made up of associative subregions in frontal cortex as well as subregions at the interface between allocortex and neocortex , such as insular and perirhinal cortices . Allocortex is the evolutionarily oldest part of cortex , and comprised of piriform cortex , hippocampus and the subiculum ( McGeorge and Faull , 1989 ) . Although the amygdaloid complex has both telencephalic ( pallial ) and subpallial origins , it is situated within allocortex , between piriform cortex and the subiculum ( Pabba , 2013 ) . Being functionally related to both the hippocampus through the limbic system and the piriform cortex with olfactory processing ( Novejarque et al . , 2011 ) , it was grouped here as part of the allocortex . Since that olfactory information does not project directly to the dorsal striatum and only very weakly to the ventral striatum and with olfactory tubercle not considered , olfactory areas and the piriform cortex were not included ( McGeorge and Faull , 1989 ) , leaving 957 injections . This was also checked through a search for olfactory to striatal projections in the AIBS Mouse Brain Connectivity Atlas ( data not shown ) . These 957 injections include both wildtype and cell-type specific cre lines , 177 of these injections are in wildtype C57BL/6J animals . However , many of the wildtype injections spanned multiple cortical subregions and had insufficient subregion specificity to map projections . Therefore , three primary sets of cre lines were also included in the search: A930038C07Rik-Tg1-Cre , Rbp4-Cre_KL100 , and Cux2-IRES-Cre . The cre lines were chosen to span cortical layers 2/3 ( L2/3 ) and 5 ( L5 ) , so as not to bias the dataset towards intratelencephalic ( IT ) or pyramidal-tract ( PT ) -type corticostriatal projections ( Harris and Shepherd , 2015; Kress et al . , 2013 ) , and contain injections in all of the cortical subregions analyzed . This added another 177 injections to the 177 wildtype injections , totaling 354 to choose from . No cortical layer 4 ( L4 ) or layer 6 ( L6 ) lines were chosen because they do not project to the striatum ( Briggs , 2010 ) . One injection each from Etv1-CreERT2 , Gpr26-Cre_KO250 , and Grp-Cre_KH288 mouse lines in auditory and insular cortices were used to supplement the lack of specific L5 or L2/3 injections in the AIBS connectivity atlas for the three primary cre lines described above ( Supplementary file 1 ) . The amygdala and hippocampus were primarily targeted by wildtype injections , but also required a different set of injections from cre lines since they have different gene expression patterns from neocortex and mesocortex . The metadata for each injection identifies the primary and secondary brain areas infected , which was used as a first screening process for subregion specificity before each brain was manually evaluated for injection targeting accuracy and specificity . Some small subregions were grouped with functionally similar areas if few or no specific injections could be identified . This includes the following grouping: LO/VO , dACC/vACC , Rhi/Tem , and AI/GI/DI ( Figure 1a ) . Injections specific to multiple areas within a single large subregion , such as visual and somatosensory cortices were selected to insure full coverage of the entire volume , and were analyzed as a single group ( e . g . , injections in VISp , VISal , VISl , and VISam for visual cortex ) . In the end 127 injections were found to specifically target 15 subregions that spanned all striatal projecting subregions originating from the cortical plate . All areas contain at least one wildtype , one L2/3 , and one L5 injection and contain eight injections on average , with considerable variability depending on the size of the subregion , with the fewest being infralimbic ( IL ) with three injections and most being somatosensory cortex ( S1/2 ) with 21 injections ( Supplementary file 1 ) . For hippocampal areas , while some injections included in this dataset had CA1 or CA3 as a primary target , only injections that at least partially covered the subiculum sent projections to the striatum ( data not shown ) . For amygdalar areas , the primary volumes of the amygdala injections in this dataset are in the basolateral amygdalar nucleus ( BLA ) , and basomedial amygdalar nucleus ( BMA ) , but they also cover parts of the central nucleus of amygdala ( CEA ) , posterior amygdalar nucleus ( PA ) , medial amygdalar nucleus ( MEA ) , and piriform-amygdalar area ( PAA ) , areas which span both pallial and subpallial parts of the amygdaloid complex ( Supplementary file 1 ) . The raw data processing methods used to generate the voxelized corticostriatal projection data and AIBS averaged template brain were described previously ( Kuan et al . , 2015 ) . Mice were injected at P14–16 with 10–20 nl of an AAV2/1 virus encoding ChR2-H134R-TdTomato ( Addgene: 28017 ) . Coronal brain slices were prepared 14 days later from mice anesthetized with an intraperitoneal injection of ketamine ( 13 mg/ml ) /xylazine ( 1 mg/ml ) ( ~0 . 01 ml/g body weight solution was injected ) and perfused transcardially with ice cold ACSF containing ( in mM ) : 127 NaCl , 25 NaHCO3 , 25 D-glucose , 2 . 5 KCl , 1 MgCl2 , 2 CaCl2 , and 1 . 25 NaH2PO4 , pH 7 . 25–7 . 35 , ~310 mOsm , and bubbled with 95% O2/5% CO2 . The brain was removed and placed into ice-cold cutting solution containing ( in mM ) : 110 choline chloride , 25 NaHCO3 , 25 D-glucose , 11 . 5 sodium ascorbate , 7 MgCl2 , 3 sodium pyruvate , 2 . 5 KCl , 1 . 25 NaH2PO4 , and 0 . 5 CaCl2 . 300-μm-thick coronal slices were vibratome sectioned ( Leica , Germany 1200 s ) . Slices were incubated in oxygenated ACSF for 45 min at 34°C , and then maintained in an oxygenated holding chamber at room temperature . Electrophysiology recordings were performed during ChR2 photostimulation , as previously described ( Hunnicutt et al . , 2014; Mao et al . , 2011 ) . The excitatory postsynaptic currents ( EPSCsCRACM ) were recorded in voltage clamp ( holding potentials were –70 mV or –75 mV ) while blue light was stimulated the thalamic axons transfected with ChR2 . Each map was repeated two to four times . The maps were averaged and a cell was counted as a positive responder if there was any excitatory postsynaptic current amplitude >6x the standard deviation of the baseline ( Figure 1—figure supplement 1 ) . The outline of the striatum was manually traced in each image set to generate a striatum mask . The front of the striatum was defined as the first slice containing the nucleus accumbens ( NAc ) , where the anterior commissure ( ac ) separates from the rostral migratory stream . The border of the dorsal striatum was determined by the lateral ventricle ( VL ) and corpus callosum ( cc ) . The ac was included in the striatum mask until it became medial of the VL . Posterior to the commissural part of ac , the ac formed the ventral border of the striatum . In posterior sections containing the globus pallidus extrenal segment ( GPe ) and the internal capsule , they were considered the medial border of the striatum . To facilitate comparison across experiments and datasets , each experimental striatum mask was aligned to the striatum of the AIBS average template brain ( Kuan et al . , 2015 ) . First , each section image was rotated about the anterior posterior ( A-P ) axis so that it was oriented vertically ( i . e . , roll rotation ) based on manually selected midpoints and down-sampled to 25 µm per pixel ( Figure 1—figure supplement 4a ) . Rotation of the images caused by an aberrant sectioning angle about the left-right ( L-R ) axis ( i . e . a pitch rotation ) was estimated using manually selected landmarks , and the rotation due to an aberrant sectioning angle about the dorsal-ventral ( D-V ) axis ( i . e . a yaw rotation ) was estimated using the center of mass of each hemisphere ( Figure 1—figure supplement 4 ) . The average template brain was rotated using these estimated angles to mimic the aberrant sectioning angle of the experimental brain . A center of mass curve was then generated from the striatum mask of this rotated average template brain , and the experimental brain sections were aligned to the rotated average template brain in the M-L and D-V axes . In the M-L axis , only the top half of the striatum was used to calculate the center of mass due to the variability in the ventral striatum masks . Additionally , the first several sections of the striatum ( a variable number depending on D-V rotation angle ) were aligned using the center of mass of the anterior commissure because the range of D-V sectioning angles made the shape of these sections too variable to implement a striatum center of mass alignment . In a case where a section displayed significant tissue damage , the section was skipped , and the sections before and after the damaged section were averaged to replace the damaged section for both the striatum mask , as well as the projection masks . The full experimental striatum was scaled in the anterior-posterior ( A-P ) axis to fit the rotated average template brain based on the first and last section containing the corpus callosum crossing the midline . A linear scaling in the D-V axis was applied based on the average distance from the top of the striatum to the center of mass of the anterior commissure in the front several sections , and this scaling for sections posterior to the anterior commissure crossing the midline was based on the average distance from the top to the bottom of the striatum . Scaling in the M-L axis was determined by an average width of the dorsal striatum above the center of mass . The section images are then iteratively aligned to the rotated average template brain in the D-V axis using the anterior commissure for the first several sections , and the dorsal border of the striatum for posterior sections , and realigned in the M-L axis based on the center of mass of the top half of the striatum . After these alignments , the experimental brains were rotated in all axes to align with the original coordinates of the average template brain , and then subjected to one more round of iterative alignment in each axis as described above . Finally , after visual inspection , if manual adjustments to the alignment were necessary , they were fed back to a point just before the average template brain is rotated to mimic the aberrant sectioning angle of the experimental brain , and the process is repeated . The corresponding thalamic projection masks were aligned concurrently with the striatum masks . The final result is the alignment of each experimental brain to the average template brain ( Figure 1h and Figure 1—figure supplement 4b–c ) . Figure 1—figure supplement 4b–c show all of the aligned striatum masks overlaid at several coronal sections in the A-P axis for all experimental striatum masks . Corticostriatal projections were identified in the AIBS images based on an AIBS custom image segmentation algorithm that identifies all fluorescent pixels and produced a full-resolution ( 0 . 35 µm/pixels ) binary mask of positive pixels ( Figure 1d ) . The images were then binned into 100 µm x100 µm x100 µm voxels , where the value of each voxel represents the fraction that contained positive fluorescence within that voxel . This data was used for the analysis of corticostriatal projections in the present study . Guided by the original images , we applied thresholds of 0 . 2 , 0 . 05 , and 0 . 005 to the voxelized data to localize the dense , moderate , and diffuse projections , respectively . Corticostriatal projection data were manually corrected to remove fluorescence resulting from fasciculated traveling axons that do not make synapses in the striatum , since the AIBS analysis did not vigorously distinguish traveling axons from axon terminals ( Figure 1—figure supplement 2 ) . The contaminating traveling axons were removed manually based on their stereotypic bundled and fasciculated morphology ( similar to fasciculated thalamostriatal axons that are functionally evaluated in Figure 1—figure supplement 1 ) using custom MATLAB software . For all injections in AIBS Mouse Brain Connectivity Atlas , the voxelized data was obtained from the AIBS and the preprocessed projection density data was obtained from the AIBS API ( http://www . brain-map . org/api/index . html ) ( Research Recourse Identifier ( RRID ) : SCR_005984 ) which contained the volume and density of projections to all brain regions defined in the AMBA ontology . This data was utilized in the present study to identify the density of corticothalamic projection in specific thalamic nuclei ( Figure 6 and Figure 6—figure supplement 1 , green ) and corticocortical projections ( Figure 7 , Figure 6—figure supplement 2 , Figure 6—figure supplement 3 and Figure 7—figure supplement 1 ) . These data describe the density of projections in each cortical subregion and each thalamic nucleus . Since these cortical and thalamic subregions are well demarcated and do not contain bundled axons requiring manual removal , as in the striatum ( Figure 1—figure supplement 2a–k ) , this data accurately represents the corticothalamic and corticocortical connectivity of each injection . To localize thalamostriatal projections and distinguish them from traveling thalamocortical axons , a machine-learning plugin for ImageJ , Trainable WEKA Segmentation ( http://fiji . sc/Trainable_Weka_Segmentation ) was used ( Figure 1—figure supplement 3 ) . To prepare the image sets for training , each image section containing striatum was background subtracted , a 12-pixel Gaussian filter was applied , and the striatum mask was used to limit the region of interest to only the striatal volume . The images were then split into single channels ( red or green ) and converted to an 8-bit grayscale format . The WEKA Segmentation program was manually trained to distinguish between three categories: ( 1 ) defasciculated axons that make synapses in the striatum , ( 2 ) fasciculated , or bundled axons that travel through the striatum to reach their final targets in the cortex , or ( 3 ) residual background fluorescence ( Figure 2 and Figure 1—figure supplement 3e ) . Visually , fasciculated traveling axons could be identified as being highly directionally oriented and generally brighter than the defasciculated thalamostriatal projections , which have a diffuse , spidery appearance ( Figure 1—figure supplement 1a–c ) . Since these morphological distinctions varied slightly for projections from different thalamic nuclei , separate training was required for each brain . For each channel , 3–6 sections ( an average of 4 ) were used for training . The Trainable Weka Segmentation parameters were as follows; six image filters were selected , Entropy , Membrane Projections , Neighbors , Structure , and Variance . Classes were homogenized , and the other settings were left on their default values ( membrane thickness: 1 , membrane patch size: 19 , minimum sigma: 1 . 0 , maximum sigma: 16 . 0 , classifier: fast random forest of 200 trees with two features per tree ) . Once the training was complete , the classifier was applied to the remaining ~80 sections of the brain containing the striatum , generating a probability map for each of the three features listed above , which conveys the certainty that a given pixel belonged to each of the three features . Only the defasciculated projection probability map was utilized ( Figure 1—figure supplement 3f ) . A threshold was selected for the defasciculated projection probability map and applied to the full probability map stack . This single-level threshold was chosen to encompass the largest possible region of correctly trained defasciculated projections throughout the striatum ( Figure 2 and Figure 1—figure supplement 3g ) . Individual images were manually inspected for accuracies in projection identification during the Trainable Weka Segmentation process , and any inaccuracy was manually corrected in MATLAB using custom programs . The final output was a binary projection mask encompassing the full thalamostriatal projection for each injection . Confidence maps , which define the thalamic origin of projections , were created to determine the likelihood that regions of the thalamus sent projections to: ( 1 ) striatal volumes that contained corticostriatal projections originating from cortical subregions ( Figure 4a ) , ( 2 ) striatal volumes that contained high- or low-diffuse corticostriatal input convergence ( Figure 5—figure supplement 1c ) , and ( 3 ) striatal subdivisions generated by clustering voxels with common cortical input patterns ( Figure 5e–f and Figure 5—figure supplement 3a ) . To control for alignment variability ( ~100 µm ) across thalamus masks ( Hunnicutt et al . , 2014 ) , an injection ‘core’ was produced by eroding the ‘full’ injection for each three-dimensional injection mask by 100 µm ( Figure 2 and Figure 3—figure supplement 1 ) . For each injection , a positive injection core adds one to the confidence level and a positive full injection adds one ( Figure 3—figure supplement 1b–c , e ) . Similarly , negative injection cores subtract one from the confidence level , and a negative full injection subtracts one . Exception: full injections were only subtracted for the two easiest to meet criteria in each grouping method ( Figure 3—figure supplement 1a , arrows , and 1d ) , and subsequent criteria only subtract negative injection cores as one . Figure 3—figure supplement 1 shows a simplified schematic of this process for case ( 1 ) listed above . A six level confidence map was generated by determining the inclusion of each injection in the following three groups; 10% of the diffuse target volume covered by the projection , 5% of the dense target volume covered by the projection , and 50% of the dense target volume covered by the projection ( Figure 3—figure supplement 1g ) . Thalamic volumes occupied by the cores of injections that did not meet any of these criteria were set to zero . For cases ( 2 ) and ( 3 ) , there was not projection density data , but instead binary volumes targeted by the thalamic projections , so the injection grouping was adjusted accordingly . For these groups , eight level confidence maps were created by determining the inclusion of each injection in the following four groups; 10% of the target volume covered by the projection , 10% of the projection volume within the target , 25% of the target volume covered by the projection , and 25% of the projection volume within the target . Thalamic volumes occupied by the cores of injections that did not meet any of these criteria were set to zero , and values of the final confidence maps below zero are also set to zero . The overall method was similar to that for case ( 1 ) , as shown in Figure 6—figure supplement 1 , except each injection is categorized based on the inclusion in each of the four groups listed above instead of the three groups shown in Figure 3—figure supplement 1 . Each voxel was assigned a point in a 15-dimensional space corresponding to the density of projections from each cortical subregion ( Figure 5 ) . The optimum distance metric was determined by comparing the cophenetic correlation coefficient across methods , and Spearman’s rank correlation metric was selected with a cophenetic coefficient of 0 . 78 . This distance metric and an average linkage were used to perform cluster analysis on the striatal voxels . The maximum number of voxel clusters was determined by applying a threshold to the resulting dendrogram . The projection regions were similarly assigned a point in 25-dimensional space corresponding to the 25 nuclei , and clustered using the same method . The chord diagrams illustrating corticocortical connectivity ( Figure 7 and Figure 6—figure supplement 2b–p ) were generated using a Circos plot with a ratio layout ( Krzywinski et al . , 2009 ) . Since corticocortical connections may be either reciprocal or unilateral , ribbons joining them may have widths on one or both ends . Corticocortical connections are shown only for connections to or from a cortical subregion included in the indicated network , i . e . a primary convergent input to either the corticostriatal projection field ( Figure 6—figure supplement 2 ) or the striatal subregion ( Figure 7 ) . For the corticostriatal projection fields , the convergence of one cortical subregion with one other cortical subregion was averaged across projection densities , i . e . the fraction of dense projections in the dense projection field , moderate projections in the moderate projection field , and diffuse projections in the diffuse projection for one cortical subregion with the corticostriatal projection field of each other cortical subregion . Corticocortical connections are indicated for projections with a density >15% in the target area , and primary convergent subregions are those where their projection fields converge with >50% the target projection field . Since the Amyg has broad projections throughout the striatum , it constituted a primary convergent input to all corticostriatal projection fields . However , in order to highlight unique interactions , the Amyg connections were left out of the corticocortical maps . The network relationship diagrams shown in Figure 6—figure supplement 3 were created using an open source network analysis software program , Gephi ( Bastian et al . , 2009 ) . The summary network diagram shown in Figure 6—figure supplement 2a is a manually modified version of a Gephi network diagram . The order of cortical nodes in each network diagram was based on the cortical subregion clustering shown in Figure 5b , the order of the striatal nodes was the same as for the cortical nodes , and the order of the thalamic nodes were based on their projection similarity , as shown in Figure 4d . Edges are shown for connections that are above a cutoff for each projection type: corticostriatal: projection density >15% in the target area; corticostriatal: projection fields converge with >50% the target projection field ( as described for the chord diagrams above ) ; thalamostriatal: thalamic nucleus with >20% of its volume contributing to the convergent projections; thalamocortical: thalamic nucleus with >20% of its volume contributing to projections to the indicated cortical subregion; corticothalamic: projections where >20% of the thalamic nucleus received projections from the corresponding cortical subregion . For the cortico-thalamo-basal ganglia circuit , it is also worth noting that since the cortical subregions used to localize the thalamocortical projections may send corticostriatal projections to more than just the associated striatal subdivision , the thalamocortical data may over-represent the association with the striatal subdivision . However , this does not diminish the relationship seen between the thalamus and cortex for subdivision-specific networks in the circuit , since the thalamocortical inputs are going to a primary input to the striatal subdivision , but it may account for the excess of thalamocortical projections not associated with corresponding thalamostriatal projections in these networks ( Figure 7e–f ) . Furthermore , the thalamostriatal confidence maps for each striatal subdivision are unrelated to the thalamostriatal confidence maps for cortical subregions since the striatal subdivisions may be either larger or smaller than the full projection fields of their corresponding primary cortical inputs . Brain slices were obtained from mice that were stereotaxically injected using methods similar to those used for the anatomical injections at postnatal day 16 with 10–20 nl of AAV serotype two expressing synapsin-CsChR-GFP , purchased from the University of North Carolina viral core ( titer 4*1012 particles/ml ) ( Klapoetke et al . , 2014 ) . Injection coordinates were deduced from Figure 2a and Figure 4a ( relative to bregma; along the anterior – posterior axis , with positive values anterior to bregma , along the medial – lateral axis relative to the midline , and along the dorsal – ventral axis relative to bregma in µm ) : d/vACC , 850 , 200 , 1750 and 1450; Vis , -3000 , 2200 , 600 and 300; Thal1 , -50 , 500 , 3400; Thal2 , -1000 , 750 , 3000 . Batches of 4–6 mice were injected within one day , and care was taken to include all four subregions in each batch . Coronal brain slices ( 300 µm ) were prepared 14–21 days post-injection with ice cold KREBS buffer containing ( in mM ) 125 NaCl , 21 . 4 NaHCO3 , 11 . 1 D-glucose , 2 . 5 KCl , 1 . 2 MgCl2 , 2 . 4 CaCl2 , 1 . 2 NaH2PO4 , ~305 mOsm , supplemented with 5 µM MK-801 and oxygenated with 95% O2/5% CO2 . Slices were incubated in oxygenated KREBS buffer supplemented with 10 µM MK-801 for 30 min at 33°C and then maintained in a holding chamber at 22–24°C . Recordings were performed at 32–33°C with oxygenated KREBS buffer containing GABAA- and GABAB-receptor antagonists , nicotinic and muscarinic acetylcholine receptor antagonists , a metabotropic glutamate receptor five antagonist , and an NMDA receptor antagonist . Two experimenters ( BCJ and WTB ) using two electrophysiology rigs performed whole-cell recordings; experimenters’ initials below note differences between experimental setups . There was no difference in results between experimenters , therefore all data were pooled . Oxygenated KREBS was supplemented with ( in µM , purchased from Tocris unless noted ) : BCJ , GABAB-receptor antagonist CGP 52432 ( 10 ) , GABAA-receptor antagonist SR 95531 ( 10 ) , nicotinic acetylcholine receptor Mecamylamine ( 10 ) , muscarinic acetylcholine receptor antagonist Scopolamine ( 10 ) , metabotropic glutamate receptor five antagonist MPEP ( 0 . 3 ) , NMDA receptor antagonist MK-801 ( 5 ) ; WTB , GABAB-receptor antagonist CGP 55845 ( 0 . 2 ) , GABAA-receptor antagonist Picrotoxin ( 10 , Sigma Aldrich ) , mecamylamine ( 1 ) , muscarinic acetylcholine receptor antagonist atropine ( 0 . 1 ) and MPEP ( 0 . 3 ) , pre-incubated in MK-801 ( 5 ) . Borosilicate pipettes ( 2 . 8–4 MΩ; Warner Instruments ) were filled with potassium gluconate-based internal solution ( in mM: 110 potassium gluconate , 10 KCl , 15 NaCl , 1 . 5 MgCl2 , 10 Hepes , 1 EGTA , 1 . 8 Na2ATP , 0 . 38 Na2GTP , 7 . 8 phosphocreatine; pH 7 . 35–7 . 40; 290 mOsm ) . Putative MSNs were identified by their morphology and stereotypic physiological properties . Evoked excitatory postsynaptic currents ( EPSCs ) were recorded in whole-cell voltage-clamp mode at −75 mV holding potential . Recordings for Figure 8 and Figure 8—figure supplement 1 were targeted to a non-striated portion of the dorsomedial striatum between the lateral ventricle and the portion of the striatum containing fasciculated traveling axons . Photostimulation was performed using a custom-made LED system , consisting of a 470 nm LED mounted on Olympus BX51WI microscopes , tuned to deliver between 0 . 1 and 2 mW ( measured after 60x objective ) 1 ms duration light pulses . For paired-pulse stimulation , two consecutive pulses at an interval of 50 ms were given and repeated every 20–40 s for at least five times . Repetitive stimulation consisted of 10 pulses at 20 Hz and was repeated every 20–40 s for at least seven times . Putative MSN with evoked EPSC of ≤−100 pA were included . Data were acquired at 10 kHz using an Multiclamp 700B ( BCJ ) with an online 2 kHz low-pass filter ( Molecular Devices ) and Ephus software ( www . ephus . org ) or using an Axopatch 200A amplifier ( Molecular Devices ) and AxoGraph X software sampled at 20 kHz and filtered online with a 5 kHz low-pass filter ( WTB ) . Data analysis was performed in Matlab , R ( http://cran . r-project . org ) , Igor Pro ( Wavemetrics ) , Excel ( Microsoft ) , Axograph X , Origin7 ( OriginLab ) and Prizm 6 ( GraphPad ) . Rise- and decay time were calculated based on 10% to 90% of EPSC peak value . For decay time calculation , the presence of a slow current was taken into account . Slow currents of single evoked EPSCs were calculated as the change in mean current ( 10 ms episode ) at 40 ms post-stimulus relative to 10 ms pre-stimulus or , in the case of repetitive stimulation , 10 ms prior to the tenth stimulus over 10 ms before the first stimulus , and normalized to EPSC peak value . Charge transfer was calculated per stimulus over a 50 ms episode starting from the stimulus onset and normalized to the charge transfer evoked by the first stimulus . For data analysis , numbers of observations represent recorded cells from ( # cells / # mice ) : d/vACC , 34/6; V1 , 26/4; Thal1 , 25/5; Thal2 , 32/5 . Due to the injection site-specific innervation patterns to the striatum , injection sites were first inspected by the experimenters . Injected animals were excluded from analysis , when thal1 or thal2 injection produced tail-contamination in the d/vACC . Statistical comparisons were performed using Kruskal-Wallis test followed by post-hoc Dunn’s test with Bonferonni correction for multiple testing ( Figure 8e , g , and Figure 8—figure supplement 1b–e ) and two-way repeated measures ANOVA with post-hoc Tukey’s multiple comparisons test ( Figure 8—figure supplement 1f–g ) . The results presented here do not show correlations with the light power used for photostimulation ( data not shown ) . Mice ( P21 ) were injected with LumaFluor red or green beads ( 1:1 diluted in sterile PBS ) in the dorsomedial ( DMS ) and posterior striatum ( PS ) . Each animal received one injection in the DMS and two in the PS . Bead color – injection region combination was assigned randomly per animal . Injection coordinates were based on Figures 5 , 15 nl bead volume per position was deposited at ( relative to bregma; along the anterior – posterior axis , with positive values anterior to bregma , along the medial – lateral axis relative to the midline , and along the dorsal – ventral axis relative to bregma in µm ) : DMS , 1000 , 1000 , 3100; PS , −1600 , 3250 , 3700 and 3400 . Mice were perfused with ice-cold 4% PFA in PBS 3 days post-injection . Brains were resected , post-fixed in 4% PFA in PBS overnight and subsequently stored in PBS at 4° . Coronal brain sections of 50 µm were produced on a vibratome and stained with 1:5000 Hoechst . Epifluorescent tiled images were made on an AxioImager N2 ( Zeiss ) . | To fully understand how the brain works , we need to understand how different brain structures are organized and how information flows between these structures . For example , the cortex and thalamus communicate with another structure known as the basal ganglia , which is essential for controlling voluntary movement , emotions and reward behaviour in humans and other mammals . Information from the cortex and the thalamus enters the basal ganglia at an area called the striatum . This area is further divided into smaller functional regions known as domains that sort sensorimotor , emotion and executive information into the basal ganglia to control different types of behaviour . Three such domains have been identified in the striatum of mice . However , the boundaries between these domains are vague and it is not clear whether any other domains exist or if the domains can actually be divided into even smaller areas with more precise roles . Information entering the striatum from other parts of the brain can either stimulate activity in the striatum ( known as an “excitatory input” ) or alter existing excitatory inputs . Now , Hunnicutt et al . present the first comprehensive map of excitatory inputs into the striatum of mice . The experiments show that while many of the excitatory inputs flowing into the striatum from the cortex and thalamus are sorted into the three known domains , a unique combination of the excitatory inputs are sorted into a new domain instead . One of the original three domains of the striatum is known to relay information related to associative learning , for example , linking an emotion to a person or place . Hunnicutt et al . show that this domain has a more complex architecture than the other domains , being made up of many distinct areas . This complexity may help it to process the various types of information required to make such associations . The findings of Hunnicutt et al . provide a framework for understanding how the striatum works in healthy and diseased brains . Since faulty information processing in the striatum is a direct cause of Parkinson’s disease , Huntington’s disease and other neurological disorders in humans , this framework may aid the development of new treatments for these disorders . | [
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"neuroscience"
] | 2016 | A comprehensive excitatory input map of the striatum reveals novel functional organization |
The enterococcal cytolysin is a virulence factor consisting of two post-translationally modified peptides that synergistically kill human immune cells . Both peptides are made by CylM , a member of the LanM lanthipeptide synthetases . CylM catalyzes seven dehydrations of Ser and Thr residues and three cyclization reactions during the biosynthesis of the cytolysin large subunit . We present here the 2 . 2 Å resolution structure of CylM , the first structural information on a LanM . Unexpectedly , the structure reveals that the dehydratase domain of CylM resembles the catalytic core of eukaryotic lipid kinases , despite the absence of clear sequence homology . The kinase and phosphate elimination active sites that affect net dehydration are immediately adjacent to each other . Characterization of mutants provided insights into the mechanism of the dehydration process . The structure is also of interest because of the interactions of human homologs of lanthipeptide cyclases with kinases such as mammalian target of rapamycin .
Cytolysin is produced by many clinical isolates of Enterococcus faecalis and consists of two post-translationally modified peptides termed cytolysin L and S ( Figure 1A ) ( Gilmore et al . , 1994; Cox et al . , 2005 ) . These peptides have lytic activity against various types of eukaryotic cells including immune cells ( Cox et al . , 2005; Bierbaum and Sahl , 2009 ) . The production of cytolysin enhances virulence in infection models of E . faecalis , and epidemiological data support an association with acute patient mortality ( Ike and Clewell , 1984; Huycke et al . , 1991; Chow et al . , 1993; Van Tyne et al . , 2013 ) . Cytolysin is a member of the lanthipeptides , a family of polycyclic peptides that are made in a two-step process involving dehydration of Ser and Thr residues to dehydroamino acids and subsequent addition of thiols of Cys residues to the dehydroamino acids ( Figure 1B ) ( Knerr and van der Donk , 2012 ) . This process , catalyzed by the enzyme CylM for cytolysin , generates the characteristic thioether crosslinks called lanthionine ( Lan ) and methyllanthionine ( MeLan ) ( Figure 1 ) . An N-terminal leader peptide in the substrates is important for substrate binding by lanthipeptide biosynthetic enzymes ( Oman and van der Donk , 2010 ) , but the post-translational modifications take place in the C-terminal core peptides . 10 . 7554/eLife . 07607 . 003Figure 1 . Biosynthesis of the enterococcal cytolysin . ( A ) Biosynthetic route to cytolysin S ( small subunit of cytolysin ) and the structure of cytolysin L ( large subunit of cytolysin ) . CylM dehydrates three Thr and one Ser in the precursor peptide CylLS to generate three Dhb residues and one Dha . The enzyme also catalyzes the conjugate addition of the thiols of Cys5 to Dhb1 and Cys21 to Dha17 . The proteases CylB and CylA then remove the leader peptide in a step-wise manner to provide cytolysin S . In similar fashion , CylM catalyzes seven dehydrations of Ser and Thr residues and three cyclization reactions during the biosynthesis of the large subunit of cytolysin . Abu-S-Ala = methyllanthionine ( MeLan ) ; Ala-S-Ala = lanthionine ( Lan ) ; Dha = dehydroalanine; Dhb = dehydrobutyrine . ( B ) Post-translational modifications carried out by CylM during cytolysin biosynthesis . Xn = peptide linker . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 00310 . 7554/eLife . 07607 . 004Figure 1—figure supplement 1 . MALDI/TOF mass spectra for CylLL ( A ) and CylLS ( B ) peptides incubated with ( magenta traces ) or without ( blue traces ) CylM . Linear CylLL , calculated M: 7 , 082 , average mass; observed M + H+: 7084 , average mass . CylM modified CylLL , calculated M—7 H2O: 6956 , average mass; observed M—7 H2O + H+: 6958 , average mass . CylLS , calculated M: 7132 , average mass; observed M + H+: 7134 , average mass . CylM modified CylLS , calculated M—4 H2O: 7060 , average mass; observed M—4 H2O + H+: 7062 , average mass . The observed masses are consistent with the expected post-translational modifications shown in Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 00410 . 7554/eLife . 07607 . 005Figure 1—figure supplement 2 . ESI MS/MS analysis of CylLL ( A ) and CylLS ( B ) core peptides modified by CylM in vitro and treated with the protease CylA that removes the leader peptide . The fragmentation pattern verifies the expected ring topology . The parent ions provided masses consistent with the expected structure and the MS/MS data corroborate the ring topology . CylM-modified CylLL core peptide , calculated ( M—7 H2O + 3 H ) 3+: 1146 . 2 , monoisotopic mass; observed ( M—7 H2O + 3 H ) 3+: 1146 . 2 , monoisotopic mass . CylM-modified CylLS core peptide , calculated ( M—4 H2O + 2 H ) 2+: 1016 . 5 , monoisotopic mass; observed ( M—4 H2O + 2 H ) 2+: 1016 . 5 , monoisotopic mass . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 005 To date , four distinct routes to lanthipeptides have been discovered , illustrating that the cyclic thioether motif is a privileged structural scaffold that has been independently accessed multiple times during evolution ( Zhang et al . , 2012 ) . The thioether bridges introduce conformational constraints that facilitate target binding and reduce proteolytic susceptibility . The four routes differ primarily in the mechanism of dehydration . For the class I , III , and IV lanthipeptides , the mechanism of dehydration has been illuminated by crystallographic characterization of the dehydratases or close sequence homologs ( Li et al . , 2006 , 2007; Goto et al . , 2010; Ortega et al . , 2015 ) , but the mechanism of dehydration for class II lanthipeptide synthetases ( LanMs ) that include CylM has remained enigmatic . These enzymes show no clear sequence homology with non-lanthipeptide proteins and despite two decades of investigation , structural information on class II lanthipeptide synthetases has been unavailable . Such information would be valuable for obtaining inhibitors of cytolysin biosynthesis that could be therapeutically valuable . In addition , LanM lanthipeptide synthetases are involved in the biosynthesis of several lanthipeptides and their derivatives that are under clinical evaluation such as actagardine and duramycin ( Grasemann et al . , 2007; Steiner et al . , 2008; Jones and Helm , 2009; Johnson , 2010; Oliynyk et al . , 2010; Crowther et al . , 2013 ) . As such , structural information on this class of synthetases will also facilitate bioengineering of improved analogs . We describe here the 2 . 2 Å resolution structure of CylM and demonstrate that its dehydration domain surprisingly has structural similarity with eukaryotic lipid kinases despite the absence of notable sequence homology . These findings may also have implications for the three eukaryotic homologs of lanthipeptide cyclases , one of which was recently shown to interact with mammalian target of rapamycin ( mTOR ) complex 2 ( Zeng et al . , 2014 ) .
CylM and its substrate peptides CylLL and CylLS were expressed in Escherichia coli as hexahistidine-tagged proteins and purified by metal affinity chromatography . Incubation of CylM with CylLL or CylLS in the presence of MgCl2 and adenosine triphosphate ( ATP ) , and subsequent removal of the leader peptides by purified CylA , a serine protease of the cytolysin biosynthetic pathway ( Booth et al . , 1996 ) , resulted in the desired number of dehydrations as determined by matrix-assisted laser-desorption time-of-flight mass spectrometry ( MALDI-TOF MS ) ( Figure 1—figure supplement 1 ) . Analysis of the peptides by tandem electrospray ionization mass spectrometry ( ESI MS ) demonstrated the formation of the correct ring structures ( Figure 1—figure supplement 2 ) . To investigate the mechanism of catalysis , we determined the 2 . 2 Å resolution structure of CylM in complex with adenosine monophosphate ( AMP ) . The structure of the ∼110 kDa polypeptide consists of two distinct domains , with an N-terminal dehydration domain , composed of residues Asn4 through Pro624 , and a C-terminal cyclization domain encompassed by Tyr641 through Glu992 ( Figure 2A ) . The protein is a monomer in the crystal and in solution as determined by gel filtration analysis . Consistent with prior predictions , the cyclization domain consists of the α/α-barrel fold observed in the structure of the stand-alone class I Lan cyclase NisC ( Li et al . , 2006 ) . As in NisC , CylM contains a single zinc ion near the center of the toroid coordinated by residues Cys875 , Cys911 , and His912 , with a water molecule completing the tetrahedral coordination geometry at the metal . This zinc site is believed to activate the thiols of the Cys residues during the cyclization reaction ( Li et al . , 2006 ) . The barrel of the NisC structure is interspersed with a structural element that resembles eukaryotic peptide-binding domains ( Figure 2B ) , which is thought to bind the leader region of the substrate peptide . The C-terminal cyclization domain of CylM lacks this element but instead contains a β-sheet region composed of three antiparallel strands that is situated near the zinc ion ( Figure 2A , red ) . This element , encompassing Ile666 through Leu690 , is located on the opposite face of the toroid relative to the putative leader peptide-binding domain in NisC , where it flanks against the base of the N-terminal dehydration domain of CylM . A similar antiparallel β-stranded element engages the leader peptide in the mechanistically unrelated class I Lan dehydratase NisB ( Ortega et al . , 2015 ) and is also found in other enzymes involved in the biosynthesis of ribosomally synthesized and post-translationally modified peptides ( RiPPs ) ( Koehnke et al . , 2013; Burkhart et al . , 2015; Koehnke et al . , 2015 ) ( Figure 2C ) . Thus , the leader peptide binding architecture may be conserved across RiPP biosynthetic enzymes , despite very high diversity of the reactions they catalyze ( Arnison et al . , 2013 ) . To investigate whether the β-stranded element in the cyclase domain is important for the dehydration reaction , the N-terminal domain ( residues 1–625 ) that lacks this element was expressed and purified with an N-terminal His6-tag . Incubation with CylLS substrate resulted in efficient dehydration ( Figure 2—figure supplement 1 ) , indicating that the β-stranded element is not required for the dehydration reaction . This observation is consistent with several very recent reports describing expression and activity of the two individual domains of various LanM enzymes and binding of their substrates to both domains ( Ma et al . , 2015; Shimafuji et al . , 2015; Yu et al . , 2015 ) . 10 . 7554/eLife . 07607 . 006Figure 2 . ( A ) Overall structure of CylM . ( B ) Structure of the class I lanthipeptide cyclase NisC illustrating the structural homology with the C-terminus of CylM . ( C ) Comparison of the putative peptide-binding β-strands of CylM with the peptide binding regions of other RiPP biosynthetic enzymes including NisB ( involved in nisin biosynthesis , PDB 4WD9 ) and LynD ( involved in cyanobactin biosynthesis; PDB 4V1T ) . ( D ) Structure of the lipid kinase PI3K that shares homology with the dehydration domain of CylM . ( E ) Domain organization of LanMs in comparison with that of lipid kinases . RBD , Ras-binding domain . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 00610 . 7554/eLife . 07607 . 007Figure 2—figure supplement 1 . MALDI-TOF mass spectrum for CylLS modified by the CylM dehydratase domain in Escherichia coli . Calculated M—4 H2O: 8330 , average mass; observed M—4 H2O + H+: 8334 , average mass . Partial gluconoylation at the N-terminus of dehydrated CylLS occurred when expressing the peptide in E . coli BL21 ( DE3 ) , resulting in a +178 Da peak in addition to the desired peptide mass ( Aon et al . , 2008 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 00710 . 7554/eLife . 07607 . 008Figure 2—figure supplement 2 . Topology diagrams for ( A ) CylM and ( B ) PI3 kinase P110γ . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 00810 . 7554/eLife . 07607 . 009Figure 2—figure supplement 3 . Structure based alignment of biochemically characterized LanM enzymes . Secondary structural elements are colored as in Figures 2A , 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 009 Although the N-terminal dehydration domain lacks detectable sequence similarities with other proteins , the CylM structure reveals that this domain is architecturally related to the catalytic core of lipid kinases , such as phosphoinositide 3-kinase ( PI3K ) ( Figure 2A , D ) ( Walker et al . , 1999; Williams et al . , 2009 ) . The structural similarity with kinases is consistent with the proposed mechanism of dehydration by LanM proteins via first phosphorylation of Ser and Thr residues , followed by elimination of the phosphate ( Chatterjee et al . , 2005; You and van der Donk , 2007 ) . Despite the structural homology , the topology of the CylM dehydration domain is quite different from those of canonical kinases ( Figure 2E ) , resulting in a distinct connectivity between conserved secondary structural features ( Figure 2—figure supplement 2 ) , which can only be gleaned through a structure-based alignment ( Figure 2—figure supplement 3 ) . Like canonical kinases , the CylM dehydration domain is composed of an N-lobe spanning residues Lys135 through Ser279 , and a C-lobe composed of residues Glu280 through Val508 ( Figure 3A ) . A number of helices formed by residues Leu5 through Asn131 and a two-helix insert created by Ile180 through Tyr200 cap the N-lobe , and hence we name these the ‘capping helices’ ( Figure 3A ) . A similar , but topologically distinct , four-helix bundle insertion ( termed the FRB domain [Chen et al . , 1995] ) cradles the N-lobe of PI3K-related protein kinases ( PIKKs ) such as , mTOR and DNA-PKc ( Figure 3B , C ) ( Choi et al . , 1996; Sibanda et al . , 2010; Yang et al . , 2013 ) . Another distinct domain that we term the kinase-activation ( KA ) domain ( see below ) is held in place through interactions with the ‘capping helices’ ( Figure 3A ) . The CylM C-lobe is appended with two helices formed by Gln589 through Pro624 that are characteristic of lipid kinases ( helices kα10 and kα11 in lipid kinase nomenclature ) ( Walker et al . , 1999; Miller et al . , 2010; Sibanda et al . , 2010; Yang et al . , 2013 ) . Helix kα11 of CylM packs against the three-β-stranded proposed leader peptide-binding region in the cyclase domain ( Figure 3A ) . In mTOR , the equivalent kα11 helix is necessary for stabilization of the activation loop ( Figure 3B ) ( Yang et al . , 2013 ) . As a result of all of these architectural additions , the CylM dehydration domain is considerably larger than the catalytic domain of other protein and lipid kinases , with the exception of the aforementioned PIKKs that contain the FRB insertion within the N-lobe ( Figure 3—figure supplement 1 ) ( Sibanda et al . , 2010; Yang et al . , 2013 ) . 10 . 7554/eLife . 07607 . 010Figure 3 . ( A–C ) Comparison of the kinase domains of ( A ) CylM , with those of ( B ) mammalian target of rapamycin ( mTOR ) and ( C ) DNA-PKc ( a PI3 kinase ) . Secondary structural elements are colored as in Figure 2A and structurally unique insertions are designated . ( D ) Close up of the CylM dehydratase active site showing the bound nucleotide , and the proximity of residues important for phosphorylation and phosphate elimination . A simulated annealing difference Fourier map ( calculated without the nucleotide ) is superimposed in blue mesh . ( E ) Solvent occluded surface showing the two possible peptide-binding grooves that flank the peptide β-strand element ( red ) . A loop = activation loop . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01010 . 7554/eLife . 07607 . 011Figure 3—figure supplement 1 . Two views , rotated by 180o , of the superposition of the kinase active site of CylM ( in purple ) with the kinase domain of PI3 kinase ( in cyan ) . Relevant structural and functional elements are noted . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01110 . 7554/eLife . 07607 . 012Figure 3—figure supplement 2 . Superposition of the active sites of CylM ( pink ) with the co-crystal structures of transition state mimics bound to mTOR ( cyan ) and cyclin-dependent protein kinase CDK2 ( green ) . Conserved active site residues implicated in the catalytic mechanism are labeled using the same color-coding as for the polypeptides . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01210 . 7554/eLife . 07607 . 013Figure 3—figure supplement 3 . Superposition of the active sites of CylM ( pink ) with cyclin-dependent protein kinase CDK2 bound to a peptide substrate ( green ) . The insertions in the kinase domain of CylM preclude binding of the CylA peptide substrate in a similar pose . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 013 All protein and lipid kinases contain a requisite ∼30-residue segment termed the activation loop that plays a major role in both regulation and function . Kinase activity is typically controlled through activation-induced conformational changes , consisting of a disorder-to-order transition of the activation loop , which aligns active site residues and provides part of the binding site for substrate . Unlike most kinases , the activation loop in the CylM dehydration domain , composed of residues Asn365 through Val383 , is well defined in the absence of bound peptide substrate ( Figures 2A , 3A ) . The orientation and stabilization of the activation loop is established through numerous interactions with the LanM-specific KA domain , which is itself held in place through interactions with the ‘capping helices’ ( Figures 2A , 3A ) . The activation loop is presumably stabilized in a catalytically competent conformation , because unlike most lipid kinases , the dehydration activity of LanM enzymes is not dependent on exogenous regulatory protein activators . The activation loop in mTOR is similarly held in a catalytically competent conformation via interactions with a highly conserved and integral ∼35 residue FATC domain , which is stabilized through packing interactions with a ∼40 residue insertion in the C-lobe termed the LBE ( Figure 3B ) ( Yang et al . , 2013 ) . In the CylM co–crystal structure , the bound AMP is located between the two lobes of the kinase domain ( Figure 3D ) . The phosphate-binding loop ( P-loop ) is composed of residues Ser247 through Thr262 and is considerably longer than the equivalent feature in PI3Ks . It contains many residues that are poised to interact with the nucleotide phosphate , including Asp252 and His254 . Mutational analysis suggests a second role for these residues in the elimination of phosphate from the phosphorylated peptide product ( see below ) . The adenine is situated in a hydrophobic binding pocket common across other structurally characterized kinases that is defined by CylM residues Val272 ( Ile831 in PI3Kγ ) , Val301 ( Tyr867 in PI3Kγ ) , Ile354 ( Met953 in PI3Kγ ) , and Val361 ( Phe961 in PI3Kγ ) . The proposed mechanism for PIKKs involves a conserved DxH motif , and LanM-conserved residues Asp347 and His349 are poised for catalysis in CylM ( Figure 3D ) . His349 may receive a proton from Ser/Thr in the substrate peptide ( Miller et al . , 2010 ) , in which case Asp347 likely orients the Ser/Thr oxygen for nucleophilic attack onto the γ-phosphate of ATP . Alternatively , Asp347 could accept the proton from substrate ( Yang et al . , 2013 ) . The invariant Lys residue that activates the γ-phosphate in kinases ( Hanks and Hunter , 1995 ) is Lys274 in CylM . Additionally , two conserved residues , Asn352 and Asp364 , are situated to act as divalent metal ligands to stabilize the incipient charge in the transition state , although slight reorientation of the side chain conformations must occur upon binding of the metal . A superposition of CylM with recent structures of the cyclin-dependent PIKK CDK2 ( Bao et al . , 2011 ) and mTOR ( Yang et al . , 2013 ) , each bound to transition state mimics , reveals a near-perfect coincidence of equivalent residues at the active site , underscoring their importance in catalysis ( Figure 3—figure supplement 2 ) . The structure also suggests a model for how the substrate peptide can bind to the two active sites . A superposition of CylM with the CDK2-substrate peptide bound structure ( Bao et al . , 2011 ) reveals that the LanM-specific KA domain occludes the canonical peptide binding sites of protein and lipid kinases ( Figure 3—figure supplement 3 ) . Instead , a solvent-excluded surface diagram demarcates a groove that leads to the nucleotide-binding site of the dehydratase domain ( Figure 3E ) . A second groove traces to the zinc ion in the cyclase domain . In order to establish the functional relevance of the observed structural similarities between CylM and lipid kinases , we measured the kinetics for ATP hydrolysis by CylM in the presence of the substrate peptide CylLS . Using a commercially available coupled luminescence assay kit that detects adenosine diphosphate ( ADP ) , the steady-state kinetic parameters for ATP consumption by CylM were measured affording an apparent KM value of 99 ± 6 μM for ATP and a kcat , app of 4 . 1 ± 0 . 1 min−1 ( Figure 4 ) ; because poor solubility precluded saturation in the peptide substrate , these are apparent values . By way of comparison , prior studies established the kinetic parameters for the kinase domain of mTOR against the 4EBP1 peptide substrate yielding a KM of 9 . 5 μM for ATP and kcat of 0 . 91 min−1 ( Tao et al . , 2010 ) . Thus , the catalytic efficiency of ATP consumption by CylM is roughly within the same order of magnitude of the basal activity of the kinase domain of mTOR ( which is enhanced ∼fivefold in mTOR complex 1 [Tao et al . , 2010] ) . 10 . 7554/eLife . 07607 . 014Figure 4 . Dependence of the rate of ADP production by CylM ( 1 μM ) on ATP concentration in the presence of 100 μM CylLS . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 014 Given the unanticipated lipid kinase fold , we focused our mechanistic studies on the CylM dehydration reaction . The aforementioned residues in the active site of CylM ( Asp347 , His349 , Asn352 , Asp364 , and Lys274 ) are conserved in the LanM family . Their importance was investigated by replacement with Ala and assessing the activity with CylL , active site ofS as substrate . No dehydration could not be detected for any of the mutants with the exception of CylM-H349A and K274A that both produced a small amount of dehydrated CylLS as determined by MALDI-TOF MS ( Figure 5 and Figure 5—figure supplements 1 , 2 and Figure 5—source data 1–3 ) . 10 . 7554/eLife . 07607 . 015Figure 5 . MALDI-TOF mass spectra of CylLS peptides co-expressed with CylM and CylM mutants in E . coli . M = unmodified CylLs; P = phosphorylation . Peaks between the highlighted masses of multiply phosphorylated CylLS correspond to intermediates resulting from both phosphorylation and partial dehydration . A table showing the calculated and observed masses of each intermediate is provided in Figure 5—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01510 . 7554/eLife . 07607 . 016Figure 5—source data 1 . Calculated and observed masses of CylLS peptides modified by CylM and CylM mutants in E . coli . All calculated masses are [M + H] . -: not observed . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01610 . 7554/eLife . 07607 . 017Figure 5—source data 2 . Calculated and observed masses of CylLS peptides incubated with CylM and CylM mutants in vitro for 30 min . All calculated masses are [M + H] . -: not observed . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01710 . 7554/eLife . 07607 . 018Figure 5—source data 3 . Calculated and observed masses of CylLS peptides incubated with CylM and CylM mutants in vitro for 10 hr . All calculated masses are [M + H] . -: not observed . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01810 . 7554/eLife . 07607 . 019Figure 5—figure supplement 1 . MALDI-TOF mass spectra of CylLS peptides incubated with CylM and CylM phosphorylation-deficient mutants in vitro for 30 min ( left ) and 10 hr ( right ) . M = unmodified CylLs; P = phosphorylation . Peaks between the masses of the highlighted multiply phosphorylated CylLS correspond to intermediates resulting from both phosphorylation and partial dehydration . A table showing the calculated and observed masses of each intermediate is provided in Figure 5—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 01910 . 7554/eLife . 07607 . 020Figure 5—figure supplement 2 . MALDI-TOF mass spectra of CylLS peptides incubated with CylM elimination-deficient mutants in vitro for 30 min ( left ) and 10 hr ( right ) . M = unmodified CylLs; P = phosphorylation . Peaks between the highlighted masses of multiply phosphorylated CylLS correspond to intermediates resulting from both phosphorylation and partial dehydration . A table showing the calculated and observed masses of each intermediate is provided in Figure 5—source data 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 020 CylM is distinct from canonical PIKKs in that it not only phosphorylates its substrate but also eliminates the phosphate to generate a dehydroamino acid . Previous mutagenesis studies identified four conserved residues in LanMs that are important for the phosphate elimination reaction ( You and van der Donk , 2007; Ma et al . , 2014 ) . Inspection of the CylM structure shows that these four residues ( Asp252 , His254 , Arg506 , and Thr512 ) are in close proximity to each other despite a separation of >250 amino acids in primary sequence , and that they are situated in or immediately adjacent to the phosphorylation site ( Figure 3D ) . Their importance was investigated using alanine substitution . Phosphorylated intermediates were detected for all four mutants , with partially dehydrated products observed for all except CylM-T512A ( Figure 5 and Figure 5—figure supplements 1 , 2 and Figure 5—source data 1–3 ) . Thus , all four residues are important for phosphate elimination . Asp252 and His254 are in the P-loop , which is thought to activate NTPs for attack during hydrolysis or substrate phosphorylation by interacting with the γ-phosphate . The mutant phenotypes suggest that these residues may play a similar role of phosphate stabilization during the phosphate elimination reaction . Arg506 and Thr512 are located within the KA domain , suggesting that , in addition to stabilizing the activation loop , this domain also provides residues to assist in the elimination of phosphate . CylM thus offers insights into how an existing fold for an enzymatic activity ( phosphorylation ) can be adopted to carry out a second activity ( elimination ) . The mechanism to achieve dehydration in CylM is decidedly different from that found in other lanthipeptide synthetases . In class I , the Ser and Thr side chain hydroxyl groups are activated by glutamylation in a glutamyl-tRNA-dependent process ( Garg et al . , 2013; Ortega et al . , 2015 ) . Class III and IV lanthipeptide synthetases are made up of separate Ser/Thr protein kinase , phosphoSer/phosphoThr elimination , and cyclase domains that are readily recognized by sequence homology ( Goto et al . , 2010 ) . The distinct phosphorylation and elimination domains in these latter enzymes require the phosphorylated peptides to translocate from the kinase to the lyase active site , accounting for the observation of phosphorylated intermediates ( Jungmann et al . , 2014 ) . The adjacency of the phosphorylation and elimination active sites in CylM provides an explanation for the lack of observed phosphorylated substrate peptides as intermediates in LanM catalysis if elimination occurs faster than peptide dissociation ( Thibodeaux et al . , 2014 ) . To further investigate the elimination step , a mixture of phosphorylated CylLS peptides carrying different numbers of phosphate esters was obtained by co-expression of CylLS with the elimination-deficient CylM-R506A mutant in E . coli . The purified phosphorylated peptides were then incubated with wild-type CylM . Without addition of nucleotides , CylM did not eliminate the phosphates . However , when ADP or ADP analogs were supplied , the phosphorylated peptides were converted to dehydrated CylLS peptides ( Figure 6 and Figure 6—figure supplements 1 , 2 ) . Collectively , our results are consistent with an ordered kinetic mechanism in which ADP needs to bind before the phosphorylated peptide or in which the presence of ADP within the active site increases the affinity for phosphorylated peptide intermediates . The results are also consistent with processive phosphorylation and elimination steps since ADP present in the active site from the phosphorylation reaction is required for the phosphate elimination reaction . 10 . 7554/eLife . 07607 . 021Figure 6 . MALDI-TOF mass spectra of phosphorylated CylLS intermediates incubated with CylM in the absence of nucleotides ( black trace ) , and in the presence of AMP ( adenosine 5′-monophosphate disodium salt ) ( blue trace ) , non-hydrolyzable ADP ( adenosine 5′- ( β-thio ) diphosphate trilithium salt ) ( magenta trace ) , or non-hydrolyzable ATP ( adenosine 5′- ( β , γ-imido ) triphosphate lithium salt hydrate ) ( red trace ) . M = unmodified CylLs; P = phosphorylation . The data are shown for non-hydrolyzable analogs of ADP and ATP to distinguish whether the observed activity is due to the presence of these nucleotides , or to the activated phosphor-anhydride groups of ADP/ATP ( see ‘Materials and methods’ for more information ) . See also Figure 6—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 02110 . 7554/eLife . 07607 . 022Figure 6—figure supplement 1 . MALDI-TOF mass spectra of CylLS peptides incubated with CylM in the absence of nucleotides ( black trace ) , and in the presence of AMP ( adenosine 5′-monophosphate disodium salt ) ( blue trace ) , non-hydrolyzable ADP ( adenosine 5′- ( β-thio ) diphosphate trilithium salt ) ( magenta trace ) , or non-hydrolyzable ATP ( adenosine 5′- ( β , γ-imido ) triphosphate lithium salt hydrate ) ( red trace ) . M = unmodified CylLs; P = phosphorylation . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 02210 . 7554/eLife . 07607 . 023Figure 6—figure supplement 2 . MALDI-TOF mass spectra of phosphorylated CylLS intermediates incubated with CylM in the absence ( black trace ) or presence of ADP ( magenta trace ) . M = unmodified CylLs; P = phosphorylation . Not only are the phosphates eliminated from pSer/pThr , ADP also is used to dehydrate non-phosphorylated Ser/Thr to afford fully , fourfold dehydrated peptide . See ‘Materials and methods’ for further discussion . DOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 023 The unexpected structural homology of bacterial LanM proteins with eukaryotic lipid kinases may also have implications for the function of three mammalian LanC-like ( LanCL ) proteins ( Chung et al . , 2007; Sturla et al . , 2009; Zhong et al . , 2012; Huang et al . , 2014 ) . LanC proteins are stand-alone Lan cyclases such as NisC ( Figure 2B ) . Both LanCL1 and LanCL2 bind glutathione ( Chung et al . , 2007 ) , with the thiol of glutathione coordinating the conserved zinc site in LanCL1 ( Zhang et al . , 2009 ) . Hence , the human proteins also appear to activate a thiol , like the LanC proteins and the homologous C-terminal domains of the bacterial LanM enzymes . Although the precise functions of LanCL proteins are currently still unresolved , human LanCL1 has been shown to be important for antioxidant activity that is key to neuronal survival ( Huang et al . , 2014 ) . Furthermore , recent studies indicated regulation of and physical interactions between the human LanCL2 and the kinases Akt and mTORC2 ( Zeng et al . , 2014 ) . The structure of CylM shows that its LanC domain interacts with the activation loop and the kα11 helix of the kinase domain . These observations provide a platform to further investigate the intermolecular interaction of LanCL proteins with mammalian kinases , such as mTOR , that have structural homology with the CylM kinase domain . The structural and biochemical analysis of the lanthipeptide synthetase CylM provided here presents the first molecular picture for installation of the thioether crosslinks in the large family of class II lanthipeptides . Leader-dependent binding of the substrate would template movement of the core peptide between the dehydration and cyclization domains . The immediacy of the phosphorylation and phosphate-elimination sites allows for both reactions to occur in a processive manner to yield the dehydroamino residue , which can then be consigned to the cyclization domain for subsequent Michael-type addition reaction . The ordered activation loop in CylM precludes the need for an activation-induced conformational change , observed for other lipid kinases , as binding of the substrate is dictated largely by the leader sequence ( Abts et al . , 2013; Thibodeaux et al . , 2015 ) . The structure of the CylM protein now allows installation of probes to monitor the movement of the substrates between the two active sites in LanM proteins to better understand the substantial motions of the substrate peptides during catalysis ( Thibodeaux et al . , 2014 ) . In addition , it facilitates inhibitor design to prevent biosynthesis of the cytolysin virulence factor in pathogenic E . faecalis , one of the causative agents of vancomycin-resistant enterococcal infections ( Van Tyne et al . , 2013 ) .
The genes encoding CylM , CylLL , and CylLS were synthesized by GeneArt ( Invitrogen , Carlsbad , CA ) with codon usage optimized for E . coli expression . All polymerase chain reactions were carried out on a C1000 thermal cycler ( Bio-Rad , Hercules , CA ) . DNA sequencing was performed by ACGT , Inc ( Wheeling , IL ) . Preparative HPLC was performed using a Waters Delta 600 instrument equipped with appropriate columns . LC-ESI-Q/TOF MS analyses were conducted using a Synapt G2 MS system equipped with Acquity UPLC ( Waters , Milford , MA ) . MALDI-TOF MS was carried out on a Bruker Daltonics UltrafleXtreme MALDI-TOF/TOF mass spectrometer ( Bruker , Billerica , MA ) . C18 zip-tip pipet tips were obtained from Millipore to desalt samples for MS analysis . Luminescence in 96-well plates was measured with a Synergy H4 Microplate Reader ( BioTek , Winooski , VT ) . Oligonucleotides were purchased from Integrated DNA Technologies ( Coralville , IA ) . Restriction endonucleases , DNA polymerases , T4 DNA ligase , and media components were obtained from New England Biolabs ( Ipswich , MA ) and Difco laboratories ( Franklin Lakes , NJ ) , respectively . Chemicals were ordered from Sigma Aldrich ( St Louis , MI ) or Fisher Scientific ( Hampton , NH ) . The ADP-Glo MAX Assay kit was obtained from Promega ( Madison , WI ) . E . coli DH5α and E . coli BL21 ( DE3 ) cells were used as host for cloning and plasmid propagation , and host for expression , respectively . Expression vectors ( pET15b and pRSFDuet-1 ) were obtained from Novagen ( Billerica , MA ) . The cylM gene was cloned into the multiple cloning site 1 of a pRSFDuet-1 vector using EcoRI and NotI restriction sites to generate pRSFDuet-1/CylM plasmid . Primer sequences used are listed in Table 1 . CylLL and cylLS genes were cloned into a pET15b vector using NdeI and BamHI restriction sites , resulting in pET15b/CylLL or pET15b/CylLS plasmids , respectively . 10 . 7554/eLife . 07607 . 024Table 1 . Primer sequences used for cloning of cylM and its mutantsDOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 024Primer namePrimer sequence ( 5′-3′ ) CylM_EcoRI_Duet_FPAAAAA GAATTCG GAAGATA ATCTGATTAA TCylM_NotI_Duet_RPAAAAA GCGGCCGC TTACAGT TCAAACAGCA GCylM_D252A_QC_FPAGGGT GCA AGCCAT AGCCGTGGTAAAACCGTT AGCCylM_D252A_QC_RPATGGCT TGC ACCCT GGC TTTCGCTAAT GCTATTCAGTCylM_H254A_QC_FPGATAGC GCT AGCCGT GGT AAAACCGTT AGCACCCTGCylM_H254A_QC_RPACGGCT AGC GCTA TC ACCCTGGC TTTCGCTAAT GCylM_D347A_QC_FPGTTACC GCT CTGCAT TATGAAAACATCATTGCCCATGGCCylM_D347A_QC_RPAT GCAG AGC GGT AAC ATTAAAC AGAAAGGCAA TGCCAATCAGCylM_H349A_QC_FPCCGATCTG GCT TATGAAAA CATCATTGCCCATGGCGAATACylM_H349A_QC_RPTTTTCATA AGC CAGATCGG T AACATTAAAC AGAAAGGCAA TGCCAATCylM_N352A_QC_FPCATTATGAA GCC ATCATTGC CCATGGCGAATATCCG GTGATTCylM_N352A_QC_RPGCAATGAT GGC TTCATAATG CAGATCGGT AACATTAAAC AGAAAGGCCylM_D364A_QC_FPGTGATTATT GCT AATGAAACC TTTTTTCAGCAGAATATTCCGATTGAATTTCylM_D364A_QC_RPGGTTTC ATT AGC AATA ATCAC CGGAT ATTCGCCATG GGCCylM_R506A_QC_FPTGATTGTG GCC AATGTTAT TCGTCCGACCCAGCGTTACylM_R506A_QC_RPA TAACATT GGC CACAATCA GA TTCTGCAGAT TATTATTAAT ATAGGCCAGACylM_T512A_QC_FPGTCCG GCC CAG C GTTATGCAGATATGCTGGAA TTTAGCCylM_T512A_QC_RPCTG GGCCGGAC GAA TAACATTGCG CACAATCAGACylM_NdeI_FPAAAAA CATATG GAAGATA ATCTGATTAA TCylM625_KpnI_RPAAAAA GGTACC TTA GTACGGGTTA TAAATATTCA G The plasmids pRSFDuet-1/CylM-D347A , pRSFDuet-1/CylM-H349A , pRSFDuet-1/CylM-N352A , pRSFDuet-1/CylM-D364A , pRSFDuet-1/CylM-D252A , pRSFDuet-1/CylM-H254A , pRSFDuet-1/CylM-R506A , pRSFDuet-1/CylM-T512A , pRSFDuet-1/CylLS/CylM-D347A-2 , pRSFDuet-1/CylLS/CylM-H349A-2 , pRSFDuet-1/CylLS/CylM-N352A-2 , pRSFDuet-1/CylLS/CylM-D364A-2 , pRSFDuet-1/CylLS/CylM-D252A-2 , pRSFDuet-1/CylLS/CylM-H254A-2 , pRSFDuet-1/CylLS/CylM-R506A-2 and pRSFDuet-1/CylLS/CylM-T512A-2 were generated using QuikChange methodology using pRSFDuet-1/CylM and pRSFDuet-1/CylLS/CylM-2 as templates , respectively ( Tang and van der Donk , 2013 ) . Primer sequences are listed in Table 1 . The cylM-1-625 gene was amplified and cloned into the MCS2 of pRSFDuet-1/CylLS to generate pRSFDuet-1/CylLS/CylM-1-625-2 . Primer sequences are listed in Table 1 . E . coli BL21 ( DE3 ) cells were transformed with pET15b/CylLL or pET15b/CylLS and plated on a LB plate containing 100 mg/l ampicillin . A single colony was picked and grown in 20 ml of LB in the presence of ampicillin at 37°C for 12 hr . The cell suspension was directly used to inoculate 2 l of fresh LB media . Cells were cultured at 37°C until the OD at 600 nm reached 0 . 5 , and isopropyl β-D-1-thiogalactopyranoside ( IPTG ) was added to a final concentration of 0 . 2 mM . Cells were cultured at 37°C for another 3–5 hr before harvesting . The cell pellet was resuspended at room temperature in LanA start buffer ( 20 mM NaH2PO4 , 500 mM NaCl , 0 . 5 mM imidazole , 20% glycerol , pH 7 . 5 at 25°C ) and lysed by sonication . The resulting sample was then centrifuged at 23 , 700×g for 30 min and supernatant was discarded . The remaining pellet was resuspended in LanA buffer 1 ( 6 M guanidine hydrochloride , 20 mM NaH2PO4 , 500 mM NaCl , 0 . 5 mM imidazole , pH 7 . 5 at 25°C ) and sonicated . Centrifugation was performed afterwards to pellet the debris and the soluble portion was passed through 0 . 45-µm syringe filters . His-tagged peptides were purified by immobilized metal ion affinity chromatography ( IMAC ) eluting with LanA elute buffer ( 4 M guanidine hydrochloride , 20 mM NaH2PO4 , 500 mM NaCl , 1 M imidazole , pH 7 . 5 at 25°C ) . The eluted fractions were desalted by preparative HPLC using a Waters Delta-pak C4 column ( 15 µm 300 Å 25 × 100 mm ) . The resulting peptides were lyophilized to dryness and kept at −20°C for future use . E . coli BL21 ( DE3 ) cells were transformed with pRSFDuet-1/CylM , pRSFDuet-1/CylM-D347A , pRSFDuet-1/CylM-H349A , pRSFDuet-1/CylM-N352A , pRSFDuet-1/CylM-D364A , pRSFDuet-1/CylM-D252A , pRSFDuet-1/CylM-H254A , pRSFDuet-1/CylM-R506A or pRSFDuet-1/CylM-T512A , and plated on a LB plate containing 50 mg/l kanamycin . A single colony was picked and grown in 20 ml of LB in the presence of kanamycin at 37°C for 12 hr . The cell suspension was directly used to inoculate 2 l of LB and cells were cultured at 37°C until the OD at 600 nm reached 0 . 5 . The culture was cooled down on ice followed by the addition of IPTG to a final concentration of 0 . 1 mM . Cells were cultured at 18°C for additional 18 hr before harvesting . The harvested cells were resuspended on ice in LanM start buffer ( 20 mM HEPES , 1 M NaCl , pH 7 . 5 at 25°C ) and lysed using a homogenizer . Insoluble debris was removed by centrifugation at 23 , 700×g for 45 min at 4°C and the supernatant was passed through 0 . 45-µm syringe filters . His-tagged proteins were purified by IMAC , eluting with a linear concentration gradient of imidazole from 30 mM to 200 mM . The eluted fractions were analyzed using SDS-PAGE . Fractions containing the desired protein were combined and concentrated using a centrifugal filtering device , and the buffer was exchanged to LanM start buffer using a gel-filtration column . Protein concentration was quantified by its absorbance at 280 nm . The extinction coefficient for His6-CylM was calculated as 140 , 110 M−1 cm−1 . Aliquoted protein solutions were flash-frozen and kept at −80°C for further usage . E . coli BL21 ( DE3 ) cells were transformed with pRSFDuet-1/CylLS/CylM-D347A-2 , pRSFDuet-1/CylLS/CylM-H349A-2 , pRSFDuet-1/CylLS/CylM-N352A-2 , pRSFDuet-1/CylLS/CylM-D364A-2 , pRSFDuet-1/CylLS/CylM-D252A-2 , pRSFDuet-1/CylLS/CylM-H254A-2 , pRSFDuet-1/CylLS/CylM-R506A-2 , pRSFDuet-1/CylLS/CylM-T512A-2 , or pRSFDuet-1/CylLS/CylM-1-625-2 , and plated on a LB plate containing 50 mg/l kanamycin . A single colony was picked and grown in 10 ml of LB in the presence of kanamycin at 37°C for 12 hr . The cell suspension was directly used to inoculate 1 l of LB and cells were cultured at 37°C until the OD at 600 nm reached 0 . 5 . The culture was cooled down on ice followed by the addition of IPTG to a final concentration of 0 . 1 mM . Cells were cultured at 18°C for 18 hr before harvesting . To obtain both fully modified and linear CylLS as well as possible intermediates ( partially modified CylLS ) and reduce the bias introduced by peptide solubility , harvested cells were resuspended and lysed directly in LanA buffer 1 ( 6 M guanidine hydrochloride , 20 mM NaH2PO4 , 500 mM NaCl , 0 . 5 mM imidazole , pH 7 . 5 at 25°C ) by sonication . Debris was removed by centrifugation and the soluble portion was passed through 0 . 45-µm syringe filters . His-tagged CylLS was purified by IMAC , eluting with LanA elute buffer ( 4 M guanidine hydrochloride , 20 mM NaH2PO4 , 500 mM NaCl , 1 M imidazole , pH 7 . 5 at 25°C ) . The eluted fractions were desalted with Strata-X polymeric reverse phase SPE columns and lyophilized to dryness . Presumably due to high hydrophobicity , the solubility of linear CylLL or CylLS peptides is extremely poor . For enzyme assays , 2 mg/ml peptide suspension was made in deionized water as stock solution for both peptides . The stock solution was vortexed to a homogenized suspension each time before any peptide was taken . However , given the presence of precipitation , the concentration of CylLL or CylLS peptides could not be tightly controlled . To reconstitute the activity of CylM in vitro , 20 µM of linear peptides were supplied in a reaction vessel with 4 mM MgCl2 , 2 mM ATP , 2 mM DTT , 1 × 10−5 U thrombin ( to remove the His-tag in situ ) and 50 mM HEPES ( pH 7 . 5 ) , followed by the addition of CylM to a final concentration of 0 . 5 µM . Reactions were incubated at room temperature for 4 hr . Control reactions were set up with all other components in the absence of CylM . Each sample was zip-tipped and analyzed by MALDI-TOF MS . Aliquoted samples were treated by CylA ( serine protease encoded in the biosynthetic pathway of cytolysin ) to remove the leader peptides , and the resulting core peptides were analyzed by LC-MS or LC-MS/MS . ESI MS analysis confirmed a mass shift of 144 or 126 Da for CylLL , corresponding to a loss of 8 or 7 water molecules , with the 7-dehydrated peptide as the major product , and a mass shift of 72 Da for CylLS , corresponding to a loss of 4 water molecules ( Figure 1—figure supplement 1 ) . The results were consistent with the reported mass of CylLL‘ and CylLS’ as well as our previous observations using an E . coli co-expression system , where 7 dehydrations and 4 dehydrations were detected ( Booth et al . , 1996; Tang and van der Donk , 2013 ) . Tandem MS ( MS/MS ) analysis indicated the desired ring systems were formed for both peptides ( Figure 1—figure supplement 2 ) . CylLS peptide ( 20 µM ) was supplied to a reaction vessel in the presence of 4 mM MgCl2 , 2 mM ATP , 2 mM DTT , 1 × 10−5 U thrombin ( to remove the His-tag in situ ) and 50 mM HEPES ( pH 7 . 5 ) . CylM and CylM mutant proteins were then added to a final concentration of 0 . 5 µM . Reactions were incubated at room temperature and aliquots were quenched by adding formic acid to a final concentration of 0 . 5% at desired time points . Each sample was then zip-tipped and analyzed by MALDI-TOF MS . With linear CylLS serving as the substrate , wild-type CylM finished the modification by eliminating 4 water molecules within 30 min of incubation when characterized using MALDI-TOF MS . In comparison , the four phosphorylation-deficient mutants were unable to convert the starting material into modified peptide using the same set up ( Figure 5—figure supplement 1 ) . A small amount of dehydrated product was observed for CylM-H349A , which afforded partially dehydrated intermediates when analyzed using the E . coli co-expression system ( Figure 5—figure supplement 1 ) . We further increased the incubation time to 10 hr at room temperature to facilitate the detection of any minimal level activity . Indeed , almost full modification of CylLS was achieved by CylM-H349A ( Figure 5—figure supplement 1 ) . Partially modified products with one dehydration and one phosphorylation were also detected for CylM-N352A , but CylLS remained unmodified in the presence of CylM-D347A and CylM-D364A even with elongated incubation time ( Figure 5—figure supplement 1 ) . In vitro characterization of the four elimination-deficient mutants of CylM also provided similar phenotypes as what was observed using the co-expression system , except that CylM-H254A afforded fully modified CylLS after elongated incubation period ( Figure 5—figure supplement 2 ) , indicating that mutating histidine 254 to alanine slows down but does not abolish the phosphate-elimination activity of CylM . The T512A mutant did not eliminate the phosphate even with increased reaction time ( Figure 5—figure supplement 2 ) , suggesting that Thr512 is critical for the elimination activity of CylM . Phosphorylated CylLS peptides carrying different numbers of phosphate esters were obtained by co-expression of His6-CylLS with CylM elimination-deficient mutant CylM-R506A . The IMAC-purified peptide mixture was dissolved in deionized water to make a 350 µM stock solution . Non-hydrolyzable ATP ( adenosine 5′- ( β , γ-imido ) triphosphate lithium salt hydrate ) , non-hydrolyzable ADP ( adenosine 5′- ( β-thio ) diphosphate trilithium salt ) and AMP ( adenosine 5′-monophosphate disodium salt ) were reconstituted in deionized water and a stock solution of 20 mM was obtained for each . For elimination reactions , CylM was present at a final concentration of 0 . 5 µM in the presence of 1 mM MgCl2 , 2 mM DTT and 50 mM HEPES ( pH 7 . 5 ) . Then adenosine derivatives ( final concentration 500 µM ) or deionized water ( negative control ) were added , followed by phosphorylated CylLS peptide to a final concentration of 35 µM , and the assay was incubated at room temperature for 2 hr . Parallel control reactions were set up using linear CylLS with a peptide concentration of 20 µM in the presence of 1 × 10−5 U thrombin ( to remove the His-tag in situ ) . Samples were zip-tipped and analyzed by MALDI-TOF MS ( Figure 6—figure supplement 1 ) . Non-hydrolyzable adenosine derivatives were used for analysis of the elimination activity because we determined that CylM could use both ATP and ADP to dehydrate its substrates ( i . e . , both ATP and ADP can be used for phosphorylation ) . Hence , when the mixture of CylLS peptides that carry 1–3 phosphate esters were supplied to CylM in the presence of ATP or ADP , both elimination and dehydration reactions proceeded , which complicated the outcome and precluded data interpretation . For example , when ADP was supplied instead of non-hydrolyzable ADP , only fully ( fourfold ) dehydrated CylLS was observed ( Figure 6—figure supplement 2 ) . Since the phosphorylated peptides carried only 1–3 phosphate esters , the additional dehydrations resulted from conversion of non-phosphorylated Ser/Thr to Dha/Dhb . Therefore , to study the elimination reaction in isolation , non-hydrolyzable ATP and ADP analogs were used . Single colonies of chemically competent E . coli Rosetta 2 cells , transformed with the pRSFDuet-1/CylM plasmid , were grown in LB media supplemented with kanamycin ( 50 µg/ml ) and chloramphenicol ( 25 µg/ml ) . A 6 ml starter culture was grown overnight and used to inoculate 1 l of LB media supplemented with the same antibiotic . Liquid cultures were grown at 37°C with vigorous shaking , and protein production was induced with the addition of 0 . 5 mM IPTG when the OD600 reached 0 . 5 followed by further shaking for additional 20 hr at 18°C and 200 rpm . Cell pellets were harvested from the cultures by centrifugation at 4°C , followed by suspension of the pellet in ∼30 ml of buffer ( 500 mM NaCl , 10% glycerol , 20 mM Tris , pH 8 . 0 ) . Frozen cell pellets were thawed and lysed by sonication , and the lysates were clarified by centrifugation at 4°C . The clear supernatant containing the soluble fraction was loaded onto a 5 ml immobilized metal ion affinity resin column ( Hi-Trap Ni-NTA , GE Healthcare ) pre-equilibrated with binding buffer ( 1 M NaCl , 5% glycerol , 20 mM Tris , pH 8 . 0 ) . The column was washed with 50 ml of 12% elution buffer ( 1 M NaCl , 250 mM imidazole , 20 mM Tris , pH 8 . 0 ) , and eluted by a linear gradient . Fractions containing the highest purity protein , as judged by Coomassie-stained SDS-PAGE , were pooled and further purified by size exclusion chromatography ( Superdex Hiload 200 16/60 , GE Healthcare ) in 500 mM KCl , 20 mM HEPES , pH 7 . 5 buffer . The purified protein was concentrated using Amicon Ultra-4 centrifugal filters ( 10 KDa molecular weight cut-off , Millipore ) and stored in liquid nitrogen until needed . The final concentration was quantified by Bradford analysis ( Thermo Scientific ) . Crystals of LanM were obtained by hanging drop vapor diffusion method , by mixing 1 μl of protein ( concentration of 2–6 mg/ml ) with an equal volume of precipitant of either 0 . 2 M CaCl , 0 . 1 M HEPES pH 7 . 5 , 10 mM betaine hydrochloride , and 28% PEG 400 ( condition 1 ) or 0 . 2 M KCl , 0 . 05 M HEPES pH7 . 5 , 10 mM barium chloride , and 33% 5/4 PO/OH ( condition 2 ) . Crystals were supplemented with either PEG 400 or 5/4 PO/OH to a final concentration of 35% ( vol/vol ) prior to vitrification by direct immersion in liquid nitrogen . Macro- and micro-seeding facilitated the formation of crystals suitable for diffraction data collection . SeMet CylM was expressed , purified , and crystallized in a similar manner . Native and SeMet data were collected at Sector 21 ID ( LS-CAT , Advanced Photon Source , Argonne National Labs , IL ) and data were integrated and scaled using HKL2000 ( Otwinowski et al . , 2003 ) or XDS ( Kabsch , 2014 ) . Crystallographic phases were determined by single wavelength anomalous diffraction methods from data collected on crystals of SeMet CylM to a resolution limit of 2 . 7 Å . Heavy atom sites were located using the SHELX ( Sheldrick , 2010 ) suite of programs and refinement of heavy atom parameters in SHARP ( Bricogne et al . , 2003 ) yielded an initial figure of merit of 0 . 273 . Multiple rounds of automated and manual model building using COOT ( Emsley et al . , 2010 ) , interspersed with rounds of crystallographic refinement using REFMAC5 ( Murshudov et al . , 2011 ) , resulted in convergence to the near final model ( free R factor of 0 . 30 ) . Ligand and water molecules were added at this stage and refinement was completed using BUSTER ( Blanc et al . , 2004 ) . The validity of all models was routinely determined using MOLPROBITY ( Chen et al . , 2010 ) and by using the free R factor to monitor improvements during building and crystallographic refinement . Relevant data collection , phasing , and refinement statistics may be found in Table 2 . 10 . 7554/eLife . 07607 . 025Table 2 . Data collection , phasing , and refinement statisticsDOI: http://dx . doi . org/10 . 7554/eLife . 07607 . 025NativeSeMetData collection Space groupP212121P212121 Unit cell: a , b , c ( Å ) 51 . 2 , 90 . 7 , 246 . 451 . 2 , 90 . 9 , 246 . 2 Resolution ( Å ) *50 . 00–2 . 2 ( 2 . 24–2 . 2 ) 50 . 00–2 . 8 ( 2 . 85– 2 . 8 ) Total reflections359 , 303169 , 660 Unique reflections58 , 18025 , 354 Rsym ( % ) 6 . 3 ( 67 . 0 ) 6 . 1 ( 53 . 8 ) I/σ ( I ) 19 . 1 ( 1 . 7 ) 16 . 7 ( 2 . 1 ) Completeness ( % ) 97 . 9 ( 87 . 6 ) 96 . 0 ( 88 . 4 ) Redundancy6 . 2 ( 5 . 1 ) 6 . 0 ( 5 . 9 ) Refinement Resolution ( Å ) 25 . 0–2 . 2 No . reflections used51 , 874 Rwork/Rfree‡23 . 7/26 . 8Number of atoms Protein7251 Solvent160 Metal/Nucleotide1/23B-factors Protein52 . 9 Solvent32 . 4 Metal/Nucleotide54 . 1/62 . 3R . m . s deviations Bond lengths ( Å ) 0 . 011 Bond angles ( ° ) 1 . 54*Highest resolution shell is shown in parenthesis . ‡R-factor = Σ ( |Fobs| − k|Fcalc| ) /Σ |Fobs|and R-free is the R value for a test set of reflections consisting of a random 5% of the diffraction data not used in refinement . Stock solutions of 800 µM ATP and ADP were prepared by diluting the Ultra Pure ATP and ADP supplied with the ADP-Glo MAX Assay kit ( Promega ) in 1× reaction buffer ( 20 mM MgCl2 , 2 mM DTT , and 50 mM HEPES pH 7 . 5 ) . Mixtures of 50 µl of ATP ( 800 µM ) and ADP were made in which the final ADP content varied from 0 to 20% ( 0 , 8 , 16 , 24 , 32 , 40 , 80 , and 160 μM ) , which represents the percent conversion of ATP to ADP in the kinetic experiments . These standard solutions were designated the 800 μM series . Mixtures in a 400 µM series were prepared by diluting 25 µl of the 800 µM series samples with 25 µl 1× reaction buffer . Similarly , 200 , 100 , 50 , and 25 µM series ( 25 µl each ) were prepared . To each sample , 25 µl of the ADP-Glo Reagent was added and incubated for 40 min , followed by the addition of 50 µl of ADP-Glo Max Detection Reagent and incubation for 1 hr . All samples were then transferred into a white 96 well plate and the luminescence was measured by a plate reader . Standard curves ( 25 , 50 , 100 , 200 , 400 , and 800 µM series ) were created to correlate the ADP concentration with the luminescence . To measure the ATP consumption of CylM in vitro , 25 µl of reaction mixtures were prepared consisting of 100 µM linear peptide , 1 µM CylM , and 1× reaction buffer , followed by the addition of ATP to final concentrations of 25 , 50 , 100 , 200 , 400 , and 800 µM . Control reactions were set up with all other components in the absence of CylM . Reactions were incubated at 25°C for 1 , 2 , 3 , and 4 min before being stopped by adding 25 µl of ADP-Glo Reagent , which depletes the remaining ATP . After incubation for 40 min , ADP-Glo Max Detection Reagent ( 50 µl ) was then added , the samples were incubated for 1 hr , and the luminescence was measured . ADP production in each sample was calculated by applying the corresponding standard curve . The curve of ATP consumption of CylM against ATP concentration was fitted using OriginPro 2015 . All reactions were carried out in duplicate . | Enterococcus faecalis is a bacterium that is usually found living harmlessly in the gut of humans and other mammals . However , over the past few decades hospitals have noted an increase in the number of hospital-acquired infections caused by antibiotic-resistant strains of E . faecalis . Many of the E . faecalis strains that cause illness and death do so by producing a toxin called cytolysin , which can destroy a range of cells , including the immune cells that normally eradicate bacterial infections . Inside the bacteria , an enzyme called cytolysin synthetase—also known as CylM—catalyzes the reactions that make the cytolysin toxin from precursor molecules . Enzymes are primarily made up of proteins . Both the sequence of the amino acids in the protein chains and the shapes and structures that these chains fold into affect how the enzyme works . CylM is made up of two parts , or ‘domains’ . One of these , known as the dehydration domain , removes water molecules from some of the precursor amino acid chains that are used to build cytolysin . This dehydration reaction forms the first stage of cytolysin production . How CylM catalyzes this reaction was not known , because CylM does not have a similar amino acid sequence to any other enzymes and no information about its structure was available . Now , Dong , Tang et al . have resolved the structure of the E . faecalis CylM enzyme using a technique called x-ray crystallography . Unexpectedly , this revealed that the dehydration domain of the enzyme has a similar structure—despite having a completely different amino acid sequence—to enzymes that are found in eukaryotic organisms ( i . e . , organisms with cells that contain a nucleus ) . These enzymes are called lipid kinases , and help to add phosphate groups to other molecules . Additional structural and biochemical analyses enabled Dong , Tang et al . to investigate how CylM catalyzes the dehydration reaction in more detail . Given its central role in toxin production , an increased understanding of how CylM makes cytolysin could eventually help to develop new treatments for the conditions caused by E . faecalis infections . | [
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The spinal cord has the capacity to coordinate motor activities such as locomotion . Following spinal transection , functional activity can be regained , to a degree , following motor training . To identify microcircuits involved in this recovery , we studied a population of mouse spinal interneurons known to receive direct afferent inputs and project to intermediate and ventral regions of the spinal cord . We demonstrate that while dI3 interneurons are not necessary for normal locomotor activity , locomotor circuits rhythmically inhibit them and dI3 interneurons can activate these circuits . Removing dI3 interneurons from spinal microcircuits by eliminating their synaptic transmission left locomotion more or less unchanged , but abolished functional recovery , indicating that dI3 interneurons are a necessary cellular substrate for motor system plasticity following transection . We suggest that dI3 interneurons compare inputs from locomotor circuits with sensory afferent inputs to compute sensory prediction errors that then modify locomotor circuits to effect motor recovery .
Like other regions of the central nervous system , the spinal cord is remarkably plastic ( Wolpaw , 2007; Grau , 2014 ) . Such plasticity has been demonstrated , for example , following spinal cord injury , when training can lead to a degree of recovery of spinal locomotor circuits such that stepping movements are restored ( Barbeau et al . , 1987; Courtine et al . , 2009; Harkema et al . , 2012; Hubli and Dietz , 2013; Martinez et al . , 2013; Takeoka et al . , 2014 ) . After complete spinal transection in cats and rodents , a treadmill-training regimen that provides rhythmic sensory input to the spinal cord leads to the re-acquisition of the complex sequence of muscle activation that produces stepping ( Barbeau and Rossignol , 1987; Sławińska et al . , 2012 ) . Multiple modalities of sensory input are likely required to promote these sustained changes in spinal circuits , as removing cutaneous inputs degrades the quality of recovery in both cat ( Bouyer and Rossignol , 2003 ) and rats ( Sławińska et al . , 2012 ) , and eliminating muscle proprioceptive afferents impairs recovery in mice ( Takeoka et al . , 2014 ) . But in addition to determining the afferent inputs involved , it is necessary to identify the spinal circuits involved in this plasticity in order to understand how the nervous system acquires new motor skills in health and injury . One approach towards understanding these circuits would be to study neurons that are interposed between sensory inputs and spinal locomotor circuits . Furthermore , since there is a relationship between short-term adaptation and longer-term plasticity ( Bastian , 2008 ) , it might be useful to focus on interposed neurons that are known to be involved in adaptive responses . We previously showed that dI3 interneurons ( INs ) , a population defined by expression of the LIM-homeodomain transcription factor Isl1 ( Liem et al . , 1997 ) , receive multimodal monosynaptic sensory afferent inputs and project to spinal motoneurons . Eliminating glutamatergic output by these neurons led to deficits in motor responses to sensory perturbation: while the mice could place their paws on wire rungs , they were unable to adjust their grasp in response to sensory stimulation provided by increasing the inclination of the rungs . This indicates that a microcircuit involving dI3 INs mediates adaptive changes in motor behaviour ( Bui et al . , 2013 ) . We thus focussed on this population to determine if they have a role in locomotor recovery . Here , we have considered the position of dI3 INs in spinal microcircuits , and show that they are indeed interposed between sensory inputs and locomotor circuits . We demonstrate that while dI3 INs are not required for normal locomotor function , they are necessary for stable recovery of locomotor activity following spinal cord transection . Specifically , we demonstrate that eliminating glutamatergic output from dI3 INs precludes locomotor recovery after spinal cord transection . Thus , dI3 INs are involved in spinal microcircuits that mediate motor system plasticity .
We first determined whether dI3 INs are an essential component of spinal locomotor circuits and thus necessary for locomotion . To do so , we genetically eliminated glutamatergic neurotransmission from dI3 INs using dI3OFF ( Isl1Cre/+;Slc17a6fl/fl ) mice ( Bui et al . , 2013 ) . Within their cages , dI3OFF mice did not reveal obvious locomotor deficits ( Bui et al . , 2013 ) . There was no difference between the weights of control ( 19 . 8 ± 2 . 3 g; n = 14 ) and dI3OFF mice ( 19 . 0 ± 2 . 7 g; n = 9 , p>0 . 44 ) . Footprint and inter-limb coordination analysis revealed subtle alterations in locomotion in adults ( Figure 1A ) . The hind paws , but not the forepaws , of dI3OFF mice were more widely spaced than those of control mice ( Figure 1B ) , and dI3OFF mice had a greater propensity to make coincident contact with the ground with three or four paws ( Figure 1A , C , and Video 1 ) , however inter-limb coordination was similar in dI3OFF and control mice ( Figure 1—figure supplement 1 ) . During treadmill locomotion ( Video 1 ) , dI3OFF mice had longer stance times on average , and a steeper relationship between stance duration and cycle period than seen in controls ( Figure 1D , E ) . Collectively , the shorter swing phases , increased time with more paws in contact with the treadmill , and more widely spaced hind paws in dI3OFF mice could result from a reduction in extensor activity and/or compensation for a reduction in stability . Taken together , these results show that while dI3 INs sculpt hind limb movement , they are not critical for the fundamental rhythm and/or pattern of locomotion . 10 . 7554/eLife . 21715 . 003Figure 1 . dI3 INs are subtly involved in locomotion . ( A ) Footprint snapshots in control ( cyan ) and dI3OFF ( magenta ) littermates at 30 ms intervals starting from the onset of the stance of right hindlimb ( top ) running at 30 cm/s . Coloured lines represent hindlimb feet spacing , circles indicate foot contact . ( B ) Front and rear foot spacing for control ( cyan , n = 6 ) and dI3OFF ( magenta , n = 3 ) animals running between 10 and 50 cm/s . Mean +/− Standard Deviation . ( C ) Proportion of time with indicated number of feet contacting the treadmill belt in same animals running between 10 and 18 cm/s . ( D ) Percentage of swing ( light shades ) and stance ( dark shades ) in same animals running between 10 and 18 cm/s . ( E ) Correlation between swing or stance duration and cycle period in same animals running between 9 . 5 cm/s and 72 cm/s . Each data point represents a single step cycle . Analysis of Covariance ( ANCOVA ) on slopes . ( B , C , D ) Two-way ANOVA followed by Sidak post-hoc multiple comparison test . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , and ****p<0 . 0001 , ns non-significant . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00310 . 7554/eLife . 21715 . 004Figure 1—source data 1 . Related to Figure 1B . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00410 . 7554/eLife . 21715 . 005Figure 1—source data 2 . Related to Figure 1C . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00510 . 7554/eLife . 21715 . 006Figure 1—source data 3 . Related to Figure 1D . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00610 . 7554/eLife . 21715 . 007Figure 1—source data 4 . Related to Figure 1E . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00710 . 7554/eLife . 21715 . 008Figure 1—source data 5 . Related to Figure 1I . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00810 . 7554/eLife . 21715 . 009Figure 1—figure supplement 1 . Interlimb coordination in dI3OFF mice is similar to controls . ( A ) Comparison of phase relationships during treadmill recording at 30 cm/s between control ( cyan shades , n = 7 ) and dI3OFF ( magenta shades n = 3 ) animals . Mean +/− Standard Deviation **p<0 . 01 , ns non-significant , two-way ANOVA followed by Sidak post-hoc multiple comparison test . ( B ) Illustration of foot coupling is provided on the right . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 00910 . 7554/eLife . 21715 . 010Figure 1—figure supplement 1—source data 1 . Related to Figure 1—figure supplement 1A . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01010 . 7554/eLife . 21715 . 011Video 1 . Foot print recordings in intact animals . Foot print recordings of control and dI3OFF animals running at 30 cm/s , recorded at 100 fps and displayed at 15 fps . Animals were recorded separately . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 011 We next sought to determine whether dI3 INs , in addition to their projections to motoneurons ( Bui et al . , 2013 ) , also have access to spinal circuits for locomotion . Considering that sensory stimulation can trigger locomotor activity ( Lundberg , 1979; Hultborn et al . , 1998 ) and that dI3 INs can be monosynaptically activated by stimulation of low-threshold sensory afferents ( Bui et al . , 2013 ) , we asked whether dI3 INs could activate spinal locomotor circuits . To do so , we studied sensory-evoked locomotion in control and dI3OFF mice , stimulating a sensory ( sural ) rather than a mixed ( tibial ) nerve to avoid stimulating motor axons . Furthermore , as sensory afferents may have multiple routes to spinal locomotor circuits , we stimulated the sural nerve , which we previously showed both anatomically and physiologically project directly to dI3 INs ( Bui et al . , 2013 ) , rather than a dorsal root . We thus studied sensory-evoked locomotor activity in neonatal ( P1-P3 ) mice by isolating their spinal cords with the sensory sural nerve in continuity ( Figure 2A ) . Given that the Vesicular glutamate transporter 2 ( vGluT2 ) coded by the gene Slc17a6 is also expressed in high threshold small to medium sized primary afferents responsible for pain , itch , and thermoception ( Brumovsky et al . , 2007; Lagerström et al . , 2010; Liu et al . , 2010; Scherrer et al . , 2010 ) , we focussed on low threshold stimulation , with threshold defined as the lowest amplitude that produced a volley in the proximal dorsal root ( A-wave in Figure 2B; control: 4 . 9 ± 6 . 4 µA , n = 13; dI3OFF: 2 . 9 ± 1 . 4 µA , n = 12; p>0 . 29 ) . 10 . 7554/eLife . 21715 . 012Figure 2 . dI3 INs activate spinal locomotor circuits . ( A ) Experimental preparation showing isolated spinal cord with sural nerve attached . Sural nerve stimulation ( arrow ) is used to evoke locomotion , while recording ipsilateral ( Left ) L5 and bilateral L2 ventral root activity ( grey electrodes ) . Stimulus strength determined by volleys in dorsal root recordings ( red electrode ) . ( B ) Dorsal root potentials in response to sural nerve stimulation ( single pulse , 0 . 25 ms ) recorded in ipsilateral L5 dorsal root of a dI3OFF spinal cord . Dotted line denotes stimulation artifact . Stimulation strength is in relation to the threshold ( Th ) to evoke a short-latency A-wave . A-wave threshold was 1 . 5 μA for this dI3OFF spinal cord . ( C ) Rectified and time-integrated ventral root recordings during sural-nerve stimulation ( 10 s train , 3 Hz , 0 . 25 ms long pulses , thick orange line ) in control mice . Brief voltage deflections in the recordings are stimulation artifacts ( # ) . ( D ) Rectified and time-integrated ventral root recordings during sural-nerve stimulation in dI3OFF mice . Note the lack of locomotor activity with stimulation strength ≤2 x Th . Brief voltage deflections in the recordings are stimulation artifacts ( # ) . ( E ) Threshold of stimulation to evoke locomotor activity by sural nerve stimulation ( left ) and percentage of preparations with sural-nerve evoked locomotor activity ( right ) in control and dI3OFF mice . The threshold of stimulation was not determined for one of the six dI3OFF spinal cords in which sural-nerve evoked locomotion was observed . Two-tailed Student’s t-test for threshold of stimulation , Fisher’s exact test for percentage of successful preparations . ( F ) Relationship between the thresholds for evoking locomotion and the long latency C-wave . Dashed line represents line of unity . Spinal cords in which locomotor but not C-wave thresholds were measured are not shown . There is no difference in C-wave thresholds ( inset above ) , but there is a significant increase in locomotor threshold ( right inset ) between control ( cyan ) and dI3OFF mice ( magenta ) . dI3 INs corresponding to recordings in panels 2C and 2D are marked by full colored circles and their respective letters . Two-tailed Student’s t-test . ( G ) Diagram depicting access of dI3 INs to spinal locomotor circuits . Stimulation of low-threshold afferents ( LTA , blue ) recruits dI3 INs , which provide drive to spinal locomotor circuits ( black and grey circle ) . *p<0 . 05 , Scale bars , 5 ms ( B ) , 2 s ( C , D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01210 . 7554/eLife . 21715 . 013Figure 2—source data 1 . Related to Figure 2E . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01310 . 7554/eLife . 21715 . 014Figure 2—source data 2 . Related to Figure 2F . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01410 . 7554/eLife . 21715 . 015Figure 2—figure supplement 1 . When evocable by sural nerve stimulation in dI3OFF mice , locomotion is similar to control mice , and when not evocable , locomotion can be initiated by dorsal root stimulation . ( A ) Phase coordination between right and left ventral roots during sural nerve evoked locomotion in control ( cyan , n = 16 step cycles from two spinal cords ) and dI3OFF ( magenta , n = 12 step cycles from two spinal cords ) . Comparisons were made between right and left flexor-related ( L1–L3 ) ventral root activity or from right and left extensor-related ( L4–L6 ) ventral root activity . Phase coordination was similar between control and dI3OFF spinal cords , p>0 . 15 , two-tailed Student’s t-test assuming unequal variances . ( B ) Frequency of locomotor activity evoked by sural nerve stimulation ( 10 s train , 3 Hz , 0 . 25 ms pulses ) was similar between control ( cyan ) and dI3OFF ( magenta ) spinal cords . Frequencies were measured for stimulation strengths where rhythmic bursts were most defined . ( ns non-significant , p>0 . 63 , two-tailed Student’s t-test assuming unequal variances . ) ( C ) Left , experimental setup of sural-nerve attached isolated spinal cord preparation . Recording electrodes ( grey electrodes ) are attached to ventral roots . The sural nerve is in continuity with the spinal cord and is stimulated ( orange electrode ) to evoke locomotor activity . Inset , right L5 dorsal root potential evoked by sural nerve stimulation . Asterisk denotes stimulation artifact , arrow denotes the A-wave . Ths is defined as threshold for evoking short-latency L5 dorsal root potential in response to sural nerve stimulation . Right , ventral root recordings during right sural-nerve stimulation ( 10 s train , 3 Hz , 0 . 25 ms long pulses , thick orange line ) in dI3OFF spinal cord . Brief voltage deflections in the recordings are stimulation artifacts . Top and bottom traces are from two separate applications of the stimulation with recording electrodes attached to different sets of ventral roots . ( D ) Same setup as in C except that stimulation was applied to the left L4 dorsal root ( orange electrode ) . Ventral root recordings during dorsal root stimulation in the same dI3OFFmouse as in C showing rhythmic locomotor-like activity . Thdr is defined as threshold for evoking short-latency L5 dorsal root potential in response to dorsal root stimulation . ∫Rt L5 , ∫Lt L3: rectified and integrated ventral root recordings . Scale bar , 2 s , applies to C and D . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 015 In 13 of 14 control spinal cords , stimulation of the sural nerve generated locomotor activity ( Figure 2C , E ) , whereas this was only possible in 6 of 12 dI3OFF mice ( Figure 2D , E , p<0 . 05 ) . Furthermore , the thresholds required for evoking locomotor activity in these 6 dI3OFF spinal cords were significantly higher than control thresholds ( Figure 2C–E ) , with activity evoked only when stimulation was equal to or greater than the threshold for producing the higher threshold C-wave ( in contrast to 5/6 controls that responded to low threshold stimulation , Figure 2B , F ) . In the dI3OFF spinal cords in which locomotor activity could be evoked , left-right alternation ( control phase: 0 . 46 ± 0 . 11; dI3OFF phase: 0 . 53 ± 0 . 14; p>0 . 15; Figure 2—figure supplement 1A ) and locomotor frequency ( control: 1 . 2 ± 0 . 1 Hz; dI3OFF: 1 . 3 ± 0 . 2 Hz; p>0 . 63; Figure 2—figure supplement 1B ) were similar to control spinal cords . The lack of response to low threshold stimulation in dI3OFF spinal cords was not due to sural nerve dysfunction , as low threshold sural nerve stimulation produced a normal volley in the dorsal root ( Figure 2B ) , nor was it due to a general inability of afferent stimulation to produce locomotor activity , as dorsal root stimulation was able to evoke locomotion in dI3OFF mice ( Figure 2—figure supplement 1C , D ) . Thus dI3 IN output is necessary for low threshold-evoked locomotor activity , demonstrating that dI3 INs can activate spinal locomotor circuits ( Figure 2G ) . To further understand the relationship between dI3 INs and locomotor circuits , we next asked whether there was a reciprocal relationship between them , that is , whether dI3 INs receive inputs from locomotor circuits . During drug-induced locomotor activity in hemisected ( Figure 3A ) or dorsal horn removed ( Figure 3C ) spinal cords from neonatal ( P1-P6 ) Isl1Cre/+;Thy1-fs-YFP mice ( n = 7 ) , whole-cell recordings of upper and lower lumbar dI3 INs revealed cyclic membrane potential depolarisations ( Figure 3B , D and Figure 3—figure supplement 1A ) , with 27 of 40 ( 68% ) dI3 INs firing rhythmic bursts of action potentials . Raster and polar plots of up to 50 randomly selected action potentials from each of the 27 rhythmically active dI3 INs showed that the majority ( 22 of 27 , 81% ) of neurons were active primarily during the extensor phase of the step cycle ( cf . 5/27 in flexor phase , Figure 3—figure supplement 1B ) . This extensor-dominant pattern of activity was seen irrespective of whether the interneurons were located in the upper or lower lumbar segments , or medial or lateral grey matter ( Figure 3E and Figure 3—figure supplement 1A ) . Peak activity of those active during extension was at the midpoint of that phase ( Figure 3F , G ) . The dominant extensor phase activity may explain the differences in locomotion between control and dI3OFF mice , as elimination of dI3 IN output could result in reduced plantar flexion ( physiological extension ) ( Bui et al . , 2013 ) during paw ground contact ( Engberg , 1964 ) , resulting in reduced weight support and rear track widening ( Donelan et al . , 2004 ) . Together , these data demonstrate that dI3 INs receive inputs – either directly or indirectly – from locomotor circuits , and are predominantly active during the ipsilateral extensor phase of the step cycle . 10 . 7554/eLife . 21715 . 016Figure 3 . dI3 INs are rhythmically active during fictive locomotion . ( A ) Schematic of hemicord preparation indicating the position of recording electrodes as well as L2/L3 ( light blue ) and L4/L5 ( dark blue ) dI3 INs . ( B ) Current-clamp recording of L5 dI3 IN rhythmically active during drug-evoked locomotion in hemicord preparation . dI3 IN: whole-cell patch clamp recording . L2 , L5: raw ventral root recordings . ∫L2 , L5: rectified and integrated ventral root recordings . ( C ) Schematic of L1-L3 dorsal horn removed preparation indicating the position of recording electrodes as well as L2/L3 ( light green ) and L4/L5 ( dark green ) dI3 INs . For recordings of L4/L5 dI3 INs , the dorsal horn overlying L4-L6 was removed . ( D ) Current-clamp recording of L3 dI3 IN rhythmically active during drug-evoked locomotion in dorsal horn removed preparation . dI3 IN: whole-cell patch clamp recording . L2 , L5: raw ventral root recordings . ∫L2 , L5: rectified and integrated ventral root recordings . ( E ) Raster plot of dI3 IN spiking during drug-evoked locomotion . 50 randomly selected spikes ( a fewer number of spikes from cells 2 , 3 , 5 , and 14 from hemicord preparations and cell 4 from dorsal horn removed preparation were recorded ) are shown for each cell . Locomotor cycles were double-normalized such that a phase of 0 marks the beginning of extensor phase , 0 . 5 marks the beginning of the flexor phase/end of the extensor phase . Only dI3 INs determined to be rhythmically active are shown ( see F , G ) . ( F ) Polar plot summarizing rhythmic activity of dI3 INs in hemicords ( n = 13 L2/L3 dI3 INs light blue , and 16 L4/L5 dI3 INs dark blue ) . Mean phase and angular concentration r calculated from 50 or fewer randomly selected spikes . Each point depicts the average phase at which spikes occurred during the step cycle . The distance away from the centre represents r . The inner circle represents r = 0 . 24 , which corresponds to significant rhythmicity ( p<0 . 05 ) for 50 randomly selected spikes . Asterisks mark cells where rhythmicity was statistically demonstrated though fewer than 50 spikes were recorded . Arrows depict mean phase ( Φ ) of dI3 INs whose average phase was during the extensor phase . ( G ) Polar plot summarizing rhythmic activity of dI3 INs in dorsal horn removed spinal cords ( n = 4 L2/L3 dI3 INs light green , and 7 L4/L5 dI3 INs dark green ) . Arrows depict mean phase ( Φ ) of dI3 INs whose average phase was during the extensor phase . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01610 . 7554/eLife . 21715 . 017Figure 3—source data 1 . Related to Figure 3E . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01710 . 7554/eLife . 21715 . 018Figure 3—source data 2 . Related to Figure 3F . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 01810 . 7554/eLife . 21715 . 019Figure 3—figure supplement 1 . Examples of rhythmic activity of dI3 INs during fictive locomotion . ( A ) Current-clamp recording of L2 dI3 IN ( light blue ) rhythmically active during extension . dI3 IN: whole-cell patch clamp recording ( light blue ) . L2 ( dark grey ) , L5 ( light grey ) : raw ventral root recording from L2 and L5 . ∫L2 , L5: rectified and integrated ventral root recordings . ( B ) L4 dI3 IN ( dark blue ) rhythmically active during flexion . dI3 IN: whole-cell patch clamp recording ( dark blue ) . L2 ( dark grey ) , L5 ( light grey ) : raw ventral root recording from L2 and L5 . ∫L2 , L5: rectified and integrated ventral root recordings . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 019 To determine the nature of this input , we performed voltage-clamp recordings of dI3 INs that were rhythmically active during extensor ( n = 8 ) or flexor ( n = 2 ) phases . We found that , regardless of their location in the lumbar spinal cord , the post-synaptic currents ( PSCs ) recorded in 7 out of 8 dI3 INs reversed between −90 and −40 mV , indicating they were inhibitory . In the neurons active in extension , these inhibitory PSCs ( IPSCs ) were phasic , indicating that they predominantly received rhythmic synaptic inhibition during the flexor phase ( Figure 4A , and Figure 4—figure supplement 1A ) . Three of these neurons also received some excitatory post-synaptic currents ( EPSCs ) during the extensor phase . Quantification of the distribution of the IPSCs across the step cycle revealed that the onset and termination of inhibitory input mirrored those of the L2 flexor bursts ( Figure 4B ) . When additional brief L2 bursts were present , these bursts also coincided with inhibition of dI3 INs , with the inhibitory input being of longer duration than the abbreviated L2 bursts ( Figure 4C ) . 10 . 7554/eLife . 21715 . 020Figure 4 . dI3 INs receive rhythmic synaptic inputs during drug-evoked locomotion . ( A ) Voltage-clamp recording ( VC ) of L5 dI3 IN during drug-evoked locomotion at different holding potentials ( VH ) . Bottom two rows are expanded time-scale representation of data in boxes seen in top two rows . Insets depict 10 postsynaptic currents of greatest magnitude within the flexor phase . Scale bars within insets , 5 ms , 25 pA . ( B ) Distribution of IPSCs through the step cycle ( n = 6622 IPSCs occurring in 116 step cycles in seven preparations ) overlaid with averaged normalised L2 ventral root recordings ( means and standard deviations shown ) . Step cycle was divided into 50 bins . Dashed line at 2% indicates the proportion of IPSCs if they were evenly distributed throughout the step cycle . ( C ) Voltage-clamp recording ( VC ) at holding potential of −40 mV of L5 dI3 IN during drug-evoked locomotion with brief flexion bursts ( cyan ) during extension . Bursts of IPSCs were observed during regular ( dark blue ) and brief flexion bursts as evidenced in integrated IPSCs trace ( top trace ) . ( D ) Diagram showing added inhibition of dI3 INs arising from flexor module of spinal locomotor circuits . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02010 . 7554/eLife . 21715 . 021Figure 4—source data 1 . Related to Figure 4B . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02110 . 7554/eLife . 21715 . 022Figure 4—figure supplement 1 . Examples of synaptic inputs to dI3 INs during fictive locomotion . ( A ) Voltage-clamp ( VC ) recording of L2 dI3 IN rhythmically active during extension at different holding potentials ( VH ) . Bottom two rows are expanded time-scale representation of data in boxes seen in top three rows . ( B ) Voltage-clamp ( VC ) recording of L4 dI4 IN ( dark blue ) rhythmically active during swing at different holding potentials ( VH ) . Bottom two rows are expanded time-scale representation of data in boxes seen in top two rows . Insets depict 10 excitatory post-synaptic currents of greatest magnitude within the extensor phase . Scale bars within insets , 2 ms and 25 pA . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 022 In contrast to the dominant extensor bursting dI3 INs , the two dI3 INs that were active during the flexor phase of the step cycle had no evident rhythmic IPSCs , but were excited during flexion ( Figure 4—figure supplement 1B ) , supporting that this subset likely belonged to a different functional sub-population of dI3 INs . Together , these data indicate dI3 INs are reciprocally connected to spinal locomotor circuits . dI3 INs excite spinal locomotor circuits , and in turn , the majority of dI3 INs are rhythmically inhibited during the flexor phase by these circuits ( Figure 4D ) . The timing of this inhibitory input suggests that locomotor circuits are transmitting an inhibitory efference copy to these dI3 INs during the flexor phase of the locomotor cycle . In light of their interposition between sensory afferents and spinal locomotor circuits , and their involvement in movement adaptation ( Bui et al . , 2013 ) , we next asked whether dI3 INs are involved in microcircuits responsible for plastic changes following spinal cord transection . To do so , we isolated spinal cords from the brain by performing lower thoracic spinal cord transections in control ( n = 22 ) , and dI3OFF ( n = 16 ) adult mice . Mice were subjected to regular treadmill training to promote spinal locomotor recovery ( Figure 5A ) . We quantified forelimb/hindlimb step ratios as initial estimates of recovered hindlimb locomotor performance over time , counting any ( however minimal ) forward excursion of the toes as a ‘step’ ( control: n = 6; dI3OFF: n = 3 ) . Step ratios of animals from both groups plateaued by 50 days following transection ( Figure 5C ) . But the number of forward toe excursions in dI3OFF mice was about half that in control mice ( Figure 5C , D , and Video 2 ) , indicating poor capacity of motor recovery by dI3OFF mice following spinal transection . 10 . 7554/eLife . 21715 . 023Figure 5 . dI3 INs are involved in locomotor recovery following complete spinal transection . ( A ) Experimental paradigm . Orange indicates period of treadmill training and locomotor performance assessments . Green indicates period of recovery following surgical procedures ( red ) . Days ( d ) from birth are indicated . ( B ) Kinematic recording snapshots in control and dI3OFF mice 50 days following complete ( and confirmed ) lower thoracic spinal transection . Arrows depict proper paw dorsiflexion in control ( cyan ) and abnormal plantarflexion in dI3OFF ( magenta ) mice during stance . Cyan segment at t = 384 ms indicates toe elevation in control , which is absent in dI3OFF during the swing phase . Pictures at 32 ms intervals . ( C ) Hindlimb/Forelimb step ratio ( S ) as a function of time ( T ) following complete spinal transection in control ( n = 6 ) and dI3OFF ( n = 3 ) animals with non-linear sigmoidal fit , S = Smax * Th / ( k1/2h + Th ) where Smax is the maximum step ratio , h is the Hill slope ( recovery rate ) , and k1/2 is the number of days to reach half the maximal step ratio . R2 = 0 . 53 and 0 . 23 respectively . ( D ) Hindlimb/Forelimb step ratio 30 days following complete spinal transection ( Tx ) in control ( n = 10 ) , and dI3OFF ( n = 6 ) animals . Mean +/− Standard Deviation . Two-tailed Student’s t-test ( ***p<0 . 0001 ) . ( E to I ) Toe coordinates along X ( horizontal forward-backward axis E , G ) and Y ( vertical elevation F , H ) axes , and knee angle ( I ) in control and dI3OFF spinal-transected animals 40 days post-surgery on a treadmill at 7 cm/s . Dashed lines represent standard deviation . Minimum weight-support was provided when necessary to complete the task . In G , H , and I , data from intact animals are shown in dark blue ( control ) and dark pink ( dI3OFF ) for comparison . Multiple t-tests corrected for multiple comparison Holm-Sidak . *p<0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02310 . 7554/eLife . 21715 . 024Figure 5—source data 1 . Related to Figure 5C . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02410 . 7554/eLife . 21715 . 025Figure 5—source data 2 . Related to Figure 5D . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02510 . 7554/eLife . 21715 . 026Figure 5—source data 3 . Related to Figure 5G . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02610 . 7554/eLife . 21715 . 027Figure 5—source data 4 . Related to Figure 5H . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02710 . 7554/eLife . 21715 . 028Figure 5—figure supplement 1 . Confirmation of anatomical and functional spinal isolation . ( A–B ) Post-mortem anatomy of control ( A ) and dI3OFF ( B ) spinal cords . Scar tissue removed . Animals included for analysis indicated by light borders , those excluded by dark borders . ( C ) Step Ratio quantification in control ( cyan shades ) and dI3OFF ( magenta shades ) before ( Surgery 1 ) and immediately after the second surgery ( Surgery 2 ) . Subsequent analysis was performed only on animals showing equivalent locomotor performance ( included , light shades ) while animals showing a decreased step ratio were excluded ( dark shades ) . ( D ) Experimental schema showing the group sizes of control ( cyan ) and dI3OFF ( magenta ) mice for spinal transection experiments . Most animals that did not survive the second surgery were assessed anatomically . Only animals that passed both functional ( similar step ratio before and after second surgery ) and anatomical ( absence of tissue in the lesion site ) assessments were included in the final kinematic analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02810 . 7554/eLife . 21715 . 029Figure 5—figure supplement 1—source data 1 . Related to Figure 5—figure supplement 1C . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 02910 . 7554/eLife . 21715 . 030Figure 5—figure supplement 2 . Locomotor function following isolation of spinal circuits . ( A–B ) Hip ( A ) and Ankle ( B ) angles in control ( cyan ) and dI3OFF ( magenta ) intact ( dark shades ) and spinal-transected ( light shades ) animals 40 days post-surgery . Dashed lines represent standard deviation . ( C , D ) Maximum toe excursion along the horizontal ( C ) and vertical ( D ) axis in spinal transected control ( cyan ) and dI3OFF ( magenta ) animals 40 days post-surgery . Mean +/− Standard Deviation . Two-tailed Student’s t-test , *p<0 . 05; **p<0 . 01 . ( E–I ) Correlation of normalized Hip ( E ) , Knee ( F ) , Ankle ( G ) angles , and Horizontal ( H ) and Vertical ( I ) Toe displacement between dI3OFF and Control intact ( row ) and transected ( Tx , second row ) mice and between intact and transected control mice ( third row ) or dI3OFF ( Fourth row ) mice . Arrow indicates direction at the stance onset . Swing: white; Stance: black . Bottom matrices indicate Pearson correlation coefficients for indicated pairs . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 03010 . 7554/eLife . 21715 . 031Video 2 . Foot print recordings following spinal cord transection . Foot print recordings of control and dI3OFF animals at 7 cm/s 50 days after spinal transection , recorded at 100 fps and displayed at 15 fps . Animals were recorded separately . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 031 To assess the quality of locomotor recovery , high-speed kinematic video recordings ( Figure 5B ) were analyzed in the 3 control and 3 dI3OFF animals that had anatomically and functionally confirmed complete transections ( Figure 5—figure supplement 1 ) . The spinal-transected control animals had some recovery of locomotion , although limb movements were reduced compared to their intact counterparts ( Figure 5E–I , Figure 5—figure supplement 2 , and Video 3 ) . On the other hand , dI3OFF mice had minimal horizontal movements of distal hind limb segments ( Figure 5E , G , and Figure 5—figure supplement 2C ) and an almost complete absence of vertical excursions ( Figure 5F , I , Figure 5—figure supplement 2D , and Video 4 ) . Furthermore , while the kinematic parameters of spinal-transected control animals for the most part were a scaled version of those in intact animals , the parameters in dI3OFF animals differed in trajectory ( Figure 5—figure supplement 2E–I ) . For example , the knee angle linearly decreased during flexion ( when it is normally biphasic ) and linearly increased during extension ( when it is normally primarily decreasing , Figure 5I ) . That is , while control mice recovered a degree of the complex sequence of muscle activation that produces stepping , the minimal movements of dI3OFF mice had linear kinematics with no resemblance to locomotion ( Figure 5H , I ) . Furthermore , during unrestrained overground locomotion ( data not shown ) , there was almost a complete absence of hindlimb movement in dI3OFF mice , in stark contrast to control mice , suggesting that the minimal movements seen during treadmill walking in dI3OFF mice were specific to the treadmill environment . 10 . 7554/eLife . 21715 . 032Video 3 . Kinematic recordings in intact animals . Kinematic recordings of control and dI3OFF animals running at 20 cm/s , recorded at 250 fps and displayed at 15 fps . Animals were recorded separately . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 03210 . 7554/eLife . 21715 . 033Video 4 . Kinematic recordings following spinal cord transection . Kinematic recordings of control and dI3OFF animals at 7 cm/s 50 days after spinal transection recorded at 250 fps and displayed at 15 fps . Weight support was provided . Animals were recorded separately . DOI: http://dx . doi . org/10 . 7554/eLife . 21715 . 033 Taken together , these results illustrate an absence of locomotor recovery in dI3OFF animals , and demonstrate the critical role of dI3 INs in driving recovery of locomotor function after spinal transection .
Sensory inputs to the spinal cord are required for the recovery of function following injury ( Dietz et al . , 2002; Rossignol and Frigon , 2011; Takeoka et al . , 2014 ) . Experiments in animal models such as rodents and cats have demonstrated that sensory inputs have access to spinal locomotor circuits , are phasically gated during the step cycle ( Forssberg et al . , 1977 ) , and their stimulation can lead to alterations in the timing and coordination of ongoing locomotor activity ( Duysens and Pearson , 1976; Loeb et al . , 1987; Gossard et al . , 1994; Stecina et al . , 2005 ) . Furthermore , stimulation of sensory afferents in in vitro isolated spinal cords can be sufficient to activate spinal locomotor circuits ( Bonnot et al . , 2002; Cherniak et al . , 2014 ) . Therefore , spinal locomotor circuits remain accessible through sensory afferents following the loss of descending inputs . Activation of sensory afferents by treadmill training is a guiding principle of locomotor rehabilitation ( Harkema , 2008 ) . This strategy has shown that activation of sensory afferents during imposed walking movements retrains spinal locomotor circuits to generate the rhythmic , patterned activation of hindlimb muscles required for locomotion ( Dietz et al . , 1995; Edgerton and Roy , 2009; Rossignol and Frigon , 2011; Harkema et al . , 2012 ) . In spinal-transected cats , at least one source of cutaneous afferents from the hindlimb is required to ensure full recovery of treadmill locomotion with plantar foot placement ( Bouyer and Rossignol , 2003 ) . And following spinal transection in rodents , recovery of hindlimb movements became highly disorganized following pharmacological block of paw cutaneous afferents , indicating the important role of these afferents in functional recovery ( Sławińska et al . , 2012 ) . Proprioceptive afferents also play an important role in recovery following incomplete spinal cord injury ( Takeoka et al . , 2014 ) . Thus , target neurons responsible for spinal mechanisms involved in sensory-mediated plasticity of locomotor circuits would be expected to receive sensory inputs from a variety of afferent types and project to spinal locomotor circuits . We have shown previously that dI3 INs receive different modalities of sensory inputs ( Bui et al . , 2013 ) , and here we show that they can activate spinal locomotor circuits . Furthermore , we now show that they are necessary for sensory-mediated recovery of function following spinal transection . That dI3 INs receive multimodal sensory inputs could explain why different sensory modalities contribute to locomotor recovery following spinal cord injury and would suggest , perhaps , that the combined inputs from different modalities , through temporal and spatial summation , may be beneficial to training strategies . The identity of the locomotor neurons excited by dI3 INs remains elusive: in addition to their outputs to motoneurons , dI3 INs also project to as yet unidentified neurons in the intermediate laminae of the cervical and lumbar spinal cord ( Bui et al . , 2013 ) . Unfortunately , although we increasingly understand locomotor circuits ( Kiehn , 2016 ) , knowledge of the neuronal substrate for activation of these circuits either by descending commands ( Jordan et al . , 2008; Bretzner and Brownstone , 2013; Bouvier et al . , 2015 ) , or by sensory inputs ( Whelan et al . , 2000; Marchetti et al . , 2001; Cherniak et al . , 2014 ) is lacking . Understanding dI3 IN target neurons in the intermediate laminae of the spinal cord may shed light on these questions , and would be a key next step in the determination of the cellular mechanisms of plasticity induced by dI3 INs . We have shown that dI3 INs are necessary for training-induced recovery of locomotor activity following spinal transection . It is unlikely that this recovery results simply from a loss of afferent input to locomotor circuits , as sensory stimulation – either by increasing stimulation intensity or by number of fibres ( by dorsal root stimulation ) – can still evoke locomotor activity in dI3OFF mouse spinal cords . We thus suggest that dI3 INs are mediating plastic changes in these circuits . In other words , the re-acquisition of locomotion can be considered to be a low level form of motor learning , in which repeated activity leads to sustained changes in the central nervous system such that spinal circuits below the site of a lesion can produce locomotor activity in the absence of descending motor commands . In this light , it is therefore instructive to consider that circuits that mediate short-term adaptation are those that lead to long-term learning through various mechanisms ( Hirano et al . , 2016 ) . We have previously shown that dI3 INs mediate short-term adaptation in regulating paw grasp in response to changing sensory stimulation ( Bui et al . , 2013 ) . Long-term changes , however , must be accompanied by homeostatic plastic mechanisms that prevent instability induced by positive feedback ( Turrigiano , 1999; Desai , 2003; Quartarone and Hallett , 2013 ) . These mechanisms could include , for example , changes in connectivity , synaptic strength , and/or morphology of spinal neurons ( Brownstone et al . , 2015 ) . Such changes have been proposed to underlie spontaneous or training-induced changes in motor output in spinal cord injury patients ( Harkema , 2008; Knikou , 2010; Dietz , 2012 ) and in animal models of spinal cord injury ( Côté and Gossard , 2004; Frigon et al . , 2009; Tillakaratne et al . , 2010; Martin , 2012; van den Brand et al . , 2012; Houle and Côté , 2013; Takeoka et al . , 2014 ) . Similar mechanisms of plasticity may also occur during learning in intact developing and mature spinal cords ( Tahayori and Koceja , 2012; Grau , 2014 ) . Studying such changes at synapses between dI3 INs and their target locomotor circuit neurons may reveal specific mechanisms underlying this plasticity . From a circuit perspective , we know from cerebellar studies that motor learning relies on comparator neurons – neurons that compare actual sensory inputs ( what did happen , or instructive inputs ) with the sensory input predicted by the motor command ( what should have happened ) ( Bastian , 2006; Shadmehr et al . , 2010; Wolpert et al . , 2011; Cullen and Brooks , 2015 ) . Predictive inputs arise from forward models derived from a copy of the motor command – an efference copy . By comparing these two inputs , comparator neurons calculate the ‘sensory prediction error , ’ which is then used to modify circuit function , either for corrective responses or sustained learning ( Shadmehr et al . , 2010; Requarth and Sawtell , 2014; Brownstone et al . , 2015 ) . Most dI3 INs receive excitatory instructive inputs from a variety of sensory afferents ( Bui et al . , 2013 ) , as well as inhibitory rhythmic input from locomotor circuits . We suggest that this inhibitory input , which mirrors the motor output , is the manifestation of a forward model and is suggestive of a negative image of the expected excitatory sensory input ( Requarth and Sawtell , 2014 ) . That is , in addition to instructive inputs , dI3 INs receive inputs indicative of a predictive forward model . These two classes of inputs position dI3 INs as comparators between actual and predicted movement , and thus calculators of sensory prediction error . We suggest that this error signal leads to plastic changes in locomotor circuits , mediating long-term learning such as that necessary for locomotor recovery after spinal cord transection ( Figure 6B ) . Within this framework , sensory information provided by training would lead to activation of locomotor circuits , which would then produce a forward model to predict the sensory consequences of the movement . Through training , the sensory prediction error is iteratively calculated by dI3 INs , and would lead to synaptic and/or cellular changes in locomotor circuits , leading to sustained recovery of motor function ( Brownstone et al . , 2015 ) . Therefore , functional removal of dI3 INs from the circuits results in the loss of sensory prediction error signals , and thus prevents the benefit of locomotor training following spinal transection ( Figure 6C ) . Motor learning is distributed across hierarchical control structures , with different levels of the hierarchy functioning together to ensure adaptation and learning ( Kawato et al . , 1987; Gordon and Ahissar , 2012 ) . We show that dI3 INs form an intra-spinal closed loop circuit , in which the microcircuits that route sensory information to locomotor circuits are themselves under the influence of the locomotor circuits that they modulate ( Figure 6A ) . These closed loops would be nested within other control loops , such as peripheral sensorimotor loops ( Dimitriou and Edin , 2010 ) and those situated between spinal motor circuits and descending motor systems ( Arshavsky et al . , 1972; Hantman and Jessell , 2010; Azim et al . , 2014 ) . Together , these nested loops create a hierarchical control system that would optimise motor learning ( Kawato et al . , 1987 ) . That one of these loops may exist in the spinal cord would be important for rehabilitative techniques: targeting intraspinal learning circuits such as those formed by dI3 INs could lead to new strategies to facilitate spinal circuit function in order to improve motor behaviour affected by a number of neurological diseases and injuries .
All animal procedures were approved by the University Committee on Laboratory Animals of Dalhousie University ( protocol 13–143 ) and conform to the guidelines put forth by the Canadian Council for Animal Care . Expression of yellow fluorescent protein ( YFP ) driven by the promoter for the homeodomain transcription factor Isl1 was obtained by crossing Isl1Cre/+ ( RRID:IMSR_JAX:024242 ) and Thy1-fs-YFP mice to yield Isl1-YFP mice . Conditional knockout of Slc17a6 ( vGluT2 ) in Isl1 expressing neurons was accomplished by crossing Isl1Cre/+ mice with a strain of mice bearing a conditional allele of the Slc17a6 ( vGluT2 ) gene to yield dI3OFF mice as previously described ( Hnasko et al . , 2010; Bui et al . , 2013 ) . Control animals consisted of Isl1Cre/+:Slc17a6flox/+ and Isl1+/+:Slc17a6flox/flox mice . No differences were observed between these two control genotypes and animals were thus pooled into a single control group . Limb movement during locomotor behaviour was described by using motion analysis techniques combined with high-speed video recordings of the behaviour ( Akay et al . , 2014 ) . Analysis of footprints during treadmill locomotion ( 10 cm/s to 50 cm/s ) was performed using an Exer Gait XL treadmill ( Columbus Instruments ) and analyzed with Treadscan Analysis System v4 . 0 ( Clever Sys ) . Analysis was performed on segments in which the mice maintained their position on the treadmill . For kinematic analysis , mice walked on a treadmill ( either custom made by the workshop of Zoological Institute , University of Cologne or an Exer Gait XL from Columbus Instruments ) at speeds ranging from 3 to 72 cm/s and recorded with a high-speed camera ( Photron PCL R2 , Photron; or IL3-100 , Fastec Imaging ) at a capture rate of 250 frames per second ( Fps ) . The animals were briefly anesthetized with isoflurane and custom-made three-dimensional reflective markers ( ~2 mm diameters ) were glued onto the shaved skin at the level of the anterior iliac crest , hip , knee ( in some cases ) , ankle , metatarsophalangeal ( MTP ) joint , and the tip of the fourth digit ( toe ) of one or both hindlimbs . Joint coordinates were automatically tracked by Vicon Motus software or by custom scripts for ImageJ ( Schneider et al . , 2012 ) ( RRID:SCR_003070 ) and R ( R Core Team , 2013 ) ( RRID:SCR_000432 ) . These coordinates were used to compute hip , knee , ankle , and paw angles . For the knee joint , in consideration of movement of the skin over the knee , the actual knee coordinates and angles were calculated geometrically using the length of the femur and tibia . Stance onset was determined by using local maxima of the position of the toe marker in the horizontal plane . Several step cycles were averaged from portion of recordings when animals were producing a steady locomotor output . Extracellular ventral and dorsal root recordings via suction electrodes were amplified 10 , 000 X in differential mode , bandpass-filtered ( 10 Hz to 3 KHz ) using a custom-built extracellular amplifier , and acquired at 10 kHz ( Digidata 1322A , pClamp nine software , Molecular Devices RRID:SCR_011323 ) . Whole-cell patch-clamp signals were obtained using a MultiClamp 700B amplifier ( Molecular Devices ) as previously described ( Bui et al . , 2013 ) . To study sensory-evoked locomotor activity , in-vitro preparations with the sural nerve in continuity with the spinal cord were prepared from Isl1-YFP or dI3OFF postnatal ( P1-P3 ) mice . Surgical procedures to isolate the spinal cord were similar to Bui et al . ( 2013 ) except that the skin of the right hindlimb was dissected and muscles removed to expose and dissect the sciatic and sural nerves . The sural , common peroneal , and tibial nerves were then transected distally . The spinal cords were left to recover for 1–2 hr before recording . Locomotion was induced by dorsal root or sural nerve stimulation using a 10 s long train of 250 µs pulses at 3 Hz using a Grass Technologies S88 square pulse stimulator ( Natus Neurology Inc . ) . The presence of more than four successive rhythmic bursts within the stimulation train in at least one ventral root was used to indicate the presence of locomotor-like activity . To record rhythmic inputs to dI3 INs during fictive locomotion , hemisected spinal cords were prepared from Isl1-YFP postnatal ( P1-P6 ) mice as previously described ( Bui et al . , 2013 ) . Following anaesthesia with xylazine and ketamine , mice were decapitated . Their spinal cords were isolated by vertebrectomy in room temperature recording ACSF ( in mM: NaCl , 127; KCl , 3; NaH2PO4 , 1 . 2; MgCl2 , 1; CaCl2 , 2; NaHCO3 , 26; D-glucose , 10 ) . Ventral and dorsal roots were dissected as distally as possible , and spinal cords were pinned with the ventral side up . A surgical blade was used to make a longitudinal incision down the midline ( in a rostro-caudal direction ) . The hemicords were then allowed to equilibrate in room temperature recording ACSF for at least one hour , then pinned , medial-side up to a base of clear Sylgard ( Dow Corning ) in a recording chamber and perfused with circulating room temperature recording ACSF . Ventral roots were placed in suction electrodes ( A-M Systems Inc . ) . In a subset of experiments examining the activity of dI3 INs during drug-evoked locomotion , bilateral dorsal cords were removed from segments L1 to L3 or from segments L4 to L6 . Spinal cords were pinned on their sides and insect pins were used to trace a line followed by a surgical blade to section the spinal cord . Fictive locomotion was elicited by application of NMDA ( 5 µM ) , serotonin ( 10 µM ) and dopamine ( 50 µM ) ( Jiang et al . , 1999 ) . Data from spinal cords in which synchronous activity was produced in flexor- and extensor-dominant ventral roots were excluded . Circular statistics ( Zar , 1996 ) using 50 randomly selected spikes for each dI3 IN were used to determine the phasic relationships between dI3 IN spiking and ventral root bursting during drug-induced locomotion . Whole-cell patch-clamp electrodes were filled with an internal solution containing ( in mM: K-methane-sulphonate , 140; NaCl , 10; CaCl2 , 1; HEPES , 10; EGTA , 1; ATP-Mg , 3; GTP0 . 4; pH 7 . 2–7 . 3 adjusted with KOH; osmolarity adjusted to ~295 mOsm with sucrose ) . In the hemisected cords , care was taken to record from cells that were in the middle of the ventral-dorsal axis of the hemicord to avoid motoneurons . In order to avoid recording from neurons close to the central canal ( possibly sympathetic preganglionic neurons ) , neurons that were near the surface were also avoided . Most dI3 INs exhibited rhythmic , alternating phases of membrane depolarization , which in some cases were accompanied by firing of action potentials , and quiescent hyperpolarization . In the cells where cyclical change of membrane potential was not accompanied by firing of action potentials , application of a bias depolarizing current led to phasic firing of action potentials during periods of membrane depolarization . The locomotor cycle was double-normalized ( Kwan et al . , 2009 ) such that each cycle was divided into extensor and flexor phases , with 0 to 0 . 5 spanning the onset to the termination of the extensor phase and 0 . 5 to 1 spanning the flexor phase of each cycle . Polar plots of rhythmic activity were used for analysis of the rhythmic activity of each cell . The angle represents the mean phase ( Φ ) whereas the distance from origin is a measure of concentration around the mean phase , which we refer to here as r . After calculating the cosine and sine of the phase of each spike , Φ was calculated as the average cosine and sine of the spikes and the angular concentration , r , was calculated as the square root of the sum of squares of the average cosine and average sine ( Zar , 1996 ) . Thus if the neuron fires once per cycle at precisely the same phase , then r = 1 . Conversely , if there were a random distribution of firing across the step cycle , then the average sine and cosine would both be 0 , and r = 0 ( i . e . all spikes are uniformly dispersed across the step cycle ) . The inner circle represents an r value of 0 . 24 , as for 50 randomly-selected spikes , values above this threshold indicate that cells were rhythmically active ( p<0 . 05 ) . To determine the rhythmicity of inputs to dI3 INs , unitary PSCs were extracted from voltage-clamp recordings at −40 mV . Outward IPSCs and inward EPSCs were detected in Clampfit using a template . The phase of each IPSC and EPSC was calculated and circular statistics were applied as above to determine whether dI3 INs received rhythmic inputs . The proportion of IPSCs that occurred within a certain phase of the step cycle was determined by calculating the proportion of IPSCs within each of 50 equal bins across the step cycle . Complete transections were performed at T9-T10 under isoflurane anaesthesia . Animals were individually housed , given analgesics for 3 days , and allowed to recover for at least one week prior to further testing . Animals were monitored twice daily and bladders expressed manually . Humane endpoints were defined as self-mutilation , improper feeding , decreased grooming , ataxia , or a loss of body weight >20% . Completeness of spinal transections was confirmed by a second surgery as described below . Locomotor training was provided at least once per week ( average twice per week ) . Animals were allowed to habituate to the treadmill environment before recordings ranging from 20 to 40 s at speeds between 3 and 7 cm/s . When required to ensure the completion of the task , animal support was provided by holding the animal in a horizontal position while maintaining permanent contact with the treadmill belt . 5 to 10 recordings were performed per sessions . Animals showing regular locomotor performance were selected for spinal transection . In sterile conditions , animals were anesthetized with isoflurane to the point of loss of hindlimb reflexes , then shaved and disinfected using Chlorexidine , 70% isopropyl alcohol and 10% betadine from the neck to the lumbar region , and placed on a heating pad . A Tegaderm film ( Ref 70200749300 , 3M ) was placed for stability and to ensure sterile conditions throughout the procedure . An incision was made , paraspinal muscles were incised and spread to expose the T9-T10 laminae , and laminectomy was performed to expose the spinal cord . Double incisions were performed on the rostral and caudal portions of the exposed cord . A Pasteur pipet melted into a custom hook was used to insert underneath the cord and remove the sectioned segment . A piece of sterile Absorbable Hemostat Surgifoam ( Ref 63713-0019-75 , Ethicon ) was placed into the created spinal space . Deep and superficial muscles were sutured with Chromic Gut 6–0 sutures ( Ref 796G; Ethicon ) , and skin with 6–0 Ethilon nylon monofilament suture ( Ref 1856G; Ethicon ) . Tissue was kept moist at all times using sterile saline solution . Animals , individually housed until suture removal about 10 days later , were allowed to recover for at least one week prior to further testing . They were monitored twice per day and bladders were expressed manually as necessary . Animal weight was monitored daily . Analgesics were administered sub-cutaneously for 3 days after the surgery ( buprenorphine: 0 . 05 mg/kg working solution , 3 µg/ml twice daily; and ketoprofen: 5 mg/kg working solution , 1 mg/ml once a day ) . Antibiotic ( enrofloxacin: 5 mg/kg , working solution 0 . 25 mg/ml ) was administered sub-cutaneously at the time of surgery then provided in drinking water ( 400 mg/l ) until complete healing . Animals were provided with accessible food , water , and soft bedding . We confirmed complete transection ( Figure 5—figure supplement 1A , B ) by proceeding with a second transection at least 40 days after the first in 11 control and 6 dI3OFF mice ( 7 and 5 survived , respectively ) . Animals were excluded ( 4 control and 2 dI3OFF mice ) from the analysis if the second surgery induced a locomotor deficit ( Figure 5—figure supplement 1C ) , indicating incomplete initial transection . In order to assess locomotor function recovery , we quantified the ratio of hindlimb to forelimb steps . A hindlimb step was counted when there was any forward movement of the toes . Ventral treadmill video recordings were analyzed using the ImageJ Cell counter plugin . An average of 40 forelimb steps were counted per recording . The number of rear steps was averaged between both hindlimbs . The ratio was calculated as the average number of hindlimb steps divided by the number of forelimb steps . For kinematic analysis , were analyzed using a custom script in R ( https://github . com/nstifani ) . The following packages were used: tcltk , zoo , and rgl , all available from the Comprehensive R Archive Network ( CRAN repository http://cran . r-project . org ) . Data recorded from intact control ( n = 12 ) and dI3OFF ( n = 6 ) animals running at 10 to 60 cm/s . Left and right hind limbs were recorded separately and processed in parallel . A total of 382 control and 172 dI3OFF step cycles were analyzed . Subsequently , step cycles ( swing and stance ) were normalized to durations of 100 data points ( equivalent to a normalised step cycle duration of 400 ms ) . Measured variables were averaged per animal and across recordings . Following transection and recovery , data recorded from spinalized controls ( n = 3 ) and dI3OFF ( n = 3 ) animals at speeds between 3 and 7 cm/s were added to the recorded data from intact animals . Steps cycles ( control: n = 46; dI3OFF: n = 16 ) were normalized as described above . | Circuits of nerve cells , or neurons , in the spinal cord control movement . After an injury to the spinal cord , the connections between the brain and spinal neurons may be severed , meaning that the spinal circuits can no longer work properly . This loss of communication between the brain and the spinal cord often leads to paralysis below the level of the injury . There are currently no effective treatments for individuals who have lost the ability to walk following spinal cord injury . However , the spinal cord retains circuits that are sufficient to restore walking and these circuits can be activated with training . That is , rehabilitative training can lead to improvements in movement by promoting spinal cord plasticity – the ability of other neurons in the spinal cord to take over the roles of the severed neurons . By understanding how rehabilitation leads to these improvements following injury , new strategies could be developed to optimize the recovery process . Previous research showed that spinal neurons called dI3 interneurons are involved in short term adjustments of movement . Could these interneurons also be involved in longer term adaptations ? Bui , Stifani et al . compared normal mice with genetically engineered mice that had dI3 interneurons “removed” from their circuits . This revealed that although dI3 interneurons in mice are integrated with spinal circuits that are involved in walking , they are not necessary for normal walking . Following the severing of the spinal cord , the experimental mice , unlike the normal mice , did not recover the ability to step . Thus , circuits comprising dI3 interneurons are necessary for recovering the ability to move after an injury . Now that Bui , Stifani et al . have identified this essential circuit , the next step is to investigate how dI3 interneurons promote spinal cord plasticity . Understanding these mechanisms could help to develop therapies that enhance rehabilitation-assisted improvement of movement following spinal cord injury . | [
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White adipose tissue ( WAT ) remodeling is dictated by coordinated interactions between adipocytes and resident stromal-vascular cells; however , the functional heterogeneity of adipose stromal cells has remained unresolved . We combined single-cell RNA-sequencing and FACS to identify and isolate functionally distinct subpopulations of PDGFRβ+ stromal cells within visceral WAT of adult mice . LY6C- CD9- PDGFRβ+ cells represent highly adipogenic visceral adipocyte precursor cells ( ‘APCs’ ) , whereas LY6C+ PDGFRβ+ cells represent fibro-inflammatory progenitors ( ‘FIPs’ ) . FIPs lack adipogenic capacity , display pro-fibrogenic/pro-inflammatory phenotypes , and can exert an anti-adipogenic effect on APCs . The pro-inflammatory phenotype of PDGFRβ+ cells is regulated , at least in part , by NR4A nuclear receptors . These data highlight the functional heterogeneity of visceral WAT perivascular cells , and provide insight into potential cell-cell interactions impacting adipogenesis and inflammation . These improved strategies to isolate FIPs and APCs from visceral WAT will facilitate the study of physiological WAT remodeling and mechanisms leading to metabolic dysfunction . Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review . The Reviewing Editor's assessment is that all the issues have been addressed ( see decision letter ) .
White adipose tissue ( WAT ) represents the principle site for safe and efficient energy storage in mammals . WAT , as a whole , is considerably heterogeneous . WAT is composed of energy-storing adipocytes , various immune cell populations , vascular cells , adipocyte precursor cells ( APCs ) , and largely uncharacterized stromal populations . The development and function of adipose tissue is highly dependent on critical interactions between adipocytes , APCs , immune cells , and endothelial cells ( Han et al . , 2011; Hong et al . , 2015 ) . WAT has a unique and remarkable capacity to expand and contract in size in response to changes in demand for energy storage . In the context of positive energy balance ( nutrient excess ) , WAT expands to meet the increased demand for energy storage , leading ultimately to the condition of obesity . The manner by which WAT expands is a critical determinant of metabolic health in obesity . It has long been appreciated that individuals who preferentially accumulate WAT in subcutaneous regions are at a relatively lower risk for developing insulin resistance when compared to equally obese individuals with central ( visceral ) adiposity ( Kissebah et al . , 1982; Krotkiewski et al . , 1983 ) . It is now widely believed that visceral and subcutaneous WAT depots represent fundamentally distinct types of WAT ( Karastergiou et al . , 2013; Lee et al . , 2013; Macotela et al . , 2012; Yamamoto et al . , 2010 ) . Indeed , visceral and subcutaneous WAT depots emanate from distinct developmental lineages ( Chau et al . , 2014 ) . Importantly , another clear determinant of metabolic health in obesity is manner in which individual WAT depots expand and ‘remodel’ ( Hepler and Gupta , 2017; Lee et al . , 2010 ) . WAT ‘remodeling’ associated with obesity can be described as both quantitative and qualitative changes in adipocyte numbers and stromal-vascular cell composition . Pathological WAT expansion is characterized by the presence of enlarged adipocytes , excessive macrophage accumulation , and fibrosis ( Divoux et al . , 2010; Gustafson et al . , 2009; Hardy et al . , 2011; Klöting and Blüher , 2014; Sun et al . , 2013 ) . The prevailing hypothesis is that as ‘overworked’ fat cells reach their storage capacity , adipocyte death , inflammation , and fibrosis ensue ( Hepler and Gupta , 2017; Sun et al . , 2011 ) . This is often associated with the deleterious accumulation of lipids in the liver , skeletal muscle , pancreas , and heart ( termed ‘lipotoxicity’ ) ( Unger and Scherer , 2010 ) . Healthy WAT expansion occurs when adipose tissue expands through adipocyte hyperplasia ( increase in adipocyte number through de novo differentiation ) ( Denis and Obin , 2013; Kim et al . , 2014; Klöting et al . , 2010 ) . This is associated with a lower degree of chronic tissue inflammation and fibrosis . These adipose phenotypes of the ‘metabolically healthy’ obese tightly correlate with sustained insulin sensitivity in these patients . To date , the factors dictating a healthy vs . unhealthy WAT expansion in obesity remain poorly defined . In particular , the array of cell types within the adipose stromal-vascular compartment contributing to the remodeling of WAT in obesity has remained largely undefined . The growing appreciation for the casual link between adipose tissue distribution and remodeling with systemic metabolic health has sparked considerable interest in defining the adipocyte precursors giving rise to fat cells in adults and the mechanisms controlling their differentiation in vivo ( Hepler et al . , 2017 ) . In male C57BL/6 mice , adipose tissues expand in diet-induced obesity in a depot-selective manner . The epididymal WAT depot expands through both adipocyte hypertrophy and adipocyte hyperplasia ( Jeffery et al . , 2015; Kim et al . , 2014; Wang et al . , 2013b ) . The inguinal subcutaneous WAT depot expands almost exclusively by adipocyte hypertrophy . We recently reported that visceral adipocytes emerging in association with HFD feeding originate , at least in part , from perivascular precursors expressing Pdgfrb ( Vishvanath et al . , 2016 ) . Pdgfrb encodes the platelet-derived growth factor receptor β chain ( PDGFRβ protein ) and is a widely used marker of perivascular cells ( Armulik et al . , 2011 ) . We previously employed a pulse-chase lineage tracing mouse model to track the fate of Pdgfrb-expressing cells in adipose tissue . Following HFD feeding , Pdgfrb-expressing cells give rise to white adipocytes within visceral WAT depots ( Vishvanath et al . , 2016 ) . The ability of these precursors to undergo de novo adipogenesis in the setting of diet-induced obesity is critical for healthy visceral WAT expansion ( Shao et al . , 2018 ) . Inducible genetic disruption of Pparg , the master regulatory gene of adipocyte differentiation , in Pdgfrb-expressing cells leads to a loss of de novo adipogenesis from Pdgfrb-expressing cells in the visceral WAT depot of diet-induced obese mice; this exacerbates the pathologic remodeling of this depot ( i . e . increased inflammation and fibrosis ) . Driving de novo adipogenesis from Pdgfrb-expressing cells through transgenic Pparg expression leads to a healthy expansion of visceral WAT ( lower inflammation and small adipocytes ) ( Shao et al . , 2018 ) . The highly adipogenic subpopulation of PDGFRβ+ cells in gonadal WAT ( gWAT ) is quantitatively enriched in the expression of Pparg , as well as its upstream regulatory factor , Zfp423 ( Gupta et al . , 2012; Tang et al . , 2008; Vishvanath et al . , 2016 ) . PDGFRβ+ cells enriched in these adipogenic factors express several mural cell ( pericyte/smooth muscle ) markers and reside directly adjacent to the endothelium in WAT blood vessels ( Gupta et al . , 2012; Tang et al . , 2008; Vishvanath et al . , 2016 ) . Using Zfp423 reporter mice ( Zfp423GFP BAC transgenic mice ) , we revealed that PDGFRβ+ cells expressing high levels of Zfp423 ( GFP+ or Zfp423High ) represent highly committed preadipocytes while Zfp423Low cells ( GFP- ) lacked significant adipogenic capacity , and exhibited significantly different global patterns of gene expression ( Vishvanath et al . , 2016 ) . These observations suggested that the pool of PDGFRβ+ cells in visceral WAT is functionally heterogeneous , with cells possessing distinct cellular phenotypes . In this study , we set out to explore the functional heterogeneity within Pdgfrb-expressing cells of visceral WAT from adult mice . Furthermore , our objective was to identify improved strategies to purify adipocyte precursor populations from these depots . Through single-cell RNA-sequencing , we identified functionally distinct subpopulations of Pdgfrb-expressing progenitor cells . We identified a unique population of cells that display fibrogenic and functional pro-inflammatory phenotypes , and lack inherent adipogenic capacity . These fibro-inflammatory progenitors ( termed here as ‘FIPs’ ) can be purified by the use of commercially available antibodies ( LY6C + PDGFRβ+ ) . On the other hand , LY6C- CD9- PDGFRβ+ cells represent a distinct pool of highly adipogenic visceral adipocyte precursor cells ( ‘APCs’ ) that robustly differentiate spontaneously in vitro in growth media containing insulin . The frequency of these PDGFRβ+ subpopulations is highly regulated under physiological conditions . These data reveal the functional heterogeneity of perivascular progenitors within visceral WAT and provide insight into how the adipose stroma can control WAT remodeling . Moreover , the molecular profiles obtained for FIPs and APCs from visceral WAT , along with the strategies to isolate these cells , will facilitate the study of physiological WAT remodeling in vivo .
We previously derived a doxycycline-inducible ( Tet-On ) lineage-tracing model that allows for the indelible labeling of Pdgfrb-expressing perivascular cells in adipose tissue of adult mice ( PdgfrbrtTA; TRE-Cre; Rosa26RmT/mG; herein , ‘MuralChaser mice’ ) ( Vishvanath et al . , 2016 ) . Prior to exposing animals to doxycyline , all cells within the stromal-vascular fraction ( SVF ) of adult gonadal WAT ( gWAT ) express membrane tdTomato from the Rosa26 locus . Following 9 days of exposure to doxycycline-containing chow diet , Cre-mediated excision of the loxP-flanked tdTomato cassette occurs in Pdgfrb-expressing cells , and membrane-bound GFP ( mGFP ) expression is constitutively activated ( Figure 1A ) . As previously reported and confirmed here , FACS analysis indicated that nearly all mGFP+ cells are PDGFRβ+ as expected , and are devoid of CD45 ( hematopoietic ) , CD31 ( endothelial ) , and CD11b ( monocyte/macrophage ) expression ( Figure 1—figure supplement 1A ) ( Vishvanath et al . , 2016 ) . Moreover , mGFP expression following transient doxycycline exposure is confined predominately to peri-endothelial cells in adult gonadal WAT ( Figure 1—figure supplement 1B ) ( Vishvanath et al . , 2016 ) . We set out to test the hypothesis that Pdgfrb-expressing perivascular cells in gonadal visceral WAT of adult mice are heterogeneous , with subpopulations harboring functionally distinct phenotypes . To this end , we performed single cell RNA-sequencing ( scRNA-seq ) of mGFP+ cells isolated from gWAT of lean ( chow fed ) 8 week-old male MuralChaser mice following 9 days of doxycycline exposure . tSNE analysis of 1045 cell transcriptomes revealed distinct cell clusters exhibiting unique transcriptional profiles ( Figure 1B , C ) . Many of the top 20 most enriched transcripts in Cluster 1A and Cluster 1B correspond to notable genes related to adipogenesis and/or adipocyte gene expression ( Figure 1D ) . In particular , the majority of cells in Clusters 1A and 1B express high levels of Pparg , Fabp4 , Hsd11b1 , and Lpl , indicating these clusters may represent the PDGFRβ+ APC population within visceral WAT ( Figure 1E ) . Interestingly , Cluster 1B further enriches in the expression of Pparg , Cebpa , and other markers of terminal adipocyte differentiation , including Plin1 , Fabp5 , Car3 , and Cd36 ( Figure 1F ) . Notably , the expression of Adipoq , Retn , and Adrb3 , genes typically characteristic of mature adipocytes , were detected within some cells within Cluster 1B ( Figure 1F ) . Unbiased gene set enrichment analysis ( GSEA ) revealed that cells of Cluster 1A/B enriched for gene sets related to ‘adipogenesis’ and cells of Cluster 1B enriched for gene signatures of ‘oxidative phosphorylation , ’ ‘adipogenesis , ’ and fatty acid metabolism ( Tables 1 and 2 ) . These data suggest Cluster 1A and 1B represent ‘adipocyte precursor cells’ ( APCs ) , with Cluster 1B representing a subpopulation of APCs that are ‘committed preadipocytes’ . The cells in Cluster 2 were highly enriched in the expression of genes associated with fibrosis and inflammation , including Fn1 , Loxl2 , Tgfb2 , and Ccl2 ( Figure 1D and G ) . GSEA revealed the enrichment of numerous gene signatures characteristic of a fibrogenic and inflammatory phenotype , including gene sets corresponding to ‘inflammatory response , ’ ‘TGFβ signaling , ’ ‘TNFα signaling , ’ and ‘hypoxia’ ( Table 3 ) . This fibro-inflammatory molecular signature of Pdgfrb-expressing cells suggested this subpopulation represents ‘fibro-inflammatory progenitors’ ( herein , termed ‘FIPs’ ) . Cluster 3 was molecularly quite distinct from Clusters 1A/B and 2 . Interestingly , Cluster 3 had a mesothelial-like cell ( herein , ‘MLCs’ ) expression profile . Mesothelial cells are epithelial cells of mesodermal origin that form a monolayer ( mesothelium ) lining the visceral serosa . Mesothelial cells and mural cells share a common developmental lineage . Multiple genetic lineage tracing studies in mice indicate that various stromal cell populations within visceral tissues , including APCs , descend from embryonic mesothelial cells ( Chau et al . , 2014; Rinkevich et al . , 2012 ) . Mesothelial cells have been linked to multiple aspects of adipose tissue development and remodeling , including adipogenesis and inflammation ( Darimont et al . , 2008; Gupta and Gupta , 2015; Mutsaers et al . , 2015 ) . Cluster 3 was enriched for genes representing common mesothelial/epithelial markers , such as Msln , Upk3b , Krt8 , and Krt14 ( Figure 1D and H ) . The presence of this cluster suggested that the PdgfrbrtTA transgene targets at least a subset of visceral WAT associated mesothelial cells . Indeed , following transient doxycycline treatment of MuralChaser mice , a few mGFP+ cells can be observed within in the outermost epithelial layer of gonadal WAT ( Figure 1—figure supplement 1C ) . Moreover , mGFP+ cells can be occasionally observed within cultures of isolated mesothelial cells obtained from gWAT of doxycycline-treated MuralChaser mice ( Figure 1—figure supplement 1D ) . We performed a second scRNA-seq analysis utilizing independently isolated mGFP+ cells from gonadal WAT MuralChaser mice ( Figure 1—figure supplement 2 ) . From the second scRNA-seq dataset , we again identified the same major subpopulations of Pdgfrb-expressing cells . All together , these scRNA-seq data reveal molecularly distinct Pdgfrb-expressing subpopulations in visceral adipose tissue . Next , we developed a strategy to isolate these molecularly distinct cell populations by flow cytometry from wild type mice . For this purpose , we treated Cluster 1A and 1B as one broad ‘APC’ population ( Figure 2A ) . Candidate cell surface markers were selected on the basis of their corresponding gene expression in the three PDGFRβ+ subpopulations and the availability of commercial antibodies suitable for FACS . Of note , Ly6c1 expression was abundant in FIPs but not APCs ( Figure 2B ) . The expression of Cd9 , a recently described marker of fibrogenic cells ( Marcelin et al . , 2017 ) , was abundantly expressed in both the FIPs and MLCs ( Figure 2B ) . Therefore , we isolated the three populations based on these markers using fluorescence-activated cell sorting . PDGFRβ+ cells ( CD31- and CD45- ) were subdivided on the basis of LY6C and CD9 immunoreactivity ( Figure 2B , C ) . Three distinct subpopulations of PDGFRβ+ cells were apparent: LY6C- CD9- ( APCs ) , LY6C+ ( FIPs ) , and LY6C- CD9+ ( MLCs ) cells ( Figure 2C ) . Flow cytometry analysis consistently revealed that LY6C+ PDGFRβ+ cells were more abundant than LY6C- CD9- PDGFRβ+ cells and Ly6C- CD9+ PDGFRβ+ cells ( Figure 2D ) . Importantly , gene expression analysis by qPCR revealed that LY6C- CD9- PDGFRβ+ cells were enriched in the expression of genes that defined the APC population ( Cluster 1 ) ( Figure 2E , F ) . LY6C+ PDGFRβ+ cells enriched for the mRNAs that initially defined the FIPs ( Cluster 2 ) ( Figure 2E , G ) , and LY6C- CD9+ PDGFRβ+ cells expressed the mesothelial/epithelial markers that defined Cluster 3 ( Figure 2E , H ) . Collectively , these data provide independent validation of the scRNA-seq data of genetically labeled Pdgfrb-expressing cells , and establish a method for isolating PDGFRβ+ subpopulations from gWAT of adult wild type mice using commercially available antibodies . The global molecular signature of LY6C- CD9- PDGFRβ+ cells ( Cluster 1 ) suggests this population represents APCs . Indeed , freshly sorted LY6C- CD9- PDGFRβ+ cells are enriched in Pparg expression when compared to LY6C+ PDGFRβ+ cells ( Figure 3—figure supplement 1A ) . We explored this hypothesis by testing the ability of these subpopulations to undergo adipocyte differentiation in vitro . We isolated and cultured all three subpopulations in growth medium containing 2% FBS and 1% ITS ( insulin , transferrin , selenium ) . These represent culture conditions that we previously established for growth and differentiation of gWAT-derived PDGFRβ+ cells ( Vishvanath et al . , 2016 ) . Under these growth conditions , LY6C+ PDGFRβ+ cells proliferate at a greater rate than LY6C- CD9- PDGFRβ+ cells; however , the two subpopulations appear morphologically indistinguishable , with both populations appearing fibroblast-like until reaching confluence ( Figure 3—figure supplement 1B , C , E ) . LY6C- CD9+ PDGFRβ+ cells ( MLCs ) grow to confluence and adopt a cobblestone-like morphology characteristic of cultured mesothelial cells ( Figure 3—figure supplement 1D ) . Remarkably , upon reaching confluence , only LY6C- CD9- PDGFRβ+ cells ( APCs ) underwent spontaneous adipocyte differentiation at a high efficiency , while very few adipocytes emerged in the other two PDGFRβ+ subpopulations or within cultures containing all PDGFRβ+ cells from gWAT ( Figure 3 ) . FIPs appeared to possess some latent capacity to undergo adipogenesis . Confluent cultures of LY6C+ PDGFRβ+ cells stimulated with a more commonly used hormonal adipogenic cocktail ( dexamethasone , IBMX , insulin , and PPARγ agonist , Rosiglitazone ) underwent to adipocyte differentiation to some degree ( Figure 3—figure supplement 2 ) . Despite this strong adipogenic stimulus , LY6C+ PDGFRβ+ cells still did not differentiate to the same extent as LY6C- CD9- PDGFRβ+ cells stimulated with insulin alone ( see Figure 3 ) . We also assessed the ability of APCs and FIPs to undergo adipocyte differentiation in vivo . We transplanted 80 , 000 cells into the remnant subcutaneous WAT depots of Adipoq-Cre; PpargloxP/loxP animals , a well-described model of lipodystrophy ( Figure 3—figure supplement 3A ) ( Wang et al . , 2013a ) . 3 weeks following cell transplantation , the WAT depots all four animals injected with LY6C- CD9- PDGFRβ+ cells contain numerous clusters of lipid-laden fat cells ( Figure 3—figure supplement 3B ) . The contralateral depots of the same animals injected with LY6C+ PDGFRβ+ cells , or matrigel alone , remained devoid of adipocytes ( Figure 3—figure supplement 3C , D ) . Collectively , these data indicate that LY6C- CD9- PDGFRβ+ cells are highly adipogenic functional gonadal white adipocyte precursors , while LY6C+ PDGFRβ+ cells are largely refractory to adipogenic stimuli . Several studies have defined APCs from gonadal WAT as SCA-1+ CD34+ CD24± cells that also express PDGFRα ( Berry and Rodeheffer , 2013; Jeffery et al . , 2015; Lee et al . , 2012; Rodeheffer et al . , 2008 ) . In fact , most studies of gonadal WAT APCs isolate these cells on the basis of these markers . Additionally , recent studies identified CD38 as a marker of committed preadipocytes ( Carrière et al . , 2017 ) . The scRNA-seq analysis and follow-up qPCR analyses of isolated subpopulations revealed that all three PDGFRβ+ subpopulations indeed expressed Pdgfra , Ly6a ( SCA-1 ) , and Cd34; however , the mRNA levels of Ly6a and Cd34 are actually lower in LY6C- CD9- PDGFRβ+ APCs than in LY6C+ PDGFRβ+ cells ( FIPs ) ( Figure 3—figure supplement 1F , G ) . As expected , all three subpopulations expressed Pdgfrb; however , mRNA levels of Pdgfrb were quantitatively lower in FIPs than in the APCs and MLCs . qPCR analysis indicated that levels of Cd24a were low in all three PDGFRβ+ subpopulations . Cd38 was present predominately in LY6C- CD9- PDGFRβ+ cells , consistent with the notion that CD38 identifies APCs from this depot ( Carrière et al . , 2017 ) ( Figure 3—figure supplement 1F , G ) . Flow cytometry analyses revealed similar patterns of surface protein expression in these subpopulations ( Figure 3—figure supplement 1H ) . Collectively , these data reveal the selection of gonadal WAT SVF cells on the basis of SCA-1/CD34 yields functionally heterogeneous cell populations , and perhaps biases against the selection of LY6C- CD9- PDGFRβ+ APCs . Recently , Burl et al . reported scRNA-seq profiles of adipose SVF cells , creating a cellular atlas of potential adipocyte precursor populations ( perivascular and non-perivascular ) ( Burl et al . , 2018 ) . Notably , the authors identified two prominent populations within the gonadal WAT depot , termed adipose stem cell ( ASC ) 1 and ASC 2 . Moreover , they identified two additional smaller ASC subpopulations that were considered ‘differentiating’ ASCs and ‘proliferating’ ASCs . The identified populations were not isolated and explored functionally in their study; however , a comparison of the molecular profiles strongly suggests that ASC 1 defined by the authors bears close resemblance to APC population defined in our study , while the ASC 2 population bears close resemblance to the FIPs discovered here ( Figure 3—figure supplement 1I ) . Markers of the differentiated/proliferative ASCs aligned closely to the committed PDGFRβ+ preadipocyte depicted in Figure 1B . Taken together , our data here suggest a refined strategy to isolate functional white adipocyte precursors from visceral WAT of adult mice . Our prior studies of Zfp423GFP reporter mice indicated that gonadal WAT PDGFRβ+ cells expressing GFP are enriched in the expression of Pparg and are highly adipogenic in vitro ( Gupta et al . , 2012; Vishvanath et al . , 2016 ) . Additional studies by others indicated that this reporter captures committed preadipocytes within the skeletal bone marrow microenvironment ( Ambrosi et al . , 2017 ) . Endogenous Zfp423 mRNA levels were found in all PDGFRβ+ subpopulations , albeit at highest levels in APCs . ( Figure 3—figure supplement 4A ) . We re-examined Zfp423GFP-High and Zfp423GFP-Low PDGFRβ+ cells isolated from gWAT ( Figure 3—figure supplement 4B ) , asking whether these labeled cells captured by this reporter allele enriched for any of the Cluster markers identified by scRNA-seq . Consistent with our prior studies , Zfp423GFP-High PDGFRβ+ cells were enriched in the expression of Pparg isoforms when compared to Zfp423GFP-Low PDGFRβ+ cells ( Figure 3—figure supplement 4C ) . Further gene expression analysis of the top cluster gene markers revealed that Zfp423GFP-High cells were enriched in the expression of the genes that define the APC clusters , but not FIPs or MLCs ( Figure 3—figure supplement 4D–G ) . In particular , Zfp423GFP-High PDGFRβ+ cells were enriched in the expression of genes that delineate the more committed preadipocytes cluster ( Cluster 1B ) identified by scRNA-seq ( Figure 3—figure supplement 4E ) . Taken all together , these data indicate that endogenous Zfp423 mRNA expression is not confined exclusively to the APC subpopulation of PDGFRβ+ cells in gWAT; however , Zfp423GFP reporter mice represent a genetic tool to localize and enrich for committed preadipocytes from this depot . Transcriptional programs of white adipocyte precursors are depot- and sex dependent ( Macotela et al . , 2012 ) . Thus , we asked whether similar functional heterogeneity exists amongst PDGFRβ+ cells within various WAT depots , and whether functionally distinct subpopulations could be selected for using the same FACS strategy described above . Indeed , the same three populations can be observed within the mesenteric and retroperitoneal depots of adult male mice , with LY6C- CD9- PDGFRβ+ cells representing the highly adipogenic subpopulation ( Figure 4A–H ) . We also examined LY6C expression within PDGFRβ+ SVF cells obtained from the inguinal and anterior subcutaneous WAT depots . We previously demonstrated that the total pool of PDGFRβ+ cells from inguinal WAT is very highly adipogenic in vitro ( Shao et al . , 2018 ) ; however , remarkably , all PDGFRβ+ cells within the inguinal and anterior subcutaneous WAT depots expressed LY6C ( Figure 4I ) . These data suggest that if heterogeneity exists amongst PDGFRβ+ cells in these subcutaneous depots , subpopulations could not be discriminated on the basis of LY6C expression . Therefore , functionally distinct perivascular cell subpopulations from visceral , but not subcutaneous , WAT depots can be revealed on the basis of LY6C and CD9 expression . We also asked whether visceral WAT in female mice contains APCs and FIPs , bearing similar molecular and functional properties . Within the SVF of peri-ovarian WAT , the same three distinct subpopulations of PDGFRβ+ cells can be discriminated , with FIPs being the predominant population ( Figure 4—figure supplement 1A , B ) . Importantly , gene expression analysis by qPCR confirmed that LY6C- CD9- PDGFRβ+ cells were enriched in the expression of genes that defined the epididymal WAT APC population ( Cluster 1 ) ( Figure 4—figure supplement 1C , F ) , including Pparg isoform 2 . LY6C+ PDGFRβ+ cells enriched for the mRNAs that initially defined the epididymal WAT FIPs ( Cluster 2 ) ( Figure 4—figure supplement 1D , G , H ) , and LY6C- CD9+ PDGFRβ+ cells expressed mesothelial/epithelial markers ( Figure 4—figure supplement 1E ) . Moreover , LY6C- CD9- PDGFRβ+ cells from peri-ovarian WAT are functional adipocyte precursors; these cells , but neither FIPs nor MLCs , differentiate spontaneously upon reaching confluence in culture ( Figure 4—figure supplement 1I–K ) . Collectively , these data provide evidence that functional APCs from both male and female visceral WAT can be isolated through this cell sorting strategy . It is notable that very little spontaneous adipocyte differentiation occurs in cultures containing the total pool of visceral adipose PDGFRβ+ cells ( Figure 3A , E , I , M , Q ) , despite the presence of numerous APCs within this population . This suggested that perhaps the presence of FIPs within these cultures influenced the differentiation capacity of neighboring APCs in vitro . Therefore , we also tested the impact of conditioned media from cultured FIPs on the differentiation capacity of APCs residing in parallel cultures . Remarkably , APCs exposed to conditioned media from FIPs , but not from parallel cultures of APCs , expressed lower levels of Pparg ( Figure 5A ) . Moreover , APCs exposed to conditioned media from FIPs lost a significant degree of adipogenic capacity ( Figure 5B , C , E ) . Conditioned media from cultures of MLCs had only a slight inhibitory effect on the terminal differentiation of APCs ( Figure 5D , E ) . Collectively , these data not only suggest that FIPs lack significant adipogenic capacity , but highlight the notion that these cells can actually be anti-adipogenic . Recently , Schwalie et al . identified anti-adipogenic stromal cells within the inguinal WAT of mice ( Schwalie et al . , 2018 ) . These cells , termed Aregs , are defined , in part , by the expression of CD142 and ABCG1 and exhibit perivascular localization . From our scRNA-seq dataset , we observed that F3 expression ( encoding CD142 ) is detected in all PDGFRβ+ clusters of gonadal WAT , albeit not enriched in FIPs ( Figure 5F ) . Abcg1 expression was not detected by the sequencing analysis in any population . We also examined the levels of mRNA for these two markers directly by quantitative PCR analysis . Consistent with the sequencing data , neither marker was enriched in FIPs ( Figure 5G ) . We also examined additional genes ( 23 in total ) whose expression defines Aregs , as identified by Schwalie et al , by assessing their expression level within our scRNA-seq dataset ( Figure 5H ) . Levels of transcripts corresponding to a number of these genes were detectable , albeit at low levels . Notably , there was no selective enrichment of the broader set of Areg markers within FIPs or APCs . As such , despite some shared functional similarities , inguinal adipose Aregs and the gonadal adipose FIPs described here appear molecularly distinct . As described above , GSEA of scRNA-seq profiles also identified a gene expression profile suggestive of active TGFβ signaling within Cluster 2 cells ( Table 3 ) . Indeed the expression of major collagens ( Col1a1 and Col3a1 ) and some of the assayed genes associated with extracellular matrix accumulation were enriched in freshly isolated LY6C+ PDGFRβ+ cells compared to the other PDGFRβ+ subpopulations ( Figure 6—figure supplement 1A ) . In vitro , cultured FIPs and APCs were both responsive to treatment with recombinant TGFβ; however , the expression of collagens examined remained higher and/or was further induced in FIPs ( Figure 6—figure supplement 1B ) . These data indicate that LY6C+ PDGFRβ+ FIPs exhibit a phenotype characteristic of fibrogenic cells . The most striking result from GSEA was the enrichment of pathways related to active ‘Tnfα signaling’ and ‘inflammatory response’ in FIPs ( Table 3 ) . Remarkably , FIPs exhibited a robust inflammatory gene expression signature following acute exposure to pro-inflammatory molecules . Lipopolysaccharide ( LPS ) treatment induced inflammatory cytokine gene expression in both APCs and FIPs; however , the response was more robust in the latter population ( Figure 6A ) . The differential response to TNFα treatment was the most striking; FIPs , but not APCs , activate the expression of several pro-inflammatory cytokines under these conditions ( Figure 6B ) . These fibro-inflammatory cells displayed increased gene expression of numerous cytokines involved in the recruitment of leukocytes and the activation of immune cells . This suggested that FIPs have the potential to activate macrophages through cytokine production . To test this , we treated cultured bone marrow derived macrophages with conditioned media from LPS-treated FIPs , APCs , and MLCs ( Figure 6C ) . Macrophage cultures exposed to conditioned media from LPS-treated FIPs had the most robust induction of pro-inflammatory genes , including Tnfα , Il1β , and Il6 ( Figure 6D ) . These data highlight the potential for FIPs to exert a functional pro-inflammatory phenotype . In the setting of caloric excess , adipose tissue undergoes a dramatic remodeling as it expands to meet increased demands for energy storage . Shortly after the onset of high-fat diet ( HFD ) feeding , adipose tissue inflammation occurs ( Hill et al . , 2014; Xu et al . , 2003 ) . After 4–5 weeks of HFD feeding ( 60% kcal from fat ) , newly formed visceral adipocytes emerging from the PDGFRβ+ lineage begin to appear ( Vishvanath et al . , 2016 ) . We asked if the frequency of FIPs and APCs were altered during the course of HFD feeding . Four weeks of HFD feeding did not appear to dramatically alter the absolute number of PDGFRβ+ cells present in gWAT; however , the ratio of FIPs to APCs begins to increase by as early as one week of HFD feeding ( Figure 7A ) . We also analyzed BrdU incorporation into the mural cell populations during one week of HFD feeding . FIPs and the MLCs displayed the greatest BrdU incorporation ( Figure 7B , C ) . BrdU incorporation into APCs was significantly lower than observed in the FIPs ( Figure 7B , C ) . These data indicate that frequencies of APCs and FIPs are differentially regulated in vivo in association with high-fat diet feeding , with FIPs exhibiting a relatively higher degree of cell proliferation under these conditions . The change in frequency of FIPs and APCs during HFD feeding prompted us to examine if their defining gene expression programs were altered under these conditions . One month of HFD feeding lead to a significant elevation in mRNA levels of Pparg isoform two expression in APCs , with a smaller increase occurring in MLCs ( Figure 7D ) . Pparg isoform two expression was not elevated in FIPs , consistent with their apparent lack of adipogenic potential ( Figure 7D ) . mRNA levels of pro-inflammatory cytokines and extracellular matrix components were further induced and/or remained more abundant in FIPs than in APCs or MLCs ( Figure 7E–M ) . Interestingly , APCs activated the expression of some of these genes ( e . g . Il6 , Tnfa , Col1a1 , Col3a1 ) during HFD feeding . These data are consistent with the in vitro analyses highlighting the potential of APCs to trigger some degree of an inflammatory response in pro-inflammatory stimuli ( see Figure 6 ) . These data reveal that PDGFRβ+ subpopulations exhibit unique transcriptional responses to HFD feeding; however , these data also suggest that APCs have some capacity to adopt characteristics of FIPs in vivo . We sought to gain insight into the potential transcriptional mechanisms regulating the pro-inflammatory and adipogenic phenotypes of PDGFRβ+ perivascular cells . A number of transcription factors were differentially expressed between FIPs and APCs; however , it was notable that the expression of all three members of the Nr4a family of nuclear hormone receptors was significantly enriched in the FIPs cluster ( Figure 8A ) . Gene expression analysis by qPCR of the isolated populations confirmed the significant enrichment of Nr4a1 , Nr4a2 , and Nr4a3 in FIPs isolated from chow-fed mice , with relatively lower expression in the APCs and MLCs ( Figure 8B ) . Members of the NR4A family , including NR4A1 ( NUR77 ) , NR4A2 ( NURR1 ) , and NR4A3 ( NOR1 ) , have been implicated in the regulation of inflammation; however , their exact impact on inflammatory signaling appears cell-type specific ( Rodríguez-Calvo et al . , 2017 ) . Following 4 weeks of HFD-feeding , the expression of Nr4a family members remained significantly enriched in FIPs ( Figure 8C ) . In vitro , the expression of all Nr4a family members in FIPs was increased following exposure to recombinant TNFα ( Figure 8D ) . Therefore , we assessed the consequences of retroviral-mediated overexpression of individual Nr4a family members on the inflammatory response of FIPs ( Figure 8E–G ) . Overexpression of Nr4a1 , Nr4a2 , or Nr4a3 , attenuated the pro-inflammatory response to TNFα ( Figure 8H–K ) . Moreover , we assessed the impact of retroviral-mediated knockdown of Nr4a1 on the inflammatory response in FIPs . Knockdown of Nr4a1 using three independent shRNAs led to an exaggerated response of FIPs to TNFα treatment ( Figure 8L–P ) . These gain- and loss of function studies suggest that FIPs activate Nr4a family members in response to pro-inflammatory stimuli , perhaps as a means to counter-regulate a sustained cellular inflammatory response . These data provide proof of concept that FIPs may be utilized as a tool to identify additional regulators of inflammatory signaling pathways .
Visceral adipose tissue dysfunction in obesity is driven , at least in part , by chronic tissue inflammation , collagen deposition , and a loss of adipocyte precursor activity . WAT remodeling involves substantial qualitative and quantitative changes to the composition of the stromal compartment of the tissue; however , the functional heterogeneity of WAT stromal-vascular fraction has remained poorly defined and tools to isolate and study distinct subpopulations have been lacking . Here , we unveil functionally distinct PDGFRβ+ stromal cell subpopulations in visceral WAT ( Figure 9 ) . Importantly , we have developed strategies to prospectively isolate these distinct populations using commercially available antibodies . Functional analyses indicate that a relatively large subpopulation of PDGFRβ+ perivascular cells in visceral gonadal WAT exert fibrogenic and pro-inflammatory phenotypes . These cells , termed here as ‘FIPs , ’ lack adipogenic capacity in vitro but instead exhibit a fibrogenic phenotype . FIPs are physiologically regulated; the frequency of these cells increases upon HFD feeding . Clement and colleagues previously reported that fibrogenic cells residing in WAT could be identified by the expression of CD9 and PDGFRα ( Marcelin et al . , 2017 ) . Indeed , FIPs express CD9 and PDGFRα; however , both CD9 and PDGFRα are also expressed in at least a subpopulation of mesothelial cells isolated from visceral WAT . As such , the selection of FIPs on the basis of LY6c and PDGFRβ expression ( LY6C+ PDGFRβ+ ) represents a strategy to prospectively and specifically isolate FIPs from mouse gonadal WAT . Importantly , our data reveal a number of previously unappreciated ways in which perivascular cells may impact WAT remodeling ( Figure 9 ) . The LY6C+ PDGFRβ+ cells described here have the capacity to inhibit adipocyte differentiation from APCs through the release of secreted factors . The presence of highly anti-adipogenic stromal cells within the total PDGFRβ+ population may explain the apparent lack of adipogenic capacity that crude/unpurified visceral PDGFRβ+ cultures possess vitro , despite the presence of APCs . It is notable that visceral adipose FIPs appear distinct from the recently identified Aregs of inguinal WAT . Stromal cell heterogeneity may be depot-specific , with different depots utilizing distinct cell types to control their function and plasticity . The exact identities of the secreted factors and mechanisms that mediate the anti-adipogenic activity of FIPs and Aregs are still unknown . Importantly , whether FIPs and Aregs act to suppress/restrain adipocyte hyperplasia under physiological settings in vivo needs to be further explored . FIPs also exert a functional pro-inflammatory phenotype in response to pro-inflammatory stimuli . It is notable that the frequency of FIPs increases following the onset of HFD feeding . This raises an intriguing hypothesis for future studies that perivascular stromal cells can modulate local tissue inflammation . This notion is in line with recent studies indicating that vascular mural cells can serve as ‘gatekeepers’ of inflammation in the lung ( Hung et al . , 2017 ) . On the other hand , one may expect that the increased frequency of FIPs would also completely blunt the differentiation of APCs in this depot . This is clearly not the case as gonadal WAT in mice is able to expand through adipocyte hyperplasia in the setting of diet-induced obesity . One possibility is that the anti-adipogenic activity of FIPs ( rather than the frequency per se ) is diminished by local signals in an attempt to facilitate adipogenesis within the depot . Another possibility lies in the spatial distribution of activated FIPs and APCs; their proximity to one another may influence their activity . The identification of these populations from the MuralChaser mice essentially places them within the perivascular compartment of adipose tissue; however , where APCs and FIPs are localized and become activated within the tissue is still unknown . Clearly , additional studies of these cells will be needed in order to determine their precise contribution to WAT inflammation and health in various settings in vivo . Furthermore , it is certainly plausible that FIPs contribute to WAT remodeling beyond fibrosis , inflammation , and adipogenesis . The ability to isolate functionally distinct subpopulations of mural cells affords the possibility of identifying factors regulating these diverse mural cell phenotypes . Our gene expression analysis revealed the enrichment in mRNA levels of Nr4a family members in FIPs . Several studies have implicated NR4A nuclear receptors as modulators of inflammatory signaling; the precise impact of NR4A members on inflammation appears context/cell type specific . In some studies , NR4A family members are observed as being pro-inflammatory ( Pei et al . , 2006 ) . In other settings , NR4A expression is induced in response to pro-inflammatory stimuli but acts as a molecular brake on inflammatory signaling . Our gain- and loss of function studies suggest that FIPs activate Nr4a family members in response to pro-inflammatory stimuli to serve as a transcriptional brake on inflammatory cytokine gene expression . This counter-regulatory response may be a mechanism to limit cellular oxidative stress and apoptosis driven by inflammatory signaling ( Rodríguez-Calvo et al . , 2017 ) . Chao et al . previously demonstrated that NR4A family members are potent regulators of adipocyte differentiation ( Chao et al . , 2008 ) . Thus , NR4A members may play multiple roles in controlling of the fate and function of adipose perivascular cells . Additional studies involving the genetic manipulation of Nr4a family members in PDGFRβ+ cells in vivo will be needed to elucidate the exact requirements of NR4A members in WAT remodeling in vivo . Importantly , the ability to isolate FIPs and APCs affords the possibility of employing several different types of genomic approaches in an effort to reveal novel molecular mechanisms controlling adipose tissue inflammation , fibrosis , and adipogenesis . As described above , there has been tremendous interest in elucidating the identity of adipocyte precursors in adult adipose tissue . Pioneering studies from Friedman and colleagues led to a now widely-used strategy to prospectively isolate adipocyte progenitor cells from freshly isolated WAT ( Rodeheffer et al . , 2008 ) . APCs have been isolated on the basis of CD29 , SCA-1 , and CD34 expression ( CD29+ CD34+ SCA-1+ CD31- CD45- ) . These markers have proven to be quite useful for the selection of APCs from inguinal WAT and other WAT depots; however , a notable observation made here is that SCA-1 expression is in fact enriched in FIPs rather than APCs of the gonadal WAT depot . As such , the selection of cells on the basis of SCA-1 expression from this particular WAT depot yields a functionally heterogeneous population that likely includes FIPs , and perhaps even enriches for these cells . This may explain , at least in part , the notable lack of adipogenic potential that isolated gonadal CD34+ SCA-1+ cells possess in vitro ( Church et al . , 2014 ) . Our prior work pointed to Zfp423 as a marker of committed preadipocytes; however , our scRNA-seq data reveal that Zfp423 expression is not confined exclusively to the APC subpopulation of PDGFRβ+ cells; Pdgfrb-expressing MLCs and FIPs also express Zfp423 . Nevertheless , the selection of GFPHigh PDGFRβ+ cells from gWAT of Zfp423GFP reporter mice enriches for committed APCs , perhaps reflecting increased promoter/transgene activity in these cells . Recent scRNA-seq analyses from Granneman and colleagues provide a cellular atlas of putative adipocyte precursor populations in adipose tissue ( Burl et al . , 2018 ) . Their analyses included all non-hematopoetic , non-endothelial cells of the isolated adipose stromal-vascular fraction . The strength of their approach is that it allows for one to capture both perivascular ( PDGFRβ+ ) and non-perivascular precursor populations . Our approach will identify precursor populations that express Pdgfrb/rtTA at the time of the pulse labeling . Pdgfrb expression declines as cell undergo differentiation into adipocytes . This means that cells even further committed to the adipocyte lineage ( i . e . no longer express Pdgfrb ) may not be captured through our analysis . Moreover , putative stem cell populations not yet expressing Pdgfrb may also be present and not captured ( e . g . Pref1rtTA targeted cells [Hudak et al . , 2014] ) . Nevertheless , it is notable that most of the adipocyte precursor populations represented in the study by Burl et al . were indeed captured in our analysis . One cannot rule out the existence of additional adipocyte progenitor populations in any particular adipose depot; however , the congruency of the two independent studies suggests the MuralChaser model can identify and target the major APC populations residing within visceral WAT of mice . Moreover , the selection of LY6C- PDGFRβ+ cells from gWAT using commercially available antibodies represents a refined and convenient strategy to isolate visceral adipocyte precursors from wild type mice or genetic mouse models of interest . Single-cell transcriptomics has become very useful in revealing molecular heterogeneity amongst seemingly homogenous populations of cells . The challenge , however , is to determine whether molecularly distinct populations of cells represent distinct ‘cell types , ’ or rather ‘cell states’ which are influenced by their local microenvironment . Here , we reveal molecular heterogeneity amongst PDGFRβ+ cells within visceral white adipose tissue and begin to define the functional differences between the identified subpopulations . Our functional analyses suggest that the properties of visceral FIPs are at least somewhat stable; FIPs are quite limited in adipogenic potential in vitro and upon transplantation . They do not readily activate Pparg expression under the conditions examined . The phenotype of visceral APCs may be less stable . Visceral APCs have the potential to adopt characteristics of FIPs . In vitro and in vivo following HFD , APCs can activate the expression of pro-inflammatory cytokines . A caveat to most of our functional studies is that the cells are studied outside their native microenvironment . Under some physiological conditions , it is certainly plausible that multiple PDGFRβ+ subpopulations give rise in vivo to adipocytes; such adipocytes might even possess unique functional characteristics . Our prior lineage-tracing studies using the MuralChaser model clearly established that adipocytes emerge from Pdgfrb-expressing cells; efforts to define the relative contribution of individual mural cell subpopulations will require more precise lineage-tracing approaches with more specific Cre drivers . Moreover , our current studies cannot exclude the possibility that even further heterogeneity exists amongst PDGFRβ+ cells or within the identified subpopulations . Deeper sequencing , refined analyses , and further functional studies may unveil even more heterogeneity than appreciated . It is noteworthy that Pdgfrb is expressed in a subset of WAT associated mesothelial cells and that Pdgfrb-expressing mesothelial cells express Pparg and some level of Zfp423 . As described above , several lines of evidence point to a lineage relationship between embryonic mesothelial cells , APCs , and perivascular stromal cells within the visceral compartment ( Chau et al . , 2014; Rinkevich et al . , 2012 ) . PDGFRβ+ MLCs did not exhibit robust adipogenic potential under the culture conditions utilized here , despite their expression of Pparg isoform two and Zfp423 . Additional signals may be needed in order to drive adipocyte differentiation from these cells . Alternatively , this subpopulation of mesothelial cells may represent developmental intermediates between mesothelial cells and perivascular progenitors . Further insight into the functional significance of these various stromal subpopulations , their developmental origins , and their cellular plasticity will require additional genetic tools to manipulate individual populations selectively in vivo . Nevertheless , the molecular profiles obtained for FIPs and APCs from visceral WAT , along with the strategies to isolate these cells , will facilitate the study of physiological visceral WAT remodeling in vivo . Ultimately , unraveling the cellular and molecular determinants of WAT expansion and remodeling may lead to strategies to improve adipose tissue function and defend against metabolic disease .
All animal experimens were performed according to procedures approved by the UTSW Animal Care and Use Committee . MuralChaser mice were derived from breeding PdgfrbrtTA ( JAX 028570 ) , TRE-Cre ( JAX 006234 ) , and Rosa26RmT/mG ( JAX 007676 ) mice , as previously described ( Vishvanath et al . , 2016 ) . Wildtype C57BL/6 mice mice were purchased from Charles River Laboratories and Zfp423GFPB6 mice were described previously ( Vishvanath et al . , 2016 ) . Mice were maintained on a 12 hr light/dark cycle in a temperature-controlled environment ( 22°C ) and given free access to food and water . Mice were fed a standard rodent chow diet , doxycyline-containing chow diet ( 600 mg/kg doxycycline , Bio-Serv , S4107 ) , or high-fat diet ( 60% kcal% fat , Research Diets , D12492i ) . For all experiments involving MuralChaser mice , 6 weeks-old mice were fed doxycycline-containing chow for 9 days , and then standard chow for additional 5 days before analysis ( doxcycyline washout ) . For the high fat diet feeding experiments , mice were placed on the high fat diet beginning at 8 weeks of age . Adipose tissues were harvested from perfused ( 4% paraformaldehyde ) mice . Paraffin embedding and tissue sectioning was performed by the Molecular Pathology Core Facility at UTSW . Indirect immunofluorescence was performed as previously described ( Vishvanath et al . , 2016 ) . Antibodies used for immunofluorescence include: anti-GFP 1:700 ( Abcam ab13970 ) , anti-perilipin 1:1500 ( Fitzgerald 20R-PP004 ) , anti-chicken Alexa 488 1:200 ( Invitrogen ) , and anti-guinea pig Alexa 647 1:200 ( Invitrogen ) . Hematoxylin and eosin staining was performed according to manufacturer’s instructions . RNA was isolated from freshly sorted cells using RNAqueous-Micro Total RNA Isolation Kit ( Thermo Fisher Scientific ) or from cell cultures using Trizol , according to manufacturer’s instructions . cDNA was synthesized using M-MLV Reverse Transcriptase ( Invitrogen ) and Random Primers ( Invitrogen ) . Relative mRNA levels were determined by quantitative PCR using SYBR Green PCR Master Mix ( Applied Biosystems ) . Values were normalized to Rps18 levels using the ΔΔ-Ct method . Unpaired Student's t-test was used to evaluate statistical significance . All primer sequences are listed within Table 4 . Adipose tissue was minced with scissors in a 1 . 5 mL tube containing 200 μL of digestion buffer ( 1X HBSS , 1 . 5% BSA , and 1 mg/mL collagenase D ) and then transferred to a 50 mL Falcon tube containing 10 mL digestion buffer . The mixture was incubated in a 37°C shaking water bath for 1 hr . The solution of digested tissue was passed through a 100 µm cell strainer , diluted to 30 mL with 2% FBS in PBS , and centrifuged at 500 x g for 5 min . The supernatant was aspirated and red blood cells in the SVF pellet were lysed by brief incubation in 1 mL RBC lysis buffer ( Sigma ) . Next , the mixture was diluted to 10 mL with 2% FBS in PBS , passed through a 40 µm cell strainer , and then centrifuged at 500 x g for 5 min . The supernatant was aspirated , and cells were resuspended in blocking buffer ( 2% FBS/PBS containing anti-mouse CD16/CD32 Fc Block ( 1:200 ) ) . Primary antibodies were added to the cells in blocking buffer for 15 min at 4°C in the dark . After incubation , the cells were washed once with 2% FBS/PBS and then resuspended in 2% FBS/PBS for sorting . Cells were sorted for collection using a BD Biosciences FACSAria cytometer or analyzed using a BD Biosciences LSR II cytometer ( UTSW Flow Cytometry Core Facility ) . Flow cytometry plots were generated with FlowJo ( V10 ) . Eight week-old mice were administered 0 . 8 mg/mL BrdU in drinking water ( replaced fresh every 2 days ) and placed on chow or high fat diet for 1 week . At the end of the treatment period , adipose tissue SVF was isolated as described above and stained with the following antibodies: CD31 , CD45 , PDGFRβ , LY6C , and CD9 . Anti-BrdU staining of fixed cells was then conducted using the BrdU Flow Kit ( BD Biosciences 559619 ) , according to the manufacturer’s protocol . Six-week-old male MuralChaser mice were fed doxycycline-containing chow diet for 9 days , followed by standard chow diet for 5 days . Following the 5 day washout period , gonadal WAT was isolated and digested as described above . tdTomato- mGFP+ cells were collected by FACS . Single cell library preparation was performed using the 10X Genomics Single Cell 3’ v2 according to the manufacturer’s instructions . After FACS isolation of gonadal WAT tdTomato- mGFP+ cells from MuralChaser mice , 10 , 000 cells were partitioned into droplets containing a barcoded bead , a single cell , and reverse transcription enzyme mix using the GemCode instrument . This was followed by cDNA amplification , fragmentation , end repair and A-tailing , adaptor ligation , and index PCR . Cleanup and size selection were performed using Dynabeads MyOne Silane beads ( Thermo Fisher Scientific ) and SPRIselect Reagent beads ( Beckman Coulter ) . Libraries were sequenced on an Illumina NextSeq 500 High Output ( 400M ) by the UT Southwestern McDermott Center Next Generation Sequencing Core . 75 paired-end reads were obtained using one flow cell with the following length input: 26 bp Read 1 , 66 bp Read 2 , 0 bp Index 1 , and 0 bp Index 2 . Cell Ranger software ( v2 . 1 . 0 ) was used to perform demultiplexing , aligning reads , filtering , clustering , and gene expression analyses , using default parameters . We recovered 1378 cells with a median UMI count of 10 , 879 per cell , a mean reads per cell of 277 , 212 , and a median genes per cell of 3278 . In order to ensure that our analysis was restricted to genetically marked Pdgfrb-expressing cells , we filtered the cells based on expression of tdTomato ( <0 ) and GFP ( >0 ) to only include cells in the final analysis that were devoid of tdTomato mRNA and expressed GFP transcript . After this screening , we obtained a total of 1 , 045 cells for the analysis shown in Figure 1 . The Cell Ranger data was imported into Loupe Cell Browser Software ( v1 . 0 . 5 ) for t-distributed stochastic neighbor embedding ( tSNE ) based clustering , heatmap generation , and gene expression distribution plots . The Cell Ranger files were imported into R Studio ( v3 . 3 . 2 ) and the Seurat ( v2 . 1 . 0 ) and Readr ( v1 . 1 . 0 ) packages were used to generate gene cluster text ( GCT ) and categorical class ( CLS ) files , using the clustering generated from Cell Ranger ( k-means = 4 for the analysis in Figure 1 and k-means = 3 for the analysis in Figure 2 ) . The GCT and CLS files were input into Gene Set Enrichment Analysis ( GSEA ) ( v3 . 0 ) using the Java GSEA implementation with default parameters . The single cell RNA-sequencing experiment was repeated using cells isolated from pooled gonadal WAT from five additional MuralChaser mice to validate the identification of APCs , FIPs , and MLCs ( Figure 1—figure supplement 1 ) . The raw sequencing data from Figures 1 and 2 has been deposited to Gene Expression Omnibus ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE111588 ) . For all cellular assays , 4–6 weeks-old C57BL/6 mice were utilized . PDGFRβ+ subpopulations were isolated from gonadal WAT SVF as described above . For spontaneous adipogenesis assays , sorted cells were plated in 48-well plates at a density of 4 × 104 cells/well in growth media containing 2% FBS and ITS supplement [60% pH7–7 . 4 low glucose DMEM , 40% pH 7 . 25 MCDB201 ( Sigma M6770 ) , 1% ITS premix ( Insulin-Transferrin-Selenium ) ( BD Bioscience 354352 ) , 0 . 1 mM L-ascorbic acid-2-2phosphate ( Sigma A8960-5G ) , 10 ng/mL FGF basic ( R and D Systems 3139-FB-025/CF ) , Pen/Strep , and gentamicin] and incubated at 37°C in 10% CO2 . Media was replaced every other day . For induced adipogenesis , confluent cultures of FIPs were treated with an adipogenesis induction cocktail ( growth media supplemented with 5 mg/ml insulin , 1 μM dexamethasone , 0 . 5 mM isobutylmethyxanthine , ±1 μM rosiglitazone ) for 48 hr . After 48 hr . , the cells were maintained in growth media . Cells were fixed in 4% PFA for 15 min at room temperature and washed twice with water . Cells were incubated in Oil Red O working solution ( 2 g Oil red O in 60% isopropanol ) for 10 min to stain accumulated lipids . Cells were then washed three times with water and bright field images were acquired using a document scanner or with a Keyence BZ-X710 Microscope . Adipose associated mesothelial cells were isolated as described previously ( Darimont et al . , 2008 ) . Epididymal adipose depots were harvested from 6 weeks-old male MuralChaser mice treated with doxycyline as described above . Intact whole adipose depots were placed in 10 mL of PBS containing 0 . 25% trypsin for 20 min at 37°C with continuous end over end rotation . Next adipose tissues were removed , and remaining solution containing isolated cells was centrifuged at 600 x g for 5 min . The media was aspirated , and the cell pellet was resuspended in growth media ( 10% FBS in DMEM/F12 ( Invitrogen ) ) and plated in a 12-well collagen-coated plate . The cells were incubated at 37°C in 10 CO2 and the media was replaced daily . Images were obtained using a Leica DMIL LED microscope and a Leica DFC3000g camera . To assess in vitro proliferation , sorted cells ( APCs and FIPs ) were plated at a density of 5 x 103 cells/well in a 48-well plate containing 2% FBS in ITS Media . Cell numbers were assessed in parallel wells every 2 days by cell counting with a hemocytometer . To study the impact of conditioned media on adipogenesis , media from equally confluent cultures of APCs , FIPs , and MLCs was harvested and placed onto APCs beginning 48 hr after culture in a 1:1 ratio with 2% FBS in ITS media . Cells were harvested at the indicated time points for RNA expression analysis . Images were obtained using a Leica DMIL LED microscope and a Leica DFC3000g camera . Bone marrow derived macrophages ( BMDMs ) were derived from bone-marrow stem cells ( BMSCs ) isolated from the femurs and tibias of male mice , as previously described ( Shan et al . , 2017 ) . BMSCs were maintained in differentiation medium derived from L929 cells for 7 days to allow for macrophage differentiation . In parallel , adipose tissue SVF was isolated and PDGFRβ+ subpopulations were sorted as described above . Sorted cells ( APCs , FIPs , and MLCs ) were plated in a 48-well plate at 4 × 104 cells/well in 2% FBS in ITS media . 48 hr later , the adipose-derived cells were treated with vehicle ( PBS ) or LPS ( 100 ng/ml ) for 3 hr . Next the adipose-derived cells were washed with PBS and fresh media was added . 24 hr later , the conditioned media was harvested and placed on the BMDMs ( at day 7 ) in a 1:1 ratio with 2% FBS in ITS media . After a 3 hr incubation , the BMDMs were harvested for RNA analysis . For TGFβ treatments , sorted cells were plated in 48-well plates at 2 × 104 cells/well in 2% FBS in ITS media . 24 hr later , vehicle ( PBS ) or 1 ng/mL recombinant TGFβ was added to the media for 3 days prior to harvest . The media was replaced daily under this period . For the LPS and TNFα treatments , cells were plated in 48-well plates at 4 × 104 cells/well in 2% FBS in ITS media . 48 hr later , the cells were treated with vehicle ( PBS ) , LPS ( 100 ng/ml ) , or TNFα ( 20 ng/ml ) . After 3 hr of treatment , cells were harvested for RNA isolation . 80 , 000 cells ( APCs and FIPs ) collected by FACS were suspended in 100 µL transplantation media ( 50% Matrigel in PBS , supplemented with 2 ng/mL FGF ) and injected subcutaneously into the remnant inguinal WAT region of 3 month old lipodystrophic mice ( Adiponectin-Cre; PpargloxP/loxP ) . Three weeks later , the remnant inguinal WAT depots were harvested for histology . The pMSCV-Nr4a1 , pMSCV-Nr4a2 , and pMSCV-Nr4a3 plasmids were previously reported ( kind gift from Dr . P . Tontonoz ) ( Chao et al . , 2008 ) . Retroviral production and packaging in phoenix cells was performed as previously described ( Shao et al . , 2016 ) . Briefly , 10 µg of the pMSCV overexpression plasmids were co-transfected with 5 µg gag-pol and 5 µg VSV-g plasmids into phoenix packaging cells using Lipofectamine LTX ( Thermo Fisher Scientific ) , according to the manufacturer’s protocol . APCs and FIPs were transduced with diluted virus ( 1:10 ratio ) in 2% FBS/ITS media containing 8 µg/ml polybrene ( Sigma ) . Following 16 hr of incubation with indicated viruses , cells were returned to 2% FBS/ITS media and assayed for TNFα responsiveness as indicated . Double-stranded DNA sequence encoding the shRNA targeting Nr4a1 was selected from the Broad Institute public database ( https://portals . broadinstitute . org/gpp/public/ ) and cloned into the AgeI/EcoRI sites of the pMKO-1 U6 retroviral vector . The pMKO-1 vector expressing shRNA targeting GFP was used as a negative control . The selected DNA sequences encoding the Nr4a1 shRNAs used in the study are as follows: shNr4a1-1317 forward oligonucleotide , 5′-CCGGTGCCGGTGACGTGCAACAATTCTCGAGAATTGTTGCACGTCACCGGCATTTTTG-3′; shNr4a1-1317 reverse oligonucleotide , 5′-AATTCAAAAATGCCGGTGACGTGCAACAATTCTCGAGAATTGTTGCACGTCACCGGCA-3′ . shNr4a1-1468 forward oligonucleotide , 5′-CCGGCGCCTGGCATACCGATCTAAACTCGAGTTTAGATCGGTATGCCAGGCGTTTTTG-3′; shNr4a1-1468 reverse oligonucleotide , 5′-AATTCAAAAACGCCTGGCATACCGATCTAAACTCGAGTTTAGATCGGTATGCCAGGCG-3′ . shNr4a1-1877 forward oligonucleotide , 5′-CCGGCTATTGTGGACAAGATCTTTACTCGAGTAAAGATCTTGTCCACAATAGTTTTTG-3′; shNr4a1-1877 reverse oligonucleotide , 5′-AATTCAAAAACTATTGTGGACAAGATCTTTACTCGAGTAAAGATCTTGTCCACAATAG-3′ . All data were expressed as the mean +SEM . We used GraphPad Prism 7 . 0 ( GraphPad Software , Inc . , La Jolla , CA , USA ) to perform the statistical analyses . For comparisons between two independent groups , a Student’s t-test was used and p<0 . 05 was considered statistically significant . For in vitro studies , we estimated the approximate effect size based on independent preliminary studies . Studies designed to characterize an in vitro difference in gene expression were estimated to have a slightly larger effect size of 30% with assumed 15% standard deviation of group means . To detect this difference at a power of 80% and an alpha of 0 . 05 , we predicted we would need four independent replicates per group . We estimated this effect size based on independent preliminary studies . Statistical information , including p values , samples sizes , and repetitions , for all datasets are provided in Supplementary file 1 . | Fat tissue , also known as white adipose tissue , specializes in storing excess calories . Much of this storage happens under the skin , but fat tissue can also build up inside the abdomen and surround organs , where it is known as ‘visceral’ fat . When visceral fat tissue is unhealthy , it may help diseases such as diabetes and heart disease to develop . Unhealthy fat tissue contains enlarged fat cells , which may die from overwork . The stress this places on the surrounding tissue activates the immune system , causing inflammation and the build-up of collagen fibers around the cells ( a condition known as fibrosis ) . Not all people develop this type of unhealthy fat tissue , but we do not yet understand why . In many tissues , blood vessels serve as a home for several types of adult stem cells that help to rejuvenate the tissue following damage . To identify these cells , Hepler et al . analyzed the genes used by more than 3 , 000 cells living around the blood vessels in the visceral fat of adult mice . Recent work had already revealed that stem cells called adipocyte precursor cells live in this region . Hepler et al . now reveal the presence of a second group of cells , termed fibro-inflammatory progenitor cells ( or FIPs for short ) . To investigate the roles of each cell type in more detail , Hepler et al . developed a new technique to isolate the adipocyte precursor cells from other cell types . When grown in the right conditions in petri dishes , the adipocyte precursor cells were able to form new fat cells . They could also make new fat cells when transplanted into mice that lacked fat tissue . By contrast , the FIPs can suppress the activity of adipocyte precursor cells and activate immune cells . They may also help fibrosis to develop . It is not yet clear whether FIPs are present in human fat tissue . But , if they are , understanding them in greater detail may suggest new ways to treat diabetes and heart disease in obese people . | [
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] | 2018 | Identification of functionally distinct fibro-inflammatory and adipogenic stromal subpopulations in visceral adipose tissue of adult mice |
One of the leading approaches to non-invasively treat a variety of brain disorders is transcranial magnetic stimulation ( TMS ) . However , despite its clinical prevalence , very little is known about the action of TMS at the cellular level let alone what effect it might have at the subcellular level ( e . g . dendrites ) . Here , we examine the effect of single-pulse TMS on dendritic activity in layer 5 pyramidal neurons of the somatosensory cortex using an optical fiber imaging approach . We find that TMS causes GABAB-mediated inhibition of sensory-evoked dendritic Ca2+ activity . We conclude that TMS directly activates fibers within the upper cortical layers that leads to the activation of dendrite-targeting inhibitory neurons which in turn suppress dendritic Ca2+ activity . This result implies a specificity of TMS at the dendritic level that could in principle be exploited for investigating these structures non-invasively .
Transcranial magnetic stimulation ( TMS ) holds great promise as a non-invasive method that can be used to both enhance and impair cognitive abilities ( Eldaief et al . , 2013 ) . As such , it has proved to be an important tool for addressing basic questions about brain function as well as for diagnostic and therapeutic purposes ( Fregni and Pascual-Leone , 2007 ) . Stimulation is produced by generating a brief , high-intensity magnetic field by passing a brief electric current through a magnetic coil . As a general rule , TMS affects the action of feedback projections ( Juan and Walsh , 2003; Hung et al . , 2005; Camprodon et al . , 2010; Zanto et al . , 2011 ) leading to a disruption in perception ( Shimojo et al . , 2001; Kammer et al . , 2005 ) . Due to this influence on higher order cognitive processing , TMS is not only useful for examining the interactions of different brain areas ( Pascual-Leone and Walsh , 2001; Silvanto et al . , 2005; Murd et al . , 2012 ) , but it has also been used as a therapeutic method to alleviate some of the symptoms of hemispatial neglect ( Nyffeler et al . , 2009 ) , schizophrenia including auditory hallucinations ( Giesel et al . , 2012 ) , pain , depression and epilepsy . Despite great interest ( Mueller et al . , 2014 ) , the cellular influence of TMS has yet to be ascertained since the precise effects of TMS at the level of a single neuron are very difficult to gauge , particularly in humans . The neural architecture of the brain means the neural processes which receive and transform most synaptic inputs , the dendrites , extend into the upper layers where TMS would be most effective . Since dendrites can shape synaptic input to be greater or less than their linear sum ( Polsky et al . , 2004; Losonczy and Magee , 2006; Larkum et al . , 2009 ) thereby altering the firing properties of the neuron ( Larkum et al . , 1999 ) , the active properties of dendrites have attracted attention and have been linked to cognitive processes and feature selectivity ( Lavzin et al . , 2012; Xu et al . , 2012; Smith et al . , 2013; Cichon and Gan , 2015 ) . Furthermore , it has been suggested that active dendritic processing underlies a more general principle of cortical operation that facilitates parallel associations between different cortical regions and the thalamus ( Larkum , 2013 ) which is controlled by dendritic inhibition ( Palmer et al . , 2012; Lovett-Barron and Losonczy , 2014 ) . Establishing the validity of this hypothesis will have important ramifications for understanding brain function as a whole . TMS presents a most promising way to study the causal relationship between active dendritic properties and cognition but only if its effect on dendrites can be understood and ultimately controlled . Using an optical fiber imaging approach , here we present a study examining the effect of TMS on sensory-evoked dendritic activity in layer 5 pyramidal neurons of the somatosensory cortex . We find that TMS suppresses dendritic Ca2+ activity evoked by tactile stimulation and that this suppression can be abolished by blocking GABAB receptors and excitatory transmission in the upper layers of the cortex . We uncover the cellular mechanisms underlying TMS-evoked inhibition , demonstrating that TMS of the rat brain activates long-range fibers that leads to the activation of dendrite-targeting inhibitory neurons in the upper cortical layers which in-turn suppress dendritic Ca2+ activity . Since indirect brain stimulation shows immense promise in treating many neurological disorders , such as epilepsy ( Berenyi et al . , 2012 ) , this study not only illustrates the cellular mechanisms underlying TMS but also highlights dendrites as potential targets for therapeutic approaches .
We recorded the Ca2+ activity in populations of layer 5 ( L5 ) pyramidal neuron dendrites in the hindlimb somatosensory cortex of urethane anesthetized rats using a custom-made fiber optic 'periscope' in vivo ( Murayama et al . , 2007 ) oriented horizontally for use in tandem with a TMS coil ( Figure 1A , Figure 1—figure supplement 1A ) . Pyramidal neurons located approximately 800 μm below the cortical surface were loaded with the Ca2+ indicator Oregon Green BAPTA1 AM ( OGB1 AM; Figure 1A inset and see 'Materials and methods' ) . Using this approach , large dendritic Ca2+ responses to brief hindpaw stimulation ( 100 V , 1 ms ) were reliably evoked after 70 min loading with OGB1 AM ( Figure 1—figure supplement 1B ) . To investigate the effects of TMS on evoked cortical network activity , the TMS coil was positioned just above the craniotomy ( Figure 1A ) and a single brief TMS pulse was evoked together with the stimulation of the hindpaw ( Figure 1B ) greater than 70 min post loading with OGB1 AM . TMS caused a significant decrease in the hindpaw-evoked dendritic Ca2+ response when triggered 50 ms before hindpaw stimulation ( Figure 1C and Figure 1—figure supplement 2A ) , both in the maximum amplitude ( control , 7 . 3 ± 1 . 5 △F/F versus TMS , 5 . 0 ± 1 . 1 △F/F , n = 17 , p<0 . 05 ) and integral ( control , 4 . 3 ± 0 . 9 △F/F•s versus TMS , 2 . 4 ± 0 . 6 △F/F•s; n = 17; p<0 . 001 , Figure 1D ) . Further , the size of the coil ( Figure 1—figure supplement 2B ) and the type of hindpaw stimulation ( Figure 1—figure supplement 2C ) did not influence the results , whereas the distance of the coil from the cortical region of interest influenced the effectiveness of the TMS inhibition on the dendritic sensory responses ( Figure 1—figure supplement 3 ) . 10 . 7554/eLife . 13598 . 003Figure 1 . TMS inhibits sensory evoked Ca2+ activity in layer 5 dendrites . ( A ) Schematic of the experimental design . Layer 5 pyramidal neurons were bulk loaded with OGB1-AM and dendritic Ca2+ activity was recorded using a flat-periscope configured horizontally and inserted underneath the TMS coil from the side . The TMS coil was placed above the dendrites in the hindpaw region of the somatosensory cortex . ( B ) Typical dendritic Ca2+ response to hindpaw stimulation ( HP ) alone ( black ) and during a single TMS pulse ( red ) and HP alone post-experiment ( grey ) . ( C ) Overlay of traces in ( b ) and ( D ) graph illustrating the decrease in Ca2+ response during TMS ( n=17 ) . p<0 . 001 ( *** ) . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00310 . 7554/eLife . 13598 . 004Figure 1—source data 1 . Integral and amplitude of evoked calcium transient . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00410 . 7554/eLife . 13598 . 005Figure 1—figure supplement 1 . Periscope position and temporal characteristics of sensory-evoked Ca2+ responses in layer 5 pyramidal neuron dendrites . ( A ) Similar to the ‘vertical’ periscope , the ‘flat’ periscope recorded an increase in Ca2+ activity during TTX application into layer 5 which is caused by TTX blocking layer 5 Martinoti cells that normally inhibit sensory evoked dendritic Ca2+ influx . These results illustrate both ‘periscopes’ are able to reliably record dendritic Ca2+ dynamics . ( B ) A large Ca2+ response was recorded after 70min using the ‘flat’ periscope indicating the length of time required for OGB1-AM to diffuse into the dendrites of layer 5 pyramidal neurons . ( left ) Schematic representation of the experimental setup . The Ca2+ indicator OGB1-AM was bulk loaded into layer 5 and the fiber optic ‘flat periscope’ was positioned horizontally above the craniotomy . ( middle ) Ca2+ responses to hindpaw stimulation ( HS ) recorded 10 , 30 , 50 , 70 , 90 , 110 min after OGB1-AM loading . ( right ) Integral of Ca2+ response normalized to the maximum at different times after OGB1-AM load . The increase over time is assumed to be due to diffusion of the indicator in the dendrites and corresponds to the time needed for stable recordings using the vertical periscope configuration . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00510 . 7554/eLife . 13598 . 006Figure 1—figure supplement 2 . The effect of TMS timing , coil size and stimulation paradigm on layer 5 dendritic sensory responses . ( A ) Layer 5 ( L5 ) pyramidal neurons were bulk loaded with the calcium indicator OGB1 AM and the effect of the timing of TMS on the dendritic response during hindpaw stimulation was investigated . Example Ca2+ response to hindpaw stimulation alone ( HS ) and during TMS generated 200 ms before HS . Inset ( left ) : overlay of HS alone ( black ) and HS+TMS ( red ) . Scale bar: 5% △F/F , 1 s . Inset ( right ) : Compared to control ( TMS 50 ms before HS ) which causes a 45 ± 5% decrease in the evoked dendritic Ca2+ response to HS ( see Figure 1; solid; n=17 ) , on average , TMS did not significantly influence the dendritic response to HS when evoked greater than 100 ms before HS ( lines; n=6 ) . ( B ) L5 pyramidal neurons were bulk loaded with the Ca2+ indicator OGB1 AM and the effect of TMS on hindpaw sensory-stimulation was tested using different sized coils . Both coils were positioned the same distance from the region of interest . There was no significant difference between the TMS evoked inhibition of the HS dendritic response using either the small ( 25 mm; n=10 ) or large ( 70 mm; n=7 ) TMS coil . ( C ) The effect of TMS on hindpaw sensory-stimulation was tested using different hindpaw stimulation protocols . The hindpaw was stimulated by either a triggered brief airpuff ( 400 ms; n=12 ) or electrical stimulation ( 100 V; n=9 ) delivered to the pad of the paw . There was no significant difference between the TMS evoked inhibition of the HS dendritic response using either hindpaw stimulation technique . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00610 . 7554/eLife . 13598 . 007Figure 1—figure supplement 3 . Increasing TMS strength did not elicite an excitatory response in layer 5 pyramidal neuron dendrites . ( A ) Ca2+ transients in a population of layer 5 pyramidal neuron dendrites during TMS delivered at increasing strengths ( 50% , 80% , 90% , 100% ) . Note , there was no excitatory Ca2+ response to TMS . ( B ) Sensory evoked Ca2+ transients in a population of layer 5 pyramidal neuron dendrites during hindpaw stimulation ( HS , black ) and combined HS and TMS delivered at decreasing distances from the craniotomy ( 10–30 mm ) . Inset , overlay of Ca2+ transient during HS+TMS at far ( 30 mm ) and near ( 10 mm ) TMS coil distances . ( C ) Integral of the sensory evoked Ca2+ transients during HS+TMS when the TMS coil is positioned is at different distances from the craniotomy . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 007 What is the cause of this TMS-induced decrease in dendritic calcium activity ? L5 pyramidal neuron dendrites have been previously shown to be strongly inhibited by the activation of post-synaptic GABAB ( Pérez-Garci et al . , 2006; Chalifoux and Carter , 2011; Palmer et al . , 2012 ) receptors . To test whether GABAB receptors are predominantly causing the TMS-induced dendritic inhibition , the GABAB antagonist CGP52432 was locally perfused into the recording region ( Figure 2A ) affecting up to 300 μm of the surrounding tissue ( Figure 2—figure supplement 1 ) . Blocking GABAB receptors prevented the TMS-evoked decrease in the Ca2+ response to hindpaw stimulation in both the integral ( controlcgp , 2 . 3 ± 0 . 5 △F/F•s versus TMScgp , 2 . 1 ± 0 . 5 △F/F•s; p=0 . 62 ) and maximum amplitude ( controlcgp , 7 . 7 ± 2 . 8 △F/F versus TMScgp , 7 . 2 ± 1 . 9 △F/F; n = 7; p=0 . 66 , Figure 2B ) . L5 dendrites have been shown to also be inhibited by the activation of GABAA ( Kim et al . , 1995; Murayama et al . , 2009 ) receptors . Although cortical application of Gabazine causes a six-fold increase in the sensory evoked dendritic response ( Figure 2—figure supplement 2 ) , block of GABAA receptors also prevented the TMS-evoked decrease in Ca2+ response to hindpaw stimulation ( HS amplitude , 130 ± 30% of control; n = 6; Figure 2—figure supplement 2 ) . Taken together , the fact that blocking both GABAA and GABAB receptors abolished the dendritic effect of TMS s . 10 . 7554/eLife . 13598 . 008Figure 2 . TMS causes GABAB-mediated inhibition of layer 5 dendrites . ( A ) Schematic of the experimental design illustrating the application of the GABAB antagonist CGP52432 on the cortical surface . ( B ) Typical dendritic Ca2+ response to hindpaw stimulation ( HS ) alone ( grey ) and during a single TMS pulse ( orange , HS+TMS ) during cortical CGP . ( C ) Block of TMS-evoked inhibition of the dendritic sensory response in the presence of CGP52432 compared with control ( prior to CGP52432; HS , black; HS+TMS , red; n=7 ) . p<0 . 05 ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00810 . 7554/eLife . 13598 . 009Figure 2—figure supplement 1 . Spread of localized injection ( A–C ) and cortical surface application ( D–F ) . Drug application characteristics were measured using fluorescent indicator , AlexFluor594 ( AF594; 50 μM ) and two-photon imaging ( 800 nm ) . ( A ) To test localized injection , AF594 was included in the drug application pipette and was puffed into the upper cortical layers at approximately 200 μm deep . ( B ) The lateral spread of the dye was approximately 300 μm . ( C ) The fluorescence reached maximal intensity within 5 s . ( D ) AF594 was placed onto the cortical surface . Two photon images of the fluorescence measured at the same laser intensity at 0 , 100 and 200 μm . ( E ) The fluorescence was only measureable at a maximal distance of 200 μm . ( F ) Therefore , cortical application of fluorophores penetrates the cortex to layer 2/3 . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 00910 . 7554/eLife . 13598 . 010Figure 2—figure supplement 2 . Cortical application of the GABAA antagonist Gabazine causes a dramatic increase in the dendritic response to hindpaw stimulation . Layer 5 ( L5 ) pyramidal neurons were bulk loaded with the Ca2+ indicator OGB1 AM , and the evoked Ca2+ response was recorded using a fiber optic . ( A ) An example L5 dendritic Ca2+ response to hindpaw stimulation ( HS ) during control ( black ) , CGP ( orange ) and CGP+Gabazine ( green ) cortical application . ( B ) Integral of the HS-evoked Ca2+ response as a proportion of the control response during cortical application of CGP+gabazine ( green ) and CGP ( orange ) . ( C ) An example L5 dendritic Ca2+ response to hindpaw stimulation ( HS , light green ) and hindpaw stimulation during TMS ( HS+TMS , dark green ) during CGP+Gabazine cortical application . Individual traces are overlaid on right . ( D ) Evoked dendritic Ca2+ response during TMS ( HS+TMS ) normalized to HS alone during CGP ( orange ) and CGP + Gabazine ( green ) . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 010 To investigate the laminar profile of the influence of TMS on neuronal activity , the Ca2+ indicator OGB1-AM was injected at different cortical depths ( L5 , 800 μm; Layer 2/3 , 300 μm; Layer 1 , 100 μm ) and the Ca2+ response to TMS alone was recorded . TMS itself did not directly activate L5 pyramidal neuron dendrites ( Figure 3A; n = 4 ) , contrasting greatly to the large TMS-evoked Ca2+ response in cells within layer 2/3 ( L2/3; 3 . 0 ± 1 . 1 △F/F; n = 3; Figure 3B ) and layer 1 ( L1; 5 . 5 ± 2 . 1 △F/F; n = 12; Figure 3B ) . Importantly , the TMS-evoked Ca2+ response in these upper cortical layers was similar to the response evoked by physiological stimulation via hindpaw stimulation ( L2/3 , 3 . 5 ± 1 . 2 △F/F•s and L1 , 6 . 4 ± 2 △F/F•s ) . The lack of a direct response to TMS in L5 pyramidal neuron dendrites implies that the inhibition of sensory evoked dendritic transients was mediated by the action of inhibitory neurons . Furthermore , the response to TMS in upper-layer neurons leaves open the possibility that local inhibitory neurons might be recruited by TMS either directly , via TMS-induced membrane activation or indirectly , via synaptic transmission . 10 . 7554/eLife . 13598 . 011Figure 3 . Upper layers of the cortex have Ca2+ transients in response to TMS . ( left ) Schematic diagram illustrating Ca2+ indicator loaded into ( A ) layer 5 , ( B ) layer 2/3 and ( C ) layer 1 . For each cortical depth , the Ca2+ indicator loading location ( green circle ) and target neurons ( green ) are indicated . ( right ) Ca2+ activity was recorded in response to a single TM pulse . ( D ) Comparison of the integrals of the TMS-evoked Ca2+ responses recorded at the different cortical depths . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 011 To test these possibilities , the Ca2+ response to hindpaw stimulation was recorded before ( 5 . 5 ± 3 . 3 △F/F•s ) and after ( 3 . 2 ± 3 . 6 △F/F•s ) blocking synaptic transmission by locally applying the AMPA antagonist CNQX to the upper cortical layers at the site of the recording ( Figure 4 ) . Under these conditions , CNQX prevented the inhibitory effect of TMS in L5 pyramidal neuron dendrites ( n = 10; Figure 4A–C and Figure 4—figure supplement 1 ) . Therefore , since blocking excitatory AMPA-mediated transmission prevented the TMS inhibitory effect , TMS-evoked inhibition in L5 pyramidal neuron dendrites must be of polysynaptic ( indirect ) origin . We next tested whether the TMS-evoked Ca2+ transient in L1 was also of synaptic origin as TMS influenced cell activity in the upper cortical layers ( Figure 4 ) and therefore possibly provides the TMS-evoked inhibition of L5 dendrites . Indeed , TMS-evoked activity in L1 neurons was suppressed by CNQX application , significantly reducing the Ca2+ response amplitude by 53 ± 7% ( n = 8; p<0 . 05; Figure 4D–F ) . Therefore , the TMS-evoked Ca2+ response in L1 neurons is of synaptic origin . Taken together , this data suggests that the inhibition of sensory-evoked L5 dendritic Ca2+ responses was primarily mediated by upper-layer inhibitory neurons driven to fire synaptically from neurons or axons recruited by TMS . 10 . 7554/eLife . 13598 . 012Figure 4 . TMS directly activates cells in the upper cortical layers . ( A ) Schematic diagram of the experimental design . Layer 5 pyramidal neurons were bulk loaded with OGB1-AM and dendritic Ca2+ activity was recorded using a side-on ( horizontal ) periscope during application of CNQX to the upper cortical layers . ( B ) Typical dendritic Ca2+ response to hindpaw stimulation ( HS ) alone ( black ) and during a single TMS pulse in the presence of cortical CNQX ( blue ) . ( C ) Ca2+ responses ( integrals ) during HS+TMS in the presence ( blue ) and absence ( red ) of CNQX normalized to control HS ( black; n=10 ) . ( D ) Schematic diagram of the experimental design . Layer 1 neurons were bulk loaded with OGB1-AM and dendritic Ca2+ activity was recorded during TMS using the side-on periscope during application of CNQX into the upper cortical layers . ( E ) Dendritic Ca2+ response to a single TMS pulse ( black ) and in the presence of cortical CNQX ( blue ) . ( F ) Amplitude of the TMS-evoked Ca2+ responses in L1 neurons during control ( black ) and CNQX ( blue ) ( n=8 ) . p<0 . 005 ( ** ) , p<0 . 001 ( *** ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 01210 . 7554/eLife . 13598 . 013Figure 4—figure supplement 1 . Comparison of CNQX application . Layer 5 pyramidal neurons were bulk loaded with the Ca2+ indicator OGB1-AM and the effect of TMS on the dendritic response during hindpaw stimulation ( HS+TMS ) was compared in control and during CNQX application . CNQX was either applied locally into layer 2/3 ( L2/3 ) by a puff pipette ( n=6; solid ) or topically onto the pia surface ( n=4; empty ) . During both application methods , CNQX caused significant increase in the integral of the Ca2+ response during HS+TMS ( p < 0 . 05 ) . There was no significant difference in the Ca2+ response between local or topical application of CNQX ( Control , p = 0 . 27; CNQX , p = 0 . 59 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 01310 . 7554/eLife . 13598 . 014Figure 5 . TMS activates an inhibitory microcircuit in the upper cortical layers . Hypothesized effect of TMS on cortical processing; TMS activates axons ( red ) which excite upper layer interneurons ( blue ) causing GABA neurotransmitter release ( green ) which provides GABA-mediated inhibition to layer 5 pyramidal neuron dendrites . ( left ) Hindpaw stimulation ( HS ) causes large Ca2+ responses in layer 5 pyramidal neuron dendrites . ( middle ) TMS directly activates upper layer neurons but does not cause a Ca2+ response in layer 5 dendrites . However , ( right ) TMS paired with HS causes a large decrease in the HS Ca2+ response . TMS , transcranial magnetic stimulation . DOI: http://dx . doi . org/10 . 7554/eLife . 13598 . 014
The results from this study demonstrate the subcellular effect of TMS on dendritic Ca2+ activity for the first time . TMS alone did not directly activate L5 pyramidal dendrites but instead activated axonal processes coursing through the upper layers and synapsing onto GABAB-mediating interneurons in L1 . A similar form of synaptic activation of interneurons leading to inhibition in L5 pyramidal neuron dendrites has been shown previously with activation of callosal input to the cortex ( Palmer et al . , 2012 ) . This form of 'silent' inhibition involves the activation of inhibitory conductances which are not detectable at the soma except through their eventual influence on the generation of action potential output . These results therefore highlight an effect of single-pulse TMS on the cortical network , which involves the activation of a specific cortical microcircuit . The action of TMS at the cellular and network levels is extremely complex and likely constitutes the activation of a range of different cell types leading to multiple effects ( Rossi et al . , 2009; Siebner et al . , 2009 ) . A recent combined experimental and theoretical investigation of the biophysical underpinnings of TMS suggested that the generation of a magnetic field is most likely to evoke firing in cell bodies as opposed to dendrites or axons ( Pashut et al . , 2014 ) . This is consistent with our finding that no dendritic activity was observed with TMS stimulation alone indicating there was no direct activation of the pyramidal cell dendrites and contrasted with the signals found in both L1 and L2/3 neurons following a TMS pulse . It is also consistent with the activation of neurogliaform interneurons in L1 that target the dendrites of L5 pyramidal neurons and suppress Ca2+ activity ( Ziemann , 2010; Terao and Ugawa , 2002; Pashut et al . , 2011; Tamás et al . , 2003; Oláh et al . , 2007; Oláh et al . , 2009 ) . This form of dendritic inhibition arises from the metabotropic inactivation of L-type ( Cav1 ) channels in the apical dendrite that underlie the dendritic Ca2+ activity ( Wozny and Williams , 2011 ) and can last several hundred milliseconds ( Pérez-Garci et al . , 2006 ) . The suppression of Ca2+ channels can significantly reduce the action potential firing of the pyramidal neuron even when the driving input to the pyramidal neuron is not predominantly dendritic ( Palmer et al . , 2012 ) . The long time-scale of this form of inhibition ( ~500 ms ) raises interesting consequences for the participation of pyramidal neurons in the cortical network . The similarity of some of the effects of TMS to interhemispheric inhibition has been noted previously in human studies ( Jiang et al . , 2013; Lee , 2014; Pérez-Garci et al . , 2013; Ferbert et al . , 1992 ) including its mediation via GABAB receptor-activation ( Ferbert et al . , 1992; Kobayashi et al . , 2003 ) , although these investigations could not examine the cellular mechanisms . For this study , we used a large coil typically used in humans and a smaller ( 25 mm ) coil designed for use in rodents . The effect on dendritic Ca2+ was the same in both cases . Clearly , the use of TMS coils with rats where the magnetic field generated is comparable to the size of the rat brain itself raises the possibility that the effects in humans may differ . However , the cortical feedback fibers which synapse onto the tuft dendrites of pyramidal neurons are located in the part of the cortex closest to the magnetic coil ( i . e . L1 ) in both rats and humans ( Larkum et al . , 1999; Larkum , 2013 ) , suggesting there would be overlap with respect to the influence of TMS . The aim of this study was to examine the effect of TMS on dendritic activity in L5 neocortical neurons rather than a general study on the overall effects of TMS at the cellular level . We were interested in this , in particular , because we have previously hypothesized that cell assemblies over different cortical regions might be associated through the activation of dendritic Ca2+ spikes in these neurons ( Larkum , 2013 ) . According to this hypothesis , dendritic activity in these neurons is a marker of important cognitive processes . The finding that TMS targets this mechanism is therefore highly relevant to the rationale of the study and may be instructive in understanding current applications of TMS . For instance , TMS has been used to alleviate some of the symptoms of hemispatial neglect ( Nyffeler et al . , 2009 ) and auditory hallucinations ( Giesel et al . , 2012 ) via unknown inhibitory processes . In conclusion , the results presented here indicate that the inhibitory actions of TMS is due to the recruitment of upper cortical layer interneurons mediating both GABAA and GABAB-receptor-activated inhibition in the dendrites of pyramidal neurons . This may have implications for the interpretation of results in humans using TMS as a form of 'virtual lesion' ( Lee et al . , 2007 ) .
Male or female Wistar rats ( P30-P40 ) were used in these experiments . Urethane ( intraperitoneal , 1 . 5 g/kg ) was used for experiments under anesthesia , according to the guidelines of the Federal Veterinary Office of Switzerland and LAGeSo Berlin . The head was fixed in a stereotaxic instrument ( Model SR-5R , Narishige , Tokyo , Japan ) and body temperature maintained at 36 to 37°C . A craniotomy above the primary somatosensory cortex ( 3 × 4 . 4 mm square ) , centered at 1 . 5 mm posterior to bregma and 2 . 2 mm from midline in the right hemisphere , was performed . The craniotomy was bathed in normal rat ringer ( in mM; 135 NaCl , 5 . 4 KCl , 1 MgCl2 1 . 8 CaCl2 , 5 HEPES ) and the dura mater surgically removed immediately before Ca2+ recording ( see below ) . In these vivo experiments , intrinsic optical imaging was used to identify the sensorimotor cortex before surgery . The cortical surface was visualized with green ( ~530 nm ) light to enhance contrast and switched to red ( ~600 nm ) light for functional imaging captured with a charge-coupled device ( CCD ) camera ( Teli ) coupled to a 50 mm and 25 mm lens ( Navitar ) . The signal was measured in alternating sweeps before and during contralateral hindpaw stimulation ( 300 ms; 30 isi ) governed by custom routines running in IgorPro ( Wavemetrics , Portland , OR . ) and Master 8 ( A . M . P . I ) . The intrinsic signal was measured as the difference in the reflected light before and during hindpaw stimulus and was mapped onto the blood vessel pattern to be targeted during experiments . Ca2+ imaging was performed as described previously by Murayama et al . ( Murayama et al . , 2007 ) . OGB-1 AM ( 50 µg; Molecular Probes , Eugene , OR ) was mixed with 5 µL of pluronic acid ( Pluronic F-127 , 20% solution in DMSO , Molecular Probes ) for 15 min . The solution was then diluted in 28 µL of HEPES-buffered solution ( 125 mM NaCl , 2 . 5 mM KCl , 10 mM HEPES ) and mixed for a further 15 min . The OGB-1 AM solution ( 1 . 3 mM ) was loaded into a glass pipette ( tip diameter: 5–15 µm ) and pressure-injected into layer 5 ( pressure: 10–22 kPa ) for 1 min . The pipette was withdrawn and the area of the craniotomy was then resubmerged with rat ringer for 2 hrs . For epifluorescence Ca2+ recordings , light from a blue light-emitting-diode ( LED , IBF+LS30W-3W-Slim-RX , Imac Co . , Ltd . , Shiga , Japan ) was passed through an excitation filter , dichroic mirror , and emission filter ( as a filter set 31001 , Chroma Technology , Rockingham , VT ) and focused onto a fiber bundle by a 10× objective ( Model E58-372 , 0 . 45 NA , Edmund Optics GmbH , Germany ) . The fiber bundle ( IGN-06/17 , Sumitomo Electric Industries , Tokyo , Japan ) was used as a combined illuminating/recording fiber and consisted of 17 , 000 fiber elements . The end face of the bundle was fitted with a prism-lens assembly , which consisted of a right-angle prism ( dimension of 0 . 5 × 0 . 5 × 0 . 5 mm , GrinTech , Jena , Germany ) attached to a GRIN lens ( a diameter of 0 . 5 mm and a NA of 0 . 5 , GrinTech ) . In previous studies , the fiber optic ‘periscope’ was vertically inserted 500 μm into the brain at a 90° angle ( Murayama et al . , 2009 ) . This ensured that the Ca2+ responses were recorded from the upper cortical layers . However , in this study , the fiber optic could not be inserted vertically due to the positioning of the TMS coil . Instead , the fiber optic was positioned horizontally on the brain surface ( ‘flat’ periscope ) . In this configuration , the ‘flat’ periscope was able to capture the same Ca2+ responses as the ‘vertical’ periscope . As previously reported by ( Murayama et al . , 2009 ) , TTX application into L5 caused a dramatic increase in L5 dendritic Ca2+ responses to hindpaw stimulation using both the ‘flat’ periscope and the ‘vertical’ periscope ( Figure 1—figure supplement 1 ) . With a focal length nominally 100 μm and 0 . 73 × magnification ( Murayama et al . , 2007 ) , the flat periscope configuration resulted in a field of view of 685 μm diameter restricted to the upper layers of the cortex . A cooled CCD camera operating at either 100 Hz ( MicroMax , Roper Scientific , Trenton , NJ ) or 2 . 7 kHz ( Redshirt imaging , Decatur , GA ) was used for collecting fluorescence . The fluorescence signals were quantified by measuring the mean pixel value of a manually selected ROI for each frame of the image stack using Igor software . Data was acquired on a PC using WinView software ( Roper Scientific ) . Regions of interest ( ROIs ) were chosen offline for measuring fluorescence changes ( see 'Data analysis' ) . TMS was applied to the rat somatosensory cortex using a MagStim 200 Monopulse and Rapid 2 system ( The MagStim Company Ltd . , Whitland , UK ) figure-eight coil , which was positioned 2–3 cm from the brain using a fixed manipulator . Experiments were typically performed with a 70-mm coil ( exception: during CNQX application , TMS was delivered via a 25-mm coil , see Figure 1—figure supplement 2 ) . Figure-of-eight-shaped coil was used as they produce a more focal current which is maximal at the intersection of the two round components ( DeFelipe , 2011 ) . The coil was centered on the craniotomy directly above the periscope fiber optic cable and angled approximately parallel to the skull curvature . TTL digital pulses triggered a single pulse TMS at 80–100% stimulation intensity ( unless otherwise stated ) with an inter-trial interval of at least 9 s to limit fluorescence bleaching . Given this experimental design , the electric field should be approximately ~150–200 V/m ( Cohen et al . , 1990 ) magnetic stimulation is comparatively indifferent to the conductive properties of the skull ( Wagner et al . , 2006 ) , and since the small ( 3 × 4 . 4 mm ) craniotomy is therefore unlikely to change the currents produced by the coil . Further , there was no behavioral response of the rat during TMS and increasing TMS strength did not elicit an excitatory response in layer 5 ( Figure 1—figure supplement 3 ) . When paired with hindpaw stimulus , the TMS was activated 50 ms before the contralateral hindpaw stimulus ( > 10 trials per animal ) . Electrical stimulation of the contralateral hindpaw was achieved by applying a brief ( 1 ms; 100 V ) current onto conductive adhesive strips ( approximately 1 cm wide by 2 cm long ) placed on the contralateral hindpaw pad . Where stated , hindpaw stimulation was also achieved by a triggered airpuff delivered to within 1 cm of the hindpaw ( ~40 psi; ~400 ms ) . All trials were interleaved in each experiment to limit time-dependent effects . The GABAB receptor antagonist CGP52432 ( 1 μM; Tocris ) and the GABAA receptor antagonist Gabazine ( 3 μM; Tocris ) were applied to the cranial surface , AMPA/kainate receptor antagonist CNQX ( 50 μM ) was either applied to the cranial surface or pressure injected into layer 2/3 . TTX ( 1 μM ) was pressure injected into layer 5 . See Figure 2—figure supplement 1 for cortical spread of drug application . The penetration of pressure injected or cortically applied drugs was measured using in vivo two-photon microscopy ( see Figure 2—figure supplement 1 ) . Brain tissue was imaged to a depth of 500 μm using a two-photon microscope ( Thorlabs A-scope ) with a titanium sapphire laser ( 860 nm; 140 fs pulse width; SpectraPhysics MaiTai Deepsee ) passed through a 40x water immersion objective ( Olympus; 0 . 8 NA ) . Images were obtained in full-frame mode ( 512 x 512 pixels ) . The fluorescence signals in vivo were quantified by measuring the mean pixel value of a manually selected ( offline ) ROI for each frame of the image stack using IgorPro software ( Wavemetrics ) . ROIs included the entire field of view and Ca2+ changes were expressed as ΔF⁄F = Ft ⁄ F0 , where Ft was the average fluorescence intensity within the ROI at time t during the imaging experiment and F0 was the mean value of fluorescence intensity before stimulation . Ca2+ responses were measured as the maximum value ( amplitude ) and total area under the positive trace ( integral ) within 1 s of the hindpaw stimulation . All numbers are indicated as mean ± s . e . m . Significance was determined using parametric tests ( paired/unpaired student t-test ) or non-parametric tests ( Unpaired , Mann-Whitney test; paired , Wilcoxon matched-pairs signed rank test ) as appropriate . p<0 . 05 ( * ) , p<0 . 01 ( ** ) and p<0 . 001 ( *** ) . | The brain’s billions of neurons communicate with one another using electrical signals . Applying a magnetic field to a small area of the scalp can temporarily disrupt these signals by inducing small electrical currents in the brain tissue underneath . The currents interfere with the brain’s own electrical signals and temporarily disrupt the activity of the stimulated brain region . This technique , which is known as transcranial magnetic stimulation , is often used to investigate the roles of specific brain regions . By examining what happens when a region is briefly taken ‘offline’ , it is possible to deduce what that area normally does . Transcranial magnetic stimulation is also used to treat brain disorders ranging from epilepsy to schizophrenia without the need for surgery or drugs . But despite its widespread usage , little is known about how transcranial magnetic stimulation affects individual neurons . Neurons are made up of a cell body , which has numerous short branches called dendrites , and a cable-like structure called the axon . Neurons signal to each other by releasing chemical messengers across junctions called synapses . The chemical signals are generally released from the axon of one neuron and bind to receptor proteins on a dendrite on another neuron to stimulate electrical activity in the receiving neuron . Murphy et al . have now investigated how transcranial magnetic stimulation affects the activity of dendrites from neurons within the cortex of the rat brain . This revealed that the magnetic fields stimulate other neurons that inhibit the activity of dendrites from neurons within the deeper layers of the cortex . The inhibition process depends on a type of receptor protein in the dendrites called GABAB receptors; blocking these receptors prevents transcranial magnetic stimulation from altering the activity of stimulated brain regions . The processes occurring in these dendrites have been linked to cognitive function . The next challenge will be to integrate the non-invasive transcranial magnetic stimulation approach with cognitive tests in humans that can now manipulate dendritic activity to test their importance under various circumstances . | [
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] | 2016 | Transcranial magnetic stimulation (TMS) inhibits cortical dendrites |
During development , tissue repair , and tumor growth , most blood vessel networks are generated through angiogenesis . Vascular endothelial growth factor ( VEGF ) is a key regulator of this process and currently both VEGF and its receptors , VEGFR1 , VEGFR2 , and Neuropilin1 ( NRP1 ) , are targeted in therapeutic strategies for vascular disease and cancer . NRP1 is essential for vascular morphogenesis , but how NRP1 functions to guide vascular development has not been completely elucidated . In this study , we generated a mouse line harboring a point mutation in the endogenous Nrp1 locus that selectively abolishes VEGF-NRP1 binding ( Nrp1VEGF− ) . Nrp1VEGF− mutants survive to adulthood with normal vasculature revealing that NRP1 functions independent of VEGF-NRP1 binding during developmental angiogenesis . Moreover , we found that Nrp1-deficient vessels have reduced VEGFR2 surface expression in vivo demonstrating that NRP1 regulates its co-receptor , VEGFR2 . Given the resources invested in NRP1-targeted anti-angiogenesis therapies , our results will be integral for developing strategies to re-build vasculature in disease .
Blood vessels provide oxygen and nutrients to cells throughout the body and are essential for tissue homeostasis and repair as well as tumor growth . The molecular mechanisms underlying angiogenesis have become increasingly clear , and VEGF is an essential player in this process ( Carmeliet et al . , 1996 , 1999; Ferrara et al . , 1996 , 2003; Iruela-Arispe and Dvorak , 1997; Miquerol et al . , 1999; Ruhrberg et al . , 2002; Stalmans et al . , 2002; Rossant and Hirashima , 2003; Maes et al . , 2004; Coultas et al . , 2005; Olsson et al . , 2006; Chung and Ferrara , 2011 ) . VEGF operates by interacting with three receptors , VEGFR1 , VEGFR2 ( KDR/Flk1 ) , and NRP1 ( Ferrara et al . , 2003; Chung and Ferrara , 2011 ) . Although these three receptors are expressed in spatially and temporally overlapping patterns , they are thought to play different roles in VEGF signaling . The main receptor for VEGF , VEGFR2 , is a receptor tyrosine kinase whose activity is crucial for VEGF signaling ( Olsson et al . , 2006 ) . Upon binding VEGF , VEGFR2 phosphorylates intracellular targets leading to a multitude of cellular responses including proliferation , migration , and transcriptional modification via signaling pathways such as PI3K , Src , and PLCϒ ( Olsson et al . , 2006 ) . In contrast , NRP1 is a multifaceted transmembrane receptor that not only binds VEGF and forms a complex with VEGFR2 but also binds a structurally and functionally unrelated family of traditional axon guidance cues , the secreted class 3 semaphorins ( SEMA3 ) ( He and Tessier-Lavigne , 1997; Kolodkin et al . , 1997; Soker et al . , 1998 ) . Consistent with these binding partners , Nrp1−/− mice are embryonically lethal with both neural and vascular defects ( Kitsukawa et al . , 1997; Kawasaki et al . , 1999 ) , indicating that NRP1 protein is instrumental for developmental angiogenesis . However , how NRP1 functions in conjunction with multiple ligands and receptors to guide vascular development remains elusive . Previous work has started to systematically dissect NRP1 function in vivo using a combination of structure–function analyses and mouse genetic approaches . In particular , endothelial-specific NRP1 knock-outs ( Tie2-Cre;Nrp1fl/− ) recapitulate the devastating vascular defects observed in Nrp1−/− mice—the vascular network is poorly developed and large endothelial cell aggregates form within the brain ( Gu et al . , 2003 ) . This result strongly demonstrates that NRP1 is a cell autonomously required in endothelial cells for its absolutely essential function in developmental angiogenesis . To pinpoint how SEMA3-NRP1 vs VEGF-NRP1 binding contributes to NRP1's critical role in vascular development , previous work generated a knock-in mouse line , Nrp1Sema− , in which SEMA3-NRP1 interactions were abolished and VEGF-NRP1 binding was maintained ( Gu et al . , 2003 ) . Nrp1Sema− mice mimicked the neural defects observed in the Nrp1−/− but did not exhibit any vascular abnormalities . These data suggest that SEMA3-NRP1 binding does not mediate NRP1's important function in vascular morphogenesis and instead point to the hypothesis that VEGF-NRP1 interactions may be integral for angiogenesis . Currently , the dominant view in the field asserts that VEGF-NRP1 binding enhances VEGFR2 activity and downstream signaling . Yet , the functional consequence of VEGF-NRP1 interactions has only been studied indirectly using in vitro methodology and blocking antibodies in vivo ( Pan et al . , 2007; Herzog et al . , 2011 ) . Specifically , an antibody inhibiting VEGF-NRP1 binding was found to interfere with retinal vascular remodeling as well as tumor angiogenesis ( Pan et al . , 2007 ) and is currently being developed as a therapeutic strategy to block vessel outgrowth . This study suggests that VEGF-NRP1 binding facilitates pathological angiogenesis . However , in vivo evidence describing a role for VEGF-NRP1 binding in vascular development is currently lacking and the precise function of NRP1 in VEGF-mediated angiogenesis urgently needs to be addressed . To delineate the role of VEGF-NRP1 interactions , we identified a single amino acid residue in the b1 domain of NRP1 that is necessary for VEGF-NRP1 binding and generated a mouse harboring this point mutation to abolish VEGF-NRP1 interactions in vivo ( Nrp1VEGF− ) . Surprisingly , although VEGF-NRP1 binding was successfully eliminated , the Nrp1VEGF− mutants survived into adulthood and did not display any of the severe vascular phenotypes seen in either the Nrp1−/− or the endothelial-specific NRP1 knock-out . Upon closer examination , NRP1-deficient blood vessels in the endothelial-specific NRP1 knock-out exhibited reduced VEGFR2 surface expression , a phenomenon not observed in the Nrp1VEGF− mutant . These results challenge the well-accepted view that NRP1 requires VEGF-NRP1 binding to facilitate developmental angiogenesis and points to a provocative new hypothesis that the angiogenic role of NRP1 lies in its capacity as a VEGFR2 co-receptor . Interestingly , retinal angiogenesis and blood flow recovery following hindlimb ischemia were mildly perturbed in the Nrp1VEGF− mutant suggesting that the postnatal vascular system is uniquely sensitive to the loss of VEGF-NRP1 binding . Together , this work not only significantly advances our basic scientific understanding of how NRP1 functions in VEGF-mediated angiogenesis , but also provides new insights that may facilitate the development of more effective NRP1-targeted anti-angiogenesis therapies .
We sought to elucidate the in vivo function of VEGF-NRP1 binding by generating a mouse line that selectively disrupts VEGF binding to NRP1 . A previous structure–function analysis revealed that the b1 domain of NRP1 is necessary and sufficient for VEGF binding ( Gu et al . , 2002 ) . However , this b1 region is also required for SEMA3-NRP1 interactions , so a series of Nrp1 variants containing smaller deletions in the b1 domain were engineered with site-directed mutagenesis to identify a region specific for VEGF-NRP1 binding ( Figure 1A ) . Based upon previous publications , we first targeted two specific sites in the b1 domain: the 7-residue binding site of the Pathologische Anatomie Leiden-Endothelium ( PAL-E ) monoclonal antibody which competes with VEGF for NRP1 binding ( Jaalouk et al . , 2007 ) and the 3-residue binding site of the VEGF analog tuftsin ( Vander Kooi et al . , 2007 ) ( Figure 1A–B ) . COS-1 cells were transfected with wild-type ( WT ) or mutant Nrp1 constructs and assessed for NRP1 expression . PAL-E and tuftsin binding site mutations did not affect NRP1 protein expression at the cell surface as examined by non-permeabilized antibody staining ( Figure 1C , Figure 1—figure supplement 1 ) . Ligand binding to NRP1 was assessed using alkaline phosphatase-tagged VEGF ( AP-VEGF ) and SEMA3A ( AP-SEMA3A ) in conjunction with alkaline phosphatase histochemistry . All of the PAL-E or tuftsin binding site variants were capable of abolishing VEGF-NRP1 binding , but unfortunately , also eliminated SEMA3-NRP1 binding ( Figure 1C , Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 03720 . 003Figure 1 . Design and assessment of Nrp1 variants harboring mutations in the VEGF-binding site . ( A ) Schematic representation of the NRP1 b1 extracellular domain and crystal structure highlighting three potential mutagenesis sites: the PAL-E binding site ( orange circle ) , tuftsin binding site ( blue circle ) , and electronegative surface ( red circle ) . ( B ) Sequence of the Nrp1 b1 domain indicating the deletion or mutation sites for the candidate constructs . ( C ) AP-SEMA3A ( top row ) or AP-VEGF ( middle row ) binding to COS-1 cells overexpressing the indicated constructs . Deletion of the entire PAL-E binding site ( Nrp1PAL-EΔ7 ) or partial deletion of the PAL-E binding site ( Nrp1PAL-EΔ6 and Nrp1PAL-E Δ5 ) eliminated both AP-SEMA3A and AP-VEGF binding . Likewise , mutations in the tuftsin binding site ( S346A , E348A , T349A or S346A , E348A ) abolished AP-SEMA3A binding and reduced AP-VEGF binding . Although mutations in the NRP1 electronegative surface ( E319K , D320K ) eliminated AP-VEGF binding and reduced AP-SEMA3A binding , the E319K mutation only slightly reduced AP-SEMA3A binding and maintained AP-VEGF binding . Antibody staining of unpermeabilized cells ( lower row ) demonstrated normal NRP1 surface expression . Scale bar: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 00310 . 7554/eLife . 03720 . 004Figure 1—figure supplement 1 . Assessment of additional Nrp1 variants containing mutations in the VEGF-binding site . AP-SEMA3A or AP-VEGF was applied to COS-1 cells overexpressing the indicated construct ( top and middle row ) . Non-permeabilized antibody staining was performed with a polyclonal anti-NRP1 antibody to detect NRP1 surface expression ( bottom row ) . Scale bar: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 004 We decided to use an unbiased approach and designed our subsequent Nrp1 variants based upon the crystal structure of the full NRP1 b1 domain . Specifically , we identified a hydrophilic region comprised of several negatively charged residues that provided a promising mutagenesis site for abolishing VEGF-NRP1 binding ( Figure 1A ) . Several of these residues were mutated to amino acids of the opposite charge in order to preserve the hydrophilic nature of the region . As with previous Nrp1 variants , NRP1 surface expression was unperturbed in transfected COS-1 cells ( Figure 1C ) . One of these mutations ( E282K ) did not affect the binding of either AP-SEMA3A or AP-VEGF , while others ( E282K and E420K ) eradicated binding of both the ligands ( Figure 1—figure supplement 1 ) . However , the D320K mutation converting aspartic acid 320 into lysine ( Nrp1D320K ) successfully abolished VEGF-NRP1 binding while conserving AP-SEMA3A binding as demonstrated through alkaline phosphatase histochemical staining on transfected COS-1 cells ( Figure 1C , Figure 2A , C ) . Moreover , the Nrp1D320K mutation also abolished the binding of other VEGF family members including Placenta Growth Factor ( PlGF ) and Vascular Endothelial Growth Factor B ( VEGFB ) ( Figure 2—figure supplement 1 ) . In a liquid alkaline phosphatase activity assay , Nrp1D320K was co-expressed with PlexinA4 ( Plex4A ) to more accurately reflect the in vivo situation in which SEMA3A signals through a holoreceptor complex of both NRP1 and PlexinA . AP-SEMA3A binding levels to WT NRP1 and NRP1D320K were indistinguishable ( Figure 2D ) , and the dissociation constant ( KD ) of SEMA3A-NRP1D320K/PlexinA4 was unchanged from that of SEMA3A-NRP1/PlexinA4 further verifying that the SEMA3A-NRP1/PlexinA4 interaction was intact ( Figure 2E ) . Finally , Western blot analysis confirmed that NRP1 protein expression levels were equivalent in COS-1 cells transfected with WT Nrp1 and Nrp1D320K ( Figure 2B ) . Taken together , these data demonstrate that the Nrp1D320K mutation is sufficient to eliminate VEGF binding and maintain SEMA3A binding in vitro . 10 . 7554/eLife . 03720 . 005Figure 2 . The Nrp1D320K mutation selectively eliminates VEGF-NRP1 binding in vitro . ( A ) AP-VEGF binding in COS-1 cells overexpressing the indicated Nrp1 construct . WT NRP1 bound AP-VEGF strongly , while AP-VEGF binding to NRP1D320K was abolished . Scale bar: 100 μm ( B ) Western blot shows that equivalent levels of NRP1 protein in COS-1 cells transfected with the WT Nrp1 and Nrp1D320K . ( C ) Quantification of the binding assay shows that AP-VEGF-NRP1D320K binding was abolished even after normalization for protein content and NRP1 expression . ( D ) Quantification of AP-SEMA3A binding shows comparable AP-SEMA3A binding to WT NRP1 and NRP1D320K . ( E ) Measurement of the dissociation constant ( KD ) of AP-SEMA3A demonstrates that AP-SEMA3A bound to the NRP1D320K/PlexA4 complex with the same affinity as the NRP1/PlexA4 complex . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 00510 . 7554/eLife . 03720 . 006Figure 2—figure supplement 1 . VEGFA , VEGFB , and PLFG binding to NRP1 was abolished in the Nrp1D320K mutant . Nrp1 constructs were overexpressed in COS-1 cells , and AP-VEGFB or AP-PlGF was applied to cells to observe ligand binding . WT NRP1 bound AP-VEGFB and AP-PlGF strongly , while these ligands did not bind to NRP1D320K . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 006 A gene replacement strategy was implemented to generate a mouse line harboring the Nrp1D320K mutation in the endogenous Nrp1 locus , delineated as Nrp1VEGF− . Specifically , two base pair mutations were introduced into exon 6 of the mouse Nrp1 gene to produce the D320K mutation in the endogenous Asp320 location ( Figure 3A ) . After recombineering , embryonic stem cells were screened via PCR and sequenced to confirm that the D320K mutation was appropriately introduced into the Nrp1 locus ( Figure 3—figure supplement 1A–C ) . Once Nrp1VEGF− mice were obtained , the presence of the D320K mutation was verified by sequencing ( Figure 3—figure supplement 1D ) . Importantly , the Nrp1VEGF− mutants expressed normal levels of NRP1 protein as assessed by Western blot on embryonic day 14 . 5 ( E14 . 5 ) lung and adult heart , brain , lung and kidney ( Figure 3C , Figure 3—figure supplement 2D ) . AP-VEGF and AP-SEMA3A binding was examined at E12 . 5 in the dorsal root entry zone ( DREZ ) , where NRP1-expressing axons from the dorsal root ganglion enter the spinal cord . Both AP-VEGF and AP-SEMA3A bound to the DREZ in control animals ( Figure 3B ) while AP-VEGF binding to the DREZ was abolished in the Nrp1VEGF− mutant ( Figure 3B ) , confirming that this mutation eliminated VEGF-NRP1 binding in vivo . Moreover , NRP1 immunostaining and AP-SEMA3A binding to the DREZ appeared similar between Nrp1VEGF− and control littermates ( Figure 3B ) . Finally , the Nrp1VEGF− mutants failed to display the perinatal lethality or cardiac defect observed in the Nrp1Sema− mutants ( Gu et al . , 2003 ) , further confirming functional SEMA3-NRP1 binding in Nrp1VEGF− mice ( Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 03720 . 007Figure 3 . Nrp1VEGF- mice selectively abolish VEGF-NRP1 binding in vivo . ( A ) Targeting vector design for the generation of Nrp1VEGF− mice . The WT genomic region contained residue D320 in exon 6 of Nrp1 . The targeting vector ( TV ) introduced the D320K mutation along with an Frt-flanked NeoR cassette to form the targeted allele ( TA ) . After FlpE-mediated excision of the NeoR cassette , the final targeted allele ( FTA ) had the D320K mutation as well as one remaining Frt site . ( B ) Section binding assays demonstrated that AP-VEGF binding to the dorsal root entry zone ( DREZ ) was abolished in the Nrp1VEGF− mutants ( arrows , left panels ) while AP-SEMA3A binding to the DREZ appeared similar between Nrp1VEGF− and control animals ( arrows , middle panels ) . Scale bar: 100 μm . ( C ) Western blot from E14 . 5 lung tissue shows that NRP1 protein level was not affected in Nrp1VEGF− animals . ( D and E ) Nrp1VEGF− mutants appear indistinguishable from controls littermates at embryonic ( E14 . 5 ) and adult stages . ( F ) Nrp1VEGF− mutants exhibit normal body weight in adulthood ( n = 7 , males ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 00710 . 7554/eLife . 03720 . 008Figure 3—figure supplement 1 . Screening and verification of ES cells for the generation of the Nrp1VEGF− mutant . ( A ) Diagram of the Nrp1 genomic region following successful homologous recombination to insert the targeting vector . The green arrows indicate the primers used in ( B ) , while the blue arrows represent the primers used in ( C ) . ( B ) PCR screening for the proper insertion of the 3′ homology arm . The 5′ primer was located in the NeoR sequence while the 3′ primer bound to an area outside of the targeting vector . Therefore , WT colonies did not produce a band , while correctly targeted clones produced a band of 1 . 7 kb . ( C ) PCR screening for the proper insertion of the 5′ homology arm . The 5′ primer was located outside of the targeting vector area and the 3′ primer was located within the genomic sequence present in the 3′ homology arm . Thus , PCR from a properly targeted clone produced a fragment that was 1 . 5 kb larger than a negative colony . ( D ) Sequencing of the D320K region in WT and Nrp1VEGF− homozygous mutants . The boxed region indicates the altered codon . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 00810 . 7554/eLife . 03720 . 009Figure 3—figure supplement 2 . The Nrp1VEGF− mutant mice exhibit normal gross morphology . ( A ) Whole-mount images of the heart at P9 show the normal cardiac morphology of the Nrp1VEGF− mutants . ( B and C ) Organ weights measured at P9 ( B ) and adulthood ( C ) demonstrate that the heart , brain , lung , and kidney undergo appropriate growth in Nrp1VEGF− animals , n ≥ 5 . ( D ) Western blots from adult heart , brain , lung , and kidney tissue demonstrate that NRP1 protein levels were not affected in Nrp1VEGF− animals . ( E ) Viability table depicts the predicted and observed frequencies for each genotype at the indicated developmental stages . The table values represent the percentage of the total number of animals genotyped per age while the total number of animals is shown in parentheses . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 009 Despite the embryonic lethality previously described in Nrp1−/− and Tie2-Cre , Nrp1fl/− animals , Nrp1VEGF− mice were born at expected Mendelian ratios and maintained their vitality into adulthood ( p > 0 . 05 for observed vs expected , Figure 3—figure supplement 2E ) . The Nrp1VEGF− mutants exhibited normal gross morphology throughout embryonic and postnatal stages ( Figure 3D , E ) and failed to develop the cardiac defects previously observed in the Nrp1−/− , Tie2-Cre;Nrp1fl/− , and Nrp1Sema− mutants ( Figure 3—figure supplement 2A ) . Moreover , Nrp1VEGF− animals displayed normal body weight ( Figure 3F ) , organ growth ( Figure 3—figure supplement 2B , C ) , and fertility . To thoroughly examine vascular integrity during development , isolectin staining was employed to visualize blood vessels in embryonic and perinatal brain sections and vessel ingression , morphology , and branching were assessed in the Nrp1VEGF− mutant . Surprisingly , Nrp1VEGF− animals did not exhibit any of the vascular abnormalities observed in the endothelial-specific NRP1 knock-out . As shown in Figure 4A and quantified in Figure 4B–C , cortical vessel ingression was nearly absent in Tie2-Cre;Nrp1fl/fl animals at E11 . 5 while ingression was unaffected in the Nrp1VEGF− mutants . In addition , Tie2-Cre;Nrp1fl/fl animals had abnormally large vascular aggregates distributed throughout the striatum at E14 . 5 while vessels were evenly dispersed without aggregates in both control and Nrp1VEGF− animals ( Figure 4D–F ) . Finally , Tie2-Cre;Nrp1fl/fl animals had a significant decrease in vessel branching in the cortex at E14 . 5 while Nrp1VEGF− animals exhibited normal vessel branching ( Figure 4G–I ) . Moreover , unlike the endothelial-specific NRP1 knock-out , the long term viability of the Nrp1VEGF− mutants allowed us to assess cortical vessel branching and coverage at P7 which was indistinguishable from control littermates ( Figure 4G–I , Figure 4—figure supplement 1 ) . Therefore , VEGF-NRP1 binding is not required for developmental angiogenesis . 10 . 7554/eLife . 03720 . 010Figure 4 . VEGF-NRP1 binding is not required for developmental angiogenesis . ( A ) Vessel staining with isolectin ( green ) revealed that Tie2-Cre;Nrp1fl/fl mutants had delayed vessel ingression into the cerebral cortex at E11 . 5 while the Nrp1VEGF− mutants exhibited normal ingression . DAPI was used to stain the nuclei ( blue ) . ( B and C ) Quantification of cortical vessel ingression shown in A , n = 3 . ( D ) Tie2-Cre;Nrp1fl/fl mutants exhibited large vessel clumps in the brain ( particularly in the striatum ) at E14 . 5 , a phenotype not observed in the Nrp1VEGF− mutants . ( E and F ) Quantification of vessel size in E14 . 5 striatum shown in D , n = 3 . ( G ) Tie2-Cre;Nrp1fl/fl mutants had reduced vessel branching in the cerebral cortex while the Nrp1VEGF− mutants displayed normal vessel branching at E14 . 5 . ( H and I ) Quantification of vessel branching in E14 . 5 cortex shown in G , n = 4 . Scale bar: 200 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 01010 . 7554/eLife . 03720 . 011Figure 4—figure supplement 1 . The Nrp1VEGF− mutant mice display normal vessel branching and coverage at postnatal stages . ( A ) Vessel staining with isolectin ( green ) demonstrates that the Nrp1VEGF− mutants have normal vessel coverage and branching in the cerebral cortex at P7 . ( B and C ) Quantification of vessel coverage and branching in P7 cortex shown in A , n = 3 . Scale bar: 200 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 011 The normal developmental angiogenesis observed in the Nrp1VEGF− mutants clearly demonstrates that VEGF-NRP1 binding is not responsible for the vascular defects observed in Nrp1−/− or endothelial-specific NRP1 knock-outs . In this regard , NRP1 must function through an alternative mechanism to regulate vascular development during embryogenesis . The intracellular domain of NRP1 does not have any obvious enzymatic activity and is not responsible for the signal transduction mediating angiogenesis ( Fantin et al . , 2011; Lanahan et al . , 2013 ) . Therefore , two apparent alternatives remain . One possibility is that a yet unidentified ligand outside the VEGF or SEMA3 family binds to NRP1 and instructs developmental angiogenesis . Alternatively , NRP1 may control vascular development by directly regulating its co-receptor , VEGFR2 . To test this second possibility , VEGFR2 expression was evaluated in the Tie2-Cre;Nrp1fl/− mutants and control littermates via Western blot on E14 . 5 lung tissue . This biochemical assay revealed that total VEGFR2 protein levels were significantly reduced in the Tie2-Cre;Nrp1fl/− mutants compared to their control littermates ( Figure 5A–B ) . To determine the cell surface expression of VEGFR2 in vivo , we used fluorescence-activated cell sorting ( FACS ) to specifically quantify VEGFR2 expression at the cell surface of non-permeabilized endothelial cells derived from the acutely dissociated lungs of Tie2-Cre;Nrp1fl/− and control embryos . Remarkably , Tie2-Cre;Nrp1fl/− mutants displayed a significant decrease in the fluorescence intensity of VEGFR2 labeling as compared to control littermates ( Figure 5E–F ) , suggesting that NRP1 functions to regulate VEGFR2 surface expression in endothelial cells . In contrast , both Western blot and FACS analysis determined that VEGFR2 protein levels were unperturbed in Nrp1VEGF− animals ( Figure 5C–D , G–F ) . In addition , co-immunoprecipitation on P7 lung tissue revealed that NRP1 and VEGFR2 are physically associated in both control and Nrp1VEGF− animals ( Figure 5—figure supplement 1B ) , validating that NRP1-VEGFR2 receptor complex formation does not require VEGF-NRP1 binding in vivo . This result mimics our co-immunoprecipitation experiments on HEK293T cells transfected with either WT Nrp1 or Nrp1D320K constructs ( Figure 5—figure supplement 1A ) . Together , these findings indicate that NRP1 plays a role in regulating the cell surface expression of VEGFR2 in endothelial cells and that VEGF-NRP1 binding is not necessary for this function in vivo ( Figure 5G ) . 10 . 7554/eLife . 03720 . 012Figure 5 . NRP1 regulates VEGFR2 expression at the cell surface independent of VEGF-NRP1 binding . ( A ) Western blot from E14 . 5 lung tissue treated with 50 ng/ml VEGF for 15 min revealed that VEGFR2 was reduced in Tie2-CreNrp1fl/− mutants while VE-cadherin expression remained at control levels . Western blot for NRP1 demonstrates that the Tie2-Cre allele successfully knocked down NRP1 expression . ( B ) Quantification of VEGFR2 expression shown in A , n = 4 . ( C ) Western blot from E14 . 5 lung tissue treated with 50 ng/ml VEGF for 15 min demonstrates that VEGFR2 , NRP1 , and VE-cadherin expression were unperturbed in the Nrp1VEGF− mutants . ( D ) Quantification of VEGFR2 expression shown in C , n = 5 . ( E ) FACS analysis plots illustrate a reduction in VEGFR2 surface expression in endothelial cells isolated from Tie2-Cre;Nrp1fl/− mice . ( F ) Quantification of the VEGFR2 fluorescence intensity from the FACS analysis shown in E , n = 5 . ( G ) FACS analysis plots demonstrate that VEGFR2 surface expression in endothelial cells isolated from Nrp1VEGF− mice remained at control levels . ( H ) Quantification of the VEGFR2 fluorescence intensity from the FACS analysis shown in G , n ≥ 7 . ( I ) Schematic of VEGFR2 and NRP1 at the cell surface illustrates VEGF ligand binding to both VEGFR2 and NRP1 . In the Nrp1VEGF− mutants , VEGF-NRP1 binding is abolished , VEGFR2 has normal cell surface localization , and vascular development proceeds appropriately . However , in Nrp1−/− mutants , VEGFR2 cell surface localization is reduced and vascular development is impaired . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 01210 . 7554/eLife . 03720 . 013Figure 5—figure supplement 1 . VEGF-NRP1 binding is not required for NRP1-VEGFR2 complex formation in vitro and in vivo . ( A ) HEK293T cells transfected with Vegfr2 and either WT Nrp1 or Nrp1D230K exhibited normal NRP1-VEGFR2 complex formation . ( B ) Lung lysates generated from the Nrp1VEGF− mutants also displayed normal NRP1-VEGFR2 complex formation comparable to littermate controls . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 01310 . 7554/eLife . 03720 . 014Figure 5—figure supplement 2 . VEGF-induced VEGFR2 phosphorylation is reduced in both the Nrp1VEGF− and Tie2-Cre;Nrp1fl/− mutants . ( A ) Western blot from E14 . 5 lung tissue shows that VEGFR2 phosphorylation upon VEGF treatment was diminished in the Nrp1VEGF− mutant . ( B ) Quantification of VEGFR2 phosphorylation shown in A , n = 7 . ( C ) Western blot from E14 . 5 lung tissue demonstrates that VEGFR2 phosphorylation is significantly reduced in the Tie2-Cre;Nrp1fl/− mutants . ( D ) Quantification of VEGFR2 phosphorylation shown in B , n = 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 014 To examine VEGF signaling in the Tie2-Cre;Nrp1fl/− and Nrp1VEGF− mutants , VEGFR2 phosphorylation was examined via Western blot on embryonic lung tissue isolated at E14 . 5 . Specifically , Tie2-Cre;Nrp1fl/− mutants had a severe reduction in VEGFR2 phosphorylation at the tyrosine residue 1175 ( Y1175 ) upon VEGF treatment ( Figure 5—figure supplement 2A , B ) . Interestingly , Nrp1VEGF− mutants also exhibited a mild reduction in VEGFR2 phosphorylation while total VEGFR2 protein levels were well maintained ( Figure 5—figure supplement 2C , D ) . Although the level of pVEGFR2 in the Nrp1VEGF− mutant was sufficiently high to support vascular development during embryogenesis , the modest reduction in pVEGFR2 may manifest in issues with angiogenesis , vascular maintenance , and regeneration in the postnatal animal . To directly test the role for VEGF-NRP1 binding in postnatal angiogenesis , whole-mount staining was performed with isolectin and an antibody against α-smooth muscle actin ( α-SMA ) to visualize the retinal blood vessels and arteries , respectively . At P9 , the Nrp1VEGF− mutants exhibited a reduction in the vascular extension and artery number but did not have any abnormalities in vessel coverage as compared with control littermates ( Figure 6A ) . In the adult , the vascular extension and vessel coverage in the retina were indistinguishable from controls ( Figure 6B ) indicating that the Nrp1VEGF− mutants experience a delay in the formation of the primary vascular plexus . However , the number of retinal arteries remained lower in Nrp1VEGF− adults . These results demonstrate that VEGF-NRP1 interactions are required to some degree for postnatal angiogenesis and artery differentiation in the retina . 10 . 7554/eLife . 03720 . 015Figure 6 . Retinal angiogenesis is perturbed in the Nrp1VEGF− mutant . ( A ) Isolectin and α-SMA staining on P9 retinal flat-mounts revealed a significant reduction in vascular extension and artery number in Nrp1VEGF− mutants . However , vessel coverage in the retina was unperturbed in the Nrp1VEGF− mutants , n = 6 . ( B ) In the adult , isolectin and α-SMA staining showed that the number of retinal arteries remained lower in the Nrp1VEGF− mutants than littermate controls while vascular extension and vessel coverage in the retina were normal , n = 4 . Scale bar: 200 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 01510 . 7554/eLife . 03720 . 016Figure 6—figure supplement 1 . The Nrp1VEGF− mutants have delayed blood flow recovery following femoral artery ligation . ( A ) Laser doppler imaging demonstrates severe hindlimb ischemia directly after femoral artery ligation in both control and Nrp1VEGF− animals ( arrows ) . Five days after surgery , blood flow recovery in the injured hindlimb was significantly greater in control vs Nrp1VEGF− animals ( arrowheads ) . ( B ) Quantification of blood flow recovery following femoral artery ligation , n = 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 03720 . 016 In addition , Nrp1VEGF− animals were also assessed for injury-induced arteriogenesis following femoral artery ligation . In this assay , the femoral artery was surgically severed in both Nrp1VEGF− and control mice , and blood flow recovery was monitored via deep penetrating laser Doppler imaging . Femoral artery ligation produced a comparable level of hindlimb ischemia in the Nrp1VEGF− mutants and controls ( Figure 6—figure supplement 1 ) . However , the Nrp1VEGF− mutants exhibited a significant delay in hindlimb re-perfusion . Building upon these results , future work will utilize the Nrp1VEGF− knock-in line to determine if VEGF-NRP1 signaling functions in pathological or physiological angiogenesis in the adult .
In this study , we identified a single amino acid within the extracellular b1 domain of NRP1 that is required for VEGF-NRP1 binding , but non-essential for SEMA3-NRP1 interactions . A point mutation in this D320 residue was incorporated into the endogenous Nrp1 locus to generate the Nrp1VEGF− mutant , a novel mouse line that selectively abolishes VEGF-NRP1 binding in vivo . Recently a cDNA knock-in NRP1 mutant , Nrp1Y297A/Y297A , was also developed to examine the role of VEGF-NRP1 binding ( Fantin et al . , 2014 ) . However , mice generated with genetically modified cDNA notoriously lack the essential intronic regions that regulate the temporal and spatial expression of the gene . Consequently , the aberrant and severe down-regulation of NRP1 protein expression in the Nrp1Y297A/Y297A hypomorph prevents any definitive conclusions from being garnered about the biological cause of phenotypes present in this mouse . In this regard , abnormalities in the Nrp1Y297A/Y297A hypomorph could originate from two potential sources: the severe reduction in NRP1 levels or the abolishment of VEGF-NRP1 binding . Unlike the Nrp1Y297A/Y297A line , our Nrp1VEGF− mutant contains a two base pair replacement in the endogenous Nrp1 locus and preserves the genetic structure of the Nrp1 gene . Consequently , Nrp1VEGF− mice maintain appropriate levels of NRP1 protein expression and allow the first unobscured in vivo assessment of VEGF-NRP1 binding in developmental angiogenesis . Our Nrp1VEGF− line provides a powerful new genetic tool for selectively interrogating the function of VEGF-NRP1 binding in broad areas of basic research and translational study . Remarkably , our Nrp1VEGF− mutant did not recapitulate the early embryonic lethality or developmental angiogenesis phenotypes of the Nrp1−/− and endothelial-specific NRP1 knock-out ( Figure 4 ) . Moreover , the Nrp1VEGF− mutant did not exhibit any of the cardiac failure , perinatal lethality , or growth defects observed in the Nrp1Y297A/Y297A hypomorph indicating that these phenotypes are attributed to the severe reduction in NRP1 protein in Nrp1Y297A/Y297A mutants rather than the lack of VEGF-NRP1 binding . However , the Nrp1VEGF− mutant did exhibit a delay in vascular extension and a reduction in the number of arteries in the postnatal retina . This retinal phenotype is significantly less severe than those observed in the Nrp1Y297A/Y297A hypomorph ( Fantin et al . , 2014 ) or in animals treated with antibodies inhibiting VEGF-NRP1 binding ( Pan et al . , 2007 ) . Together , these results reveal that the retina relies on both VEGF-NRP1 dependent and independent mechanisms to establish the retinal vasculature . Our surprising results challenge the well-accepted view that NRP1 depends on VEGF-NRP1 binding to facilitate angiogenesis and points to a provocative new hypothesis that NRP1 functions independently of VEGF-NRP1 binding perhaps via its interaction with an unidentified ligand or in its capacity as a co-receptor for VEGFR2 . Our study demonstrates that the NRP1-deficient endothelial cells have reduced VEGFR2 expression at the cell surface , a phenomenon that was not observed in the Nrp1VEGF− mutants . This result provides the first in vivo evidence that NRP1 controls VEGFR2 levels at the cell membrane and offers the first in vivo phenotypic characterization linking NRP1 regulated VEGFR2 surface expression to vascular development . Consistent with our in vivo observations , several lines of in vitro work using multiple cell culture systems demonstrate that NRP1 is essential for the proper presentation , recycling , and degradation of VEGFR2 ( Shintani et al . , 2006; Holmes and Zachary , 2008; Ballmer-Hofer et al . , 2011; Hamerlik et al . , 2012 ) . The loss of function and gain of function studies in human umbilical vein endothelial cells ( HUVECs ) found that VEGFR2 protein levels were decreased in the absence of NRP1 while Vegfr2 mRNA levels were unaffected by Nrp1 siRNA ( Shintani et al . , 2006; Holmes and Zachary , 2008 ) . Similarly , Hamerlik et al . ( 2012 ) examined human glioblastoma multiforme cells and found that shRNA mediated knock-down of NRP1 resulted in dramatically decreased VEGFR2 protein levels accompanied by a lower surface presentation of VEGFR2 and a decrease in cell viability . Moreover , cell surface protein biotinylation and immunofluorescence staining with confocal microscopy confirmed the co-localization of VEGFR2-NRP1 with the early/recycling endosome . Finally , Ballmer-Hofer et al . , ( 2011 ) used stably transfected porcine aortic endothelial cell ( PAEC ) lines in conjunction with immunostaining to visually follow VEGFR2 trafficking in the presence and absence of NRP1 . Their experiments revealed that upon VEGF stimulation , VEGFR2 is internalized in Rab7 vesicles for degradation . However , in the presence of NRP1 , VEGFR2 is stabilized in Rab11 vesicles and recycled back to the cell surface . In conjunction with our in vivo results , these data demonstrate that NRP1 guides vascular development through its capacity as a VEGFR2 co-receptor rather binding to VEGF . In this manner , NRP1 regulates angiogenesis by controlling the amount of VEGFR2 expression at the cell surface and consequently the level of VEGFR2-VEGF signaling . The modulation of co-receptors may function as a general mechanism for regulating cell signaling and behavior . A prior in vitro study identified a similar relationship between the membrane protein , neural cell adhesion molecule ( NCAM ) and fibroblast growth factor receptor-1 ( FGFR1 ) ( Francavilla et al . , 2009 ) . This previous work discovered that NCAM induced sustained FGFR1 activation by controlling the intracellular trafficking of the FGFR1 receptor . Specifically , NCAM was capable of re-targeting internalized FGFR1 from the lysosomal degradation pathway to Rab11-postive recycling vesicles and increased FGFR1 expression at the cell surface . In this regard , the co-receptor interaction between NRP1 and VEGFR2 may be representative of a more universal phenomenon in which membrane proteins function to regulate the cell surface expression and subsequent downstream signaling of receptors . Ultimately , our findings mark a pivotal step toward understanding the role of NRP1 in developmental angiogenesis and indicate that NRP1-VEGFR2 interactions rather than VEGF-NRP1 binding underlie NRP1's critical function in VEGF-mediated vascular development . Given the substantial resources invested in NRP1-targeted anti-angiogenesis therapies for vascular disease and cancer , the information gleaned from this study will be invaluable in identifying the cellular and molecular mechanisms underlying angiogenesis and ultimately using this information to instruct the development of new therapeutic approaches .
Rat Neuropilin1 cDNA was re-cloned from pMT21 into pCS2+ using the original EcoRI and XhoI sites present in both vectors . Mutations were made using PCR , and the mutated fragment was subcloned back into pCS2-Nrp1 using endogenous restriction sites . The targeting vector ( TV ) was constructed using a combination of traditional cloning and recombineering along with point mutagenesis . Genomic DNA was obtained from the 129S7-AB2 . 2 BAC library , clone #bMQ-373E22 . The short ( 3′ ) arm ( 1 . 3 kb ) was cloned into the HpaI and EcoRI sites of 4600C-loxP . Two short homology arms ( 900 bp , total ) were created and cloned into the XhoI and NotI sites of 4600C-loxP , with the two arms joined by a SalI site . The homology arms were ligated in a triple ligation to 4600C-loxP as well as to each other . The vector was then linearized with SalI and electroporated into modified electrocompetent DH10B cells containing the previously mentioned BAC in order to facilitate homologous recombination to insert the remainder of the long arm . Recombineering was performed as described by the NCI-Frederick . After a full-length TV was made , the D320K mutation was introduced . The final TV was linerarized and electroporated into ES cells . All primer sequences used for the targeting vector construction are provided in Supplementary file 1 . HEK293T cells were transfected with AP-SEMA3A , AP-VEGF A , AP-VEGF B , or AP-PlGF expression constructs using a calcium phosphate transfection method . Media was changed after 6 hr . Cells were cultured for an additional 48 hr in DMEM + 10% FBS . After 48 hr the media were collected , filtered to remove the cell debris , and AP activity was measured . The ligands were frozen at −80°C until use . COS-1 cells were grown in DMEM + 10% fetal bovine serum ( FBS ) + 1% Penicillin-Streptomycin . Cells were transfected with the indicated expression vectors using Lipofectamine-2000 ( Invitrogen , Carlsbad , CA ) in 6-well plates . 24 hr later , transfected cells were split into 24-well plates for parallel AP-binding and antibody staining . 24 hr after splitting , binding was performed using AP-tagged ligands ( AP-VEGF A , AP-SEMA3A , AP-VEGF B , AP-PlGF ) . The binding protocol was as follows: cells were washed 1× with HBHA ( 1× HBSS , 0 . 5 mg/ml BSA , 0 . 5% sodium azide , and 20 mM HEPES [pH 7] ) , then incubated for 75 min with 0 . 3 ml of 2 nM ligand . Cells were then washed 7× with HBHA on a rotating platform and 110 µl of cell lysis buffer ( 1% Triton X-100 and 10 mM Tris–HCl [pH 8] ) was added to each well . Cells and buffer were scraped into Eppendorf tubes , then vortexed for 5 min to fully lyse them . The lysates were then spun down for 5 min , and the supernatant was heat inactivated at 65°C for 10 min to inactivate endogenous alkaline phosphatases . AP-activity was measured by adding 2× SEAP buffer ( 50 ml 2 M diethanolamine [pH 9 . 8] , 50 µl 1 M MgCl2 , 224 mg L-homoarginine , 50 mg BSA , 445 mg p-nitrophenylphosphate ) and measuring optical absorbance at 405 nm every 15 s for 1 min . Antibody staining of these cells was done as follows: non-specific binding was blocked with 5% Normal Goat Serum in DMEM for 30 min at 4°C . Cells were then incubated with primary antibody ( Rabbit anti-NRP1 , gift of Dr David Ginty ) for 2 hr at 4°C . They were then washed 6× with cold HBHA , then incubated with a secondary antibody ( AP-tagged anti-rabbit ) for 1 . 5 hr at 4°C . Cells were then washed 3× in cold HBHA , then lysed as described above . AP-activity was measured from lysed extracts . Binding of AP-tagged ligands was normalized to protein content of each well and to antibody staining with an anti-NRP1 antibody . Each AP-binding assay was independently repeated three times . Nrp1VEGF− , Tie2-Cre , Nrp1fl , and Nrp1− ( Gu et al . , 2003 ) mice were maintained on a C57Bl/6 background . Nrp1VEGF− mice were genotyped with traditional PCR techniques . The expected WT band is 305 bp , while the targeted allele is 350 bp due to the remaining presence of one FRT site . To sequence the mutation site , PCR was performed to generate a fragment around the mutation site . The primer sequences for genotyping and sequencing are included in Supplementary file 1 . Tie2-Cre , Nrp1fl , and Nrp1− genotyping was performed as previously published . All animals were treated according to institutional and NIH guidelines approved by IACUC at Harvard Medical School . Embryos were dissected and frozen immediately in liquid nitrogen , then stored at −80°C until use . Sections were cut at 25 µm with a cryostat , then fixed for 8 min in ice-cold methanol . Sections were then washed 3× in PBS + 4 mM MgCl2 . Non-specific binding was reduced by blocking the sections with DMEM + 10% FBS for 45 min . After fixation , sections were incubated with 2 nM AP-tagged ligand , diluted with PBS + 4 mM MgCl2 , and buffered with HEPES , pH 7 for 1 . 5 hr at room temperature in a humidified chamber . The sections were washed 5× in PBS + 4 mM MgCl2 , then fixed with a fixative solution ( 60% acetone , 1% formaldehyde , 20 mM HEPES , pH 7 ) . Sections were washed 3× in PBS and incubated in PBS at 65°C for 2 hr to heat inactive endogenous alkaline phosphatases and then incubated overnight in developing solution ( 100 mM Tris–HCl pH 9 . 5 , 100 mM NaCl , 5 mM MgCl2 ) with NBT ( nitro-blue tetrazolium chloride ) and BCIP ( 5-bromo-4-chloro-3′-indolyphosphate p-toluidine ) . AP-ligand binding was analyzed in sections from at least three animals across two different litters per genotype . For immunoblotting , E14 . 5 lung samples were loaded on 8% polyacrylamide gels and run until the appropriate protein separation was achieved . Samples were electrophoretically transferred onto the PVDF membrane . Non-specific binding was blocked by a 1 hr incubation in 5% non-fat milk in TBST ( Tris-buffered saline + 0 . 1% Tween-20 ) . The membranes were then incubated overnight with the following primary antibodies , as indicated below , at 4°C: anti-NRP1 ( #ab81321 Abcam , Cambridge , MA or gift of Dr David Ginty , see Ginty et al . , 1993 for details ) , anti-VEGFR2 ( gift of Procter and Gamble , see Gu et al . , 2003 for details ) , anti-VE-cadherin ( #ab33168 Abcam , Cambridge , MA ) , anti-p-VEGFR2 ( p1175 ) ( #2478 Cell Signaling Technology , Danvers , MA ) , and anti-α-Tubulin ( #T5168 Sigma-Aldrich , Natick , MA ) . After incubation with primary antibodies , the membranes were washed 3× in TBST then incubated with the appropriate HRP-labeled secondary antibody in TBST or 5% milk in TBST for 1 hr at room temperature . Membranes were then washed 3× with TBST then developed with regular or super ECL ( GE Amersham , United Kingdom or Thermo Scientific , Waltham , MA ) . The intensity of individual bands was quantified using ImageJ . At the indicated stages , embryos were dissected , fixed with 4% paraformaldehyde , equilibrated in a sucrose gradient , embedded in OCT , and sectioned in the coronal plan at 12 µm with a Leica CM3050S cryostat . Likewise , the brains of postnatal pups ( P7 ) were dissected , fixed , cryo-protected , and sectioned at 20 µm . Tissue sections were washed 3× for 5 min in 0 . 2% PBT ( 0 . 2% Triton X-100 in PBS ) , incubated with Isolectin GS-IB4 ( #I21411 Life Technologies , Grand Island , NY ) overnight at 4°C , washed 3× for 5 min in PBS , and coverslipped with using ProLong Gold/DAPI antifade reagent ( #P36935 Molecular Probes , Eugene , OR ) . Sections were imaged by fluorescence microscopy using a Nikon Eclipe 80i microscope equipped with a Nikon DS-2 digital camera . Quantification was performed using ImageJ . Vessel coverage delineates the percent of cortical pixel area covered by isolectin-positive pixels while vessel size quantifies the pixel area of each discrete vascular aggregate identified by isolectin staining . E14 . 5 mouse lungs were dissected in cold PBS and minced finely using a razor blade . The tissue was then incubated with plain EBM ( Lonza , Switzerland ) or EBM containing 50 ng/ml VEGF for 15 min at 37°C . Lysis buffer ( 50 mM Tris/HCl [pH 7 . 5] , 150 mM NaCl , 1% Triton X-100 , 2 mM EDTA , and 2 mM DTT ) containing complete proteinase inhibitors ( Roche , Switzerland ) , PhosSTOP ( Roche , Switzerland ) , and sodium orthovanadate was added to the tissue , which was then pulverized with a pestle and incubated for 30 min while rotating at 4°C . Tissue was spun down and protein quantification was performed . The tissue was treated as described in the Western blotting section . HEK293T cells were transfected with the indicated constructs using Lipofectamine-2000 ( Invitrogen , Carlsbad , CA ) . They were then grown in DMEM + 10% fetal bovine serum + 1% Penicillin-Streptomycin and 48 hr after transfection cells were washed and harvested in ice-cold PBS . Cells were lysed using lysis buffer ( 50 mM Tris/HCl [pH 7 . 5] , 150 mM NaCl , 1% Triton X-100 , 2 mM EDTA , and 2 mM DTT ) containing complete proteinase inhibitors ( Roche , Switzerland ) . After 30 min of rotation in the cold room and subsequent centrifugation , protein was quantified and 20 µg of protein was frozen down as input controls . 0 . 5 µg of anti-VEGFR2 antibody ( gift of Procter and Gamble , see Gu et al . , 2003 for details ) was added to 500 µg of protein and rotated in the cold room for 1 hr . Then , 20 µl of protein A/G beads ( Thermo Scientific , Waltham , MA ) were added to the protein and rotated overnight in the cold room . Beads were washed 3× with lysis buffer and two times with wash buffer ( lysis buffer with 300 mM NaCl ) . Protein was eluted by the addition of 2× SDS-PAGE sample buffer and boiling for 10 min . Co-immunoprecipitation was also performed on P7 lung lysates isolated from control and Nrp1VEGF− animals treated with VEGF as described above . Analysis of E14 . 5 mouse embryos were performed on single cells from dissociated lungs . In brief , microdissection techniques were used to isolate the lung . Lungs were then rinsed in PBS and incubated in 2 mg/ml collagenase and 20 μg/ml DNase I 3× for 15 min at 37°C and gently pipetted . The collagenase was inactivated using 5 ml of ice-cold 10% FBS/PBS , centrifuged at 1000×g for 5 min , and suspended in 400 µl of red blood cell ( RBC ) lysis buffer ( Sigma-Aldrich , Natick , MA ) . Following a 5 min incubation at room temperature , 2 ml of ice-cold 5% FBS/PBS was added and cells were centrifuged at 1000×g for 5 min at 4°C . Cells were then blocked in Fc-blocking solution ( #553142; BD ) for 20 min on ice , centrifuged , incubated with the labeled conjugated primary antibodies–PE-anti-CD31 ( PECAM ) ( #553373 BD Pharmingen , Franklin Lakes , NJ ) and APC-anti-Flk1-1 ( VEGFR2 ) ( #560070 BD Pharmingen , Franklin Lakes , NJ ) , for 30 min on ice with agitation every 10 min . After incubation , the cells were spun down , the supernatant was removed , and the cell pellet was resuspend in 1:10K Sytox in PBS/5%FBS . Cells were analyzed on a LSR II Flow Cytometer . Cells incubated with no antibody , APC-anti-Flk1 , or PE-anti-CD31 only served as the control population . Whole-mount retina immunohistochemistry was performed as previously described in Kim et al . , ( 2011 ) . Briefly , eyes were extracted and fixed in 4% paraformaldehyde for 10 min at room temperature . Retinas were dissected in PBS and post-fixed in 4% paraformaldehyde overnight at 4°C . Retinas were then permeabilized in PBS , 1% BSA , and 0 . 5% Triton X-100 at 4°C overnight , washed 2× for 5 min in 1% PBT ( 1% Triton X-100 in PBS ) , and incubated in Isolectin GS-IB4 ( 1:200 , #I21411 Life Technologies , Grand Island , NY ) and anti-αSMA Cy3 ( 1:100 , #C6198 Sigma-Aldrich , Natick , MA ) in 1% PBT overnight at 4°C . Retinas were washed 3× for 5 min and flat-mounted using ProLong Gold antifade reagent ( #P36934 Molecular Probes , Eugene , OR ) . Flat-mounted retinas were analyzed by fluorescence microscopy using a Nikon Eclipe 80i microscope equipped with a Nikon DS-2 digital camera and by confocal laser scanning microscopy using an Olympus FV1000 confocal microscope . Quantification was performed using MetaMorph Image Analysis Software and ImageJ . At least four retinal leaves were quantified per animal to determine the vascular extension ratio , both eyes were examined in each animal for artery number , and three representative images were quantified from each animal for vascular coverage ( representing the total isolectin-positive pixel area per image ) . Ketamine ( 80–100 mg/kg ) and xylazine ( 5–10 mg/kg ) delivered by IP injection were used to anesthetize 12-week old male Nrp1VEGF− and control littermates . After anesthesia was achieved , the bilateral hindlimbs and lower abdomen were cleared of hair and cleaned with 10% betadine and 70% alcohol . An incision of 3–4 mm was made in the right inguinal area to visualize the femoral artery . Two 6–0 silk sutures were tied in the proximal femoral artery and the deep femoral and epigastric artery branches were cauterized . The femoral artery was then ligated between the two sutures . The skin was sutured with one 4–0 prolene sutures . Immediately before and after surgery , each animal was scanned with a non-invasive laser doppler imaging system ( moorLD12-HR Moor Instruments , Wilmington , DE ) under 1–3% isofluorane anesthesia . Blood flow recovery in the hindlimbs was further assessed on 3 , 5 , and 7 days post-surgery and quantified via Moor LDI Software . The standard error of the mean was calculated for each experiment and error bars in the graphs represent the standard error . A paired Student's t-test was used to determine the statistical significance of differences between samples , and the genotype distribution was analyzed using a Chi-square test . Statistical analyses were performed with Prism 4 ( GraphPad Software ) and p values are indicated by * ≤ 0 . 05 , ** ≤ 0 . 01 , and *** ≤ 0 . 001 . | Blood flows through blood vessels to carry oxygen and nutrients towards , and waste away from , the cells of the body . New blood vessels are formed not only during development but also throughout life as part of normal tissue growth and repair . However , blood vessels may also form as a consequence of diseases , such as cancer . For example , tumors often stimulate the growth of new blood vessels to ensure a good supply of blood carrying nutrients and oxygen . As such , some anti-cancer therapies try to stop blood vessels from developing in an attempt to slow down or prevent tumor growth . New blood vessels often form by branching off from existing vessels . One molecule that stimulates this branching process is called vascular endothelial growth factor ( or VEGF for short ) . Three ‘receptor’ proteins found on the outside of cells can bind to the VEGF molecule and then trigger a response inside the cell that guides the development of new blood vessels . VEGF and its receptor proteins—including one called NRP1—are being investigated as a possible target for drugs that could treat cancer and other diseases affecting blood vessels . However , the exact mechanisms that control the formation of new blood vessels are not fully understood , which makes it difficult to develop these treatments . Now Gelfand et al . have created mice whose NRP1 receptors cannot bind VEGF . These mice unexpectedly survive to adulthood and develop normal blood vessels . This outcome is in contrast to mice that lack NRP1 , which normally die as embryos and have severe defects with their nerves and blood vessels . Gelfand et al . instead found that mice that only lack NRP1 in the cells of their blood vessels had less of another receptor protein called VEGFR2 on the surface of these cells . This result suggests that NRP1 controls blood vessel development , not by binding to VEGF but by affecting how much of the VEGFR2 receptor is available to interact with VEGF . These findings challenge the long-held view of how NRP1 functions and lead Gelfand et al . to suggest a new mechanism: NRP1 interacts with VEGFR2 , rather than with VEGF , to control the formation of new blood vessels . Future work will aim to uncover how these interactions regulate the normal development of blood vessels , and if other molecules that bind to NRP1 are involved in this process . Furthermore , these findings may help to guide the on-going efforts to develop drugs that target NRP1 into treatments that are effective against diseases that involve problems with blood vessels—including diabetes , immune disorders , and cancer . | [
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] | 2014 | Neuropilin-1 functions as a VEGFR2 co-receptor to guide developmental angiogenesis independent of ligand binding |
Iron-sulfur ( Fe-S ) clusters are essential for many cellular processes , ranging from aerobic respiration , metabolite biosynthesis , ribosome assembly and DNA repair . Mutations in NFU1 and BOLA3 have been linked to genetic diseases with defects in mitochondrial Fe-S centers . Through genetic studies in yeast , we demonstrate that Nfu1 functions in a late step of [4Fe-4S] cluster biogenesis that is of heightened importance during oxidative metabolism . Proteomic studies revealed Nfu1 physical interacts with components of the ISA [4Fe-4S] assembly complex and client proteins that need [4Fe-4S] clusters to function . Additional studies focused on the mitochondrial BolA proteins , Bol1 and Bol3 ( yeast homolog to human BOLA3 ) , revealing that Bol1 functions earlier in Fe-S biogenesis with the monothiol glutaredoxin , Grx5 , and Bol3 functions late with Nfu1 . Given these observations , we propose that Nfu1 , assisted by Bol3 , functions to facilitate Fe-S transfer from the biosynthetic apparatus to the client proteins preventing oxidative damage to [4Fe-4S] clusters .
A severe syndrome characterized by the dysfunction of multiple mitochondrial enzymes has been described for a series of patients with mutations in four mitochondrial proteins IBA57 , ISCA2 , NFU1 and BOLA3 ( Seyda et al . , 2001; Cameron et al . , 2011; Navarro-Sastre et al . , 2011; Ferrer-Cortès et al . , 2013; Nizon et al . , 2014; Baker et al . , 2014; Debray et al . , 2015; Lossos et al . , 2015; Al-Hassnan et al . , 2015 ) . Patients with this Multiple Mitochondria Dysfunctions Syndrome ( MMDS ) are afflicted with lactic acidosis , nonketotic hyperglycinemia and infantile encephalopathy typically leading to death in their first year of life . The syndrome is associated with an impairment of lipoic acid-dependent 2-oxoacid dehydrogenases arising from defective lipoate synthesis and defects in respiratory complexes I and II in select tissues including muscle and liver . These phenotypes arise from defective iron-sulfur ( Fe-S ) cluster assembly within the mitochondria . The deficiency in protein lipoylation is due to impaired activity of lipoic acid synthetase , which requires two [4Fe-4S] cluster cofactors ( Hiltunen et al . , 2010 ) . The hyperglycinemic phenotype arises from failed lipoylation of the glycine cleavage enzyme . Whereas IBA57 and ISCA2 are known components of the ISA complex , along with ISCA1 , which functions in the formation of [4Fe-4S] clusters within mitochondria ( Mühlenhoff et al . , 2011; Gelling et al . , 2008; Sheftel et al . , 2012 ) , the functions of NFU1 and BOLA3 in Fe-S cluster assembly remain an enigma . Yeast cells lacking Nfu1 are partially compromised in mitochondrial [4Fe-4S] cluster formation , but the defect is not as pronounced as in cells lacking components of the ISA complex ( Isa1 , Isa2 and Iba57 ) ( Navarro-Sastre et al . , 2011; Schilke et al . , 1999 ) . As in patient cells with mutations in NFU1 , yeast nfu1∆ cells have diminished protein lipoylation levels ( Navarro-Sastre et al . , 2011 ) . Humans and yeast have two mitochondrial BolA proteins termed BolA1 ( Bol1 in yeast ) and BolA3 ( Bol3 in yeast ) ( Cameron et al . , 2011; Willems et al . , 2013 ) , but little is known concerning their physiological function . The similarities of phenotypes in patients with MMDS mutations in NFU1 and BOLA3 suggest that BOLA3 may likewise function in mitochondrial Fe-S biogenesis ( Cameron et al . , 2011 ) . Fe-S cluster synthesis within the mitochondria occurs on a scaffold complex and preformed clusters are subsequently transferred to recipient proteins ( Lill et al . , 2012 ) . The initial cluster formed is a [2Fe-2S] cluster assembled on the ISU scaffold complex consisting of five proteins , Nfs1 , Isd11 , Yfh1 , Yah1 and Isu1 ( or Isu2; yeast nomenclature ) ( Lill et al . , 2012; Schmucker et al . , 2011; Tsai and Barondeau , 2010; Lange et al . , 2000; Webert et al . , 2014 ) . The sulfide ions are provided by the Nfs1 cysteine desulfurase , along with its effector proteins Isd11 and Yfh1 ( Tsai and Barondeau , 2010; Lill and Mühlenhoff , 2008; Gerber et al . , 2003; Biederbick et al . , 2006; Bridwell-Rabb et al . , 2014; Parent et al . , 2015; Fox et al . , 2015 ) . Assembled [2Fe-2S] clusters on Isu1 are transferred to the monothiol glutaredoxin Grx5 through the action of the Ssq1 ATPase and the DnaJ protein Jac1 ( Ciesielski et al . , 2012; Majewska et al . , 2013; Uzarska et al . , 2013 ) . Two [2Fe-2S] clusters transferred by Grx5 are condensed into a [4Fe-4S] cluster on the downstream ISA complex ( Isa1 , Isa2 and Iba57 ) prior to transfer to client proteins ( Mühlenhoff et al . , 2011; Gelling et al . , 2008; Sheftel et al . , 2012; Brancaccio et al . , 2014 ) . Nfu1 has been implicated to function as a late Fe-S maturation factor in bacteria and fungi ( Navarro-Sastre et al . , 2011; Bandyopadhyay et al . , 2008; Py et al . , 2012 ) , an alternate scaffold protein for cluster synthesis ( Cameron et al . , 2011; Tong et al . , 2003 ) or as a persulfide reductase associated with the sulfide transfer ( Liu et al . , 2009 ) . The lack of NfuA in Escherichia coli and Azotobacter vinelandii is associated with decreased viability under stress conditions ( Bandyopadhyay et al . , 2008; Py et al . , 2012; Angelini et al . , 2008 ) . Nfu proteins from most species are multidomain proteins . E . coli NfuA and human Nfu1 are two domain proteins with the C-terminal domain containing the functionally important CxxC motif that is known to bind a [4Fe-4S] cluster at a homodimer interface ( Bandyopadhyay et al . , 2008; Tong et al . , 2003; Angelini et al . , 2008; Gao et al . , 2013 ) . The N-terminal domains differ between the E . coli and human proteins and lack a related CxxC motif . Recombinant expression and purification of Azotobacter NfuA or human Nfu1 did not result in Fe-S cluster bound to the purified protein , but in vitro Fe-S reconstitution studies followed by Mössbauer spectral studies demonstrated the presence of a [4Fe-4S] cluster ( Bandyopadhyay et al . , 2008; Py et al . , 2012; Tong et al . , 2003; Angelini et al . , 2008 ) . Synechocystis NifU was reported to bind a [2Fe-2S] cluster ( Yabe et al . , 2004; Nishio and Nakai , 2000 ) , but Mössbauer spectral studies were not done to validate the assignment . The ability of Nfu1 to bind a [4Fe-4S] cluster supported the suggestions that Nfu1 was either an alternative scaffold protein involved in Fe-S cluster formation or involved in a late cluster transfer step . The ability of bacterial NfuA to transfer its cluster to apo-aconitase in vitro is consistent with a role in a late step of cluster transfer ( Bandyopadhyay et al . , 2008; Angelini et al . , 2008 ) . BolA proteins are also known to coordinate Fe-S clusters in conjunction with monothiol glutaredoxins ( Li and Outten , 2012 ) . One of the three BolA proteins in Arabidopsis thaliana BolA1 was shown to bind a [2Fe-2S] cluster in a complex with glutaredoxin ( Grx ) ( Roret et al . , 2014 ) . The cluster associated with the BolA:Grx complex is coordinated by two thiolate ligands , one from Grx and the other from an associated glutathione , and two histidine ligands from BolA1 . Likewise , the cytosolic BolA2 proteins of yeast and humans coordinate [2Fe-2S] clusters at the heterodimer interface with monothiol glutaredoxins ( Li and Outten , 2012; Li et al . , 2012 ) . Little is known about the physiological function of mitochondrial BolA proteins , designated Bol1 and Bol3 . BolA proteins are found only in aerobic species ( Willems et al . , 2013 ) . Depletion of the mitochondrial BolA1 in HeLa cells caused an oxidative shift in the mitochondrial thiol/disulfide redox ratio ( Willems et al . , 2013 ) . We set out to define the functional steps of Nfu1 and two mitochondrial BolA proteins in yeast . We report that Nfu1 and Bol3 function at a late step in the transfer of Fe-S clusters from the ISA complex to mitochondrial client proteins as a protective measure for [4Fe-4S] clusters from oxidative stress damage . In contrast to Bol3 , the related mitochondrial Bol1 shows an interaction with Grx5 but not with the ISA complex or [4Fe-4S] client proteins .
S . cerevisiae cells lacking the mitochondrial Nfu1 protein ( nfu1∆ cells ) are markedly impaired in growth on synthetic complete medium with acetate as a carbon source ( Figure 1A ) . However , the mutant cells display only a slight growth impairment on glycerol/lactate medium , suggesting a partial respiratory growth defect that is exacerbated with acetate as the carbon source . It was previously reported that nfu1∆ cells exhibit specific but partial defects in the formation of [4Fe-4S] clusters analogous to phenotypes seen in patients with mitochondrial dysfunction syndrome ( Navarro-Sastre et al . , 2011; Schilke et al . , 1999 ) . We confirmed the defects in [4Fe-4S] client enzymes reported for nfu1∆ cells showing that aconitase and succinate dehydrogenase ( SDH ) activities are markedly impaired , yet residual activity persists ( Figure 1B ) . Aconitase activity is markedly attenuated in nfu1∆ yeast cells , whereas its activity is not significantly depleted in human nfu1 patients ( Cameron et al . , 2011; Navarro-Sastre et al . , 2011 ) . No defect was observed in the yeast mutant in respiratory complex III , cytochrome bc1 , which requires a [2Fe-2S] cluster in its Rieske Rip1 subunit or in cytochrome oxidase that requires a [2Fe-2S] cluster in Yah1 for heme a formation ( Figure 1B ) . 10 . 7554/eLife . 15991 . 003Figure 1 . Nfu1 functions with both the ISA [4Fe-4S] assembly complex and [4Fe-4S] client proteins . Cells lacking Nfu1 exhibit defects in [4Fe-4S] cluster enzymes in mitochondria . ( A ) Respiratory growth defects revealed by yeast drop-test . Cells harboring empty vectors ( EV ) or high-copy plasmids expressing designated genes were pre-cultured in liquid synthetic complete ( SC ) glucose media lacking uracil . Serially diluted cells ( 10-fold ) were spotted on SC media plates at 30°C . Grx5 is a monothiol glutaredoxin involved in mitochondrial Fe-S biogenesis . Isa1 , Isa2 and Iba57 are subunits of the ISA scaffold complex required for [4Fe-4S] cluster synthesis . Yap1 is a transcription factor that induces expression of anti-oxidant genes . Glu is 2% glucose and Ace is 2% acetate . ( B ) The relative activity of aconitase , SDH , cytochrome bc1 , cytochrome c oxidase ( CcO ) , and malate dehydrogenase ( MDH ) were measured in isolated mitochondria from cells cultured in SC media with 2% raffinose . Data are shown as mean ± SE ( n = 3 ) ( CcO , n = 4 ) . ( C ) Steady-state protein levels measured by SDS-PAGE followed by immunoblotting in isolated mitochondria . Anti-LA antibody is an antibody specific to lipoic acid ( LA ) that is conjugated to proteins . PDH is pyruvate dehydrogenase and KDH is α-ketoglutarate dehydrogenase . Sdh2 is the Fe-S cluster subunit of SDH . Aco1 is mitochondrial aconitase . Por1 is a mitochondrial loading control . ( D ) Restoration of LA moieties on PDH and KDH shown by SDS-PAGE followed by immunoblotting in isolated mitochondria from nfu1∆ cells over-expressing ISA1 and ISA2 . ( E ) Enzymatic activity of SDH in mitochondria isolated from nfu1∆ cells over-expressing ISA1 and ISA2 . Data are shown as mean ± SE ( n = 3 ) . ( F ) Strep-tag affinity purification of Nfu1-Strep revealed the Nfu1 interaction with Isa1 and Isa2 . Mitochondria were solubilized with 0 . 1% n-dodecyl maltoside ( DDM ) . Clarified lysates were incubated with Strep-Tactin superflow beads for 16 hr . After washing , proteins were eluted with 2 . 5 mM desthiobiotin , and then analyzed by immunoblotting . ( G ) Strep-tag affinity purification of Nfu1-Strep in the presence of ectopically expressed Aco2-HA . Nfu1m-Strep is the G/T>H mutant described in Figure 4 . ( H ) Strep-tag affinity purification of Nfu1-Strep in the presence of ectopically expressed Lys4-HA . Lys4 and Aco2 are both nuclear DNA-encoded mitochondrial proteins that require a [4Fe-4S] cluster for each function in the lysine biosynthetic pathway in yeast . Nfu1m-Strep is the G/T>H mutant described in Figure 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 003 Consistent with the known defects of nfu1∆ yeast cells and human nfu1 patients , lipoic acid ( LA ) conjugates on pyruvate dehydrogenase ( PDH ) and oxoglutarate dehydrogenase ( KDH ) were attenuated in nfu1∆ cells ( Figure 1C ) ( Navarro-Sastre et al . , 2011 ) . As mentioned , lipoic acid formation is dependent on the [4Fe-4S] lipoic acid synthase Lip5 ( Hiltunen et al . , 2010 ) . Steady-state protein analysis by SDS-PAGE showed diminished Sdh2 levels , the Fe-S subunit of SDH . Sdh2 contains three distinct Fe-S clusters ( [2Fe-2S] , [4Fe-4S] , and [3Fe-4S] clusters ) , which transfer electrons from the catalytic Sdh1 subunit to ubiquinone . In the absence of Fe-S cluster insertion , Sdh2 stability is compromised ( Kim et al . , 2012 ) ( Figure 1C ) In contrast , the aconitase protein stability is not dependent on the presence of its [4Fe-4S] cluster ( Gelling et al . , 2008 ) . Two enzymes involved in yeast lysine biosynthesis Aco2 and Lys4 contain [4Fe-4S] clusters ( Fazius et al . , 2012 ) . Whereas yeast lacking the ISA complex are auxotrophic for lysine and accumulate homocitrate as a metabolic intermediate , nfu1∆ cells propagate normally in medium lacking lysine and do not accumulate homocitrate as shown by GC-MS metabolomic studies ( data not shown ) . Thus , sufficient [4Fe-4S] cluster synthesis and distribution occurs in nfu1∆ cells for lysine synthesis . The growth defect of nfu1∆ yeast cells on acetate medium was severe , creating an opportunity to conduct screening for genetic suppressors of the respiratory defect . In a screen using transformants with a high-copy yeast DNA library , we isolated respiratory competent vector-borne clones of nfu1∆ BY4741 cells containing NFU1 , ISA2 , and the YAP2 transcriptional activator . Each gene was recloned into yeast vectors and nfu1∆ transformants of both BY4741 and W303 genetic backgrounds were analyzed for growth on acetate medium for respiratory function . Although Isa2 is a component of the mitochondrial ISA heterotrimeric complex comprised of Isa1 , Isa2 and Iba57 , overexpression of Isa2 was the only ISA component capable of partially restoring respiratory growth of nfu1∆ cells on acetate medium ( Figure 1A ) . ISA2 transformants of nfu1∆ cells showed a partial restoration lipoylation of KDH and SDH activity suggesting that the respiratory capacity of the mutant cells was partially restored by elevated Isa2 levels ( p value ~0 . 14 ) ( Figure 1D and E ) . Thus , the respiratory function of Nfu1 can be partially replaced by super-physiological levels of the Isa2 component of the ISA complex . An association of Nfu1 with the mitochondrial ISA complex was suggested by the observed suppression of the respiratory defect of nfu1Δ cells by ISA2 overexpression along with defects in [4Fe-4S] mitochondrial enzymes . We tested if Nfu1 physically interacts with the ISA complex by co-immunoprecipitation studies using a functional C-terminal Strep tagged chimera of Nfu1 . Affinity purification of Nfu1-Strep with Strep-Tactin beads showed co-purification of Isa1 and Isa2 ( Figure 1F ) . In addition to the interaction with Isa1 and Isa2 , Nfu1 associated with three [4Fe-4S] client proteins Aco1 , Aco2 and Lys4 , but not the [2Fe-2S] client protein Rip1 ( Figure 1G and H ) . The partial respiratory function of nfu1∆ cells was also restored by overexpression of Yap2 or its paralogue Yap1 ( Figure 1A ) . Yap1 and Yap2 are transcriptional activators that induce the expression of a battery of antioxidant genes , including thioredoxin , thioredoxin reductase and glutathione reductase , in response to oxidative stress ( Fernandes et al . , 1997 ) . To confirm that the suppression of nfu1Δ cells by the YAP transcription factors was specifically due to a recovery of the [4Fe-4S] centers , we analyzed mitochondria from the transformants to test for restoration of lipoic acid conjugates of PDH and KDH and observed a clear restoration of LA-associated PDH ( Figure 2A ) . The identification of YAP1 and YAP2 as high copy suppressors of nfu1Δ cells suggested a role for Nfu1 during oxidative stress . Consistent with this postulate , the respiratory growth of nfu1Δ cells was partially restored with the addition of the antioxidants , GSH and N-acetyl cysteine ( NAC ) to the growth medium ( Figure 2B ) . These results support a role for Nfu1 during oxidative metabolism . 10 . 7554/eLife . 15991 . 004Figure 2 . Nfu1 has a heighted importance during times of oxidative stress and is expendable in anoxic conditions . Defects in cells lacking Nfu1 are pronounced under oxidative stress conditions . ( A ) Steady-state levels of proteins in isolated mitochondria from nfu1∆ cells harboring high-copy NFU1 plasmids or YAP1 plasmids . ( B ) Yeast drop-test with 5 mM n-acetyl cysteine ( NAC ) and 2 mM glutathione ( GSH ) . Gly/Lac is SC medium with 2% glycerol and 2% lactate as carbon sources . ( C ) Steady-state levels of proteins in isolated mitochondria from cells cultured under normoxic conditions or anaerobic conditions . ( D ) Relative activity of SDH and aconitase in mitochondria from panel C . Data are shown as mean ± SE ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 004 Since Nfu1 is important under oxidative conditions , we tested whether Nfu1 is dispensable during anoxic growth . WT and nfu1Δ cells were cultured to mid-log growth in normoxic or anoxic conditions . Mitochondria isolated from the cells were analyzed by steady-state protein analysis and enzymatic function of various [4Fe-4S] cluster enzymes . As previously described normoxic nfu1Δ cells exhibited the expected marked attenuation in SDH and aconitase activities and reduced lipoic acid adducts; however , the anoxic cells did not exhibit a significant difference between WT and nfu1Δ cells ( Figure 2C and D ) . It should be noted that anoxic WT cells showed a marked reduction in mitochondrial enzymatic activities and steady-state protein levels compared to normoxic WT cells ( ~30% of normoxia ) , yet anoxic nfu1Δ cells did not show a marked further attenuation in SDH and lipoic acid conjugates . Thus , the cells are more dependent on Nfu1 during oxidative metabolism . Nfu1 consists of two domains in addition to the N-terminal mitochondrial targeting sequence ( MTS ) based on sequence homologies ( Figure 3A ) . The N-terminal domain ( NfuN , residues 22–126 ) is only conserved within eukaryote species , while the C-terminal NifU-like domain ( NfuC , residues 143–256 ) is widely conserved in all species and contains the important the Fe-S binding CxxC motif ( Figure 3A ) . To test the functional importance of the two domains , both domains were separately expressed in nfu1Δ cells with the endogenous MTS of Nfu1 ( 1–21 ) to ensure proper delivery to the mitochondrial matrix . 10 . 7554/eLife . 15991 . 005Figure 3 . The CxxC motif of C-terminal domain of Nfu1 is essential for function . The CxxC motif is critical for Nfu1 function . ( A ) A schematic representation of Nfu1 domains . MTS , the mitochondrial targeting sequence; NfuN , the N-terminal domain of Nfu1; NfuC , the C-terminal domain harboring the highly conserved CxxC motif . The human NfuC tertiary structure ( PDB: 2M5O ) and primary sequences showing the CxxC motif ( red ) and adjacent amino acids indicated in partial sequences ( green ) . ( B ) The respiratory growth defect of nfu1∆ cells was rescued with NfuC . Nfu1 , NfuN , and NfuC were all fused with a C-terminal Strep-tag and expressed exogenously using low-copy plasmids . ( C ) Restoration of Nfu1 target proteins by NfuC expression in nfu1∆ cells . ( D ) Respiratory growths of nfu1∆ cells that express Nfu1 sequence variants were tested . All variants were fused with a Strep-tag and expressed on low-copy plasmids . ( E ) Steady-state levels of LA-conjugated proteins and Sdh2 in nfu1∆ cells that express Nfu1 variants . ( F ) BN-PAGE and SDS-PAGE analysis of [4Fe-4S] cluster independent enzymes in the dominant negative backgrounds nfu1Δ + G>C and bol1/3Δ + H101C . ( G ) Strep-tag purification of Nfu1 sequence variants as described in Figure 3B immunoblotting for [4Fe-4S] cluster client proteins Aco1 and Sdh2 . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 00510 . 7554/eLife . 15991 . 006Figure 3—figure supplement 1 . Yeast growth tests evaluating respiratory growth ( Gly/Lac ) of nfu1Δ + G>C cells following treatment with 5’-Fluoroorotic acid ( 5-FOA ) to show cells have not lost their mitochondrial DNA ( rho- ) . 5-FOA was used to induce shedding of the pRS416 vector . Glu-URA is SC media with 2% glucose lacking Uracil to show the successful loss of the pRS416 vector . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 00610 . 7554/eLife . 15991 . 007Figure 3—figure supplement 2 . Affinity purification using Strep-Tactin to immobilize Strep tagged Nfu1 and the Nfu1 AxxA variant expressed ectopically in the BY4743 background with a single copy of Lys4 chromosomally tagged with GFP . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 007 Cells containing only the Nfu1 NfuC domain were capable of respiratory growth on either glycerol/lactate or acetate medium ( Figure 3B ) , whereas cells harboring only the Nfu1N domain failed to propagate . Additionally , cells with the NfuC , but not the NfuN , domain showed normal Sdh2 and lipoic acid levels . Although the NfuN domain failed to restore Nfu1 function , the fragment was well expressed in cells , unlike the functional C-terminal domain that was markedly attenuated in protein stability ( Figure 3C ) . The functionality of the NfuC domain suggests that only minimal levels of Nfu1 are important for function . The NfuN domain exhibited a putative dimeric species , analogous to the intact Nfu1 on the denaturing gels . To further address the functional importance of the NfuC domain , we generated a series of amino acid substitutions within and near the conserved CxxC motif to the full-length Nfu1 protein ( Figure 3A ) . One Nfu1 variant generated had the two cysteinyl residues in the CxxC motif ( highlighted in red in Figure 3A ) replaced with alanines . Cells harboring Nfu1 with the two CxxC cysteinyl residues replaced by alanines exhibited a respiratory growth defect analogous to nfu1Δ cells suggesting a loss-of-function phenotype ( Figure 3D and E ) . The critical role of the CxxC motif cysteines was previously shown in the E . coli NfuA ( Angelini et al . , 2008 ) . A conserved glycine just upstream of the CxxC motif is commonly mutated to a cysteine in patients with MMDS ( Navarro-Sastre et al . , 2011; Nizon et al . , 2014 ) . We generated amino acid substitution of this Gly to Cys or His residues and replaced the conserved threonine between the two Cys residues by a His . Each mutant of Nfu1 was expressed in nfu1Δ cells and tested for function . The most striking substitution was the G>C mutant that mimics the MMDS1 patient allele , which displayed a severe synthetic sick phenotype on glycerol/lactate medium ( Figure 3D ) . SDH biogenesis was markedly decreased and in addition Rip1 levels were low suggesting a block in bc1 biogenesis ( Figures 3E and G , lane 5 ) . This dominant negative phenotype was reversed when cells were plated on medium containing 5-fluoroorotic acid ( 5-FOA ) to shed the URA3-containing plasmid harboring the G194C Nfu1 mutant ( Figure 3—figure supplement 1 ) . Thus , the synthetic phenotype did not arise from mtDNA loss or any other irreversible pleiotropic defects . In addition , co-expression of a wild-type Nfu1 with the G194C Nfu1 mutant failed to restore respiratory growth , demonstrating the dominant negative nature of this mutant ( Figure 3D , bottom panel ) . Although nfu1Δ cells with the G194C mutant retained its mtDNA , mitochondrial translation was likely impaired due to attenuated levels of the assembled F1F0 ATPase on BN-PAGE and Cox2 steady-state levels ( Figure 3F ) . Although the assembled F1F0 ATPase complex is markedly diminished , the steady-state levels of Atp2 in the F1 sector are normal . Cells impaired in lipoic acid formation are deficient in tRNA processing by RNase P leading to attenuation in mitochondrial translation ( Schonauer et al . , 2008; Hiltunen et al . , 2009 ) . Diminished mitochondrial translation of the bc1 cytochrome b subunit would account for the reduced Rip1 levels observed ( Figure 3F and G ) . In contrast to cells harboring the Nfu1 G194C patient mutation , nfu1∆ cells have normal F1F0 ATPase levels on BN-PAGE and Cox2 steady-state levels suggesting that mitochondrial translation is normal without Nfu1 . We tested whether the dominant negative effect arises from changes in interactions between Nfu1 and client proteins . We performed affinity purification of Nfu1-Strep on Strep-Tactin beads for the WT and mutant alleles . The loss-of-function AxxANfu1mutant failed to show a detectable interaction with Aco1 ( Figure 3G , lane 8 ) and was impaired in its interaction with Lys4 ( Figure 3—figure supplement 2 ) . In contrast , the G194C Nfu1mutant exhibited an enhanced interaction with Aco1 ( Figure 3G , lane 10 ) . An interaction with Sdh2 is unclear , since Sdh2 levels are markedly depleted in G194C Nfu1 cells . These data show the functional importance of the NfuC domain and its CxxC motif . MMDS2 patients have been reported to have mutations in the mitochondrial BOLA3 protein ( Seyda et al . , 2001; Cameron et al . , 2011; Baker et al . , 2014 ) . The clinical phenotypes of patients with mutations in NFU1 or BOLA3 were similar with neurological regression , infantile encephalopathy and hyperglycinemia ( Cameron et al . , 2011; Navarro-Sastre et al . , 2011 ) . In addition , biochemical defects in protein lipolyation and succinate dehydrogenase were observed . Due to the clinical and biochemical similarities in mutant NFU1 or BOLA3 patients , we tested the function of the yeast BOLA3 homolog , Bol3 , and the related Bol1 protein ( Figure 4A ) . In human cells , BOLA1 and BOLA3 are known to be mitochondrial proteins ( Willems et al . , 2013 ) . We confirmed that Bol1 and Bol3 are likewise localized within the mitochondria of yeast cells ( data not shown ) . Yeast devoid of either Bol1 or Bol3 lacks a clear respiratory phenotype , but a double bol1Δbol3Δ null strain displayed a growth defect on acetate medium and to a lesser extent on glycerol/lactate medium ( Figure 4B ) . Mitochondria isolated from single mutants and the double null mutant were used for biochemical characterization studies . As with nfu1Δ cells , protein lipoylation was partially impaired in KDH in the bol3∆ null , but the defect in KDH lipoylation was enhanced in the bol1Δbol3Δ null strain ( Figure 4C ) . SDH and aconitase activities were depressed in the double null strain , but not significantly changed in the individual single mutants ( Figure 4D ) . The attenuation of aconitase activity in both bol1Δbol3Δ null and nfu1Δ cells is in contrast to BOLA3 and NFU1 patient mutant cells . A modest attenuation was seen in bc1 activity in the bol1Δbol3Δ null strain , but this was not observed in nfu1Δ cells . 10 . 7554/eLife . 15991 . 008Figure 4 . The Mitochondrial Bol1 and Bol3 proteins function in Fe-S biogenesis . Bol1 and Bol3 play roles in Fe-S cluster biogenesis in mitochondria ( A ) Partial sequences of yeast and human mitochondrial BolA proteins . Boxed are conserved motifs with proposed ISC ligands that were mutated in this work . ( B ) Respiratory growth defects of bol1∆ cells , bol3∆ cells and bol1∆bol3∆ double mutants and complementation by plasmid-borne BOL1 or BOL3 . ( C ) Steady-state levels of LA-conjugated proteins and Sdh2 in cells lacking Bol1 and/or Bol3 . ( D ) Relative activity of SDH , cytochrome bc1 complex and aconitase were measured . Data are shown as mean ± SE ( n=3 ) . ( E ) Observation of LA moieties on PDH and KDH and Sdh2 steady-state levels by SDS-PAGE followed by immunoblotting in isolated mitochondria from bol1/3∆ cells over-expressing the indicated Fe-S cluster gene . ( F ) Respiratory function of Bol1 and Bol3 sequence variants in conserved residues were examined by yeast drop-test . All Bol1 variants were fused with a C-terminal Strep-tag and expressed on low-copy plasmids . All Bol3 variants were fused with a N-terminal Strep-tag between the MTS and the remainder of the protein and expressed on low-copy plasmids . ( G and H ) Steady-state levels of LA-conjugated proteins in cells lacking Bol1 and Bol3 with Bol1 variants ( G ) and Bol3 variants ( H ) exogenously expressed . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 00810 . 7554/eLife . 15991 . 009Figure 4—figure supplement 1 . Yeast growth tests evaluating the viability of cells expressing mitochondrial Bol1 and Bol3 N-terminal ligands mutated to lysine in the bol1/3Δ background . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 009 Since the respiratory growth defect of nfu1Δ cells was partially suppressed by overexpression of ISA2 , we tested whether overexpression of a series of late mitochondrial Fe-S cluster assembly genes would likewise rescue the respiratory defect of bol1Δbol3Δ cells . No growth restoration was observed on acetate or glycerol/lactate media , and only minimal lipoylation of PDH and KDH was observed in cells harboring elevated levels of Grx5 , Isa1 , and Isa2 ( Figure 4E ) . BolA proteins are implicated in binding Fe-S clusters . Whereas Nfu1 is known to bind a [4Fe-4S] cluster at the homodimer interface , BolA proteins have been shown to bind [2Fe-2S] clusters in association with glutaredoxins as heterodimers ( Li and Outten , 2012; Roret et al . , 2014; Li et al . , 2012 ) . We evaluated the roles of potential Fe-S cluster ligands in Bol1 and Bol3 . Bol1 has conserved His56 and His93 residues ( Figure 4A ) , which in the case of Arabidopsis thaliana BolA1 the corresponding His residues are apparent ligands to a [2Fe-2S] cluster in association with a monothiol glutaredoxin ( Roret et al . , 2014 ) . Bol3 has conserved Cys64 and His101 residues in corresponding loops to that of Bol1 and are expected to serve as ligands for a Fe-S cluster . We replaced the conserved histidine residues with alanines or cysteines and tested phenotypic effects . We observed that the C-terminal His in each BolA protein was important for the respiratory growth of cells ( Figure 4F , G and H ) . Whereas the H101A Bol3 mutant was non-functional , the variant containing a H101C substitution exhibited a synthetic sick phenotype in that the respiratory growth on glycerol/lactate medium was more impaired relative to the starting bol1Δbol3Δ null strain ( Figure 4F and H lane 6 ) . The Bol3 C64A mutant was only a partial loss-of-function allele . In contrast , the Bol1 H93A or H93C mutants exhibited similar loss-of-function phenotypes without any observed dominant negative effects . The upstream Bol1 H56A mutant retains function , but the H56C allele was a partial loss of function mutant ( Figure 4F and G , lane 5 ) . Since substitutions in the upstream conserved His56 in Bol1 and Cys64 in Bol3 failed to yield a significant phenotype , they may not contribute to a candidate FeS cluster binding . To confirm this prediction , we converting the His56 in Bol1 and Cys64 in Bol3 to lysine residues to create electrostatic repulsion to a candidate FeS cluster Fe atom . Bol1 H56K and Bol3 C64K mutants did not exhibit an enhanced phenotype ( Figure 4—figure supplement 1 ) ruling out that they are important FeS cluster ligands . Together , these data show a functional importance of Bol1 and Bol3 in mitochondrial Fe-S cluster biogenesis and highlights the need for the C-terminal conserved His in each protein for physiological function . Bol1 and Bol3 , like Nfu1 , are not essential for mitochondrial Fe-S protein biogenesis , as a bypass exists enabling limited respiratory growth on glycerol/lactate medium . To glean further insights into the function of Nfu1 , Bol1 and Bol3 in mitochondrial Fe-S cluster biogenesis , we performed proteomic analyses on affinity purified Nfu1 , Bol1 and Bol3 proteins with each expressed as Strep fusions . Purification of each protein was accomplished on Strep-Tactin resin and protein eluates were analyzed by mass spectrometry . Multiple independent proteomic analyses were conducted on WT proteins as well as mutant proteins of each ( G/T>H Nfu1 , H93C Bol1 and H101C Bol3 ) ( Figure 5A and B; Figure 5—source data 1 ) . Of the mutant proteins , BolA3 H101C was synthetic sick in the bol1Δbol3Δ null strain ( Figure 4F ) ; Nfu1 G/T>H mimicked the severe dominant negative mutant , G194C found in patients , however the substitutions were less detrimental to growth ( Figure 3D ) ; and the Bol1 H93C variant was a loss-of-function mutant without a dominant negative characteristic ( Figure 4F ) . Inspection of datasets of protein interactors revealed a common set of [4Fe-4S] client proteins associating with both Nfu1 and Bol3 . These include Aco1 , Aco2 , Lys4 , Sdh2 , Lip5 and Bio2 . For all client proteins except Sdh2 , the observed total spectral count that was markedly higher for clients purified with mutant Nfu1 and Bol3 variants ( Figure 5A and Figure 5—source data 1 ) . Additionally , the mutant forms of Bol3 and Nfu1 both co-purified with the ISA complex component , Isa2 ( Figure 5B and Figure 5—source data 1 ) . The physical interactions of Nfu1 with the clients , Aco1 , Lys4 , Aco2 and Sdh2 , and with the ISA complex are consistent with the results shown by affinity purification experiments followed by SDS-PAGE and immunoblotting ( Figure 1F , G and H ) . Two Bol3 pulldown studies revealed limited levels of copurified Nfu1 , although we never observed Bol3 in the pulldown of Nfu1 ( Figure 5—source data 1 ) . 10 . 7554/eLife . 15991 . 010Figure 5 . Proteomic analysis of Nfu1 , Bol1 and Bol3 establishes function within mitochondrial Fe-S for Bol1 and Bol3 . ( A and B ) Percentages of spectral counts identified by MS proteomics . Percentages were calculated by the number of spectral counts identified for a denoted protein in an individual Strep-tagged protein divided by the total number of spectral counts for that protein identified from all seven samples . Strep-tagged proteins were expressed from low-copy plasmids in corresponding single deletion mutants . Samples were Strep-affinity purified as in Figure 3 . Bol1m is the H93C variant . Bol3m is the H101C variant . Nfu1m is the G/T>H variant . WT is wild-type BY4741 expressing an empty vector . All were fused with a C-terminal Strep-tag . WT is BY4741 wild type harboring a low-copy empty plasmid . ( C ) Human GLRX5 or NFU1 were used in apo- and holo- form and mixed at increasing concentrations with 200 nM fluorescently labelled BOLA1 or BOLA3 . Microscale thermophoresis were performed and dissociation constants ( Kd ) were determined . Error bars indicate the SD ( n=3 ) . ( D ) Strep-tag affinity purification of Nfu1-Strep in the presence of ectopically expressed Ilv3-FLAG . ( E ) Affinity purification using Strep-Tactin agarose beads to purify Nfu1-Strep from an nfu1∆ background expressing either WT Aco1 or Aco1 AxxA mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 01010 . 7554/eLife . 15991 . 011Figure 5—source data 1 . ( Table 1 ) Spectral counts , unique peptides , and coverage of mitochondrial Fe-S client proteins , bait proteins , and Fe-S assembly machinery identified by MS proteomics . Bol1m is the H93C variant . Bol3m is the H101C variant . Nfu1m is the G/T>H variant . ( Table 2 ) Spectral counts , unique peptides , and coverage of mitochondrial Fe-S client proteins ( Aco1 and Lip5 ) , Fe-S assembly machinery protein ( Grx5 ) and the mitochondrial peroxiredoxin Prx1 comparing two unique biological replicates by affinity purification and subsequent MS analysis . Bol1m is the H93C variant . Bol3m is the H101C variant . Nfu1m is the G/T>H variant . Prx1 was a reproducible interactor with Bol1 , but the significance of this interaction remains to be established . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 01110 . 7554/eLife . 15991 . 012Figure 5—figure supplement 1 . Interaction studies of human BOLA proteins with GLRX5 . ( A ) UV–visible absorption spectrum of apo-GLRX5 ( black line ) and chemically reconstituted GLRX5 ( dashed line ) . Reconstituted human GLRX5 ( 100 µM ) showed absorption bands at 320 nm and 425 nm besides the protein absorption at 280 nm , characteristic for the [2Fe–2S] cluster bound to GLRX5 . ( B ) Iron and sulfide determination of chemically reconstituted GLRX5 . Reconstituted human GLRX5 contains about 0 . 85 Fe2+ and 0 . 8 S2- per monomer indicating a bridging [2Fe-2S] cluster between two GLRX5 monomers . ( C–F ) Quantification of the interaction between the human apo- or holo-GLRX5 and the BOLA proteins . Microscale thermophoresis was performed using the indicated fluorescently labeled human BOLA proteins and apo-GLRX5 ( C;E ) or holo-GLRX5 ( D;F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 01210 . 7554/eLife . 15991 . 013Figure 5—figure supplement 2 . Ilv3 activity assay using wild-type and nfu1Δ purified mitochondria along with wild-type overexpressing Ilv3 as a control . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 013 Unlike Bol3 , Bol1 purification did not lead to appreciable co-purification of [4Fe-4S] client proteins , but Grx5 was isolated as a reproducible interactor with WT but not the loss-of-function H93C Bol1 mutant ( Figure 5B ) . Grx5 was a significantly less abundant interactor with Bol3 or Nfu1 . Human BOLA1 was previously shown to associate with Grx5 in HEK293 cells ( Willems et al . , 2013 ) . We conducted in vitro experiments to verify the observed selective interaction of Bol1 with Grx5 using human orthologs . Solution binding analyses were conducted using microscale thermophoresis , which assesses molecular diffusion in a microscopic temperature gradient . Due to their higher stability , we used the human proteins . Apo-GLRX5 or the holo-GLRX5 dimer containing the bridging [2Fe-2S] cluster were incubated with either recombinant human BOLA1 or BOLA3 proteins for assessment binding ( Figure 5C and Figure 5—figure supplement 1 ) . Holo-GLRX5 associated with BOLA1 with a 50-fold greater affinity than with BOLA3 , while similar affinities of apo-GLRX5 were observed for the two human BOLA proteins . As a control , no significant interaction of the BOLA proteins with the human [2Fe-2S] ferredoxin FDX2 was observed . Together , these results indicate a strong preference of BOLA1 for the holoform of GLRX5 . We predict , based on the yeast Bol1 H93C mutant , that BOLA1:GLRX5 interaction is mediated by a [2Fe-2S] cluster . The Nfu1 and Bol3 proteomics experiments did not identify any novel mitochondrial [4Fe-4S] cluster client proteins . Interestingly , the [2Fe-2S] enzyme dihydroxyacid dehydratase ( Ilv3 ) was recovered in multiple independent mass spectrometry analyses in Nfu1 and Bol3 samples . However , we were unable to verify that interaction when using a FLAG-tagged Ilv3 chimera in the Nfu1-Strep affinity capture ( Figure 5D ) . Furthermore , enzymatic activity of Ilv3 was not altered in nfu1∆ cells ( Figure 5—figure supplement 2 ) . Thus , Nfu1 does not appear to be important for the function of the [2Fe-2S] Ilv3 enzyme . We sought to address whether the binding of Nfu1 to a client protein was mediated through a bridging [4Fe-4S] cluster . Aco1 binds its [4Fe-4S] cluster through a conserved CxxC motif and one distant Cys residue in the primary sequence . We generated a double AxxA mutant and tested its ability to associate with Nfu1-Strep . No difference in binding was observed between WT and the AxxA Aco1 proteins with Nfu1 ( Figure 5E ) . The distinct overlap of [4Fe-4S] client protein interactors between Bol3 and Nfu1 suggested a potential overlap or partnership in the function of the two proteins in late step [4Fe-4S] cluster transfer . We tested whether a genetic linkage exists between the proteins by evaluating whether a synthetic phenotype exists in cells lacking Bol1 , Bol3 and Nfu1 . The triple deletion cell ( bol1Δbol3Δnfu1Δ , designated bΔΔnfu1Δ ) exhibited a strong synergistic growth defect on glycerol/lactate medium ( Figure 6A ) . While the defects are too severe to see the synergism by protein lipolyation and Sdh2 steady-state levels , the enzymatic activities of SDH and aconitase do reflect a synergistic effect ( Figure 6B and C ) . In addition , the level of assembled F1F0 ATPase was markedly reduced in the triple mutant ( Figure 6D ) . The severity of the phenotype and the impairment in F1F0 ATPase in the triple mutant likely arises from reduced mitochondrial translation likely through RNaseP , similar to the dominant negative Nfu1 G>C mutant discussed above ( Figure 3F ) . The growth defect of the triple mutant can be partially rescued by re-expression of BOL1 or NFU1 , but not by BOL3 ( Figure 6E ) . This may suggest that Bol3 requires Nfu1 for its function . 10 . 7554/eLife . 15991 . 014Figure 6 . Nfu1 and Bol3 function together in [4Fe-4S] delivery . ( A ) Exacerbated respiratory growth defects of bol1∆bol3∆nfu1∆ triple mutants ( designated bΔΔnfu1Δ ) compared to nfu1∆ single mutants and bol1∆bol3∆ double mutants on non-fermentable carbon sources . ( B ) Steady-state levels of LA-conjugated proteins and Sdh2 in the absence of Bol1 , Bol3 or Nfu1 . ( C ) Relative activity of SDH and aconitase in the absence of Bol1 , Bol3 or Nfu1 . Data are shown as mean ± SE ( n=3 ) ( D ) BN-PAGE and SDS-PAGE analysis of [4Fe-4S] cluster independent enzymes in the bΔΔnfu1Δ triple deletion mutant background . F1β is a subunit of ATP synthase . ( E ) Respiratory growth of bΔΔnfu1Δ triple mutants harboring plasmid-borne BOL1 , BOL3 and NFU1 , respectively . ( F ) Strep-tag purification of Nfu1-Strep in the absence of Bol1 and Bol3 . ( G ) Strep-tag purification of Nfu1-Strep in the absence of Isa2 . ( H ) Steady-state levels of Bol1-Strep ( upper panel ) and Bol3-Strep ( bottom panel ) in response to overexpression of genes as indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 01410 . 7554/eLife . 15991 . 015Figure 6—figure supplement 1 . SDS-PAGE followed by immunoblotting to evaluate the different steady state levels of Nfu1-Strep , Bol1-Strep , and Bol3-Strep while being expressed under the same heterologous MET25 promoter and CYC1 terminator . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 015 Affinity purification of Nfu1-Strep expressed in the bol1Δbol3Δnfu1Δ triple null mutant was carried out to test the effect of loss of the two BolA proteins on the interaction of Nfu1 with [4Fe-4S] client proteins . As can be seen in Figure 6F there was enhanced co-purification of Aco1 in the absence of Bol1 and Bol3 . Likewise , a similarly enhanced interaction between client proteins and Nfu1 was apparent in cells lacking a functional ISA complex in isa2∆ cells ( Figure 6G ) . These data are consistent with a role of Nfu1 in [4Fe-4S] cluster transfer from the ISA complex to client proteins . Given the strong genetic interaction between the mitochondrial BOLA genes and NFU1 , we attempted to substantiate the linkage . The proteomic results suggested an association of Bol1 and Grx5 , whereas Bol3 was associated with Nfu1 function . Mitochondrial BolA proteins are low abundance molecules ( Figure 6—figure supplement 1 ) making co-immunoprecipitation studies challenging . Because of this , we tested whether increasing the levels of candidate interacting proteins would alter the abundance of Bol1 or Bol3 . As can be seen in Figure 6H , the steady-state levels of Bol3 , but not Bol1 , were dramatically increased in cells with elevated levels of Nfu1 . Additionally , ISA1 and ISA2 overexpression resulted in a modest increase in Bol3 , but not Bol1 , protein levels . In contrast , Grx5 overexpression led to a marked enhancement in Bol1 levels without altering Bol3 ( Figure 6H ) . In these studies Strep-chimeras of Bol1 and Bol3 were expressed from heterologous promoters , so the changes in protein levels are likely occurring through post-transcriptional stabilization . These stabilization experiments corroborate the genetic and proteomic experiments , all of which suggest that Bol3 ( BOLA3 ) functions with Nfu1 in [4Fe-4S] cluster transfer to client proteins and Bol1 functions with Grx5 for a yet to be determined purpose . Mitochondrial lysates were subjected to gel filtration studies to assess the extent of interaction between Nfu1 and Bol3 . Mitochondrial lysates prepared from either Nfu1-Strep or Bol3-Strep cells were separately chromatographed and fractions were assayed for Nfu1 or Bol3 . The bulk of Nfu1 eluted in fractions corresponding to a globular mass of ~47 kDa , consistent with a homo-dimeric complex ( Figure 7A ) . Nfu2 from Arabidopsis is a dimeric species both as an apo-protein and with a [4Fe-4S] cluster ( Gao et al . , 2013 ) . In contrast , Bol3 eluted predominantly in fractions corresponding to a globular mass of 13 kDa consistent with a monomeric protein . No significant co-elution was observed between Nfu1 and Bol3 , indicating that any Nfu1/Bol3 interaction is transient in nature . An additional set of chromatographic studies were done with mitochondrial lysates containing Nfu1-Strep in which lysates were treated with either 0 . 1 mM DTT or 2 mM dithionite to assess whether the apparent Nfu1 dimer was a disulfide-linked homo-dimer or a Fe-S cluster bridged complex ( Figure 7B ) . The abundance of Nfu1 in fraction 13 assessed by immunoblotting indicated that the elution properties of Nfu1 were unaffected by preincubation with DTT ( followed by alkylation of cysteines by Iodoacetamide ) , whereas treatment with dithionite attenuated the apparent dimeric complex abundance . Fe-S clusters are susceptible of disassembly with dithionite treatment , suggesting that a significant fraction of steady-state Nfu1 in WT yeast mitochondria may be Fe-S loaded . In support of this conclusion is the observation that the Nfu1 AxxA mutant fractionates predominantly as a monomer . 10 . 7554/eLife . 15991 . 016Figure 7 . Nfu1 exists as a dimer bridged by a Fe-S cluster . ( A ) Immunoblotting of fractions from nfu1∆ + Nfu1-Strep or bol3∆ + Bol3-Strep lysates separated by size exclusion chromatography . Molecular weight standards [bovine serum albumin ( BSA ) , carbonic anhydrase ( CA ) and cytochrome c] are displayed above the corresponding fractions were used to create a standard curve to calculate apparent molecular weights . Fraction 13 has an apparent molecular weight of 47 . 6 KDa and Fraction 26 has an apparent molecular weight of 13 . 2 kDa . ( B ) Immunoblotting of fraction 13 from nfu1∆ + Nfu1-Strep lysates pretreated with nothing , 100 mM DTT followed by 200 mM iodoacetamide , or 2 mM dithionite followed by 200 mM iodoacetamide were separated by size exclusion chromatography . ( C ) GAL-NFS1 shutdown was induced over 8 hr with over-expression of NFU1 , BOL3 , and ACO1 and LA levels were observed by immunoblotting . Nfu1 protein levels were reduced with increasing amounts of methionine by utilizing a MET25 promoter is repressed with excess methionine ( 1x = 0 . 6 mM methionine ) . ( D ) A working model of late stage mitochondrial iron-sulfur cluster biogenesis and delivery . Two potential pathways of cluster transfer are shown in A and B . DOI: http://dx . doi . org/10 . 7554/eLife . 15991 . 016 If Nfu1 exists in a FeS-loaded conformer , the question arose whether [4Fe-4S]-Nfu1 serves as a reservoir of [4Fe-4S] clusters for client proteins . We specifically focused on Lip5 that catalyzes formation of lipoic acid . In its catalytic cycle , one of its [4Fe-4S] clusters is consumed to provide two sulfur atoms needed to generate lipoic acid ( Cicchillo and Booker , 2005; Cicchillo et al . , 2004 ) . Thus , [4Fe-4S] cluster regeneration is needed to support Lip5 catalysis and lipoic acid levels . To address a role of Nfu1 in cluster regeneration in Lip5 , we utilized cells containing a chromosomal NFS1 under the control of GAL1 promoter enabling glucose-mediated repression of Nfs1 expression . The GAL-NFS1 strain was transformed with an empty vector or a vector containing either NFU1 under the control of the regulatable MET25 promoter , BOL3 or ACO1 . If Nfu1 were a reservoir of [4Fe-4S] clusters , we predicted that elevated levels of holo-Nfu1 would enable sufficient [4Fe-4S] cluster transfer to Lip5 to support lipoic acid formation in cells depleted of Nfs1 . Cells pre-cultured in galactose were shifted to glucose-containing medium to repress NFS1 expression and mitochondrial lysates were collected 8 hr later ( Figure 7C ) . The lipoic acid level in pyruvate dehydrogenase was reduced , rather than maintained , in cells expressing elevated levels of Nfu1 ( low 1x methionine cultures ) ( Figure 7C , lane 8 , top band ) , but unaffected in cells overexpressing Bol3 ( lane 11 ) . Overexpression of the aconitase mimicked the reduced lipoic acid levels seen with elevated Nfu1 ( lane 12 ) . These results do not support a reservoir function of [4Fe-4S]-Nfu1 and suggest that elevated levels of a [4Fe-4S] protein , e . g . [4Fe-4S] Nfu1 , may negatively impair routing [4Fe-4S] transfer to Lip5 for lipoic acid formation .
A role of Nfu1 in Fe-S cluster biogenesis has long been implicated ( Jacobson et al . , 1989; Schilke et al . , 1999 ) ; however , its molecular mechanism has not been definitely established . Patients harboring mutations in NFU1 , as well as BOLA3 , exhibit biochemical abnormalities in a set of [4Fe-4S] enzymes leading to speculation that Nfu1 , and BolA3 , function as a late Fe-S maturation factor ( Navarro-Sastre et al . , 2011; Py et al . , 2012 ) or that Nfu1 is an alternate Fe-S cluster synthesis scaffold protein used for a subset of specific Fe-S client proteins ( Cameron et al . , 2011; Tong et al . , 2003 ) . The phenotypic similarity between Nfu1 and BolA3 mutations suggests the two proteins function in a common step of the Fe-S protein maturation pathway . We demonstrate in studies using yeast as a model system that the yeast orthologs of human NFU1 and BOLA3 function in a late step of transfer of [4Fe-4S] clusters to specific client proteins . Yeast lacking Nfu1 are partially deficient in the [4Fe-4S] enzymes aconitase , succinate dehydrogenase and lipoic acid synthase . The defect in lipoic acid synthase is highlighted by the pronounced defect in protein lipoylation in mitochondria . The defect in yeast lacking Bol3 is modest , but is exacerbated in cells lacking both mitochondrial Bol3 and Bol1 . The double null cells show related partial defects in [4Fe-4S] enzymes aconitase , succinate dehydrogenase and lipoic acid synthase , although the defects are not as pronounced as in nfu1∆ cells . Yeast lacking all three proteins Nfu1 , Bol1 and Bol3 show an exaggerated phenotype approaching the defect seen in cells lacking the ISA complex required for [4Fe-4S] cluster synthesis . Clearly , nfu1∆ cells do not exhibit any defects in enzymes dependent on [2Fe-2S] centers , suggesting that Nfu1 functions in the [4Fe-4S] cluster transfer pathway . Our systematic approach to identify endogenous binding partners of Nfu1 , Bol1 and Bol3 revealed the step in Fe-S cluster biogenesis in which they function . Affinity purification of Nful1 coupled with mass spectrometry led to the identification of [4Fe-4S] client proteins as physically associating proteins of Nfu1 . It is of interest that the G194C Nfu1 variant exhibiting a partial dominant negative effect showed enhanced interaction with the same client proteins . This yeast mutant mimics the known G208C patient mutation in human NFU1 that causes MMSD . Recombinant Nfu1 has been shown to bind a [4Fe-4S] cluster and 55Fe in vivo labeling studies showed a strong increase in 55Fe binding by the patient mimic G194C Nfu1 yeast variant ( Navarro-Sastre et al . , 2011 ) . Moreover , it is also noteworthy that Gly194 is in juxtaposition to the CxxC motif , which has been shown to bind Fe-S clusters . Therefore , it is plausible that the dominant negative effect of the G194C Nfu1 variant may result from the inefficient release of [4Fe-4S] clusters from the Nfu1 variant to client proteins . The dramatic phenotype of cells harboring G194C Nfu1 is likely due to secondary effects of impaired lipoic acid formation . As mentioned , yeast lacking enzymes involved in octanoic acid formation or lipoic acid synthase are deficient in tRNA processing by RNase P leading to attenuation in mitochondrial translation ( Schonauer et al . , 2008; Hiltunen et al . , 2009 ) . Consistent with impaired RNase P function , G194C Nfu1 cells are markedly attenuated in levels of the F1F0 ATPase and Cox2 steady-state levels . In contrast , the RNase P function is normal in either nfu1∆ cells or bol1∆bol3∆ cells based on normal F1F0 ATPase assembled complexes . The physical interactions of Nfu1 with Isa1 and Isa2 corroborate our model that Nfu1 functions in [4Fe-4S] cluster transfer to client proteins . Interestingly , we isolated Isa2 as a suppressor of the respiratory defect of nfu1∆ cells . Whereas the condensation of two [2Fe-2S] to form a single [4Fe-4S] cluster requires the participation of Isa1 , Isa2 and Iba57 , Isa2 is capable of forming homo-dimers that may exert a limited transfer function as proposed for Nfu1 . The same [4Fe-4S] client proteins were pulled down in affinity purification of Bol3 , but not Bol1 , compared to proteins interacting with Nfu1 . In the case of Bol3 , the dominant negative H101C Bol3 variant also showed enhanced interactions with [4Fe-4S] client proteins . The dominant negative phenotype of the H101C Bol3 mutant ( putative Fe-S ligand ) but only loss of function phenotype for the H101A mutant is consistent with a model that Bol3 His101 participates in Fe-S cluster transfer . The partial deficiency of [4Fe-4S] enzyme activities in nfu1∆ cells suggests that the function of Nfu1 may be conditionally important in [4Fe-4S] cluster transfer and that a bypass mechanism exists in yeast . We demonstrate that Nfu1 in yeast has a heightened importance in cells undergoing oxidative metabolism as opposed to anoxic metabolism . In addition , nfu1∆ cell growth defect is partially suppressed with supplemental GSH in the growth medium . Identification of the Yap1 and Yap2 , transcription factors that are important for oxidative stress tolerance , as high copy suppressors emphasized the importance of Nfu1 during oxidative metabolism . One curiosity is that human patients with mutations in NFU1 or BOLA3 lack defects in mitochondrial aconitase , whereas the yeast mutants , nfu1∆ and the double bol1∆ bol3∆ , exhibit a partial aconitase defect . There are two implications of this result . First , Nfu1 may exhibit different client selectivity in the actual transfer of [4Fe-4S] clusters . Although Nfu1 binds many [4Fe-4S] client proteins , it may facilitate cluster transfer to select clients and this may differ between human and yeast cells as in the case of aconitase . This postulate is supported by the observed role for Nfu1 in Aco1 and Lip5 activation , but not the function of Aco2 and Lys4 . Second , since the partial respiratory function persists in nfu1∆ cells , Nfu1 may facilitate cluster transfer in oxidative growth conditions and this may differ between yeast and human cells . One dramatic phenotype in human and yeast Nfu1 mutant cells is impaired protein lipoylation . Yeast and human cells require lipoylation on E2 subunits of pyruvate dehydrogenase , 2-oxoglutarate dehydrogenase and the glycine cleavage enzyme complex . In addition , the human branched chain 2-oxoacid dehydrogenase requires lipoylation for function . Lip5 catalyzing formation of the lipoate coenzyme binds two [4Fe-4S] clusters , one of which serves as the sulfur donor for lipoic acid formation in a radical S-adenosylmethionine dependent reaction ( Cicchillo and Booker , 2005; Cicchillo et al . , 2004 ) . Two sulfide ions from this auxiliary cluster are used for formation of lipoate resulting in disassembly of the cluster . Each catalytic cycle of the enzyme requires repair or replacement of the auxiliary cluster ( Cronan , 2014 ) . Nfu1 may have a specialized role in cluster repair in lipoic acid synthase or alternatively provides a [4Fe-4S] replacement . For most [4Fe-4S] client proteins , Nfu1 appears to have evolved to shield its [4Fe-4S] cluster from endogenous oxidants during the cluster transfer step . Oxidants are generated by 2-oxoacid dehydrogenases ( Boutigny et al . , 2013 ) , so Nfu1-mediated cluster transfer may be critical to ensure intact [4Fe-4S] insertion . Nfu1 may also serve as a chaperone of apo-client protein , preventing their aggregation in the absence of a bound Fe-S cluster . Additional studies are necessary to define these candidate roles . Bol3 , but not Bol1 , was found to associate with [4Fe-4S] client proteins , whereas Bol1 reproducibly associated with Grx5 both in in vivo and in vitro studies . BolA:glutaredoxin complexes reported to date only bind [2Fe-2S] clusters ( Li and Outten , 2012 ) . Thus , Bol1 is anticipated to function with Grx5 in a [2Fe-2S] cluster step , whereas Bol3 is likely to function , independent of Grx5 , in a Nfu1-mediated [4Fe-4S] cluster step . These studies suggest that Bol1 and Bol3 have specialized functions within the same pathway , such that cells lacking both Bol1 and Bol3 have a synthetic defect . In summary , the present work suggests that Nfu1 has a significant role in a late step transfer of [4Fe-4S] clusters to select client proteins . Nfu1 binds the client proteins independent of the ISA complex and its association with the ISA complex may serve to recruit apo-clients to the ISA complex where [4Fe-4S] clusters are formed ( Figure 7D ) . Some [4Fe-4S] client proteins may get their [4Fe-4S] cluster directly from the ISA complex , whereas others may derive their clusters after prior transfer of a [4Fe-4S] cluster to Nfu1 . In these cases Nfu1 facilitates the process as an adapter protein in oxidatively growing cells . Additional work is required to discern the client selectivity in [4Fe-4S] cluster transfer by Nfu1 . This model of eukaryotic Nfu1 function resembles the role of the E . coli Nfu1 ortholog NfuA , which binds a subset of Fe-S apo-client proteins and facilitates cluster transfer especially under oxidative stress conditions ( Py et al . , 2012; Angelini et al . , 2008; Boutigny et al . , 2013 ) . Likewise , the Azobacter NfuA is reported to be critical under oxidative growth conditions ( Bandyopadhyay et al . , 2008 ) . In the case of E . coli , NfuA cluster transfer is likely mediated directly by NfuA ( Py et al . , 2012 ) . Bol3 likely functions with Nfu1 in cluster transfer , but its mechanism remains nebulous . Clearly , interaction studies separate Bol1 and Bol3 into two distinct classes , with Bol3 working with Nfu1 in [4Fe-4S] client binding and Bol1 working with Grx5 , which has one known function upstream of the ISA complex ( Uzarska et al . , 2013; Kim et al . , 2010; Banci et al . , 2014 ) . However , cells lacking both mitochondrial BolA proteins show a synthetic defect . The Bol3 protein may facilitate [4Fe-4S] cluster dissociation from either the ISA complex or Nfu1 in [4Fe-4S] cluster transfer . Additional work will be required to discern their mechanisms .
BY4741 strains were used unless indicated otherwise . Deletion strains were generated by homologous recombination and confirmed by PCR analyses of loci as described earlier ( Longtine et al . , 1998 ) . Plasmids used in this study were constructed using general subcloning techniques . For mutagenesis or adding epitope tags , Phusion DNA Polymerases ( Thermo Fisher Scientific , Waltham , MA ) were used . All plasmid-borne genes were expressed under the MET25 promoter and the CYC1 terminator unless indicated otherwise . Affinity purifications of Strep-tagged proteins were conducted using Strep-Tactin superflow beads ( Qiagen , Germany ) following the manufacturer’s instruction with slight changes . Briefly , isolated mitochondria were solubilized with 0 . 1% n-dodecyl maltoside ( DDM ) in the lysis buffer , 50 mM NaH2PO4 ( pH 8 . 0 ) , 300 mM NaCl and 1x protease inhibitor ( cOmplete mini , Roche , Switzerland ) , for 30 min on ice . After clarification of solubilized mitochondria by high-speed centrifugation , the supernatants were incubated with Strep-Tactin superflow beads for 16 hr at 4°C . The beads were washed five times with the lysis buffer . Strep-tagged proteins bound to the beads were eluted with 2 . 5 mM dethiobiotin in the lysis buffer , which were subjected to mass spectrometry analyses or immunoblotting . Activity assays for aconitase , succinate dehydrogenase ( SDH ) , cytochrome bc1 complex and cytochrome c oxidase were performed as described previously ( Atkinson et al . , 2011; Na et al . , 2014 ) . Aconitase activity was determined by measuring the initial rate of conversion of 100 mM cis-aconitate to isocitrate in 50 mM Tris ( pH 7 . 4 ) at 240 nm . Soluble fractions of mitochondria were obtained by repetitive freeze-thaw . SDH activity was measured by quinone-mediated reduction of dichlorophenolindophenol ( DCPIP ) upon succinate oxidation at 600 nm . For cytochrome bc1 complex activity , the reduction rate of cytochrome c was measured upon the oxidation of reduced decylubiquinol at 550 nm . Cytochrome c oxidase activity was determined by measuring the initial rate of oxidation of cytochrome c oxidation ( Pierrel et al . , 2007 ) . Dihydroxy acid dehydratase ( Ilv3 ) catalytic activity was assayed using an end point assay measuring 2 , 4-dinitophenylhydrazine ( DNPH ) as a proton acceptor as described previously ( Limberg et al . , 1995 ) . Purified mitochondria ( 30 μg ) were lysed by sonication in assay buffer ( 20 mM KPO4 and 10 mM MgCl2 ) , spun at 20 , 000 ×g for 15 min , before incubation with 100 mM dihydroxyisovalerate for 10 min in a total volume of 1 ml . The reaction was quenched with 100 μl of 50% TCA . Next 200 μl of DNPH ( saturated in 2 N HCl ) was added for 15 min when 500 μl of 2 . 5N NaOH was added to quench the reaction . The absorbance was measured at 540nm by a UV-VIS spectrophotometer . The purified Strep-tagged protein complexes were reduced , alkylated and digested as described ( Kaiser and Wohlschlegel , 2005; Wohlschlegel , 2009 ) . The digested peptide mixture was desalted using C18-packed pipette tips ( Thermo Fisher Scientific ) and fractionated online using a 75 µM inner diameter fritted fused silica capillary column with a 5 µM pulled electrospray tip and packed in-house with 15 cm of Luna C18 ( 2 ) 3 µM reversed phase particles . The gradient was delivered via an easy-nLC 1000 ultra high-pressure liquid chromatography ( UHPLC ) system ( Thermo Fisher Scientific ) . MS/MS spectra were collected on a Q-Exactive mass spectrometer ( Thermo Fisher Scientific ) ( Kelstrup et al . , 2012; Michalski et al . , 2011 ) . Data analysis was carried out using the ProLuCID and DTASelect2 implemented in the Integrated Proteomics Pipeline - IP2 ( Integrated Proteomics Applications , Inc . , San Diego , CA ) ( Cociorva et al . , 2007; Tabb et al . , 2002; Xu et al . , 2006 ) . Protein and peptide identifications were filtered using DTASelect and required at least two unique peptides per protein with a peptide-level false positive rate of 5% as estimated by a decoy database strategy ( Elias and Gygi , 2007 ) . Normalized spectral abundance factor ( NSAF ) values were calculated as described ( Florens et al . , 2006 ) and multiplied by a factor of 105 for readability . Purified mitochondria ( 1 . 5 mg ) were lysed by sonication in 50 mM NaPO4 150 mM NaCl ( pH 7 . 0 ) buffer . Lysates were precleared and filtered prior being applied to a HiLoad Superdex 75 PG 16/600 column ( GE Healthcare Life Sciences , United Kingdom ) with a flow rate of 1 mL/Min . Fractions of 1 . 33 mL were collected , TCA precipitated and analyzed by immunoblotting . For MicroScale Thermophoresis the proteins were fluorescently labeled using the Monolith NT Protein Labeling Kit RED with NT-647 dye as recommended by the supplier ( NanoTemper Technologies , Germany ) . The fluorescently labelled protein ( 200 nM ) was titrated with serial dilutions of unlabeled protein ( from 200 µM to 6 . 1 nM ) in buffer containing 50 mM KPi , pH 7 . 4 , 150 mM NaCl , 5% glycerol , 0 . 05 mg/mL BSA , and 0 . 05% Tween20 . Thermophoresis assays were performed using Monolith NT . 115 at 21°C ( LED power – between 40% and 60% , IR laser power 75% ) in standard capillaries under anaerobic conditions . At least three independent experiments were recorded at 680 nm . The thermophoresis data were processed by Nano Temper Analysis 1 . 2 . 009 and GraphPad Prism5 software to estimate the Kd values . Chemical reconstitution was done in a COY ( Grass Lake , MI ) anaerobic chamber using freshly dissolved stock solutions . Protein solutions were reduced with 5 mM DTT for 2–3 hr on ice in reconstitution buffer ( 50 mM Tris-HCl , pH 8 . 0 , 150 mM NaCl , 5% glycerol ) . Reconstitution was started at room temperature by the addition of a 2-3-fold excess of ferric ammonium citrate by inverting the tube . After 5 min a 2-3-fold excess of lithium sulfide was added slowly . Reconstituted proteins were desalted after 2 hr incubation on a PD-10 column equilibrated with reconstitution buffer . Incorporation of the Fe/S clusters into apoproteins was monitored by UV-Vis ( V-550 , Jasco Inc . , Easton , MD ) and CD spectroscopy ( J-815 , Jasco Inc ) . Yeast mitochondria isolation was performed using the method of Glick and Pon ( Glick and Pon , 1995 ) . Standard procedures were performed for SDS-PAGE and immunoblotting . Anti-Sdh2 was from the previous study ( Kim et al . , 2012 ) . BN-PAGE was performed as described previously with mitochondrial lysates in 1% digitonin solution ( Schilke et al . , 1999 ) . Anti-Strep was purchased from Qiagen . Antibodies against LA-conjugated proteins were from Calbiochem ( San Diego , CA ) . Anti-Myc and anti-HA were from Santa Cruz Biotechnology ( Dallas , TX ) . Anti-Por1 was purchased from Molecular Probes and anti-FLAG was from Sigma-Aldrich . ( St . Louis , MO ) Protein concentration was determined by the Bradford assay . | Proteins perform almost all of the tasks necessary for cells to survive . Some of these proteins need to contain collections of iron and sulfur ions known as iron-sulfur clusters to work properly . The iron-sulfur clusters are first assembled from individual ions and then attached to the correct target proteins . In humans , yeast and other eukaryotic cells , the first step of this process happens in compartments called mitochondria and makes a cluster that contains two of each ion , known as [2Fe-2S] clusters . These [2Fe-2S] clusters can either be directly incorporated into target proteins , or they may be used to make larger iron-sulfur clusters – such as [4Fe-4S] clusters – in the mitochondria or the main compartment of the cell ( the cytoplasm ) . Defects that affect the assembly of proteins with iron-sulfur clusters are associated with severe diseases that affect metabolism , the nervous system and the blood . Mitochondria contain at least 17 proteins involved in making iron-sulfur proteins , but there may be others that have not yet been identified . For example , a study on patients with a rare human genetic disease suggested that proteins called BOLA3 and NFU1 might also play a role in this process . Melber et al . used genetics to study how [4Fe-4S] clusters are assembled in the mitochondria of yeast cells . The experiments show that the yeast equivalents of NFU1 and BOLA3 ( known as Nfu1 and Bol3 ) act to incorporate completed [4Fe-4s] clusters into their target proteins . This process is particularly important when iron-sulfur clusters are in high demand , such as when a cell needs to produce a lot of energy . Melber et al . also showed that a protein called Bol1 – which is closely related to Bol3 – is needed in an earlier stage of iron-sulfur cluster assembly . The next steps following on from this work will be to look more closely at how Nfu1 and Bol3 deliver iron-sulfur clusters to the right target proteins . A future challenge will be to find out how other types of iron-sulfur clusters are transferred to their target proteins . | [
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] | 2016 | Role of Nfu1 and Bol3 in iron-sulfur cluster transfer to mitochondrial clients |
Food consumption is thought to induce sleepiness . However , little is known about how postprandial sleep is regulated . Here , we simultaneously measured sleep and food intake of individual flies and found a transient rise in sleep following meals . Depending on the amount consumed , the effect ranged from slightly arousing to strongly sleep inducing . Postprandial sleep was positively correlated with ingested volume , protein , and salt—but not sucrose—revealing meal property-specific regulation . Silencing of leucokinin receptor ( Lkr ) neurons specifically reduced sleep induced by protein consumption . Thermogenetic stimulation of leucokinin ( Lk ) neurons decreased whereas Lk downregulation by RNAi increased postprandial sleep , suggestive of an inhibitory connection in the Lk-Lkr circuit . We further identified a subset of non-leucokininergic cells proximal to Lkr neurons that rhythmically increased postprandial sleep when silenced , suggesting that these cells are cyclically gated inhibitory inputs to Lkr neurons . Together , these findings reveal the dynamic nature of postprandial sleep .
Despite accumulating evidence of interactions between sleep and metabolism , few studies have documented an increased propensity for sleep that animals might experience after meals . Results on human postprandial behavior have shown an increased sleep propensity following a meal , a lagging response , or no effect at all ( Orr et al . , 1997; Zammit et al . , 1995; Wells et al . , 1998; Harnish et al . , 1998; Stahl et al . , 1983 ) . It has been proposed that postprandial sleep is not invariable; it may be absent in particular individuals or heavily dependent on specific food properties ( Stahl et al . , 1983 ) . While the behavior likely exists , these intrinsic variabilities have limited our ability to study its molecular basis . A thorough examination of this behavior would be facilitated by use of an animal model although , at present , no clear model exists ( Watson , 2014 ) . In Drosophila there is a well-documented sleep-metabolism interaction in which flies suppress sleep or increase locomotion when starved ( Lee and Park , 2004; Thimgan et al . , 2010; Keene et al . , 2010 ) . However , the acute effects of food consumption on sleep have not been tested , largely because there is no system available to do so . Here we establish a system for simultaneous measurement of discrete feeding events and sleep , allowing us to examine the mechanisms underlying their interaction .
To simultaneously measure sleep and feeding of individual adult flies , we designed the Activity Recording CAFE ( ARC ) , a system that couples machine vision tracking of capillary-based food consumption and animal motion ( Figure 1A–C ) ( Ja et al . , 2007; Donelson et al . , 2012; Deshpande et al . , 2014 ) . Previous studies have shown that flies inactive for 5 min exhibit multiple hallmarks of sleep ( Shaw et al . , 2000; Huber et al . , 2004; Hendricks et al . , 2000 ) . Using this standard , we extracted sleep parameters by defining sleep as periods of inactivity >5 min . Despite different housing and food consistency , we found sleep architecture to be similar to previous studies using high resolution object tracking , a method which has greater resolution of total sleep and sleep duration than the infrared-beam based alternative ( Figure 1C , Figure 1—figure supplement 1A–D ) ( Donelson et al . , 2012 ) . Flies appeared to move less after meals than before—an effect more pronounced with larger meals—suggesting that sleep might also be greater after eating ( Figure 1D ) . Animals also positioned themselves closer to the food following each meal ( Figure 1E–F ) , consistent with observations that isolated flies sleep close to food ( Hendricks et al . , 2000 ) and the spatial tendency of flies driven to sleep by activation of neurons expressing short neuropeptide F , a satiety signaling hormone ( Shang et al . , 2013; Lee et al . , 2004 ) . 10 . 7554/eLife . 19334 . 003Figure 1 . Activity Recording CAFE ( ARC ) facilitates simultaneous , high resolution measurements of food intake and motion in individual flies . ( A ) Schematic of ARC apparatus . Independent computer-controlled cameras record images of capillaries for feeding measurements and of flies for activity and sleep determination . ( B ) Raw image ( one out of 1440 taken over 24 hr ) of capillaries containing liquid food ( top ) . Capillaries have an external reference mark and an internal dyed oil band overlaid the aqueous food . Images are thresholded to binary , from which cumulative pixel distances between the reference mark and dye band can be calculated from the image series ( bottom ) . Δpixels per frame reveals individual feeding events , selected as events greater than 3 . 5 standard deviations above noise ( red dashed line ) . ( C ) Activity , sleep ( black shading ) and feeding ( µl ) of an individual fly ( activity and feeding in 1 min bins , sleep in 30 min bins ) . ( D ) Example motion traces of individuals before ( black ) and after ( red ) a meal ( yellow circle ) . Motion traces and averages associated with small ( <0 . 06 µl ) or large ( ≥0 . 06 µl ) meals are shown . n = 661 meals from 30 flies , w1118; *p<0 . 05 , ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . ( E ) Raw and convolved spatial heat map showing time spent at each pixel coordinate in the 20 min before and after meals ( 291 feeding events from 15 w1118 flies , Gaussian convolution ) . ( F ) Kymograph of average vertical position in time relative to meals ( shaded line represents mean ± s . e . m . ) . The inset graph shows the average position over 20 min pre-meal ( −20 ) and post-meal ( +20 ) with the axis scaled to the parent graph . ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . All bars represent mean ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 00310 . 7554/eLife . 19334 . 004Figure 1—figure supplement 1 . Influence of recording apparatus on sleep architecture . ( A ) Illustration of motion estimation using the Drosophila Activity Monitor in which the number of times a fly breaks an infrared beam in a glass tube is counted . ( B ) Illustration of motion quantification by first detecting an object and determining the frame-frame distance of the object centroids . ( C ) A comparison of circadian sleep profiles derived from the Drosophila Activity Monitor versus object tracking reveals general conservation of sleep patterning between systems . ( D ) Measurement of total sleep and sleep duration are lower using video tracking , indicating a higher resolution of waking motion . 25–27 flies per condition , **p<0 . 01 , ***p<0 . 001 , Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 004 To examine the acute effects of feeding on sleep , we quantified the probability of sleep ( Psleep ) relative to individual feeding events . The animals experienced a sharp decline in Psleep in the time approaching each meal , as expected since flies cannot sleep and feed simultaneously . Subsequent to the meal , Psleep rose rapidly and exceeded the respective pre-meal Psleep ( Figure 2A ) . To quantify changes in sleep subsequent to each meal , we calculated the change in Psleep of respective time points before and after each event ( i . e . t+1 – t−1 , t+2 – t−2 , … ) . This analysis revealed a rise in sleep lasting approximately 20–40 min with a maximum ΔPsleep amplitude of 0 . 15 and 0 . 2 for the control strains , w1118 and Canton-S , respectively ( Figure 2B ) . The effect was observed in both males and females ( Figure 2—figure supplement 1A ) . While the duration of sleep events prior to a meal is inherently limited by the time until an animal wakes to eat , sleep initiation is not limited and was more probable following a meal ( Figure 2—figure supplement 1B ) . 10 . 7554/eLife . 19334 . 005Figure 2 . Animals exhibit increased sleep and arousal threshold after eating . ( A ) Probability of sleep ( Psleep ) preceding or following each meal ( t = 0 ) in Canton-S ( top ) or w1118 ( bottom ) males . Data are shown as averages of 1 min bins . ΔPsleep is defined as the difference between postprandial Psleep and the corresponding time-matched pre-meal Psleep ( i . e . t1 – t−1 , t2 – t−2 , … ) . For clarity , a mirror image of pre-meal Psleep is replotted in the postprandial period ( dashed line ) . Inset graphs show average Psleep ( ± s . e . m . ) for the 20 min before ( −20 ) and after ( +20 ) meals , with the axis scaled to the parent graph . n = 757 meals from 50 flies , Canton-S; 661 meals from 30 flies , w1118; ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . ( B ) ΔPsleep calculated from data in A . ( C ) Diagram of stimulus delivery system showing shaft-less vibration motors attached to the back of ARC chamber . Increasing vibrations are delivered to the chamber via a microcontroller using pulse width modulation . ( D ) Arousal threshold shows an initial increase with the time an animal is inactive . 180 flies , 11 , 479 arousal events , Canton-S; 5 min bins , circles represent mean ± s . e . m . , a secondorder polynomial trendline is shown . ( E ) Superimposition of time inactive ( blue ) over Psleep ( black ) relative to meals during periodic vibrational stimuli . The inset graph shows Psleep and time inactive in the 20 min before and after each meal . n = 2245 meals from 180 flies , Canton-S; ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . ( F ) Stimulus response from 0–20 , 20–40 , and 40–60 min pre- and post-meal ( red ) superimposed onto Psleep ( black ) . Arousal events are filtered to 5 min intervals for prior time inactive to control for sleep depth ( 0–5 mins shown , minimum 1 s inactivity ) . Circles represent mean ± s . e . m . ; n = 2245 meals from 180 flies , Canton-S; *p<0 . 05 , **p<0 . 01 , Mann Whitney test . ( G ) Percent of calculated sleep that is actual immobility versus grooming . The inset graph shows the percent grooming in the 20 min before and after each meal . n = 55 meals from seven flies , Canton-S; p=0 . 69 , Wilcoxon matched-pairs sign rank test . ( H ) Comparison of Psleep before and after meals calculated using immobility criteria "I" versus immobility criteria paired with grooming criteria "I , -G" . Inset shows average Psleep ( ± s . e . m . ) in the 20 min before and after each meal . ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 00510 . 7554/eLife . 19334 . 006Figure 2—figure supplement 1 . Postprandial sleep is sex independent . ( A ) Canton-S males ( blue ) and females ( red ) demonstrate similar increases in postprandial sleep . n = 672 meals from 30 males; 730 meals from 30 females; ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . ( B ) Probability of sleep initiation in 1 min windows surrounding feeding events for Canton-S ( gray ) and w1118 ( red ) , with a quantification of sleep bout frequency in the 20 min before ( +20 ) and after ( −20 ) feeding events . ***p<0 . 001 , Wilcoxon matched-pairs sign rank test . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 00610 . 7554/eLife . 19334 . 007Figure 2—figure supplement 2 . Arousal threshold surrounding meals with increasing sleep duration state . ( A ) Stimulus response from 0–20 , 20–40 , and 40–60 min pre- and post-meal ( red ) superimposed onto Psleep ( black ) representing the entire data set . Arousal events are filtered to 5-min intervals for prior time inactive ( 0–5 min duplicated from Figure 2 ) to control for sleep depth . ( B ) Correlation between Psleep and arousal threshold for 0–20 , 20–40 , and 40–60 min bins surrounding feeding events ( Pearson correlation , six time-bins derived from 2245 meals from 180 flies , Canton-S ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 00710 . 7554/eLife . 19334 . 008Figure 2—figure supplement 3 . Grooming event influence on immobility based sleep . Ethograms of individual fly behavior in the 20 min before and after each meal , including immobility ( purple ) , sleep ( grey ) , grooming ( blue ) , and recalculated sleep ( red ) . n = 55 meals from seven flies , Canton-S . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 008 In addition to periods of quiescence , increased arousal threshold is regularly used to demonstrate sleep in flies . To test if arousal threshold is increased after a meal , we adapted methodology from the Drosophila arousal tracking system in which animals are exposed to a series of increasing vibrational stimuli ( 0 . 8–3 . 2 g ) once every hour in order to measure the intensity at which inactive animals respond ( Figure 2C ) ( van Alphen et al . , 2013; Faville et al . , 2015 ) . Consistent with previous studies , we found that arousal threshold initially increases with the amount of time that a fly is inactive , with the greatest change occurring in the first 5 min ( Figure 2D ) ( Shaw et al . , 2000; Huber et al . , 2004; van Alphen et al . , 2013; Faville et al . , 2015 ) . Since the maximum duration of inactivity is lower following a meal , irrespective of sleep probability ( Figure 2E ) , we filtered arousal events to compare animals in similar stages of sleep . While animals inactive for more than 5 min are experiencing change in sleep depth , animals inactive for 0–5 min are transitioning into sleep . In this transition state animals showed a significantly greater arousal threshold in the 20 , 40 , and 60 min post-meal compared to animals during the respective pre-meal times ( Figure 2F ) . Interestingly , the correlation between arousal threshold and postprandial sleep grew weaker when comparing animals in deeper stages of sleep ( Figure 2—figure supplement 2A–B ) . This suggests that food intake transiently influences sleep induction rather than depth , which might be shaped solely by the amount of time that an animal is inactive . We further considered the possibility that increases in immobility-derived sleep and arousal threshold could be artifacts of grooming behavior . To test this , we manually scored videos of animals during an ARC recording and annotated grooming events . Falsely calculated sleep , in which animals were grooming rather than immobile , accounted for ~7% of sleep surrounding meals and had statistically indistinguishable occurrence before and after a meal ( Figure 2G , Figure 2—figure supplement 3 ) . Re-analysis of sleep surrounding a meal , where sleep was defined as periods of immobility and a lack of grooming exceeding 5 min , lowered total sleep but did not alter the rise in postprandial sleep ( Figure 2H ) . We next asked how the quantity of food consumed might influence postprandial sleep . We plotted Psleep or ΔPsleep for different consumption volumes and found that sleep following meals increased as a function of meal size , while pre-prandial sleep did not ( Figure 3A , Figure 3—figure supplement 1A ) . Since average postprandial sleep lasted between 20–40 min , and since arousal threshold differences were most apparent in the first 20 min , we re-calculated ΔPsleep for the 20 min surrounding each meal . We found a significant correlation between ΔPsleep and consumption volume when meals were analyzed individually ( Figure 3—figure supplement 1B ) or binned ( Figure 3B ) . Since this analysis used individual meals rather than individual animals , we tested whether animals that consumed meals more frequently could bias the observed effect . We performed a Monte Carlo simulation by iteratively recapitulating the analysis with an equal number of randomly selected meals from each animal and found that equal sampling accurately portrays the full data set ( Figure 3—figure supplement 2A–B ) . Since sleep is derived from periods of inactivity , we considered whether increased consumption could affect mobility , which might in turn appear to be sleep . The change in movement rate after a meal ( Δspeed ) showed a much weaker correlation to meal size than ΔPsleep ( Figure 3—figure supplement 2C ) . Since speed is a strong indicator of mobility , we reasoned that immobility is not likely responsible for the sleep effect . 10 . 7554/eLife . 19334 . 009Figure 3 . Postprandial sleep correlates with food intake quantity . ( A ) Sleep probability of Canton-S and w1118 surrounding meals of varying size ( lines represent mean; color grading corresponds to meal size ) . Only groupings with n > 7 are shown for visualization . ( B ) 20 min ΔPsleep as a function of meal size for each grouping ( Pearson correlation: p<0 . 001 for Canton-S and w1118 . ) Shaded lines represent mean ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 00910 . 7554/eLife . 19334 . 010Figure 3—figure supplement 1 . Sleep probability and ΔPsleep response to meal size . ( A ) Time-course of ΔPsleep for meals grouped by volume ( 0 . 01 µl meal groupings , circles represent 1 min binned averages , lines represent spline fit ) . ( B ) Correlation between meal size and ΔPsleep in 20 min after feeding events for Canton-S and w1118 ( Spearman rank-order correlation: p=9 . 0 × 10−14 , Canton-S; p=4 . 1 × 10−8 , w1118 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 01010 . 7554/eLife . 19334 . 011Figure 3—figure supplement 2 . Unequal meal sampling frequency and motor ability effects on meal size-sleep correlation . ( A ) Monte Carlo simulation of ΔPsleep as a function of meal size using fixed sampling frequency from individual flies . Simulations contained 3000 trials of randomly sampled meals , where thin black lines represent individual trials , and the red line represents the entire data set ( three samples/fly , Canton-S; eight samples/fly , w1118; 0 . 01 µl meal groupings ) . ( B ) Histogram of linear regression slope for each trial fit with a Gaussian distribution ( red line ) . The black arrow indicates the slope value of the entire data set . ( C ) Average Δspeed for the 20 min relative to meals versus meal size ( Spearman rank-order correlation: p=0 . 0096 , Canton-S; p=0 . 0095 , w1118 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 011 Although meal size has an apparent impact on postprandial sleep , the standard diet contains numerous physiological and nutritive components . To determine the potential contribution of individual components to postprandial sleep , we designed a paradigm that allowed us to test a range of feeding volumes or of ingested nutrients . To test volumetric effects , animals were fed a low protein concentration food ( 0 . 25% tryptone ) to elicit feeding without impacting baseline sleep , since sucrose and sweet tasting substances have been shown to modulate sleep ( Keene et al . , 2010; Linford et al . , 2012 ) . We found a highly significant correlation between meal volume and 20 min ΔPsleep ( Figure 4A , Figure 4—figure supplement 1A–B ) . Larger meal volumes are also associated with a strong but transient increase in ΔPsleep amplitude ( Figure 4A , Figure 4—figure supplement 2A ) . 10 . 7554/eLife . 19334 . 012Figure 4 . Influence of meal components on postprandial sleep . ( A ) Average 20 min ΔPsleep as a function of each meal component ( Canton-S , Pearson correlation: p<0 . 001 , volume; p<0 . 001 , protein; p<0 . 005 , salt; p=0 . 065 , sucrose ) . Shaded lines represent mean ± s . e . m . ( B ) Histograms representing the distribution of volume and protein consumed in each meal for data from A . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 01210 . 7554/eLife . 19334 . 013Figure 4—figure supplement 1 . Meal component correlates to postprandial sleep . ( A ) Scatter plots representing individual meals showing 20 min ΔPsleep versus meal volume or consumed protein , salt ( NaCl ) , or sucrose . Non-normal data in each plot can arise from the tendency for fully awake animals to stay awake ( ΔPsleep = 0 ) and from low variation in nutrient consumption within animals given a low nutrient , fixed diet ( protein , NaCl , or sucrose consumption = 0 µg ) . Spearman rank-order correlation: p=1 . 52 × 10−25 , volume; p=1 . 73 × 10−19 , protein; p=6 . 84 × 10−6 , NaCl; p=0 . 055 , sucrose ) . ( B ) Time-courses of sleep probability for component groupings ( lines represent mean for each grouping ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 01310 . 7554/eLife . 19334 . 014Figure 4—figure supplement 2 . Time-course analysis of sleep in response to meal components reveals differential kinetics . ( A ) Time-course analysis of ΔPsleep for meals of graded volume , protein , or salt consumption ( circles represent 1 min binned averages , lines represent spline fit ) . n = 811 meals , volume; 714 , protein; 478 , salt; 1131 , sucrose . ( B ) ΔPsleep amplitude for gradings of volume , protein , and salt plotted against total ΔPsleep during the decay phase 20 min following the maximum . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 014 To determine if individual nutrients could modulate postprandial sleep , we fed animals a concentration series of protein ( tryptone ) , salt ( NaCl ) , or sucrose . All diets contained a base of 0 . 25% protein to induce feeding while minimizing effects on baseline sleep that might arise by differences in sucrose consumption . Within each series we compared nutrient consumption within a narrow volume range ( 0 . 02–0 . 04 µl , representing 33–38% of recorded meals ) to limit the contribution of ingested volume on sleep . We found that ΔPsleep was significantly correlated to both protein and salt ingestion ( Figure 4A , Figure 4—figure supplement 1A–B ) . Interestingly , despite reported effects of sucrose on total sleep ( Keene et al . , 2010; Linford et al . , 2012 ) , we found no correlation between ingested sucrose and ΔPsleep ( Figure 4A , Figure 4—figure supplement 1A–B ) . For all analyses , only the meal parameter of interest varied , while all others were kept within a restricted range ( Figure 4B ) . To compare the kinetics of ΔPsleep for each effective driver , we quantified maximum ΔPsleep amplitude and the total ΔPsleep during its decay . For any given increase in total ΔPsleep during the decay , meal volume induced a greater rise in amplitude than protein or salt ( Figure 4—figure supplement 2B ) . We hypothesize that multiple mechanisms operating on different time scales might underlie postprandial sleep regulation by the various drivers . We next sought to identify a neuronal mechanism by which feeding drives postprandial sleep . A previous study showed that Lk was involved in meal size regulation , suggesting that this system acts rapidly during feeding to signal a behavioral shift ( Al-Anzi et al . , 2010 ) . To test if Lk signaling regulates postprandial sleep , a subset of Lkr neurons labeled by the line , R65C07 ( hereafter referred to as LkrGAL4 ) , were silenced by overexpressing Kir2 . 1 , an inward rectifying K+ channel ( Johns et al . , 1999 ) . This manipulation eliminated any rise in postprandial sleep and instead caused slight arousal ( Figure 5A ) . There was no significant change in postprandial activity ( Δmotion for 20 min after meal: Kir2 . 1 / wCS , −0 . 006 ± 0 . 003; LkrGAL4 / wCS , −0 . 001 ± 0 . 002; Kir2 . 1 / LkrGAL4 , 0 . 004 ± 0 . 004; p=0 . 28 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . The expression pattern of LkrGAL4 recapitulates that of Lkr neurons which were found to regulate meal size ( Al-Anzi et al . , 2010 ) , revealing several cell populations stemming from the lateral horn which innervate the dorsal fan-shaped body of the central complex—a region of the brain which functions in sleep homeostasis , visuomotor function , and hunger-related behavior ( Figure 5B ) ( Donlea et al . , 2011 , 2014; Seelig and Jayaraman , 2013; Krashes et al . , 2009 ) . This circuitry suggests that the Lk system may act directly on the sleep and motor controllers of the fly brain to regulate postprandial sleep . 10 . 7554/eLife . 19334 . 015Figure 5 . Leucokinin receptor neurons regulate protein-induced postprandial sleep . ( A ) Overexpression of Kir2 . 1 channel in cells labeled by LkrGAL4 ( R65C07 ) results in a defective postprandial sleep response ( 20 flies per genotype , n = 697 meals , Kir2 . 1 / wCS; 762 , LkrGAL4 / wCS; 450 , Kir2 . 1 / LkrGAL4; *p<0 . 05; ***p<0 . 001 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( B ) LkrGAL4 drives mCD8-GFP expression in neurons innervating the dorsal fan-shaped body , stemming from cell bodies in the lateral horn ( scale bar = 50 µm ) . ( C ) Average 20 min ΔPsleep for Kir2 . 1-silenced LkrGAL4 animals and controls given low nutrient food ( 1% sucrose , 0 . 25% tryptone ) for observing volumetric effects ( n = 1589–1736 meals per genotype ) . ( D ) Average 20 min ΔPsleep for Kir2 . 1-silenced LkrGAL4 animals and controls given salt ( 2 . 5% sucrose + 1% salt ) or protein ( 2 . 5% sucrose + 1 . 7% tryptone ) diet to test meal component influences on postprandial sleep . The silenced line shows a negative response to protein supplemented diet ( ***p<0 . 001 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . n = 257–444 meals per genotype . ( E ) Expression of Kir2 . 1 can be restricted by using temperature-sensitive GAL80ts to suppress GAL4 activity at 21°C throughout development . ( F ) Silencing of LkrGAL4-labeled cells in adulthood ( 30°C ) is sufficient to reduce postprandial sleep ( 24 flies per genotype , n = 493 meals , TubGAL80ts; Kir2 . 1/ wCS; 499 , LkrGAL4/ wCS; 528 , TubGAL80ts; Kir2 . 1/ LkrGAL4; *p<0 . 05; **p<0 . 01 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 01510 . 7554/eLife . 19334 . 016Figure 5—figure supplement 1 . General behavior in Lkr neuronal-silenced animals . ( A ) Total sleep , ( B ) meal size , and ( C ) feeding of LkrGAL4 / Kir2 . 1 and controls on sucrose + protein diet ( Student’s t-test or one-way ANOVA followed by Tukey-Kramer post hoc test for multiple comparisons ) . Points represent individual animals and lines represent mean ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 016 We hypothesized that Lkr circuitry could receive convergent signals from volume , protein , and salt to drive postprandial sleep . Alternatively , it could specifically relay the signals of a single driver to some point of convergence . We first tested whether the subset of Lkr neurons labeled by LkrGAL4mediated the sleep response to meal volume by feeding a low nutrient diet to LkrGAL4-silenced animals . Silenced animals and their controls showed similar ΔPsleep in response to ingested volume , indicating that the LkrGAL4 neurons are not responsible for the volumetric modulation of postprandial sleep ( Figure 5C ) . To test the role of Lkr neurons in the postprandial sleep response mediated by nutrients , we supplemented a sucrose diet with either salt or tryptone . LkrGAL4-silenced animals and their controls showed statistically indistinguishable ΔPsleep responses to salt manipulation ( Figure 5D ) . In contrast , while protein ingestion increased postprandial sleep in controls , LkrGAL4-silenced animals showed a strong waking response , or a negative shift in postprandial sleep ( Figure 5D ) . Interestingly , this waking response exceeded that of control animals on sucrose alone , indicating that protein can also drive wakefulness in the absence of LkrGAL4 neuronal activity . We did not observe changes in meal size , total sleep , or total consumption , indicating that these behaviors are independent of Lkr influence on postprandial sleep ( Figure 5—figure supplement 1A–C ) . To test whether this phenotype was caused by changes in development , we conditionally silenced the cells by co-expressing a temperature sensitive GAL4 suppressant protein , GAL80ts , throughout development ( Figure 5E ) . Inactivation of GAL80ts to silence LkrGAL4-labeled cells in adulthood was sufficient to induce a postprandial sleep defect ( Figure 5F ) . Subsets of Lk neurons have been found in the lateral horn , with the diffuse puncta in close proximity to the cell bodies of the Lkr neurons , suggesting a neuromodulatory connection between these cells ( Al-Anzi et al . , 2010; de Haro et al . , 2010; Cavey et al . , 2016 ) . Recent findings also show that incubating Lkr neurons with Lk inhibits their response to the cholinergic agonist carbachol , suggesting that Lk inhibits Lkr neuronal activity ( Cavey et al . , 2016 ) . We first sought to confirm that cells labeled by the LkGAL4 driver express Lk . Indeed , immunostaining of Lk overlapped with LkGAL4-labeled cells in the lateral horn , as well as in two cells in the suboesophageal ganglion ( SOG ) ( Figure 6A ) . Using thermogenetic activation of these cells with the transient receptor potential channel TrpA , we found that postprandial sleep was significantly reduced upon activation ( Figure 6B ) , furthering the notion that these cells inhibit Lkr neurons . To see if Lk was necessary to drive this inhibitory effect , we used two independent RNAi lines to knockdown Lk in cells labeled by LkGAL4 . We found that this knockdown increased postprandial sleep , consistent with the idea that expression of Lk in LkGAL4-labeled cells is necessary for the inhibitory effect on Lkr neuronal activity ( Figure 6C ) . 10 . 7554/eLife . 19334 . 017Figure 6 . Leucokinin neurons inhibit postprandial sleep . ( A ) Confocal reconstruction of immunostaining for anti-Lk ( magenta ) in the brain of LkGAL4>mCD8::GFP reveals Lk co-localization with GFP-expressing neurons ( green ) in the lateral horn ( LHLK ) and suboesophageal ganglion ( SELK ) . The neuropil marker nc82 ( gray ) is used as background ( scale bar = 50 µm ) . ( B ) Stimulation of Lk neurons at 30°C by expressing Transient receptor potential channel , TrpA , causes a reduction in postprandial sleep in comparison to the unstimulated state at 18°C ( 10 flies per genotype , 30°C , n = 83 meals , TrpA/ wBerlin; 63 , LkGAL4 / wBerlin; 25 , LkGAL4 / TrpA; *p<0 . 05 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( C ) Downregulation of Lk in LkGAL4-labeled cells , using two independent RNAi lines , increases postprandial sleep . ( 60 flies per genotype , n = 893 meals , LkGAL4 / GD60000; 841 , LkGAL4 / LkRNAi ( VDRC ) ; 953 , LkGAL4 /mCherryRNAi; 1060 , LkGAL4 / LkRNAi ( TRIP ) ; **p<0 . 01 , ***p<0 . 001 Mann-Whitney test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 017 The broad arborizations of the Lkr neurons suggest that these cells might receive inputs from a number of spatially or type distinguished cells in the brain . R20G03 ( 20G03 ) , a line expressing GAL4 driven by an enhancer fragment 316 base pairs upstream of Lk , labels cell bodies in the lateral horn and SOG , as well as a subset of mushroom body output neurons ( MBONs ) ( Figure 7A ) , all of which coincide with Lkr neurons labeled by the LkrGAL4driver ( Figure 7B ) . The cholinergic MBONs of 20G03 have been shown to play a role in short-term appetitive memory following food intake ( Aso et al . , 2014 ) . These features , paired with their neuroanatomical distribution , suggested that these cells might play a role in postprandial sleep . To test this , we again employed electrical silencing with Kir2 . 1 and found that constitutive and adult-restricted silencing of these neurons increased postprandial sleep , suggesting that these cells might directly inhibit the activity of proximal Lkr neurons ( Figure 7C–D ) . Interestingly , immunostaining of Lk did not co-localize with any of the 20G03 cells , nor did downregulation of Lk in these cells affect postprandial sleep ( Figure 7—figure supplement 1A–B ) , suggesting that their inhibitory action stems from an alternative neuropeptide or neurotransmitter . By analyzing behavior throughout a 24 hr period , we found that the increase in postprandial sleep driven by 20G03 silencing was most pronounced at ZT 12 ( Figure 7E–F ) . Closer examination within this period showed that , rather than raising sleep evenly within ZT 10–14 , the sleep after but not before each meal was increased ( Figure 7G ) , implying that these cells activate in response to food intake . 10 . 7554/eLife . 19334 . 018Figure 7 . 20G03 neurons inhibit postprandial sleep in a circadian manner . ( A ) 20G03GAL4 drives mCD8-GFP expression in neurons with cell bodies positioned proximal to cell arborizations of those labeled by LkrGAL4 . ( B ) Overlaid images of 20G03GAL4 and LkrGAL4 expression show close proximity between cell types in the lateral horn and SOG . ( C ) Overexpression of Kir2 . 1 channel in 20G03GAL4-labeled cells results in an enhanced postprandial sleep response ( 30 flies per genotype , n = 805 meals , Kir2 . 1 / w1118; 488 , 20G03GAL4 / w1118; 285 , Kir2 . 1 / 20G03GAL4; **p<0 . 01; ***p<0 . 001 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( D ) Conditional silencing of 20G03GAL4-labeled cells in adulthood is sufficient to increase postprandial sleep ( 24 flies per genotype , n = 493 meals , TubGAL80ts; Kir2 . 1/ wCS; 575 , 20G03GAL4 / wCS; 528 , TubGAL80ts; Kir2 . 1/20G03GAL41; *p<0 . 05; **p<0 . 01 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( E ) Time-course of postprandial sleep reveals that effect of 20G03GAL4 silencing is most prominent from ZT 10–14 ( time-course partitioned into 4 hr bins , shaded region indicates dark period; *p<0 . 05 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( F ) Conditional silencing effects are also stronger at dusk ZT 10–14 ( **p<0 . 01 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( G ) Comparison of Psleep in the 20 min before and after each meal for both 20G03GAL4 manipulations ( color codes from C and D; *p<0 . 05; **p<0 . 01 , ***p<0 . 001 , Kruskal-Wallis test followed by Dunn’s multiple comparisons ) . ( H ) Proposed model for regulation of postprandial sleep by dietary components and neuronal circuitry . Meal volume , ingested salt , and protein drive postprandial sleep . Sleep induced by ingested protein acts through Lkr neurons . Protein also induces a waking response independent of Lkr neuronal activity . Leucokininergic ( Lk ) or non-leucokininergic ( 20G03 ) cell populations can independently inhibit postprandial sleep , possibly through modulation of Lkr neuronal activity . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 01810 . 7554/eLife . 19334 . 019Figure 7—figure supplement 1 . Lk immunostaining and knockdown in 20G03 neurons . ( A ) Confocal reconstruction of 20G03GAL4>mCD8::GFP . GFP-expressing neurons ( green ) with immunostaining for anti-Lk ( magenta ) and neuropil marker nc82 ( gray ) , shows no co-localization of Lk in 20G03GAL4-labeled neurons . ( B ) RNAi of Lk in 20G03GAL4 neurons does not affect postprandial sleep ( 60 flies per genotype; n = 1118 meals , 20G03GAL4 / mCherryRNA; 1249 , 20G03GAL4 / LkRNAi [TRIP] ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19334 . 019
The ARC is the first platform capable of simultaneously measuring sleep and resolving discrete feeding events in individual flies . Using this tool , we found that flies exhibit a rise in sleep following a meal . Interestingly , consumption of a meal did not necessarily affect the depth of sleep when controlling for time inactive . There are many instances in which genes or neural processes have been found to influence particular features of sleep while leaving others unaffected ( Harbison et al . , 2009; Shi et al . , 2014; Shang et al . , 2011 ) . Similarly , our work suggests that sleep probability and sleep depth can also be dissociated . By further examination of the shift in sleep probability , we found that it increased as a function of meal size . This may be paralleled in humans where cranial EEG power increases with meal size and during certain stages of sleep ( Reyner et al . , 2012 ) . In examining individual meal components , we identified volume , protein , and salt as effectors of postprandial sleep . While human studies have been unable to resolve a link between meal volume and measures of wakefulness ( Landström et al . , 2001 ) , artificial distension of the gut has been shown to increase sleep in rats ( Lorenz et al . , 1998 ) . Although there are no studies on the effect of protein on postprandial sleep , protein consumption modulates sleep in Drosophila and positively correlates with napping in humans ( Catterson et al . , 2010; Grandner et al . , 2010 ) . Similarly , although there are no studies on the influence of salt on postprandial sleep , salt intake increases mammalian oxytocin signaling , which has been implicated in sleep-wake behavior ( Krause et al . , 2011; Lancel et al . , 2003 ) . Sucrose has been shown to play a critical role in long-term sleep homeostasis and architecture in flies ( Keene et al . , 2010; Linford et al . , 2012 ) , although our findings indicate that it does not modulate postprandial sleep . Although we demonstrate the effectiveness of these dietary components in isolation , we note that meals typically incorporate multiple elements . Thus , further work is necessary to identify the mechanisms by which each constituent is sensed and the potential interactions between them . Lipids are also present in many fly food sources and future examination of their influence on postprandial sleep might be facilitated by adding fat solubilizers to the liquid food used in the ARC . We demonstrate that the Lk system plays a role in postprandial sleep ( Figure 7H ) . A subset of Lkr neurons were necessary to initiate postprandial sleep in the presence of protein . While we expected that animals defective in protein sensing would experience postprandial sleep similar to that of animals fed only sucrose , we found instead that they had a waking response . This suggests that ingested protein promotes both sleep and wakefulness , and that the wakefulness is normally counterbalanced by Lkr neuronal activity . While this study does not specifically identify the waking output for protein , it was previously shown that Translin acts through Lk-expressing cells to drive wakefulness in response to sucrose starvation ( Murakami et al . , 2016 ) . While this does not explain rapid wake promotion in response to protein ingestion , it does demonstrate that Lk-expressing cells are capable of promoting wakefulness . Another recent finding identified Lkr neurons as circadian output neurons , which modulate activity rhythmically . The same study found that incubation of Lkr cells with Lk shunts their calcium response to the cholinergic agonist carbachol ( Cavey et al . , 2016 ) , suggesting an inhibitory connection between Lk and downstream Lkr neurons . By stimulating these same Lk neurons , we observed a reduction of postprandial sleep matching the effect of Lkr silencing , providing additional evidence that the lateral horn harbors an inhibitory node between Lk and Lkr neurons . We also found that knockdown of Lk in these cells increased postprandial sleep , supporting this notion . However , it has been suggested that Lk may have additional receptors ( Terhzaz et al . , 1999 ) , and there is still a lack of definitive evidence for the inhibitory mechanism between Lk and Lkr neuronal activity in the lateral horn . Additional genetic tools , including lines with restricted expression patterns , will be needed to fully uncover the genetic interactions and neuronal connectivity of this system . We also found a subset of cells labeled by 20G03 that modulate postprandial sleep during the period near ‘dusk’ or ‘lights-off’ . Interestingly , the subset harbors acetylcholine MBONs—cells necessary for the short-term associative memory between sucrose consumption after starvation and an odor ( Aso et al . , 2014 ) . This suggests that these cells activate following a feeding event , in agreement with our results . This is further supported by the anatomical distribution of cells in the SOG , a region of the fly brain that regulates feeding behavior . The distribution also shows overlap with Lkr neurons and regions of the neuronal clock network , suggesting a diversity of regulatory inputs and outputs . Further studies might employ GFP reconstitution across synaptic partners ( GRASP ) ( Gordon and Scott , 2009 ) or neuronal co-labeling to demonstrate these connections . The apparent diversity of neuronal modulators of postprandial sleep opens the door to future work on determining the various genes and circuits involved . The tachykinin family to which Lk belongs has been implicated in the regulation of satiety and sleep , however any physiological connections between these behavioral outputs has not been explored ( Massi et al . , 1988; Zielinski et al . , 2015 ) . The orexin system is regulated by a diversity of signaling molecules ( Scammell and Winrow , 2011 ) and has already shown promise as a target for sleep therapeutics . Such genetic diversity and therapeutic potential may also be true of systems governing postprandial sleep . Accordingly , the ARC provides a platform for future studies aimed at uncovering additional genes , circuitry , and physiological roles of postprandial sleep . Furthermore , the ability to measure both sleep and feeding in individual animals may shed new light on the fundamental relationship between these behaviors in other paradigms .
Flies were developed in 6 oz . bottles on a standard cornmeal-sucrose-yeast medium consisting of ( w/v ) 5 . 8% cornmeal , 3 . 1% active dry yeast , 0 . 7% agar , 1 . 2% sucrose , and ( v/v ) 1% propionic acid and 0 . 22% Tegosept ( w/v , pre-dissolved in ethanol ) . Typically , males were collected 0–2 days after eclosion under gentle CO2 anesthesia and maintained on standard diet at ~20 flies per vial ( Polystyrene , 25 × 95 mm ) . All flies were maintained in a humidity and temperature controlled incubator at 25°C and 65% relative humidity under a 12/12 hr light/dark cycle . All experiments were carried out with 5–9 day old males . R20G03 ( 20G03 , RRID:BDSC_48907 ) and LkrGAL4 ( R65C07 , RRID:BDSC_39344 ) neuronal driver expression patterns were identified by and obtained from Janelia Farm ( Jenett et al . , 2012 ) via the Bloomington stock center . UAS-Kir2 . 1 ( Baines et al . , 2001 ) and TubGAL80ts ( McGuire et al . , 2004 ) were previously described . LkRNAi ( TRIP , RRID:BDSC_25798 ) was obtained from the Bloomington Stock Center . LkRNAi ( VDRC , 14091 ) was obtained from the Vienna Drosophila Resource Center . UAS-TRPA was provided by Ulrike Heberlein . LkGAL4 and the anti-Lk antibody were provided by Bader Al-Anzi . Liquid diets were prepared by boiling ingredients in 100 ml ddH2O followed by filtration ( 0 . 2 µm cellulose acetate syringe filter , VWR , Radnor , PA ) . Listed percentages of Bacto Tryptone , Bacto yeast extract , sucrose , or NaCl were calculated as weight/volume . All reagents were obtained from Fisher Scientific ( Waltham , MA ) or VWR . Feeding behavior was recorded on a standard liquid diet of 2 . 5% sucrose + 2 . 5% yeast extract unless otherwise noted . Animals were housed in a plastic chamber ( acrylonitrile butadiene styrene ) containing small half-cylindrical cells ( 7 mm wide , 4 . 5 mm depth , 1 . 15 mm spacing ) fit with a 2 mm thick clear acrylic panel for visualization . Strips of infrared led lights were placed ~16 cm behind each chamber on a heat sink to provide backlighting for feeding and motion tracking . Each animal was given access to a capillary filled with liquid food . Above the chambers was a 2 mm gap with a small overhang allowed for a sliding gate ( 5 mm × 2 mm ) to seal individual fly cells . The gate contained evenly spaced 2 . 5 mm vertically oriented holes above the center of each fly cell . This allowed for the insertion of tightly fitting 200 µl pipette tip sleeves that were cut to hold the food-containing capillaries ( 5 µl glass capillaries , VWR ) . Sleeves were made by cutting the narrow end of 200 µl pipette tips to fit capillaries tightly . The dye used for liquid meniscus tracking was composed of 75% mineral oil , 25% dodecane , and 1% Copper ( II ) 1 , 4 , 8 , 11 , 15 , 18 , 22 , 25-octabutoxy-29H , 31H-phthalocyanine ( Sigma-Aldrich , St . Louis , MO ) . Dye solution was vortexed for 1 min and centrifuged briefly . Supernatant dye was collected and re-dispensed into 1 . 5 ml tubes and vortexed for 1 min . Dye solution was loaded into each capillary to make a 1 mm plug before loading liquid diet . Food was loaded until dye bands were approximately 0 . 5 mm below the capillary reference mark . To improve visualization of the dye and reference mark , thin white construction paper was gently attached to the back of capillaries using double-sided tape . For ARC serial image capture , we used a Lifecam 1080p HD webcam ( Microsoft , Redmond , WA ) with the infrared filter replaced by an infrared pass filter . The camera was mounted with a custom bracket at equal height to the base of the capillaries . PhenoCapture software was used to capture one image per min using the time-lapse image capture tool ( www . phenocapture . net ) . Each capillary had an exterior reference mark and was pre-loaded with an oil-based dye band , marking the meniscus of the liquid food . ImageJ was used for image processing ( Schneider et al . , 2012 ) . Images were cropped to include all capillaries and dye bands . The images were background subtracted using the built-in subtract background function with a rolling ball radius of five and disabled smoothing . The images were then thresholded manually until the dye bands and reference marks were faithfully displayed without background noise . The images were split to contain image stacks of individual capillaries and tracked using a custom ImageJ plugin . The relative distance between the food meniscus and the reference mark was calculated at each frame using the custom plugin . We used a meal selection algorithm that estimates noise by an iterative filtration of distal values 4 . 0 s . d . above the Δpixel mean ( see Supplementary file 1 ) . Meals were then selected as values which were 3 . 5 s . d . above the estimated noise , based on the one frame/min sampling rate . In a setup tracking 30 capillaries , this method provides 10 nL resolution although greater resolution can be achieved by increasing camera resolution and sampling rate . Though infrequent , temporally adjacent feeding bouts were combined and time stamped with the latter meal time . Thus , feeding bouts followed by at least 1 min of non-feeding were considered meals . Each feeding event was corrected for effects of evaporation and luminance fluctuation on observed meal size by subtracting the average noise value from all non-feeding frames . Meal output contains meal associated time and corrected volume . Behavioral tracking was carried out with a custom , freeware , cross-platform analysis framework ( JavaGrinders Library , available for free download at http://iEthology . com/ ) . This library utilizes and extends machine vision functions provided by the OpenCV project for high-resolution analysis of spatial and behavioral data . With an integrated interface for standard USB video device class ( UVC ) cameras , the software implements multi-stage object detection and analysis for 8-bit grayscale/32-bit color frame sequences in real-time . Following the subtraction of a reference frame , the tracker locates the frame coordinate with the highest remaining pixel value . Provided this value exceeds the object's threshold , the detection algorithm proceeds outwards until it identifies a starting point along the object's thresholded border . By following this edge until it returns to the starting point , the detection algorithm characterizes the object's outline as a polygonal shape . The object's center location is estimated by the polygon's centroid , directional attributes ( e . g . , long axis ) which are obtained via a Singular Value Decomposition of the polygon's outline . The projected size is represented by the shape's area . The size of a fly tracked in the ARC measured approximately 15–20 pixels in length . The present study used a Microsoft Lifecam Studio camera with the infrared filter replaced by an infrared pass filter . Although much higher rates are possible within this system , a reduced sampling rate of 1 Hz was chosen as it proved sufficient for identifying the extended periods of inactivity indicative of sleep . The maximum frame rate capability depends on a number of factors , including the video's resolution , the object's size , the performances of processor and graphics card , and the specifications of the hardware drivers used to interface with the camera . Distance traveled was obtained from Euclidean measures between object centers in consecutive frames . Motion values less than 50% of the fly body length ( FBL ) were discarded . Dropped positions were reset to the last known location for motion and sleep calculation purposes . This system has been previously tested for Drosophila tracking sufficiency and demonstrated enhanced motion/sleep characterization compared to alternative methods ( Donelson et al . , 2012 ) . An animal was considered sleeping during any period of immobility exceeding 5 min , based on previous studies showing that this period of inactivity is highly correlated with hallmarks of sleep ( Shaw et al . , 2000; Huber et al . , 2004 ) . Speed of each motion event was calculated as the distance traveled during that event divided by its uninterrupted duration . This is the first time , to our knowledge , that sleep has been measured in vertically oriented chambers , and on a liquid diet . However , we found sleep patterning in the ARC to be in line with the more commonly used Drosophila Activity Monitor ( TriKinetics , Waltham , MA ) . Notably , object detection provides a higher resolution for sleep bout duration ( Figure 1—figure supplement 1D ) . Each fly well was loaded with 300 µl of 1% ( w/v ) Bacto agar to allow ad libitum access to water . Flies were loaded into chambers using a standard mouth aspirator to avoid behavioral perturbation from CO2 anesthetization . After loading each fly , a sleeve and capillary were quickly inserted to prevent escape . Flies were habituated in the recording chamber for 20–24 hr with access to high nutrient food ( 5% sucrose + 5% yeast extract ) to obviate the need for more than one food change every 24 hr . All experiments had daily food changes from Zeitgeber time ( ZT ) 0–0 . 5 hr . Infrequently , food consumption exceeded the volume administered requiring an additional food change at ZT 8–12 . Motion and feeding data were analyzed using custom python-based software and custom or built-in Matlab functions ( The Mathworks , Natick , Massachusetts , USA ) . Instructions and 3D print files for ARC setup and data analysis software are available upon request . Probability of sleep is calculated as the population mean of sleep probability/unit time . 1-min bins were used for all Psleep plots . ΔPsleep represents a comparison of pre-meal to post-meal sleep by calculating the difference in Psleep between post-meal time bins and their respective pre-meal time bins ( i . e . ( t0–1 min post-meal ) – ( t0–1 min pre-meal ) , ( t1–2 min post-meal ) – ( t1–2 min pre-meal ) … ) . This was either calculated in 1-min bins for time-course analysis , or in 20-min time bins following the meal . The 20-min bin was selected since this length of time covered the mean duration of postprandial sleep for both Canton-S and w1118 . To test arousal threshold , we adapted methodology previously used for quantifying sleep and sleep depth ( van Alphen et al . , 2013; Faville et al . , 2015 ) . Animals were exposed hourly to a series of vibrations of increasing intensity ranging from 0 . 8 to 3 . 2 g , in steps of 0 . 6 g . Stimuli trains were composed of 200 ms vibration with 800 ms inter-vibration interval and 15 s inter-stimuli train interval . Stimulation intensity and timing were controlled using pulse-width modulation via an Arduino UNO and shaft-less vibrating motors ( Precision Microdrives , model 312–110 ) ( Figure 2C ) . Arousal to a given stimulus was assigned when an animal ( 1 ) was inactive at the time of the stimulus , ( 2 ) satisfied a given inactivity criteria at the time of the stimulus , and ( 3 ) moved within the inter-stimuli train period ( 15 s ) of that stimulus . To determine the impact of grooming behavior on calculated sleep , we simultaneously recorded video of flies during a normal ARC experiment using equipment and videocapture methods previously described ( King et al . , 2016 ) . Grooming start and stop times were manually noted , where the observer was blind with respect to feeding times . Times were then computer annotated as a binary ( grooming = 1 , not grooming = 0 ) for all time points within the experiment . Individual housing precluded the need for annotation of courtship behaviors . For meal volume gradings , animals were fasted for 20 hr and then placed on a 0 . 25% tryptone diet . Feeding events were recorded for 24 hr . This paradigm provided a high range of feeding volumes while maintaining a small but sufficient amount of nutrients to elicit feeding . Meal volumes and associated sleep events were grouped into 0 . 01 µl bins according to volume consumed . Bins greater than the 97th percentile were excluded to maintain sufficient sample size for each bin . Alternative experiments for testing volumetric effects used small amounts of sucrose ( 1% ) or both sucrose and tryptone ( 1% sucrose + 0 . 25% tryptone ) and gave similar results ( data not shown ) . For sucrose grading , the same paradigm was used with a low tryptone diet ( 0 . 25% ) supplemented with varying concentrations of sucrose ( 0 , 1 , 5 , 15 , and 25% ) . Feeding events were then filtered to include volumes within the range 0 . 02–0 . 04 µl which included 37% of all recorded meals ( 1131/3038 meals ) . This range was chosen to maintain a small and narrow volumetric input , and because all dietary groups maintained meal volumes within this range . Meals were grouped into 1 . 333 µg bins of sucrose consumed during the meal ( meal volume × nutrient concentration ) . Salt grading was performed similarly but with varying concentrations of salt ( 0 , 0 . 25 , 0 . 3 , 0 . 4 , 0 . 5 , 0 . 6 , 0 . 75 , and 1% ) and 0 . 05 µg bins , and included 33% of all recorded meals ( 478/1446 meals ) . Protein grading was performed by providing diets of varying concentrations of tryptone ( 0 . 25 , 1 , 2 . 5 , 3 , 4 , and 5% ) and analyzed similarly to sucrose and salt experiments using 0 . 333 µg bins , and included 38% of all recorded meals ( 714/1872 meals ) . For LkrGAL4 silencing experiments , flies were fed standard liquid diets: 2 . 5% sucrose + 2 . 5% yeast extract , respectively . To test volume response deficiency , animals were fed a low nutrient ( 1% sucrose + 0 . 25% tryptone ) food which elicited a large range and frequency of meals . To test protein response deficiency , animals were fed 2 . 5% sucrose supplemented with the standard diet equivalent of protein ( 1 . 67% tryptone ) . The standard diet contained a negligible amount of salt ( with respect to inducing a postprandial sleep response ) . To test a salt response deficiency , animals were fed 2 . 5% sucrose supplemented with 1% NaCl . Average meal size between groups did not differ—hence , meal size filtering was not applied . Brains were imaged as described previously ( Murakami et al . , 2016 ) . Statistical analysis was performed using Matlab statistical toolbox or GraphPad Prism version 5 . 04 ( GraphPad Software , La Jolla , CA ) . All reported values are mean ± s . e . m . Shapiro-Wilk test was used to determine data normality . Data were analyzed using either Wilcoxon matched-pairs sign rank test for comparisons of non-parametric Psleep of animals before and after meals , Kruskal-Wallis test followed by Dunn’s multiple comparisons for non-parametric comparisons of ΔPsleep , or the Mann-Whitney test when comparing two non-parametric groups , non-paired observations . Comparisons of meal size to given features were analyzed using Spearman rank correlation , due to non-parametric distribution , while binned data were analyzed using Pearson correlation . | Many of us have experienced feelings of sleepiness after a large meal . However , there is little scientific evidence that this “food coma” effect is real . If it is , it may vary between individuals , or depend on the type of food consumed . This variability makes it difficult to study the causes of post-meal sleepiness . Murphy et al . have now developed a system that can measure fruit fly sleep and feeding behavior at the same time . Recordings using this system reveal that after a meal , flies sleep more for a short period before returning to a normal state of wakefulness . The sleep period lasts around 20-40 minutes , with flies that ate more generally sleeping more . Further investigation revealed that salty or protein-rich foods promote sleep , whereas sugary foods do not . By using genetic tools to turn on and off neurons in the fly brain , Murphy et al . identified a number of brain circuits that play a role in controlling post-meal sleepiness . Some of these respond specifically to the consumption of protein . Others are sensitive to the fruit fly’s internal clock , reducing post-meal sleepiness only around dusk . Thus , post-meal sleepiness can be regulated in a number of different ways . Future experiments are now needed to explore the genes and circuits that enable meal size and the protein or salt content of food to drive sleep . In nature , sleep is likely a vulnerable state for animals . Thus , another challenge will be to uncover why post-meal sleep is important . Does sleeping after a meal boost digestion ? Or might it help animals to form memories about a food source , making it easier to find similar food in the future ? | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"neuroscience"
] | 2016 | Postprandial sleep mechanics in Drosophila |
Schistosomes infect more than 200 million of the world's poorest people . These parasites live in the vasculature , producing eggs that spur a variety of chronic , potentially life-threatening , pathologies exacerbated by the long lifespan of schistosomes , that can thrive in the host for decades . How schistosomes maintain their longevity in this immunologically hostile environment is unknown . Here , we demonstrate that somatic stem cells in Schistosoma mansoni are biased towards generating a population of cells expressing factors associated exclusively with the schistosome host-parasite interface , a structure called the tegument . We show cells expressing these tegumental factors are short-lived and rapidly turned over . We suggest that stem cell-driven renewal of this tegumental lineage represents an important strategy for parasite survival in the context of the host vasculature .
Neoblasts are pluripotent stem cells essential for regeneration and tissue homeostasis in a variety of free-living flatworms , most notably freshwater planarians ( Newmark and Sánchez Alvarado , 2002; Wagner et al . , 2011 ) . Previously , it was shown that schistosomes , like their free-living relatives , also possess neoblasts , capable of self-renewal and differentiation into tissues such as the intestine and muscle ( Collins et al . , 2013 ) . However , the role these cells play in the biology of the parasites in their mammalian host was unexplored . To decipher the cellular functions of schistosome neoblasts , we compared the short-term and long-term transcriptional consequences for the parasite following neoblast depletion ( Figure 1a ) . 10 . 7554/eLife . 12473 . 003Figure 1 . Identification of genes down-regulated after long-term stem cell depletion . ( a ) Scheme for transcriptional profiling studies . ( b ) Venn Diagram showing number of genes significantly down-regulated after short-term ( green ) and long-term ( magenta ) stem cell depletion . ( c ) Heat map showing relative gene expression for various treatments and time points . Only a subset of representative genes is displayed . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 00310 . 7554/eLife . 12473 . 004Figure 1—figure supplement 1 . histone H2B is required to maintain proliferative neoblasts . Parasites were treated with either control or histone H2B dsRNA for four days and then labeled at Day 11 overnight with 10 µM EdU and fixed the following day . Parasites treated with histone H2B dsRNA display a rapid and robust loss of neoblasts . n > 5 parasites . Scale bar: 200 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 00410 . 7554/eLife . 12473 . 005Figure 1—figure supplement 2 . val-8 expression is increased 48 hr following irradiation . Quantitative real time PCR analysis of val-8 48 hr post-irradiation . Levels of tsp-2 and cyclin B gene expression are shown as negative and positive controls , respectively . n=3 biological replicates , *p<0 . 005 , Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 005
To examine the transcriptional effects of neoblast ablation , we exploited the observation that expression of genes in differentiated tissues ( e . g . , the intestine ) is unaffected at 48 hr following irradiation , whereas the neoblasts are irreversibly killed ( Collins et al . , 2013 ) . Previously , we demonstrated that many genes down-regulated at 48 hr following irradiation were associated with the schistosome neoblasts ( Collins et al . , 2013 ) . Thus , we reasoned that by comparing the gene expression profiles of parasites shortly after neoblast ablation ( 48 hr ) to parasites two weeks after their neoblasts had been killed , we could characterize the long-term consequences of neoblast depletion . Specifically , we expected genes down-regulated at both early and late time points to be neoblast-enriched factors , whereas genes only down-regulated at later time points would be genes that require neoblasts for maintaining their expression . To add specificity to this dataset , removing genes whose expression could be influenced non-specifically by irradiation ( Solana et al . , 2012 ) , we also profiled parasite transcriptomes after long-term RNA interference ( RNAi ) targeting either of two genes required for the maintenance of proliferating neoblasts: fgfrA ( Collins et al . , 2013 ) or histone H2B ( Figure 1—figure supplement 1 ) . From our transcriptional profiling experiments of male somatic tissues we identified 135 genes that were down regulated ( ≥1 . 25x , p<0 . 05 ) in both our irradiation and RNAi datasets ( Figure 1b , Supplementary file 1 ) . As anticipated , this gene set included a number of known stem cell- ( e . g . , nanos2 and ago2-1[Collins et al . , 2013] ) and cell cycle-specific ( cyclinB and mcm2 ) genes ( Figure 1b , c and Supplementary file 1 ) . More importantly , we identified 105 genes that were not down-regulated at D2 post-irradiation but were significantly down regulated ( ≥1 . 25x , p<0 . 05 ) at D14 post-irradiation and following RNAi of either fgfrA or histone H2B ( Figure 1b , c and Supplementary file 1 ) . For brevity , we will refer to these 105 genes as delayed irradiation-sensitivity ( DIS ) genes . We also noted a small class of genes that were modestly down regulated at early time points and highly down regulated after long-term stem cell depletion ( Supplementary file 1 ) . The most striking example of this class was the schistosome orthologue of the planarian p53 ( Pearson and Sánchez Alvarado , 2010 ) , which was down-regulated ~2 fold at 48 hr and nearly 150 fold at D14 post-irradiation ( Supplementary file 1 ) . To validate our transcriptional profiling experiments , we examined a subset of these DIS genes by whole-mount in situ hybridization at D2 and D7 following irradiation . As anticipated , expression of cathepsin B , a gene expressed in differentiated intestinal cells , was unaffected at either time point ( Figure 2a ) . Conversely , the expression of genes associated with the neoblasts ( fgfrA and nanos2 ) was substantially reduced at D2 and the expression of these genes did not return by D7 ( Figure 2a ) , confirming that stem cells are irreversibly depleted by irradiation . Consistent with our RNAseq data , the number of cells expressing p53 is modestly reduced at D2 post-irradiation and dramatically reduced by D7 ( Figure 2a ) . In contrast to the neoblast-expressed genes and p53 , at D2 the number of cells expressing the DIS genes tsp-2 , sm13 , sm29 , and val-8 was unaffected ( Figure 2a ) . However , by D7 post-irradiation the expression of these genes was severely depleted ( Figure 2a ) . We did note in our RNAseq experiments , and in independent qPCR experiments , a modest increase in val-8 mRNA levels 48 hr post-irradiation ( Figure 1 and Figure 1—figure supplement 2 ) . Since , the number of val-8+ cells did not appear to dramatically change at 48 hr post-irradiation ( Figure 2a ) , it is possible that some val-8+ cells had elevated levels of the val-8 mRNA . To directly examine the relationship between genes expressed in neoblasts and the DIS genes , we performed double fluorescence in situ hybridization ( FISH ) experiments with histone H2B , p53 , and tsp-2 . We observed no co-expression of the DIS gene tsp-2 with histone H2B Figure 2—figure supplement 1 , suggesting that DIS genes are expressed in a population of cells other than neoblasts . Consistent with our observations following irradiation , we observed that p53 was expressed in both the histone H2B+ neoblasts and tsp-2+ cells ( Figure 2—figure supplement 1 ) . Together , these data strongly support the model that the DIS genes are expressed in an irradiation-sensitive population of cells that is molecularly distinct from the neoblasts . 10 . 7554/eLife . 12473 . 006Figure 2 . Cells expressing DIS genes are lost following stem cell depletion and express genes associated with the schistosome tegument . ( a ) Whole-mount in situ hybridization to detect genes expressed in: the intestine ( Cathepsin B ) ; neoblasts ( fgfrA , nanos2 ) ; or cells expressing DIS genes ( tsp-2 , sm13 , sm29 , val-8 ) in either untreated parasites or worms at D2 or D7 following irradiation . p53 is also shown as an example of a gene modestly down-regulated at early time points and highly down-regulated at late time points after neoblast ablation . Expression of DIS genes is unaffected at day 2 following irradiation but is substantially reduced by day 7 . n > 3 for each data point . ( b ) Left , cartoon showing the organization of the schistosome tegument . Right , fluorescence in situ hybridization and DAPI labeling overlaid on a Differential Interference Contrast ( DIC ) micrograph showing the distribution of tsp-2+ cells relative to the tegument . Although some cells expressing lower levels of tsp-2 are located more internally , a majority of tsp-2+ cells were located just beneath the parasite muscle layer . ( c ) Double fluorescence in situ hybridization showing co-localization of tsp-2 with the indicated tegumental factors . Images are representative of parasites ( n > 3 ) recovered from two separate groups of mice . Scale bars: ( a ) 100 µm , ( b , c ) 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 00610 . 7554/eLife . 12473 . 007Figure 2—figure supplement 1 . DIS genes are expressed in a population of cells that is distinct from the neoblasts . Double fluorescence in situ hybridization showing expression of tsp-2 , p53 , and the stem cell marker histone H2B . tsp-2 is not expressed in histone H2B+ stem cells , whereas p53 is expressed in the histone H2B+ cells . tsp-2 and p53 are co-expressed . Thus , neoblasts and tsp-2+cells are distinct and both express p53 . Images are representative of parasites ( n > 3 ) recovered from at least two separate groups of mice . Scale bars , 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 007 Upon closer examination we noted that a number of the DIS genes encoded proteins previously shown by immunological and/or proteomic approaches to be associated with the parasite's surface ( e . g . , tsp-2 ( Tran et al . , 2006; Pearson et al . , 2012; Wilson , 2012 ) , sm13 ( Abath et al . , 2000; Wilson , 2012 ) , sm29 ( Braschi and Wilson , 2006; Cardoso et al . , 2008; Wilson , 2012 ) , sm25 ( Abath et al . , 1999; Castro-Borges et al . , 2011; Wilson , 2012 ) ) . The schistosome surface is covered by a continuous syncytial structure , called the tegument ( Figure 2b ) , which serves as the primary barrier between the parasite and its host . This unique tissue is connected by cytoplasmic bridges to nucleated cell bodies that sit in the mesenchyme , beneath the parasite’s body-wall muscles ( Morris and Threadgold , 1968; Wilson and Barnes , 1974 ) ( Figure 2b ) . To determine if these DIS genes are expressed in a tegument-associated cell population , we performed double FISH experiments . We first examined the distribution of the mRNA for a Tetraspanin , TSP-2 , that encodes a well-characterized tegument-specific factor ( Braschi and Wilson , 2006; Tran et al . , 2006; Pearson et al . , 2012 ) . TSP-2 is currently being explored as an anti-schistosome vaccine candidate due to its presence on the parasite surface ( Hotez et al . , 2010 ) . Consistent with tsp-2 being expressed in a tegument-associated cell population , we found that a majority of tsp-2+ cells are located immediately beneath the parasite’s body-wall muscle layer ( Figure 2b ) . To further examine this tsp-2+ cell population , we performed double FISH with other DIS genes known to encode proteins expressed in the tegument . We observed that DIS genes encoding a panel of known tegumental factors , including sm13 ( Smp_195190 ) , sm29 ( Smp_072190 ) , sm25 ( Smp_195180 ) , an amino acid transporter ( Smp_176940 ) ( Wilson , 2012 ) , a dysferlin protein ( Smp_141010 ) ( Braschi and Wilson , 2006; Wilson , 2012 ) , an endophillin B1 ( Castro-Borges et al . , 2011; Wilson , 2012 ) , and a cd59-like molecule ( Smp_081920 ) ( Wilson , 2012 ) were expressed in a largely overlapping population of cells with tsp-2 immediately beneath the dorsal body-wall muscles ( Figure 2c ) . Given their position in the parasite , and their expression of many known tegumental genes , our data indicate that tsp-2+ cells represent a population of tegument-associated cells . Our data suggest that tsp-2+ cells co-express many known tegumental factors and are lost within a few days following stem cell depletion . We envision two models to explain these observations . First , tsp-2+ cells could represent a relatively long-lived population that requires the continual presence of the somatic neoblasts for their survival . Alternatively , the tsp-2+ cells could be a short-lived cell population that requires a pool of stem cells for its continuous renewal . In the absence of this renewal , the tsp-2+ cells are rapidly depleted . To distinguish between these possibilities , we performed pulse-chase experiments with the thymidine analogue EdU ( Salic and Mitchison , 2008 ) . This approach allows us to specifically label neoblasts at S-phase and monitor their differentiation over time ( Collins et al . , 2013 ) . In these experiments , parasite-infected mice were injected with EdU and the distribution of EdU+ cells relative to the tsp-2+ cells was monitored every other day for 11 days ( Figure 3a ) . If the tsp-2+cells are long-lived and turn over slowly , we would anticipate that few tsp-2+cells would become EdU+ over the chase period . However , if these cells were renewed rapidly by the neoblasts , we would expect a large fraction of tsp-2+ cells to become EdU+ . Furthermore , since EdU levels are diluted following cell division , over time differentiating neoblasts would contain less EdU , resulting in a reduction in the EdU levels in tsp-2+ cells . 10 . 7554/eLife . 12473 . 008Figure 3 . tsp-2+ cells are renewed by stem cells and then rapidly turned over . ( a ) Cartoon showing EdU pulse-chase strategy to examine the differentiation of stem cells into tsp-2+ cells . ( b ) Quantification of the number of EdU+tsp-2+or EdU+cathepsin B+ cells following a single pulse of EdU given to parasites in vivo . Percentages of EdU+ tsp-2+/total tsp-2+ cells were D1 0 . 22% ( 2/917 ) , D3 41% ( 323/787 ) , D5 52% ( 299/575 ) , 13% ( 57/437 ) , D9 8 . 1% ( 49/603 ) , D11 1 . 4% ( 8/567 ) . Percentages of EdU+ Cathepsin B/ total Cathepsin B+ cells were D1 0% ( 0/1570 ) , D3 2 . 4% ( 26/1057 ) , D5 2 . 9% ( 61/2044 ) , D7 4 . 2% ( 58/1359 ) , D9 4 . 3% ( 106/2469 ) , D11 3 . 9% ( 64/1646 ) . Data were collected from > 5 male parasites recovered from two separate mice , except for cathepsin B labeling at D11 where parasites were recovered from a single mouse . ( c , d ) Fluorescence in situ hybridization showing the EdU labeling of tsp-2+ or cathepsin B+ cells at various time points following an EdU pulse . Scale bars , 15 µm . ( e ) Potential models for tegumental cell differentiation . DOI: http://dx . doi . org/10 . 7554/eLife . 12473 . 008 At D1 following an EdU pulse , <0 . 25% of tsp-2+ cells were EdU+ ( Figure 3b , c ) , indicating that the tsp-2+ cells are not proliferative . After a 3-day chase period , however , we noted that over 40% of tsp-2+cells were newly born EdU+ cells ( Figure 3b , c ) . This result suggests that stem cells initially incorporating EdU were capable of replenishing nearly half of the tsp-2+ cells within three days . Beyond D5 , we noted a rapid reduction in the number of tsp-2+ EdU+ cells and an overall reduction in the EdU levels per cell ( Figure 3b , c ) . These data suggest that tsp-2+ cells are a short-lived cell population that is continuously renewed by the neoblasts during the parasite’s life in its definitive host . To determine if this rapid rate of neoblast-driven renewal was unique to tsp-2+ cells , we also examined the kinetics of EdU labeling of the schistosome intestine . In contrast to the tsp-2+ cells , only 2 . 5% of cathespin B+ intestinal cells were EdU+ at D3 , and this level remained fairly constant throughout the 11D time course ( Figure 3b , d ) . Thus , the kinetics of tegumental cell birth differs from that of intestinal cells . Taken together , our data suggest that on a population level neoblasts are 'biased' toward the rejuvenation of tsp-2+ cells over other lineages . In planarians , a population of postmitotic neoblast progeny displays similar sensitivity to irradiation as this tsp-2+ tegument-associated cell population ( Eisenhoffer et al . , 2008 ) . These planarian neoblast progeny similarly express a p53-like protein as well as a large collection of planarian-specific molecules ( Eisenhoffer et al . , 2008; Zhu et al . , 2015 ) . Most importantly , these planarian cells serve as progenitors to terminally differentiated epidermal cells ( van Wolfswinkel et al . , 2014; Tu et al . , 2015 ) . Thus , it appears that free-living and parasitic flatworms utilize similar developmental strategies for epidermal maintenance . Presently , electron microscopy is the only methodology to unambiguously identify tegumental cell bodies in schistosomes . Therefore , with current technology , it is not possible to determine which of the irradiation-sensitive cells expressing DIS genes are terminally differentiated tegumental cell bodies . In light of this limitation , our data are consistent with two models ( Figure 3e ) . In the first model , proliferating neoblasts differentiate to produce a short-lived population of terminally differentiated tegumental cell bodies expressing tsp-2 and other DIS genes ( i . e . , sm13 , sm25 , sm29 , etc . ) . In the alternative model , cells expressing tsp-2 ( and other DIS genes ) represent a population of short-lived progenitors to terminally differentiated tegumental cell bodies . Regardless of which model ( or combination of these models ) is correct , our data suggest that a primary function of the schistosome neoblasts is to generate cells that contribute to the tegument . The mammalian bloodstream would appear to be a rather inhospitable niche for a pathogen . In the case of schistosomes , there is little dispute about the importance of the tegument in defending the parasite from host immunity ( McLaren , 1980; Skelly and Alan Wilson , 2006 ) , yet the properties of this tissue that afford the parasite protection in blood are unclear . Indeed , numerous ideas have been proposed to explain this phenomenon , including tegumental absorption of host antigens ( Smithers et al . , 1969; Clegg et al . , 1971 ) and the turnover of the unique tegumental outer membranes ( Perez and Terry , 1973; Wilson and Barnes , 1977 ) . Based on our data , we suggest that neoblast-driven tegumental regeneration may play a key role in the parasite’s ability to survive in the mammalian host . By undergoing continuous tegumental renewal , the parasite is likely capable of rapidly turning over the tegument and regenerating damage inflicted inside the host ( e . g . , via immune attack ) . Thus , an important goal for future studies is to address the role of neoblasts in parasite survival and tegumental function in vivo .
Adult S . mansoni ( 6–8 weeks post-infection ) were obtained from infected mice by hepatic portal vein perfusion with 37°C DMEM ( Mediatech , Manassas , VA ) plus 5% Fetal Bovine Serum ( FBS , Hyclone/Thermo Scientific Logan , UT ) and heparin ( 200–350 U/ml ) . Parasites were rinsed several times in DMEM + 5% FBS and cultured ( 37°C/5% CO2 ) in Basch’s Medium 169 ( Basch , 1981 ) and 1x Antibiotic-Antimycotic ( Gibco/Life Technologies , Carlsbad , CA 92008 ) . Media were changed every 1–3 days . For transcriptional profiling of irradiated worms , parasites were harvested from mice , suspended in Basch medium 169 , and exposed to 200 Gy of γ-irradiation using a Gammacell-220 Excel with a Co60 source ( Nordion , Ottawa , ON , Canada ) . Control parasites were mock irradiated . Parasites were cultured in Basch Medium 169 and 48 hr or 14D post-irradiation males were separated from female parasites using 0 . 25% ethyl 3-aminobenzoate methanesulfonate . Following separation , the head and testes of males were amputated with a sharpened tungsten needle ( Collins et al . , 2013 ) and purified total RNA was prepared from the remaining somatic tissue from pools of 9–18 parasites using Trizol ( Invitrogen , Carlsbad , CA ) and DNase treatment ( DNA-free RNA Kit , Zymo Research , Irvine , CA ) . Three independent biological replicates were performed for both control and irradiated experimental groups . For RNAi of fgfrA and histone H2B , parasites were treated with dsRNA as previously described ( Collins et al . , 2013 ) , and RNA was extracted at D21 using similar procedures as used for the irradiated parasites . Detailed files of the RNAseq results can be found in Supplementary file 2 . Three biological replicates were performed for fgfrA ( RNAi ) and two biological replicates for H2B ( RNAi ) . Control RNAi treatments with an irrelevant dsRNA synthesized from the ccdB and camR-containing insert of plasmid pJC53 . 2 ( Collins et al . , 2013 ) were performed alongside fgfrA and H2B dsRNA treatments . To measure transcriptional differences , RNAseq analysis was performed on an Illumina HiSeq2000 and analyzed using CLC Genomics Workbench as described previously ( Collins et al . , 2013 ) . To define genes down-regulated in all treatment groups and genes specifically down-regulated following long-term stem cell depletion , we first compared the lists of genes down-regulated ( >1 . 25 fold change , p<0 . 05 , t-test ) at D2 and D14 post-irradiation . This list was then cross-referenced to our RNAi datasets to define the DIS genes and the 135 genes down regulated in both the irradiation and RNAi treatments . To reduce false negatives we only required genes to be significantly down-regulated in either the fgfrA ( RNAi ) or the H2B ( RNAi ) treatments . For quantification of gene expression RNA was reverse transcribed ( iScript , Biorad ) and quantitative real-time PCR was performed on a BioRad CFX96 Real Time System with iTaq Universal SYBR Green Supermix ( Biorad ) . Relative expression was determined using the ΔΔCt method and mean ΔCt values of biological replicates were used to make statistical comparisons between treatments . Oligonucleotide sequences are listed in Supplementary file 3 . Whole-mount in situ hybridization and EdU labeling of parasites grown in mice were performed as previously described ( Collins et al . , 2013 ) Tyramide Signal Amplication for double fluorescence in situ hybridization was performed essentially as previously described ( Collins et al . , 2010 ) except 100mM sodium azide was used to quench peroxidase activity between rounds of signal development . cDNAs used for RNAi or in situ hybridization were cloned in plasmid pJC53 . 2 using TA-based cloning ( Collins et al . , 2010 ) or Gibson assembly ( New England Biolabs Gibson Assembly Master Mix , E2611S ) ; oligonucleotide primer sequences are listed in Supplementary file 3 . Imaging of specimens was performed similar to previous studies ( Collins et al . , 2010; 2011 ) using either a Zeiss LSM 710 or Zeiss LSM 700 for confocal imaging or a Leica MZ205 or Zeiss AxioZoom for brightfield imaging . For whole-mount in situ hybridizations on irradiated parasites , parasites were recovered from mice , exposed to 200 Gy of X-ray irradiation using a CellRad irradiator ( Faxitron Bioptics , Tucson , AZ ) or 100 Gy of Gamma Irradiation on a J . L . Shepard Mark I-30 Cs137 source , and cultured in vitro for indicated periods of time . | Schistosomes are parasitic worms that infect and cause chronic disease in hundreds of millions of people in the developing world . A major reason these parasites are so damaging is that they are capable of living and reproducing in the human bloodstream for decades . Previous research had shown that schistosomes have a population of stem cells that are proposed to promote the parasite’s survival inside the host’s bloodstream . However , it was not clear what role these cells play in the worms . Collins et al . have now found that , in a parasitic worm called Schistosoma mansoni , a large number of these stem cells are destined to become cells that generate the parasite’s skin . This unique tissue is known as the tegument , and had long been thought to have evolved in parasitic flatworms to help them survive in their host and evade its immune defenses . Therefore , Collins et al . ’s findings suggest a new mechanism by which stem cells can promote the survival of a parasite inside its host . In the long-term , these findings could lead to new treatments for parasitic infections and may shed light on the evolution of parasitic flatworms . An important future challenge will be to determine if disrupting these parasites’ stem cells , and their ability to generate new tegumental cells , has any effect on the parasite inside its host . | [
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] | 2016 | Stem cell progeny contribute to the schistosome host-parasite interface |
The molecular pathways underlying tumor suppression are incompletely understood . Here , we identify cooperative non-cell-autonomous functions of a single gene that together provide a novel mechanism of tumor suppression in basal keratinocytes of zebrafish embryos . A loss-of-function mutation in atp1b1a , encoding the beta subunit of a Na , K-ATPase pump , causes edema and epidermal malignancy . Strikingly , basal cell carcinogenesis only occurs when Atp1b1a function is compromised in both the overlying periderm ( resulting in compromised epithelial polarity and adhesiveness ) and in kidney and heart ( resulting in hypotonic stress ) . Blockade of the ensuing PI3K-AKT-mTORC1-NFκB-MMP9 pathway activation in basal cells , as well as systemic isotonicity , prevents malignant transformation . Our results identify hypotonic stress as a ( previously unrecognized ) contributor to tumor development and establish a novel paradigm of tumor suppression .
Many malignancies result from loss-of-function mutations in one or more tumor suppressor genes whose normal function is concerned with the inhibition of cell division , the induction of apoptosis and/or the inhibition of metastasis . Most tumor suppressors affect one or more of these processes in a cell-autonomous manner , being produced by and acting within the tumor precursor cells themselves ( Sherr , 2004; Sun and Yang , 2010 ) , whereas comparably few genes are known to block tumorigenesis in a non-cell-autonomous manner ( Chua et al . , 2014 ) . Tumor suppressors often act by inhibiting or antagonizing proto-oncogenic factors . The phosphatase PTEN for instance is the antagonist of phosphatidyl-inositol-3-kinases ( PI3Ks ) , critical coordinators of intracellular signaling in response to extracellular stimuli such as growth factors and cytokines . Hyperactivity of PI3K signaling cascades , including that involving AKT/PKB ( protein kinase B ) , is one of the most common events in human cancers ( Altomare and Testa , 2005; Thorpe et al . , 2015 ) . One of the transcription factors regulated by PI3K/AKT via the mTORC1 complex is NFκB ( Dan et al . , 2008 ) . Increased NFκB activity is observed in many carcinomas ( solid malignancies derived from epithelial cells ) , promoting cell survival , proliferation and metastasis ( Karin et al . , 2002 ) . However , the actual genetic cause of NFκB activation is unknown in most of these cases ( Ben-Neriah and Karin , 2011 ) . Furthermore , in other instances , anti-tumorigenic effects of NFκB have been described ( Ben-Neriah and Karin , 2011 ) . In epithelial cells , carcinogenesis can also be caused by compromised functioning of genes involved in the formation and maintenance of epithelial cell polarity ( Martin-Belmonte and Perez-Moreno , 2012; Ellenbroek et al . , 2012 ) . Prominent examples of affected proteins include CRB3 , a member of the Crumbs complex; PAR3 , a member of the partitioning defective ( PAR ) / aPKC complex , which like the Crumbs complex promotes apical identities; and LGL1 ( Lethal giant larvae-1 ) , DLG ( Discs large ) and SCRIB , members of the Scribble complex , which promote basolateral identities . In Drosophila , a second pro-basolateral complex has been described , consisting of a Na , K-ATPase , Coracle , Yurt and Neurexin IV , which acts in partial functional redundancy with the Scribble complex ( Paul et al . , 2007; Laprise et al . , 2009 ) . Na , K-ATPases are ion pumps composed of a catalytic α-subunit and a regulatory β-subunit that is required for proper trafficking , localization , and functionality of the α-subunit ( Geering , 2008 ) . Four α-subunit and three β-subunit genes have been described in mammals , of which α1 and β1 are the major isoforms in epithelial cells . These subunits transport Na and K ions across the cell membrane , and thereby play well-characterized roles in generating electrochemical gradients in multiple cell types , and in sodium and water balancing in renal tubules , thereby regulating body fluid composition and volume . However , additional pump-independent and evolutionary conserved functions for Na , K-ATPase proteins have been described in the context of epithelial cell adhesiveness and polarity ( Vagin et al . , 2012 ) . Here , the β-subunits play a prominent role . These are type II transmembrane proteins with glycosylated extracellular domains that can form β1-β1 trans-bonds between neighboring cells , thereby promoting both epithelial polarity and intercellular adhesiveness of cultured cells . β-subunit glycosylation has a positive impact on cell-cell adhesiveness , most likely by stabilizing E-cadherin cell adhesion proteins ( Vagin et al . , 2008 ) . Consistently , in transformed Madin-Darby canine kidney epithelial cells ( MDCK ) cells , the expression of both E-cadherin and the Na , K-ATPase β1-subunit is drastically reduced , and epithelial polarity and junctional complexes can only be re-established upon repletion of both molecules ( Rajasekaran et al . , 2001 ) . Na , K-ATPase β1-subunit levels are also reduced in cell lines derived from various carcinomas . This evidence , together with functional studies with transformed MDCK cells , has led to the proposal that the Na , K-ATPase β1 subunit has a potential tumor-suppressor function ( Inge et al . , 2008 ) , but direct genetic evidence for this notion had been missing to date . Here , we report that a loss-of-function mutation in the β-subunit Atp1b1a causes epidermal malignancy in zebrafish psoriasis mutant embryos ( Webb et al . , 2008 ) , correlated with reduced E-cadherin levels and a genetic interaction with the formerly described epithelial polarity regulator and tumor suppressor Lgl2 ( Sonawane et al . , 2005; Reischauer et al . , 2009 ) . During the affected stages , the epidermis is normally bi-layered , consisting of a tight junction-bearing outer periderm and a basal layer of keratinocytes . The latter are transformed in atp1b1a mutants , leading to their overgrowth and invasion of dermal compartments . Keratinocyte transformation is transduced via aberrant activation of a PI3K-AKT-mTORC1-NFκB-MMP9 ( metalloprotease 9 ) pathway . Chemical inhibition of PI3K , mTORC1 and NFκB rescues all aspects of malignancy , whereas knockdown of MMP9 alleviates only epidermal invasiveness but not hyperplasia , pointing to a specific role of this matrix metalloprotease as one of the mediators of metastasis , and to the involvement of additional relevant NFκB targets . Epidermal malignancy is also fully suppressed upon incubation of embryos in isotonic ( rather than the natural hypotonic ) medium , and the remaining basal cell polarity and adhesiveness defects can be rescued by concomitant re-introduction of wild-type Atp1b1a in peridermal cells . Together with other presented data , these findings indicate that epidermal malignancy results from a combined loss of the β-subunit’s trans-layer function in promoting basal cell polarity via the periderm , and its osmoregulatory function in suppressing hypotonic stress . Possible tumor-promoting effects of hypotonicity during human carcinogenesis are discussed .
The zebrafish psoriasis mutant was isolated in a phenotype-based screen after undirected ethyl methanesulfonate ( EMS ) -mutagenesis and has been described as developing edema as well as epidermal aggregates during embryogenesis ( Figure 1a , b ) ( Webb et al . , 2008 ) . Aggregates were preferentially found on the median fin folds , but also on the flanks , the yolk sac and the head ( Figure 1c–g ) . Analysis with specific molecular markers revealed that the aggregates consist of both peridermal cells and basal keratinocytes ( see Figure 1h for schematic of embryonic skin ) , and can already be detected at 48 hours post-fertilization ( hpf ) ( Figure 1i–k ) . Initial defects were more pronounced in the outer periderm , in which the normally flat cells started to round up ( Figure 1i , j ) . In addition , cell membranes , in particular in basal domains facing the underlying basal keratinocytes , displayed reduced levels of the cell-cell adhesion molecule E-cadherin ( Cdh1 ) ( Figure 1i’ , j’ ) . Altered organization of basal keratinocytes themselves was only obvious in more advanced aggregates , in which cells had lost their regular epithelial shape and their mono-layered organization ( Figure 1k ) . At 56 hpf , hyper-proliferation was evident in basal cells , whereas peridermal cells did not proliferate ( Figure 1l , m ) . 10 . 7554/eLife . 14277 . 003Figure 1 . Epidermal aggregates in psoriasis mutants display hyperplasia . ( a–g ) Live images of wt siblings ( a , c ) and psoriasis mutants ( b , d–g ) ; psoriasis mutants develop pericardial edema ( pe ) and epidermal aggregates on the medium fin fold ( b , d ) , on the yolk sac ( e ) , on the flank ( f ) ( all at 54 hpf ) , and on the head ( g; 72 hpf ) . ( h ) Schematic of embryonic skin . Peridermal cells ( PC , green ) with apical tight junctions ( TJ , red ) are located above p63 basal keratinocytes ( BC , red nuclei ) . The basement membrane ( BM , red ) separates the basal epidermal layer from the dermis containing collagen fibers ( COL , green ) . ( i–k ) IF of periderm-specific GFP ( green ) , Cdh1 ( red ) , and p63 ( red ) on transverse sections through 48 hpf Tg ( krt4:GFP ) wt and psoriasis mutant embryos , counterstained with DAPI ( blue ) . ( i’–k’ ) show magnified views of regions framed in ( i–k ) , without the green channel . In wt , the epidermis is bi-layered , with flat cells ( i ) and Cdh1 is localized at cell borders between peridermal and basal cells ( i’; arrows ) . In an early-stage aggregate of the mutant , peridermal cells have rounded up ( j ) , and Cdh1 levels are reduced at cell borders ( j’ , k’; arrows ) . An advanced aggregate ( k ) contains multi-layered basal epidermal cells ( k’ ) . Scale bar: 10 µm . ( l , m ) Whole mount IF of incorporated BrdU ( red ) and periderm-specific GFP ( green ) in 54 hpf Tg ( krt4:GFP ) wt sibling ( l ) and psoriasis mutant ( m ) , showing elevated numbers of BrdU-positive non-peridermal cells in aggregates . ( m ) Maximum intensity projection of a confocal Z stack through aggregate; ( m’ ) single focal plane . Scale bars: 20 µm . Abbreviations: BC , basal cell; BM , basement membrane; COL , collagen fibers; PC , peridermal cell; pe , pericardial edema; wt , wild-type . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 003 Live time-lapse imaging revealed partial epithelial-mesenchymal transitions ( EMTs ) in the basal keratinocytes of mutant embryos at 48 hpf , characterized by the formation of cellular processes and dynamic dissociations and re-associations of cells ( Figure 2a , b and Videos 1 , 2 ) . Transmission electron microscopy ( TEM ) and immunofluorescence ( IF ) analyses of the median fin folds of psoriasis mutants revealed that at 58 hpf basal keratinocytes had loosened their intercellular connections ( Figure 2c , d ) . In addition , the basement membrane ( BM ) underneath the basal layer was disintegrated to variable degrees ( Figure 2e–j; Figure 2—figure supplement1 ) . Furthermore , numerous basal cells had entered the dermal compartment ( Figure 2i , j ) where they were in close contact with the remnants of the actinotrichia , dermal collagenous structures that were significantly disassembled in regions with epidermal aggregates ( Figure 2c , d , g , h; Figure 2—figure supplement 1a–d ) . Together , this evidence demonstrates that basal keratinocytes of psoriasis mutants display both hyperplasia and invasive behavior , characteristics of epidermal malignancy . 10 . 7554/eLife . 14277 . 004Figure 2 . psoriasis keratinocytes display partial EMT and invasive behavior . ( a , b ) Stills from in vivo time-lapse recordings ( Videos 1 and 2 ) of clones of membrane-bound GFP-labelled ( Tg ( Ola . Actb:Hsa . hras-egfp ) ) basal keratinocytes in a mosaic wt ( a ) or an atp1b1a morphant ( b ) embryo; 'n min' indicates elapsed time since the start of the recordings at 48 hpf ( Videos 1 and 2 ) . Wild-type cells form a rigid epithelium and maintain their shapes and relative positions ( a ) , whereas atp1b1a morphant cells form cellular processes ( arrows ) , dynamically dis- and re-associate ( arrowheads ) and eventually crawl on top of each other ( cell 1; b , first panel ) . Cell 1 moves out of the focal plane after 60 min , cell 6 changes its shape from roundish to more hexagonal and vice versa . Scale bars: 20 µm . ( c , d ) Transverse TEM sections through median fin fold , at 58 hpf . In wt ( c ) , an intact basement membrane ( BM; black arrowhead ) separates the compact layer of basal cells from the underlying dermis , which contains actinotrichia ( small asterisks ) . The psoriasis mutant ( d ) displays large intercellular gaps ( large asterisk ) between basal cells , cellular protrusions ( arrows ) of basal cells , a discontinued BM ( arrowheads to remaining BM ) , direct contacts between epidermal cells , and disassembling dermal actinotrichia ( small asterisks ) that lose their regular shape and striated pattern . bc: basal cells; pc: peridermal cell . Scale bars: 1 µm . ( e , f ) Laminin and peridermal-specific GFP double IF , counterstained with DAPI , at 58 hpf . Transverse sections through the fin fold of Tg ( krt4:GFP ) transgenics reveal basement membrane fragmentation ( arrowhead ) below an epidermal aggregate in the mutant ( f ) . ( g , h ) Laminin and type II collagen double IF , counterstained with DAPI at 58 hpf; view of fin folds of whole mounts , showing basement membrane fragmentation and actinotrichia disassembly in the mutant ( h ) . ( i , j ) Laminin and p63 double IF , counterstained with DAPI at 58 hpf; transverse section through the yolk sac . Arrowheads in ( j ) point to holes in the basement membrane of the mutant . Note the presence of p63 keratinocytes below the basement membrane in the dermal space . Scale bar: 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 00410 . 7554/eLife . 14277 . 005Figure 2—figure supplement 1 . psoriasis mutants display local degradations of the skin basement membrane and of underlying collagenous actinotrichia of the dermis . ( a-d ) IF of laminin ( red; a ) and type II collagen ( green; b ) in a whole mount 58 hpf psoriasis mutant fin fold , counterstained with DAPI ( blue; c ) . The basement membrane is disintegrated ( a ) and actinotrichia ( b ) are disassembled below an epidermal aggregate ( d; merged channels ) . ( e–g ) IFs of laminin ( red ) show examples of holes in the basement membrane in different 58 hpf psoriasis mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 00510 . 7554/eLife . 14277 . 006Figure 2—figure supplement 2 . psoriasis mutants display skin inflammation , which does not contribute to the formation of epidermal aggregates . ( a–d ) WISH of mpx labeling neutrophils in 54 hpf psoriasis-/- and psoriasis-/- ; mpx MO embryos . mpx-positive neutrophils ( arrows ) have migrated into epidermal aggregates in the psoriasis-/- embryo ( a , c ) . No reduction of epidermal aggregate formation and no mpx-positive neutrophils are observed in psoriasis mutants , in which the myeloid lineage has been ablated by pu . 1 MO injection ( b , d ) . ( e ) Quantification of phenotypes of psoriasis mutants injected with pu . 1 MO and un-injected controls . n = 16–18 . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 00610 . 7554/eLife . 14277 . 007Figure 2—figure supplement 2–source data 1 . Source data for Figure 2—figure supplement 2e . Quantification of the phenotypes of embryos obtained from in-crosses of psoriasis /- fish injected with pu . 1 MO and un-injected controls . Numbers and percentage of psoriasis mutants obtained with pericardial edema ( pe ) , edema and weak aggregates ( weak ) , edema and medium aggregates ( medium ) , and edema and strong aggregates ( strong ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 00710 . 7554/eLife . 14277 . 008Video 1 . In vivo time-lapse recordings revealing stable epithelial integrity of basal keratinocytes in wild-type embryo . In vivo time-lapse recordings of mosaic wt embryo with clones of basal keratinocytes labeled with membrane-bound GFP . The Tg ( Ola . Actb:Hsa . hras-egfp ) -bearing progenitors of these clones had been transplanted into a non-labeled host at 6 hpf . At 48 hpf , the mosaic embryo was mounted in 1 . 5% low-melting agarose in E3 medium and z-stacks of clusters of GFP-positive cells were recorded every five minutes with a Zeiss laser scanning microscope ( Zeiss LSM710 META ) for 95 min . Maximum intensity projections were processed using ImageJ software . Stills of the Videos are shown in Figure 2a . Similar results were obtained for nine recordings from 9 different individuals . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 00810 . 7554/eLife . 14277 . 009Video 2 . In vivo time-lapse recordings revealing partial EMT of basal keratinocytes in atp1b1a morphant embryo . In vivo time-lapse recordings of mosaic atp1b1a morphant embryo with clones of basal keratinocytes labeled with membrane-bound GFP . The Tg ( Ola . Actb:Hsa . hras-egfp ) -bearing progenitors of these clones had been transplanted into a non-labeled host at 6 hpf . At 48 hpf , the mosaic embryo was mounted in 1 . 5% low-melting agarose in E3 medium and z-stacks of clusters of GFP-positive cells were recorded every five minutes with a Zeiss laser scanning microscope ( Zeiss LSM710 META ) for 95 min . Maximum intensity projections were processed using ImageJ software . Stills of the videos are shown in Figure 2b . Similar results were obtained for nine recordings from 9 different individuals . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 009 In addition , psoriasis mutants displayed moderate skin inflammation , characterized by increased numbers of mpx-positive neutrophils and lyz-positive macrophages at 54 hpf ( Figure 2—figure supplement 2a , c; and data not shown ) . This inflammation does not , however , seem to contribute to epidermal malignancy , as epidermal aggregates of unaltered numbers and sizes were also obtained in psoriasis mutants after ablation of the myeloid lineage by pu . 1 antisense morpholino oligonucleotide ( MO ) injection ( Carney et al . , 2007 ) ( Figure 2—figure supplement 2a–e ) . Applying meiotic mapping , Webb et al . had placed the psoriasis mutation within a defined region on LG 6 , but the causing lesion had not been identified ( Webb et al . , 2008 ) . We conducted whole exome sequencing of pooled mutant siblings and their ( heterozygous ) parents and confirmed the linkage to LG 6 ( Figure 3a , b ) . In addition , we identified a C to T transition in exon 6 of the gene atp1b1a , which was confirmed by Sanger sequencing ( Figure 3c ) . By contrast , exons of all other annotated genes in this region ( from 1 . 32 Mb / 22 genes North to 1 . 43 Mb / 20 genes South of atp1b1a ) contained no nonsense , non-conservative missense or splice site mutations ( Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 14277 . 010Figure 3 . The psoriasis phenotype is caused by a loss-of-function mutation in atp1b1a , which is expressed in multiple epithelia , but not in basal keratinocytes . ( a-d ) Exome sequencing links the psoriasis mutation to LG6 and identifies a C to T transition in atp1b1a . ( a , b ) Heat maps showing the density of variant loci over the whole genome ( a ) and on chromosome 6 ( b ) . The Y-axis shows the absolute value of the difference in the percentage of DNA harboring the variation between the pool of affected offspring and the pool of their parents . Most of the genome shows a difference close to 0% , indicating that the parental DNA segregated randomly in the affected offspring . Only one peak on chromosome 6 shows low density at a difference of 0% , but high density at between 25% and 50% difference , which is expected at the linked locus under the assumption of a recessive mode of inheritance . ( c ) Chromatographs of Sanger sequencing of sibling and mutant DNA showing the psoriasis mutation ( * ) . ( d ) Schematic representation of the atp1b1a locus . Red arrowhead indicates the position of the mutation . ( e ) Live images of 54 hpf wt siblings , psoriasis mutants , and atp1b1a morphants . MO-based knockdown of atp1b1a in wt embryos phenocopies both the pericardial edema and epidermal aggregates . Scale bars: 500 µm; 250 µm ( magnifications ) . ( f–i ) WISH detects atp1b1a RNA ( blue ) in heart and multiple epithelia of 48 hpf wt embryos , including the pronephric duct and the periderm but not in the basal keratinocytes ( f–h ) , as seen at higher magnification after counterstaining of nuclei of basal keratinocytes for p63 protein ( i ) . The atp1b1a RNA signal is not detected around p63 nuclei , but is detected in hexagonal peridermal cells with p63– nuclei . Abbreviations: h , heart; le , lense; oe , olfactory epithelium; ov , otic vesicle; pd , periderm; pfb , pectoral fin bud; pn , pronephros; pnd , pronephric duct . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01010 . 7554/eLife . 14277 . 011Figure 3—figure supplement 1 . Schematic of the genomic region between 27 , 899 , 072–30 , 685 , 841 on Chromosome 6 of Ensembl Danio rerio version 84 . 10 ( GRCz10 ) . Merged Havana/Ensemble annotated genes are indicated . The interval between the two simple sequence length polymorphism ( SSLP ) markers BX487C and z12094 , to which atp1b1a had been meiotically mapped by Webb et al . , 2008 , is indicated by black bars . Exons of genes indicated in grey and red have been exclusively sequenced in this study , and a nonsense mutation has been identified in atp1b1a ( red ) . Genes indicated in blue have been sequenced in this study as well as by Webb et al . , 2008 without identification of mutagenic lesions . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01110 . 7554/eLife . 14277 . 012Figure 3—figure supplement 1—source data 1 . List of annotated genes , in syntenic order , contained in the genomic 2 . 76 Mb region shown in Figure 3—figure supplement 1 , together with their chromosomal location , and their sequencing status . In addition to atp1b1a ( in red ) , the exons of the other 42 genes in the region were sequenced during this study . Furthermore , the exons or cDNAs of 10 of these 42 genes had already been sequenced by Webb et al . ( 2008 ) . Apart from the described nonsense mutation in atp1b1a , no mutagenic lesions were found . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 012 atp1b1a encodes a β-subunit of a Na , K-ATPase , an ion pump that drives the directional transport of Na and K ions across cell membranes . The functional pump consists of two subunits , the catalytically active α-subunit and the regulatory β-subunit , which is a type II transmembrane protein . The identified C to T transition ( C760T ) results in the generation of a premature stop codon ( Q254* ) ( Figure 3d ) , removing the highly conserved C-terminus of the β-subunit’s extracellular domain . Upon knocking down atp1b1a in wild-type embryos with a previously described MO blocking atp1b1a translation ( Blasiole et al . , 2006 ) , we obtained edema and epidermal phenotypes indistinguishable from those of the psoriasis mutants ( Figure 3e ) . This indicates that the identified mutation is indeed the causative lesion and has a loss-of-function effect . To gain first insights into potential sites of essential Atp1b1a functions , we determined the β-subunit’s expression pattern in 24– 48 hpf wild-type embryos via whole mount in situ hybridization ( WISH ) . Consistent with former reports ( Thisse et al . , 2001 ) , atp1b1a was strongly expressed in the pronephros , the heart , and multiple ( other ) epithelia ( Figure 3f , g ) . In addition , we found previously unrecognized expression in peridermal cells , but not in the basal keratinocytes of the epidermis ( Figure 3h , i ) . The zebrafish Na , K-ATPase α-subunit Atp1a1a . 1 has been formerly described as required for heart tube elongation and proper heart-beating ( Cheng et al . , 2003 ) . psoriasis mutants also displayed a mildly reduced heartbeat rate at 33 hpf , which was more pronounced at 50 hpf ( Figure 4c ) , but no defects in heart tube elongation were apparent at 34 hpf ( Figure 4a , b ) . This suggests that atp1b1a is required for proper functioning of the embryonic heart rather than for its development , although the cellular basis of the heart malfunction in psoriasis mutants remains unclear . We could , however , identify specific defects in the organization of epithelial cells in the pronephros and its ducts . Antibodies raised against the chicken Na , K-ATPase α-subunits α5 and α6F have been reported to detect zebrafish α-isoforms in the embryonic kidney ( Drummond et al . , 1998 ) and in ionocytes ( Lin et al . , 2006 ) ( but not in the heart and periderm; own data not shown ) . α5/α6F immunolabelling was completely absent in the pronephric duct of psoriasis mutants , but not in their ionocytes ( Figure 4g–j ) , demonstrating that the mutation in the β-subunit results in a complete failure to target these α-subunits to the basolateral membrane of pronephric cells . A comparable mislocalization of α-subunits in the zebrafish pronephros has been associated with compromised osmoregulatory function and edema formation in several other instances ( Drummond et al . , 1998; Hentschel et al . , 2005; Martin-Belmonte and Perez-Moreno , 2012 ) . 10 . 7554/eLife . 14277 . 013Figure 4 . atp1b1a is required for proper heart and pronephric function . ( a , b ) WISH of cmlc2 in 34 hpf embryos reveals normal heart tube elongation in psoriasis mutants . ( c ) psoriasis mutants exhibit a reduction of the heart beat . n = 12 ( mutants ) , 24 ( siblings ) . p values: 32 hpf: 3 . 8E-04 , 36 hpf: 6 . 8E-07 , 50 hpf: 1 . 2E-11 . ( d– f ) psoriasis mutants show compromised clearance / excretion of rhodamine-dextrane injected into the cardinal vein at 34 hpf; confocal images of live embryos of wt sibling ( e ) and mutant ( f ) embryos at 50 hpf; RDex , rhodamine-dextrane; scale bar: 200 µm . ( f ) Quantification of mean fluorescence intensity of a defined area in confocal images , determined with ImageJ software; n = 3 for each condition . Error bars represent standard deviations . ( g–j ) IF of Atp5a ( red ) and aPKC ( green ) , counterstained with DAPI ( blue ) , on transverse sections of 54 hpf wt ( g , i ) and psoriasis mutant ( h , j ) embryos . Atp5a signal is absent from the pronephric duct ( pnd , arrowheads ) but not from ionocytes ( io ) or spinal cord ( sc ) in psoriasis mutants ( g , h ) . The apical marker aPKC is still present in the pronephric duct cells of psoriasis mutants , outlining the lumen of the ducts , whereas Atp5a is missing from the basolateral site ( arrows; i , j ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01310 . 7554/eLife . 14277 . 014Figure 4—source data 1 . Source data for Figure 4 . Source data for Figure 4c . Heart beat score of psoriasis mutants and their wt siblings . Mutants are indicated in yellow . p values are determined using an unpaired two-tailed Student’s t-test . Source data for Figure 4f . Quantification of the mean fluorescence intensity of Rhodamin dextran of a defined area in confocal images , determined with ImageJ software . p values are determined using an unpaired two-tailed Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 014 Together , this evidence points to an essential role for Atp1b1a in the proper positioning of the Na/K pump in the basolateral membrane domain of kidney epithelial cells , which is crucial for the kidney’s osmoregulatory function . Together with the reduced heart beat , these kidney defects should lead to compromised renal water secretion . Indeed , clearance of rhodamin-dextrane injected into the cardinal vein was significantly reduced in psoriasis mutants at 24 hr post injection ( Figure 4d–f ) . The osmoregulatory demands of an animal depend on its environmental conditions . Zebrafish live in freshwater and are estimated to have an internal osmolarity of 230–300 mOsm , whereas freshwater has 10 mOsms ( Enyedi et al . , 2013 ) . As a consequence of this external hypotonicity , the embryos face a continuous passive influx of water that needs to be actively excreted via the pronephros . Therefore , compromised function of Atp1b1a in pronephros and heart causes edema formation as a result of increased water content , which is indicative of hypotonicity in interstitual compartments . To bypass the need for Atp1b1a for osmoregulation , we incubated psoriasis mutant embryos in E3 medium containing 250 mM mannitol , which is isotonic to the interior of the embryo ( Enyedi et al . , 2013 ) . This treatment led to the expected abrogation of edema formation ( Figure 5a–d ) . In addition and more surprisingly , it also completely rescued epidermal hyperplasia and aggregate formation ( Figure 5a–k ) . Similar results were obtained by incubating mutant embryos in isotonic Ringer’s solution ( Figure 5i ) . This indicates that deregulation of the osmotic state in psoriasis mutants results in epidermal hyperplasia , and that the osmoregulatory function of atp1b1a is required for proper epidermal homeostasis . 10 . 7554/eLife . 14277 . 015Figure 5 . Epidermal hyperplasia is dependent on hypotonic conditions . ( a–k ) psoriasis mutants raised in isotonic conditions do not develop epidermal hyperplasia . Live images of 60 hpf embryos ( a–d ) show epidermal aggregates and pericardial edema ( pe ) in the mutant raised in hypotonic E3 ( b ) but not in the mutant raised in isotonic E3 ( d ) . IF of BrdU incorporation ( white ) in 72 hpf wt or atp1b1a morphant tail fins ( e–h ) reveals excessive cell proliferation in the fin fold of the morphant embryo ( f ) raised in hypotonic E3 but not in the morphant embryo ( h ) raised in isotonic E3 . ( i ) Quantification of embryos as shown in ( a–d ) , obtained from incross of two psoriasis /- parents . n = 86–122 . ( j ) Representative gel with PCR products subjected to MwoI restriction digest to genotype embryos raised in isotonic medium . All mutants raised in isotonic medium and shown as representative examples in this and the following figures had been positively genotyped . ( k ) Quantification of embryos as shown in ( e–h ) , scored is the number of BrdU-positive cells in a given area of the median fin fold . n = 3–7 per condition . Error bars represent standard deviation . p values are as follows: a: 0 . 0416 , b: 0 . 9139 , c: 0 . 8956 , d: 0 . 0017 . ( l–n ) Live images of 56 hpf wt embryos ( l , l’ ) , psoriasis mutant embryos ( m , m’ ) , and wt embryos treated with 3mM ouabain ( n , n’ ) starting from 33 hpf . Blockage of Na , K-ATPase pump function by ouabain results in pericardial edema ( pe ) as in psoriasis mutants ( n = 122/125; compare n to m ) , but not in epidermal aggregates ( n = 0/125; compare n’ to m’ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01510 . 7554/eLife . 14277 . 016Figure 5—source data 1 . Source data for Figure 5 . Source data for Figure 5i . Quantification of phenotypes of embryos obtained from incross of two psoriasis /- parents raised in E3 medium , E3 medium 250 mM mannitol , and Ringer's solution . Source data for Figure 5k . Quantification of BrdU-labeled cells . p values are determined using an unpaired two-tailed Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01610 . 7554/eLife . 14277 . 017Figure 5—figure supplement 1 . Live images of the otic vesicles of 48 hpf embryos show two otoliths in a untreated wt embryo ( a ) whereas in an embryo treated with 3 mM ouabain starting from 10 hpf , otoliths have failed to form ( b; n = 76/99 ) or are much smaller ( not shown; n = 23/99 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 017 But is the loss of this osmoregulatory function also sufficient to induce the epidermal defects ? To test this , we incubated wild-type embryos in 3 mM ouabain , an inhibitor of the pumping function of the α-subunit . Treated embryos displayed numerous formerly reported phenotypes ( Figure 5—figure supplement 1; and data not shown ) , typical of those caused by genetic mutations in other Na , K-ATPases ( Blasiole et al . , 2006 ) , as well as pericardial edema ( Figure 5l–n ) . These embryos did not , however , develop epidermal aggregates ( Figure 5n’ , compare to Figure 5m’ ) , indicating that the loss of the osmoregulatory function of Atp1b1a is necessary but not sufficient for epidermal hyperplasia . This points to an additional , pump-independent function of Atp1b1a , which makes keratinocytes more resistant to hypotonic stress . Several functions additional to their role in ion homeostasis have been described for Na , K-ATPases , such as a role in epithelial polarity that promotes basolateral membrane identities , as well as a role in intercellular adhesion ( see Introduction ) . To study the subcellular localization of zebrafish Atp1b1a , we transiently expressed an atp1b1a-gfp fusion construct in peridermal cells , one of its endogenous expression sites ( Figure 3i ) , under the control of the krt4 promoter . As described for Na , K-ATPases in other epithelial cells , Atp1b1a-GFP was localized in the basolateral domain of peridermal cells , together with E-cadherin ( Cdh1 ) , while it was absent from the apical region marked by the tight junction protein Tjp1/ZO-1 ( Kiener and Hunziker , 2007 ) ( Figure 6a ) . Consistent basolateral defects were found in psoriasis mutants even when incubated in isotonic medium to exclude effects resulting from tonicity-related stress . TEM and IF studies revealed unaltered morphologies of tight junctions ( Figure 6d , e ) and an unaltered distribution of Tjp1 in the periderm of mutant embryos at 52 hpf ( Figure 6—figure supplement 1 ) . By contrast , the lateral regions of peridermal cells were less organized ( Figure 6d , e ) , and aberrant gaps were observed between peridermal cells and underlying basal keratinocytes ( Figure 6b–f ) , Furthermore , the membranous localization of Cdh1 and Lgl2 proteins was strongly diminished in both the peridermal and basal cells of psoriasis mutants , independent of whether they had been incubated in hypotonic or isotonic medium ( Figure 7a–h ) . At later stages ( 84 hpf ) , basal keratinocytes of psoriasis mutants displayed a strong and tonicity-independent reduction in normally basally localized cytokeratins ( Figure 7i–l ) , similar to that previously reported for lgl2 mutants ( Sonawane et al . , 2005 ) . Together , this evidence points to a requirement for Atp1b1a for proper establishment of epithelial polarity and integrity , not only in the periderm but also in the basal layer . 10 . 7554/eLife . 14277 . 018Figure 6 . Atp1b1a is required for epidermal cell adhesion . ( a ) IF of GFP ( a , green ) , Cdh1 ( a’ , red ) and tight junction marker Tjp1 ( a’’ , magenta ) on a transverse section of the epidermis of a 48 hpf wt embryo expressing peridermal-specific krt4:atp1b1a-gfp , counterstained with DAPI ( blue; a’’’ with merged channels ) . Atp1b1a and Cdh1 are co-localized on the basolateral side of peridermal cells , but are excluded from tight junctions and the apical side of these cells . ( b–f ) . Transverse TEM sections through the medium fin fold of wt and psoriasis mutant embryos raised in isotonic conditions , at 58 hpf . In the mutant epidermis ( c , c’ ) , aberrant gaps between peridermal cells ( false-colored in green ) and underlying keratinocytes ( false-colored in red ) are apparent when compared to the wt epidermis ( b , b’ ) . ( d–f ) Higher magnifications reveal tight junctions ( indicated by arrows ) of unaltered morphology in the mutant ( e ) compared to a wt sibling ( d ) , but less organized lateral regions between peridermal cells ( e ) , and large gaps ( * ) between peridermal and basal cells ( e , f ) in the mutant . bc , basal cell; pc , peridermal cell . Scale bars: 1 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01810 . 7554/eLife . 14277 . 019Figure 6—figure supplement 1 . Localization of the tight junction protein Tjp1 is unaltered in psoriasis mutants . IF of Tjp1 ( a , b; magenta ) and periderm-specific GFP ( a’ , b’; green; counterstained with DAPI in blue; merged channels ) ; transverse section through epidermis of wt ( a , a' ) and psoriasis mutant ( b , b' ) embryos carrying Tg ( krt4:GFP ) transgene; 48 hpf . , Tjp1 ( arrowed ) shows unaltered apical localization in peridermal cells of mutant . Scale bar: 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 01910 . 7554/eLife . 14277 . 020Figure 7 . Atp1b1a is required for epidermal integrity and polarity . ( a–h ) . Whole mount IFs of Cdh1 ( red ) and Lgl2 ( green ) in 54 hpf embryos raised in hypotonic ( a , b , e , f ) and isotonic ( c , d , g , h ) conditions . In mutants , localization of Cdh1 ( b , d , f , and h; compare to wt a , c , e , g ) and Lgl2 ( b’ , d’ , f’ , and h’; compare to wt a’ , c’ , e’ , g’ ) is compromised in both peridermal cells ( a–d ) and basal cells ( e–h ) . Images show regions of the trunk epidermis not yet affected by aggregate formation . ( i– l ) . Whole mount IFs of cytokeratin ( red ) in 84 hpf embryos show a reduction of basally localized cytokeratin in mutants raised in hypotonic ( j ) and isotonic ( l ) conditions . Images show regions of the trunk epidermis above the yolk sac extension . ( m–p ) . Live images of the tail fins of 54 hpf embryos either with full MO knockdown of atp1b1a ( m ) or with partial MO knockdown of lgl2 ( n ) , of atp1b1a ( o ) , or of both ( p ) . ( q ) Quantification of the phenotypes of 54 hpf embryos in synergistic enhancement studies; n = 31–88 . Similar results were obtained in two additional independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02010 . 7554/eLife . 14277 . 021Figure 7—source data 1 . Source data for Figure 7q . Quantification of phenotypes of 54 hpf embryos in synergistic enhancement studies . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02110 . 7554/eLife . 14277 . 022Figure 7—figure supplement 1 . atp1b1a and lgl2 interact genetically to enhance edema formation and AKT phosphorylation and mmp9 expression in basal keratinocytes of embryos raised in hypotonic medium . ( a–c ) pAKT IF , at 54 hpf , revealing low pAKT levels in embryos injected with low amounts of atp1b1a MO ( a ) or with lgl2 MO ( b ) , but strongly increased levels in a double-injected embryo ( c ) . For comparison with pAKT levels in wild-type and psoriasis mutants ( full loss of Atp1b1a activity ) , see Figure 8f , g . ( d–f ) . mmp9 WISH , at 54 hpf , revealing low mmp9 expression in embryos injected with low amounts of atp1b1a MO ( d ) or with lgl2 MO ( e ) , but strongly increased levels in double-injected embryo ( f ) . For comparison with mmp9 expression levels in wild-type and psoriasis mutant , see Figure 9j , j" . ( g–j ) . Embryos co-injected with sub-phenotypic amounts of atp1b1a MO and lgl2 MO display epidermal aggregates in conjunction with pericardial edema . Treatment with the PI3K inhibitor Wortmannin , the mTORC1 inhibitor Rapamycin or the NFκB inhibitor Withaferin A , starting at 34 hpf , leads to a complete loss of epidermal aggregates , whereas edema persist . ( g–i ) Representative live images of DMSO-treated controls ( g ) , and of Wortmannin-treated ( h ) and Rapamycin-treated ( i ) embryos , at 54 hpf; pe , pericardial edema . ( j ) Quantification of the phenotypes of control , Wortmannin- , Rapamycin- or Withaferin A-treated embryos as shown in ( g– i ) , at 54 hpf . For classification of phenotypic strengths , see Figure 9a–d . ( k–m ) . IF of Atp5a ( red ) and aPKC ( green ) , counterstained with DAPI ( blue ) , on transverse sections at 54 hpf , revealing high amounts of Na , K-ATPase α-subunits in the basolateral membrane domains of pronephric duct epithelial cells in embryos injected with low amounts of atp1b1a MO ( k ) or with lgl2 MO ( l ) , but strongly decreased levels in double-injected embryo ( m ) . Scale bar:10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02210 . 7554/eLife . 14277 . 023Figure 7—figure supplement 1–source data 1 . Source data for Figure 7—figure supplement 1j . Quantification of phenotypes of control , Wortmannin- , Rapamycin- or Withaferin A-treated embryos co-injected with sub-phenotypic amounts of atp1b1a MO and lgl2 MO . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 023 In lgl2 mutants , the epidermal defects only become apparent comparably late ( from 96 hpf onwards ) ( Reischauer et al . , 2009; Sonawane et al . , 2005 ) , but a recent study uncovered a pro-basal effect of lgl2 on basal keratinocytes as early as 30–36 hpf ( Westcot et al . , 2015 ) . This suggests that Atp1b1a might regulate epithelial polarity and epidermal integrity at least in part by cooperating with Lgl2 and by ensuring its proper localization . To test whether , in line with this notion , lgl2 and atp1b1a genetically interact , we performed synergistic enhancement studies . When either lgl2 or atp1b1a MOs were injected at a low , sub-phenotypic concentration , all embryos displayed wild-type morphology . By contrast , when the two MOs were co-injected at the same concentrations , 62% of the embryos ( n = 74 ) developed pericardial edema , a loss of Na/K-ATPase α-subunits from the basolateral domains of pronephric epithelial cells , epidermal aggregates , and an upregulation of the malignancy markers pAKT and mmp9 in basal keratinocytes , similar to the phenotype after full knockdown of atp1b1a ( Figure 7m–q; Figure 7—figure supplement 1 ) . This indicates that lgl2 and atp1b1a cooperate to promote proper epithelial cell polarity and integrity in the epidermis as well as proper osmoregulation in the kidney , thereby suppressing epidermal malignancy . In contrast to lgl2 expression ( Sonawane et al . , 2005; Sonawane et al . , 2009 ) , atp1b1a expression in the epidermis is restricted to the periderm , suggesting that the effect of this gene on basal keratinocytes must be indirect . To prove this more directly , and to rule out indirect effects caused by hypotonicity , we analyzed chimeric embryos under isotonic conditions . Wild-type basal cell precursors that were transplanted into atp1b1a morphant hosts later exhibited distorted keratin distribution similar to that of their morphant neighbors ( Figure 8a ) . Vice versa , atp1b1a morphant basal cells transplanted into wild-type hosts were indistinguishable from their wild-type neighbors and displayed normal keratin distribution ( Figure 8b ) . This indicates that keratin localization in basal keratinocytes and the epithelial polarity of these cells are under the control of Atp1b1a function in a tissue other than the basal epidermal layer and the osmoregulatory organs . To show that this tissue is the periderm , we established a stable line of the aforementioned krt4 transgene driving expression of atp1b1a-gfp exclusively in peridermal cells . In psoriasis mutants carrying this transgene that were raised under isotonic conditions , keratin localization in basal keratinocytes was fully restored and indistinguishable from that in wild-type siblings ( Figure 8c–e; n = 28/28; 3 independent experiments ) . We conclude that atp1b1a is required in the periderm to establish proper epithelial polarity and organization in the underlying basal keratinocytes . Furthermore , this function is independent of the osmoregulatory role of Atp1b1a , as the corresponding defects are present even in mutants kept in isotonic medium , thus in the absence of hypotonic stress . When kept in hypotonic medium , psoriasis mutants carrying the krt4:atp1b1a-gfp transgene displayed a partial restoration of epidermal cell polarity both in the periderm and in the basal layer ( cytokeratin and Lgl2 localization; Figure 8—figure supplement 1e , I , m and f , j , n ) and a partial , but significant amelioration of basal cell malignancy ( Figure 8—figure supplement 1a–c ) , including reduction in the expression of the malignancy markers pAKT and mmp9 ( Figure 8f–h; Figure 8—figure supplement 1g , k , o ) . The failure of full restoration under hypotonic conditions suggests that hypotonic stress further challenges the epidermal polarity system , which becomes more difficult to repair than is the case under isotonic conditions . In addition , the data reveal that upon hypotonic stress , the degrees of epidermal polarity defects and epidermal malignancy are proportionally linked . 10 . 7554/eLife . 14277 . 024Figure 8 . atp1b1a is required in peridermal cells to establish epithelial organization of basal keratinocytes , and to suppress hypotonicity-induced upregulation of pAKT levels in basal keratinocytes . ( a–b ) IF of cytokeratins ( Ker; red ) and GFP ( green ) in 84 hpf chimeric embryos raised in isotonic conditions . Wild-type ( wt ) basal keratinocytes expressing Tg ( Ola . Actb:Hsa . hras-egfp ) -encoded membrane-tagged GFP transplanted into atp1b1a morphant hosts display reduced cytokeratin localization similar to that of host cells ( a ) , whereas the cytokeratin distribution of morphant donor cells ( green ) in wt hosts is indistinguishable from that in neighboring wt cells ( b ) . Images show regions of the trunk epidermis above the yolk sac extension . ( c–e ) . Maximum intensity projections of confocal images of IF of cytokeratin ( red ) and periderm-specific Atp1b1a-GFP ( green ) in 84 hpf embryos obtained from an in-cross of psoriasis +/- ; Tg ( krt4:atp1b1a-gfp ) parents , raised in isotonic conditions . Embryos were genotyped after imaging . Cytokeratin localization in basal keratinocytes is distorted in non-transgenic psoriasis-/- embryos ( d; compare to wt in c ) , but restored in psoriasis-/- ; Tg ( krt4:atp1b1a-gfp ) embryos ( e ) . Images show regions of the trunk epidermis above the yolk sac extension . ( f–k ) pAKT IF ( red ) in 54 hpf embryos . pAkt is upregulated in psoriasis mutants raised in hypotonic ( g ) but not in isotonic ( j ) medium , compared to wt siblings ( f , i ) . pAkt levels are ameliorated in psoriasis-/- ; Tg ( krt4:atp1b1a-gfp ) embryos kept in hypotonic medium ( h ) . pAkt is not upregulated in wt embryos incubated in hypotonic medium after addition of 3 mM ouabain , starting from 33 hpf ( k ) . Images show regions of the trunk epidermis not yet affected by aggregate formation . Abbreviation: iso , isotonic medium ( E3 250 mM mannitol ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02410 . 7554/eLife . 14277 . 025Figure 8—figure supplement 1 . Tg ( krt4:atp1b1a-gfp ) -driven atp1b1a expression in the periderm of psoriasis mutants kept in hypotonic medium leads to a partial rescue of the polarity defects and malignant transformation in basal keratinocytes . ( a ) Quantification of phenotypes of non-transgenic psoriasis mutants and psoriasis mutants carrying the krt4:atp1b1a-gfp transgene , raised in hypotonic E3 , at 54 hpf . For classification of phenotypic strengths , see Figure 9a–d . ( b–c ) Representative live images of a psoriasis mutant lacking the transgene ( no peridermal GFP; b’ ) , which has pericardial edema and epidermal aggregates ( b ) , and a psoriasis mutant expressing the transgene ( peridermal GFP; c’ ) , which has pericardial edema but no epidermal aggregates ( c ) . ( d-o ) IF of GFP ( transgene-encoded Atp1b1a-GFP ) in periderm ( green; d , h , l; 54 hpf ) , Lgl2 in periderm ( red; e , i , m; 54 hpf ) and cytokeratin in basal keratinocytes ( red; f , j , n; 84 hpf ) , and WISH of mmp9 transcripts in basal keratinocytes ( g , k , o; 54 hpf ) of transgenic wt siblings ( d-–g ) , non-transgenic psoriasis mutants ( h–k ) and psoriasis mutants carrying the krt4:atp1b1a-gfp transgene ( l–o ) . Periderm-specific expression of atp1b1a in mutant embryos leads to a partial restoration of the polarity markers Lgl2 and cytokeratin , and to a reduction of the malignancy marker mmp9 . However , obtained levels are still higher than in the wt sibling control . For the reduction of the malignancy marker pAKT , see Figure 8f–h . Scale bars: 20 µm ( d–f , h–j . l–n ) and 50 µm ( g , k , o ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02510 . 7554/eLife . 14277 . 026Figure 8—figure supplement 1–source data 1 . Source data for Figure 8—figure supplement 1a . Quantification of the phenotypes of psoriasis mutant embryos from an in-cross of psoriasis /-; krt4:atp1b1a-gfp parents , at 54 hpf , incubated in E3 . pe , pericardial edema; weak , edema and weak aggregates; medium , edema and medium aggregates; strong , edema and strong aggregates . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 026 We next explored the molecular pathways mediating the malignant transformation within basal keratinocytes of psoriasis mutants . In cultured human keratinocytes , hypotonic culturing conditions induce , among other effects , the phosphorylation and activation of the kinase AKT/PKB ( Kippenberger et al . , 2005 ) , while a PI3K-AKT-NFκB-MMP9 ( metalloprotease 9 ) pathway has been implicated with cellular invasiveness ( Dilly et al . , 2013; Wang et al . , 2011 ) . Furthermore , AKT has been shown to activate NFκB via mTORC1 ( a mechanistic target of rapamycin complex 1 ) ( Dan et al . , 2008 ) . psoriasis mutants that were raised in hypotonic conditions displayed strongly increased pAKT levels in both peridermal and basal cells , whereas wild-type siblings showed very little pAKT staining ( Figure 8f , g ) . By contrast , pAKT levels were not elevated in atp1b1a mutants raised in isotonic medium ( Figure 8i , j ) or in wild-type embryos treated with ouabain ( Figure 8k ) . Together , this evidence indicates that the pAKT pathway is only activated by hypotonic stress in conjunction with the epithelial polarity / adhesiveness defects caused by the loss of Atp1b1a function in the periderm . To investigate the functional involvement of PI3K , mTORC1 and NFκB in malignant basal cell transformation of psoriasis mutants , we blocked them with the PI3K inhibitors Wortmannin , PIK90 or LY294002 , the mTORC1 inhibitors Rapamycin or AZD8055 , and the NFκB inhibitor Withaferin A . We determined the effects of these drugs on epidermal aggregate and edema formation , epidermal polarity ( cytokeratin localization ) , pAKT levels , pS6RP levels ( as a readout of mTORC1 activity ) ( Hoesel and Schmid , 2013 ) , NFκB activity ( transgenic NFκB responder line; Candel et al . , 2014 ) , expression levels of mmp9 ( which in mammals is a direct transcriptional NFκB target ( Rhee et al . , 2007 ) implicated in invasiveness ) , and finally epidermal proliferation . In addition to distorted keratin distribution ( Figure 9f , f’ ) and increased pAKT levels ( Figure 9g , g’ ) and proliferation rates ( Figure 9k , k` ) as described above ( Figures 5 , 7 , 8 ) , basal keratinocytes of atp1b1a mutants and morphants displayed strongly upregulated pS6RP levels ( Figure 9h , h’ ) , NFκB activity ( Figure 9i , i’ ) and mmp9 expression ( Figure 9j , j’ ) . Upon treatment with any of the inhibitors , mutants continued to display edema ( Figure 9a–e ) and distorted keratin distribution in basal keratinocytes ( Figure 9f’’–f’’’’ ) , but they lacked epidermal aggregates ( Figure 9a–e ) and displayed mmp9 expression levels ( Figure 9j’’–j’’’’ ) and keratinocyte proliferation rates ( Figure 9k’’–k’’’’ ) that were back to wild-type levels . 10 . 7554/eLife . 14277 . 027Figure 9 . Hyperplasia and transcriptional upregulation of mmp9 in basal keratinocytes of psoriasis mutants is mediated via an aberrant activation of a linear PI3K-Akt-mTorC1-NFκB pathway . ( a–e ) Blockade of PI3K , mTorC1 , and NFκB signaling rescues epidermal aggregate but not pericardial edema formation in psoriasis mutants . ( a–d ) Representative live images of phenotypic strength classes of psoriasis -/- embryos at 54 hpf , all with pericardial edema of comparable strengths , but strong ( a ) , intermediate ( b ) , weak ( c ) , or no ( d ) epidermal aggregates . ( e ) Quantification of the phenotypes of psoriasis mutants incubated in E3 medium containing 1 µM Wortmannin , 5 µM PIK90 , 25 µM LY94002 , 1 . 1 µM Rapamycin , 30 µM AZD8055 , or 30 µM Withaferin A compared to the corresponding DMSO controls ( n = 16–30 ) . Drugs were added at 34 hpf and embryos scored at 54 hpf . Similar results were obtained in at least two additional independent experiments . For representative live images , see Figure 9—figure supplement 1 . f–k . A linear PI3K-Akt-mTORC1-NFκB pathway mediates hyperplasia and upregulation of mmp9 expression in basal keratinocytes . All embryos had been kept in ( hypotonic ) E3 medium , supplemented with the indicated drugs starting at 34 hpf . ( f-–f’’’’ ) IF of cytokeratins ( red ) at 84 hpf . Distorted keratin localization in the psoriasis mutant ( f’ , compare to wt ( f ) ) is not restored by Wortmannin ( f’’ ) , Rapamycin ( f’’’ ) , or Withaferin A ( f’’’’ ) . Scale bar: 50 µm . ( g–g’’’’ ) IF of pAkt ( red ) , counterstained with DAPI ( blue ) ; transverse sections of 54 hpf psoriasis mutants raised in E3 medium . Elevated pAkt levels in the mutant ( g’ , compared to the wt ( g ) ) are lowered by Wortmannin ( g’’ ) , but not by Rapamycin ( g’’’ ) or Withaferin A ( g’’’’ ) . Scale bar: 20 µm . ( h–h’’’’ ) IF of pS6RP and p63 of whole mounts , at 54 hpf . Elevated pS6RP levels in mutant ( h’ , compared to wt ( h ) ) are alleviated by Wortmannin ( h’’ ) , PIK90 ( not shown ) and Rapamycin ( h’’’ ) , but not by Withaferin A ( h’’’’ ) . Scale bar: 20 µm . ( i– i’’’’ ) Confocal images of GFP fluorescence in the tail fin of a live 48 hpf wt embryo ( i ) and an atp1b1a morphant ( i’ ) , both carrying the Tg ( NFκB-RE:eGFP ) transgene . The atp1b1a morphant shows strong upregulation of NFκB activity in keratinocytes , which is restored by treatment with Wortmannin ( i’’ ) , Rapamycin ( i’’’ ) , and Withaferin A ( i’’’’ ) . Scale bar: 100 µm . For quantification , see Figure 9—figure supplement 2 . ( j–j’’’’ ) mmp9 WISH at 54 hpf . Elevated mmp9 expression in the mutant epidermis ( j’ , compare to wt ( j ) ) is downregulated by Wortmannin ( j’’ ) , Rapamycin ( j’’’ ) , and Withaferin A ( j’’’’ ) . Scale bar: 50 µm . ( k–k’’’’ ) IF of incorporated BrdU ( red ) , counterstained with DAPI ( blue ) at 56 hpf . Elevated cell proliferation in mutant epidermis ( k’ , compare to wt ( k ) ) is downregulated by Wortmannin ( k’’ ) , Rapamycin ( k’’’ ) , and Withaferin A ( k’’’’ ) . Scale bar: 100 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02710 . 7554/eLife . 14277 . 028Figure 9—source data 1 . Source data for Figure 9e . Quantification of phenotypes of psoriasis mutants at 54 hpf , incubated in E3 medium containing 1 µM Wortmannin , 5 µM PIK90 , 25 µM LY94002 , 1 . 1 µM Rapamycin , 30 µM AZD8055 , or 30 µM Withaferin A compared to the corresponding DMSO controls . pe: , pericardial edema; weak , edema and weak aggregates; medium , edema and medium aggregates; strong , edema and strong aggregates . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02810 . 7554/eLife . 14277 . 029Figure 9—figure supplement 1 . Chemical inhibiton of PI3K , mTORC1 or NFkB rescues the epidermal malignancies , but not the pericardial edema of psoriasis mutants . ( a–h ) Representative live images of psoriasis -/- embryos at 54 hpf , treated with the PI3K inhibitors Wortmannin ( 1 µM; c , c’ ) , PIK90 ( 5 µM; d , d’ ) and LY 294002 ( 25 µM; e , e’ ) , the mTORC1 inhibitors Rapamycin ( 1 . 1 µM; f , f’ ) and AZD8055 ( 30 µM; g , g’ ) , and the NFκB inhibitor Withaferin A ( 30 µM; h , h’ ) , as quantified in Figure 9e . Treatment with the different inhibitors of components of the PI3K-Akt-mTorC1-NFκB pathway do not rescue the pericardial edema ( c– h , compare to wt ( a ) and non-treated mutant ( b ) ) , but do rescue the epidermal malignancies of mutant embryos ( c’–h’ , compare to wt ( a’ ) and non-treated mutant ( b’ ) ) . Scale bars: 500 µm ( a ) , 250 µm ( a’ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 02910 . 7554/eLife . 14277 . 030Figure 9—figure supplement 2 . Quantification of NFκB activity in wt embryos , atp1b1a morphants and atp1b1a morphants treated with Wortmannin , Rapamycin , and Withaferin A , as shown in Figure 9i–i’’’’ . Mean fluorescence intensities of GFP in the posterior part of the tail fin were measured in maximum intensity projections of confocal images using ImageJ software . n = 4–16 . Error bars represent standard deviations . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 03010 . 7554/eLife . 14277 . 031Figure 9—figure supplement 2–source data 1 . Source data for Figure 9—figure supplement 2 . Quantification of the mean fluorescence intensities of GFP in the posterior part of the tail fins of wt fish , atp1b1a morphants and atp1b1a morphants treated with Wortmannin , Rapamycin , and Withaferin A . Intensities were measured in maximum intensity projections of confocal images using ImageJ software . p values are determined using an unpaired two-tailed Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 031 In addition , PI3K inhibition led to a downregulation and normalization of pAKT , pS6RP and NFκB activity levels ( Figure 9g’’ , h’’ , i’’ ) , whereas the mTORC1 inhibitor affected the pS6RP and NFκB but not the pAKT levels ( Figure 9g’’’ , h’’’ , i’’’ ) , and the NFκB inhibitor the NFκB but not the pS6RP and pAKT levels ( Figure 9g’’’’ , h’’’’ , i’’’’ ) . Together , this evidence points to the involvement of a linear PI3K-Akt-mTorC1-NFκB pathway in mediating epidermal malignancy downstream of the epidermal polarity and hypotonicity defects . A normalization of epidermal hyperplasia to that described above was also obtained upon blockage of cell proliferation by incubating mutant embryos in 50 mM hydroxyurea; by contrast , edema ( Figure 10e; Figure 10—figure supplement 1a , b ) , compromised cytokeratin localization and increased pAKT , NFκB activity and mmp9 expression levels persisted ( Figure 10a–d; Figure 10—figure supplement 1c; and data not shown ) . By contrast , mmp9 knockdown via MO injection failed to rescue epidermal hyperplasia ( Figure 10e ) , but alleviated epidermal invasiveness . Thus , the degradation of the skin basement membrane was significantly , but not fully , blocked in mmp9-MO-injected psoriasis mutant embryos ( Figure 10f , g , Figure 10—figure supplement 2 ) . In addition , basal keratinocytes remained within the epidermal compartment ( Figure 10h ) , in contrast to their ectopic localization in dermal compartments of mutant controls ( Figure 1j ) . This indicates that Mmp9 is an essential downstream effector of the PI3K-AKT-NFκB pathway and is primarily involved in mediating epidermal invasiveness , whereas epidermal hyperplasia is mediated by other NFκB targets . 10 . 7554/eLife . 14277 . 032Figure 10 . Blockage of cell proliferation results in the normalization of epidermal hyperplasia , whereas blockage of Mmp9 activity reduces epidermal invasiveness in psoriasis mutants . ( a–b ) . Confocal images of GFP in the tail fin of live 48 hpf atp1b1a morphant Tg ( NFκB-RE:eGFP ) transgenics , showing that elevated NFκB activity in atp1b1a morphants ( a ) is not reduced by hydroxyurea treatment ( b ) . For quantification , see Figure 10—figure supplement 1 . ( c–d ) mmp9 WISH of 54 hpf psoriasis mutants raised in hypotonic E3 . Elevated mmp9 expression in mutant epidermis ( c ) is not reduced by hydroxyurea ( HU ) treatment ( d ) . ( e ) Quantification of the phenotypes of psoriasis mutants , either treated with 50 mM hydroxyurea or injected with mmp9 MO , compared to their respective siblings . e , pericardial edema; wa , weak epidermal aggregates; ma , medium epidermal aggregates; sa , strong epidermal aggregates . n = 17–47 . Similar results for each condition were obtained in two additional independent experiments . ( f–g ) mmp9 knockdown alleviates basement membrane fragmentation . Laminin IF , counterstained with DAPI , in psoriasis mutants at 58 hpf , epidermal aggregates of comparable sizes . In the un-injected psoriaris mutant ( f ) , the aggregate is associated with BM fragmentation , while the underlying BM is largely intact in the psoriasis mutant injected with mmp9 MO ( g ) . For more images and numbers , see Figure 10—figure supplement 2 . ( h ) mmp9 knockdown alleviates epidermal invasiveness . Laminin and p63 IF of transverse sections , counterstained with DAPI , through the yolk sac of a psoriasis mutant ( 58 hpf ) injected with mmp9 MO . The basement membrane is largely intact ( arrowhead to small remaining region with thinner basement membrane ) , and p63 keratinocytes are confined to the epidermal compartment above the basement membrane . For un-injected mutant and wt controls , see Figure 2i , j . ( i ) Diagram of the identified pathway in which the two required non-cell-autonomous effects caused by loss of Atp1b1a in periderm and osmoregulatory organs converge in basal cells . The pathway subsequently diverges downstream of NFκB to mediate overgrowth versus invasiveness of transformed keratinocytes . Question marks indicate components that have not yet been identified . For details , see text . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 03210 . 7554/eLife . 14277 . 033Figure 10—source data 1 . Source data for Figure 10e . Quantification of the phenotypes of psoriasis mutants , either treated with 50 mM hydroxyurea or injected with mmp9 MO , compared to those of their respective siblings . pe , pericardial edema; weak , edema and weak aggregates; medium , edema and medium aggregates; strong , edema and strong aggregates . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 03310 . 7554/eLife . 14277 . 034Figure 10—figure supplement 1 . Morphology rescue of psoriasis mutant upon treatment with hydroxyurea and quantification of the non-alleviating effect of the treatment on NFκB activity in embryos as shown in Figure 10a , b . ( a–b ) Live images of a 54 hpf psoriasis mutant treated with 50 mM hydroxyurea ( HU ) in E3 from 34 hpf onwards ( b ) and control mutant raised in E3 ( a ) . Hydroxyurea does not rescue the pericardial edema ( a , b ) , but blocks epidermal aggregate formation ( a’ , b’ ) . ( c ) Mean fluorescence intensities of GFP in the posterior part of the tail fin were measured in maximum intensity projections of confocal images using the ImageJ software . n = 7–8 . Error bars represent standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 03410 . 7554/eLife . 14277 . 035Figure 10—figure supplement 1—source data 1 . Source data for Figure 10—figure supplement 1 . Quantification of the mean fluorescence intensities of GFP in the posterior part atp1b1a morphants and atp1b1a morphants treated with hydroxyurea . Intensities were measured in maximum intensity projections of confocal images using ImageJ software . p values are determined using an unpaired two-tailed Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 03510 . 7554/eLife . 14277 . 036Figure 10—figure supplement 2 . mmp9 knockdown alleviates epidermal invasiveness . Examples of whole mount immunofluorescence of laminin ( red ) and type II collagen ( green ) of the tail fin of 58 hpf psoriasis-/- embryos ( a , b ) and psoriasis-/- ; mmp9 MO embryos ( c , d ) - , counterstained with DAPI ( blue ) . Actinotrichia disassembly ( a’–d’ ) and basement membrane disruption ( a’’-b’’ ) observed below epidermal aggregates ( arrows in a’’’–d’’’ ) are strongly reduced upon knockdown of mmp9 . In total ( 5 embryos examined per condition ) , 5/22 medium-sized fin fold aggregates were associated with BM fragmentation in mmp9 MO-injected mutants , compared to 19/19 in un-injected mutant controls . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 036
atp1b1a is expressed in multiple organs and epithelia , including the periderm of the epidermis , the tubules / ducts of the pronephros and the heart ( Figure 3 ) . Accordingly , atp1b1a mutant embryos display compromised heart function ( Figure 4 ) , consistent with results reported in mouse ( Barwe et al . , 2009 ) . In addition , they display a loss of the α-subunit of the Na/K-ATPase , the actual pump , from the basolateral domains of pronephric epithelial cells ( Figure 4 ) , possibly reflecting more general defects in the cells’ epithelial polarity and severely compromising the kidney’s osmoregulatory function . Together with the reduction in the blood pressure , these defects should lead to compromised water elimination in the pronephros , as reflected by the reduced clearance of rhodamin-dextran from the blood ( Figure 4 ) . The resulting increase in water content in turn causes edema formation and a corresponding reduction in the osmolarity of interstitial compartments ( Drummond et al . , 1998; Hentschel et al . , 2005 ) . In addition , and independently of its osmoregulatory functions , Atp1b1a is required in the periderm to assure proper epithelial polarity and cell-cell adhesiveness , both in the periderm and in the underlying basal keratinocytes . Thus , even when the osmoregulatory role of Atp1b1a has become dispensable upon incubation in isotonic medium , peridermal cells of mutant embryos display reduced levels of E-cadherin and of Lgl2 , a pro-basal polarity regulator , as well as increased spacing between epidermal cells . Strikingly , basal keratinocytes themselves also display specific defects , such as reduced membraneous Lgl2 and E-cadherin levels , as well as a mislocalization of cytokeratins in basal domains . These defects could be rescued when transgene-encoded wild-type Atp1b1a was re-introduced into peridermal cells , indicating that Atp1b1a from the periderm regulates the epithelial organization and adhesiveness of basal keratinocytes in a tonicity-independent manner ( Figure 8 ) . But what are the molecular mechanisms that underlie this trans-layer effect ? Trans-bonds between ATPase β-subunits , previously reported to mediate cell-cell adhesiveness in other instances ( Vagin et al . , 2012 ) , could account for the adhesiveness among peridermal cells , but not for that between peridermal and basal cells , as the latter lack atp1b1a expression . The cell-cell adhesion molecule E-cadherin might be the relevant mediator . In psoriasis mutants , E-cadherin membrane localization is severely diminished both in peridermal and in basal cells ( Figure 7 ) . The reduction of E-cadherin in peridermal cells could be a direct consequence of the loss of Atp1b1a , with which it is normally co-localized in basolateral regions ( Figure 6 ) . This is consistent with the formerly reported physical interaction between ATPase α-subunits and E-cadherin via the anchoring protein ankyrin and the spectrin-actin cytoskeleton ( Vagin et al . , 2012 ) . In addition , Atp1b1a could promote E-cadherin localization via other epithelial polarity regulators , consistent with reports in MCDK cells ( Qin et al . , 2005 ) . We propose that it is this loss of proper E-cadherin in peridermal cells that compromises not only their adhesion to the underlying basal cells ( due to the loss of trans-bonds between the basal side of peridermal and the apical side of basal cells; Figure 1 ) but also the adhesion among basal cells themselves , as well as their epithelial polarity . Consistent with the latter notion , loss of E-cadherin has been shown to affect apical-basal polarity in numerous systems ( Desai et al . , 2009; Stephenson et al . , 2010 ) . E-cadherin and epithelial polarity regulators , including Lgl2 , are well-known tumor suppressors ( Birchmeier , 1995; Martin-Belmonte and Perez-Moreno , 2012; Ellenbroek et al . , 2012 ) . Atp1b1a is another cell adhesion molecule and epithelial polarity regulator to add to the list , , as its loss causes crucial features of carcinogenesis ( hyperplasia and infiltration of other tissues ) in vivo . Strikingly , however , epidermal malignancy only occurs upon the combined loss of Atp1b1a’s osmoregulatory and epithelial polarity-regulating functions . Thus , hypotonic stress per se does not lead to basal cell malignancy , as demonstrated in this work by the treatment of wild-type embryos with ouabain . Consistently , none of the described kidney mutants , although displaying massive edema formation as a result of internal hypotonicity , have had reported skin defects ( Drummond et al . , 1998; Drummond , 2002; Hentschel et al . , 2005 ) . Moreover , epidermal malignancy in atp1b1a mutants is suppressed in the absence of hypotonic stress , although epithelial polarity and adhesiveness in the embryonic skin remain compromised . Carcinogenesis is commonly regarded as a multistep process , to which aberrant polarity signaling can contribute as one of multiple causative factors ( Sherr , 2004; Sun and Yang , 2010; Ellenbroek et al . , 2012 ) . In this light , carcinogenesis subsequent to the combined loss of different functions of one and the same factor is an astonishing and thus far unrecognized variation of this common concept . In addition , it is remarkable that one of these genetically caused effects ( hypotonicity ) can also be achieved by environmental insults ( injury ) , constituting an interesting variation of the common concept of tumorigenesis as a result of combined genetic and environmental factors . Another epithelial polarity regulator that has a tumor-suppressing function in the epidermis of zebrafish embryos is Lgl2 . Like atp1b1a mutants , lgl2 mutants display aberrant apical-basal polarity of basal keratinocytes ( mislocalized cytokeratin ) and E-cadherin displacement , as well as epidermal hyperplasia and increased expression of mmp9 ( Sonawane et al . , 2005; Reischauer et al . , 2009 ) , which we now can interpret as a sign of invasiveness . Although not yet addressed , carcinogenesis in lgl2 mutants might also involve hypotonic stress , and lgl2 , like atp1b1a , might act both in the epidermis and in the kidney . Thus , we found that atp1b1a and lgl2 not only genetically interact during epidermal cell polarity and malignancy but also during edema formation and Na/K-ATPase α-subunit localization ( Figure 7—figure supplement 1 ) , in line with previous reports of pronephric defects and mild edema formation in lgl2 morphants ( Tay et al . , 2013 ) . However , atp1b1a and lgl2 mutants also display differences . Although in conjunction with other mutations , loss of Lgl2 function leads to defects in earlier developmental stages ( Westcot et al . , 2015; this work ) , epidermal hyperplasia in lgl2 single mutants begins significantly later ( 4 days post fertilization; dpf ) than that in atp1b1a mutants ( 2 dpf ) . An even more pronounced requirement in different time windows has been reported for Na , K-ATPase and Lgl during epithelial polarity regulation in Drosophila embryos . Thus , defects in lgl mutants are manifested during gastrulation , when ATPase is dispensable , whereas corresponding defects in ATPase mutants are first detected during early organogenesis stages , when the defects in lgl mutants begin to recover ( Laprise et al . , 2009; Laprise and Tepass , 2011 ) . This suggests that as in flies , Atp1b1a and Lgl2 in zebrafish might act in different basolateral-promoting complexes , which have differential temporal but partially redundant functions . In addition , although eventually converging at similar or even identical ( mmp9 ) effector genes , Atp1b1a and Lgl2 can fulfill their tumor-suppressing role by blocking different pathways . Lgl2 has been reported to act by blocking ErbB and MAPK in later epidermal malignancies ( Reischauer et al . , 2009 ) , but earlier it cooperates with Atp1b1a to inhibit aberrant activation of a PI3K-AKT-mTORC1-NFκB pathway ( this work ) . Of note , chemical inhibition of the PI3K-AKT-mTORC1-NFκB pathway in atp1b1a mutants blocks epidermal malignancy , but it does not rescue the edema or epithelial polarity phenotype . This indicates that the pathway acts downstream of , and integrates , these two primary effects caused by the loss of Atp1b1a , which per se are necessary but not sufficient for pathway activation ( Figure 10m ) . Parts of the PI3K-AKT-mTORC1-NFκB pathway had previously been reported to mediate hypotonic stress or tumorigenesis in other contexts . Thus , hypotonic stress induces the activation of the EGF receptor ( EGFR ) , AKT and several MAP kinases ( ERK1/2 , p38 ) in cultured human keratinocytes ( Kippenberger et al . , 2005 ) , in line with our observed increase in pAKT levels ( but not in pERK levels; JH and MH , unpublished data ) . Furthermore , PI3K , AKT and NFκB mediate the tumorigenic effects of different cytokines in cultured prostate , gastric and leukemic cancer cells ( Dilly et al . , 2013; Kang et al . , 2011; Wang et al . , 2011 ) , while mTORC1 controls NFκB activity downstream of pAKT in prostate and breast cancer cells ( Dan et al . , 2008; Davis et al . , 2014 ) . The effects of the transcription factor NFκB in the context of tumorigenesis are complex , and multiple mechanisms and effectors have been described . NFκB can affect cancer cell survival , proliferation and invasiveness , and can act in cancer cells themselves or on the tumor microenvironment , for instance by regulating tissue inflammation , which in turn further stimulates tumor progression ( Ben-Neriah and Karin , 2011; Hoesel and Schmid , 2013 ) . In atp1b1a mutants , however , inflammation seems to be of minor relevance: epidermal aggregates only display moderately increased numbers of innate immune cells , and genetic ablation of the entire myeloid lineage does not alleviate epidermal malignancy ( Figure 2—figure supplement 2 ) . One prominent direct transcriptional target of NFκB is mmp9 ( Rhee et al . , 2007 ) , a matrix-metalloprotease with collagenase activity that destabilizes basement membranes and connective tissue , thereby facilitating tumor progression and metastasis ( Kessenbrock and Werb , 2010; Kang et al . , 2011; Dilly et al . , 2013 ) . In addition , MMPs can promote tumor vascularization and inflammation as well as tumor cell proliferation ( Kessenbrock and Werb , 2010 ) . In zebrafish atp1b1a mutants , however , the NFκB-dependent increase in mmp9 expression only contributes to epidermal invasiveness , while epidermal hyperplasia is mediated by NFκB targets other than mmp9 ( Figure 10m ) . Thus , in contrast to mmp9 knockdown , treatment of mutant embryos with hydroxyurea rescues epidermal hyperproliferation in the persistent presence of high NFκB activity . The nature of these proliferation-promoting NFκB targets remains unknown . CyclinD1 ( ccnd1 ) , a described direct transcriptional NFκB target in mammals ( Hinz et al . , 1999 ) , seems an unlikely target , as its expression levels are unaltered in atp1b1a mutants ( JH and MH , unpublished data ) . What also remains unknown is the player upstream of PI3K that is directly affected by hypotonicity and by the compromised epithelial polarity / adhesiveness of basal keratinocytes ( Figure 10m ) . EGFR would be a strong candidate . It acts as the upstream regulator of PI3K to mediate hypotonic stress in human keratinocytes ( Kippenberger et al . , 2005 ) , and is activated by hypotonicity in several other , MAPK-mediated responses ( Lezama et al . , 2005 ) . Like Atp1b1a , EGFR is normally targeted to the basolateral side of epithelial cells ( He et al . , 2002 ) , and its signaling strength is under the control of the epithelial polarity system ( Hobert et al . , 1999; Vermeer et al . , 2003 ) . Nevertheless , treatment of atp1b1a mutant embryos with the chemical pan-ErbB inhibitor PD168393 failed to rescue epidermal malignancies ( JH and MH , unpublished data ) . This suggests that other receptor tyrosine kinases or their modifiers are the relevant players at the convergence point of hypotonic stress and aberrant epithelial polarity signaling . Na , K-ATPases and hypotonicity might also play a role during human carcinogenesis . Although not usually mentioned in this context ( Martin-Belmonte and Perez-Moreno , 2012; Ellenbroek et al . , 2012 ) , one study has proposed the β1-subunit as a potential tumor-suppressor ( Inge et al . , 2008 ) . This notion was based on functional analyses performed with virus-transformed MDCK cells , and the down-regulation of the β1-subunit in different human carcinoma cell lines ( Espineda et al . , 2004 ) , and is strongly supported by our genetic in vivo data presented here . There are also several indirect lines of evidence for a tumor-promoting effect of hypotonic stress during human carcinogenesis . For instance , untransformed human keratinocytes display strongly increased proliferation when cultured in hypotonic medium ( Gönczi et al . , 2007 ) . Systemic hypotonicity can also occur in humans in vivo . A major human condition that leads to systemic hypotonicity in conjunction with reduced Na levels ( hypotonic or hypoosmolar hyponatremia ) is increased nephric water re-absorption caused by ADH ( antidiuretic hormone ) hyperactivity . In SIADH ( Syndrome of inappropriate antidiuretic hormone secretion ) , this is usually linked to carcinogenesis and results from ectopic ADH production by the tumors , which in general are rather aggressive ( Ellison and Berl , 2007; Grohé and Berardi , 2015 ) . In fact , hyponatremia is quite common in malignant solid tumors beyond SIADH ( up to 25% of all patients ) . It is used as a prognostic and predictive value and is associated with high morbidity rates; its early diagnosis and treatment significantly improves the patients’ prognosis ( Schutz et al . , 2014; Grohé and Berardi , 2015; Balachandran et al . , 2015 ) . This correlation suggests that systemic hypotonicity / hyponatremia increases carcinoma incidence and aggressiveness . In addition , the carcinogenesis risk seems to be increased by locally restricted hypotonic stress . Such stress might occur , for instance , during injuries of the lung or the esophagus ( Ribeiro et al . , 1996; Islami et al . , 2009; Goldkorn and Filosto , 2010; Maret-Ouda et al . , 2016 ) , when epithelial cells become exposed to the airway surface fluid ( Joris and Quinton , 1993 ) or to saliva ( Edgar , 1992 ) , respectively , both of which are hypotonic compared to the internal milieu . In this light , and in light of the data presented in this work , it makes sense to include treatments that target osmotic conditions in therapies against certain carcinoma types ( Balachandran et al . , 2015; Grohé and Berardi , 2015 ) . Furthermore , lavages with distilled water during cancer surgery ( Iitaka et al . , 2012 ) , or hypotonic approaches to improve the uptake of chemotherapeutics by tumor cells ( Stephen et al . , 1990 ) , should be used with caution .
The mutant line psoriasism14 ( Webb et al . , 2008 ) and the transgenic lines Tg ( Ola . Actb:Hsa . hras-egfp ) vu119Tg ( ubiquitous expression of membrane-tagged EGFP ) ( Cooper et al . , 2005 ) , Tg ( krt4:GFP ) gz7Tg ( periderm-specific expression of GFP ) ( Gong et al . , 2002 ) , and Tg ( NFκB-RE:eGFP ) sh235Tg ( NFκB responder ) ( Candel et al . , 2014 ) have been described previously . The krt4:atp1b1a-gfp construct was generated using the Tol2 kit ( Kwan et al . , 2007 ) with the described krt4 promoter ( Gong et al . , 2002 ) . The primers 5’- GGGGACAAGTTTGTACAAAAAAGCAGGCTCCACCATGCCCGCAAATAAAGATGG-3’ and 5’-GGGGACCACTTTGTACAAGAAAGCTGGGTATGACTTGGTTTTGATGGTGAAC-3’ were used to amplify the atp1b1a cDNA , which was cloned into pDONR221 ( Invitrogen , Carlsbad , CA ) . The construct was used to generate the stable transgenic line Tg ( krt4:atp1b1a-gfp ) fr36Tg by standard injection and screening procedures . Genotyping of the psoriasis mutation was conducted by PCR with the primers 5’-TCCGAGAATCCAAAATGAGC-3’ and 5’-CACTCGTCTCCGTTTATTCG-3’ followed by an MwoI digestion of the PCR product . Embryos were raised in E3 medium ( 5 mM NaCl , 0 . 17 mM KCl , 0 . 33 mM CaCl2 , 0 . 33 mM MgSO4; hypotonic ) , E3 medium containing 250 mM mannitol , or Ringer’s solution . All zebrafish experiments were approved by the national animal care committees ( LANUV Nordrhein-Westfalen; 8 . 87–50 . 10 . 31 . 08 . 129; 84–02 . 04 . 2012 . A251; City of Cologne; 576 . 1 . 36 . 6 . 3 . 01 . 10 Be ) and the University of Cologne . Genomic DNA was extracted from a pool of 20 affected embryos and from their healthy parents using a Maxwell 16 instrument from Promega , according to the manufacturer's protocol . The DNAs underwent individual library preparation and enrichment ( SureSelectXT Zebrafish Kit 5190–5450 , Agilent Technologies , Santa Clara , CA ) , using 3ug DNA fragmented to 150bp by sonication ( bioruptor , Diagenode , Liège , Belgium ) and the standard protocol SureSelectXT Target Enrichment for Illumina Paired-End Multiplexed Sequencing . After validation ( Agilent 2200 TapeStation ) and quantification ( Invitrogen Qubit System ) , we performed a qPCR by using the Peqlab KAPA Library Quantification Kit and the Applied Biosystems 7900HT Sequence Detection System ( Applied Biosystems , Foster City , CA ) . Pools of 2–4 libraries were sequenced on one lane using an Illumina TruSeq PE Cluster Kit v3 and an Illumina TruSeq SBS Kit v3-HS on an Illumina HiSeq2000 sequencer with a paired-end ( 101x7x101 cycles ) protocol . The alignment to the zebrafish reference version Zv9 was performed using the Burrows-Wheeler Aligner ( BWA ) . PCR duplicate marking was performed using Picard , both realignment around indels and variant calling were performed using the Genome Analysis ToolKit ( GATK ) . The annotation was performed using Annovar and the variations were filtered according to the predicted effect at the protein level and according to their presence in a control set of 5 unrelated zebrafish Whole-Exome Sequencing datasets . For mapping , all variant loci with a coverage of at least 20 were selected from both the affected pool and the parental DNA , with the exclusion of loci in which both datasets were homozygous for the variant allele . At every locus , the percentage of reads showing the variation in the parental DNA was subtracted from the percentage of reads showing the variation in the affected pool . The absolute value of this difference was then plotted against the physical position of the locus and heatmaps were generated for the whole genome and for each chromosome . The chromosomes were then visually analyzed to identify the regions in which the majority of the loci show a difference of either 25% or 50% . Embryos were fixed in 4% paraformaldehyde ( PFA ) and WISH was performed as previously described ( Carney et al . , 2007 ) . DIG-labeled probes were synthesized with the Roche digoxygenin RNA synthesis kit , using cmlc2 , mmp9 and mpx cDNA templates as described ( Carney et al . , 2007; Reischauer et al . , 2009 ) . For atp1b1a , a cDNA was amplified using the following primers 5’-ATGCCCGCAAATAAAGATGG-3’ and 5’-TCATGACTTGGTTTTGATGG-3’ , cloned into pGEM T Easy ( Promega ) and linearized with SacII for Sp6 RNA pol-dependent antisense RNA synthesis . Combined colorimetric WISH and immunostainings were performed as described ( Carney et al . , 2007 ) . Images were taken on a Axioplan2 microscope ( Zeiss ) using AxioVision software ( Zeiss ) . 1 . 5 nl of 1 mg/ml rhodamine-dextran ( Molecular Probes , Eugene , OR; D1816 ) in PBS was injected into the common cardinal vein of 34 hpf embryos anaesthetized with Tricaine . Injected embryos were incubated in E3 until 50 hpf , when remaining rhodamine-dextran was detected by confocal microscopy . Cryosections were generated as previously described ( Westcot et al . , 2015 ) . For TEM analysis , embryos were anesthetized with Tricaine , heads were removed using a scalpel and subjected to genotyping , and tails were processed for ultrathin sectioning and TEM as described ( Feitosa et al . , 2011 ) . To determine cell proliferation , embryos were incubated in 10mM BrdU in E3 for 1 or 2 hr , followed by a one-hour wash with E3 and fixation in 4% PFA . BrdU incorporation was detected by anti-BrdU immunolabelling . IF analyses were performed essentially as previously described ( Carney et al . , 2007 ) . Embryos were fixed in 4% PFA for stainings using the following primary antibodies: mouse anti-Tjp1 ( Zymed , San Francisco , CA; 33–9100 , 1:200 ) , mouse anti-chicken ATPa5 ( Developmental Studies Hybridoma Bank; DSHB , 1:200 ) , mouse anti-chicken ATPa6F ( DSHB , 1:200 ) , rabbit anti-Cdh1 ( Anaspec , Fremont , CA; 1:200 ) , mouse anti-Cdh1 ( BD Biosciences , 610188 , 1:200 ) , rabbit anti-laminin ( Sigma Aldrich , St . Louis , MO; L9393 , 1:200 ) , mouse anti-BrdU ( Roche , Basel , CH; 1170376 , 1∶100 ) , mouse anti-collagen II ( DSHB II-II6B3-c , 1:200 ) , chicken anti-GFP ( Invitrogen; A10262 , 1:300 ) , mouse anti-p63 BC4A4 ( Zytomed , 1:200 ) , rabbit anti-zebrafish Lgl2 ( Sonawane et al . , 2009 ) ( 1:400 ) , rabbit anti-aPKC ( C-20 , Santa Cruz Biotechnologies , Dallas , TX; sc-216 , 1:200 ) , rabbit anti-phospho-S6 ribosomal protein ( pS6RP , Ser240/241; Cell Signaling Technology , Danvers , MA; #2215 , 1∶300 ) . For stainings with the mouse anti-panKeratin1-8 ( Progen Pharmaceuticals , Darra , Australia; 61006 , 1:10 ) , embryos were fixed with Dent’s fixative ( 80% Methanol , 20% DMSO , at -20°C overnight ) . For pAkt stainings , embryos were fixed in EAF ( 40% ethanol , 5% acetic acid , 4% formaldehyde in PBS ) and either washed with PBS-TritonX , followed by an antigen retrieval in 10 mM Tris , 1 mM EDTA , pH 9 . 0 for 60 min at 60°C , blocking in 5% sheep serum and antibody incubation with rabbit anti-pAkt ( S473 ) ( Cell Signaling Technology #4060 , 1:50 ) or processed for cryosectioning as described ( Westcot et al . , 2015 ) . Secondary antibodies were anti-mouseCy3 , anti-rabbitCy3 , anti-mouseAlexa488 , anti-mouseAlexa647 , and anti-chickenAlexa488 ( all Invitrogen , Carlsbad , CA; 1:200 ) . Images were taken using a Zeiss Confocal ( LSM710 META ) and processed using the ImageJ software . Ventral ectodermal cells from Tg ( Ola . Actb:Hsa . hras-egfp ) vu119 donor embryos either un-injected or injected with atp1b1a MO were transplanted into the ventral ectoderm of wild-type or atp1b1a morphant recipients at 6 hpf . Homotypic mosaics ( wt to wt , morphant to morphant ) embryos were raised in E3 medium until 48 hpf , mounted in 1 . 5% LMP agarose in E3 , and clusters of GFP+ cells were recorded by time-lapse confocal microscopy ( Figure 2A , B and Videos 1 , 2 ) . Heterotypic chimeras ( wt to morphant , morphant to wt ) were raised in E3 250 mM mannitol until 84 hpf , fixed in Dent’s fixative and subjected to anti-GFP and cytokeratin IF ( Figure 8a , b ) . Ouabain ( Sigma-Aldrich; St . Louis , MO ) and hydroxyurea ( Merck , Kenilworth , NJ ) were dissolved in water , Wortmannin ( Sigma-Aldrich ) , PIK90 ( Merck ) , LY294002 ( TocrisBioscience , Bristol , UK ) , Rapamycin ( Merck ) , AZD8055 ( Santa Cruz Biotechnologies ) , and Withaferin A ( TocrisBioscience ) in DMSO , and solutions were further diluted in E3 embryo medium to concentrations of 3mM ( ouabain ) , 50mM ( hydroxyurea ) , 1 µM ( Wortmannin ) , 5 µM ( PIK90 ) , 25 µM ( LY294002 ) , 1 . 1 µM ( Rapamycin ) , 30 µM ( AZD8055 ) , and 30 µM ( WithaferinA ) . Embryos were incubated in inhibitor solution starting from 34 hpf and scored at 54 hpf . The following morpholinos were obtained from GeneTools ( Philomath , CA ) and 1 . 5 nl injected in 1-cell stage embryos according to standard protocols:10 . 7554/eLife . 14277 . 037Table 1 . Sequences and concentrations of antisense morpholino oligonucleotides ( MOs ) used . DOI: http://dx . doi . org/10 . 7554/eLife . 14277 . 037MOSequenceConcentrationRef . pu . 1GATATACTGATACTCCATTGGTGGT0 . 8 mM ( Carney et al . , 2007 ) atp1b1aCGGTATTTAGTTCCCTTTTTGGTGG0 . 75 mM ( full ) , 0 . 01 mM ( subphenotypic ) ( Blasiole et al . , 2006 ) lgl2GCCCATGACGCCTGAACCTCTTCAT0 . 05 mM ( Sonawane et al . , 2005 ) mmp9CGCCAGGACTCCAAGTCTCATTTTG0 . 2 mM ( LeBert et al . , 2015 ) Quantitative experiments were repeated at least three times , reaching similar results . No outliers were encountered . Mean values and standard deviations of all individual specimens ( biological samples; n ) from one representative or all independent experiments are presented , as specified in the respective figure legends . Numbers of biological samples analyzed were decided on depending on obtained standard deviations and statistical significances . Quantifications of phenotypes were conducted by comparing treated and control progeny of the same parental fish . Fluorescence intensities were determined using ImageJ software . Significance of differences was determined using an unpaired two-tailed Student’s t-test , and obtained p values are mentioned in the respective figure legends . Numerical data from all quantitative analyses are also provided in the Supplement as a source data file . Data illustrated in representative images that are shown without statistical calculations were obtained from at least 15 out of 15 investigated specimens from at least three independent experiments . | Cancer can develop when cells in the body gain mutations that allow them to grow and divide rapidly . Some of these mutations may affect the activity of genes that usually act to prevent cancer from developing . Several such “tumor suppressor” genes have been identified , but it is likely that many remain undiscovered and it is far from fully understoodhow all these genes work . One way to identify new tumor suppressor genes is to examine tumors to search for genes that have gained mutations that block their activity , known as loss-of-function mutations . Hatzold et al . identified a new and rather unexpected tumor suppressor gene by studying a zebrafish mutant that develops skin cancer as the embryo grows . The experiments showed that cells in the skin of the developing embryos of this mutant grow excessively and start to invade deeper tissues in the body . This behavior is caused by loss-of-function mutations in a gene called atp1b1a . This gene encodes part of an ion pump protein that helps to control the amount of water and ions in cells and in body fluids . Further experiments showed that this tumor suppressor gene does not act in the skin cells themselves but in other cells of organs such as the kidney . The kidney is involved in controlling the water and ion content of the body ( known as osmoregulation ) , and the atp1b1a mutants have lower levels of ions and increased levels of water than normal zebrafish . Cancer formation could be completely blocked when the mutant embryos were kept in a solution that had the same salt and water content as the animals , instead of regular fresh water . This suggests that exposure of cells to body fluids with decreased ion and increased salt contents , a condition also called hypotonic stress , increases the risk of developing some tumors . Osmoregulatory organs that are not working efficiently , or injuries that expose cells to different ion and water levels can both cause hypotonic stress . The next steps are to investigate whether this stress also promotes cancer formation in mammals , including humans . | [
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] | 2016 | Tumor suppression in basal keratinocytes via dual non-cell-autonomous functions of a Na,K-ATPase beta subunit |
The diaphragm muscle is essential for breathing in mammals . Its asymmetric elevation during contraction correlates with morphological features suggestive of inherent left–right ( L/R ) asymmetry . Whether this asymmetry is due to L versus R differences in the muscle or in the phrenic nerve activity is unknown . Here , we have combined the analysis of genetically modified mouse models with transcriptomic analysis to show that both the diaphragm muscle and phrenic nerves have asymmetries , which can be established independently of each other during early embryogenesis in pathway instructed by Nodal , a morphogen that also conveys asymmetry in other organs . We further found that phrenic motoneurons receive an early L/R genetic imprint , with L versus R differences both in Slit/Robo signaling and MMP2 activity and in the contribution of both pathways to establish phrenic nerve asymmetry . Our study therefore demonstrates L–R imprinting of spinal motoneurons and describes how L/R modulation of axon guidance signaling helps to match neural circuit formation to organ asymmetry .
The diaphragm is the main respiratory muscle of mammalian organisms , separating the thoracic and abdominal cavities . Many diseases , including congenital hernia , degenerative pathologies and spinal cord injury , affect diaphragm function and thereby cause morbidity and mortality ( Greer , 2013; McCool and Tzelepis , 2012 ) . Despite the large interest given to diaphragm function in various physiological and pathological contexts ( Lin et al . , 2000; Misgeld et al . , 2002; Strochlic et al . , 2012 ) , little attention has been paid to the embryological origin of left–right ( L/R ) asymmetries in diaphragm morphology and contraction , in part because they were inferred to be simply an adaptation to the structure of other , surrounding asymmetric organs such as the lungs ( Laskowski et al . , 1991; Whitelaw , 1987 ) . In the present study , we investigated the origin and the mechanisms responsible for the establishment of the diaphragm asymmetries , including motor innervation by the left and right phrenic motoneurons that arise in the spinal cord at cervical levels C3 to C5 ( Greer et al . , 1999; Laskowski and Owens , 1994 ) . Our findings show that both the diaphragm muscle and phrenic nerves have asymmetries , which are established independently of each other during early embryogenesis .
As many L/R asymmetries are determined prenatally ( Sun et al . , 2005 ) , we analyzed the diaphragm innervation of mouse embryos on embryonic day ( E ) 15 . 5 , when synaptic contacts begin to be established in this organ ( Lin et al . , 2001 ) . We observed that the phrenic nerves split into primary dorsal and ventral branches when reaching the lateral muscles , whereby the distance from the end-plate to the nerve entry point differs between the left and right side and results in a characteristic ‘T’ -like pattern on the left and ‘V’ -like pattern on the right ( Figure 1A; Figure 1—figure supplement 1A , B ) . Similar differences in the L/R branching patterns are present in the human diaphragm ( Hidayet et al . , 1974 ) ( Figure 1—figure supplement 1C ) . Additionally , we observed an asymmetric number of branches defasciculating from the left and right primary nerves to innervate the motor end-plates ( Figure 1A; Figure 1—figure supplement 1A , B ) . We further found that the L/R distribution of acetylcholine receptor ( AchR ) clusters at the nascent neuromuscular junctions also differed , with a 2 . 1 ± 0 . 2-fold increase in the medio-lateral scattering of AchR clusters on the right side of the diaphragm compared to the left side ( N = 11 , p<0 . 001 Wilcoxon ) ( Figure 1B; Figure 1—figure supplement 2A , B ) . The time course analysis revealed that these asymmetric nerve patterns arose at E12 . 5 , concomitantly with branch formation ( Figure 1C–E; Figure 1—figure supplement 3A–C ) . Thus , phrenic branch patterns exhibit clear asymmetries before synapse formation and fetal respiratory movements ( Lin et al . , 2001 , 2008 ) , and are therefore unlikely to be induced by nerve activity or muscle contraction . 10 . 7554/eLife . 18481 . 003Figure 1 . L/R asymmetries of the phrenic nerve patterns are established from the onset of diaphragm innervation . ( A ) Neurofilament ( NF ) staining showing the branching patterns of the left and right phrenic nerves in whole-mount E15 . 5 mouse diaphragm . Left and right primary branches are pseudocolored ( middle panel ) in green and red , respectively . ( See Figure 1—figure supplement 1A , for complete branch traces ) . L/R asymmetry is especially apparent after superimposing the left and right primary branches ( right panel ) . Arrows point to the nerve entry points . Images are top views of the whole diaphragm , oriented as indicated in the top left hand corner of the left panel ( V , Ventral; D , Dorsal; L , Left; R , Right ) . ( B ) NF and Bungarotoxin staining showing the asymmetry of acetylcholine receptor clusters and nerve domains on the left ( left panel , green frame ) and right ( right panel , red frame ) diaphragm muscles of an E15 . 5 embryo ( see Figure 1—figure supplement 2 for quantification ) . ( C ) NF staining showing the patterns of left and right phrenic nerves at E13 . 5 and E14 . 5 . Green- and red-framed panels show enlarged images of the left and right phrenic nerves , respectively . ( D ) Schematics showing the method used to quantify the defasciculation distance ( shown in blue ) , from the nerve entry point to the dotted line and histogram of the defasciculation distance at E13 . 5 , E14 . 5 and E15 . 5 ( E13 . 5 — left 32 . 76 ± 11 . 01 , right 94 . 82 ± 21 . 94 , N = 9 , p=0 . 0106; E14 . 5 — left 42 . 56 ± 4 . 16 , right 135 . 71 ± 10 . 20 , N = 8 , p=0 . 00015; E15 . 5 — left 77 . 16 ± 7 . 32 , right 188 . 51 ± 7 . 01 , N = 18 , p=4 E-10 , Mann-Whitney ) . ( E ) Schematics showing the method used to quantify the secondary branch number by counting the number of NF-positive fascicles that crossed the dotted line positioned at 80% of the defasciculation distance and histogram of the secondary branch number at E13 . 5 , E14 . 5 and E15 . 5 ( E13 . 5 — left 5 . 55 ± 0 . 96 , right 8 . 88 ± 0 . 65 , N = 9 , p=0 . 0288; E14 . 5 — left 7 . 5 ± 0 . 38 , right 10 . 88 ± 0 . 69 , N = 8 , p=0 . 00117; E15 . 5 — left 5 . 94 ± 0 . 31 , right 10 . 7 ± 0 . 3 , N = 18 , p=2 . 35 E-7 , Mann-Whitney ) . Histograms show the mean ± SEM for each stage . Scale bars: 200 μm ( A , C ) ; 100 μm ( B ) . Numerical values used to generate the graphs are accessible in Figure 1—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00310 . 7554/eLife . 18481 . 004Figure 1—source data 1 . Left and right measures of the defasciculation distance and branch number in E13 . 5 , E14 . 5 and E15 . 5 mouse embryos . This file provides the mean , SEM , statistical report and individual measures used to create the histograms shown in Figure 1D , E . Defasciculation distances measured on left and right hemi-diaphragms are shown in the first sheet and numbers of secondary branches between the two primary branches on the second sheet . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00410 . 7554/eLife . 18481 . 005Figure 1—figure supplement 1 . Phrenic nerve patterns and quantification in mice and L/R nerve asymmetry in a human diaphragm . ( A ) NF staining showing the branching patterns of the left and right phrenic nerves in a whole-mount E15 . 5 mouse diaphragm . In the right panel , the primary , secondary and tertiary branches of the left and right phrenic nerves are traced in green and red , respectively . The left and right crural phrenic nerves are traced in blue . ( B ) Example of quantification on an NF-labelled wholemount diaphragm . ( C ) L versus R differences of nerve pattern in human diaphragms , the left ( green ) and right ( red ) branches are innervating the lateral muscle ( grey regions ) . Reproduced from the original figure of Hidayet et al . ( 1974 ) . The L/R asymmetry is especially apparent after superimposing the left and right nerve pattern ( right panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00510 . 7554/eLife . 18481 . 006Figure 1—figure supplement 2 . L/R differences of acetylcholine clusters during synaptogenesis . ( A ) Bungarotoxin staining on the left and right sides of an E15 . 5 mouse diaphragm and plot profile showing the asymmetry of the clusters of acetylcholine receptor indicative of the endplate thickness ( left and right in green and red , respectively ) . ( B ) Histogram showing the quantification of the endplate thickness ( left: 254 . 9 ± 22 . 2 , right: 529 . 3 ± 53 . 0 , N = 11 , p=0 . 00097 , Wilcoxon signed rank ) . Scale bars: 200 μm . Numerical values used to generate the graphs are accessible in Figure 1—figure supplement 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00610 . 7554/eLife . 18481 . 007Figure 1—figure supplement 2—source data 1 . Left and right endplate thicknesses measured from Bungarotoxin labeling in E15 . 5 mouse embryos . This file provides the mean , SEM , statistical report and individual measures used to create the histograms shown in Figure 1—figure supplement 2B . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00710 . 7554/eLife . 18481 . 008Figure 1—figure supplement 3 . Stereotypy and variability of L/R asymmetry of the phrenic nerve patterns . ( A ) NF staining showing the patterns of left and right phrenic nerves at E12 . 5 and E18 . 5 . Green- and red-framed panels show enlarged images of the left and right phrenic nerves , respectively . Note that at E12 . 5 , the dorsal and ventral branches have already split with different angles on the left and right sides ( left: 166° ± 4°; right: 132° ± 4°; N = 8 ) . ( B ) Ladder graph showing the stereotypy of the left and right defasciculation distances for eight E14 . 5 embryos ( E1 to E8 ) ( ratio shown in brackets ) . ( C ) NF staining of whole-mount diaphragms from E14 . 5 mouse embryos showing the phrenic nerve pattern variability at that stage . Left ( green ) and right ( red ) primary branch traces are shown in the lower panels . Scale bars: 200 μm , 100 μm for enlargement panels . The numerical values used to generate the graphs are accessible in Figure 1—figure supplement 3—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 00810 . 7554/eLife . 18481 . 009Figure 1—figure supplement 3—source data 2 . Paired analysis of left and right defasciculation distances in E14 . 5 mouse embryos . This file provides the individual measurements used to create the ladder graph shown in Figure 1—figure supplement 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 009 We therefore asked whether diaphragm nerve asymmetry was genetically hard-wired downstream of Nodal signaling , which initiates a left-restricted transcriptional cascade to establish visceral asymmetry ( Komatsu and Mishina , 2013; Nakamura and Hamada , 2012 ) . To answer this question , we examined two complementary types of mouse mutants that have defective Nodal signaling and ensuing lung isomerism . First , we examined Pitx2∆C/∆C embryos lacking PITX2C , a transcription factor downstream of Nodal ( Essner et al . , 2000; Liu et al . , 2001; Schweickert et al . , 2000 ) . In the absence of PITX2C , Nodal signaling is interrupted , which causes a right pulmonary isomerism ( i . e . the left lung has three main lobes like the right lung , instead of only one ) ( Liu et al . , 2001 , 2002 ) . Second , we examined Rfx3–/– embryos lacking RFX3 , which is essential for cilia function that helps to distribute Nodal to the left side of the body . As a result , some Rfx3–/– embryos exhibit bilateral Nodal expression and left pulmonary isomerism ( i . e . the right lung has one lobe like the left lung ) ( Bonnafe et al . , 2004 ) . We found that diaphragm L/R nerve asymmetries were lost in both Pitx2∆C/∆C and Rfx3–/– embryos with impaired visceral asymmetries at E14 . 5 ( Figure 2A–E ) ( number of secondary branches , Wt versus mutant with lung isomerism: PITX2C , p=4 . 493E-5; RFX3 , p=0 . 002884; defasciculation distance , Wt versus mutant with lung isomerism: PITX2C , p=0 . 001268; RFX3 , p=2 . 719E-6 , Mann-Whitney ) . Thus , the Nodal pathway is essential for the establishment of diaphragm nerve asymmetry . 10 . 7554/eLife . 18481 . 010Figure 2 . L/R asymmetries of the phrenic nerve patterns require Nodal signaling . ( A ) NF staining of E14 . 5 diaphragms from wild-type , Pitx2∆C/ΔC and Rfx3–/– embryos with the respective superimposed L/R nerve pattern and the Nodal expression . ( B–C ) Schematic of the secondary branches quantification and histograms of the R/L ratios of secondary branches: Pitx2∆C/+and Pitx2+/+ 2 . 23 ± 0 . 20 , versus Pitx2∆C/∆C with lung isomerism 1 . 09 ± 0 . 05 , p=4 . 493E-5 ( B ) ; Rfx3+/+ and Rfx3–/+ 1 . 75 ± 0 . 12 , versus Rfx3–/– with lung isomerism 1 . 07 ± 0 . 10 , p=0 . 002884 , Mann-Whitney ( C ) . ( D–E ) Schematic of the defasciculation distance measurements and histograms of the R/L ratios of defasciculation distance for: Pitx2∆C/+and Pitx2+/+ 4 . 63 ± 0 . 26 , versus Pitx2∆C/∆C with visceral isomerism: 2 . 28 ± 0 . 59 , p=0 . 001268 , Mann-Whitney ( D ) ; Rfx3+/+ and Rfx3–/+ 4 . 62 ± 0 . 43 , versus Rfx3-/- with visceral isomerism 1 . 35 ± 0 . 19 , p=2 . 719E-6 , Mann-Whitney ( E ) . Note that there is no lung isomerism in wild-type embryos . Histograms show the mean ± SEM . Numbers above bars indicate the number of embryos analysed . ni , non-isomeric ( embryos that did not exhibit visceral isomerism ) ; i , isomeric . Scale bars: 200 μm . Numerical values used to generate the graphs are accessible in Figure 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01010 . 7554/eLife . 18481 . 011Figure 2—source data 1 . Ratios of the defasciculation distance and branch number in E14 . 5 mouse embryos of Pitx2C and Rfx3 lines . The file provides the mean , SEM , statistical report and individual values used to create the histograms shown in Figure 2B , C , D and E . Branch numbers ratios found in Pitx2C∆C/+ and Pitx2C∆C/∆C embryos as well as those found in Rfx3+/+ and Rfx3–/– embryos are shown on the first and second sheet , respectively . Defasciculation distance ratios measured in Pitx2C∆C/+ and Pitx2C∆C/∆C embryos or in Rfx3 +/+ and Rfx3–/– embryos are shown on the third and fourth sheet , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 011 We next asked whether phrenic nerve asymmetry has an environmental origin , because it is conceivable that the lung buds confer L/R asymmetry-inducing signals to nerves that are navigating close by ( Figure 3A , B ) . However , the analysis of Pitx2∆C/∆C and Rfx3–/– mutants showed that the pattern of nerve asymmetry did not always correlate with the pattern of lung asymmetry; for example , in 2/10 Pitx2∆C/∆C embryos , nerve patterns were normal even though the lungs were isomerized ( 20%; Figure 3C; Figure 3—figure supplement 1A , B ) . Moreover , 1/13 Rfx3–/– embryos exhibited nerve isomerism together with pulmonary situs inversus , and nerve patterns were reversed in 1/13 embryo with lung isomerism ( 7 . 7% and 7 . 7%; Figure 3C; Figure 3—figure supplement 1A , B ) . Alternatively , it is conceivable that muscle asymmetry controls nerve asymmetry . In agreement with this possibility , L/R asymmetry of the lateral diaphragm muscles was lost in both Pitx2∆C/∆C and Rfx3–/– mutants ( Figure 3B ) . However , muscle width did not correlate with changes in nerve patterns in 2/10 Pitx2∆C/∆C embryos or in 6/13 Rfx3–/– embryos ( 20% and 46 . 2% , respectively ) . For example , muscle isomerism could be observed in 1/13 Rfx3–/– embryos that have normal nerve patterns ( 7 . 7% ) or in 1/13 Rfx3–/– embryos with reversed nerve patterns ( 7 . 7% ) . Finally , nerves were isomerized in 2/13 Rfx3–/– embryos that exhibit normal L/R muscle asymmetry ( 15 . 4% ) ( Figure 3C; Figure 3—figure supplement 1C ) . Together , these findings raise the possibility that phrenic motoneurons possess intrinsic L/R differences that are established independently of visceral and muscle asymmetries . 10 . 7554/eLife . 18481 . 012Figure 3 . The asymmetry of phrenic circuits results from an intrinsic neuronal program . ( A ) Schematic representation of the organisation of the phrenic nerves as they pass through the lungs and reach the diaphragm . ( B ) Photomicrographs of the expected L/R asymmetry of lungs and diaphragm muscles at E14 . 5 in wild-type embryos and the altered L/R asymmetry observed in the Rfx3–/– and Pitx2∆C/∆C mutant embryos . Quantification of diaphragm muscle asymmetry: Pitx2+/+ and Pitx2∆C/+ 6 . 25 ± 0 . 68 , N = 20 , versus Pitx2∆C/∆Ciso 0 . 26 ± 0 . 6 , N = 6; Rfx3 +/+ and Rfx3-/+ 7 . 02 ± 0 . 74 , N = 17 versus Rfx3–/-–iso 0 . 72 ± 1 . 6 , N = 7 ( see methods ) . ( C ) Schematic representation of L/R asymmetries in the lungs , diaphragm muscles and phrenic nerves . A colour code is used to show the uncoupling occurring between phrenic nerve and lung asymmetries or phrenic nerve and diaphragm muscle asymmetries . Any structure represented in green is indicative of its left characteristics , whether it is observed on the left or the right side of the embryo , whereas red structures represent right characteristics . ( D ) Pou3f1 ( Oct6 ) staining showing the pool of phrenic motoneurons , projection formed by serial sections of the entire cervical region of an E11 . 5 spinal cord embryo . ( E ) Histogram showing the area positive for the Pou3f1 ( Oct6 ) labeling in the left and right cervical motoneuron domains ( N = 3 , p=0 . 5 , Wilcoxon signed rank ) . ( F ) GFP staining of ventral cervical spinal cord explants from E12 . 5 HB9::GFP embryos; the dashed line is indicative of the explant border . ( G ) Quantification of the area occupied by GFP-positive axons for left and right explants ( left — 100% ± 17 . 4; right — 214% ± 30 . 2 , p=0 . 0045 , Mann-Whitney ) . ( H ) Quantification of the width ratio ( see Figure 3—figure supplement 1 for quantification details ) ( left —100% ± 7 . 3; right — 127% ± 8 . 0 , p=0 . 0127 , Mann-Whitney ) . Numbers above bars indicate the numbers of explants analysed . Histograms show the mean ± SEM . Scale bars: 100 μm ( D ) , 200 μm ( F ) . Numerical values used to generate the graphs are accessible in Figure 3—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01210 . 7554/eLife . 18481 . 013Figure 3—source data 1 . Pool size and in vitro axon growth from left and right motoneurons . This file provides the mean , SEM , statistical report and individual values used to create the histograms shown in Figure 3D , G and H . Left and right Oct6-labeled surfaces are shown on the first sheet . The surface and the defasciculation index of motoneurons axons ( GFP+ ) growing from left and right explants are shown on the second and third sheet , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01310 . 7554/eLife . 18481 . 014Figure 3—figure supplement 1 . Uncoupling between lung or muscle and nerve asymmetry and intrinsic L/R differences of axon growth from cultured cervical motoneuron explants . ( A ) Distribution of the defasciculation distance ratio amongst E14 . 5 Pitx2+/ΔC and Pitx2∆C/ΔC embryos . Values for Pitx2+/+ and Pitx2+/ΔC embryos are pooled . Two clearly separated groups are visible amongst the Pitx2∆C/ΔC embryos , one below the dashed line composed of embryo with nerve isomerism and one above the line composed of normally asymmetric nerves . ( B–C ) Table showing the uncoupling observed between the nerve pattern and lung morphology ( B ) or the nerve pattern and muscle morphology ( C ) in the Pitx2C and Rfx3 mutant embryos and its frequency . ( D ) Photomicrograph of the GFP signal observed from HB9::GFP spinal cord . The blue dashed line outlines the ventral spinal cord and the white dashed line outlines the spinal cord . The arrows delimit the area of interest . ( E ) Photomicrographs illustrating the defasciculation behaviours of GFP-labelled axons extending from left ( top panel ) or right ( bottom panel ) ventral cervical spinal cord explants from E12 . 5 HB9::GFP embryos . ( F ) Quantification method used to calculate the area occupied by GFP=positive axons and defasciculation index . The binary image ( left panel ) shows the GFP-positive area extracted with the ImageJ plugin NeuriteJ that was used to calculate the area . The proximal ( yellow in left panel ) and the distal ( blue in left panel ) selections are created using the same plugin . The width of each fascicule crossing the proximal and distal selections was measured and the defasciculation index calculated . Scale bar: 300 μm ( F ) . Numerical values used to generate the graphs are accessible in Figure 3—figure supplement 1—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01410 . 7554/eLife . 18481 . 015Figure 3—figure supplement 1—source data 1 . Distribution of defasciculation ratios in the Pitx2C mouse line . This file provides the individual values used to generate the graph plot shown in Figure 3—figure supplement 1A . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 015 3D reconstructions of cervical spinal cord tissue immunolabeled with Pou3f1/Oct6 , whose expression has been reported in motoneurons ( Philippidou et al . , 2012 ) , did not reveal any obvious differences in the L/R organization of the cervical motoneuron pools in the spinal cord ( Figure 3D–E ) . We therefore explanted phrenic motoneuron-enriched Hb9::GFP spinal cord tissue ( Wichterle et al . , 2002 ) to follow the behavior of motor axons as they extended from the explants independently of the surrounding organs ( Figure 3—figure supplement 1D ) . We observed that axons explanted from right tissue extended over longer distances and were organized differently than axons explanted from left tissue ( Figure 3F–H; Figure 3—figure supplement 1E–F ) . This observation suggests that intrinsic factors present within the ventral spinal cord confer different behaviors to left and right motoneuron axons . To identify molecular determinants of L/R differences in phrenic axon growth , we laser-captured left versus right GFP-positive cervical motoneurons from Hb9::GFP transgenic E11 embryos for microarray analysis ( Figure 4A ) . The presence of several markers for phrenic motoneurons ( e . g . Pou3f1/Oct6 , Islet1 and ALCAM ) in the microarray data demonstrated the accuracy of the dissection procedure ( Figure 4—figure supplement 1A–B ) . Consistent with the lack of obvious anatomical differences distinguishing left and right Pou3f1/Oct6+ cervical motoneuron populations , none of these markers had asymmetric expression levels . We further observed that amongst 22 , 600 transcripts expressed above background , 146 were enriched on the left and 194 on the right , with a predominance of transcripts encoding nuclear proteins ( differentially enriched transcripts: right 35 . 56% versus left 26 . 02%; Figure 4B; Figure 4—source data 1 and 2 ) . Immunoblotting confirmed that Morf4l1 , a protein involved in histone acetylation/deacetylation and chromatin remodeling and reported to be essential for neural precursor proliferation and differentiation ( Chen et al . , 2009; Boije et al . , 2013 ) , was enriched in the left cervical motoneuron domain ( L/R fold-change 1 . 81 ± 0 . 163 , p=0 . 0022 , Mann-Whitney; Figure 4C–E ) . Xrn2 , a protein regulating RNA processing and miR stability that regulates miR expression in neurons ( Kinjo et al . , 2013 ) , was also enriched in the left cervical motoneuron domain ( L/R fold-change 1 . 37 ± 0 . 13 , p=0 . 028; Mann-Whitney; Figure 4—figure supplement 1C ) . Thus , cervical motoneurons are intrinsically L/R-specified . 10 . 7554/eLife . 18481 . 016Figure 4 . L/R molecular signature of cervical motoneurons . ( A ) Transverse sections of E11 . 5 Hb9::GFP embryo cervical spinal cord , illustrating the areas used for laser-capture microdissection . ( B ) Pie charts showing the proportion of left-enriched and right-enriched genes according to their Gene Ontology ‘cellular component’ terms . The ‘nucleus’ component is detached from the pie . ( C ) Ladder graph showing the left and right expression of Morf4l1 in three embryos . Average L/R fold-change shown in brackets . ( D ) Immunodetection of Morf4l1 and loading control tubulin ( Tub . ) in left and right ventral cervical spinal cord tissues . ( E ) Graph showing normalized protein levels of Morf4l1 in left and right ventral cervical spinal cords from E11 . 5 mouse embryos . Individual values observed for the six western-blots ( dots ) and mean ± SEM are represented ( L/R ratio: 1 . 81 ± 0 . 163 , L versus R; p=0 . 0022 , Wilcoxon signed rank ) . Average L/R fold-change shown in brackets . Scale bar: 100 μm . Numerical values used to generate the graphs are accessible in Figure 4—source data 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01610 . 7554/eLife . 18481 . 017Figure 4—source data 1 . List of enriched genes in the left cervical motor neurons of HB9::GFP embryos at E11 . 5 . Genes are included on this list if the average change in expression was > 1 . 5 ( or −0 . 58< in log2 ) between the left and right sides . The listed genes had the same enrichment trend in all embryos with a fold-change > 1 . 5 in at least in two embryos . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01710 . 7554/eLife . 18481 . 018Figure 4—source data 2 . List of enriched genes in the right cervical motor neurons of HB9::GFP embryos at E11 . 5 . Genes are included in the list if the average change in expression was > 1 . 5 ( or > 0 . 58 in log2 ) between the right and left sides . The listed genes had the same enrichment trend in all embryos with a fold-change superior to 1 . 5 in at least in two embryos . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01810 . 7554/eLife . 18481 . 019Figure 4—source data 3 . Lateralization expression of Morf4l1 in cervical motoneurons . This file provides the statistical reports and individual values used to create the ladder graphs shown in Figure 4C and E . RNA expression is shown on the first sheet . Normalized protein levels are shown on the second sheet . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 01910 . 7554/eLife . 18481 . 020Figure 4—figure supplement 1 . Symmetric expression of phrenic motoneuron markers , and lateralized Xrn2 expression . ( A ) Schematic representation of the spinal cord that depicts the expression domain of Hox genes and brachial-specific Pea3 ( Etv4 ) transcription factor . Boxes represent present/absent call tests indicating that markers of dorsal spinal cord and brachial motoneurons are absent . By contrast , generic markers of motoneurons as well as markers that are enriched in cervical and phrenic motoneurons are present in all three embryos . ( B ) Ladder graphs showing the normalized expression signals in the left and right laser-captured samples of the three embryos for probes that detect Ret , Pou3f1 ( Oct6 ) , HoxA5 , HoxC5 , ALCAM and Mnx1 ( Hb9 ) RNA . The average log2 ( R/L ratios ) are indicated in brackets in black for probe one and blue for probe 2 . None of these probes showed significant L/R difference according to the threshold used ( see Materialsand methods ) . ( C ) Ladder graph of Xrn2 RNA expression in left and right samples from the three embryos ( left panel ) , average log2 ( R/L ratio ) indicated in brackets . Graph showing the left and right normalized protein levels of Xrn2 in ventral cervical spinal cord from E11 . 5 embryos . Values of the four western-blots ( dots ) and mean ± SEM are represented ( L/R fold-change 1 . 37 ± 0 . 13 , L versus R , p=0 . 028; Mann-Whitney ) ( right panel ) . Numerical values used to generate the graphs are accessible in Figure 4—figure supplement 1—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02010 . 7554/eLife . 18481 . 021Figure 4—figure supplement 1—source data 1 . RNA level of motoneuron markers and asymmetric expression of Xrn2 . This file provides the individual values used to create the ladder graphs shown in Figure 4—figure supplement 1B and C . RNA expression of motoneuron markers is shown on the first sheet . RNA and normalized protein levels of Xrn2 are shown on the second and third sheets . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 021 To determine whether molecular differences in L/R specification manifest themselves in differential axon guidance responses , we studied mice lacking Slit/Robo signaling , which is known to regulate the fasciculation of phrenic axons ( Jaworski and Tessier-Lavigne , 2012 ) . In agreement with prior reports , we observed defective nerve defasciculation in Robo1–/–;Robo2–/– double mutants ( Figure 5A ) . Notably , defasciculation of the left nerve was as high as that of the right nerve and assumed a similar pattern in the left and right diaphragm , rather than adopting the normal asymmetric pattern seen in wild-type littermates ( Figure 5A ) . Partial symmetrization was observed in double heterozygous mutants , indicating concentration-dependent sensitivity of phrenic nerve axons to Slit signals ( Figure 5A ) . 10 . 7554/eLife . 18481 . 022Figure 5 . Slit/Robo signalling and MMP2 control asymmetry of L/R phrenic nerves . ( A ) NF staining of E14 . 5 diaphragm from Robo1+/+ and Robo2+/+ and Robo1–/– and Robo2–/–- embryos , left and right primary branches are pseudocolored in green and red , respectively , and superimposed to show the lack of asymmetry in the Robo1 and 2–/– embryos . Histogram showing the branch number and the defasciculation distance in Robo1+/+ and Robo2+/+ , Robo1+/– and Robo2+/– and Robo1–/– and Robo2–/– embryos ( R/L branch ratio: Robo1+/+ and Robo2+/+ 2 . 30 ± 0 . 37 , versus Robo1–/– and Robo2–/– 1 . 06 ± 0 . 06; p=0 . 00048; R/L distance ratio: Robo1+/+ and Robo2+/+ 4 . 99 ± 0 . 89 , versus Robo1–/– and Robo2–/– 1 . 05 ± 0 . 07; p=3E-6 , Mann-Whitney ) . ( B ) Immunodetection of Robo1 and loading control ( Tub ) in left and right HB9::GFP ventral cervical spinal cord and distribution of the relative amount of the two shorter forms ( pink arrowheads ) to the full-length form ( black arrowhead ) . The graph shows the normalized left and right values obtained for the five western-blots ( dots , 6–8 embryos per sample ) and the mean ± SEM ( R versus L: p=0 . 01587 , Wilcoxon singed rank ) ; average fold-change is shown in brackets ( 1 . 22 ± 0 . 10 ) . Normalization between lines was done on the Robo1 long form . ( C ) Ladder graph showing the left and right expression of Mmp2 detected by microarray in three embryos . Average Log2 ( R/L ratio ) shown in brackets . ( D ) Photomicrograph of cultured ventral cervical spinal cord motoneuron . The combination of in situ zymmography with DQ-Gelatin and Islet1/2 staining enables the identification of motoneuron with MMP gelatinase activity . Histogram showing the amount of motoneuron with gelatinase activity in left and right samples ( left 23 . 37% ± 2 . 7 , N = 792 versus right 37 . 94% ± 2 . 1 , N = 797; p=0 . 00109 , Mann-Whitney ) . Histogram showing the gelatinase activity measured in cultures from Rfx3–/– embryos with symmetric lungs ( Iso ) and in cultures from Rfx3+/+ , Rfx3+/– embryos ( Rfx3 wt: — 1 . 4 ± 0 . 08; Rfx3 iso — 0 . 96 ± 0 . 08 , p=0 . 0013 , Mann-Whitney ) . ( E ) NF staining of E14 . 5 diaphragms from wild-type and Mmp2–/– embryos . Left ( green ) and right ( red ) primary and secondary branch traces shown in the middle panel are superimposed in the right panel to compare the left and right patterns . Histograms showing the R/L ratios of branch number and defasciculation distances . Ratio of secondary branches: Mmp2+/+ and Mmp2–/+ 1 . 74 ± 0 . 07 , versus Mmp2–/– 1 . 21 ± 0 . 10; p=0 . 00029; defasciculation distance: Mmp2+/+ and Mmp2–/+ 5 . 33 ± 0 . 44 , versus Mmp2–/– 3 . 49 ± 0 . 38; p=0 . 022 , Mann-Whitney . Scale bar: 200 μm ( A , E ) , 10 μm ( D ) . Numerical values used to generate the graphs are accessible in Figure 5—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02210 . 7554/eLife . 18481 . 023Figure 5—source data 1 . Slit/Robo signalling controls asymmetry of L/R phrenic nerves and Robo1 exhibits different processing levels in left and right cervical motoneurons . This file provides the statistical report and individual values used to create the histograms and ladder graphs shown in Figure 5A , B , C , D and E . The ratios of branch numbers and defasciculation distances in Robo1+/+ and Robo2+/+ , Robo1+/– and Robo2+/– and Robo1–/– and Robo2–/– embryos are shown on the first and second sheets . The third sheet contains left and right normalized values of short Robo1 forms presented in the graph of Figure 5B . RNA levels of Mmp2 are shown on fourth sheet . The percentage of left and right motoneurons ( Islet+ ) exhibiting gelatinase activity from wild-type embryos are shown on fifth sheet and the ratio found in Rfx3–/– embryos with lung isomerism and Rfx3+/+ and Rfx3+/– on the sixth sheet . Branch number and defasciculation distance ratio measured in Mmp2+/+ and Mmp2–/– embryos are shown on the seventh and eighth sheets . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02310 . 7554/eLife . 18481 . 024Figure 5—figure supplement 1 . Post-translational regulation of Robo1 . ( A ) Ladder graph of Robo1 RNA expression detected by the two probes present on the microarray for each of the three embryos ( probe1 — black; probe2 — blue; average Log2 ( R/L ratio ) shown in brackets ) . ( B ) Histogram showing the average R/L ratio of expression ( in log2 ) assessed by the microarray probes targeting Slit1 , Slit2 and Slit3 . Error bars represent SEM . ( Note that 1 . 5 fold-change gives 0 . 5849 in log2 . ) ( C ) Immunodetection of Robo1 in spinal cord lysates of Robo1–/– and Robo2–/– and wild-type tissues . The antibody detects three specific bands . Black arrowhead points to the expected full-length Robo1 and the two pink arrowheads point to the two shorter forms . ( D ) Graph shows the normalized left and right values obtained for the four western-blots ( 6–8 embryos per sample ) and the mean ± SEM ( R versus L: p=0 . 028 , Mann-Whitney; average fold-change is 1 . 22 ± 0 . 11 ) . Normalization between lines was done on the tubulin band . Numerical values used to generate the graphs are accessible in Figure 5—figure supplement 1—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02410 . 7554/eLife . 18481 . 025Figure 5—figure supplement 1—source data 1 . Post-translational regulation of Robo1 and biased expression of Mmp2 . This file provides the statistical report and individual values used to create the histograms and ladder graphs shown in Figure 5—figure supplement 1A and B . Left and right levels or ratios for the Robo1 and the Slits transcripts are shown on the first and second sheet , respectively . The third sheet contains left and right tubulin normalized values of short Robo1 forms presented in the graph of Figure 5—figure supplement 1D . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02510 . 7554/eLife . 18481 . 026Figure 5—figure supplement 2 . Asymmetric expression of MMP2 . ( A ) Histogram showing the average ratio of Mmp2 RNA expression assessed by qPCR in cervical motoneurons from two E11 embryos . Expression normalized to GAPDH . ( B ) Histogram showing the R/L ratio ( in log2 ) of the surface labelled by the RNAscope MMP2 probe in the motoneuron region . Each bar shows the R/L ratio for one section of a series of serial sections that cover the entire cervical spinal cord region . The dashed line highlights the log2 value that corresponds to a 1 . 5-fold change . ( C ) Mmp2 RNAscope in situ hybridization on an E12 transversal spinal cord section at cervical levels . ( D ) Mmp2 in situ hybridization combined with Pou3f1 immunolabeling on E11 transversal spinal cord sections . Enlarged panels of the motoneuron domain ( right ) show that Mmp2 transcripts are detected within the Pou3f1-positive domain . ( E ) Schematics of the dissection and the in situ zymography ( ISZ ) procedure for E12 . 5 ventral cervical spinal cord . Cleavage-induced fluorescence of DQ-Gelatin ( green ) is overlaid over the phase contrast image . Islet1-positive motoneurons exhibit gelatinase activity in different cellular regions , including the axon and the growth cone ( lower right panel ) . ( F ) Ladder graph showing the expression signals in the left and right laser-captured samples of the three embryos detected with the microarray Mmp14 , Mmp15 , Mmp16 and Mmp17 probes , average Log2 ( R/L ratio ) shown in brackets . Scale bars: 100 μm ( C ) and ( D left panel ) ; 200 μm ( D right panel ) and 10 μm ( E ) . Numerical values used to generate the graphs are accessible in Figure 5—figure supplement 2—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 02610 . 7554/eLife . 18481 . 027Figure 5—figure supplement 2—source data 2 . Asymmetric expression of Mmp2 in cervical motoneurons and expression of other MMPs . Mmp2 expression ratios quantified by qRT-PCR and quantitative in situ hybridization ( RNAscope ) are presented on the first and second sheets , respectively . The third sheet shows the microarray data used to generate the ladder graphs shown in Figure 5—figure supplement 2F . DOI: http://dx . doi . org/10 . 7554/eLife . 18481 . 027 To determine whether differential levels of Slit/Robo signaling dictate the L/R pattern of phrenic nerve fasciculation , we examined their transcript levels , but found no evidence for lateralized expression of the transcripts for Robo1 , the major regulator of diaphragm innervation ( Jaworski and Tessier-Lavigne , 2012 ) , or the ligands of Robo1: Slit1 , Slit2 and Slit3 ( Figure 5—figure supplement 1A–B ) . By contrast , we identified L/R differences in Robo1 protein by immunoblotting of phrenic motor neuron-enriched cervical spinal cord tissue . Robo1 was detected in one long and two short forms ( Figure 5—figure supplement 1C ) , whereby the long Robo1 form migrating as a 250 kDa protein was enriched in the left samples and the short forms migrating as 120 kDa and 130 kDa proteins were enriched in the right samples ( R/L ratio— 1 . 22 ± 0 . 1 , p=0 . 01587; Mann-Whitney , Figure 5B , Figure 5—figure supplement 1D ) . Even though 12 alternatively spliced isoforms have been predicted for mouse Robo1 , the predicted changes in protein sequence are unlikely to account for the short forms we observed in our immunoblots , because they are predicted to change the molecular weight by just 7 . 1 kDa . However , both human and drosophila Robo1 have been shown to be processed by metalloproteases ( Seki et al . , 2010; Coleman et al . , 2010 ) , and potential cleavage fragments have been reported in mouse brain tissues ( Clark et al . , 2002 ) . These findings raise the possibility that differential post-translation processing of Robo proteins may be involved in creating L/R asymmetries in diaphragm innervations . Next , we investigated whether axon guidance effectors that were revealed by our transcriptomic analysis to exhibit asymmetric expression levels could also contribute to the L/R phrenic nerve patterns . Given that metalloproteases have emerged as important regulators of axonal behaviors during development and regeneration ( Bai and Pfaff , 2011; Łukaszewicz-Zając et al . , 2014; Small and Crawford , 2016; Verslegers et al . , 2013b ) , we concentrated on these effectors . Consistent with previous expression data ( GSE41013 ) ( Philippidou et al . , 2012 ) , our transcriptome analysis indicated that cervical motoneurons express several metalloproteases . Interestingly , among the 7 Mmps and 13 ADAMs expressed by cervical motoneurons , Mmp2 and ADAM17 were expressed at higher levels in the right motoneurons . We focused on MMP2 because it was shown to control axon development in mouse and motor axon fasciculation in drosophila ( Miller et al . , 2011; Gaublomme et al . , 2014; Zuo et al . , 1998; Miller et al . , 2008 ) . The microarray analysis showed that Mmp2 transcripts were enriched in the Hb9-positive right motoneurons , which was confirmed using qRT-PCR and quantitative in situ hybridization ( log2 ( R/L ) Embryo 1 — 0 . 22 ± 0 . 08; Embryo 2 — 0 . 63 ± 0 . 11 , RNAscope ) ( Figure 5C; Figure 5—figure supplement 2A–D ) . Moreover , in situ zymography with DQ-Gelatin ( Hill et al . , 2012 ) , which is effectively cleaved by MMP2 ( Snoek-van Beurden and Von den Hoff , 2005 ) , showed that gelatinase activity on the axon shaft and growth cones was 1 . 6 times higher in right than in left motoneuron cultures ( Figure 5D; Figure 5—figure supplement 2E ) . Remarkably , this difference was absent in motoneuron cultures prepared from Rfx3–/– embryos with phenotypic left isomerism ( Figure 5D ) . These results provide evidence that the differential L/R MMP activity is controlled by the Nodal pathway and further suggest that MMP2 contributes to the establishment of phrenic nerve asymmetry . We therefore analyzed the diaphragm nerve patterns in Mmp2–/– mice ( Itoh et al . , 1997 ) . At E14 . 5 , Mmp2–/– embryos exhibited normal lung asymmetry and well-developed phrenic branches on both sides ( Figure 5E ) . Interestingly , we observed a partial symmetrization of the phrenic branches , with a right pattern that resembled the one observed on the left in control littermates in E14 . 5 Mmp2–/– embryos ( Figure 5E ) . Thus , higher right MMP2 activity could contribute to promote the right pattern of phrenic nerve defasciculation .
Taken together , our work shows that the first asymmetry instruction in diaphragm patterning is provided by early Nodal signaling , which sets the L/R axis and visceral asymmetry of the embryo . Beyond this early mechanism , phrenic motoneurons have an intrinsic , genetically encoded L/R asymmetry that manifests itself in the differential activation of molecules that have key roles in axon guidance , including Robo1 and MMP2 . Future work should aim to address how and at which stage phrenic motoneurons are imprinted . For example , an early Nodal signal might be propagated from the lateral plate mesoderm ( LPM ) to the cervical spinal cord . In agreement with this idea , it has been suggested that Lefty expression in the prospective floor plate of the neural tube prevents Nodal diffusion to the left LPM ( Shiratori and Hamada , 2006 ) . Moreover , Lefty expression is confined to the left prospective floor plate and is reversed or expanded bilaterally in ‘iv’ or ‘inv’ mutants , which exhibit reverse visceral asymmetry ( Meno et al . , 1997 ) . Given the key role of the floor plate in the patterning and specification of spinal cord neuronal lineages ( Goulding et al . , 1993; Placzek et al . , 1991 ) , left and right floor plate cells might also differently imprint left and right spinal cord . Alternatively , or additionally , endothelial cells invading the ventral spinal cord could convey early Nodal signaling from the LPM to the spinal cord . Indeed , these cells exhibit L/R asymmetries that can be preserved during their migration ( Chi et al . , 2003; Klessinger and Christ , 1996 ) . The resulting L/R imprints could occur early on during neurogenesis or later on during motoneuron differentiation . The two hypothesizes might not be exclusive . Indeed , recent work in zebrafish habenula suggests that differences in both the timing of neurogenesis and exposure to lateralized signal during neuron differentiation act in parallel to set L/R asymmetries ( Hüsken et al . , 2014 ) . Interestingly , early imprinting of progenitors in Caenorhabditis elegans induces an epigenetic mark for L/R identity that drives differential genetic programs during neuron differentiation ( Cochella and Hobert , 2012; O'Meara et al . , 2010 ) . Our work provides evidence to show that a L/R imprint confers specific axon behaviors to the left and right phrenic motoneurons . For example , we found that Slit/Robo signaling is required for the establishment of asymmetric nerve patterns , which suggests that left and right phrenic motoneurons have different Slit/Robo signaling levels . Interestingly , Slit/Robo signaling has previously been shown to control phrenic axon fasciculation ( Jaworski and Tessier-Lavigne , 2012 ) . Consistent with an intrinsic control of Slit/Robo signaling as the cause of this axonal asymmetry , we discovered L/R differences in Robo1 protein in motoneurons , which may arise through differential proteolysis and may help to modulate responsiveness to Slit signaling , even though Slit and Robo genes are expressed similarly in the left and right motoneuron pools . In support of the idea that differential proteolysis contributes to the emergence of different Robo1 forms in the left and right phrenic motoneuron pools , Robo processing has previously been reported in other contexts ( Seki et al . , 2010; Coleman et al . , 2010 ) . This Robo1 processing could have different outcomes on Slit/Robo signaling . In drosophila , cleavage of Robo by ADAM10 is required for recruitment of downstream signaling molecules and the axon guidance response ( Coleman et al . , 2010 ) . Metalloproteases can also decrease the amount of available receptors and/or terminate adhesion and signaling ( Bai and Pfaff , 2011; Hinkle et al . , 2006; Romi et al . , 2014; Hattori et al . , 2000; Gatto et al . , 2014 ) . Slit/Robo signaling can control many different aspects of axon development , such as axon growth , branching , guidance or fasciculation . As primary and secondary branches are formed by selective defasciculation and because Slit/Robo signaling controls phrenic axon fasciculation , our interpretation is that the different Slit/Robo signaling abilities of left and right phrenic axons result in different axon–axon fasciculation states , with right axons having greater defasciculation behavior than the left ones . Alternatively , the Slit/Robo pathway may differentially regulate axon and branch growth , or branch trajectories , as it does for other systems of neuronal projections ( Wang et al . , 1999; Brose et al . , 1999; Blockus and Chédotal , 2016 ) . These ideas have to be taken cautiously . Differential Robo forms were assessed from spinal cord tissue essentially containing neuronal soma , and not peripheral phrenic axons . Furthermore , the tissue samples , although enriched in phrenic motoneurons by the procedure , contained additional neuronal sub-types . Further investigations are thus needed to assess with more specific tools Robo protein dynamics and distribution along phrenic axons and in the growth cones . This work will provide a better characterization of the functional outcome determined by the balance of short and long Robo forms in the establishment of phrenic nerve patterns . Asymmetries in several genes implicated in axon guidance were observed in our transcriptome analysis . In particular , we found differences in the expression of regulators of guidance receptors activities , such as metalloproteases . Mmp2 expression level and gelatinase activity were found to be higher in right cervical motoneurons . Moreover , differential gelatinase activity between left and right motoneurons was lost in cultures from Rfx3–/-– mutants with symmetrical Nodal signaling , suggesting that early Nodal signaling impacts on gelatinase activity in motoneurons . Mmp2 genetic loss reduced the asymmetry of the diaphragm branch pattern , suggesting that asymmetric expression of Mmp2 in motoneurons contributes to set phrenic nerve patterns . However , in contrast to embryos lacking Pitx2 and Rfx3 , embryos lacking Mmp2 only exhibited a partial symmetrization of the phrenic nerve branches . Rfx3 and Pitx2C transcription factors act at the onset of the left–right imprinting , and their genetic loss is therefore expected to abolish the entire program of L/R nerve asymmetry . By contrast , the subsequent construction of individual neuronal circuits relies on the concerted action of many different signaling pathways , whereby loss of a single pathway is not expected to disrupt the entire asymmetry program . The partial defect may be due to the presence of other effectors of the Nodal pathway that contribute to L/R nerve asymmetries independently of MMP processing , to the co-expression of several MMPs acting with partial redundancies with each other ( Prudova et al . , 2010; Kukreja et al . , 2015 ) and to the fact that MMPs have many different substrates with potentially opposite effects on the same biological process . For example , proteomic studies have identified more than 40 secreted and transmembrane substrates for MMP2 ( Dean and Overall , 2007 ) , of which we found 32 to be expressed in cervical motoneurons including Adam17 , which is enriched in right motoneurons ( Figure 5—figure supplement 2F , Figure 4—source data 2 ) . The MMP substrates that are responsible for asymmetric phrenic nerve patterning remain to be determined , but Slit/Robo signaling appears to be an obvious candidate . First , cleavage of human Robo1 has been suggested to be MMP-dependent , although in drosophila , Robo1 is cleaved by Adam10/Kuzbanian ( Coleman et al . , 2010; Seki et al . , 2010 ) . Second , short forms of Robo1 , lprobably generated by proteolysis , are enriched in right motoneurons , in which MMP activity is the highest . In support , incubation of cervical spinal cord tissue with active MMP2 significantly increased the short Robo1 forms ( fold change: 1 . 60 ± 0 . 23 , p=0 . 00285 , Mann-Whitney , four independent western blots , Supplementary file 1 ) . Nevertheless , the L/R ratio of Robo protein forms in cervical motoneuron tissue collected from Mmp2 null embryos , although showing a tendency towards reduction , was not statistically different from the wild-type ratio ( WT: 1 . 22 ± 0 . 10 , N = 5; Mmp2–/–: 1 . 14 ± 0 . 01 , N = 3; p=0 . 78 , Mann-Whitney; Supplementary file 2 ) . This might be due to an insufficient number of tested embryos . Alternatively , because short Robo1 forms were still detected , this L/R ratio might rather reflect the activity of other proteases , either compensating for MMP2 loss or also contributing to Robo processing . An additional MMP candidate is NCAM , which is highly expressed by developing phrenic axons , controls axon-axon fasciculation , and is cleaved by MMPs ( Dean and Overall , 2007; Hinkle et al . , 2006 ) . In the light of MMP redundancy and the possible involvement of other proteases in the processing of axon guidance receptors and their ligands , the in vivo assessment of these hypotheses will be challenging . Finally , the genetic program for L/R identity in spinal cord motoneurons that we have described here may provide important insights into motoneuron development and diseases . For example , the L/R imprinting of spinal motoneuron might also explain why right-sided fetal forelimb movements are far more frequent than left-sided movements at developmental stages when motoneurons have not yet received any input from higher brain centers ( Hepper et al . , 1998 ) . In addition , our description of early events controlling diaphragm formation may have broad implications for our understanding of several human conditions . Examples include congenital hernias , which generally affect the left hemi-diaphragm and can cause perinatal lethality ( Pober , 2008 ) , and some types of congenital myopathies that impair diaphragm function only on one side ( Grogan et al . , 2005 ) . Our data thus provide a novel basis for investigations of molecular diversity in spinal cord neurons and for functional studies of diaphragm physiology and pathology .
This work was conducted in accordance with the ethical rules of the European community and French ethical guidelines . Genotyping of transgenic mouse lines was performed as described in Liu et al . ( 2001 ) for Pitx2∆C ( original line: RRID:MGI:3054744 ) , in Bonnafe et al . ( 2004 ) for Rfx3 ( RRID:MGI:3045845 ) , in Delloye-Bourgeois et al . ( 2015 ) for Robo1 and Robo2 ( RRID:MGI:5522691 ) , in Verslegers et al . ( 2013b ) for Mmp2 ( RRID:MGI:3577310 ) and in Huber et al . ( 2005 ) for the HB9::GFP ( RRID:IMSR_JAX:005029 ) . Diaphragms were dissected from embryos fixed overnight in 4% paraformaldehyde . After permeabilization and blocking in PBS with 5% BSA with 0 . 5% Triton X-100 , diaphragms were incubated overnight at room temperature with the primary antibody , Neurofilament 160 kDa ( 1/100 , RMO-270 , Invitrogen , France; RRID:AB_2315286 ) . Diaphragms were then incubated with the secondary antibody , α-mouse Alexa-555 ( 1/400 , Invitrogen , France ) with or without Alexa488-coupled α-BTX ( 1/50 , Molecular probes , ThermoFischer Scientific , France; RRID:AB_2313931 ) , for 4 hr at room temperature in blocking solution . The procedure was performed entirely on freely floating diaphragms . Diaphragm imaging was then performed under an inverted microscope and a montage was constructed using the metamorph software ( Molecular device , Sunnyvale , CA ) . Cryosections ( 20 µm ) were obtained from embryos fixed overnight in 4% paraformaldehyde , embedded in 7 . 5% gelatin with 15% sucrose . For immunolabeling , embryonic sections or cultured neurons were incubated overnight at 4°C with Oct6 antibody ( 1/50; Santa Cruz , Germany ) and then for 2 hr at room temperature with anti-goat secondary antibody , Alexa-488 ( 1/400; Invitrogen , France ) . Nuclei were stained with bisbenzimide ( Promega , Madison , WI ) . In situ hybridization was performed as described previously ( Moret et al . , 2007 ) . The probes were synthetized from the Mmp2 IMAGEclone plasmid ( n6813184 ) . Mmp2 in situ hybridization and Pou3f1 ( Oct6 ) immunolabeling were performed on adjacent sections because the antibody could no longer recognize the Oct6 epitope after in situ hybridization . Serial Pou3f1/Oct6-labeled sections were imaged using a confocal microscope . Series of images were converted into a single stack using the ImageJ plugin Stack Builder . Images were aligned manually using morphological structures and labeled nuclei were extracted . A three-dimensional reconstruction of the Pou3f1 ( Oct6 ) labeling from the cervical to the brachial part of the embryos was then generated in ImageJ ( 3D Project command ) . All quantifications were done using ImageJ . For quantification of defasciculation distance , we first traced the tangential straight line of the endplate ( Figure 1—figure supplement 1B ) . We then traced a perpendicular line to the tangent that goes through the nerve entry point . Finally , we measured the distance from the entry point to the intersection of both lines . For branch number quantification , we traced a parallel to the tangential straight line of the endplate . The line was placed at a distance of one quarter of the defasciculation distance . We then counted the number of secondary branches that crossed the line . The endplate thickness was evaluated from the α-Btx staining . The α-Btx-positive region was outlined and divided into 30 rectangles . The average width of the rectangles was calculated . Width evaluation of endplate from the plot profile of α-Btx staining gave similar values . Cervical ventral spinal cords were dissected from E11 . 5 HB9::GFP embryos in cold HBSS with 6% glucose ( as shown in Figure 4—figure supplement 1D ) and directly frozen in dry-ice cooled eppendorf tubes . Typically , left and right dissected tissues from 6–8 embryos were pooled and lysed in RIPA buffer with protease inhibitors for 30 min at 4°C . Western blots were performed using primary antibody ( Anti-Morf4l1 ( 1:1000 , Abcam , France – ab183663 ) , anti-Xrn2 ( 1:1000 , Abcam , France – ab72181 , RRID:AB_2241927 ) , anti-Robo1 ( 1:500 [Seki et al . , 2010] ) and secondary antibody ( Anti-goat or -mouse HRP [A5420 and A4416 , Sigma-Aldrich , France] at 1/5000 ) . Image quantification was done with Image Lab4 . 0 software ( Bio-Rad , France ) . Left and right data were normalized to the tubulin level for Morf4l1 and Xrn2 and to Robo1 full-length or tubulin for Robo1 short forms . To allow comparison between replicates left and right values were then normalized to have the same left plus right sum for all western blots . E12 . 5 GFP-positive mouse embryos ( 4–6 per experiment ) from HB9::GFP transgenic mice were selected and dissected using the fluorescence GFP-positive pool . Ventral cervical spinal cords were isolated ( left and right parts separated ) and cut into explants . Explants were cultured as described in Moret et al . ( 2007 ) . Immunohistochemistry was performed using Anti-Tuj1 ( 1:100 , Millipore , France – MAB1637 , RRID:AB_2210524 ) and anti-GFP ( 1:100 , Invitrogen , France – A11122 , RRID:AB_221569 ) . Axon outgrowth was calculated using the ImageJ plugin NeuriteJ ( Torres-Espín et al . , 2014 ) , which creates regions of interest ( ROI ) corresponding to radial concentric rings separated by 25 pixels . NeuriteJ extracted the signal from GFP-positive axons and measured the labeled surfaces between two ROIs . To quantify the total area occupied by GFP-positive axon , we summed the surface of all ROIs . To calculate the proximo-distal index , the width of the labeled axons was calculated in the second ROI ( proximal ring ) and in the ROI at 30% of the maximal distance of growth ( distal ring ) ( see Figure 4—figure supplement 1F ) . The index was calculated by dividing the width of the proximal fascicles by the width of the distal fascicles . For dissociated motoneuron culture , left and right cervical ventral spinal cord tissues were dissected from E12 . 5 OF1 or Rfx3 pregnant mice . Neurons were dissociated and cultured as described previously ( Charoy et al . , 2012; Cohen et al . , 2005 ) . After 24 hr in vitro , neurons were incubated for 10 min at 37°C with DQ-Gelatin 20 μg/mL ( Invitrogen , France ) . Cells were washed twice with warm PBS and fixed in 4% paraformaldehyde both containing 25 μM of GM6001 MMP inhibitor ( Millipore , France-CC1100 ) . The cultures were incubated with Islet 1/2 antibody ( 1/50; DSHB , Iowa , USA – 39 . 4D5 ) overnight at 4°C then for 2 hr with α-mouse Alexa-555 ( 1/400; Invitrogen , France ) to detect motoneurons . Nuclei were counter-stained with bisbenzimide ( Promega ) . For quantification , the number of cells expressing Islet1/2 with gelatinase activity is reported realtive to the total number of Islet-1/2-positive cells . The GFP+ motor pool was laser-captured from E11 . 5 GFP+ mouse embryos from HB9::GFP transgenic mice frozen in −45°C isopentane . Captured tissues were lysed in the lysis buffer provided with the RNA purification kit ( RNAeasy microkit , Qiagen , France ) . RNA quality was assessed on an Agilent 2100 bioanalyser ( Agilent Technologies , USA ) . L/R matching samples that had a RNA integrity number ( RIN ) above 9 were amplified ( ExpressArt PICO mRNA amplification kit , Amp-tec-Exilone , France ) and reverse transcribed ( BioArray HighYield RNA Transcript Labeling , ENZO , France ) . cDNA quality was assessed on an Agilent 2100 bioanalyser before fragmentation and hybridization on an Affymetrix microarray ( GeneChip Mouse 430 2 . 0 , Affymetrix , ThermoFischer scientific , France ) . Expression normalizations and present or absent calls were performed in Affymetrix Expression Console Software . Fold change and filtering were performed in Excel . Transcripts were considered as being expressed if scored as present in at least one sample of each embryo . Transcripts were classified as differentially expressed if the fold change ( FC ) between left and right samples had the same trend for all embryos ( same sign for log2 ratio ) and was over 1 . 5 ( FC > 0 . 58 or FC < −0 . 58 in log2 ) on average and for at least two of the embryos . Transcripts with very low expression ( maximal normalized expression <200 ) were removed . Raw data are available on GEO under the accession number GSE84778 . Real-time PCR was performed using MIQE pre-validated Mmp2 ( qMmuCID0021124 ) and Mnx1 ( qMmuCED0040199 ) primers ( BioRad , france ) . Data were normalized to GAPDH expression values ( primers Fw: AGAACATCATCCCTGCATCC; Rv: ACACATTGGGGCTAGGAACA ) . Real time PCRs were performed in duplicate on amplified RNA prepared as described for the microarray . Laser-capture microdissection , RNA preparation , microarray and qPCR were performed at the ProfileXpert core facility ( France ) . RNAscope in situ hybridization ( Advanced Cell Diagnostic , Ozyme , France ) was performed on 14–20-µm cryosections according to the manufacturer's recommendations for fresh frozen samples , using Mmp2 C3 and C1 proprietary probes ( references 315937 and 315931-C3 , ACD , Ozyme , France ) . Both probes gave the same pattern , which mirror the distribution observed using the conventional in situ procedure . UBC and DapB probes were used as positive and negative controls , respectively ( references 310777 and 312037 , ACD , Ozyme , France ) . All incubations were performed in the HyBez hybridation system ( ACD , Ozyme , France ) . Sections were fixed in 4% paraformaldehyde for 15 min before dehydratation and incubated in pretreat buffer 4 ( Advance Cell Diagnostic , Ozyme , France ) for 15 min at room temperature . DAPI staining was performed at the end of the procedure . The left and right side of the cervical spinal cord were imaged at 20x on a FV1000 confocal microscope ( Olympus , France ) using the same acquisition parameters . Labeled surfaces were quantified in ImageJ in ROI drawn from the DAPI staining . The threshold calculated on the sum of the Z-stack image of one side was applied to the other side . Surface ratios were calculated after normalization to the selection area . Control and mutant embryos were from the same litters . All analyzable samples ( diaphragm , western blots , cells or explants ) were included , no outliers were removed . Left and right samples were from the same embryos . Analyses of the diaphragm innervation and Mmp2 quantitative in situ were performed blind . No blinding was done on other data collections or analyses . Sample sizes , statistical significance and tests are stated in each figure and figure legend . All statistical analyses were done using Biostat-TGV ( CNRS ) . Mann-Whitney ( method: Wilcoxon rank sum ) or Wilcoxon signed rank were used for small-sized samples or when distributions were not normal . Wilcoxon signed rank was used when paired analysis was needed ( left versus right from the same embryo ) . | The diaphragm is a dome-shaped muscle that forms the floor of the rib cage , separating the lungs from the abdomen . As we breathe in , the diaphragm contracts . This causes the chest cavity to expand , drawing air into the lungs . A pair of nerves called the phrenic nerves carry signals from the spinal cord to the diaphragm to tell it when to contract . These nerves project from the left and right sides of the spinal cord to the left and right sides of the diaphragm respectively . The left and right sides of the diaphragm are not entirely level , but it was not known why . To investigate , Charoy et al . studied how the diaphragm develops in mouse embryos . This revealed that the left and right phrenic nerves are not symmetrical . Neither are the muscles on each side of the diaphragm . Further investigation revealed that a genetic program that establishes other differences between the left and right sides of the embryo also gives rise to the differences between the left and right sides of the diaphragm . This program switches on different genes in the left and right phrenic nerves , which activate different molecular pathways in the left and right sides of the diaphragm muscle . The differences between the nerves and muscles on the left and right sides of the diaphragm could explain why some muscle disorders affect only one side of the diaphragm . Similarly , they could explain why congenital hernias caused by abdominal organs pushing through the diaphragm into the chest cavity mostly affect the left side of the diaphragm . Further studies are now needed to investigate these possibilities . The techniques used by Charoy et al . to map the molecular diversity of spinal cord neurons could also lead to new strategies for repairing damage to the spinal cord following injury or disease . | [
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] | 2017 | Genetic specification of left–right asymmetry in the diaphragm muscles and their motor innervation |
Gastrulation constitutes a fundamental yet diverse morphogenetic process of metazoan development . Modes of gastrulation range from stochastic translocation of individual cells to coordinated infolding of an epithelial sheet . How such morphogenetic differences are genetically encoded and whether they have provided specific developmental advantages is unclear . Here we identify two genes , folded gastrulation and t48 , which in the evolution of fly gastrulation acted as a likely switch from an ingression of individual cells to the invagination of the blastoderm epithelium . Both genes are expressed and required for mesoderm invagination in the fruit fly Drosophila melanogaster but do not appear during mesoderm ingression of the midge Chironomus riparius . We demonstrate that early expression of either or both of these genes in C . riparius is sufficient to invoke mesoderm invagination similar to D . melanogaster . The possible genetic simplicity and a measurable increase in developmental robustness might explain repeated evolution of similar transitions in animal gastrulation .
The evolution of shape and form is generally associated with changes of cell and tissue behavior during the course of development ( Carroll et al . , 2013 ) . Such changes in morphogenesis can originate from modifications of molecular patterning , by adjusting the interpretation and integration of molecular patterning within and between cells , or by altering the translation of cellular decisions into changes of cytoskeleton and cell behavior , either alone or in combination ( Davies , 2013 ) . Understanding principal transitions in the evolution of morphogenesis thus requires the precise description of differences in cell and tissue behavior , the identification of coincident changes in gene activity , and a functional validation to support the link between molecular and morphogenetic divergence . The evolutionary gain or loss of gene activity has been shown to be a major source of morphological innovation , in particular between closely related species and in systems where patterns of gene expression correspond to a direct phenotypic output like insect wing and body pigmentation ( Arnoult et al . , 2013; Gompel et al . , 2005; Prud'homme et al . , 2006; Wittkopp et al . , 2002a , 2002b ) . Well-studied examples of morphogenetic evolution have correlated differences in tissue and cell behavior to modifications in gene expression ( Cleves et al . , 2014; Indjeian et al . , 2016; Shapiro et al . , 2004 ) , occasionally supported by experimental evolution ( Abzhanov et al . , 2006 , 2004 ) , but rarely have analyzed morphogenesis in reference to a genetically well-understood developmental context ( Rafiqi et al . , 2012 ) . It is currently not known whether major , macro-evolutionary changes of morphogenesis between distantly related species are driven through accumulative genetic tinkering , the recruitment or modification of pre-existing developmental modules in a switch-like fashion , or by the acquisition of entirely new gene function; neither is it understood how many or what kinds of modifications are required to instruct transitions between different modes of morphogenesis . To address these questions , we have studied gastrulation in the fruit fly Drosophila melanogaster and the midge Chironomus riparius , two fly species that shared their last common ancestor about 250 million years ago ( Wiegmann et al . , 2011 ) . Their evolutionary history is characterized by an evolutionary transition between two distinct modes of cell internalization during gastrulation , i . e . mesoderm invagination in D . melanogaster and mesoderm ingression in C . riparius ( Figure 1 ) . Similar transitions between distinct modes of cell internalization have been observed repeatedly and in either direction during the evolution of metazoan gastrulation ( Leptin , 2005; Nielsen , 2012; Solnica-Krezel and Sepich , 2012 ) . In insects , the extremely fast and coordinated invagination of mesoderm cells constitutes a derived morphogenetic process that evolved from stochastic migration of individual cells ( Johannsen and Butt , 1941; Roth , 2004 ) . Mesoderm invagination in flies has been studied extensively in D . melanogaster and thus provides an excellent reference system ( Leptin , 2005 ) ; mesoderm ingression is less well understood but has been previously reported for C . riparius as well as other species representing the most basal branches of flies ( Goltsev et al . , 2007; Ritter , 1890 ) . 10 . 7554/eLife . 18318 . 003Figure 1 . To assess gastrulation differences between C . riparius and D . melanogaster , nuclear position and embryonic axis elongation provide faithful proxies for cell position and stage of mesoderm internalization . ( A , B ) Mesoderm internalization shown for C . riparius ( A ) and D . melanogaster ( B ) in transversal embryonic sections stained for DNA ( DAPI ) and F-actin ( phalloidin ) . ( C ) Cell positions as a readout of cell behavior were determined in transversal sections and whole embryos based on staining of DNA ( cell nucleus ) and F-actin ( cell outline ) , cell length was measured along the yellow bar . ( D ) The centre of masses determined in embryo sections is indicated as circles overlaid on an F-actin stained micrograph ( colors indicate identical cells ) . ( E , E’ ) Differences in the approximation of the cell position by cell nucleus and outline were assessed as a deviation along cell height , width , and breadth for sections ( E ) and whole embryos ( E’ ) . The percentage of cells for which the two methods deviated by more than 10% cell length are indicated ( blue dashed area ) . ( F ) Progression of germband extension ( GBE ) in C . riparius and D . melanogaster served as measure to stage mesoderm internalization . The extension was measured as displacement of the invaginating posterior midgut from the posterior pole in percent egg length ( 0% corresponds to the non-extended germband ) . Gastrulating embryos of C . riparius and D . melanogaster were classified according to the degree of GBE . To enable automated image segmentation routines to extract the ventral embryo surface , embryos in which the ventral opening was equal or smaller than one nucleus size ( red asterisk ) were considered as closed along the ventral midline . In C . riparius , embryos with less than 10% GBE showed no internalization , mesoderm internalization was observed in embryos showing 13 to 25% GBE , and embryos with more than 25% GBE were closed along the ventral midline . In these embryos , putative mesoderm nuclei were barely detectable ( arrow heads ) , presumably because they were few and adhered closely to the ectoderm . In D . melanogaster , mesoderm internalization was observed in embryos before and after the onset of GBE , and embryos with more than 15% GBE were closed along the ventral midline ( classes and number of embryos within class indicated as grey bars ) . For subsequent quantification of mesoderm internalization , C . riparius embryos were staged between 13–22% GBE , and D . melanogaster embryos between onset and 10% GBE . Mitosis in C . riparius was observed in spatially distinct and temporally successive domains similar as in D . melanogaster ( Foe , 1989 ) . Scale bars , 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 003 Mesoderm invagination in D . melanogaster builds on molecular patterning through the transcription factors Twist ( Twi ) and Snail ( Sna ) along the ventral midline of the blastoderm embryo . These transcription factors repress mitosis ( Grosshans and Wieschaus , 2000; Seher and Leptin , 2000 ) and instruct specific shape changes in the cells of the mesoderm anlage ( Costa et al . , 1994; Kölsch et al . , 2007; Leptin , 1991; Manning et al . , 2013; Martin et al . , 2009; Rauzi et al . , 2015 ) . Their positional information is relayed to the cytoskeleton by G-protein coupled receptor ( GPCR ) signaling through Folded gastrulation ( Fog ) as ligand , Mist as receptor , and Concertina ( Cta ) as associated Gα-unit . Upon binding of Fog to Mist , Cta is released and activates RhoGEF2 , which is enriched at the cell apex of mesoderm cells through the transmembrane anchor T48 . In turn , RhoGEF2 dependent activation of Rho1 invokes changes in cell shape through constriction of the apical actomyosin network ( reviewed in Leptin , 2005; Manning and Rogers , 2014 ) . We used this defined morphogenetic module as reference to identify the genetic innovations that distinguish mesoderm invagination in D . melanogaster from ingression in C . riparius . We then asked whether the evolutionary transition between ingression and invagination could be experimentally reproduced , and finally tested whether the origin of mesoderm invagination in flies has been associated with putative developmental advantages .
An initial comparison of mesoderm internalization in C . riparius and D . melanogaster indicated differences in the mode of cell internalization , which were consistent with an evolutionary transition from ingression to invagination ( Figure 1A , B ) . To establish whether differences between ingression and invagination could be studied by a careful analysis of cell positions in fixed tissue , we used staining of F-actin and DNA as a proxy for the cytoskeletal cell outline and nuclei , respectively ( Figure 1C , D ) . In both species , we found that the position of mesoderm cells during progression of mesoderm formation could be approximated by their nuclei ( Figure 1E , E’ ) . To establish equivalent time points of mesoderm internalization during the courses of gastrulation in C . riparius and D . melanogaster , we compared the timing of mesoderm internalization in both species relative to a conserved and independent process of fly gastrulation , i . e . , germband extension ( Anderson , 1966 ) . We found that relative to the onset of germband extension , mesoderm internalization started and ended earlier in D . melanogaster than in C . riparius ( Figure 1F ) . In transversal sections of C . riparius embryos that were about halfway through internalization , mesodermal cells clustered on top of a shallow groove along the ventral midline , while at a comparable stage in D . melanogaster they formed part of a deep ventral furrow ( Figure 1A , B , F ) . Taken together , the net behavior of mesoderm cells in the two species appeared to be either ingressive ( C . riparius ) or invaginative ( D . melanogaster ) . Notably , when we used cell position in transversal sections to assess the coherence of mesoderm cell behavior in individual embryos ( see Materials and methods ) , we found mesoderm cell behavior to vary substantially along the anterior-to-posterior axis ( Figure 2A–B’’ ) . These differences were more pronounced in C . riparius embryos than in D . melanogaster and indicated that the classification of cell internalization in fixed embryos as either ingressive or invaginative could not be based on qualitative measures of transversal sections alone . Thus , to account for variation in the mode of cell internalization within and between individuals , we established measures to quantitatively distinguish mesoderm ingression and invagination in whole embryos . Specifically , we quantified cell behavior during mesoderm internalization by using parameters that have been used to characterize this process in D . melanogaster . These included the total number of cells within the future mesoderm that underwent mitosis , the width and depth of the ventral furrow , the number of cells along the ventral midline that were internalized , a measurement of epithelial integrity , and the maximal depth at which an internalized cell could be observed ( Costa et al . , 1993; Kam et al . , 1991; McMahon et al . , 2008 ) . Mitosis was detected by antibody staining against phosphorylated histone H3 . All remaining features were quantified by approximating cell position based on the position of the nuclei relative to the computationally reconstructed egg and epithelial surfaces ( Figure 2C , D , Figure 2—figure supplement 1 ) . In the assessment of maximal cell internalization and furrow depth , the analysis based on nuclear cell position proved particularly beneficial as it was independent of cell orientation and avoided discrimination against distinct modes of mesoderm internalization . All features were evaluated for nine transversal sections of a ventral window for each embryo ( see Materials and methods , Figure 2—figure supplement 1 ) . While mitosis was rarely detected in either species during mesoderm internalization , wild-type embryos of C . riparius and D . melanogaster exhibited significant divergence in all other parameters ( Figure 2E ) . 10 . 7554/eLife . 18318 . 004Figure 2 . Quantitative analysis of mesoderm internalization in C . riparius and D . melanogaster characterizes differences between mesoderm ingression and invagination . ( A–B’’ ) Ventral view ( A , B ) transversal sections ( A’ , B’ ) and stacks of transverse centerlines ( A’’ , B’’ ) at comparable positions and corresponding stages of mesoderm internalization in C . riparius ( A–A’’ , blue; 17% GBE ) and D . melanogaster ( B–B’’ , red; 17% GBE ) . ( A , B ) Boxes indicate the region for which stack centerlines were calculated , arrowheads and dashed lines indicate the position of selected transversal section ( A’ , B’ ) . The stacks of transverse centerlines provide a visual measure for variation of cell behavior along the anterior-to-posterior axis of the embryo . ( C , D ) Exemplary quantitative analysis of cell behavior based on nuclear position as outlined in Figure 2—figure supplement 1 on transversal sections of C . riparius ( C ) and D . melanogaster ( D ) . Cell position based on segmented nuclei within a defined region of interest ( ROI , see Materials and methods ) are indicated as circles , lines are used to indicate the computationally reconstructed ventral egg surface and the outline of ventral epithelium . Automated classification of cells is indicated as color code ( legend in C ) . Measures of furrow depth , and max cell depth were based on cell position and measured in nuclear diameter , furrow width was measured as a proportion of total embryo width ( see Materials and methods ) . ( E ) Collective quantitative analysis of cell behavior for mesoderm internalization in transversal sections of C . riparius ( blue ) and D . melanogaster ( red ) embryos that separate mesoderm ingression from invagination . Medians of the distributions are indicated ( solid line , C . riparius; dashed line , D . melanogaster ) . The significance of differences in the median was assessed by a Wilcoxon rank sum test . ( F–F’’’ ) Biplots of a principal component analysis for all transversal sections of all analyzed C . riparius and D . melanogaster embryos . Shown is the contribution for each feature to the two first principal components ( F ) , the separation of the two species and how they are most sensitively separated along integrity , furrow depth , and internalization ( F’ ) , the position of individual transversal sections along the anterior-posterior axis of the embryo ( F’’ ) , and progression of gastrulation measured by % GBE ( F’’’ , Figure 1F ) . Furrow width and maximal cell depth align with the progression of gastrulation and were thus not considered as time-independent parameters of mesoderm internalization in C . riparius and D . melanogaster , respectively . Scale bars , 50 µm ( A , A’ , B , B’ ) and 20 µm ( C , D ) . **p≤0 . 01; ****p≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 00410 . 7554/eLife . 18318 . 005Figure 2—figure supplement 1 . Cartoon summarizing image processing and analysis pipeline for the quantification of ventral cell behavior . High resolution laser scanned confocal microscope ( LSCM ) image stacks were acquired from embryos stained for DNA and phosphorylated Histone H3 ( pHis H3 ) . Nuclei , cell surface and the ventral egg surface ( corresponding to the vitelline membrane ) were segmented using iLastik ( Sommer et al . , 2011 ) and Matlab routines ( see Materials and methods ) . Parameters for cell internalization were analyzed in the region of interest ( ROI ) , defined by the central 50% of the embryo length and 30% of the embryo width . Internalization , maximal depth , epithelial integrity , and furrow depth were quantified using Matlab scripts based on segmented image data for nine transversal sections; the furrow width was measured manually on raw images ( see Materials and methods ) . Critical features for the quantitative analysis are indicated in yellow: 'mitosis' was measured by overlap of nuclear position with a positive pHis H3 staining , 'furrow width' was measured as distance between edges of furrow abutting nuclei ( furrow width , 'A' ) relative to embryo width ( embryo width , 'B' ) , 'furrow depth' was measured as maximal distance between reconstructed egg surface and nuclei within ventral epithelium , 'internalization' was measured as number of nuclei inside the embryo with a minimal distance of one nuclear diameter to the reconstructed egg surface ( threshold indicated by the black line ) , 'integrity' was measured by the number of nuclei within the ventral epithelium divided by the total number of internalized nuclei , and 'max cell depth' was measured as the maximal distance between the deepest internalized nucleus and the reconstructed egg surface ( indicated as grey line beneath furrow ) . The calculation of integrity was adapted in case of Cri-sna knockdown to account for high tissue integrity without internalization ( see Materials and methods ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 005 To address the extent to which each individual parameter contributed to the global differences in C . riparius and D . melanogaster mesoderm formation and detect biases in the analysis , we performed a principal component analysis of all the sections and parameters . We found three principal parameters that , regardless of intra-species variation in space and time , permitted the best discrimination between ingression and invagination: cell internalization , the depth of the ventral furrow and epithelial integrity ( Figure 2F–F’’’ ) . Accordingly , invagination in D . melanogaster was characterized by high tissue integrity , a deep ventral furrow , and a high number of internalized cells , while ingression in C . riparius was characterized by low tissue integrity , a shallow ventral furrow , and a lower number of internalized cells . The number of internalized cells as well as the depth of the ventral furrow may be in part depended on the size of the mesoderm anlage and thus contain a species-specific component , whereas integrity was independent of the size of the mesoderm anlage . Collectively , the identified features made it possible to distinguish between ingressive and invaginative mesoderm formation in C . riparius and D . melanogaster precisely and quantitatively , despite variations within and between individuals . To identify the genetic basis for these differences , we used the established genetic network of D . melanogaster mesoderm internalization as a reference ( Figure 3A ) and asked how the expression and function of orthologous genes differed in C . riparius . In D . melanogaster , mesoderm internalization is orchestrated by two zygotic transcription factors , Twist ( Twi ) and Snail ( Sna ) ( Leptin and Grunewald , 1990 ) . Both genes are expressed along the ventral midline of the embryo ( Figure 3B–C’ ) , where they instruct zygotic expression of fog ( Figure 3D , D’ ) , t48 ( Figure 3E , E’ ) , and mist ( Figure 3F , F’ ) , while cta and RhoGEF2 are expressed ubiquitously ( Figure 3G–H’ ) ( Costa et al . , 1994; Kölsch et al . , 2007; Manning et al . , 2013 ) . Orthologues of twi and sna , t48 , and RhoGEF2 , along with genes encoding the GPCR ligand Fog , the receptor Mist , and the Gα subunit Cta , could be identified in C . riparius and in major families of winged insects where genome sequence data is available ( Pterygota , Figure 3—figure supplement 1 ) . The universal conservation of this gene set demonstrates its evolutionary importance and suggests that it originated over 400 million years ago ( Misof et al . , 2014 ) . 10 . 7554/eLife . 18318 . 006Figure 3 . Mesoderm internalization in C . riparius lacks input by fog and t48 . ( A ) Genetic regulation of mesoderm formation in D . melanogaster relays spatial instructions provided by the transcription factors Twi and Sna to RhoGEF2-dependent activation of non-muscle myosin via a GPCR signaling cascade with the ligand Fog , the G protein coupled receptor Mist , and its associated Gα subunit Cta; T48 functions as an apical anchor of RhoGEF2 . The expression domains for each gene are indicated in schematized transversal sections for maternal ( left ) and zygotic ( right ) contribution ( blue ) . ( B–P’ ) The comparison of blastoderm expression patterns of twi ( B , B’ ) , sna ( C , C’ ) , fog ( D , D’ ) , t48 ( E , E’ ) , mist ( F , F’ ) , cta ( G , G’ ) , and RhoGEF2 ( H , H’ ) in D . melanogaster with the expression of the C . riparius orthologues Cri-twi ( I , I’ ) , Cri-sna ( J , J’ ) , Cri-fog1 ( K , K’ ) , Cri-fog2 ( L , L’ ) , Cri-t48 ( M , M’ ) , Cri-mist ( N , N’ ) , Cri-cta ( O , O’ ) , and Cri-RhoGEF2 ( P , P’ ) indicated potentially crucial differences in the expression of fog and t48 in the future mesoderm . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 00610 . 7554/eLife . 18318 . 007Figure 3—figure supplement 1 . Phylogenetic occurrence of morphogenetic key players of D . melanogaster mesoderm invagination . Genes were considered as present in the last common ancestor of insects if they were included in the insect ( i ) as well as the metazoan ( m ) or arthropod ( a ) OrthoDB orthology group ( Waterhouse et al . , 2013 ) : cta , EOG7HF1JF ( mai ) , evolutionary rate 0 . 81; mist , EOG7J70G6 ( mi ) , evolutionary rate 1 . 04; RhoGEF2 , EOG75J0M5 ( mai ) , evolutionary rate 1 . 18; twist , EOG7TJ3M9 ( mai ) , evolutionary rate 0 . 79 . Because snail originated from a dipteran-specific gene duplication and belongs to a family of three genes with redundant functionality ( snail , escargot , and worniu ) ( Ashraf et al . , 1999 ) , its presence in the last common ancestor of insects was assessed by the snail family representing orthology group ( EOG7P2XSG ( mai ) , evolutionary rate 0 . 71 ) . t48 was restricted to the insect orthology group ( EOG7RC98C , evolutionary rate 1 . 27 ) , and fog to the dipteran orthology group ( EOG7HQZRP , evolutionary rate 1 . 44 ) . OrthoDB attributed higher evolutionary rates to t48 and fog than to the other morphogenetic key players of D . melanogaster gastrulation , suggesting that the identification of additional orthologues within insects was affected fast sequence turnover . To identify additional insect orthologues of t48 and fog despite fast sequence evolution , we followed previously described position weight matrix based blast searches aimed to test for the presence of orthologues in representative insect genomes ( Materials and methods ) ( Klomp et al . , 2015 ) . Phylogenetic tree and age estimates of branching as described previously ( Misof et al . , 2014 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 00710 . 7554/eLife . 18318 . 008Figure 3—figure supplement 2 . Evidence for maternal contribution of selected GPCR signaling components in D . melanogaster . ( A ) Summary of modENCODE expression profiles of cta , fog , mist , RhoGEF2 , t48 , twi , and sna at various stages of development , ( Attrill et al . , 2016 ) , expression levels shown as RPKM values . ( B–E’ ) Expression of fog at pole bud formation ( B , B’ ) , during blastoderm cellularization ( C , C’ ) , at the onset of gastrulation ( D , D’ ) and during germband extension ( E , E’ ) . Expression of t48 at pole bud formation ( F , F’ ) , during blastoderm cellularization ( G , G’ ) , at the onset of gastrulation ( H , H’ ) and during germband extension ( I , I’ ) . Expression of fog and t48 at pole bud formation was strong , ubiquitous , and not restricted to 'nuclear dots' of possibly early zygotic expression ( Pritchard and Schubiger , 1996 ) . ( J–K’ ) Expression of Cri-fog1 ( J , J’ ) and Cri-t48 ( K , K’ ) during germband extension . Expression of fog and t48 during germband extension in the presumptive neuroectoderm is conserved in D . melanogaster ( E , E’ , I , I’ ) and C . riparius ( J , J’ , K , K’ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 008 To assess how members of this conserved set of genes contributed to gastrulation in C . riparius , we analyzed their expression ( Figure 3I–P’ ) and function ( Figure 4 ) . We found that Cri-twi and Cri-sna were both expressed along the ventral midline of the blastoderm embryo ( Figure 3I–J’ ) . The knockdown of Cri-twi by RNAi resulted in embryos that lacked a visible ventral groove . Despite the knockdown of Cri-twi , nuclei of internalized cells could still be observed within the ventral-most 10–15% of the embryonic circumference ( Figure 4A , B ) , which corresponded to a slightly narrower domain than that of wildtype Cri-twist expression . The knockdown of Cri-sna produced an abnormally thin ventral epithelium with spherical nuclei and a failure of proper mesoderm internalization ( Figure 4C ) . Similar expression and phenotypes have been reported in experimental manipulations of twi and sna in D . melanogaster ( Leptin and Grunewald , 1990 ) , indicating that mesoderm cells receive and require essentially the same zygotic patterning information in C . riparius and D . melanogaster . Expression of Cri-mist along the ventral midline and in the domain of Cri-twi expression ( Figure 3N , N’ ) , as well as ubiquitous expression of Cri-cta ( Figure 3O , O’ ) and Cri-RhoGEF2 ( Figure 3P , P’ ) was consistent with a RhoGEF2-mediated machinery of cytoskeletal regulation like in D . melanogaster . 10 . 7554/eLife . 18318 . 009Figure 4 . Mesoderm internalization in C . riparius depends on conserved patterning by Cri-twi and Cri-sna . ( A–C ) Compared to control injections with water ( A ) , proper mesoderm internalization in C . riparius relied on patterning by Cri-twi and Cri-sna . Knockdown ( KD ) of Cri-twi showed a slight decrease in cell internalization but was mostly characterized by an absence of the shallow groove and seemingly random ingression throughout the ventral 10% of the embryonic circumference ( B ) . Knockdown of Cri-sna produced embryos that failed to internalize the mesoderm , reflected by a highly significant decrease in furrow depth and max cell depth , as well as a highly significant increase in integrity when using a modified algorithm to account for lack of internalization ( indicated as dashed bar , see Materials and methods ) ( C ) . ( D–E ) Knockdown of Cri-mist ( knockdown efficiency 90% ) appeared very similar to control injection , with a slight decrease in overall internalization and a small but significant increase in furrow depth ( D ) ; knockdown of Cri-fog2 ( knockdown efficiency 60% ) was similar to control injections with a small but significant increase in furrow depth and maximal cell depth ( E ) . Representative transversal sections , quantification of parameters , statistical analyses , and nuclear centerlines as in Figure 2 . Scale bar , 20 µm . ns , p>0 . 05; *p≤0 . 05; **p≤0 . 01; ***p≤0 . 001; ****p≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 009 Analysis of fog and t48 gene expression patterns yielded significant differences between the two species ( compare Figure 3D–E’ with Figure 3K–M’ ) . In D . melanogaster , both molecules are initially subject to maternal loading ( Figure 3—figure supplement 2 ) ( Costa et al . , 1994; Strutt and White , 1994; Zusman and Wieschaus , 1985 ) and are later expressed zygotically along the ventral midline of blastoderm embryos in a Twist-dependent manner ( Costa et al . , 1994; Kölsch et al . , 2007 ) . In C . riparius , Cri-fog1 and Cri-t48 transcripts were not detected until mesoderm internalization had been completed ( Figure 3K , K’ , M , M’ , Figure 3—figure supplement 2 ) . A second fog orthologue , Cri-fog2 , was expressed prior to mesoderm internalization in the central third of the embryo , in a domain broader than that seen for the expression of Cri-twi ( Figure 3L , L’ ) . This suggested that the GPCR ligand it encodes and , by extension , its receptor Cri-Mist , may not play the same prominent role in C . riparius mesoderm formation as they do in D . melanogaster . This interpretation was consistent with a visual inspection of embryos after RNAi knockdown of either gene . Following the quantitative analysis , we found that the median cell internalization and furrow depth differed in Cri-mist RNAi embryos from water injected embryos ( Figure 4D ) . In Cri-fog2 RNAi embryos , the difference was smaller and significant only for an increase in the furrow and maximal cell depth ( Figure 4E ) . All effects after Cri-mist and Cri-fog2 knockdown were substantially smaller than in Cri-twi and Cri-sna RNAi embryos and close to the sensitivity of our method ( <10% of nuclear size ) , and we cannot exclude that either Cri-fog or Cri-mist do not contribute to mesoderm formation . Based on the observed differences in fog and t48 expression in D . melanogaster and C . riparius , we wondered whether the lack of substantial pre-gastrulation activity of both genes in C . riparius could be functionally linked to differences in the modes of mesoderm internalization between the two species . Specifically , we asked whether mesoderm invagination might be provoked in C . riparius by emulating Drosophila-like gene expression of fog and t48 . We tested this hypothesis by injecting in vitro transcribed mRNA of the D . melanogaster genes fog and t48 into C . riparius embryos at the early blastoderm stage . In response to this unspecific and early ubiquitous expression of fog and t48 , C . riparius embryos exhibited an invagination of the mesoderm . This invagination was seen in several ways . First , we observed the formation of a distinct continuous ventral furrow , qualitatively comparable to that of D . melanogaster ( Figure 5A , B ) . Then in particular , the induced invagination in C . riparius embryos was characterized by increased epithelial integrity and an increased furrow depth ( Figure 5B ) . 10 . 7554/eLife . 18318 . 010Figure 5 . Ubiquitous expression of fog and t48 promotes Drosophila-like mesoderm internalization in C . riparius embryos . ( A ) Mesoderm internalzation was characterized by a shallow furrow and little overall epithelial integrity in individual transversal section in control injected embryos ( raw data as in Figure 4A ) . ( B–D ) Ubiquitous expression of fog and t48 ( B ) , Cri-fog and Cri-t48 ( C ) , or either gene alone ( D ) increased epithelial integrity . ( E ) In D . melanogaster embryos , knockdown of t48 activity in embryos without ventral fog expression led to loss of epithelial integrity and tissue coherence comparable to C . riparius wildtype . Representative transversal sections , quantification of parameters , statistical analyses , and nuclear centerlines as in Figure 2 . Scale bar , 20 µm . ns , p>0 . 05; *p≤0 . 05; **p≤0 . 01; ***p≤0 . 001; ****p≤0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 01010 . 7554/eLife . 18318 . 011Figure 5—figure supplement 1 . Comparison of individual embryos after either Cri-fog or Cri-t48 injection suggests differences in the within-embryo variation of epithelial integrity . ( A , B ) Distribution of epithelial integrity in transversal sections following Cri-fog injections ( A , n=9 ) and Cri-t48 injections ( B , n=9 ) . Pink dot: mean; pink line: standard deviation . ( C ) Distribution of standard deviations with mean indicated by dotted line . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 011 To test whether the induction of mesoderm invagination in C . riparius required the activity of the D . melanogaster-specific protein sequence of fog and t48 , we repeated the experiments with Cri-fog and Cri-t48 . Following early ubiquitous expression of Cri-fog and Cri-t48 , C . riparius embryos responded with an invagination of the mesoderm which resembled that seen with the D . melanogaster forms of fog and t48 ( Figure 5C ) , suggesting that a gain of early ubiquitous expression of one or both of these molecules was sufficient for the induction of mesoderm invagination . To test whether both fog and t48 had been necessary for the evolution of mesoderm invagination , we tested whether a single factor alone was sufficient to induce invagination in C . riparius . Upon early ubiquitous expression of either Cri-fog or Cri-t48 alone , C . riparius embryos displayed induced furrows overall , with increased epithelial integrity and deeper internalization very similar to the features observed in Cri-fog and Cri-t48 double-injected embryos ( Figure 5D ) . Differences were noted in the behavior of cells within individual embryos: these appeared less variable in embryos expressing Cri-fog than in embryos expressing Cri-t48 ( Figure 5—figure supplement 1 ) . Our results indicate that fog and t48 operate differently in transmitting instructions to the cytoskeleton , while at the same time demonstrating that gaining early expression of either gene alone was sufficient to invoke a change in the mode of mesoderm internalization . This interpretation was further supported by converse experiments in D . melanogaster . While the loss of either fog or t48 activity in the mesoderm alone affects ventral furrow formation , it does not block it ( Costa et al . , 1994; Kölsch et al . , 2007 ) , but the combined knockdown of fog and t48 functions in the ventral blastoderm abolished ventral furrow formation and resulted in ingression-like mesoderm cell behavior reminiscent of C . riparius wildtype development ( Figure 5E ) . In D . melanogaster , the expression of fog and t48 both contribute to coordinated mesoderm invagination ( Figure 3A ) ( Manning and Rogers , 2014 ) . Our observations in C . riparius suggested that expression of either Cri-fog or Cri-t48 was already sufficient for a morphogenetic response that appeared indistinguishable from the expression of both genes together . This seemingly redundant control of mesoderm invagination in D . melanogaster prompted us to speculate that features beyond cell internalization were associated with a tissue-wide invagination . In comparison with individual cells , a coherent epithelium provides increased mechanical stability ( Lecuit and Lenne , 2007 ) . In the context of mesoderm internalization , this stability of a coordinated invagination could provide robustness against physical perturbations that are widely present during fly gastrulation as the germband expands and the midgut invaginates ( Costa et al . , 1993 ) . To test whether variation within C . riparius gastrulation could be reduced by early ectopic expression of Cri-fog and Cri-t48 , we collected and compared all time-independent measures of mesoderm internalization between control-injected and fog/t48 injected embryos ( Figure 6A ) . Following the induced expression of Cri-fog and Cri-t48 , we observed an overall decrease in the variability of phenotypes ( Figure 6B ) . The decrease in the variability of the measured features was observed within embryos ( Figure 6C ) and may reflect the increase in cell coordination due to the induced change in the mode of gastrulation . Consistent with conceptual consideration of robustness ( Félix and Barkoulas , 2015 ) , we detected a decrease in variability also between embryos ( Figure 6C ) , which indicated that C . riparius embryonic development became more robust against cellular mechanical perturbations . 10 . 7554/eLife . 18318 . 012Figure 6 . Experimentally induced invagination increases developmental robustness and efficiency of mesoderm internalization in C . riparius . Variation in gastrulation was assessed for each embryo by sampling the complete ventral ROI in seventeen transversal sections along the anterior-posterior axis . Each section was characterized by the time-independent parameters internalization , furrow depth , maximal cell depth , and integrity and takes one point in that 4D space . The similarity of cell behavior in different sections was calculated as the Euclidean distance between points . ( A ) Example of variation for two embryos in a reduced 3D data space ( internalization , furrow depth , integrity; asterisk indicates normalization of parameter values by z-score normalization ) : each embryo was characterized by 17 cross sections ( yellow and green points in 3D space ) , the Euclidean distance provided a measure for variation within the two embryos . Variation between embryos ( black line ) was computed as the Euclidean distance between the embryo centroids ( black dots: calculated as the mean value for all sections in an embryo ) . ( B ) Variation across all transversal sections was significantly decreased after ectopic expression of Cri-fog and Cri-t48 . ( C ) Both variations within individual embryos ( values for embryos shown in A are indicated by arrows ) as well as variation between embryos was decreased . ( D ) Ectopic expression of Cri-fog and Cri-t48 increased the efficiency of mesoderm internalization and was measured as the percentage of internalized cells in the ROI at a comparable stage of GBE . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 012 To test whether the expression of Cri-fog and Cri-t48 affected developmental timing , we counted how many cells had been internalized immediately after the onset of germband extension . Following the expression of Cri-fog and Cri-t48 , we found a significant increase in the number of cells that were internalized ( Figure 6D ) . An increased robustness against mechanical perturbation and developmental efficiency did not appear to trigger conflicts with subsequent embryonic muscle formation and enervation: after 48 hr , twitching and moving was observed in embryos injected with Cri-fog and Cri-t48 at a frequency ( 40%; n=500 ) similar to control-injected embryos ( 44%; n=531 ) . Our results thus indicate that , beyond a simple coordination of cell behavior , mesoderm invagination induced by fog and t48 is associated with a measurable increase in the robustness and efficiency of gastrulation . Given the simplicity of the system and the extent of its fixation over long periods of evolutionary time , such traits could have provided an adaptive advantage in specific environments and contexts encountered by the predecessors of D . melanogaster after divergence from the last common ancestor shared with C . riparius .
Our analysis of C . riparius mesoderm morphogenesis has allowed us to associate two distinct modes of mesoderm internalization with specific differences in developmental gene expression . We could demonstrate that mesoderm ingression in C . riparius correlates at the genetic level with the absence of significant fog and t48 activity in the early embryo . In comparison with mesoderm invagination in D . melanogaster , these results suggested that an evolutionary gain of early fog and t48 activity may constitute a genetic switch to change the mode of fly gastrulation . We functionally tested this hypothesis by early ubiquitous expression of fog and t48 in C . riparius embryos , and we found that the activities of either gene alone or both genes together were sufficient to invoke a Drosophila-like invagination of the mesoderm in C . riparius embryos . Our data suggest that mesoderm invagination in C . riparius increases developmental robustness and efficiency . Additionally , invagination preserves the epithelial character of the mesoderm during internalization and thus decouples epithelial-to-mesenchymal transition in the mesoderm from the positional cell exchange during germband extension . As a result , the anterior-to-posterior order of mesoderm cells may be more effectively preserved throughout gastrulation . During the course of evolution , this could have facilitated the use of positional information of pre-gastrulation blastoderm patterning for post-gastrulation differentiation of the internalized mesoderm . In the following , we are building on our results in C . riparius to assess the evolution of highly coordinated mesoderm invagination as described for D . melanogaster . Unspecific injection of fog and t48 mRNA , which at best corresponds to a strong maternal loading , had a specific effect on C . riparius mesoderm internalization instead of leading to pleiotropic morphogenetic defects ( Figure 5 ) . This observation was unexpected and surprising . In order for C . riparius embryos to respond to the early ubiquitous expression of fog or t48 in a spatially restricted manner along the ventral midline , the activity of either gene was apparently realized differently in different regions of the C . riparius blastoderm embryo . A strong and exclusively ventral response to fog and t48 activity in C . riparius could explain such a local change in the mode of morphogenesis in spite of ubiquitous gene expression . Recent work has shown that ancestral modes of gastrulation can be built upon a gene repertoire that is in part distinct from the network known in D . melanogaster ( Pers et al . , 2016; Stappert et al . , 2016 ) , and the local response to ubiquitous fog and t48 activity in C . riparius embryos may thus be elicited by an unknown fog/t48 sensitive pathway under control of twist and/or snail . Alternatively , ectopic fog and t48 in C . riparius act through a conserved genetic cascade present also in D . melanogaster . In this case , the activity of at least one component of this pathway downstream of fog and t48 would have to be locally restricted to the ventral blastoderm . A possible candidate for such a conserved component with spatially restricted activity downstream of fog and t48 is the G-protein coupled receptor Mist . In embryos of both species , C . riparius and D . melanogaster , mist is expressed in a narrow domain along the ventral midline ( Figure 3F , F’ , N , N’ ) . Mist transduces signaling of the extracellular ligand Fog to the intracellular receptor-coupled G-protein Gα12/13 Cta , which during D . melanogaster mesoderm invagination results in apical RhoGEF2 activation . RhoGEF2 is intracellularly enriched at the apical cell membrane by its transmembrane anchor T48 , and apical RhoGEF2 activation subsequently results in constriction of the apical actomyosin network and thus coordinated cell shape changes that lead to ventral invagination ( Kerridge et al . , 2016; Manning et al . , 2013; Parks and Wieschaus , 1991; Dawes-Hoang et al . , 2005; Morize et al . , 1998; Barrett et al . , 1997; Häcker and Perrimon , 1998; Fox and Peifer , 2007; Kölsch et al . , 2007 ) . In blastoderm embryos of D . melanogaster , fog and mist are expressed in partially overlapping domains , and tissue invagination is observed only where the expression of both genes coincides ( Manning et al . , 2013 ) . Similarly , the activity of ectopic ubiquitous fog and t48 expression in C . riparius appears to be limited to a ventral domain that overlaps with endogenous Cri-mist expression . Based on the expression of Cri-cta and Cri-mist and the induction of mesoderm invagination through ectopic fog and t48 , we speculate that mesoderm ingression as an ancestral mode of cell internalization is based on comparatively low-level RhoGEF2 activity ( Figure 7A ) . This low level of RhoGEF2 activity may be invoked by scattered expression of the ligand Fog , or , as has been suggested before , by low-frequency self-activation of Mist ( Manning and Rogers , 2014 ) . Our results in wildtype C . riparius embryos suggest that under such conditions , cells undergo an early epithelial-to-mesenchymal transition and leave the blastoderm stochastically due to individual rather than collective cell shape changes . This cell behavior changes under conditions that increase RhoGEF2 activity at the apex of mesoderm cells , which may be achieved in two ways: either non-cell-autonomously , by increasing GPCR signaling strength through elevated levels of the secreted ligand Fog , or cell-autonomously , by improving GPCR signaling efficiency through the apically enriched membrane anchor T48 ( Kölsch et al . , 2007 ) . It is tempting to speculate that nature used similar changes during fly evolution ( Figure 7B ) , which ultimately led to the highly efficient GPCR signaling in the ventral blastoderm of D . melanogaster ( Figure 7C ) . 10 . 7554/eLife . 18318 . 013Figure 7 . Evolutionary scenario for cell-biological changes underlying the origin of coordinated mesoderm invagination in D . melanogaster . ( A ) Cartoon model of putative ancestral mesoderm GPCR signaling based on findings in C . riparius . According to the model , ancestral mesoderm ingression in insects is characterized by low-level RhoGEF2 activity in the absence of high level fog and t48 activity . ( B , C ) Evolutionary gain of either high fog or high t48 expression levels elevates RhoGEF2 activity ( B ) and thus invokes tissue-level coordination of mesoderm cell behavior as in D . melanogaster ( C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18318 . 013 Although C . riparius and D . melanogaster diverged from a last common ancestor about 250 million years ago ( Wiegmann et al . , 2011 ) , our functional analysis of mesoderm internalization in both species suggests that surprisingly simple genetic changes are sufficient to reversibly switch between cell ingression and tissue invagination . Without an overt genetic directionality , mesoderm formation in flies has thus evolved either from a Drosophila-like ancestor that essentially lost maternal as well as zygotic fog and t48 expression in C . riparius , or from a Chironomus-like ancestor that gained early embryonic fog and t48 expression in successive steps leading to the developmental network described for D . melanogaster . We cannot exclude that C . riparius evolved from a Drosophila-like ancestor by a secondary loss of early fog and t48 expression , and recent lineage-specific gene duplications , resulting , e . g . , in two fog copies or pangolin ( Klomp et al . , 2015 ) , suggest that C . riparius may have diverged significantly from the last common ancestor of flies . Overall however , we favor the hypothesis that C . riparius rather than D . melanogaster is more reminiscent of ancestral mesoderm internalization in flies . Supporting this view , mesoderm ingression has been reported for an independent family at the base of the insect order of flies ( Diptera ) , A . gambiae ( Goltsev et al . , 2007 ) , in the beetle Tribolium castaneum ( Handel et al . , 2005 ) , and reflects the general view that mesoderm formation in insects evolved from an ancestor with an ingression-like mode of cell internalization ( Roth , 2004 ) . Experimental induction of mesoderm invagination by early fog or t48 expression in C . riparius embryos suggests that the evolutionary origin of the D . melanogaster gene network is linked to the gain of a ubiquitous maternal and a ventral zygotic enhancer for fog and t48 . Our results in C . riparius suggest that high levels of maternal-like ubiquitous expression of fog and t48 are sufficient to invoke mesoderm invagination . Consistent with a critical role of maternal fog and t48 in the evolutionary origin of mesoderm invagination , both genes are also expressed ubiquitously in pre-blastoderm embryos of D . melanogaster ( Costa et al . , 1994; Strutt and White , 1994 ) ( Figure 3—figure supplement 2 ) . However , so far only a weak maternal contribution has been functionally demonstrated for fog ( Zusman and Wieschaus , 1985 ) , and the main function of both genes in D . melanogaster mesoderm invagination is derived from their zygotic , twist-dependent ventral expression ( Costa et al . , 1994; Kölsch et al . , 2007 ) . Thus , mesoderm invagination in D . melanogaster originated by the origin of highly specific Twist-dependent enhancers in fog or t48 and later the gain of maternal expression in both genes , or vice versa . We favor the idea that the origin of mesoderm invagination was built on the gain of a maternal enhancer , which may evolve more likely de novo from random sequence turnover . fog and t48 may have acquired their role in coordinating cell behavior during epithelial morphogenesis coincedently with the evolution of fly gastrulation . However , in D . melanogaster , the functions of fog , mist , and RhoGEF2 do not appear to be limited to gastrulation; the genes have been additionally implicated in gastrulation-like folding of epithelia during wing , leg , and salivary gland morphogenesis ( Manning et al . , 2013; Nikolaidou and Barrett , 2004; Ratnaparkhi and Zinn , 2007 ) , and the presence of fog and t48 orthologues in insect genomes overlaps with the phylogenetic occurrence of winged appendages and salivary glands ( Krenn and Aspöck , 2012 ) ( Figure 3—figure supplement 1 ) . We therefore favor a model in which fog and t48 have played a role in epithelial morphogenesis , at least during late stages of development , long before the origin of mesoderm invagination . Both genes were then subsequently recruited for a function in modern , Drosophila-like gastrulation with a delayed epithelial-to-mesenchymal transition . Such recruitments may occur independently and repeatedly , and it will be informative to study a possible role of fog and t48 in hymenopterans . Species in this insect order also internalize their mesoderm in a coordinated way , but as a stiff , coherent plate along the ventral midline ( Fleig and Sander , 1988; Roth , 2004; Sauer , 1954 ) . Recruiting genes with roles in later developmental stages into the maternal germline may provide the missing link between the divergence seen in late phases of a species' development with diversity that appears earlier – often referred to as a developmental 'hour glass' ( Duboule , 1994; Raff , 1996; Kalinka and Tomancak , 2012 ) . This provides a simple , and possibly non-gradual , evolutionary path toward altering early embryonic stages prior to the onset of zygotic transcription , and it may reflect a more general mechanism that facilitated the diversification of early embryonic development .
The laboratory culture of Chironomus riparius ( Meigen ) originates from the culture of Gerald K . Bergtrom ( University of Wisconsin , Milwaukee , WI ) and was obtained from Urs Schmidt-Ott ( The University of Chicago , Chicago ) ( Klomp et al . , 2015 ) . The culture was maintained as previously described at 25°C and a constant 17/7-hr day/night cycle ( Caroti et al . , 2015 ) . For Drosophila melanogaster , the sequenced reference from Kyorin University ( y[1] , oc[R3 . 2]; Gr22b[1] , Gr22d[1] , cn[1] , bw[1] , sp[1]; LysC[1] , lab[R4 . 2] , MstProx[1] , GstD5[1] , Rh6[1] ) was used for wild type analysis and fog;hkb::fog ( Seher et al . , 2007 ) to analyze fog loss-of-function in the mesoderm . Cri-cta ( KX009472 ) , Cri-fog1 ( KX009473 ) , Cri-fog2 ( KX009479 ) , Cri-mist ( KX009474 ) , Cri-RhoGEF2 ( KX009475 ) , Cri-sna ( KX009476 ) , Cri-t48 ( KX009477 ) and Cri-twi ( KX009478 ) were identified from transcriptome sequences and cloned after PCR amplification from cDNA . Full length coding sequences of fog and t48 were amplified by PCR from pB26H ( Costa et al . , 1994 ) and pNB40 ( Kölsch et al . , 2007 ) , CDSs of Cri-fog1 and Cri-t48 were amplified by PCR from cDNA . Fragments were cloned into the expression vector pSP35T ( Amaya et al . , 1991 ) using an adapted C . riparius specific Koszac sequence ( Klomp et al . , 2015 ) to generate capped mRNAs using the mMessage mMachine Kit with SP6 RNA polymerase ( Ambion , NY ) . Synthesized mRNA was dissolved in H2O . Double-stranded RNAs ( dsRNAs ) was synthesized essentially as described ( Stauber et al . , 2000 ) on templates that were amplified by PCR from TOPO-TA-pCRII ( Life Technologies , NY ) plasmids using vector-specific primers with attached T7 promoter sequences; dsRNAs comprised the following gene fragments ( pos . 1 refers to first nucleotide in ORF ) : Cri-fog2 , pos . −92 to 1739; Cri-mist , pos . +137 to +1111; Cri-sna , pos . +201 to +1008; Cri-twi , pos . +114 to +1107; t48 , pos −527 to +1801 . Embryos were collected , prepared for injection , and injected essentially as described ( Caroti et al . , 2015 ) . Embryos were injected before the start of cellularization ( approximately four hours after egg deposition ) , and then kept in a moist chamber until the onset of gastrulation . Throughout all procedures , embryos were kept at 25°C ( ± 1°C ) . Owing to their small size , C . riparius embryos ( 200 µm length ) were always injected into the center of the yolk ( 50% of anterior-posterior axis ) . Embryos were heat-fixed using previously established protocols ( Rafiqi et al . , 2011 ) . Embryos were injected with dsRNA typically at concentrations of 300 to 700 ng/µl; mRNA was injected at 0 , 16 µg/µl ( fog; Cri-fog ) and 1 , 1 µg/µl ( t48; Cri-t48 ) , which corresponded to about 0 , 2 mol/l of Cri-fog transcripts and 2 mol/l of Cri-t48 transcripts . The efficiency of transcript knockdown by RNAi was evaluated by qRT-PCR as described previously with minor changes ( Stappert et al . , 2016 ) . Total RNA of C . riparius knockdown and wildtype embryos at stages ranging from cellular blastoderm to onset of gastrulation was isolated and transcribed using SuperScriptTM III RT ( Invitrogen ) . Transcript quantities were compared between injected and wildtype embryos using SYBR Green ( Thermo Scientific ) . The qPCR reaction was performed according to the manual ( SYBR Green PCR master mix , ThermoScientific ) . The cycling profile was adjusted to 95°C 15 min , 40 cycles 95°C 15 s , 55°C 15 s , 72°C , 15°C and extension at 72°C for 7 min; a melting curve program was added . For the calculation of the RNA ratios the delta delta Ct method was used . Primers used were 5’-CCGGAAAAATATTCCAACGA/5’-GCGAGTGTTGCAATCAGAAA for Cri-mist , 5’-GTGACTTCGAGCTCGCTCTT/5’-CGACGACAACAACAACAACC for Cri-fog2 , and 5’-AGAAGGCAAAGCTGATGGAA/5’-GGAGCGAAAACAACAACCAT for Cri-EF1α as reference . For whole mount in situ hybridization , antibody , and nuclear staining , embryos were heat fixed and devitellinized using 1+1 n-heptane and methanol as described ( Rafiqi et al . , 2011 ) . For staining with phalloidin , embryos were fixed using 4% formaldehyde and devitellinized by substituting methanol with 95% ethanol ( Mathew et al . , 2011 ) . RNA probe synthesis , whole mount in situ hybridization , and detection was carried out as described ( Lemke and Schmidt-Ott , 2009 ) . The following cDNA fragments were used as probes: ( pos . 1 refers to first nucleotide in ORF ) : cta , pos +401 to +1180; Cri-cta , pos +104 to +982; Cri-fog1 , pos −9 to +1926; Cri-fog2 , pos −92 to +1739; Cri-mist , pos +137 to +1111; Cri-sna , pos +201 to +1008; Cri-t48 , pos −413 to +1765; Cri-twi , pos +114 to +1107; fog , pos −38 to +2804; mist , pos +518 to +1317; sna , pos −146 to +1515; t48 , pos −527 to +1801; twi , pos −376 to +2249 . Antibody and nuclear staining was carried out as described ( Jimenez-Guri et al . , 2014 ) with modifications: before the staining procedure , embryos were rehydrated after storage in methanol in PBT ( 0 . 1% Tween 20 in PBS ) , digested with Proteinase K ( 1:2500 , 0 . 08 U/ml; Invitrogen ) for 2 min at room temperature , immediately washed with PBT , and fixed in 5% formaldehyde for 25 min . Phosphorylated histone H3 detected using p-Histone H3 antibody ( 1:100; Santa Cruz Biotechnology ) as primary and anti-rabbit Alexa488 ( 1:200; Jackson Immuno Research ) as secondary antibody; nuclei were stained using 4' , 6-diamidino-2-phenylindole ( DAPI ) . Phalloidin staining was performed as described ( Panfilio and Roth , 2010 ) with modifications ( 1:200; 200 units/ml stock , Life technologies ) in PBT for two hours at room temperature . To preserve shape and allow for free rotation along the anterior-to-posterior axis , D . melanogaster embryos were mounted in glycerol ( in PBS , v/v; D . melanogaster 75% , C . riparius 50% ) with support for the cover glass . Histochemical staining was recorded with DIC on a Zeiss Axio Imager M1 using 20x ( dry , 20x/0 . 8 ) for C . riparius and both 20x and 10x ( dry , 10x/0 . 45 ) for D . melanogaster; fluorescent staining was recorded by single-photon confocal imaging on a Leica system ( DMI 4000 B and SP8 ) using a 40x oil objective ( HC PL APO CS2 40x/1 . 3 ) for C . riparius and a 20x immersol objective ( HC PL APO CS2 20x/0 . 75 ) for D . melanogaster embryos . Image stacks were acquired with a voxel size of 0 . 28 × 0 . 28 × 0 . 42 µm for C . riparius and 0 . 51 × 0 . 51 × 0 . 42 µm for D . melanogaster by oversampling in z and comprised at least 30–40% of embryo depth orthogonal to the ventral midline . A bright-field lateral view of each analyzed embryo was recorded for developmental staging . Images and stacks were processed using Fiji ( 2 . 0 . 0-rc-34/1 . 50a ) , Matlab ( R2014b ) , and Vaa3d ( 2 . 801 ) and assembled into figures in Adobe Photoshop and Adobe Illustrator . Each embryo was imaged as confocal z-stack with three 8-bit channels containing immunofluorescence of pHis H3 , DAPI , and/or phalloidin . The image stacks were aligned to have the embryo ventral midline run in parallel to the x axis . From the aligned stacks cell positions , a boundary of the ventral epithelium , and an outline of the egg shell were extracted . The developmental progression of fly gastrulation was assessed by the degree of germ band extension in lateral mid-sections of bright-field images . The deepest indentation of the infolding posterior midgut was taken as reference to the posterior end of the germband , and germband extension was measured in percent egg length relative to the anterior-posterior axis of the embryo ( % GBE , Figure 1F ) . To quantify morphological features of mesoderm formation , a region of interest ( ROI ) was defined by projection of a rectangle that encompassed all nuclei in the ventral 30% of the embryonic circumference and from 25% to 75% egg length along the anterior-to-posterior axis . Coherence of mesoderm cell behavior along the anterior-posterior axis was qualitatively assessed in individual embryos by nuclear centre-lines . Centre-lines were generated for each transversal section of the ROI by determining the shortest path from the left to the right side of the ventral epithelium and successively passing through the centre of mass of each nucleus . The stack of center-lines was used as a visual and qualitative measure to judge coherence along the anterior-to-posterior of individual embryos . To compare tissue coherence between species , corresponding stages of mesoderm internalization were identified according to germband extension ( Figure 1F ) . Morphological parameters of mesoderm formation were quantified within the ROI by determining the distance of cells to the epithelial surface of the embryo and the egg circumference using Matlab scripts ( https://github . com/lemkelab/mesoderm ) . The ROI was sampled by nine equal-sized intervals , each containing approximately 30 cells . Based on empirical thresholding , a cell was classified as internalized if the distance between its nucleus center and the reconstructed epithelial surfaceegg shell exceeded the length of one cell nucleus; it was independently classified as ingressed if the distance between nucleus center and the reconstructed reconstructed epithelial surface exceeded the length of one cell nucleus . Morphological parameters were then defined as follows: internalization - percentage of internalized nuclei in ROI; maximal cell depth - maximal measured distance between any nucleus center and the reconstructed egg shell in a transversal section; integrity: percentage of internalized cells that are not ingressed; furrow depth: maximal measured distance between internalized nucleus center and reconstructed egg shell in a transversal section . The furrow width of the respective cross sections was measured manually as a percentage of the embryo width directly on the zy image stacks in Fiji . As our definition of integrity as quantitative measure was measured only for internalized cells , this method cannot directly account for cases where cells stay within an epithelium in close contact with the egg outline ( e . g . Cri-sna RNAi ) In this case , the quantification of integrity was extended to all cells in ROI , regardless of internalization . The principal component analysis included all transversal sections for C . riparius and D . melanogaster , each described by its values for the five parameters: internalization , maximal cell depth , integrity , furrow depth and furrow width . All parameters were normalized by z-score normalization . Based only on parameter values and covariance , the analysis identified the two main axes along which variation in our data set was maximal . For all wild type sections , the resulting two principal components ( PC1 and PC2 ) accounted for 57% and 21% of variance , respectively . A biplot for the PCA was generated to visualize the contribution of each of the parameters to the two principal components . To test whether parameter separation was dependent on variation in space ( i . e . the position of a given transversal section along the anterior-to-posterior axis of an embryo ) and developmental age ( as measured by % of GBE ) , we color-coded cross sections according to their position and developmental age . Analysis of within and between-embryo variation was performed on all transversal sections and in the z-score normalized time-independent morphospace for C . riparius defined by internalization , epithelial integrity , furrow depth and maximal cell depth . Variation in cell behavior between transversal sections was calculated as Euclidean distance between transversal sections in morphospace . Variation between groups ( H2O; +fog / +t48 ) was measured as the average distance between all cross sections of all embryos within this group . Variation between embryos was measured as the average distance of the cross sections of one embryo to the cross sections of all other embryos in one group . Variation within an embryo was measured as the standard deviation from the average distance of all cross sections within a given embryo . Efficiency of mesoderm internalization was measured by the number of internalized cells in embryos with 15 to 25% GBE . Statistical comparisons of distributions were performed as comparison of their medians via the Matlab implementation of the Wilcoxon rank sum test , as most variables analyzed were not normally distributed . p-values of this test are indicated in the figure legend . Genes were considered as present in the last common ancestor of insects if they were included in the insect as well as the metazoan or arthropod OrthoDB orthology group ( Waterhouse et al . , 2013 ) . In addition , t48 and fog sequences were identified by recursive searches of insect genomes with a position-specific scoring matrix ( pssm ) . The pssm was generated in a psi-blast ( Altschul et al . , 1997 ) against non-redundant translated genome databases of representative insect genomes using two iterations and standard parameters . To identify orthologue candidates , this pssm was used in a psi-blast of individual insect genomes with an e-value cut-off at 0 . 001 . For each searched genome , the top three contigs were selected and submitted to a reciprocal blast to identify the closest homologous gene match as well as putative species-specific gene duplications . In the first iteration , the pssm was generated using the D . melanogaster proteins T48 ( GenBank , CAA55003 . 1 ) and Fog ( GenBank , NP_523438 ) , and the reciprocal blast was performed in the translated D . melanogaster transcriptome ( BDGP5 ) . In each following iteration , the pssm was generated using the closest homologous gene match identified in the species that was most closely related to the species used to generate the pssm in the previous search . Major taxa were represented by available genome ( g ) and transcriptome ( t ) sequence assemblies: Chelicerata ( Limulus polyphemus , g ) , Myriapoda ( Strigamia maritima , g ) Crustaceans ( Daphnia pulex , g ) , Diplura ( Catajapyx aquilonaris , g ) , Odonata ( Ladona fulva , g ) , Ephemeroptera ( Ephemera danica , g ) , Orthoptera ( Locusta migratoria , g ) , Phasmatodea ( Timema cristinae , g ) , Blattodea ( Blattella germanica , g ) , Isoptera ( Zootermopsis nevadensis , g ) , Thysanoptera ( Frankliniella occidentalis , g ) , Hemiptera ( Oncopeltus fasciatus , g; Acyrthosiphon pisum , g ) , Psocodea ( Pediculus humanus , g ) , Hymenoptera ( Apis mellifera , g; Nasonia vitripennis , g ) , Megaloptera ( Sialis lutaria , t ) , Neuroptera ( Chrysopa pallens , g ) , Strepsiptera ( Mengenilla moldrzyki , g ) , Coleoptera ( Tribolium castaneum , g ) , Trichoptera ( Limnephilus lunatus , g ) , Lepidoptera ( Bombyx mori , g; Papilio polytes , g ) , Siphonaptera ( Archaeopsylla erinacei , t ) , Diptera ( Chironomus riparius , g; Megaselia abdita , g; Drosophila melanogaster , g ) . Assemblies were obtained from insect base ( Yin et al . , 2016 ) , except for L . polyphemus ( GenBank , GCA_000517525 . 1 ) , S . maritima ( GenBank , GCA_000239455 . 1 ) , D . pulex ( GenBank , GCA_000187875 . 1 ) , C . riparius ( Genbank , GCA_001014505 . 1 ) , M . abdita ( GenBank , GCA_001015175 . 1 ) , and D . melanogaster ( flybase , 6 . 07 ) . Non-redundant protein BLAST databases were generated for each of the genome assemblies by translating the DNA sequence into amino acid sequences using transeq ( EMBOSS; Rice et al . , 2000 ) and the NCBI BLAST+ suite ( Camacho et al . , 2009 ) . | In animals , gastrulation is a period of time in early development during which a sphere of cells is reorganized into an embryo with cells arranged into three distinct layers ( called germ layers ) . The process has changed substantially during the course of evolution and thus provides a great experimental system to investigate the genetic basis for differences in animal form and shape . As an example , true flies use at least two different mechanisms to make the middle germ layer ( the mesoderm ) . In both cases , the mesoderm is made up of cells that move inwards from the boundary of the outer germ layer . In midges and some other flies , these cells migrate individually , while in others including fruit flies , the cells move together as a sheet . Fruit flies and midges shared their last common ancestor 250 million years ago and although the genes that make the mesoderm in fruit flies are well understood , little is known about how the mesoderm forms in midges . Urbansky , González Avalos et al . investigated which genes were responsible for the evolutionary transition between the different types of cell migration seen in flies . The experiments identified two genes – called folded gastrulation and t48 – that seem to operate as a simple switch between the two ways that mesoderm cells migrate . Both of these genes are active in fruit fly embryos and are required for the group migration of mesoderm cells . However , the genes do not appear to play a major role in the movement of individual mesoderm cells in midges . Further experiments demonstrate that switching on these genes in midge embryos is sufficient to invoke group mesoderm cell migrations similar to those seen in fruit flies . These findings show that it is possible to identify genetic changes that underlie substantial differences in animal form and shape over hundred million of years . The ease by which Urbansky , González Avalos et al . were able to switch between the two types of mesoderm migration might explain why similar transitions in gastrulation have evolved repeatedly in animals . The next step is to test this hypothesis in other animals . | [
"Abstract",
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"developmental",
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] | 2016 | Folded gastrulation and T48 drive the evolution of coordinated mesoderm internalization in flies |
Notch signaling controls a wide range of cell fate decisions during development and disease via synergistic interactions with other signaling pathways . Here , through a genome-wide genetic screen in Drosophila , we uncover a highly complex Notch-dependent genetic circuitry that profoundly affects proliferation and consequently hyperplasia . We report a novel synergistic relationship between Notch and either of the non-receptor tyrosine kinases Src42A and Src64B to promote hyperplasia and tissue disorganization , which results in cell cycle perturbation , JAK/STAT signal activation , and differential regulation of Notch targets . Significantly , the JNK pathway is responsible for the majority of the phenotypes and transcriptional changes downstream of Notch-Src synergy . We previously reported that Notch-Mef2 also activates JNK , indicating that there are commonalities within the Notch-dependent proliferation circuitry; however , the current data indicate that Notch-Src accesses JNK in a significantly different fashion than Notch-Mef2 .
A relatively small number of highly conserved cellular signaling pathways are responsible for a broad array of distinct , specific biological processes in metazoan development . It is clear that these pathways must interact in a combinatorial and context-dependent manner to produce the wide and diverse range of downstream events required for development , homeostasis , and disease . One of these fundamental signaling mechanisms is the Notch pathway , which is conserved amongst all metazoans and controls a wide range of cell fate decisions ( Artavanis-Tsakonas et al . , 1999; Louvi and Artavanis-Tsakonas , 2006 ) . Aberrant Notch signaling levels can lead to developmental defects and various pathological conditions including cancer ( Artavanis-Tsakonas and Muskavitch , 2010; Ranganathan et al . , 2011; Louvi and Artavanis-Tsakonas , 2012 ) . In addition to the well-documented causative role of activating Notch mutations in T-cell acute lymphoblastic leukemia ( Ellisen et al . , 1991; Weng et al . , 2004 ) , Notch activity has been positively correlated with a number of solid cancers , including those of the breast , prostate , skin , brain , lung , colon , and pancreas ( Koch and Radtke , 2010; Ranganathan et al . , 2011 ) . However , Notch can also act as a tumor suppressor in other contexts ( Dotto , 2008 ) . The specific mechanisms by which Notch contributes to oncogenesis remain largely opaque , although it seems clear that integration with other genes in a context-dependent fashion are key for mediating the action of Notch and its involvement in pro-oncogenic events as proliferation and metastasis . Ligand binding to the Notch cell surface receptor results in a series of cleavages that release the active C-terminus , which subsequently translocates to the nucleus and modulates the transcription of target genes ( Artavanis-Tsakonas and Muskavitch , 2010; Hori et al . , 2013 ) . Given that Notch is highly pleiotropic , it is clear that the developmentally crucial and highly specific downstream responses to Notch signal activation depend on its cellular context and the integration of the signal with other signaling pathways ( Hurlbut et al . , 2007; Bray and Bernard , 2010 ) . Indeed , a number of genetic screens in our lab and others have revealed a staggering number of genes that interact with Notch to modulate downstream phenotypes; given that the results from these studies , which encompass various biological processes , have surprisingly little overlap , it seems likely that the full gamut of genes that can genetically interact with and influence Notch has not yet been identified ( Kankel et al . , 2007; Hurlbut et al . , 2009; Shalaby et al . , 2009; Saj et al . , 2010; Guruharsha et al . , 2012 ) . We previously reported a synergistic interaction between activated Notch and the transcription factor Mef2 , and showed that it activates the JNK signaling pathway ( Pallavi et al . , 2012 ) . In addition to its classical role as a cell stress mediator , JNK , like Notch , plays roles in multiple morphogenetic processes including proliferation , cell death , and cell shape changes ( Rios-Barrera and Riesgo-Escovar , 2013 ) . In this work , we report the results of a systematic , genome-wide modifier screen in Drosophila to dissect and define the genetic circuitry that interacts with Notch to affect proliferation events . We investigate the mechanism of a novel synergistic interaction via JNK between Notch and the Drosophila Src genes , which regulate proliferation , apoptosis , adhesion , and motility and whose human orthologs are abnormally activated in numerous types of primary and metastatic tumors ( Stewart et al . , 2003; Pedraza et al . , 2004; Vidal et al . , 2007; Kim et al . , 2009; Wheeler et al . , 2009; Guarino , 2010 ) .
We performed a genome-wide screen for modifiers of an activated Notch ( Nact ) -induced large eye phenotype to uncover novel genes that interact with Notch to affect proliferation using the Exelixis collection of insertional mutations , which covers approximately 50% of the genome ( Thibault et al . , 2004; Kankel et al . , 2007; Pallavi et al . , 2012 ) . We screened for enhancement or suppression of the large eye phenotype ( Figure 1A ) . As a result , we identified 360 Drosophila genes that are predicted to affect Notch-induced proliferation in the eye; of particular interest are the 206 genes that have clear human orthologs ( Supplementary file 1 ) . Gene Ontology ( GO ) analysis reveals that 42 GO categories are significantly enriched among the 360 genes ( Figure 1B , and Supplementary file 2 ) . The majority of these enriched GO terms fall into three broad categories: genes involved in morphogenesis and development , genes involved in cell division and the cell cycle , and genes involved in transcription . Notably , 84 of the 360 genes did not have any associated GO or INTERPRO annotation . The majority of the genes identified in this screen have not previously been linked to Notch . For example , analysis of known and predicted interactions using the GeneMania platform between Notch and the 31 annotated cell cycle genes shows that only one ( inscuteable , insc ) was previously directly linked to Notch ( Figure 1C ) . 10 . 7554/eLife . 05996 . 003Figure 1 . A genetic screen for modifiers of Notch-induced proliferation in the Drosophila eye . ( A ) Examples of screen phenotypes . E1>Nact results in larger eyes ( second panel ) , compared to wild-type ( E1Gal4 alone ) controls ( top panel ) . Examples of three enhancers , c01597 ( fng ) , c03191 ( Mef2 ) , and d07478 ( Lck ) , and one suppressor , d09869 ( Cad99C ) , are shown . ( B ) Analysis of enrichment of GO terms among the 360 Drosophila genes identified in the screen . Only enriched terms with corrected p-value < 0 . 05 ( using Benjamini–Hochberg correction ) are shown . For numerical p-values , please see Supplementary file 2 . ( C ) Gene association analysis among cell cycle genes identified in the genetic screen . Genetic interactions , physical interactions , predicted interactions , and shared protein domains were mapped using GeneMania ( www . genemania . org ) between the 31 cell cycle genes from our screen ( black circles ) and Notch ( yellow ) . Genes labeled with grey circles are part of the network but were not identified in our screen . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 003 This unbiased genetic screen reveals the unexpected complexity of the genetic circuitry capable of influencing proliferation events in combination with Notch signals . We previously reported that the transcription factor Mef2 , a gene identified in our screen ( Figure 1A ) , synergizes with Notch to induce hyperproliferative and metastatic effects through activation of the JNK signaling pathway ( Pallavi et al . , 2012 ) . We therefore asked whether any of the other genes identified in the screen might also be a component of the Notch/Mef2/JNK signaling axis . We retested 26 of the hits from the screen for JNK activation using qPCR to explore changes in expression of puckered ( puc ) , a direct JNK target ( Martin-Blanco et al . , 1998 ) , and MMP1 , an indirect target ( Uhlirova and Bohmann , 2006 ) , in Drosophila wing discs in an MS1096Gal4; UAS-Nact background . We found that only two of the 26 lines were able to induce both puc and MMP1 ( Supplementary file 1 ) . These two lines were d08184 ( predicted to overexpress Eip75EF ) and d10338 ( predicted to overexpress Src42A ) . Of these two lines , d10338 was a much stronger activator of both puc and MMP1 . The combination of d10338 with UAS-Nact , under the E1Gal4 driver , results in a strong enhancement ( Figure 2A ) of the Nact large eye phenotype ( Figure 2C ) . We also often observe outgrowths of eye tissue protruding from the borders of the eye ( arrow in Figure 2A ) . Notably , d10338 alone produced smaller eyes ( Figure 2B ) than wild-type controls ( Figure 2D ) . 10 . 7554/eLife . 05996 . 004Figure 2 . Synergy between Notch and Src in the eye and wing causes hyperplastic phenotypes and activates JNK . ( A–H ) Various UAS-Src constructs were driven by E1Gal4 along with UAS-Nact in the developing eye . When d10338 , an Exelixis allele that causes Gal4-dependent overexpression of Src42A , and Nact are coexpressed ( A ) , the Nact large eye phenotype ( C ) is enhanced; in addition , occasional outgrowths of eye tissue can be seen ( arrow ) . Note that d10338 alone ( B ) results in decreased eye size , whereas Nact alone ( C ) results in increased eye size compared to the control ( D ) . Src42ACA and Src64B both cause a similar phenotype ( E , G ) when coexpressed with Nact under E1Gal4 , and both also result in decreased eye size in the absence of Nact ( F , H ) . ( I–L ) UAS-Nact and UAS-Src42ACA were driven in the developing wing using the vgGal4 driver . When Nact and Src42ACA are co-expressed ( I ) , wing discs are overgrown compared to either Src42ACA ( J ) or Nact ( K ) alone and display a characteristic ‘crumpled ball’ phenotype indicative of tissue disorganization and cell migration . Note that Src42ACA alone ( J ) causes disorganization but not overgrowth . ( M–P ) Puc-LacZ reporter assay for JNK signal activation in wing discs expressing UAS constructs as indicated under the vgGal4 driver in a pucE69/+ background . Coexpression of Nact and Src42ACA ( M ) causes strong , global activation of the pucLacZ reporter . In contrast , expression of either gene alone ( N , O ) causes weaker activation that is limited in scope . Scale bars: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 00410 . 7554/eLife . 05996 . 005Figure 2—figure supplement 1 . d10338 is a UAS allele of Src42A . ( A , B ) UAS-GFP/d10338; dppGal4/+ wing discs were stained for anti-phosphoY418-Src ( p-Src , red ) , which labels activated Src . Scale bar: 100 μm . ( C ) qPCR for Src42A in MS1096Gal4/+; d10338/+ wing discs ( blue bar ) or MS1096Gal4/+ controls ( red bar ) . Mean values are shown for two independent biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 00510 . 7554/eLife . 05996 . 006Figure 2—figure supplement 2 . Src64B also synergizes with Nact in the wing disc . When driven with vgGal4 , Src64B and Nact synergize to produce an overgrown , disorganized disc ( A ) , whereas Src64B alone causes disorganization ( B ) and Nact alone causes large but organized discs ( C ) compared to control ( D ) . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 006 We corroborated that gain-of-function of Src42A is indeed responsible for the synergy by repeating the eye experiment with a constitutively active Src42A allele ( UAS-Src42ACA ) ( Tateno et al . , 2000 ) . Indeed , Src42ACA also causes hyperplastic eye growth in combination with Nact ( Figure 2E ) and reduced eye size when expressed on its own ( Figure 2F ) . As expected , Gal4-driven expression of d10338 results in an upregulation of both the Src42A gene product and the active , phosphorylated form of Src in vgGal4/d10338 wing discs ( Figure 2—figure supplement 1 ) . VgGal4-driven expression of UAS-Nact and UAS-Src42ACA in the wing disc also resulted in a hyperplastic phenotype; the wing discs are not only overgrown but also noticeably disorganized , with a characteristic ‘crumpled ball’ phenotype ( Figure 2I ) . Furthermore , these larvae fail to pupate and develop a ‘giant larvae’ phenotype . Src42ACA alone causes disorganization but no increase in overall disc size ( Figure 2J ) , whereas Nact alone causes increased disc size but minimal apparent disorganization ( Figure 2K ) . There are two distinct Src family members in Drosophila , Src42A and Src64B . Previous reports have shown that they are both widely expressed and may be redundant in many cases ( Takahashi et al . , 1996; Tateno et al . , 2000; Takahashi et al . , 2005 ) . Therefore , we asked whether Src64B also synergizes with Nact . Consistent with the notion that Src42A and Src64B function similarly , we find that Src64B also interacts with Nact to produce hyperplastic eyes with outgrowths and overgrown , disorganized wing discs ( Figure 2G and Figure 2—figure supplement 2 ) . In order to confirm that JNK signaling acts downstream of Notch and Src ( henceforth N/Src ) , we used the puc-LacZ reporter to visualize JNK signal activation in vivo ( Martin-Blanco et al . , 1998 ) . Coexpression of Nact and Src42ACA in vgGal4 wing discs results in strong , widespread LacZ expression ( Figure 2M ) . In contrast , Nact or Src42ACA alone each induced far weaker , spatially restricted puc-LacZ activation ( Figure 2N , O ) . Given that previous studies associated increased JNK signaling with both invasiveness and apoptosis ( Uhlirova and Bohmann , 2006; Pallavi et al . , 2012 ) , we tested for expression of MMP1 , a matrix metalloprotease associated with invasive phenotypes and cleaved caspase 3 ( cl-casp3 ) , an apoptotic marker . Coexpression of Nact and Src42ACA caused high levels of both MMP1 and cl-casp3 ( Figure 3A , G ) . This is in striking contrast to Nact+Mef2 , which results in robust MMP1 activation but little to no apoptosis , consistent with our previous report ( Figure 3F , L ) ( Pallavi et al . , 2012 ) . 10 . 7554/eLife . 05996 . 007Figure 3 . N/Src synergy induces both MMP1 and apoptosis . ( A–L ) Immunofluorescence for MMP1 ( A–F ) and cleaved caspase 3 ( cl-casp3 , G–L ) in wing discs expressing UAS constructs under vgGal4 . Together , Nact and Src42ACA cause robust activation of both MMP1 ( A ) and cl-casp3 ( G ) , which is strongly reduced by BskDN ( E , K ) . The combination of Nact and Mef2 results in an increase in MMP1 ( F ) but little effect on cc3 ( L ) . ( M ) qPCR for egr and wgn in wing discs overexpressing genes as indicated under the vgGal4 driver reveals that both transcripts are strongly downregulated when Nact and Src42ACA are coexpressed . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 00710 . 7554/eLife . 05996 . 008Figure 3—figure supplement 1 . Gal4/UAS titration does not affect the N/Src phenotype . ( A , C ) One copy each of UAS-Nact , UAS-Src42ACA , and UAS-GFP ( three UAS transgenes total ) were driven with vgGal4 in the wing disc , and compared to ( B , D ) wing discs expressing only UAS-Nact and UAS-Src42ACA ( two UAS transgenes total ) with vgGal4 . Discs were stained for MMP1 ( A , B ) or cleaved caspase 3 ( C , D ) . Scale bar: 100 μM . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 00810 . 7554/eLife . 05996 . 009Figure 3—figure supplement 2 . A heterozygous null mutation of Notch can rescue lethality and phenotype of Src alone . N55e11/FM7C;UAS-Src64B virgins were crossed to vgGal4 males at 18°C ( A , B ) and the resultant female progeny were scored . N55e11/+;vgGal4/UAS-Src64B flies were more viable ( B , n = 126 over four independent experiments ) than their FM7C/+;vgGal4/UAS-Src64B siblings ( A , n = 16 ) , and show a rescued phenotype similar to that of N55e11/+;vgGal4/+ controls ( C ) . FM7C/+;vgGal4/UAS-Src64B ( A ) wings were indistinguishable from vgGal4/UAS-Src64B ( D ) wings . ( E–H ) Immunostaining for MMP1 ( E , G ) or cleaved caspase 3 ( F , H ) in wing discs with genotypes ( D , E ) FM7iGFP/+;vgGal4/UAS-Src64B or ( F , G ) N55e11/+;vgGal4/UAS-Src64B . Scale bar: 100 μm . ( I–K ) d10338 ( Exelixis Src42A allele ) lethality and phenotype at 25°C can be partially rescued by Notch RNAi . vgGal4/d10338 flies ( I ) are largely pupal lethal ( n = 3 viable adults compared to 62 vgGal4/CyO-Tb siblings from the same cross ) and the few escapers have no wings . In contrast , vgGal4/d10338 , UAS-NRNAi flies ( J ) have narrow , short , and shriveled wings and much lower lethality ( n = 67 , compared to 108 vgGal4/CyO siblings from the same cross . ) The wing phenotype appears to be a more severe version of the phenotype of vgGal4/UAS-NRNAi flies ( K ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 009 To examine whether JNK signaling is responsible for the observed phenotypes , we used a dominant negative form of the Drosophila JNK gene Basket ( UAS-BskDN ) to block JNK signaling in N/Src wing discs . We found that BskDN reduced both MMP1 and cl-casp3 to near-wildtype levels ( Figure 3E , K ) . In order to rule out the formal possibility that the observed rescue is caused by titration of Gal4 by an additional UAS rather than by a bona fide effect of the BskDN transgene , we coexpressed UAS-Nact and UAS-Src42ACA with UAS-GFP and observed no discernible rescue ( Figure 3—figure supplement 1 ) . In our earlier work , we reported that N/Mef2 activates JNK through the TNF ligand eiger ( egr ) ( Pallavi et al . , 2012 ) ; we therefore asked whether this is also the case for N/Src . We find that Nact+Src42ACA actually causes a synergistic downregulation of both egr and its receptor wengen ( wgn ) , suggesting that unlike the N/Mef2 synergy , N/Src activates JNK via a TNF-independent mechanism ( Figure 3M ) . Since the vestigial ( vg ) gene is known to be a target of Notch ( Klein and Arias , 1998 ) , we wished to rule out the possibility that the phenotypes we observe could be complicated by an effect of Nact directly on the vgGal4 driver . Therefore , we repeated the above experiment using the dppGal4 driver , which is expressed in the anterior-posterior boundary of the wing disc . Just as with vgGal4 , dppGal4-driven Nact+Src42ACA induces MMP1 and cl-casp3 in the wing disc , and this effect is rescuable by BskDN ( Figure 4 ) . Furthermore , we included a UAS-GFP transgene in these experiments to mark the domain of transgene expression and determine whether or not the effects we see are cell-autonomous . Indeed , we find that some GFP-positive cells , notably those in the far ventral region of the disc , do not obviously express either MMP1 or cl-casp3 ( green arrows in Figure 4 ) . Additionally , some apoptosis is detected in GFP-negative domains of both Nact+Src42ACA and Src42ACA wing discs ( red arrows in Figure 4 ) , suggesting a non-cell-autonomous effect , although we cannot rule out the possibility that since these cells are dying , they have also stopped expressing GFP . 10 . 7554/eLife . 05996 . 010Figure 4 . dpp-Gal4 driven expression of Nact and Src42ACA also upregulates MMP1 and induces apoptosis . UAS transgenes as indicated were driven with dppGal4 along with UAS-GFP at 18°C . Controls express an extra copy of UAS-GFP . Wing discs were stained with anti-MMP1 ( A–L ) or anti-cleaved caspase 3 ( cl-casp3 , M–X ) . The combination of Nact and Src42ACA induces both MMP1 ( A , G ) and cl-casp3 ( M , S ) , and Src42ACA alone does the same to a lesser extent ( B , H , N , T ) . Green arrows: GFP positive cells that do not express MMP1 ( G , H ) or cl-casp3 ( S , T ) . Red arrows: cl-casp3-positive cells that do not express GFP , indicating a potentially non-cell-autonomous effect . This effect can be largely rescued with BskDN ( E , K , Q , W ) . Similarly , the combination of Src64B and Nact also induces both MMP1 ( F , L ) and cl-casp3 ( R , X ) . Scale bar: 100 μM . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 010 These observations were also corroborated by determining that Src64B , in concert with Nact , also strongly activates both MMP1 and cl-casp3 ( Figure 4F , L , R , X ) . Interestingly , it appears to induce correspondingly higher levels of MMP1 and lower levels of apoptosis than Src42ACA; we note , however , that the Src64B allele we used is a WT allele , whereas the Src42ACA is constitutively active . Taken together , these findings reveal that Notch and Src act together to induce JNK signaling and subsequent downstream consequences such as MMP1 activation and induction of apoptosis , but that this JNK activation differs subtly from that induced by Notch and Mef2 . It is noteworthy that Src42A or Src64B alone can often generate apparently weaker versions of the N/Src synergistic phenotype ( Figures 2–4 ) . Notch is endogenously active in both the eye and wing discs during the time when the Src transgenes are expressed in our experiments ( Johnston and Edgar , 1998; Baonza and Garcia-Bellido , 2000; Baonza and Freeman , 2005; Herranz et al . , 2008 ) . We therefore suspected that the observed effects of Src alone could be caused by synergy between exogenous Src and endogenous Notch . To test this , we used the N55e11 heterozygous mutation to decrease levels of endogenous Notch in the developing wing . When UAS-Src64B is driven by vgGal4 in a wild-type background ( FM7C/+;vg-Gal4/UAS-Src64B ) , we observe a high degree of lethality , with the few escapers ( n = 16 over four independent experiments ) displaying small , shriveled , vestigial wings . In contrast , N55e11/+;vgGal4/UAS-Src64B siblings demonstrated reduced lethality ( n = 126 ) and fully extended , notched wings similar to the notched wing phenotype of N55e11/+;vgGal4/+ controls ( Figure 3—figure supplement 2A–D ) . MMP1 and cl-casp3 induced by Src64B were also significantly reduced in N55e11/+;vgGal4/UAS-Src64B wing discs ( Figure 3—figure supplement 2E–H ) . A similar rescue occurs when we combine the d10338 Src42A allele with UAS-NotchRNAi ( NRNAi ) . d10338 alone , when driven with vgGal4 , is largely lethal , with the few escapers ( n = 3 , compared to 62 balancer siblings ) having only wing stumps . In contrast , when UAS-NRNAi is added , lethality is largely rescued ( n = 67 , vs 108 balancer siblings ) and the wings form long , thin spikes similar to those generated by NRNAi alone ( Figure 3—figure supplement 2I–K ) . We finally note that the above experiment does not rule out the possibility that the observed rescue could be caused by an effect of N55e11/+ or UAS-NRNAi on the vgGal4 driver itself . Unfortunately , attempts to repeat these experiments using other Gal4 drivers ( C96Gal4 or dppGal4 ) or Src-activating mutants ( Cskj1d8/j1d8 or Cskj1d8/+ , pucE69/+ [Langton et al . , 2007] ) were unsuccessful due to high levels of lethality . The cell cycle is often misregulated during hyperplastic or cancerous growth . We thus performed a DNA content analysis to examine the cell cycle distribution of GFP-positive cells from vgGal4;UAS-GFP wing discs expressing Src64B and Nact . Whereas wild type and Nact cells have 23% and 25% of cells in G1 phase respectively , Nact+Src64B cells showed a complete loss of G1 phase . Src64B alone caused a partial loss of G1 phase ( 4% ) . In both cases , a corresponding increase in the proportion of S phase cells was also observed . The effect on the cell cycle appears to be dependent on JNK signaling , as blocking JNK with BskDN strongly reversed the G1 bypass ( 19% of cells in G1 ) . Both Src42A alleles ( Src42ACA and d10338 ) produced cell cycle distribution profiles ( 2% and 3% in G1 phase respectively ) similar to Src64B when coexpressed with Nact . Likewise , wing disc cells coexpressing Nact and Mef2 displayed a decreased number of cells in G1 phase ( 5% ) ( Figure 5A ) . Cells from dppGal4;UAS-GFP wing discs , similar to those from vgGal4; UAS-GFP discs , coexpressing Src64B and Nact also displayed a similar , JNK-dependent loss of the G1 peak ( Figure 5—figure supplement 1 ) . 10 . 7554/eLife . 05996 . 011Figure 5 . N/Src synergy disrupts the cell cycle . ( A ) DNA content analysis was performed on Hoechst-labeled dissociated cells from vgGal4;UAS-GFP wing discs expressing UAS-Src64B;UAS-Nact ( dark green trace ) , UAS-Src64B ( light blue ) , UAS-Nact ( red ) , WT control ( black ) , UAS-BskDN;UAS-Src64B;UAS-Nact ( light green ) , UAS-Nact;UAS-Src42ACA ( purple ) , UAS-Nact;d10338 ( dark blue ) or UAS- Nact;UAS-Mef2 ( orange ) . Comparative histograms show relative frequencies on the y-axis , normalized to total number of counts for each sample . ( B–E ) EdU incorporation assay in dppGal4;UAS-GFP wing discs expressing d10338;UAS-Nact ( B ) , d10338 ( C ) , UAS-Nact ( D ) , or UAS-GFP ( E ) at 22°C . A closeup of the areas denoted by boxes is shown below each image , and the GFP-positive area is marked with dotted yellow lines . Whereas UAS-Nact alone expands the ZNC ( zone of non-proliferating cells ) and also non-cell-autonomously induces proliferation in the dorsal-posterior region of the disc , thus increasing the size of the dorsal compartment ( D ) , the combination of d10338 and UAS-Nact eliminates the expansion of the non-proliferative zone and causes cells within the ZNC proper to begin incorporating EdU; furthermore , the area of increased proliferation in the dorsal compartment appears to be expanded ( B ) . ( F–J ) Nact and Src42ACA together cause a reduction in dacapo ( dap ) levels . ( F ) qPCR for dap expression in wing discs expressing Nact and/or Src42ACA or Mef2 under the vgGal4 driver . ( G–J ) A dap-LacZ reporter assay was used to visualize dap expression in vgGal4 wing discs in a dapk07309/+ background . Both Nact and Src42ACA together ( G ) and Src42ACA alone ( H ) show a reduction in dap-LacZ compared to both Nact alone ( I ) and vgGal4 controls ( J ) . Scale bars: 100 μM . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 01110 . 7554/eLife . 05996 . 012Figure 5—figure supplement 1 . Elimination of G1 phase of the cell cycle also occurs in dppGal4 wing discs expressing Nact and Src64B . DNA content analysis was performed on Hoechst-labeled dissociated cells from dppGal4;UAS-GFP wing discs expressing UAS-Src64B;UAS-Nact ( green trace ) , UAS-Src64B ( blue ) , UAS-Nact ( red ) , WT control ( grey ) , or UAS-BskDN;UAS-Src64B;UAS- Nact ( yellow-green ) . Comparative histograms show relative frequencies on the y-axis , normalized to total number of counts for each sample . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 012 Cell cycle arrest in G1 phase is a hallmark of cells in the Zone of Non-proliferating Cells ( ZNC ) located in the D-V boundary of the wing disc ( Johnston and Edgar , 1998 ) . If N/Src could cause G1 bypass , then , we reasoned , it might also cause ZNC disruption . We visualized the ZNC by incorporating EdU , which labels cells in S phase , in dppGal4;UAS-GFP wing discs . We used the d10338 Src42A allele , as the more modest disruption of disc organization caused by this weaker allele allowed us to better identify the ZNC . Control ZNC cells that are arrested in G1 do not enter S phase and therefore do not incorporate EdU ( Figure 5E ) ; Nact alone causes ZNC expansion and a partially non-cell autonomous increase in EdU incorporation in the dorsal region of the disc ( Figure 5D ) , as previously reported ( Go et al . , 1998; Johnston and Edgar , 1998; Herranz et al . , 2008 ) . In contrast , discs expressing Nact and d10338 show a reduced ZNC with correspondingly more EdU-labeled cells in the D–V boundary , and the area of Notch-induced increased proliferation in the dorsal region is expanded , extending all the way down to the ZNC and greatly increasing the size of the dorsal compartment ( Figure 5B ) . Given that the cyclin-dependent kinase ( CDK ) inhibitor dacapo ( dap ) blocks G1 to S transition and is important for cell cycle exit in G1 ( de Nooij et al . , 1996; Lane et al . , 1996 ) we asked whether N/Src affects transcription of dap . qPCR reveals that Nact+Src42ACA expression is indeed associated with a downregulation of both Notch-induced and endogenous dap transcription; we note that Nact+Mef2 similarly reduced dap levels , although to a lesser extent ( Figure 5F ) . We corroborated the qPCR result using the dapk07309 enhancer trap line , which functions as a dap-LacZ reporter ( Mitchell et al . , 2010 ) . Nact alone activates dap-LacZ , and Nact+Src42ACA suppresses not only this increase but also the endogenous expression of the reporter ( Figure 5G , I , J ) . Src42ACA alone also causes a significant decrease in reporter expression ( Figure 5H ) . We conclude that N/Src synergy results in bypassing the G1 phase of the cell cycle , likely via the downregulation of the CDK inhibitor dacapo . Disorganized , hyperplastic growth has been associated with JAK/STAT signaling in Drosophila; furthermore , both Src and Notch individually can activate JAK/STAT ( Tateno et al . , 2000; Read et al . , 2004; Reynolds-Kenneally and Mlodzik , 2005 ) . We thus probed whether N/Src expression could affect JAK/STAT signaling . To assess JAK/STAT signal activation , we used qPCR to measure expression levels of the JAK/STAT ligands unpaired/outstretched ( upd/os ) , unpaired2 ( upd2 ) , and unpaired 3 ( upd3 ) . We found that all three upd genes were strongly upregulated in a synergistic manner by Nact+Src42ACA; furthermore , this upregulation was largely suppressed by the addition of BskDN , indicating that it is dependent on JNK signals . Nact+Mef2 also activated the upd ligands , but to a far lesser extent ( Figure 6A ) . An upd-LacZ reporter line reveals similar results , with the combination of UAS-Nact and UAS-Src42ACA driven by vgGal4 inducing strong reporter activation compared to either gene alone or controls expressing vgGal4 alone ( Figure 6B–E ) . 10 . 7554/eLife . 05996 . 013Figure 6 . N/Src synergy activates the JAK/STAT signaling pathway . ( A ) qPCR for unpaired family ligands in vgGal4 discs expressing UAS constructs as indicated . All three upd family genes are highly upregulated by the combination of Nact and Src42ACA ( dark purple bars ) , and this upregulation is dependent upon JNK signaling as BskDN rescues it ( lavender bars ) . Coexpression of Nact and Mef2 ( orange bars ) induces a much lower level of the upd ligands . Note that the y-axis is on a logarithmic scale . ( B–E ) An upd-LacZ reporter assay in vgGal4 wing discs validates the qPCR data and demonstrates that Nact+Src42ACA causes strong , widespread activation of upd transcription ( B ) ; in contrast , either gene alone ( C , D ) causes lower , more restricted levels of upd upregulation . ( F–J ) The 10XStatGFP reporter was used to assess JAK/STAT signal activation in vgGal4 discs grown at 18°C . Nact+Src42ACA strongly upregulates 10XStatGFP ( G ) , whereas either gene alone ( H , I ) only weakly upregulates the reporter . The addition of BskDN ( F ) reduces the 10XStatGFP induced by Nact+Src42ACA ( G ) to levels similar to those of Nact alone ( I ) . Note that since the upd-LacZ discs were grown at 25°C and the 10XSTATGFP discs were grown at 18°C , the latter displays a somewhat weaker phenotype , hence the difference in disc size between B/D and G/I . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 013 To directly visualize JAK/STAT signal activation in vivo , we used the 10XSTATGFP reporter ( Bach et al . , 2007 ) . We find that , whereas Src42ACA or Nact alone each caused weak 10XSTATGFP activation , the combination of the two resulted in strong reporter activation throughout the disc ( Figure 6G–J ) . Notably , the patterns of expression of 10XSTATGFP and upd-LacZ are similar . Moreover , consistent with the observation that blocking JNK reduces the expression of the upd genes , BskDN also reduced 10XSTATGFP reporter activation ( Figure 6F ) . Thus , N/Src synergy results in activation of JAK/STAT signaling by the upregulation of the upd ligands in a JNK-dependent manner . To examine the gamut of transcriptional targets downstream of N/Src coexpression , we performed an RNA-sequencing ( RNA-seq ) analysis in vgGal4 wing discs . We defined ‘synergistic targets’ as those genes that were significantly up- or down-regulated ( adjusted p-value < 0 . 05 ) in Nact+Src42ACA discs as compared to Nact alone , Src42ACA alone , or WT controls . By these criteria , we identified 187 genes , of which 87 were downregulated and 100 were upregulated; the effects on expression of 130 of these genes was reversed by BskDN ( Supplementary file 3 ) , consistent with the notion that their expression is dependent on JNK . We validated 44 of these genes with qPCR; 4/44 were thus determined to be false positives . We note that of these 187 Drosophila genes , 120 have clear human orthologs ( Supplementary file 3 ) . It is noteworthy that this analysis did not uncover several known N/Src targets ( the upd genes , MMP1 , puc , and dap ) . We attribute this to the observation that Src alone often causes a milder version of the N/Src phenotype; our analysis is not always sensitive enough to assign significance to relatively small N/Src vs Src differences . For MMP1 , os/upd , upd2 , and upd3 , the raw data reveals that the N/Src vs Src comparison was small enough to be insignificant , although upd2 and upd3 appeared significant prior to false positive correction . For dap , we observe a reduction of Notch-induced dap with the addition of Src42ACA; however , we did not detect the same reduction in endogenous dap between N/Src and WT discs that we saw with qPCR and the dap-LacZ reporter . We cannot at present explain this discrepancy . Finally , puc did not score as a significant target in our analysis , possibly due to a false positive reading in the RNA-seq data for the WT condition , as there is no evidence to suggest that puc is highly upregulated in WT discs ( Supplementary file 4 ) . One synergistically upregulated gene was Enhancer of split mγ ( E ( spl ) mγ or HLHmgamma ) , a member of the E ( spl ) complex and a target of Notch itself . We therefore asked whether other E ( spl ) complex members were similarly affected . Six of the seven members ( excepting E ( spl ) m5 ) are expressed in the wing disc . However , only E ( spl ) mγ was synergistically upregulated . The other five expressed genes in the locus ( E ( spl ) m8 , E ( spl ) m3 , E ( spl ) m7 , E ( spl ) mβ , and E ( spl ) mδ ) were , as expected , upregulated by Nact alone , but , interestingly , this upregulation actually appeared to be suppressed by the addition of Src , sometimes all the way back to WT levels; BskDN reversed both the N/Src-induced enhancement of E ( spl ) mγ ( albeit weakly ) and the suppression of the other five E ( spl ) genes ( Figure 7A ) . In contrast , Nact+Mef2 caused suppression of all of the E ( spl ) genes , including E ( spl ) mγ ( Figure 7A ) . We corroborated these observations by using an E ( spl ) mγ reporter consisting of a 234-bp mγ enhancer region , which has been shown to recapitulate the endogenous E ( spl ) mγ expression pattern in the wing disc , fused to LacZ ( Nellesen et al . , 1999 ) . While , as expected , Nact causes an increase in the number of cells that express E ( spl ) mγ-LacZ , Nact+Src42ACA does not; interestingly Src42ACA alone eliminates the endogenous pattern of E ( spl ) mγ-LacZ , possibly due to cell death ( Figure 7—figure supplement 1 ) . This observation raises the possibility that E ( spl ) mγ activation by N/Src is driven by a genomic region distinct from the 234-bp mγ enhancer region . 10 . 7554/eLife . 05996 . 014Figure 7 . Notch targets are differentially affected by N/Src synergy . ( A ) qPCR assay for expression levels of E ( spl ) complex members in vgGal4 wing discs expressing UAS constructs as indicated . ( B–E ) Immunostaining with anti-cut ( red ) in vgGal4 wing discs . Nact alone ( D ) induces cut expression , which is suppressed in Nact+Src42ACA discs ( B ) . Note that both ectopic and endogenous cut appear to be suppressed . ( F ) NRE-GFP expression in wing discs expressing Nact+Src42ACA under vgGal4 . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 01410 . 7554/eLife . 05996 . 015Figure 7—figure supplement 1 . E ( spl ) mγ reporter staining in N/Src wing discs . VgGal4 wing discs expressing UAS-Nact and/or UAS-Src42ACA in an E ( spl ) mγ-LacZ/+ background were stained for anti-β-gal . E ( spl ) mγ-LacZ consists of the 234-bp mγ enhancer region fused to a LacZ reporter ( Nellesen et al . , 1999 ) . Note that E ( spl ) mγ induced by Nact ( C ) is not suppressed by the addition of Src42ACA ( A ) . Src42ACA alone ( B ) seems to suppress the endogenous E ( spl ) mγ staining ( D ) in the proneural cells . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 015 Because the E ( spl ) complex is an important Notch target , we asked whether the expression pattern of cut , another well-known Notch target , was affected by N/Src . As expected , cut staining was upregulated in a broad swath when Nact alone was driven by vgGal4 ( Figure 7D ) . Strikingly , this ectopic cut expression appeared to be completely absent in Nact+Src42ACA wing discs ( Figure 7B ) . Furthermore , even endogenous cut disappeared in both Nact+Src42ACA ( Figure 7B ) and Src42ACA ( Figure 7C ) wing discs . Thus , cut is not upregulated like E ( spl ) mγ by N/Src , but rather suppressed like the other five E ( spl ) genes . Since the major downstream effector of Notch is Suppressor of Hairless ( Su ( H ) ) exerting its action by binding to Notch-dependent promoter sites , we asked if the NRE-GFP reporter , which is activated by Notch binding to Su ( H ) sites , could be activated by N/Src . We find that Nact+Src42ACA is still able to strongly activate the NRE-GFP reporter ( Figure 7F ) . These analyses demonstrate that N/Src synergy affects a diverse set of genes largely in a JNK dependent manner , including known Notch targets , which however seem to be differentially regulated .
Notwithstanding the fact that the genetic circuitry surrounding Notch signals is very complex ( Guruharsha et al . , 2012 ) , we were surprised to find such an abundance and diversity of genes that can cooperate with activated Notch signals to modulate cellular proliferation , a fact that has obvious potential consequences for oncogenesis as well as normal development . It is important to note that our screen is not saturating because the Exelixis mutant collection disrupts only approximately 53% of the fly genome and has other inherent limitations , which have been described in detail elsewhere ( Thibault et al . , 2004; Kankel et al . , 2007 ) . In the current screen , we tested all lines ( with the exception of insertions on the X chromosome , which would be hemizygous in male progeny ) heterozygously , so weaker or recessive effects may not be observed . We also did not score combinations that were 100% lethal . Therefore , we do not expect to find every member of a complex or pathway or all redundant genes . In light of the above information , we presume that the true number of genes that can modify Notch-induced proliferation phenotypes is far greater ( perhaps at least double ) than the 360 we have identified in this work , indicating a surprising complexity and diversity of the potential genetic circuitry that can , in conjunction with Notch signals , affect proliferation . Despite the diversity of the identified gene set , there are nevertheless some commonalities . Most notably , 31 genes are annotated as cell cycle genes , representing a more than twofold enrichment . In particular , genes involved in mitosis are highly represented . Given the phenotypic parameters of the screen , it is not surprising that cell cycle genes have been identified as modifiers . Interestingly , we identified nanos ( d06728 , which is predicted to be a GOF allele ) as a suppressor of the large eye phenotype . This finding is corroborated by the observation that nanos can directly repress Cyclin B ( CycB ) in the germline , thereby preventing mitosis ( Kadyrova et al . , 2007 ) . However , we also identified c04775 , a predicted disruption of Cyclin B3 ( CycB3 ) , as an enhancer; the evidence would suggest that loss of mitotic cyclins should , like nanos GOF , suppress rather than enhance hyperplasia . It is conceivable that this may point to a context-specific feedback regulation that , if indeed true , may have implications for oncogenesis . Such an interesting possibility , however , awaits experimental corroboration especially in view of the fact that additional analysis would be necessary to ensure that c04775 is indeed a true loss-of-function mutation of CycB3 . We should also note that the screen did not always identify plausibly predicted genes . For example , CycB , which acts redundantly with CycB3 in embryogenesis ( Lee and Orr-Weaver , 2003 ) , was not identified in our screen despite there being three predicted alleles in the Exelixis collection . On a similar note , we identified Bub1 ( c04512 ) as a strong enhancer , but not any of the other key components of the spindle assembly checkpoint ( BubR1 , Mad2 , and Bub3 ) ( Lara-Gonzalez et al . , 2012 ) , even though they are represented in the Exelixis collection . We cannot at present say if these observations imply some specificity of the Notch response for CycB3 and Bub1 or whether they simply reflect limitations of our screening system . Previous studies have demonstrated colocalization of Notch and c-Src proteins in pancreatic cancer cells , where Src is required for proteolytic activation of Notch ( Ma et al . , 2012 ) , and of Notch and the T-cell-specific Src family member Lck in T cells ( Sade et al . , 2004 ) , but a functional relationship between the two genes had not been demonstrated prior to our study . Given that the majority of N/Src phenotypes and changes in almost 70% of the transcriptional targets can be effectively reversed upon JNK inhibition it seems clear that the major target of the N/Src synergy is the JNK pathway . Our previously reported N/Mef2 synergy activates JNK via the TNF ligand egr ( Pallavi et al . , 2012 ) , which is clearly not the case for N/Src , which actually causes suppression of both egr and its receptor . Additionally , N/Src causes a great deal of apoptosis , whereas N/Mef2 does not , and only N/Src is capable of differentially upregulating E ( spl ) mγ . Finally , although both combinations activate JAK/STAT ligands , the degree of upregulation is more than an order of magnitude greater for N/Src vs N/Mef2 . These observations suggest that while JNK activation may be a common thread in Notch-related hyperplastic phenotypes , there may be multiple routes through which a single pathway ( JNK ) is activated , and this difference in access may subsequently contribute to differential downstream outputs ( Figure 8 ) . 10 . 7554/eLife . 05996 . 016Figure 8 . Model of convergence and divergence of the Notch/Mef2/JNK and Notch/Src/JNK signaling axes . N/Mef2 and N/Src synergies converge on JNK , through eiger-dependent and eiger-independent means respectively . Some downstream processes are common to both synergies , such as MMP1 activation and bypass of G1 phase of the cell cycle via dap downregulation . Other downstream outputs , such as apoptosis , level of JAK/STAT activation , and regulation of Notch target genes , diverge between N/Src and N/Mef2 synergy . DOI: http://dx . doi . org/10 . 7554/eLife . 05996 . 016 It is worth noting that the Notch/JNK signaling axis may represent a primary pathway through which the cell can not only regulate proliferation but may also affect cell movement . Two groups have recently reported a role for actin polymerization upstream of JNK activation by Src ( Fernandez et al . , 2014; Rudrapatna et al . , 2014 ) ; given that we observed increased actin in N/Mef2 tissues ( Pallavi et al . , 2012 ) , we suggest that the same may be true for the N/Src axis . In addition to N/Src and N/Mef2 , a third Notch synergy that acts through JNK has been reported: the loss of the epithelial polarity gene scribble ( scrib ) induces JNK activation leading to cell death; however , in the presence of active Notch , scrib mutant cells instead overgrow and become invasive ( Brumby and Richardson , 2003 ) . Whether N/scrib converges with either N/Src or N/Mef2 or defines a third JNK-dependent axis remains to be seen . The vast diversity of signal outputs suggests that signal integration may occur not only at the level of Notch interactions but also at the level of JNK . Although the determinants of such specificity remain to be identified , we consider our identification of N/Src transcriptional targets to be a starting point for future studies . Overexpression of Notch alone causes local expansion of the ZNC and distant hyperproliferation ( Johnston and Edgar , 1998; Herranz et al . , 2008 ) . However , our data indicate that the combination of Notch and Src actually leads to loss of the ZNC and expansion of the hyperproliferative zone , suggesting that Src modulates the activity of Notch in this situation and leads to proliferation rather than differentiation ( as is also implied by the loss of cut in the D–V boundary ) . Our observation of differential regulation of the E ( spl ) complex genes supports the notion that Src modulates the target specificity of Notch . The NRE-GFP reporter is strongly upregulated by N/Src ( similar to Notch alone ) , suggesting that whatever leads to suppression of Notch targets in the presence of Src either does not affect Su ( H ) binding or requires the modulatory action of additional , differentially acting factors . Among the most striking N/Src phenotypes are the complete loss of the G1 phase of the cell cycle and an immense degree of hyperplasia . Our observation that the cell cycle regulator dap is downregulated by N/Src and N/Mef2 suggests a potential mechanism for the G1 bypass . The G1 phase is important during normal development and homeostasis to allow newly-divided cells to increase in size and replenish protein and energy stores; additionally , regulatory checkpoints during G1 control cell fate choices such as the decision to exit the cell cycle and differentiate ( Lee and Orr-Weaver , 2003; Hindley and Philpott , 2013 ) . Dap is a member of the p21/p27 family of Cdk inhibitors , and is required during Drosophila embryogenesis for proper timing of cell cycle exit at G1 ( de Nooij et al . , 1996; Lane et al . , 1996 ) . Early Drosophila embryos undergo their first 16 cell divisions without G1 or G2 , resulting in rapid cell division without a corresponding increase in compartment size ( Lee and Orr-Weaver , 2003 ) . In contrast , we observe hyperplasia of N/Src wing discs , indicating that compartment growth must occur even in the absence of a detectable G1 phase . In the case of Ras or Myc overexpression , the other phases of the cell cycle are prolonged to compensate for decreased time in G1 , and the overall growth rate also increases ( Johnston et al . , 1999; Prober and Edgar , 2000 , 2002 ) . We do observe an increase in the percentage of N/Src cells in S phase , suggesting that the lack of G1 is at least partially compensated for by a longer S . The N/Src cell cycle profile , with absent G1 and a high proportion of cells in S phase , is reminiscent of that of mammalian embryonic stem cells ( ESCs ) , which have an abbreviated G1 and longer S; recent studies have suggested that this altered cycle is necessary to maintain pluripotency , although the mechanisms are still unclear ( Orford and Scadden , 2008; Hindley and Philpott , 2013 ) . Like ESCs , N/Src cells remain in an actively proliferating state rather than undergoing G1 arrest followed by differentiation . Therefore , this raises the very interesting possibility that N/Src synergy may interfere with cell fate determination , conferring pluripotency . This could have important implications for tumorigenicity as well as for the maintenance of a differentiated state . A recent report found that Src42A and Src64B in Drosophila intestinal stem cells ( ISCs ) could stimulate the expansion of a transit-amplifying population of Notch-positive cells ( Kohlmaier et al . , 2014 ) . In light of our findings , we suggest that Src may actually interact with Notch in this cell population to induce proliferation . It will therefore be informative to compare our list of N/Src transcriptional targets with the genes induced by Src in ISCs or other pluripotent cell types in both mammals and flies . Several previous studies have shown that Src acts through JNK to induce invasive phenotypes , including activation of MMP1 ( Ma et al . , 2014; Rudrapatna et al . , 2014 ) . However , invasiveness is only one part of the oncogenic equation . Once they have migrated into a tissue , metastatic cells also need to grow and proliferate , preferably in an unchecked fashion . Our work suggests that the addition of Notch promotes hyperplastic growth while still retaining and perhaps even enhancing Src-driven invasive behavior . Strikingly , this occurs even in the presence of significant amounts of apoptosis . Should an additional mutation or gene activation occur that suppresses this cell death , the N/Src cells may become even more malignant . A clue might be found in the comparison with N/Mef2 , where tissues overgrow but very little cell death is observed . Other studies have reported that loss of the Src antagonist Csk in the wing disc can induce activation of JNK and JAK/STAT signaling as well as morphological disorganization ( Read et al . , 2004; Vidal et al . , 2006; Langton et al . , 2007; Vidal et al . , 2007 ) . We hypothesize that these Csk phenotypes may result from activation of endogenous Src , which then interacts with endogenous Notch . The Src family members Src , Fyn , and Yes in mammals , as well as Src42A and Src64B in Drosophila , are widely , even ubiquitously expressed , and kept in check partly by the similarly widespread expression of Csk ( Takahashi et al . , 1996; Tateno et al . , 2000; Takahashi et al . , 2005; Wheeler et al . , 2009 ) . Notch is likewise expressed in a large number of tissues ( Artavanis-Tsakonas et al . , 1999; Artavanis-Tsakonas and Muskavitch , 2010 ) . Interestingly , although the activity and levels of both Src and Notch are often increased in cancers , activating mutations in the genes themselves are rare ( Ishizawar and Parsons , 2004; Ranganathan et al . , 2011; Louvi and Artavanis-Tsakonas , 2012 ) . One explanation suggested by our data is that synergy between even relatively low levels of active Notch and Src can activate JNK and cause exponentially increased levels of hyperplastic growth and stimulation of oncogenic events . Additionally , an inactivating mutation in Csk could have devastating consequences in both development and cancer by triggering a Notch/Src synergistic response . Among the human orthologs of our N/Src targets , genes involved in metabolism and stress response are both overrepresented . Thus these cells may have an increased metabolic rate ( which may also explain the observed overgrowth in the absence of G1 ) and heightened protection from stress , both of which could contribute to a favorable environment for oncogenesis . In particular , this may explain how N/Src cells survive and indeed hyperproliferate despite the strong pro-apoptotic JNK signal . An earlier study revealed that activated Notch could act as a tumor suppressor in v-Src-transformed quail neuroepithelial cells , causing a reversion of the transformed phenotype along with suppression of JNK signaling ( Mateos et al . , 2007 ) . This report may seem on the surface to contradict our findings . However , Notch can act as either an oncogene and a tumor suppressor depending on context ( Lobry et al . , 2014 ) . Here , too , context , including other genes that interact with the N/Src axis , is likely to be essential; further analysis of the hits from our genetic screen may shed light on this issue as well as on the larger question of how Notch switches between oncogenic and tumor suppressive behavior . Our observation that the N/Mef2 and N/Src synergies both activate the same pathway ( JNK ) but display differences in downstream phenotype suggests that it may be possible to identify targeted , refined signatures for different types of Notch , Src , and JNK-related cancers . While single gene mutations can occasionally trigger an oncogenic state , it is more often the context-dependent interplay between genes that causes or modulates cancerous growth . Understanding the mechanistic consequences of cross-talking gene activities is essential if we are to unveil the molecular basis of oncogenic events and develop rational therapeutic interventions .
Crosses were carried out at 25°C under standard conditions unless otherwise noted . Exelixis lines used in this work can be obtained at https://drosophila . med . harvard . edu/ ( Artavanis-Tsakonas , 2004; Parks et al . , 2004; Thibault et al . , 2004 ) . Other fly lines used were: UAS-Nact ( Go et al . , 1998 ) , UAS-Src42ACA ( Tateno et al . , 2000 ) , UAS-Src64B ( Nicolai et al . , 2003 ) , pucE69 ( Martin-Blanco et al . , 1998 ) , UAS-BskDN ( Adachi-Yamada et al . , 1999 ) , upd-LacZ ( gift from N Perrimon ) , dapk07309 ( Mitchell et al . , 2010 ) , 10XStatGFP ( Bach et al . , 2007 ) , N55e11 ( Rulifson and Blair , 1995 ) , E ( spl ) mγKX-LacZ ( Nellesen et al . , 1999 ) , NRE-GFP ( Saj et al . , 2010 ) , UAS-Mef2 ( Bour et al . , 1995 ) , and UAS-NotchRNAi ( Hori et al . , 2004 ) . Gal4 lines used were E1Gal4 ( gift from G Rubin [Pallavi et al . , 2012] ) , vgGal4 , MS1096Gal4 and dppGal4 ( all available from the Bloomington Drosophila Stock Center , Bloomington , IN ) . Immunostaining and EdU staining were performed as described in our previous work ( Pallavi et al . , 2012 ) . The following primary antibodies were used: 3A6B4 anti-MMP1 ( 1:100; Developmental Studies Hybridoma Bank ( DSHB ) , Iowa City , IA ) , anti-cleaved caspase 3 ( 1:300; Cell Signaling Technology , Danvers , MA ) , D5 . 1 anti-GFP ( 1:300; Cell Signaling Technology ) , anti-β-gal ( 1:1000-1:2000 , MP Biomedicals , Santa Ana , CA ) , anti-Src-pY418 ( 1:1000 , Abcam , Cambridge , MA ) and 2B10 anti-cut ( 1:10 , DSHB ) . AlexaFluor conjugated secondary antibodies ( Life Technologies , Carlsbad , CA ) were used at 1:1000 . Fluorescence microscopy ( with the exception of the EdU incorporation assay ) was performed on a Zeiss Axioplan microscope with a 10× objective and images were minimally processed using Adobe Photoshop CS5 . The discs for the EdU incorporation assay ( Figure 5 ) were imaged using a Nikon TE2000 with C1 Point Scanning Confocal at the Nikon Imaging Center at Harvard Medical School . UAS-Nact/CyO-tub-Gal80; E1Gal4 fly stocks were generated and virgins were crossed to males from each line in the Exelixis mutant collection ( Artavanis-Tsakonas , 2004; Thibault et al . , 2004; Pallavi et al . , 2012 ) to screen for enhancers and suppressors of the Notch-induced large eye phenotype . All positive hits were rescreened a second time to eliminate false positives; furthermore , they were crossed to E1Gal4 alone to eliminate Notch-independent effects . We identified 332 Exelixis lines that either enhanced or suppressed the large eye phenotype . The determination of genes predicted to be affected by each Exelixis line was performed as previously described ( Sen et al . , 2013 ) . In some cases , more than one gene may be affected either due to multiple insertions or to insertion of an element in or near overlapping or neighboring genes . Mapping of Drosophila genes to human orthologs was performed using tables generated by Mark Gerstein's group at Yale University , where Drosophila-human ortholog pairs were identified based on three sources—InParanoid , OrthoMCL and TreeFarm ( http://info . gersteinlab . org/Ortholog_Resources ) . Here , we provide only the top human ortholog ( identified by the most number of sources ) for each Drosophila gene . GO term enrichment analysis was performed using DAVID ( http://david . abcc . ncifcrf . gov/ ) with the total Exelixis gene list as background; statistical significance was determined using the Benjamini–Hochberg correction . The interaction map shown in Figure 1C was generated using GeneMania ( www . genemania . org ) , using all available default datasets for genetic interactions , physical interactions , predicted interactions , and shared protein domains , and a limit of 20 related genes . Wing discs from wandering third instar larvae were collected in 1× PBS and dissociated in 9× Trypsin-EDTA ( Life Technologies ) with 0 . 5 μg/ml Hoechst 33 , 342 and 1× PBS for approximately 4 hr at room temperature with gentle agitation ( de la Cruz and Edgar , 2008 ) . FACS was performed on dissociated cells using a BD FACSAria II SORP UV , and data was analyzed using FlowJo and ModFit software . Total RNA for qPCR was extracted from wing discs from wandering third instar larvae . Discs were isolated in PBS and RNA was extracted using TRIzol reagent ( Life Technologies ) ; gDNA was removed and RNA was cleaned up using the RNEasy plus micro kit ( Qiagen , Valencia , CA ) . cDNA was generated using the High Capacity RNA to cDNA kit ( Life Technologies ) . All qPCR reactions were performed in technical triplicate using Taqman assays ( Life Technologies ) on a Life Technologies 7900HT machine . Unless otherwise noted , at least three biological replicates were performed for each genotype . VgGal4 females were crossed to males from the UAS lines of interest . Total RNA for RNA-seq was isolated and purified as for qPCR from at least 60 wing discs per sample . All samples were run in biological triplicate . Ribosomal RNA depletion was performed using the Ribo-zero rRNA removal kit ( Epicentre , Madison , WI ) . Library preparation using the PrepX SPIA RNA-seq kit ( IntegenX , Pleasanton , CA ) , library quality control , and sequencing on an Illumina HiSeq2000 machine was performed by the Biopolymers Facility at Harvard Medical School ( http://genome . med . harvard . edu ) . We ran 100 cycle paired-end reads for each sample . Sequencing data was analyzed using Galaxy ( http://usegalaxy . org/ ) ( Giardine et al . , 2005; Goecks et al . , 2010; Blankenberg and Hillman-Jackson , 2014 ) . Specifically , we used TopHat to align reads to the Drosophila melanogaster reference genome ( BDGP R5/dm3 ) and CuffDiff with replicate analysis using a false discovery rate of 0 . 05 and no additional parameters to compare sample pairs ( Garber et al . , 2011; Trapnell et al . , 2012 ) . Genes were defined as synergistically up- or down- regulated by N/Src only if the adjusted p-value was less than 0 . 05 for all of the following pairs: N/Src vs N , N/Src vs Src , and N/Src vs WT control . Synergistic targets were further defined as rescued by BskDN if the adjusted p-value for BskDN/N/Src vs N/Src was less than 0 . 05 . Mapping of Drosophila genes to human orthologs was performed as for screen hits above . | The cells within animals are organized into tissues and organs that perform particular roles . To develop and maintain these structures , the ability of individual cells to divide and grow is strictly controlled by the activities of many proteins , including one called Notch . This protein is found in all multicellular organisms and allows cells to communicate with each other . Mutations in the gene that encodes Notch can cause cells to divide excessively and lead to cancer and other diseases . Notch regulates the growth and division of cells by interacting with many other proteins . For example , Mef2 works with Notch to activate a communication system called the JNK pathway . This pathway is involved in controlling cell division , cell death , and cell movement . However , it is thought that Notch may also interact with other proteins that have not yet been identified . Now , Ho et al . have conducted a genome-wide screen in fruit flies to find proteins that interact with Notch . The experiments used flies that develop abnormally large eyes because they have an over-active Notch protein . Ho et al . identified hundreds of fruit fly genes that could increase or decrease the size of the flies' eyes in the presence of Notch activity . Many of these genes are known to be involved in development , cell division , or in controlling the activity of other genes . Ho et al . found that two of these genes encode similar proteins called Src42A and Src64B , which are similar to the Src proteins that are involved in many types of human cancers . The experiments show that both proteins interact with Notch to promote uncontrolled cell division and lead to tissues in the flies becoming more disorganized . The JNK pathway is also activated by Notch working with Src42A or Src64B , but in a different manner to how it is activated by Mef2 and Notch , and with different consequences for cells . This study provides new insights into how genes work together in order to influence cell division and other events in development . Also , it suggests that Notch activity may regulate the growth of cancers linked with defects in the Src proteins . | [
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] | 2015 | The Notch-mediated hyperplasia circuitry in Drosophila reveals a Src-JNK signaling axis |
Mutualistic interactions between free-living algae and fungi are widespread in nature and are hypothesized to have facilitated the evolution of land plants and lichens . In all known algal-fungal mutualisms , including lichens , algal cells remain external to fungal cells . Here , we report on an algal–fungal interaction in which Nannochloropsis oceanica algal cells become internalized within the hyphae of the fungus Mortierella elongata . This apparent symbiosis begins with close physical contact and nutrient exchange , including carbon and nitrogen transfer between fungal and algal cells as demonstrated by isotope tracer experiments . This mutualism appears to be stable , as both partners remain physiologically active over months of co-cultivation , leading to the eventual internalization of photosynthetic algal cells , which persist to function , grow and divide within fungal hyphae . Nannochloropsis and Mortierella are biotechnologically important species for lipids and biofuel production , with available genomes and molecular tool kits . Based on the current observations , they provide unique opportunities for studying fungal-algal mutualisms including mechanisms leading to endosymbiosis .
Mutualistic symbioses are defined as those in which partners interact physically and metabolically in mutually beneficial ways . Mutualisms underlie many evolutionary and ecological innovations including the acquisition of plastids and mitochondria , and evolution of symbiotic mutualisms such as mycorrhizas , lichens and corals ( Little et al . , 2004; Service , 2011; Tisserant et al . , 2013; Spribille et al . , 2016 ) . An understanding of the underlying principles that govern microbial mutualisms informs microbial ecology and efforts to engineer synthetic microbiomes for biotechnological applications ( Egede et al . , 2016 ) . Terrestrialization of Earth has been associated with lineages of early diverging fungi belonging to the Mucoromycota . However , recent analyses indicate that fungal colonization of land was associated with multiple origins of green algae prior to the origin of embryophytes ( Lutzoni et al . , 2018 ) . Research indicating that plants were genetically pre-adapted for symbiosis with fungi , has renewed interest in fungal-algal associations ( Delaux et al . , 2015; Spatafora et al . , 2016 ) . The most well-known mutualisms that exist between algae and fungi are lichens , which were estimated to radiate 480 million years ago ( Lutzoni et al . , 2018 ) . Lichen symbiosis is adaptive in that it allows mycobiont and photobiont symbionts to survive in habitats and environments that would otherwise be uninhabitable by either species growing alone , such as on a rock outcrop or in a desert crust . Lichenized fungi have been shown to have multiple independent origins in Ascomycota and Basidiomycota , and are themselves meta-organisms that include communities of chlorophyte algae , cyanobacteria , in addition to basidiomyceteous yeasts ( Spribille et al . , 2016 ) . Nutrient exchange often underlies mutualisms between photobionts and mycobionts . For example , reciprocal transfer of carbon and nitrogen was shown for synthetic consortia composed of Chlamydomonas reinhardtii and a diverse panel of ascomycete fungi , demonstrating a latent capacity of ascomycetous yeasts and filamentous fungi to interact with algae ( Hom and Murray , 2014 ) . In a separate study , the filamentous ascomycetous fungus Alternaria infectoria was demonstrated to provision nitrogen to C . reinhardtii in a long-lived bipartite system , whereby the nitrogen-starved alga responded favorably to the growing fungus ( Simon et al . , 2017 ) . A non-lichen algal-fungal mutualism was described involving the chytrid fungus Rhizidium phycophilum and the green alga Bracteacoccus providing evidence that early diverging fungi have evolved mutualisms with algae based on solute exchange ( Picard et al . , 2013 ) . However , in all known examples of fungal-algal symbioses algal cells remain external to fungal hyphae and are not known to enter living fungal cells . While studying a synthetic co-culture composed of two biotechnologically important oil-producing organisms , the soil fungus Mortierella elongata and the marine alga Nannochloropsis oceanica , we observed an interaction between fungal and algal cells that led to changes in metabolism of both partners ( Du et al . , 2018a ) . This biotrophic interaction showed high specificity and resulted in close physical contact of partners , with the eventual incorporation of functional algal cells within fungal mycelium . Here , we describe this apparent symbiosis in detail . We further demonstrate through isotope tracer experiments that bidirectional nutrient exchange underlies the described algal-fungal interactions .
Nannochloropsis oceanica cells flocculated in dense clusters around M . elongata mycelium when they were incubated together ( Figure 1A and B ) . After 6 days co-cultivation , scanning electron microscopy ( SEM ) revealed a wall-to-wall fungal-algal interface between the organisms grown in co-culture , ( Figure 1C ) with the morphology of N . oceanica cells differing from those of cells grown in the absence of fungus . Specifically , SEM images showed that N . oceanica cells incubated alone in f/2 medium have a smooth outer layer ( Figure 1D and Figure 1—figure supplement 1A ) , which was fragmented or lacking after co-culture with M . elongata AG77 and fibrous extensions underneath the smooth outer layer were exposed ( Figure 1E and Figure 1—figure supplement 1B ) . While it is possible that the fibrous extensions have been elicited by the contact with the fungus , remnant pieces of the outer coat covering the underlying extensions are evident in our observations ( Figure 1E and F and Figure 1—figure supplement 1B ) . Therefore , it seems likely that these extensions are present underneath the outer smooth layer . In addition , the fibrous extensions appeared to contribute to anchoring the algae to hyphae and irregular tube-like extensions were formed between the two interacting cell types ( Figure 1F ) . Further SEM revealed that the fibrous extensions only were exposed in N . oceanica cells that were in physical contact with live fungal hyphae . N . oceanica cells maintained a smooth outer wall covering ( Figure 1—figure supplement 1 ) when they were co-cultured without physical contact with live fungi , and when they were co-cultured with M . elongata mycelium that had been killed in a 65°C water bath . We demonstrated that a combination of enzymes , including 4% fungal hemicellulase and 2% driselase , could partially digest the outer smooth cell wall layer of N . oceanica and expose the fibrous extensions to mimic the morphological change observed during the physical interaction between live M . elongata and N . oceanica cells ( Figure 1—figure supplement 2 ) . To test whether nutrient , that is carbon or nitrogen exchange underlies the interaction between M . elongata and N . oceanica , we conducted a series of tracer experiments using reciprocally 14C- and 15N-labeled algal and fungal partners . For carbon exchange assays , algal cells were labeled with [14C]-sodium bicarbonate and were co-cultivated with actively growing non-labeled fungal hyphae for 1 week in flasks . Conversely , fungal hyphae were grown in either [14C]-glucose- or [14C]-acetate-containing medium for labelling . Labeled fungi were then co-incubated with non-labeled algal cells in flasks that allowed the two organisms to interact physically . Co-cultured algal and fungal cells were separated from each other by cellulase digestion and mesh filtration ( Figure 2—figure supplement 1A–E ) . Algal and fungal cells were collected and analyzed for 14C exchange , separately . Isotope analyses indicated that a significant amount of 14C-carbon was transferred from the alga to the fungus , and nearly 70% of the total transferred 14C-carbon was incorporated into the fungal lipid pool , with the remaining incorporated into free amino acids ( FAAs ) , proteins , soluble compounds , and carbohydrates ( Figure 2A , left ) . Similarly , 14C-carbon transfer was observed from the labeled fungus to its algal recipient ( Figure 2A , right ) . Fractions of algal cells attached to the fungal hyphae acquired more 14C than unattached cells sampled in the supernatant ( Figure 2A and Figure 2—figure supplement 1F and G ) . To assess whether a physical interaction is required for carbon exchange between the photosynthetic alga and the putative fungal heterotroph , we used membrane inserts to physically separate reciprocally 14C-labeled algal and fungal partners ( Figure 2—figure supplement 2A–C ) . We observed that the physical contact between the algae and fungus is essential for 14C-carbon transfer to the fungus ( Figure 2B and C ) but is not necessary for 14C-carbon transfer to the algal cells ( Figure 2B and D and Figure 2—figure supplement 2D ) . Considering that Mortierella is commonly regarded as a saprotroph that acquires carbon from dead organic matter ( Phillips et al . , 2014 ) , we tested whether alga-derived carbon obtained by M . elongata was due to the consumption of algal detritus . First , we repeated the 14C-labeling experiment described above using a 65°C water bath to kill 14C-labeled cells prior to algal-fungal reciprocal pairings . We found that M . elongata incorporates only a small amount ( 1 . 3% ) of 14C-carbon from dead algal cells , compared to 14C-carbon acquired from living algal cells ( 12 . 7% ) ( Figure 2C and Figure 2—figure supplement 2E ) . In contrast , the algal cells attached to fungal hyphae ( Att ) and those free in the medium ( Free ) acquired more 14C-carbon ( Att , 2 . 4%; Free , 15 . 8% ) from dead fungal cells ( Figure 2D ) . The total abundance of 14C-carbon was higher in the free algal cells , because most of the N . oceanica cells in the medium were free and contained a similar amount of 14C-carbon per mg compared to attached cells ( Figure 2—figure supplement 2F ) . Second , we used confocal microscopy and Sytox Green staining to assess whether fungal and algal cells remained alive during co-culture . Over 95% of algal cells were alive during the period of reciprocal co-cultivation with 14C-carbon-labeled cells , and no dead fungal cells were observed ( Figure 2—figure supplement 3A–I ) . Moreover , the micrographs show that the heat treatment was effective in killing algal and fungal cells ( Figure 2—figure supplement 3C–E ) . Together these data indicate that carbon-transfer from the alga to the fungus is dependent upon physical interaction between living partners . In contrast , the algal cells are able to utilize carbon from the fungus grown in the same culture regardless of whether the hyphae are alive , dead or physically connected . Nitrogen is a major macronutrient that limits net primary productivity in terrestrial and aquatic ecosystems , especially for microalgae such as N . oceanica ( Howarth et al . , 1997; Vieler et al . , 2012; Zienkiewicz et al . , 2016 ) . To determine whether nitrogen exchange occurs between M . elongata and N . oceanica , we grew algal cells with [15N]-potassium nitrate and the fungus with [15N]-ammonium chloride as the sole nitrogen source . The labeled cells were co-cultivated with unlabeled partners for 1 week , and then were separated and analyzed for 15N . We detected 15N-nitrogen transfer between algal and fungal partners , irrespective of whether they were in physical contact or not ( Figure 2E and Figure 2—figure supplement 4 ) . Over twice as much 15N ( ~1 . 6 μmol/mg biomass/d ) was transferred from the 15N-fungus to the algal recipient , than from the 15N-algal cells to the fungus ( ~0 . 7 μmol/mg biomass/day; Figure 2E ) , demonstrating a net nitrogen benefit for the alga when co-cultivated with the fungus . The N transfer under conditions of no-contact between the algae and fungi is relatively high compared to the experiment allowing physical-contact , possibly due to the differences in the culturing system . The physical-contact culture was grown in 125-mL flask containing 25 ml medium , while the no-contact culture was incubated in the 6-well culture plates with 5 ml medium in each well , which is a denser culture with the two species only separated by a thin membrane . To test whether the algae and fungi benefit from the interaction and the exchange of nutrients , we observed growth in macro- and micronutrient-deficient media . During nitrogen or carbon deprivation in f/2 medium , N . oceanica had significantly increased viability when co-cultivated with M . elongata ( Figure 3A–C ) . No impact of micronutrients was detected . Element analysis of the culture supernatant showed an increase in total organic carbon and dissolved nitrogen when the living M . elongata hyphae were incubated alone in f/2 medium ( Figure 3D and E ) . This is indicative of extracellular release of nutrients by the hyphae , and may explain why physical contact is not required for the 14C-carbon transfer from the fungus to the alga . In addition , following 10-day-prolonged incubation in regular f/2 medium N . oceanica cells showed significant higher levels of chlorophyll with the presence of M . elongata compared to algal cells grown alone , suggesting that the co-cultured algae likely had a higher photosynthetic capacity ( Figure 3—figure supplement 1A ) . On the other hand , since the viability of M . elongata was not obviously affected following nutrient deprivation ( Figure 3—figure supplement 1B–F ) , the biomass and growth of Mortierella were estimated using a fatty acid biomarker that can be readily quantified by gas chromatography ( GC ) , and light microscopy , respectively . It was not practical to directly determine fungal biomass , because of the difficulty of completely separating algal and fungal cells without lysing cells or losing significant biomass . To address this issue , we used fatty acid profiling of N . oceanica and M . elongata to identify a biomarker , linolenic acid ( C18:3 ) , which is a fatty acid that is predominantly present in the fungus ( Du et al . , 2018a ) . Thus , we used linolenic acid as a proxy to quantify the fungal biomass taking into account that the linolenic acid composition in the fungal biomass was consistent following the incubation in N-deprived f/2 medium ( Figure 3—figure supplement 2A ) . Due to the tight interaction between the algae and fungi , it is impractical to accurately determine the correlation of the biomarker with fungal biomass under co-culturing conditions . Instead , we used the correlation of C18:3 with biomass of the fungus grown in N-depleted f/2 medium as a proxy for the fungal biomass in the co-cultures . We made the assumption that relative change of C18:3 in co-cultured and free cells were insignificant allowing for an accurate estimate of fungal biomass in both conditions . Linolenic acid was quantified by GC of its fatty acid methyl ester derivative , from which fungal biomass was calculated . The algal biomass was calculated by subtraction of fungal biomass from the total biomass of alga-fungus aggregates . Significant increases in biomass were observed for the co-cultured alga and fungus , but not when the alga or fungus were grown by themselves ( Figure 3—figure supplement 2B ) . Therefore , both partners benefitted in this interaction . M . elongata was able to grow in nutrient-deprived conditions ( PBS buffer ) in the presence of the algal photobiont , but not when it was incubated by itself in PBS buffer without carbon ( Video 1 ) . Thus , both N . oceanica and M . elongata appear to benefit from their interaction and nutrient exchanges . Numerous lineages of fungi have evolved to interact with plants and algae . The question arises whether the interaction we observed is unique to Mortierella or , alternatively , if it is conserved across diverse lineages of fungi . We addressed this through a series of interaction experiments pairing N . oceanica with a panel of 20 fungi ( Figure 3—figure supplement 3A ) . These phylogenetically diverse fungal isolates represented three phyla , 9 orders and 13 families of fungi across trophic strategies from plant-associated fungal mutualists to pathogens and included the yeast Saccharomyces cerevisiae , as well as filamentous ascomycetes , basidiomycetes , and mucoromycetes ( Bonito et al . , 2016 ) . Mortierella elongata showed the most obvious phenotype of flocculating alga , which consisted of algal cells clustered around the fungal mycelium ( Figure 3—figure supplement 3B ) . Aside from a few Mortierella species tested , interactions between the other fungi and the alga were neutral or negative . It is worth noting that N . oceanica cells maintained an intact and smooth outer layer when co-cultured with the negatively interacting fungi such as Clavulina sp . PMI390 and Morchella americana GB760 ( Figure 3—figure supplement 4 ) . Microbial consortia may persist in a stable state , improving each other’s resilience to fluctuating environments and stresses ( Brenner et al . , 2008 ) . To assess whether the observed interaction between N . oceanica and M . elongata was stable or transient , we carried out a series of long-term incubations ( from 1 to 3 months ) in which the partners were grown together and nutrients refreshed biweekly . After ~1 month of co-culture , confocal microscopy was used to visualize cells inside the thick aggregates that formed between the alga and the fungus . To delineate cell walls , we used a wheat germ agglutinin conjugate cell wall probe , which binds to N-acetylglucosamine , a component in both the Mortierella and Nannochloropsis cell walls ( Javot et al . , 2007; Scholz et al . , 2014 ) . Microscopic observations indicated the presence of algal cells within fungal hyphae ( Figure 4—figure supplement 1A–C and Video 2 ) . Subsequent light and transmission electron microscopy ( TEM ) were used to further observe this phenomenon , whereby algal cells had been incorporated within hyphae . Differential interference contrast ( DIC ) microscopy showed the morphology of the ‘green hyphae’ after long-term co-culture , corroborating the presence of intact and presumably functional algal cells attached to the hyphal tip ( Figure 4A ) and present inside the fungal hyphal cells ( Figure 4B–E and Video 3 ) . After long-term co-culture , algae-fungi aggregates became thick and difficult to observe well with light microscopy ( Figure 4—figure supplement 1D and E ) . The viability of M . elongata was demonstrated by transferring M . elongata-N . oceanica aggregates to fresh PDB/2 plates ( Figure 4—figure supplement 1F ) . Additional imaging with TEM was performed to characterize the M . elongata-N . oceanica aggregates . Algal cells were seen outside of fungal cells surrounded by the fungal mycelium ( Figure 4—figure supplement 1G–I ) ; however , some algal cells are clearly present within the hyphae ( Figure 4F and G and Figure 4—figure supplement 2 ) . Fungal cytoplasmic contents were visible suggesting fungal cells containing algae were alive and functional ( Figure 4 ) . While there is no indication that algae are transmitted vertically through fungal reproductive structures , the algal cells remained viable ( growing and dividing ) during 2 months of co-culture ( Video 4 ) . We were not able to capture the exact transitional stage of entry of N . oceanica into hyphae of M . elongata by TEM; however , through DIC and time-lapse microscopy , we repeatedly observed that internalization of algae is preceded by dense aggregation of algal cells around the hyphal tip ( Figure 4—figure supplement 3 ) . Dense clusters of algal cells at the tip of a hypha were consistently observed when algal cells were found within fungal hyphae growing in a semisolid medium ( Figure 4—figure supplement 4 ) . Furthermore , hyphae proximal from these tips were often green , and the number of algae within these cells increased over time ( Figure 4A–E ) . In fact , trapped algal cells were able to grow and divide within their host ( Video 4 ) . To further examine the viability of green hyphae , confocal microscopy with SYTOX Green was carried out in the 1 ~ 2 months alga-fungus aggregates . Exclusion of the dye in this case is primarily an indicator of living fungal hyphae , while persistent green chlorophyll and dividing cells are hallmarks of living algae . The results are consistent with the notion that both fungal host and internalized algae within the hyphae are alive ( Figure 4—figure supplement 5 ) . DIC microscopy also confirmed that the algal cells inside green hyphae are surrounded by fungal organelles , especially what appear to be lipid droplets ( Figure 4—figure supplement 6 and Video 5 ) .
Through stable- and radio-isotope-tracer experiments , metabolic analysis and microscopy , we report that the globally distributed early-diverging terrestrial fungus M . elongata interacts intimately with the marine alga N . oceanica in a mutualism that leads to the incorporation of intact living algal cells within fungal hyphae . This symbiosis appears to be based upon an exchange of carbon and nitrogen between the cells . M . elongata is the first taxon in the Kingdom Fungi that has been shown to internalize actively photosynthesizing eukaryotic cells .
The marine alga Nannochloropsis oceanica CCMP1779 was obtained from the Provasoli-Guillard National Center for Culture of Marine Phytoplankton and incubated as previously described ( Vieler et al . , 2012 ) . In brief , N . oceanica cells were grown in f/2 medium containing 2 . 5 mM NaNO3 , 0 . 036 mM NaH2PO4 , 0 . 106 mM Na2SiO3 , 0 . 012 mM FeCl3 , 0 . 012 mM Na2EDTA , 0 . 039 μM CuSO4 , 0 . 026 μM Na2MoO4 , 0 . 077 μM ZnSO4 , 0 . 042 μM CoCl2 , 0 . 91 μM MnCl2 , 0 . 3 μM thiamine HCl/vitamin B1 , 2 . 05 nM biotin , 0 . 37 nM cyanocobalamin/vitamin B12 , and 20 mM sodium bicarbonate and 15 mM Tris buffer ( pH 7 . 6 ) to prevent carbon limitation . The cultures were incubated in flasks under continuous light ( ~80 μmol/m2/s ) at 22°C with agitation ( 100 rpm ) . Log-phase algal cultures ( 1 ~ 3×107 cells/mL ) were used for co-cultivation with fungi . Cell size and density of algal cultures were determined using a Z2 Coulter Counter ( Beckman ) . Mortierella elongata AG77 and NVP64 isolates were made from soil samples collected in North Carolina ( AG77 ) and Michigan ( NVP64 ) , USA . M . elongata AG77 and NVP64 are known to contain an endosymbiotic bacterium , Mycoavidus cysteinexigens , and were cleared of this endosymbiont through a series of antibiotic treatments as previously described ( Partida-Martinez and Hertweck , 2007; Uehling et al . , 2017 ) . The resultant Mycoavidus-free strains were used for the co-cultivation with N . oceanica . Other fungal strains used in this study were obtained from the fungal culture suppliers and isolated from sporocarps , soils , and from healthy surface-sterilized Populus roots obtained from the Plant-Microbial Interfaces project ( Bonito et al . , 2016 ) . Fungi were incubated in flasks containing PDB medium ( 12 g/L potato dextrose broth and 5 g/L yeast extract , pH5 . 3 ) at room temperature ( RT , ~22°C ) . For co-culturing N . oceanica and fungi , fungal mycelia were briefly blended into small pieces ( 0 . 5 to 2 cm ) using a sterilized blender . After a 24-hr recovery in PDB medium , fungal cells were collected by centrifugation ( 3000 g for 3 min ) , washed twice with f/2 medium and resuspended in ~15 mL f/2 medium . A portion of fungal mass ( 3–4 mL ) was used for the calculation of dry biomass: 1 mL was transferred and filtered through pre-dried and pre-weighed Whatman GF/C filters and dried overnight at 80°C . A similar method was used for the measurement of algal biomass . About a 3:1 ratio of fungal:algal biomass was used for co-cultivation on a shaker ( ~60 rpm ) under continuous light ( ~80 μmol/m2/s ) at RT . After 18-day co-culture , the shaker was turned off to allow free settling of the algal and fungal cells overnight . The supernatant was removed and the same volume of fresh f/2 medium containing 10% PDB was added to the culture . After that , the alga-fungus co-culture was refreshed biweekly with f/2 medium supplemented with 10% PDB . Nutrient deprivation of the co-culture was performed according to a published protocol for N . oceanica ( Vieler et al . , 2012 ) . Mid-log-phase N . oceanica cells ( ~1×107 cells/mL ) grown in f/2 media ( 25 mL ) were harvested by centrifugation and washed twice with nutrient-deficient f/2 media [without carbon ( -C ) , nitrogen ( -N ) or phosphorus ( -P ) ] and resuspended in 25 mL nutrient-deficient f/2 media , respectively . AG77 mycelia grown in PDB medium were washed twice with the nutrient-deficient f/2 and added into respective N . oceanica cultures for co-cultivation . To block air exchange , the flasks of -C cultures were carefully sealed with Parafilm M over aluminum foil wrap . Cell viabilities were analyzed by confocal microscopy after 10-day co-culture of -N and 20 days of -C . Interaction and symbiosis between the alga and the fungus were examined with an inverted microscope with differential interference contrast ( DIC ) and time-lapse modules ( DMi8 , Leica ) . DIC images were taken from the alga-fungus aggregates after short- ( 6 days ) and long-term ( over 1 month ) co-cultivation . To characterize the algal endosymbiosis in the fungus , DIC and time-lapse photography were performed after long-term co-culture of the alga and fungus ( from 1 to 3 months ) . For viewing alga-fungus aggregates grown in flasks , the samples were transferred to 35-mm-microwell dishes ( glass top and bottom , MatTek ) and embedded in a thin layer of semisolid f/2 medium supplemented with 10% PDB and 0 . 25% low-gelling-temperature agarose ( Sigma-Aldrich ) to immobilize the cells . The morphology of green hyphae ( AG77 hyphae containing intracellular N . oceanica cells ) was recorded in DIC micrographs , as well as real-time videos that showed four groups of green hyphae ( Video 3 ) . Videos were assembled side by side in Video 3 using video-editing software VideoStudio X9 ( Corel ) . To investigate the establishment of algal cells living inside fungal hypha , randomly selected alga-fungus aggregates were sub-cultured from 35-day co-cultures in 35-mm-microwell dishes containing semisolid f/2 medium with 10% PDB and 0 . 25% agarose and observed directly in 35-mm-microwell dishes containing semisolid f/2 medium ( Figure 4—figure supplement 3; Figure 4—figure supplement 4 ) and through time-lapse photographs that were combined together with the software VideoStudio to create Video 4 . SEM was performed at the Center for Advanced Microscopy of Michigan State University ( CAM , MSU ) to investigate the physical interaction between N . oceanica and M . elongata . Alga-fungus aggregates from 6-day co-cultures of N . oceanica and fungal strains were used for interaction analysis , including M . elongata AG77 , NVP64 and Clavulina PMI390 and Morchella Americana GB760 , which do not have interaction phenotype when co-cultured with algae . N . oceanica cells grown alone in f/2 medium were used as a control . We also observed N . oceanica cells co-cultured with 65°C-killed AG77 mycelium , and algal cells from the supernatant of living M . elongata-N . oceanica co-cultures that were unattached from fungal-algal aggregates . To mimic the exposed fibrous extensions of N . oceanica cells following physical interaction with M . elongata , different enzymes were tested to digest the out layer of algal cell wall . N . oceanica cells were washed with PBS buffer and incubated with different combination of enzymes in PBS buffer at RT for 3 hr: 4% hemicellulase ( mixture of glycolytic enzymes such as xylanase and mananase , Sigma-Aldrich ) ; 2% driselase ( mixture of carbohydrolases including laminarinase , xylanase , and cellulase , Sigma-Aldrich ) ; 4% hemicellulase and 2% driselase; 1% chitinase ( Sigma-Aldrich ) ; 1% lysing enzymes ( mixture of glucanase , protease , and chitinase , Sigma-Aldrich ) . The samples were fixed in 4% ( v/v ) glutaraldehyde solution and dried in a critical point dryer ( Model 010 , Balzers Union ) . After drying , the samples were mounted on aluminum stubs using high vacuum carbon tabs ( SPI Supplies ) and coated with osmium using a NEOC-AT osmium coater ( Meiwafosis ) . Processed tissues were examined using a JSM-7500F scanning electron microscope ( Japan Electron Optics Laboratories ) . Light microscopy and SEM showed a close physical interaction between N . oceanica and M . elongata that led us to examine whether there is metabolite exchange between N . oceanica and M . elongata by isotope labeling and chasing experiments with carbon and nitrogen ( 14C and 15N ) , two of the most important nutrients for N . oceanica and M . elongata . 14C assays were performed according to published protocols with modifications ( Li et al . , 2012 ) . 20 μL of [14C]-sodium bicarbonate ( 1 mCi/mL , 56 mCi/mmol , American Radiolabeled Chemicals ) was added to 20 mL of early log-phase culture of N . oceanica ( ~2×106 cells/mL ) and incubated for 5 days when the 14C incorporation reached ~40% . The 14C-labeled N . oceanica cells were harvested by centrifugation ( 4000 g for 10 min ) and washed three times with f/2 medium . The supernatant of the last wash was analyzed in Bio-Safe II counting cocktail ( Research Products International ) using a scintillation counter ( PerkinElmer 1450 Microbeta Trilux LSC ) , to confirm that 14C-labeling medium was washed off . The pellet of 14C-labeled N . oceanica was resuspended in 20 mL f/2 medium . Subsequently , non-labeled M . elongata AG77 mycelia ( ~3 x algal biomass , intact cells without blending ) grown in PDB medium were washed twice with f/2 medium and added to the 20 mL 14C-labeled algal culture for 7-day co-cultivation . Alga-fungus aggregates were then harvested by PW200-48 mesh ( Accu-Mesh , first filtration ) and NITEX 03-25/14 mesh ( mesh opening 25 μm , SEFAR , second filtration ) . Algal cells in the flow through were collected by centrifugation ( 4000 g for 10 min ) and kept as the first part of 14C-labeled alga control . Alga-fungus aggregates were intensively washed in 50-mL conical centrifuge tubes containing 40 mL of f/2 medium using a bench vortex mixer ( ~1500 rpm , 15 min ) . Fungal mycelia were collected with NITEX 03-25/14 mesh; algal cells in the flow through were harvested by centrifugation and stored as the second fraction of 14C-labeled alga control . Mesh-harvested fungal mycelia ( with obviously reduced the number of algal cells attached ) were placed in microcentrifuge tubes containing 300 μL of PBS buffer ( pH 5 . 0 ) supplemented with 4% hemicellulase and 2% driselase for overnight incubation at 37°C to digest the algal cell walls as previously described ( Chen et al . , 2008 ) . After cell-wall digestion , 700 μL of f/2 medium were added and the algal cells were separated from hyphae by vortexing for 15 min . The hyphae were collected by NITEX 03-25/14 mesh , and the flow-through containing algal cells was kept as the last fraction of alga control . The fungal hyphae were washed three times with f/2 medium and then used for biomass and radioactivity measurements . The three fractions of 14C-labeled alga controls were combined together for further analyses . Half of the algal and fungal samples were dried and weighed for biomass and the rest was used for 14C measurements . The 14C radioactivity of each sample was normalized to the respective dry biomass . To examine cross contamination after alga-fungus isolation , non-radioactive samples were processed the same way and analyzed by light microscopy ( Figure 2—figure supplement 1A–C ) and PCR using primers specific for the N . oceanica gene encoding Aureochrome 4 ( AUREO4 ) , a blue light-responsive transcription factor that is unique in photosynthetic stramenopiles such as N . oceanica ( Figure 2—figure supplement 1D ) : Aureo4pro F+ ( 5’-AGAGGAGCCATGGTAGGAC-3’ ) and Aureo4 DNAD R- ( 5’-TCGTTCCACGCGCTGGG-3’ ) , and primers specific for M . elongata genes encoding translation elongation factor EF1α and RNA polymerase RPB1 ( Figure 2—figure supplement 1E ) : EF1αF ( 5’-CTTGCCACCCTTGCCATCG-3’ ) and EF1αR ( 5’-AACGTCGTCGTTATCGGACAC-3’ ) , RPB1F ( 5’-TCACGWCCTCCCATGGCGT-3’ ) and RPB1R ( 5’-AAGGAGGGTCGTCTTCGTGG-3’ ) . Isolated algal and fungal cells were frozen in liquid nitrogen and ground into fine powders with steel beads and TissueLyser II ( QIAGEN ) , followed by lipid extraction in 1 . 2 mL chloroform:methanol ( 2:1 , v/v ) by vortexing for 20 min . After addition of double-distilled water ( ddH2O , 100 μL ) , the samples were briefly vortexed and then centrifuged at 15 , 000 g for 10 min . The organic phase was collected for total lipids . One mL of 80% methanol ( v/v ) was added to the water phase and cell lysis to extract free amino acids ( FAAs ) . After centrifugation at 20 , 000 g for 5 min , the supernatant was kept as total FAAs and the pellet was air-dried; 200 μL of SDS buffer ( 200 mM Tris-HCl , 250 mM NaCl , 25 mM EDTA , 1% SDS , pH7 . 5 ) was added to the pellet with incubation at 42°C for 15 min . After centrifugation at 10 , 000 g for 10 min , while the pellet was kept for carbohydrate analyses , the supernatant ( ~200 μL ) was collected for further protein precipitation ( −20°C , 1 hr ) with the addition of 800 μL cold acetone . After the 1 hr precipitation , total proteins ( pellet ) and soluble compounds ( supernatant ) were separated by centrifugation at 20 , 000 g for 15 min . The pellet of total proteins was resuspended in 200 μL of SDS buffer for scintillation counting . The pellet of carbohydrates was air-dried , resuspended in 200 μL ethanol , transferred to a glass tube with Teflon-liner screw cap , and then dissolved in 2 to 4 mL of 60% sulfuric acid ( v/v ) according to described protocols ( Velichkov , 1992; Scholz et al . , 2014 ) . As needed , vortexing and incubation at 50°C were performed . Total lipids and soluble compounds were counted in 3 mL of xylene-based 4a20 counting cocktail ( Research Products International ) , whereas total FAAs , proteins and carbohydrates were counted in 3 mL of Bio-Safe II counting cocktail . 14C radioactivity of the samples ( dpm , radioactive disintegrations per minute ) was normalized to their dry weight ( dpm/mg ) . To examine carbon transfer from the fungus to the alga , 200 μL of 0 . 1 mCi/mL [14C]-D-glucose ( 268 mCi/mmol , Moravek Biochemicals ) or 100 μL of 1 mCi/mL [14C]-sodium acetate ( 55 mCi/mmol , American Radiolabeled Chemicals ) were added to 20 mL of M . elongata AG77 grown in modified Melin-Norkrans medium [MMN , 2 . 5 g/L D-glucose , 0 . 25 g/L ( NH4 ) 2HPO4 , 0 . 5 g/L KH2PO4 , 0 . 15 g/L MgSO4 , 0 . 05 g/L CaCl2] . After 5-d 14C-labeling , fungal mycelia were harvested and washed three times with f/2 medium . The supernatant of the last wash was confirmed to be free of 14C by scintillation counting . 14C-labeled hyphae were added to 20 mL of N . oceanica culture for 7-day co-culture . Alga-fungus aggregates were harvested using PW200-48 and NITEX 03-25/14 meshes . Algal cells in the flow-through were harvested and washed twice with f/2 medium by centrifugation and kept as free N . oceanica ( unbound algal cells ) . The remaining steps of sample preparation and 14C measurement were performed as described above . To test whether physical contact is necessary for the carbon exchange between N . oceanica and M . elongata , 14C-label experiments were carried out using standard six-well cell culture plates ( 5 mL medium of each well ) with inserts that have a bottom composed of hydrophilic polytetrafluoroethylene membrane filters ( pore size of 0 . 4 μm , Millipore ) to grow the alga and fungus together , which allows metabolite exchange but no physical contact . 14C-labeling was performed in the same way as described above . For alga-fungus co-culture , 14C-labeled algal cells ( or fungal hyphae ) were added in either plate wells or cell culture inserts while respective hyphae ( or algal cells ) were grown separately in the inserts or plate wells to examine cross contamination ( Figure 2—figure supplement 2A ) . After 7-day co-culture , algal and fungal cells grown in the insert-plate system were easily separated by moving the insert to an adjacent clean well ( Figure 2—figure supplement 2B and C ) . Samples were then processed following the protocol described above ( without the steps of mesh filtration and cell-wall digestion ) . Considering that Mortierella fungi are saprotrophic ( Phillips et al . , 2014 ) , we performed 14C-label experiments using heat-killed 14C-cells to test whether the alga and fungus utilize 14C from dead cells . Briefly , 14C-labeled algal or fungal cells were washed three times with f/2 medium and incubated in a water bath at 65°C for 15 min , which killed the cells without causing significant cell lyses . Heat-killed 14C-algal cells ( or fungal hyphae ) were co-cultivated with unlabeled hyphae ( or algal cells ) for 7 days in flasks . Subsequently , the algal and fungal cells were separated by cell-wall digestion and mesh filtration , and 14C radioactivity of the samples was measured by scintillation counting as described above . Nitrogen is another major nutrient for N . oceanica ( Vieler et al . , 2012; Zienkiewicz et al . , 2016 ) and Mortierella ( Thornton , 1956 ) . Nitrogen exchange between N . oceanica and M . elongata was tested by 15N-labeling and chasing experiments using isotope ratio mass spectrometry . For 15N labeling of algal and fungal cells , N . oceanica cells were inoculated and grown in 200 mL of 15N-f/2 medium containing ~5% of [15N]-potassium nitrate [15N/ ( 15N+14N ) , mol/mol] , while M . elongata mycelia were inoculated and incubated in 2 L of 15N-MMN medium containing ~5% of [15N]-ammonium chloride for two weeks . The algal culture was maintained in log phase by the addition of fresh 15N-f/2 medium into a larger volume . Eventually , 15N-N . oceanica cells from a 4 L culture and 15N-M . elongata mycelium from a 2-L culture were harvested by centrifugation , with a portion of each sample kept as 15N-labeled controls . The remainder of the samples was added to unlabeled cells in flasks ( with physical contact ) or 6-well-culture plates with inserts ( no physical contact ) for 7-day co-cultivation . Algal and fungal cells were separated after the co-culture as described above . Samples were washed three times with ddH2O . Fungal mycelia were homogenized in a TissueLyser II ( QIAGEN ) using steel beads . The algal and fungal samples were then acidified with 1 . 5 to 3 mL of 1 N HCl , dried in beakers at 37°C and weighed for biomass . Isotopic composition of the samples [Atom% 15N , 15N/ ( 15N+14N ) 100%] and N content ( %N ) were determined using a Eurovector ( EuroEA3000 ) elemental analyzer interfaced with an Elementar Isoprime mass spectrometer following a standard protocol ( Fry , 2007 ) . The N uptake rates ( μmol N/mg biomass/d ) of 15N-N . oceanica cells from the medium ( medium-N , isotope dilution ) and that of AG77 from 15N-N . oceanica-derived N ( 15N ) were calculated based on the Atom% 15N , %N and biomass following a published protocol ( Ostrom et al . , 2016 ) . The N uptake rates of 15N-AG77 from the medium and that of recipient N . oceanica from 15N-AG77-derived N ( 15N ) were calculated in the same way . Viability of N . oceanica and M . elongata cells during their co-culture was determined by confocal microscopy using a confocal laser scanning microscope ( FluoView 1000 , Olympus ) at CAM , MSU . SYTOX Green nucleic acid stain ( Molecular Probes , Thermo Fisher Scientific ) , a green-fluorescent nuclear and chromosome counterstain impermeant to live cells , was used to indicate dead cells following a published protocol ( Tsai et al . , 2014 ) . Briefly , 1 μL of 5 mM SYTOX Green was added to 1 mL of cell culture and incubated for 5 min at RT in the dark . Samples were washed twice with f/2 medium before observation ( SYTOX Green , 488 nm excitation , 510 to 530 nm emission; chlorophyll , 559 nm excitation , 655 to 755 nm emission ) . Viability of N . oceanica cells was calculated using ImageJ software . Cell viability was analyzed during the alga-fungus co-culture in flasks containing f/2 medium ( 1 , 4 and 7 days , Figure 2—figure supplement 3F–I ) to investigate whether the cells were alive or dead during the 7-day co-culture of 14C- and 15N-labeling experiments . Viability of N . oceanica cells co-cultivated with M . elongata AG77 and NVP64 under nutrient deprivation ( -N and -C ) was tested to show whether N . oceanica benefits from the co-culture with Mortierella fungi . Viability of M . elongata AG77 was analyzed during the 30-day incubation in f/2 medium ( 9 , 18 and 30 days ) to check whether the cells were alive or dead ( Figure 3—figure supplement 1 ) when the culture media were collected using 0 . 22 μm Millipore filters after 18-day incubation for nutrient analyses ( total organic C and dissolved N ) . Viability of green hyphae containing algal cells was analyzed in the randomly selected alga-fungus aggregates after 1–2 months co-culture . Localization of N . oceanica cells in alga-fungus aggregates was investigated by cell-wall staining using Wheat Germ Agglutinin Conjugate Alexa Fluor 488 ( WGA , Thermo Fisher Scientific ) following the manufacturer’s instruction ( Figure 4—figure supplement 1A–C ) . In brief , alga-fungus aggregates were collected by centrifugation and washed once with PBS buffer ( pH7 . 2 ) , followed by addition of 5 μg/mL WGA and incubation at 37°C for 10 min . Samples were washed twice with f/2 medium and observed under a FluoView 1000 microscope ( WGA , 488 nm excitation , 510 to 530 nm emission; chlorophyll , 559 nm excitation , 655 to 755 nm emission ) . M . elongata AG77 hyphae contain many lipid droplets visible by light microscopy . To confirm the distribution of lipid droplets in hyphae grown in single and co-culture , confocal microscopy was carried out using BODIPY 493/503 ( Thermo Fisher Scientific ) , a lipophilic probe for lipid droplets ( 488 nm excitation , 510 to 530 nm emission ) . Total organic C ( TOC ) and dissolved N ( TDN ) in the media of Mortierella cultures were measured with a TOC-Vcph carbon analyzer with total nitrogen module ( TNM-1 ) and ASI-V autosampler ( Shimadzu ) at Kellogg Biological Station , MSU . M . elongata strains AG77 and NVP64 were incubated for 18 days in flasks containing 25 mL of f/2 medium . Fungal tissues were removed by filtration with 0 . 22 micron filters ( Millipore ) and the flow-through was subject to TOC and TDN analyses following published protocols ( Heinlein , 2013; Lennon et al . , 2013 ) . Chlorophyll measurement was performed as previously described ( Du et al . , 2018b ) . In brief , N . oceanica cells were incubated in f/2 medium until they reached stationary-phase ( 0 day control ) , and the cells were further incubated for 10 days within the same medium or with the addition of about three-times biomass of f/2-washed and blot-dried AG77 mycelium . Algal cells were collected from 1 ml culture of N . oceanica controls and unbound cells from alga-fungus co-culture by centrifugation . Chlorophyll of the algal cells was extracted by 900 μl of acetone:DMSO ( 3:2 , v/v ) for 20 min with agitation at RT , and then measured with a spectrophotometer ( Uvikon 930 , Kontron ) . Lipid extraction and fatty acid analysis were performed following a published protocol ( Du et al . , 2018a ) . Linolenic acid ( C18:3 ) was used as a biomarker , as it is present in M . elongata AG77 but not in N . oceanica cells and its abundance in total biomass was steady following the incubation in N-deprived f/2 medium . Thus , C18:3 was quantified by gas chromatography of its methyl ester derivative and used for the calculation of fungal biomass in dense alga-fungus aggregates , when it was not feasible to physically separate algal and fungal cells without significant loss of biomass or cellular lysis . Briefly , alga-fungus aggregates were collected with mesh filtration and total lipid was extracted with methanol/chloroform/88% formic acid ( 1:2:0 . 1 by volume ) and washed with 0 . 5 vol of 1 M KCl and 0 . 2 M H3PO4 . After phase separation by centrifugation ( 3000 g for 3 min ) , total lipids were collected for the preparation of fatty acid methyl esters by transesterification and analysis by gas chromatography . The remaining cell lysate were dried at 80°C overnight to provide the nonlipid biomass . Total dry biomass of alga-fungus aggregates was obtained by combining the lipid and non-lipid parts . Fungal biomass within alga-fungus aggregates was quantified using the C18:3 content-based calculation . Algal biomass in aggregates was determined by subtracting fungal biomass from the total biomass . DNA was extracted from fungal isolates by placing a small amount of mycelium into 20 μL of extraction solution ( Sigma-Aldrich ) and heating at 95°C for 10 min , after which 60 μL of bovine serum albumin ( BSA , 3% ) was added to the lysate and PCR was employed to directly amplify the nuclear-encoded ribosomal RNA genes ( rDNA ) : ITS ( internal transcribed spacer ) with the primers ITS1f ( 5’-CTTGGTCATTTAGAGGAAGTAA-3’ ) and ITS4 ( 5’-TCCTCCGCTTATTGATATGC-3’ ) , and 28S rDNA with primers LROR ( 5’-ACCCGCTGAACTTAAGC-3’ ) and LR3 ( 5’-CCGTGTTTCAAGACGGG-3’ ) following a published PCR protocol ( Bonito et al . , 2016 ) . Amplicons were sequenced with an ABI3730XL automated sequencer ( Applied Biosystems ) . The resultant sequences were identified by BLAST in the NCBI nucleotide database ( Altschul et al . , 1990 ) , and by sequence alignment in MUSCLE ( Edgar , 2004 ) . Unalignable regions were excluded in Mesquite ( Maddison and Maddison , 2009 ) . Phylogenetic relationships among isolates were inferred with PAUP* ( Swofford , 2002 ) using the neighbor joining optimization criterion and were visualized with FigTree ( Rambaut , 2007 ) . The alignment of sequences used in this study has been deposited in TreeBase ( #20243 ) . TEM was performed at CAM , MSU using N . oceanica and Mortierella aggregates co-cultured for ~1 month . Randomly collected alga-fungus aggregates were fixed overnight at 4°C in sodium cacodylate buffer ( 50 mM , pH 7 . 2 ) supplemented with 2 . 5% ( v/v ) glutaraldehyde . The fixed samples were washed three times with sodium cacodylate buffer , post-fixed in 1% OsO4 ( v/v ) for 2 hr at RT and then washed three times with sodium cacodylate buffer . After dehydration with a graded series of ethanol and acetone , the samples were infiltrated through a series of acetone/resin Epon/Araldite mixtures and finally embedded in resin Epon/Araldite mixture ( Electron Microscopy Sciences ) . Ultrathin sections ( 70 nm ) were cut with an ultramicrotome ( RMC Boeckeler ) and mounted onto 150 mesh formvar-coated copper grids , followed by staining with uranyl acetate for 30 min at RT . The sections were then washed with ultrapure water and stained 10 min with lead citrate and used for observation . Images were taken with a JEOL100 CXII instrument ( Japan Electron Optics Laboratories ) with SC1000 camera ( Model 832 , Gatan ) and were processed with ImageJ . | Yeast , molds and other fungi are found in most environments across the world . Many of the fungi that live on land today form relationships called symbioses with other microbes . Some of these relationships , like those formed with green algae , are beneficial and involve the exchange carbon , nitrogen and other important nutrients . Algae first evolved in the sea and it has been suggested that symbioses with fungi may have helped some algae to leave the water and to colonize the land more than 500 million years ago . A fungus called Mortierella elongata grows as a network of filaments in soils and produces large quantities of oils that have various industrial uses . While the details of Mortierella’s life in the wild are still not certain , the fungus is thought to survive by gaining nutrients from decaying matter and it is not known to form any symbioses with algae . In 2018 , however , a team of researchers reported that , when M . elongata was grown in the laboratory with a marine alga known as Nannochloropsis oceanica , the two organisms appeared to form a symbiosis . Both the alga and fungus produce oil , and when grown together the two organisms produced more oil than when the fungus or algal cells were grown alone . However , it was not clear whether the fungus and alga actually benefit from the symbiosis , for example by exchanging nutrients and helping each other to resist stress . Du et al . – including many of the researchers involved in the earlier work – have now used biochemical techniques to study this relationship in more detail . The experiments found that there was a net flow of carbon from algal cells to the fungus , and a net flow of nitrogen in the opposite direction . When nutrients were scarce , algae and fungi grown in the same containers grew better than algae and fungi grown separately . Further , Mortierella only obtained carbon from living algae that attached to the fungal filaments and not from dead algae . Unexpectedly , further experiments found that when grown together over a period of several weeks or more some of the algal cells entered and lived within the filaments of the fungus . Previously , no algae had ever been seen to inhabit the living filaments of a fungus . These findings may help researchers to develop improved methods to produce oil from M . elongata and N . oceanica . Furthermore , this partnership provides a convenient new system to study how one organism can live within another and to understand how symbioses between algae and fungi may have first evolved . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"ecology"
] | 2019 | Algal-fungal symbiosis leads to photosynthetic mycelium |
Impaired angiogenesis is a hallmark of metabolically dysfunctional adipose tissue in obesity . However , the underlying mechanisms restricting angiogenesis within this context remain ill-defined . Here , we demonstrate that induced endothelial-specific depletion of the transcription factor Forkhead Box O1 ( FoxO1 ) in male mice led to increased vascular density in adipose tissue . Upon high-fat diet feeding , endothelial cell FoxO1-deficient mice exhibited even greater vascular remodeling in the visceral adipose depot , which was paralleled with a healthier adipose tissue expansion , higher glucose tolerance and lower fasting glycemia concomitant with enhanced lactate levels . Mechanistically , FoxO1 depletion increased endothelial proliferative and glycolytic capacities by upregulating the expression of glycolytic markers , which may account for the improvements at the tissue level ultimately impacting whole-body glucose metabolism . Altogether , these findings reveal the pivotal role of FoxO1 in controlling endothelial metabolic and angiogenic adaptations in response to high-fat diet and a contribution of the endothelium to whole-body energy homeostasis .
Obesity is a growing problem worldwide ( Moller and Kaufman , 2005; Tchernof and Després , 2013 ) and thus an urgent need exists to identify molecular processes and signaling pathways that may serve as novel therapeutic targets to hinder obesity-induced pathologies . Although the underlying causes of obesity-related complications are multifactorial , the dysfunction of adipose tissue plays a central role in the development of peripheral tissue metabolic disturbances , ultimately reflecting systemically in dyslipidemia , insulin resistance and hyperglycemia ( Moller and Kaufman , 2005; Fuster et al . , 2016 ) . Capillary endothelial cells ( EC ) are well-known regulators of tissue adaptation to pathologic challenges through their prominent role in blood vessel formation and remodeling . During expansion of visceral adipose tissue , impaired vascular remodeling promotes hypoxia , inflammation , and fibrosis ( Corvera and Gealekman , 2014; Fuster et al . , 2016 ) . Conversely , forced stimulation of vascular growth in adipose tissue of obese rodents improves adipose tissue function ( Sun et al . , 2012; Robciuc et al . , 2016; Seki et al . , 2018 ) , counteracting obesity-related metabolic disorders ( Sung et al . , 2013; Seki et al . , 2018 ) . These findings indicate that the remodeling capacity of microvascular ECs during obesity is vital not only for the adipose tissue function but also for the development of systemic metabolic disturbances . However , surprisingly little is understood about the signaling pathways that limit the angiogenic response of EC in obesity . Forkhead Box O1 ( FoxO1 ) signaling is essential to the homeostasis of EC and restricts vascular growth ( Wilhelm et al . , 2016 ) . In addition to the control of angiogenesis-related genes ( Potente et al . , 2005; Paik et al . , 2007; Milkiewicz et al . , 2011; Roudier et al . , 2013; Wilhelm et al . , 2016 ) , FoxO1 is a gatekeeper of EC metabolism; its overexpression reduces the metabolic rate of EC and enforces a state of endothelial quiescence ( Wilhelm et al . , 2016 ) . Thus , this transcription factor is one of the major regulators of angiogenic capacity , since the switch from a quiescent to an angiogenic phenotype requires a coordinated increase in EC metabolic activity to meet the higher demand for energy and biomass production associated with proliferation and migration ( De Bock et al . , 2013; Schoors et al . , 2015; Kim et al . , 2017 ) . Compelling observational evidence indicates that endothelial FoxO1 dysregulation coincides with obesity-associated metabolic disturbances . For instance , FoxO1 protein levels were elevated in capillaries from skeletal muscle of mice fed a high-fat diet ( Nwadozi et al . , 2016 ) and the activity of endothelial FoxO1 correlated with adipose insulin resistance of obese subjects ( Karki et al . , 2015 ) . Additionally , in vitro conditions that mimic hyperglycemia and insulin resistance increase FoxO1 protein and activity in EC ( Tanaka et al . , 2009; Nwadozi et al . , 2016 ) . Nevertheless , to our knowledge , the contribution of FoxO1 signaling to vascular remodeling during obesity has not been addressed experimentally . To date , only a few reports have assessed the relevance of endothelial FoxO proteins in diet-induced disorders , but none have examined the influence on adipose tissue . Moreover , those studies employed simultaneous EC-specific depletion of multiple FoxOs ( FoxO1 , FoxO3 , and FoxO4 ) and transgenic lines in which gene targeting was not exclusive to EC ( Tanaka et al . , 2009; Tsuchiya et al . , 2012; Nwadozi et al . , 2016 ) , preventing discrimination of the specific functions of endothelial FoxO1 . Notably , it has been shown that in vitro conditions associated with FoxO1 dysregulation can also compromise EC metabolism ( Zhang et al . , 2000; Du et al . , 2003; Jais et al . , 2016 ) . Although this suggests that the interplay between endothelial FoxO1 levels and EC metabolic activity may be critically implicated in limiting vascular remodeling in obesity , this concept demands validation . The converging roles of FoxO1 in the angiogenic phenotype and the metabolism of quiescent EC led us to hypothesize that FoxO1 is a critical nodal point in determining the response of capillary EC to obesity . Consequently , we postulated that targeted endothelial-specific depletion of FoxO1 would provoke capillary growth , preventing obesity-driven adipose tissue dysfunction , and provide a valuable tool to unmask the role of the microvascular endothelium metabolism in the pathophysiology of obesity .
To assess the involvement of EC-FoxO1 in the control of vascular growth in the adipose tissue of adult mice , we utilized a mouse model of EC-selective depletion of FoxO1 expression ( referred to ‘EC-FoxO1 KD’ mice hereafter ) through inactivation of the Foxo1 gene specifically in EC . Foxo1 floxed ( Foxo1f/f ) mice were crossbred with Pdgfb-iCreERT2 mice that express tamoxifen-activated Cre recombinase in EC and Cre-mediated recombination of Foxo1 was induced in adult mice . Littermate mice homozygous for the floxed Foxo1 allele but not expressing Cre recombinase were used as controls . After tamoxifen injection , Foxo1 recombination was observed within adipose and skeletal muscle but not within the liver , as endothelial Pdgfb expression is undetectable in this organ ( Hellström et al . , 1999 ) . The endothelial cell specificity of the recombinase activity was confirmed in microvascular EC isolated from adipose tissue ( Figure 1A ) . Consequently , Foxo1 transcript level , as measured by qPCR , was decreased by 50% in microvascular EC of EC-FoxO1 KD mice relative to control littermates 8 weeks after the administration of tamoxifen , confirming effective and stable Foxo1 depletion in these cells ( Figure 1B ) . Of note , Foxo3 mRNA expression was unaltered in microvascular EC ( Figure 1B ) , demonstrating a lack of compensation by this FoxO family member in response to the depletion of Foxo1 . Moreover , consistent with the previously described absence of Pdgfb-Cre activity within macrophages ( Claxton et al . , 2008 ) , no significant changes in Foxo1 mRNA levels were detected in CD16/CD32+ immune cells from white adipose tissue ( Figure 1C ) , indicating that Cre-mediated recombination did not occur in these stromal cells . The depletion of EC-Foxo1 in microvascular beds of EC-FoxO1 KD mice also was validated via assessment of FoxO1 protein levels by Western blotting . In agreement with the lower mRNA levels observed in microvascular ECs from adipose tissue , protein levels of FoxO1 were diminished by 70% in capillary fragments from skeletal muscle of EC-FoxO1 KD mice compared to control littermates 6 weeks after the administration of tamoxifen ( Figure 1D ) . Together , these results not only imply that successful Foxo1 depletion was constrained to the endothelial cell compartment , particularly microvascular beds , but also support the use of EC-FoxO1 KD mice as an appropriate model to assess the relevance of endothelial FoxO1 for vascular remodeling during adipose tissue expansion . EC-FoxO1 KD mice maintained on a normal chow ( NC ) diet for 16 weeks exhibited no gross abnormalities and similar body weight gain compared to control counterparts ( 8 . 32 ± 1 . 3 vs . 7 . 75 ± 1 . 17 g , n = 6/group ) , but significantly increased mRNA levels of the EC marker Pecam1 in eWAT ( Figure 2A ) . When blood vessels were visualized by whole-mount staining with G . simplicifolia lectin , it was evident that the vascular density of visceral adipose tissue from EC-FoxO1 KD mice ( Figure 2B–C ) was significantly higher . EC-FoxO1 depletion did not alter the number of vessel branch points ( Figure 2D ) . On the other hand , vessels in the adipose of EC-FoxO1 KD mice were significantly enlarged , showing increased vessel diameter , compared to control littermates ( Figure 2E ) , which was consistent with the reported influence of EC-Foxo1 depletion in vascular development in retinas ( Wilhelm et al . , 2016 ) . No difference in the expression of Pecam1 was detected in other assessed tissues , such as skeletal muscle and liver ( Figure 2A ) . To determine whether EC-FoxO1 depletion evokes vascular growth during adipose expansion in response to excess caloric consumption , we challenged mice with a prolonged high-fat diet ( HF ) and assessed tissue angiogenesis . Gene expression analysis indicated that EC-FoxO1 depletion resulted in higher Pecam1 mRNA levels in multiple adipose tissue depots: eWAT , subcutaneous and brown adipose tissue ( BAT , Figure 3A ) . In line therewith , transcript levels of other EC markers , von Willebrand factor ( Vwf ) and endothelial nitric oxide synthase ( Nos3 ) were elevated in the eWAT of HF-fed EC-FoxO1 KD mice ( Figure 3B ) . Whole-mount staining of adipose tissue revealed remarkable increases in vascular area and number of vessel branch points in the eWAT of HF-fed EC-FoxO1 KD ( Figure 3C–E ) . Consistently , quantitative histological analysis showed that capillary number per adipocyte ( capillary to adipocyte ratio ) was significantly higher in eWAT of HF-fed EC-FoxO1 KD mice , further validating the greater microvascular content in eWAT of these mice compared to HF-fed control counterparts ( Figure 3F–G ) . Furthermore , EC-FoxO1 depletion led to significant capillary enlargement in eWAT ( Figure 3H–I ) . Of note , the increase of vessel diameter in HF-fed EC-FoxO1 KD mice was greater than observed in NC-fed EC-FoxO1 KD mice ( 1 . 8 vs . 1 . 3-fold increase ) , suggesting that the enlargement of capillaries promoted by EC-FoxO1 depletion is exacerbated by HF feeding . Subsequent gene expression analysis showed that EC-FoxO1 depletion did not change transcript levels of Pecam1 in the liver ( corresponding with the lack of Cre recombination in this organ ) but did upregulate its expression in skeletal muscle , suggesting that under the stimulus of HF diet , EC-FoxO1 depletion also induces microvascular remodeling in this tissue ( Figure 4A ) . Skeletal muscle of HF-fed EC-FoxO1 KD mice displayed a trend towards higher capillary:fiber ratio ( p = 0 . 06 ) compared to control mice ( Figure 4B–C ) . Transmission electron microscopy revealed increased capillary endothelial cross-sectional area and capillary lumen diameters in skeletal muscle of EC-FoxO1 KD mice , demonstrating a modest expansion of the size of individual capillaries ( Figure 4D–F ) . Taken together , these data indicate that EC-FoxO1 depletion results in remarkable vascular growth in response to HF diet , which is particularly pronounced within visceral adipose tissue . The vasculature is critical for maintenance of adipose tissue homeostasis during obesity-driven adipocyte enlargement . Thus , we inferred that the increased vascular density observed with EC-FoxO1 depletion may hinder adipose tissue expansion and dysfunction induced by high-fat diet . Although HF-fed EC-FoxO1 KD mice showed only a trend towards reduced body weight gain ( p = 0 . 06 ) , these mice displayed less fat accumulation , showing lower trunk fat content , smaller retroperitoneal ( rWAT ) and subcutaneous fat pads compared to control mice ( Figure 5A–C and Table 1 ) . The phenotype was not explained by changes in food consumption ( Figure 5—figure supplement 1A ) . HF-fed EC-FoxO1 KD mice also displayed lower fed levels of serum triglycerides and glycerol , and less hepatic lipid accumulation ( Figure 5—figure supplement 1B–D ) , suggesting an improvement in the capacity to handle dietary nutrient excess in these mice . Moreover , histological analysis revealed that increased vascular growth in adipose tissue of HF-fed EC-FoxO1 KD mice was associated with smaller-sized and generally spherical adipocytes , whereas adipocytes from HF-fed control mice were large with irregular polygonal shapes ( Figure 5D–E ) , which was previously related to cellular stress ( Giordano et al . , 2013 ) . Of note , adipocytes from EC-FoxO1 KD mice retained a unilocular structure ( Figure 5D ) rather than the hallmark multilocular morphology of brown fat . Furthermore , no change in the mRNA levels of browning markers Ucp1 and Prdm16 ( Figure 5F ) was detected with EC-FoxO1 depletion . Correspondingly , we did not observe any difference in mitochondrial protein content of eWAT nor in ADP-stimulated respiration through either Complex I ( pyruvate/malate , glutamate ) or Complex II ( succinate ) ( Figure 5—figure supplement 2A–C ) . Isoproterenol-stimulated phosphorylation of the hormone-sensitive lipase ( HSL ) was unaffected ( Figure 5—figure supplement 2D–E ) , indicating that EC-FoxO1 depletion did not impact the adipose tissue sensitivity to lipolytic stimuli . In contrast , and consistent with an improved function , eWAT from HF-fed EC-FoxO1 KD mice displayed enhanced Akt phosphorylation in response to insulin ( Figure 5G–H ) , which was accompanied by higher Adiponectin mRNA levels and concomitant lower Leptin expression ( Figure 5I ) . Collectively , these findings demonstrate that depletion of EC-FoxO1 signaling exerts a protective effect against obesity-induced metabolic remodeling of adipose tissue without promoting a browning phenotype . Notably , the transcripts levels of Vegfa and Apelin were also higher in eWAT from HF-fed EC-FoxO1 KD mice ( Figure 5J ) , providing evidence that the improvements in adipose phenotype include a more pro-angiogenic adipose tissue microenvironment . To better understand the metabolic consequences of EC-FoxO1 depletion , whole-body metabolic functions were monitored for 48 hr in a 2nd cohort of HF-fed mice . Surprisingly , EC-FoxO1 KD mice exhibited reduced VO2 and increased RER during the dark cycle ( Figure 6A , C ) with equivalent CO2 production and activity levels compared to control mice ( Figure 6B , D ) . These data unexpectedly indicated that EC-FoxO1 KD mice increased oxidation of carbohydrate relative to fatty acid as an energy substrate , suggesting that EC-FoxO1 depletion shifted whole-body energy homeostasis towards glucose oxidation . Consistent with these findings , HF-fed EC-FoxO1 KD mice displayed more rapid glucose clearance from the blood during glucose tolerance tests ( Figure 6E–F ) . However , higher glucose tolerance was not associated with altered whole-body insulin sensitivity , based on insulin tolerance tests ( ITT ) ( Figure 6G–H ) or insulin-mediated Akt phosphorylation in the skeletal muscle ( Figure 6—figure supplement 1 ) . Despite the effects on glucose metabolism observed in HF-fed EC-FoxO1 KD mice , no change in whole-body glucose metabolism was detected in NC-fed EC-FoxO1 KD mice compared to control counterparts ( Figure 6—figure supplements 1 and 2 ) . Interestingly , fasting glycemia was significantly lower ( Figure 6I ) whereas serum lactate levels were elevated in HF-fed EC-FoxO1 KD mice compared to their littermates ( 10 . 9 ± 0 . 39 vs . 9 . 4 ± 0 . 55 mmol/L , respectively , p = 0 . 04 , n = 7/group ) . These findings imply that altered whole-body glucose metabolism of EC-FoxO1 KD mice on a HF may be due to higher glucose turnover , leading us to postulate that increased glycolytic rates at the tissue level contribute to the metabolic phenotype of HF- EC-FoxO1 KD mice . To address this question , we first assessed the expression of main glycolytic genes , including the constitutive glucose transporter GLUT1 ( Slc2a1 ) , the rate-limiting enzymes hexokinase 2 ( Hk2 ) and phosphofructokinase ( Pfkm ) and phosphofructokinase-2/fructose-2 , 6-bisphosphatase-3 ( Pfkfb3 ) . As anticipated , the mRNA levels of most glycolytic genes , with the exception of Pfkfb3 , were upregulated in the eWAT from HF-fed EC-FoxO1 KD mice compared to control mice , ( Figure 6J ) . Furthermore , mRNA levels of the lactate transporter , monocarboxylate transporter 5 , Slc16a4 , were also increased in eWAT of HF-fed EC-FoxO1 KD mice ( Figure 6K ) , consistent with an increased glycolytic flux of glucose to lactate in the adipose tissue of these mice . EC rely dominantly on glycolysis to support angiogenesis ( De Bock et al . , 2013 ) and a previous study reported that FoxO1 overexpression represses EC metabolism ( Wilhelm et al . , 2016 ) . Therefore , we hypothesized that the changes in glucose utilization were due , at least in part , to increased metabolic activity of EC resulting from EC-FoxO1 depletion . To explore this possibility , we isolated the EC fraction from white adipose tissue depots of mice fed a HF diet for 7 weeks and first assessed gene expression of main glycolytic pathway genes . Consistent with the findings observed with whole adipose tissue , increased mRNA levels of glycolytic genes Slc2a1 , Pfkm and Pfkfb3 were detected in the EC fraction from adipose tissue of HF-fed EC-FoxO1 KD mice ( Figure 7A–B ) . We also tested whether the elevated gene expression of glycolytic markers in EC from EC-FoxO1 KD mice would correspond with greater glycolytic capacity , as assessed by cellular glucose uptake and changes in glucose consumption and the accumulation of lactate . In agreement with higher transcript levels of Slc2a1 , EC freshly isolated from adipose tissue of HF-fed EC-FoxO1 KD mice displayed increased glucose uptake than EC from floxed controls ( Figure 7C ) . Moreover , rates of glucose consumption and lactate production were higher in EC with FoxO1 depletion compared to control cells ( Figure 7D–E ) . Additionally , we observed elevated Mki67 mRNA in the EC fraction from EC-FoxO1 KD mice , indicating that an enhanced proliferative state coincides with the glycolytic activity of these EC ( Figure 7F ) . To corroborate that dysregulation of FoxO1 signaling is directly involved in disruption of glycolytic processes , we cultured skeletal muscle EC in low ( 5 mmol/L ) and high glucose ( 25 mmol/L ) conditions , as previous in vitro studies have shown that hyperglycemia can both increase FoxO1 activity ( Tanaka et al . , 2009 ) and stall EC metabolism ( Du et al . , 2000; Zhang et al . , 2000; Du et al . , 2003 ) . As expected , high-glucose significantly increased FoxO1 protein levels in cultured EC ( Figure 8A–B ) and provoked changes in established FoxO1 target genes Cdkn1b ( p27 ) and Ccnd1 ( cyclin D1 ) ( Figure 8—figure supplement 1A–B ) that are involved in cell proliferation . Consistent with increased FoxO1 levels and activity , high-glucose conditions also lowered the mRNA levels of glycolytic pathway components Slc2a1 , Hk2 , and Pfkfb3 ( Figure 8C–E ) . Importantly , pharmacological inhibition of FoxO1 significantly reversed the high-glucose-induced reduction of each of these genes ( Figure 8C–E ) , which correlated with elevated protein levels of HK2 and PFKFB3 ( Figure 8F–I ) . Accordingly , treatment with FoxO1 inhibitor AS1842856 increased cellular glucose uptake and consumption in microvascular ECs , which corresponded with higher extracellular lactate levels ( Figure 8J–L ) . Collectively , these findings indicate that lower FoxO1 levels and activity increase glycolytic and proliferative activities of EC . This induces a profound increase in glucose consumption by these cells , which consequently leads to higher glucose utilization at the tissue level , ultimately impacting whole-body glucose homeostasis . FoxO1 and FoxO3 can demonstrate overlapping functions in EC ( Potente et al . , 2005 ) and previous studies reported that double depletion of Foxo1 and Foxo3 can either demonstrate similar effects as FoxO1 deficiency or have significant additive effects ( Zhang et al . , 2012; Haeusler et al . , 2014 ) . This led us to investigate whether double depletion of endothelial Foxo1 and Foxo3 would result in a greater angiogenic response in HF-fed mice . HF-fed EC-FoxO1 , 3 KD mice ( generated using the tamoxifen-inducible , endothelial-specific Cre driver: Pdgfb-CreERT2 ) presented lower levels of fasting glucose ( Figure 9A–C ) and reduced adiposity , as evidenced by lighter subcutaneous and rWAT depots , compared to their littermate controls ( Supplementary file 1 ) . Similar to what was observed in EC-FoxO1 KD mice on a HF diet , increased expression of Pecam1 was detected in eWAT and skeletal muscle of HF-fed EC-FoxO1 , 3 KD mice ( Figure 9D ) , indicating greater EC content in these tissues . Additionally , increased expression of glycolytic markers was observed in eWAT and EC fraction from adipose tissue of HF-fed EC-FoxO1 , 3 KD mice ( Figure 9E–F ) . Consistent with these findings , increased glucose uptake ( Figure 9G ) and lactate production ( Figure 9H ) were also detected in the EC fraction from HF-fed EC-FoxO1 , 3 KD mice . Notably , these measurements demonstrated that combined depletion of EC-Foxo1 and Foxo3 elicits a similar , but not additive effect , when compared to EC-Foxo1 alone , indicating that FoxO1 is the dominant regulator of the EC response to HF diet .
Herein , we provide evidence that endothelial FoxO1 is critical to the development of metabolic disorders in obesity through the converging actions of controlling metabolic activity and angiogenic fate of the endothelium . Our data underscore that the manipulation of endothelial FoxO1 levels profoundly modifies the endothelial phenotype under an obesogenic diet , with lower levels of EC-FoxO1 evoking increased endothelial metabolism and capillary growth , most robustly detected within visceral adipose . These effects were sufficient not only to prevent the detrimental obesity-driven alterations in visceral adipose tissue but also to elicit increased glucose clearance leading to higher glucose tolerance in HF-fed mice ( Figure 10 ) . Broadly , these findings provide support for the emerging concept that intrinsic metabolic properties of EC actively influence whole-body energy balance . A marked increase in vascular density in the adipose tissue was the bona fide phenotypic consequence of EC-FoxO1 depletion , which was strikingly evident under the stress of obesity-related tissue expansion . This demonstrates that endothelial FoxO1 is a prime regulator of adipose tissue microvascular remodeling in adult mice , underlining our hypothesis that FoxO1 levels are directly implicated in limiting the angiogenic response of ECs in obesity . Despite the enlargement of capillaries that was observed in EC-FoxO1 KD mice , these mice displayed a healthier adipose phenotype that lacked the metabolic dysfunctions typically caused by obesity . This finding is in line with previous evidence suggesting that enhanced EC-FoxO1 activity is associated with reduced adipocyte insulin sensitivity in the adipose tissue of obese individuals ( Karki et al . , 2015 ) . Therefore , our findings indicate not only that EC-FoxO1 depletion beneficially increases adipose vascular density but also emphasize the intimate interplay of EC and adipocytes and the crucial role of angiogenesis in the maintenance of adipose tissue functions ( Corvera and Gealekman , 2014; Crewe et al . , 2017 ) . In contrast , EC-FoxO1 depletion induced relatively modest expansion of skeletal muscle vasculature . Our findings suggest that the tissue-restricted pattern of FoxO1-driven vascular growth is highly dependent on the co-presence of angiogenic factors within the local environment , which is impacted by nutritional status . Besides the higher levels of angiogenic mediators detected in adipose tissue of EC-FoxO1 KD mice , another argument favoring this hypothesis is provided by our observation that EC-FoxO1 depletion was associated with increased EC content within skeletal muscle only under HF feeding . It is noteworthy that previous reports showed that HF-feeding increases VEGF-A protein within the skeletal muscle ( Silvennoinen et al . , 2013; Nwadozi et al . , 2016 ) and FoxO1 levels within skeletal muscle capillaries ( Nwadozi et al . , 2016 ) . In this context , our findings also support the concept that the impaired vascular growth reported with sustained HF diet results from the repressive action of EC-FoxO1 ( Milkiewicz et al . , 2011; Roudier et al . , 2013 ) rather than the lack of a pro-angiogenic stimulus . Interestingly , EC from HF-fed EC-FoxO1 KD mice demonstrated markedly enhanced glycolytic activity , based on the increased expression of glycolytic markers and concomitant increase in glucose uptake , glucose consumption and lactate production . Although the regulation of endothelial metabolism by Foxo1 overexpression was reported in cultured EC ( Wilhelm et al . , 2016 ) , our data provide novel evidence of the impact of lower FoxO1 levels on endothelial metabolism and its consequences to whole-body homeostasis . It has become recently clear that endothelial metabolic activation represents an important feature of excessive angiogenesis , and that its repression holds therapeutic promise particularly within the tumor microenvironment ( Schoors et al . , 2014; Cantelmo et al . , 2016 ) . Our study drives this concept from the opposite angle by highlighting the potential for induction of endothelial metabolic activity as an approach to overcome the impairments in adaptive capillary growth that prevail in obese individuals . In fact , our data strongly indicate that the metabolic endothelial adaptation seen in response to FoxO1 depletion results in beneficial expansion of microvascular EC content and prevents obesity-related disorders . The lack of additional influence of combined depletion of EC Foxo1 and 3 with respect to expression of Pecam1 and glycolytic pathway genes and glucose uptake reinforces FoxO1 as the dominant regulator of EC metabolic homeostasis . More importantly , these findings indicate FoxO1 as a central target for the manipulation of capillary EC response to obesity-induced conditions . The finding that increased endothelial glycolysis induced by EC-FoxO1 depletion significantly impacts whole-body energy homeostasis is intriguing . Improvements in whole-body energy homeostasis subsequent to microvascular expansion have been observed previously ( Sun et al . , 2012; Nwadozi et al . , 2016; Robciuc et al . , 2016; An et al . , 2017 ) . Nonetheless , these effects had been ascribed to the improved passive exchange of oxygen , nutrients , and hormones to parenchymal tissues due to the increased capillary EC surface area . Our findings add another dimension to the provocative idea that EC can actively impact metabolism at the tissue level by bringing to light the dynamic contribution of EC metabolic activity to whole-body energy homeostasis during obesity . Although currently available tools do not provide the resolution required for a quantitative in vivo assessment of glucose consumption specifically by microvascular EC , our data strongly suggest that increasing EC metabolic activity leads to increased glucose uptake from the circulation and thus positively influences systemic glucose usage and tolerance . In addition , several pieces of available knowledge provide support for our hypothesis . First , EC are uniquely positioned at the interface between the bloodstream and the tissue parenchymal cells , which provides them with preferential access to circulating metabolic substrates . Second , glucose uptake by these cells is mediated via glucose transporter 1 ( GLUT1 ) , an insulin-independent transporter that is widely recognized to regulate basal glucose disposal . Interestingly , a direct connection between endothelial GLUT1 level and whole-organ glucose metabolism under physiological conditions was reported in a mouse model of EC-Hif1a depletion ( Huang et al . , 2012 ) . Third , EC are among the most abundant cell types in the human body , accounting for 2–7% of the total cell number ( Bianconi et al . , 2013; Sender et al . , 2016 ) , with the majority of these EC residing within capillaries . Therefore , it is plausible that the combined increase in EC metabolic activity with the expansion of capillary EC number can impact the overall systemic glucose homeostasis . The exact contribution of EC metabolism to whole-body substrate utilization , however , merits further investigation , as EC-FoxO1 depletion reprogrammed both metabolic activity and angiogenic responses of EC . Beyond the remarkable effect that we observed on whole-body glucose metabolism , our findings support the notion that the substantial increase in vascular growth resulting from EC-FoxO1 depletion also impacted lipid handling under HF feeding conditions . In fact , HF-fed EC-FoxO1 KD mice displayed lower adiposity and serum levels of triglycerides and glycerol and less lipid accumulation in the liver . Although this could constitute a direct consequence of vascular growth , as fatty acid oxidation is used to support de novo nucleotide synthesis in EC ( Schoors et al . , 2015 ) , it is also likely to involve secondary compensatory effects triggered by sustained imbalances in global glucose homeostasis that result from increased EC metabolic activity . A number of previous reports have shown that lower expression of EC-Foxo1 leads to disrupted vascular remodeling ( Furuyama et al . , 2004; Sengupta et al . , 2012; Dharaneeswaran et al . , 2014 ) , which seemingly contradicts the phenotype we observed . A significant part of this discrepancy may arise from differences in experimental design and approach . Those studies used Cre-deleter models ( Tie2-Cre and Cdh5-Cre ) that broadly affect all vascular beds ( including lymphatic endothelium ) beginning at an early embryonic stage and also exhibit substantial Cre recombinase activity within hematopoietic lineages ( Chen et al . , 2009; Tang et al . , 2010 ) . In contrast , we employed a model of inducible EC-Foxo1 depletion in adult mice , in which Cre activity was restricted to mature microvascular EC . Consistent with our findings , the induced depletion of EC-Foxo1 in newborn mice resulted in increased EC growth and vessel enlargement within mouse retina ( Wilhelm et al . , 2016 ) , although to a greater extent than the phenotype observed in our study . Interestingly , microvascular EC possess distinct tissue-specific molecular signatures ( Nolan et al . , 2013 ) . In addition , it has been shown that EC-depletion of FoxO transcription factors can result in unique biological consequences in different tissue contexts ( Paik et al . , 2007 ) . Thus , it is likely that both the developmental stage and the tissue microenvironment contribute substantially to phenotype observed with EC-Foxo1 depletion . These study-specific features emphasize that the methodological details need to be carefully considered in the interpretation of data from EC-Foxo1 depletion and highlight that the impact of EC-depletion cannot necessarily be extrapolated to different tissue contexts . Altogether , our study reveals that EC-FoxO1 depletion evokes increased glycolytic capacity of endothelial cells and enables microvascular expansion in conditions where an angiogenic stimulus is present , as exemplified by the capillary expansion seen in white adipose depots in HF-fed mice . The repercussions of these combined influences include profound improvements in white adipose tissue capacity to cope with the stimulus of sustained nutrient excess and systemic enhancement of glucose clearance . In conclusion , these effects clearly define FoxO1 as the major regulator of the EC response to HF diet through the repression of beneficial metabolic and angiogenic adaptations in response to the stimulus of nutrient excess . Finally , this study brings to light an unappreciated role of EC as a distinctive metabolic entity rather than a simple exchange interface and highlights the modulation of endothelial metabolic and angiogenic activity as a potential target in the treatment of obesity-related disturbances .
Foxo1f/f mice and Foxo1 , 3f/f mice were derived from outbreeding of Foxo1 , 3 , 4f/f mice ( Paik et al . , 2007 ) with wild-type FVB/n mice , and genotyped to ensure homozygosity of the floxed allele ( s ) . To permit the inducible endothelial-specific manipulation of Foxo1 and Foxo3a , these mice were bred with Pdgfb-iCreERT2 mice ( C57Bl/6 background ) ( Claxton et al . , 2008 ) . Offspring were back-crossed with Foxo1f/f or Foxo1 , 3f/f founders for a minimum of 3 generations prior to experimentation to establish genotypes Cre-;Foxo1f/f and Cre+;Foxo1f/f or Cre-;Foxo1 , 3f/f and Cre+;Foxo1 , 3f/f , respectively . We performed five separate animal studies using only male mice . To generate mice with postnatal endothelial cell-specific deletion of FoxO1 ( EC-FoxO1 KD ) , or double deletion of Foxo1 and Foxo3 ( EC-FoxO1 , 3 KD ) , Cre-mediated recombination was induced in 4–6 weeks old Cre+;Foxo1f/f; mice and Cre+;Foxo1 , 3f/f; mice by five consecutive i . p . injections of 200 µL tamoxifen ( 15 mg/mL in corn oil ) . Recombination of Foxo1 ( and Foxo3 ) alleles was confirmed via PCR analysis of genomic DNA ( Paik et al . , 2007 ) . In all experiments , EC-FoxO1 KD and EC-FoxO1 , 3 KD mice were compared with age-matched tamoxifen-injected Cre-;Foxo1f/f or Cre-;Foxo1 , 3f/f littermates ( referred to as Control ) . No statistical method was used to predetermine the sample size . At 6–8 weeks of age , male mice within each litter were assigned randomly , according to their genotypes , to either normal chow ( NC , 11% kcal from fat ) or high-fat ( HF , 58% kcal from fat , Surwit Diet ) groups . Water and diet were provided ad libitum . Body weights and food intake of mice included in studies 1 and 2 were recorded weekly . Specific animal groups and the tests conducted were as followed: Group 1: Control and EC-FoxO1 KD mice received 16 week NC or HF diets ( n = 6–7/group ) and underwent body composition imaging at week 13 , GTT and ITT ( weeks 14 , 15 respectively ) and tissue collection at week 16 for whole tissue analyses ( histology and RNA ) . Based on the apparent lack of influence of Foxo1 deletion under NC diet in this group ( see Results ) , subsequent testing focused on comparing the genotype differences observed in the HF condition: Group 2: Control and EC-FoxO1 KD mice ( n = 7 and 8 , respectively ) underwent 14 week HF diet , metabolic testing ( CLAMS ) at week 13; ITT and tissue collection at week 14 for mitochondrial respiration , electron microscopy and serum analyses of TGs and glycerol . Group 3 and 4: Control and EC-FoxO1 KD mice ( n = 5/group ) and Group 5: Control and EC-FoxO1 , 3 KD ( n = 6/group ) underwent HF diet for 7 , 5 and 14 weeks , respectively . Groups 3 and 5 were used to analyze 4 hr fasting glucose , and groups 3 , 4 and 5 were used to isolate endothelial cell fractions from adipose depots for RNA and for glucose uptake , glucose consumption and lactate release assays . Animal studies were conducted in accordance with the American Physiological Society’s guiding principles in the Care and Use of Animals , following protocols approved by the York University Committee on Animal Care . Body composition was examined by micro-computed tomography ( Skyscan 1278; Bruker ) using the step and shoot function , averaging four images/frame with a rotation step of 0 . 75 degrees . Mice were scanned at a voltage of 50kV , a current of 200 A with a 0 . 5 aluminum filter while anesthetized with isoflurane . Images were reconstructed using NRecon ( local ) and the entire trunk area fat mass was analyzed using CTAn . Mice were monitored individually for oxygen consumption , carbon dioxide production , respiratory exchange ratio and locomotor activity using Indirect Calorimetry with the Columbus Comprehensive Lab Animal Monitoring System ( CLAMS , Columbus Instruments , USA ) . Mice acclimated to CLAMS cages for 24 hr then data were recorded every 5 min for a 48 hr period . Mice had ad libitum access to food and water . VO2 and CO2 were normalized to body weight . Respiratory exchange ratio ( RER ) was calculated as the volume of CO2 relative to the volume of oxygen ( VCO2/VO2 ) . For ITT , Control and EC-FoxO1 KD mice were fasted for 4 hr and received an i . p . injection of insulin ( 0 . 75 U/kg BW ) . GTTs were conducted in overnight-fasted control and EC-FoxO1 KD mice using an i . p . injection of glucose ( 1 . 75 g/kg BW ) . Blood glucose was measured by glucometer ( Freestyle Lite , Abbott Diabetes Care , ON , Canada ) at post-injection time-points 0 , 20 , 40 and 60 min ( ITT ) or 0 , 30 , 60 , 90 and 120 min ( GTT ) . Insulin stimulation of skeletal muscle was conducted as described previously ( Nwadozi et al . , 2016 ) and the phosphorylation state of Akt was assessed by Western blotting . Epididymal fat pads were cut into ~ 80 mg pieces and pre-incubated with low glucose DMEM containing 1% fatty acid-free BSA for 30 min ( 37°C ) before 30 min incubation with insulin ( 25 mU/mL ) or isoproterenol ( 10 µmol/L ) . Tissue explants were snap frozen in liquid nitrogen and the phosphorylation states of Akt or HSL were assessed by Western blotting . Respirometry studies in freshly extracted epididymal white adipose tissue ( eWAT ) were performed using high-resolution respirometry ( Oroboros Oxygraph-2k , Oroboros Instruments , Crop , Innsbruck , Austria ) . EWAT fat pads were prepared as done previously ( MacPherson et al . , 2016 ) , minced in MIR05 buffer , weighed and immediately placed in separate Oroboros Oxygraph-2k in respiration media ( MIR05 ) containing 20 mmol/L creatine ( Saks et al . , 1995 ) . State three respiration was stimulated with the addition of ADP [5 mmol/L] in the presence of pyruvate [5 mmol/L] and malate [4 mmol/L] followed by glutamate [10 mmol/L] and succinate [20 mmol/L] . Respiration experiments were completed at 37°C before the oxygraph chamber reached 150 mmol/L [O2] . Mitochondrial membrane integrity was tested with the addition of 10 mmol/L cytochrome c oxidase at the end of each protocol . Total protein extraction from isolated cells or tissues was performed as previously described ( Milkiewicz et al . , 2011 ) . Primary antibodies were as follows: FoxO1 , Ser473-pAkt , Akt , Ser563-pHSL , HSL , HK2 , PFKFB3 , α/β-tubulin , β-actin and Mitoprofile Total OXPHOS Cocktail . Secondary antibodies were goat anti-rabbit or anti-mouse IgG-horseradish peroxidase . Membranes were developed using enhanced chemiluminescence and densitometry analysis was performed with ImageJ Analysis Software ( NIH ) . For microvascular quantification , pieces of eWAT were fixed in 4% formaldehyde and stained with BODIPY 493/503 ( 0 . 25 µg/mL ) and rhodamine-Griffonia Simplicifolia lectin ( 1:100 ) to visualize adipocytes and microvessels , respectively . Images were taken with a Zeiss LSM700 confocal microscope ( 10x or 20x objectives ) using identical gain settings for all samples . Microvascular content and branchpoint numbers were quantified from 3 to 4 10x fields of view per animal , and vessel diameters from 20x images , using Image J Analysis Software ( NIH ) . For morphometric analysis of adipocytes , eWAT was fixed in 4% formaldehyde . Paraffin embedding , sectioning and hematoxylin and eosin staining were carried out by The Centre for Phenogenomics ( Toronto , Canada ) . Adipocyte area was analyzed in three randomly selected fields of view ( 4x objective ) using ImageJ Analysis Software ( NIH ) . For quantification of capillaries , de-paraffinized sections were stained with fluorescein isothiocyanate-conjugated Griffonia Simplicifolia Lectin-1 ( 1:100 ) and Rhodamine Wheat Germ Agglutinin ( 1:200 ) . Images were acquired using a 10X objective on a Zeiss inverted microscope equipped with a digital cooled CCD camera , capturing 3–4 independent fields of view per mouse . Image J software was used to quantify the numbers of capillaries and adipocytes in corresponding Griffonia and Wheat Germ Agluttinin-stained images , respectively . Cross-sections of EDL were stained with Griffonia simplificolia-FITC for assessment of capillary to fiber ratio ( Nwadozi et al . , 2016 ) . Pieces of EDL muscles from HF-fed Control and EC-FoxO1 KD mice ( n = 4/group ) were fixed in 2% glutaraldehyde and 4% paraformaldehyde in 0 . 1M phosphate buffer ( pH = 7 . 4 ) and sent for EM processing at the Hospital for Sick Children ( Toronto , Canada ) . Cross-sectional images of capillaries ( identified by lack of smooth muscle cell coverage ) were captured by a blinded EM technician . Luminal diameters and endothelial cross-sectional areas of all detected capillaries within each sample were assessed using Image J software . Serum triglycerides and glycerol levels were measured using commercial kits . Triglycerides were also measured in homogenates of liver and gastrocnemius muscle . Lactate levels were assessed in serum from HF-fed Control and EC-FoxO1 KD mice and in phenol-free cell culture medium of adipose-derived endothelial cells and SMEC also using a commercial kit . SMEC were isolated from collagenase digested skeletal muscle using rat anti-mouse VE-cadherin antibody-coated Dynabeads and biotinylated rat anti-mouse CD31 antibody-coated streptavidin-coupled beads , cultivated as described previously ( Roudier et al . , 2013 ) , and used in experiments between passages 4 and 7 . For glucose and FoxO1 inhibitor treatments , cells were plated and after overnight attachment , culture medium was replaced by low ( 5 mmol/L ) or high-glucose ( 25 mmol/L ) DMEM 10% FBS and incubated for 48 hr and 1 µmol/L AS1842856 was added to the medium 18 hr prior to testing . To examine the influence of FoxO1 inhibition on protein levels of glycolytic enzymes , SMEC were plated sparsely . After overnight attachment , culture medium ( high-glucose DMEM with 10% FBS ) was replaced by high-glucose DMEM with 0 . 1% FBS and the cells were stimulated with 1 µmol/L AS1842856 for 24 hr before lysis in RIPA buffer for protein extraction . For the isolation of adipose-derived endothelial cells , white adipose depots were pooled and digested with 0 . 5% Type I collagenase for 20 min at ( 37°C ) with shaking . Centrifugation ( 300xg for 5 min ) was used to separate adipocytes from the stromal vascular fraction ( SVF ) . The re-suspended SVF was passed through a 100 μm-cell strainer , then pre-incubated with rat anti-mouse CD16/CD32 coupled to Dynabeads to deplete immune cells . Endothelial cells were selected using rat anti-mouse VE-cadherin antibody-coated Dynabeads and biotinylated rat anti-mouse CD31 antibody-coated streptavidin-coupled beads . Cells were plated on gelatin-coated plates and maintained in high-glucose DMEM ( 10% FBS ) until utilization the following day . Glucose uptake was assessed in freshly adipose-derived endothelial cells and in SMEC using Glucose Uptake-Glo Assay following manufacturer’s instructions . To examine the effects of Foxo1 depletion on the glycolytic capacity of endothelial cells , freshly isolated adipose-derived endothelial cells were incubated with high-glucose DMEM plus 10% dialyzed FBS for 32 hr . The effects of FoxO1 inhibition were examined in SMEC pre-treated with 1 µmol/L AS1842856 for 24 hr in high-glucose DMEM plus 0 . 1% FBS following incubation with high-glucose DMEM plus 10% dialyzed FBS for 32 hr . Glucose consumption and lactate production were determined in cell culture medium at different time points ( 0 , 8 and 24 hr ) using commercial kits . Total RNA was isolated from liver , skeletal muscle ( plantaris ) , adipose tissue ( BAT , eWAT and subcutaneous ) , adipose-derived endothelial cell and CD16/CD32 fractions and SMEC using RNeasy Mini Kit ( Qiagen Inc . ) , reverse-transcribed and analyzed by real-time PCR on the Rotor-Gene Q platform ( Qiagen Inc . ) using Fast TaqMan Master Mix and TaqMan primer sets ( listed in Supplementary file 2 ) . Each target gene was calculated relative to Hprt1 or Actb levels and expressed as 2-ΔCt . All data reported are for independent biological replicates; each mouse being considered as one biological replicate . Averaged values were used when technical replicates ( analysis of the same sample in duplicates ) were performed . For in vitro assays , experiments were repeated at least three times and a sample size of ≥ 3 biological replicates was used . Samples or data points were excluded only in the case of a technical equipment or human error that caused a sample to be poorly controlled . Statistical analyses were performed using Prism 5 ( GraphPad Software Inc . ) . Significance was established at p < 0 . 05 , by unpaired Student’s t-test or 2-way ANOVA with Bonferroni post hoc tests , as appropriate . Data are shown as means ± SEM and P values are indicated in each Figure as *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 . | In the body , thread-like blood vessels called capillaries weave their way through our tissues to deliver oxygen and nutrients to every cell . When a tissue becomes bigger , existing vessels remodel to create new capillaries that can reach far away cells . However , in obesity , this process does not happen the way it should: when fat tissues expand , new blood vessels do not always grow to match . The starved fat cells can start to dysfunction , which causes a range of issues , from inflammation and scarring of the tissues to problems with how the body processes sugar and even diabetes . Yet , it is still unclear why exactly new capillaries fail to form in obesity . What we know is that a protein called FoxO ( short for Forkhead box O ) is present in the cells that line the inside of blood vessels , and that it can stop the development of new capillaries . FoxO controls how cells spend their energy , and it can force them to go into a resting state . During obesity , the levels of FoxO actually increase in capillary cells . Therefore , it may be possible that FoxO prevents new blood vessels from growing in the fat tissues of obese individuals . To find out , Rudnicki et al . created mice that lack the FoxO protein in the cells lining the capillaries , and then fed the animals a high-fat diet . These mutant mice had more blood vessels in their fat tissue , and their fat cells looked healthier . They also stored less fat than normal mice on the same diet , and their blood sugar levels were normal . This was because the FoxO-deprived cells inside capillaries were burning more energy , which they may have obtained by pulling sugar from the blood . These results show that targeting the cells that line capillaries helps new blood vessels to grow , and that this could mitigate the health problems that arise with obesity , such as high levels of sugar ( diabetes ) and fat in the blood . However , more work is needed to confirm that the same cellular processes can be targeted to obtain positive health outcomes in humans . | [
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] | 2018 | Endothelial-specific FoxO1 depletion prevents obesity-related disorders by increasing vascular metabolism and growth |
How the human brain controls hand movements to carry out different tasks is still debated . The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints . However , whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown . Here , we combined kinematic , electromyography , and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects . Hand postural information , encoded through kinematic synergies , were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements , significantly outperforming alternative somatotopic or muscle-based models . Importantly , hand postural synergies were predicted by neural activation patterns within primary motor cortex . These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses .
Unique among primates , the human hand is capable of performing a strikingly wide range of movements , characterized by a high degree of adaptability and dexterity that enables complex interactions with the environment . This is exemplified by the hand’s ability to mold to objects and tools by combining motion and force in the individual digits so to reach a variety of hand postures . The multiple ways in which the hand can perform a given goal-directed movement arise from anatomical , functional , and kinematic redundancies , i . e . , a large number of degrees of freedom ( DoFs ) ( Bernstein , 1967 ) . Such an organization results highly advantageous from an operational perspective , as redundant DoFs enable the hand to flexibly adapt to different task demands , or to switch among multiple postural configurations , while maintaining grasp stability ( Bernstein , 1967; Santello et al . , 2013 ) . At the same time , this organization raises the question about how the central nervous system deals with these redundancies and selects a set of DoFs to accomplish a specific motor task ( Latash et al . , 2007 ) . While some models propose the notion of “freezing” of redundant DoFs ( Vereijken et al . , 1992 ) or the implementation of optimization strategies ( Flash and Hogan , 1985; Todorov and Jordan , 2002; Todorov , 2004 ) , further studies have favored an alternative solution based on linear dimensionality reduction strategies or motor synergies ( Latash , 2010 ) . From a theoretical perspective , synergies represent functional sensorimotor modules that result from the combination of elementary variables and behave as single functional units ( Turvey , 2007; Latash , 2010 ) . From an experimental viewpoint , synergy-based models have been applied with success to electrophysiological and kinematic data acquired in frogs ( d'Avella and Lacquaniti , 2003; Cheung et al . , 2005 ) , monkeys ( Overduin et al . , 2012 ) and humans ( Bizzi et al . , 2008 ) . With regard to hand control in humans , synergies have been defined at different levels . Kinematic synergies correspond to covariation patterns in finger joint angles and are quantified through kinematic recordings ( Santello et al . , 1998; Gabiccini et al . , 2013; Tessitore et al . , 2013 ) . Muscle synergies represent covariation patterns in finger muscle activations and are typically extracted from electromyography ( EMG ) signals ( Weiss and Flanders , 2004; d'Avella and Lacquaniti , 2013 ) . The first quantitative description of kinematic hand synergies was obtained by analyzing hand postures used by subjects for grasping imagined objects that varied in size and shape ( Santello et al . , 1998 ) . Three hand postural synergies were identified through a principal component analysis ( PCA ) that accounted for a high fraction ( >84% ) of variance in the kinematic data across all hand postures and characterized hand configurations as linear combinations of finger joints ( Santello et al . , 1998 ) . Notably , other studies achieved similar results using kinematic data acquired during grasping of real , recalled and virtual objects ( Santello et al . , 2002 ) , exploratory procedures ( Thakur et al . , 2008 ) , or during different movements , such as typing ( Soechting and Flanders , 1997 ) , as well as with EMG signals from finger muscles during hand shaping for grasping or finger spelling ( Weiss and Flanders , 2004 ) . Given that final hand postures can be described effectively as the linear combination of a small number of synergies , each one controlling a set of muscles and joints , the question arises whether kinematic or muscular hand synergies merely reflect a behavioral observation , or whether instead a synergy-based framework is grounded in the human brain as a code for the coordination of hand movements . According to the latter hypothesis , motor cortical areas and/or spinal modules may control the large number of DoFs of the hand through weighted combinations of synergies ( Gentner and Classen , 2006; Santello et al . , 2013; Santello and Lang , 2014 ) , in a way similar to that demonstrated for other motor acts , such as gait , body posture , and arm movements ( Cheung et al . , 2009 ) . Furthermore , the biomechanical constraints of the hand structure that group several joints in nature ( e . g . , multi-digit and multi-joint extrinsic finger muscles whose activity would generate coupled motion ) , are compatible with the synergistic control of hand movements . Previous brain functional studies in humans are suggestive of a synergistic control of hand movements . For instance , in a functional magnetic resonance imaging ( fMRI ) study , synergistic/dexterous and non-synergistic hand movements elicited different neural responses in the premotor and parietal network that controls hand posture ( Ehrsson et al . , 2002 ) . Equally , transcranial magnetic stimulation ( TMS ) induced hand movements encompassed within distinct postural synergies ( Gentner and Classen , 2006 ) . Despite all the above pieces of information , however , whether and to what extent the representation of hand movements is encoded at a cortical level in the human brain directly as postural synergies still remains an open question . Alternative solutions to synergies for hand control have been proposed as well . Above all , classic somatotopic theories postulated that distinct clusters of neuronal populations are associated with specific hand muscles , fingers , or finger movements ( Penfield and Boldrey , 1937; Penfield and Rasmussen , 1950; Woolsey et al . , 1952 ) . However , whereas a coarse arrangement of body regions ( e . g . , hand , mouth , or face ) has been shown within primary motor areas , the intrinsic topographic organization within limb-specific clusters remains controversial . In hand motor area , neurons controlling single fingers are organized in distributed , overlapping cortical patches without any detectable segregation ( Penfield and Boldrey , 1937; Schieber , 1991 , Schieber , 2001 ) . In addition , it has been recently shown that fMRI neural activation patterns for individual digits in sensorimotor cortex are not somatotopically organized and their spatial arrangement is highly variable , while their representational structure ( i . e . , the pattern of distances between digit-specific activations ) is invariant across individuals ( Ejaz et al . , 2015 ) . The present study was designed to determine whether and to what extent synergistic information for hand postural control is encoded as such at a neural level in the human brain cortical regions . An identical experimental paradigm was performed in two distinct sessions to acquire kinematic and electromyographic ( EMG ) data while participants performed grasp-to-use movements towards virtual objects . Kinematic data were analyzed according to a kinematic synergy model and an individual-digit model , based on the independent representation of each digit ( Kirsch et al . , 2014 ) , while EMG data were analyzed according to a muscle synergy model to obtain independent descriptions of each final hand posture . In a separate fMRI session , brain activity was measured in the same participants during an identical motor task . Hence , encoding techniques ( Mitchell et al . , 2008 ) were applied to brain functional data to compare the synergy-based model with the alternative somatotopic and muscular models on the basis of their abilities to predict neural responses . Finally , to assess the specificity of the findings , we applied a decoding procedure to the fMRI data to predict hand postures based on patterns of fMRI activity .
The hand kinematic data , acquired from the motion capture experiment , provided a kinematic synergy description , created using PCA on digit joint angles , and an individual digit description , i . e . , a somatotopic model based on the displacements of single digits , calculated as the average displacement of their joint angles . The EMG data provided a muscle synergy description . To obtain comparable descriptions of hand posture , three five-dimensions models were chosen . A validation procedure based on a rank-accuracy measure was performed to assess the extent to which static hand postures could be reliably discriminated by each behavioral model , regardless of its fraction of variance accounted for . All the three models were able to significantly distinguish between individual hand postures ( average accuracy ± standard deviation -SD-; chance level: 50%; kinematic synergy: 91 . 1 ± 3 . 6%; individual digit: 85 . 9 ± 5%; muscle synergy: 72 ± 7 . 7% ) ( Supplementary file 1A ) . Specifically , the kinematic synergy model performed significantly better than both the individual digit and muscle synergy models while the individual digit model was significantly more informative than the muscle synergy model ( Wilcoxon signed-rank test , p<0 . 05 , Bonferroni-Holm corrected ) . Three independent encoding procedures ( Mitchell et al . , 2008 ) were performed on the fMRI data to assess to what extent each model ( kinematic synergy , individual digit or muscle synergy ) would predict brain activity . The discrimination accuracy was tested for significance against unique null distributions of accuracies for each participant and model obtained through permutation tests . Overall , the encoding procedure based on the kinematic synergy model was highly successful across all participants ( average accuracy ± SD: 71 . 58 ± 5 . 52% ) and always significantly above chance level ( see Supplementary file 1B for single subject results ) . The encoding of the individual digit model was successful in five of nine participants only ( 63 . 89 ± 6 . 86% ) . Finally , the muscle synergy model successfully predicted brain activity in six out of eight participants , with an average accuracy that was comparable to the individual digit model ( 63 . 9 ± 6 . 5% ) . The kinematic synergy model outperformed both the individual digit and the muscle synergy models ( Wilcoxon signed-rank test , p<0 . 05 , Bonferroni-Holm corrected ) , whereas no significant difference was found between the individual digit and muscle synergy models ( p=0 . 95 ) . To obtain a measure of the overall fit between neural responses and behavioral performance , we computed the R2 coefficient between the fMRI data and each behavioral model across voxels , subjects , and acquisition modalities . The group averages were 0 . 41 ± 0 . 06 for the kinematic synergies , 0 . 37 ± 0 . 03 for the individual digits , and 0 . 37 ± 0 . 06 for the muscle synergies . Therefore , 40 . 8% of the BOLD signal was accounted for by the kinematic synergies , whereas the two other behavioral models explained a relatively smaller fraction of the total variance . The group analysis was performed only on the encoding results obtained from kinematic synergies , as this was the most successful model and the only one that performed above chance level across participants . The single-subject encoding results maps – containing only the voxels recruited during the procedure – were merged , with a threshold of p>0 . 33 to retain consistently informative voxels , overlapping in at least four participants . The group-level probability map , which displays the voxels recruited in at least four subjects , consisted of a well-recognizable network of hand-related regions , specifically bilateral precentral cortex , supplementary motor area ( SMA ) , ventral premotor and supramarginal areas , left inferior parietal and postcentral cortex ( Figure 1; coordinates in Supplementary file 1C ) . 10 . 7554/eLife . 13420 . 003Figure 1 . This probability map shows the voxels that were consistently engaged by the encoding procedure across subjects , i . e . , those voxels whose activity was predictable on the basis of the kinematic synergies . A hand-posture- related network comprising the left primary and supplementary motor areas , the superior parietal lobe and the anterior part of intraparietal sulcus ( bilaterally ) was recruited with high overlap across subjects . Despite additional regions ( i . e . , Brodmann Area 6 ) resulted from the encoding analyses , they are not evident in the map due to their deep location . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 00310 . 7554/eLife . 13420 . 004Figure 1—source data 1 . This compressed NIfTI file in MNI152 space represents the voxels that were recruited by the encoding procedure in more than three subjects . The value of each individual voxel corresponds to the number of subjects in which that voxel was recruited . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 00410 . 7554/eLife . 13420 . 005Figure 1—source data 2 . his compressed NIfTI file in MNI152 space represents the Region of Interest chosen for encoding brain activity from the visual region , defined on the basis of a t-test of the overall brain activity ( i . e . , task versus rest condition ) five seconds after the visual stimulus onset , corrected for multiple comparisons with False Discovery Rate ( q<0 . 01 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 005 Since postural synergies were obtained in each subject independently , a procedure to assess the stability of the principal components ( PCs ) across participants was performed ( see Materials and methods section ) . For visualization purposes , we focused on the first three PCs , which could explain more than 80% of the variance across the entire hand kinematic dataset , and were also highly consistent across participants ( Video 1 ) . 10 . 7554/eLife . 13420 . 006Video 1 . This video shows the meaning of the kinematic synergies measured in this study , by presenting three movements from the minimum to the maximum values of kinematic synergies 1 , 2 , and 3 , respectively , expressed as sets of twenty-four joint angles averaged across subjects . It can be observed that the first synergy modulates abduction-adduction and flexion-extension of both the proximal and distal finger joints , while the second synergy reflects thumb opposition and flexion-extension of the distal joints only . Maximizing the first synergy leads , therefore , to a posture resembling a power grasp , while the second one is linked to pinch movements directed towards smaller objects , and the third one represents movements of flexion and thumb opposition ( like in grasping a dish or a platter ) ( Santello et al . , 1998; Gentner and Classen , 2006; Ingram et al . , 2008; Thakur et al . , 2008 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 006 Accordingly to the aforementioned results , the first three kinematic PCs were mapped onto a flattened mesh of the cortical surface . This map displayed the fitting of each synergy within the voxels that were recruited by the encoding procedure across participants . Figure 2 shows that the group kinematic synergies are represented in the precentral and postcentral cortex in distinct clusters that are arranged in a topographical continuum with smooth supero-inferior transitions . The procedure developed to assess the topographical arrangement of synergies ( see Materials and methods ) was statistically significant ( C=0 . 19; p=0 . 038 ) , indicating that anatomically close voxels exhibited similar synergy coefficients ( see Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 13420 . 007Figure 2 . Cortical flattened map depicting the topographical organization of the first three synergies across primary motor , somatosensory , and parietal regions . The portion of cerebral cortex represented in the map corresponds to the area enclosed in the rectangle in the brain mesh ( top , right ) . M1: Primary Motor Cortex . CS: Central Sulcus . S1: Primary Somatosensory cortex ( postcentral gyrus ) . aIPS: anterior intraparietal sulcus . SPL: Superior Parietal lobule . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 00710 . 7554/eLife . 13420 . 008Figure 2—figure supplement 1 . Topography assessment: map and feature spaces . The two maps represent the map space ( upper image ) , which depicts the pairwise physical distance ( i . e . , standardized Euclidean distance ) between the voxels of the results map , and the feature space ( lower image ) , which depicts the distance ( i . e . , standardized Euclidean distance ) between the goodness-of-fit ( R2 ) of the first three kinematic PCs in each voxel . For further details , see Materials and methods and Yarrow et al . , 2014 . There was a significant similarity between the two spaces , assessed with the permutation test described in the Methods ( C=0 . 192; p-value=0 . 0383 ) . Voxels were reordered accordingly to their physical distance to improve readability of the two maps . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 008 Representational Spaces , drawn separately for the three models and fMRI data ( using the activity from a region consistently activated across all the grasping movements ) , were compared at a single subject and group level to assess the similarity between each behavioral model and the neural content represented at a cortical level . All group correlations , both between fMRI and behavioral data and between behavioral models were highly significant ( p<0 . 0001 ) ( for details see Supplementary file 1D , E and Figure 3—figure supplement 1 ) . Moreover , a MDS procedure was performed to represent data from kinematic synergies and fMRI BOLD activity . Figure 3 shows the high similarity between these two spaces . 10 . 7554/eLife . 13420 . 009Figure 3 . This picture displays the mMultidimensional sScaling ( MDS ) results for kinematic synergies ( left ) and fMRI brain activity ( right ) . With the exception of few postures ( e . g . , dinner plate , frisbee and espresso cup ) that were misplaced in the fMRI data with respect to the kinematic synergies representation , the other object-related postures almost preserved their relative distances . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 00910 . 7554/eLife . 13420 . 010Figure 3—figure supplement 1 . Average correlations between behavioral models and fMRI data . The histogram reports the correlation values ( transformed to z-scores and averaged across subjects ) between each behavioral model and the fMRI data . Error bars represent the SEM . The noise ceiling , estimated using the procedure described by Ejaz et al . , 2015 is also reported . The two dashed lines describe the upper and lower bounds , respectively . The single-subject correlation values are reported in Supplementary file 1D . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 010 To confirm the presence of a neural representation of hand synergies at a cortical level and that that this information can be used to specifically control hand postures based on brain activity , we applied decoding methods as complementary approaches to encoding analyses ( Naselaris et al . , 2011 ) . Hand posture ( expressed as a matrix of 24 joints angles by 20 hand postures ) was therefore predicted with a multiple linear regression procedure from fMRI data . Specifically , this procedure could reliably reconstruct the different hand postures across participants . The goodness-of-fit ( R2 ) between the original and reconstructed joint angle patterns related to single movements , averaged across subjects , ranged between 0 . 51 and 0 . 90 ( Supplementary file 1F ) . Three hand plots displaying original and reconstructed postures from a representative subject are shown in Figure 4 . Notably , this decoding attempt reveals that brain activity elicited by our task can effectively be used to reconstruct the postural configuration of the hand . Moreover , the rank accuracy procedure specifically designed to test the extent to which each decoded posture could be discriminated from the original ones yielded significant results in six of nine participants ( Supplementary file 1G ) . 10 . 7554/eLife . 13420 . 011Figure 4 . This picture represents the postures obtained from the fMRI data and those originally recorded through optical tracking . The figure shows three pairs of hand plots corresponding to three postures from a representative subject , and the goodness-of-fit between the original and decoded sets of joint angles . In these plots , the two wrist angles are not rendered . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 01110 . 7554/eLife . 13420 . 012Figure 4—source data 1 . This compressed NIfTI file in MNI152 space represents the Region of Interest chosen for RSA and posture decoding , defined on the basis of a t-test of the overall brain activity ( i . e . , task versus rest condition ) , corrected for multiple comparisons with False Discovery Rate ( q<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 01210 . 7554/eLife . 13420 . 013Figure 4—figure supplement 1 . Marker placement for kinematic hand posture data acquisition: The picture depicts the hand of a subject with the complete set of optical markers used to define hand posture through optical tracking . This set of markers corresponds to the joint and bones positions originally recorded; the rendering in Figure 4 was performed with reference to this acquisition protocol . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 01310 . 7554/eLife . 13420 . 014Figure 4—figure supplement 2 . ROI used for performing RSA and posture decoding: This map represents the Region of Interest which contained all the voxels used for performing Representational Similarity Analysis and hand posture decoding . The region was obtained with a t-test of the overall brain activity ( i . e . , task versus rest condition ) , corrected for multiple comparisons with False Discovery Rate ( q<0 . 05 ) . The population of voxels represented here was subsequently reduced with a PCA accounting for most of the variance as described in the Methods . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 014 Since motor and premotor regions supposedly contains neuronal populations that respond to visual stimuli ( Kwan et al . , 1985; Castiello , 2005; Klaes et al . , 2015 ) , one may argue that the visual presentation of objects in the current experiment contributes to the synergy-based encoding of BOLD activity in those regions . To exclude this possibility , an encoding procedure using the kinematic synergy model was performed within the region of interest ( ROI ) chosen for RSA and posture reconstruction , using exclusively the neural activity related to visual object presentation , measured five seconds after the stimulus onset . The procedure was unsuccessful in all participants , thus indicating that the kinematic synergy information in motor and premotor regions was purely related to motor activity ( Supplementary file 1H ) . The encoding maps of kinematic synergies never included visual areas . Nonetheless , visual areas are likely to participate in the early stages of action preparation ( Gutteling et al . , 2015 ) and the motor imagery might have played a role during the task in the fMRI session . For this reason , we first defined a ROI by contrasting visual related activity after stimulus presentation and rest ( q<0 . 01 , FDR corrected ) , thus to isolate regions of striate and extrastriate cortex within the occipital lobe . Subsequently , an encoding analysis was performed similarly to the above-mentioned procedures . The results were at the chance level in seven out of nine participants ( see Supplementary file 1I ) , suggesting that visual imagery processes in the occipital cortex did not retain kinematic synergy information .
Validation of behavioral data was performed as the first stage of analysis to assess the information content and the discriminability of the postures from the kinematic or EMG data . This procedure showed that each posture could be successfully classified above chance level by kinematic synergy , individual digit , and muscle synergy models . These results are highly consistent with the existent literature on synergies suggesting that just five PCs are sufficient to classify and reconstruct hand postures when computed only on hand kinematic data ( Santello et al . , 1998 , Santello et al . , 2002; Gentner and Classen , 2006 ) , or both kinematic and EMG data ( Weiss and Flanders , 2004; Klein Breteler et al . , 2007 ) . In the current work , we also demonstrate that kinematic synergies result in a higher discrimination accuracy of hand postures than individual digits and muscle synergies . In addition , the encoding procedures on fMRI-based neural responses show that kinematic synergies are the best predictor of brain activity , with a significantly higher discrimination accuracy across participants , indicating that kinematic synergies are represented at a cortical level . Even if previous studies suggest that the brain might encode grasp movements as combinations of synergies in the monkey ( Overduin et al . , 2012 ) , or indirectly in humans ( Gentner and Classen , 2006; Gentner et al . , 2010 ) , to the best of our knowledge , no direct evidence has been presented to date for a functional validation and characterization of neural correlates of synergy-based models . The results from RSA suggest that the three models used to predict brain activity may have similar , correlated spaces . However , each model provides a unique combination of weights for each posture across different dimensions ( e . g . , synergies or digits ) , thus resulting in distinct descriptions of the same hand postures . It should be noted that both the individual digit model and the muscle synergy model failed to predict brain activity in four and two participants , respectively . Thus , while they discriminated hand postures at a behavioral level , these models are clearly less efficient than the kinematic synergy model in predicting neural activity . Finally , the descriptive procedures ( RSA and MDS ) were performed to assess the differences between the fMRI representational space and the single-model spaces . The results indicate a high similarity between fMRI and kinematic synergies , as reflected in the largely overlapping representations obtained from kinematic data and fMRI as depicted in Figure 3 . A recent study employed descriptive procedures ( i . e . , RSA ) to demonstrate that similar movement patterns of individual fingers are reflected in highly correlated patterns of brain responses , that , in turn , are more correlated with kinematic joint velocities than to muscle activity , as recorded through high-density EMG ( Ejaz et al . , 2015 ) . Our paper introduces a methodological and conceptual advancement . While , in Ejaz et al . , full matrices of postural , functional or muscle data have been considered in the RSA , here we focused on descriptions with lower dimensionality , which lose only minor portions of information . Consequently , by showing that brain activity in motor regions can be expressed as a function of a few meaningful motor primitives that group together multiple joints , rather than as combinations of individual digit positions , our results suggest that a modular organization represents the basis of hand posture control . The group probability maps of our study indicate that the regions consistently modulated by kinematic synergies , that include bilateral precentral , SMA and supramarginal area , ventral premotor , left inferior parietal and postcentral cortex , overlapped with a network strongly associated with the control of hand posture ( Castiello , 2005 ) . Specifically , we show that the combination of five synergies , expressed as PCs of hand joint angles , predicts neural activity of M1 and SMA , key areas for motor control . While previous studies in humans showed differential activations in M1 and SMA for power and precision grip tasks ( Ehrsson et al . , 2000 ) and for different complex movements ( Bleichner et al . , 2014 ) , to date no brain imaging studies directly associated these regions with synergy-based hand control . Beyond primary motor areas , regions within parietal cortex are involved in the control of motor acts ( Grafton et al . , 1996 ) . Inferior parietal and postcentral areas are engaged in higher-level processing during object interaction ( Culham et al . , 2003 ) . Since grasping , as opposed to reaching movements , requires integration of motor information with inputs related to the target object , these regions may integrate the sensorimotor features needed to preshape the hand correctly ( Grefkes et al . , 2002; Culham et al . , 2003 ) . Consistently , different tool-directed movements were decoded from brain activity in the intraparietal sulcus ( Gallivan et al . , 2013 ) , and it has been reported that this region is sensitive to differences between precision and power grasps ( Ehrsson et al . , 2000; Gallivan et al . , 2011 ) . The current motor task , even if performed with the dominant right hand only , also recruited motor regions of the right hemisphere . Specifically , bilateral activations of SMA were often described during motor tasks ( Ehrsson et al . , 2001; Ehrsson et al . , 2002 ) and a recent meta-analysis indicated a consistent recruitment of SMA in grasp type comparisons ( King et al . , 2014 ) . Equally , a bilateral , but left dominant , involvement of intraparietal cortex for grasping has been reported ( Culham et al . , 2003 ) . Moreover , some authors have hypothesized recently that action recognition and mirror mechanisms may rely on the extraction of reduced representations of gestures , rather than on the observation of individual motor acts ( D'Ausilio et al . , 2015 ) . The specific modulation of neural activity by kinematic synergies within the action recognition network seems in agreement with this proposition . The map of voxels whose activity is modulated by postural synergies extends beyond the central sulcus to primary somatosensory cortex , suggesting a potential two-fold ( sensory and motor ) nature of hand synergies . Indeed , at least some subdomains ( areas 2 and 3a ) contain neurons that respond to multiple digits ( Iwamura et al . , 1980 ) , despite the evidence supporting specific single finger representations in S1 ( Kaas , 1983 ) . Finally , the width of our probability maps , measured on the cortical mesh , was ca . 1cm , which corresponds to the hand area , as defined by techniques with better spatial resolution , including ultra-high field fMRI or electrocorticography ( ECoG ) ( Siero et al . , 2014 ) . To exclude that the results from the encoding analysis can be driven by differences between classes of acts , i . e . , precision or power grasps , rather than reflect the modulation of brain activity by kinematic synergies , the similarity between the 20 hand postures was evaluated in a pairwise manner . Specifically , the accuracy of the encoding model was estimated for each pair of distinct movements , unveiling the extent to which individual hand postures could be discriminated from each other based on their associated fMRI activity . In the result heat map ( Figure 5 ) , two clusters can be identified: one composed mainly by precision grasps directed towards small objects , and a second one composed mainly by power grasps towards heavy tools . The remaining postures did not cluster , forming instead a non-homogeneous group of grasps towards objects that could be either small ( e . g . , espresso cup ) or large ( e . g . , jar lid , PC mouse ) . 10 . 7554/eLife . 13420 . 015Figure 5 . Discrimination accuracies for single postures as represented by kinematic synergies . Two clusters of similar postures are easily identifiable ( i . e . , precision grip and power grasps ) . However , other postures were recognized without showing an evident clustering , suggesting that the encoding procedure was not biased by a coarse discrimination of motor acts . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 01510 . 7554/eLife . 13420 . 016Figure 5—figure supplement 1 . Workflow of the encoding analysis . This diagram depicts the workflow of the multiple linear regression procedure applied on fMRI data using the matrices obtained from the data acquired in the kinematic and EMG experiments as encoding models . The pairwise discrimination accuracy was estimated in the decoding phase , represented as the final step of this diagram . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 016 These results indicate that goal-directed hand movements are represented in the brain in a way that goes beyond the standard distinction between precision and power grasps ( Napier , 1956; Ehrsson et al . , 2000 ) . Other authors have proposed a possible 'grasp taxonomy' in which multiple , different types of grasps are described according to hierarchical criteria rooted on three main classes: precision , power and intermediate ( Feix et al . , 2009 ) . By combining these three elementary grasps , it is possible to generate a wide number of postures . Notwithstanding the advancements of these taxonomies in describing hand posture , much less effort has been made to understand how the wide variety of human hand postures can be represented in the brain . Our results indicate that a synergy framework may predict brain activity patterns underlying the control of hand posture . Of note , the highest-ranked kinematic synergies can be clearly identified as grasping primitives: the first synergy modulates abduction-adduction and flexion-extension of both the proximal and distal finger joints , while a second synergy reflects thumb opposition and flexion-extension of the distal joints only . Maximizing the first synergy leads therefore to a posture resembling a power grasp , while the second one is linked to pinch movements directed towards smaller objects , and the third one represents movements of flexion and thumb opposition ( like in grasping a dish or a platter ) ( Santello et al . , 1998; Gentner and Classen , 2006; Ingram et al . , 2008; Thakur et al . , 2008 ) ( Video 1 ) . For this reason , the description of hand postures can benefit from reduction to combinations of few , meaningful synergies , which can provide more reliable results than clustering methods based on a small number of categories ( Santello et al . , 2002; Ingram et al . , 2008; Thakur et al . , 2008; Tessitore et al . , 2013 ) . In the present study , we examined five hand postural synergies . This number was selected based on previous behavioral studies that showed that three and five PCs can account for at least 80% and 90% of the variance , respectively ( Santello et al . , 1998 , Santello et al . , 2002; Weiss and Flanders , 2004; Gentner and Classen , 2006; Gentner et al . , 2010; Overduin et al . , 2012 ) . Indeed , a model with five synergies could successfully predict brain activation patterns . The first three synergies examined in the present study also show a high degree of stability as the order of the most relevant PCs is highly preserved across the nine participants . Moreover , the synergies described in the current study are consistent with those reported by other authors ( Santello et al . , 1998 , Santello et al . , 2002; Gentner and Classen , 2006; Ingram et al . , 2008; Thakur et al . , 2008 ) , although a larger number of both postures and subjects would be required for the definitive characterization of the stability of hand postural synergies . The first three synergies are displayed on a flattened map of the cortical surface in Figure 2 . The map suggests that the PCs are topographically arranged , forming clusters with a preference for each of the three synergies , separated by smooth transitions . This organization resembles that observed in the retinotopy of early visual areas ( Sereno et al . , 1995 ) or in auditory cortex as studied with tonotopic mapping ( Formisano et al . , 2003 ) . This observation strongly suggests that primary motor and somatosensory brain regions may show specific , organized representations of synergies across the cortical surface . Such an observation is unprecedented , since the large number of previous studies adopted techniques , such as single cell recording ( Riehle and Requin , 1989; Zhang et al . , 1997 ) or intracortical microstimulation ( ICMS ) ( Overduin et al . , 2012 ) , which can observe the activity of single neurons but do not capture the functional organization of motor cortex as a whole . Motor cortex has historically been hypothesized to be somatotopically organized in a set of sub-regions that control different segments of the body ( Penfield and Boldrey , 1937 ) . However , whereas subsequent work confirmed this organization ( Penfield and Welch , 1951 ) , a major critical point remains the internal organization of the single subregions ( e . g . , hand , leg or face areas ) . To date , a somatotopy of fingers within the hand area appears unlikely: as each digit is controlled by multiple muscles , individual digits may be mapped in a distributed rather than discrete fashion ( Penfield and Boldrey , 1937; Schieber , 2001; Graziano et al . , 2002; Aflalo and Graziano , 2006 ) . An alternative view posits that movements are represented in M1 as clusters of neurons coding for different action types or goals ( Graziano , 2016 ) . In fact , mouse motor cortex is organized in clusters that encode different motor acts ( Brown and Teskey , 2014 ) . Similarly , stimulation of motor cortex in monkeys produces movements directed to stable spatial end-points ( Graziano et al . , 2002; Aflalo and Graziano , 2006 ) and may have a synergistic organization ( Overduin et al . , 2012 ) . Recently , it has been demonstrated in both monkeys and humans that complex movements can be recorded from parietal as well as premotor and motor areas ( Aflalo et al . , 2015; Klaes et al . , 2015; Schaffelhofer et al . , 2015 ) . Interestingly , a successful decoding can be achieved in those regions both during motor planning and execution ( Schaffelhofer et al . , 2015 ) . These observations about the internal organization of motor cortex were demonstrated also in humans , revealing that individual representations of digits within M1 show a high degree of overlap ( Indovina and Sanes , 2001 ) and that , despite digits may be arranged in a coarse ventro-dorsal order in somatosensory cortex , their representations are intermingled so that the existence of digit specific voxels is unlikely ( Ejaz et al . , 2015 ) . In contrast , individual cortical voxels may contain enough information to encode specific gestures ( Bleichner et al . , 2014 ) . Finally , we questioned whether the information encoded in M1 could be used to reconstruct hand postures . To this aim , each individual posture was expressed as a set of synergies that were derived from the fMRI activity on an independent cortical map . The results were reported as correlation values between the sets of joint angles originally tracked during kinematic recording and the joint angles derived from the reconstruction procedure . Overall , hand postures can be reconstructed with high accuracy based on the neural activity patterns . This result yields potential applications for the development of novel brain computer interfaces: for instance , previous studies demonstrated that neural spikes in primary motor cortex can be used to control robotic limbs used for performing simple or complex movements ( Schwartz et al . , 2006; Schwartz , 2007; Velliste et al . , 2008 ) . Previous studies in monkeys suggest that neural activity patterns associated with grasp trajectories can be predicted from single neuron activity in M1 ( Saleh et al . , 2010; Saleh et al . , 2012; Schaffelhofer et al . , 2015 ) and recently neuronal spikes have been associated with principal components ( Mollazadeh et al . , 2014 ) . In humans , cortical activity obtained through intracranial recordings can be used to decode postural information ( Pistohl et al . , 2012 ) and proper techniques can even lead to decode EMG activity from fMRI patterns ( Ganesh et al . , 2008 ) or from ECoG signals ( Flint et al . , 2014 ) . So far , decoding of actual posture from fMRI activity in M1 was possible at individual voxel level , albeit with simplified paradigms and supervised classifiers that identified only four different movements ( Bleichner et al . , 2014 ) . In contrast , by proving that posture-specific sets of joint angles – expressed by synergy loadings – can be decoded from the fMRI activity , we show that information about hand synergies is present in functional data and can be even used to identify complex gestures . Other authors similarly demonstrated that a set of few synergies can describe hand posture in a reliable way , obtaining hand postures that correlated highly with those recorded with optical tracking ( Thakur et al . , 2008 ) . While nine subjects may appear to be a relatively limited sample for a fMRI study , our study sample is comparable to that of most reports on motor control and posture ( e . g . , Santello et al . , 1998; Weiss and Flanders , 2004; Ingram et al . , 2008; Thakur et al . , 2008; Tessitore et al . , 2013; Ejaz et al . , 2015 ) as well as to the sample size of fMRI studies that use encoding techniques , rather than univariate analyses ( Mitchell et al . , 2008; Huth et al . , 2012 ) . In addition , the data of our multiple experimental procedures ( i . e . , kinematic tracking , EMG , and fMRI ) were acquired within the same individuals , so to minimize the impact of inter-subject variability and to facilitate the comparison between different models of hand posture . Finally , robust descriptive and cross-validation methods complemented single-subject multivariate approaches , which are less hampered by the number of participants than univariate fMRI procedures at group level . A further potential criticism may involve the use of imagined objects – instead of real objects – as targets for grasping movements . The use of imagined objects allows to avoid confounding variables including grasping forces , difficulty in handling objects within a restricting environments , that could play a role in modulating motor acts . In previous behavioral reports , synergies were evaluated using contact with real objects ( Santello et al . , 2002 ) and participants could also explore them in an unconstrained manner instead of concentrating on single actions ( e . g . , grasping ) ( Thakur et al . , 2008 ) . Another study tracked hand motion across many gestures performed in an everyday life setting ( Ingram et al . , 2008 ) . Interestingly , the dimensionality reduction methods were adopted with high consistency in these reports , despite the wide variety of experimental settings , and the first few PCs could explain most of the variance across a very wide number of motor acts . Moreover , when motor acts were performed toward both real and imagined objects , the results obtained from synergy evaluation were highly similar ( Santello et al . , 2002 ) . It can be argued that the better performance for kinematic synergies as compared to the other two alternative models may be due to the differences in the intrinsic signal and noise levels of the optical motion tracking and EMG acquisition techniques . Moreover , the muscle synergy model is inevitably simplified , since only a fraction of the intrinsic and extrinsic muscles of the hand can be recorded with surface EMG . Since all these factors may impact our ability to predict brain activity , we tested whether and to what extent different processing methods and EMG channel configurations could affect the performance of the muscle synergy model in discriminating single gestures and encoding brain activity . Therefore , we performed an additional analysis on an independent group of subjects , testing different processing methods and EMG channel configurations ( up to 16 channels ) . The results , reported in the Appendix , demonstrate that EMG recordings with a higher dimensionality ( Gazzoni et al . , 2014; Muceli et al . , 2014 ) or a different signal processing ( Ejaz et al . , 2015 ) do not lead to better discrimination results . These findings are consistent with previous reports ( Muceli et al . , 2014 ) , and indicate that , in the current study , the worst performance of the muscle model relates more to the signal-to-noise ratio of the EMG technique per se , rather than to shortcomings of either the acquisition device or the signal processing methods adopted here . While our data suggest that synergies may be arranged topographically on the cortical surface , the assessment of such a mapping is currently limited to the first three unrotated PCs . Additional studies are needed to investigate how topographical organization may be affected by the rotation of the principal components . Indeed , such an assessment requires the definition of stable population-level synergies to allow for the identification of optimally rotated components and to test their topographical arrangements across subjects; for this reason , it falls beyond the aims of the current study . Our work demonstrates that the topography of synergies , as defined as a spatial map of the first three PCs , is resistant to different arrangements; however , alternative configurations ( rotated solutions within the PCA ) can be encoded as well in sensorimotor cortical areas . The relatively low C index obtained in the mapping procedure and the total variance explained by the kinematic synergy model during the encoding procedure leave the door open to better models and different topographical arrangements . In summary , our results provide strong support for the representation of hand motor acts through postural synergies . However , this does not imply that synergies are the only way the brain encodes hand movements in primary motor cortex . In our data , only a portion ( 40% ) of the total brain activity could be accounted for by kinematic synergies . Hand motor control results from complex interactions involving the integration of sensory feedback with the selection of motor commands to group of hand muscles . Similarly , motor planning is also a complex process , which requires selecting the desired final posture based on the contact forces required to grasp or manipulate an object . These elements must be continuously monitored to allow for on-line adaptation and corrections ( Castiello , 2005 ) . Previous studies demonstrated that only a small fraction of variance in M1 is related to arm posture ( Aflalo and Graziano , 2006 ) and that grasping force can be efficiently decoded from electrical activity , suggesting that at least a subset of M1 neurons processes force-related information ( Flint et al . , 2014 ) . In addition , motor areas can combine individual digit patterns on the basis of alternative non-synergistic or nonlinear combinations and the correlated activity patterns for adjacent fingers may depend on alternative mechanisms such as finger enslaving ( Ejaz et al . , 2015 ) . It is likely that sensorimotor areas encode also different combinations of synergies , based – for instance – on the rotated versions of kinematic PCs: the encoding of synergies and of their rotated counterparts may represent a wider repertoire of motor primitives which can improve the flexibility and adaptability of modular control . Moreover , the information encoded may be related to the grasping action as a whole , not only to its final posture . Dimensionality reduction criteria can also be applied to hand posture over time , leading to time-varying synergies that encode complete preshaping gestures without being limited to their final position ( Tessitore et al . , 2013 ) . This is consistent with EMG studies , which actually track muscle activity over the entire grasping trajectory ( Weiss and Flanders , 2004; Cheung et al . , 2009 ) and can add information about the adjustments performed during a motor act . Information about the temporal sequence of posture and movements may therefore be encoded in M1 and a different experimental setup is needed to test this hypothesis . It should also be noted that studies in animal models bear strong evidence for a distributed coding of hand synergies beyond motor cortex , i . e . , spinal cord ( Overduin et al . , 2012; Santello et al . , 2013 ) . The question about the role of M1 – i . e . , whether it actually contains synergic information or simply act as a mere selector of motor primitives that are encoded elsewhere – still remains open . Our study provides a relatively coarse description of the role of M1 neurons . According to the redundancy principle , only a part of M1 neurons may be directly implied in movement or posture control ( Latash et al . , 2007 ) , whereas the remaining neurons may deal with force production or posture adjustments and control over time , allowing for the high flexibility and adaptability which are peculiar features of human hand movements . Altogether , the coding of motor acts through postural synergies may shed new light on the representation of hand motor acts in the brain and pave the way for further studies of neural correlates of hand synergies . The possibility to use synergies to reconstruct hand posture from functional activity may lead to important outcomes and advancements in prosthetics and brain-machine interfaces . These applications could eventually use synergy-based information from motor cortical areas to perform movements in a smooth and natural way , using the same dimensionality reduction strategies that the brain applies during motor execution .
Nine healthy volunteers ( 5F , age 25 ± 3 yrs ) participated in the study . The subjects were right-handed according to the Edinburgh Handedness Inventory ( Oldfield , 1971 ) . All participants had normal or corrected-to-normal visual acuity and received a medical examination , including a brain structural MRI scan , to exclude any disorder that could affect brain structure or function . The kinematic , EMG , and fMRI data were acquired during three separate sessions that were performed on different days , in a randomly alternated manner across participants . Eight of nine subjects performed all the three sessions , while EMG data from one participant were not recorded due to hardware failure . Across the three sessions , participants were requested to perform the same task of grasp-to-use gestures towards 20 different virtual objects . A training phase was performed prior to the sessions to familiarize participants with the experimental task . The kinematic and EMG experiments were performed to obtain accurate descriptions of the final hand posture . Three models of equal dimensions ( i . e . , five dimensions for each of the twenty postures ) were derived from these two sessions: a kinematic synergy model based on PCA on kinematic data , an additional kinematic description which considers separately the displacements of each individual digit for each posture , and an EMG-based muscle synergy model . The models were first assessed using a machine-learning approach to measure their ability to discriminate among individual postures . The models were then used in a comparable method ( i . e . , encoding procedure ) aimed at predicting the fMRI activity while subjects performed the same hand grasping gestures . Finally , fMRI activity was used to reconstruct the hand postures ( i . e . , decoding procedure ) . The first experimental session consisted of kinematic recording of hand postures during the execution of motor acts with common objects . More specifically , we focused on the postural ( static ) component at the end of reach-to-grasp movements . Kinematic postural information was acquired with the model described in a previous study ( Gabiccini et al . , 2013 ) , which is a fully parameterized model , reconstructed from a structural magnetic resonance imaging of the hand across a large number of postures ( Stillfried et al . , 2014 ) . Such a model can be adapted to different subjects through a suitable calibration procedure . This model is amenable to in vivo joint recordings via optical tracking of markers attached to the skin and is endowed with a mechanism for compensating soft tissue artifacts caused by the skin and marker movements with respect to the bones ( Gustus et al . , 2012 ) . During the recordings , participants were comfortably seated with their right hand in a resting position ( semipronated ) and were instructed to lift and shape their right hand as to grasp a visually-presented object . Stimuli presentation was organized into trials in which pictures of the target objects were shown on a computer screen for three seconds and were followed by an inter-stimulus pause ( two seconds ) , followed by an auditory cue that prompted the grasping movements . The interval between two consecutive trials lasted seven seconds . In each trial , subjects were requested to grasp objects as if they were going to use them , and to place their hands in the resting position once the movement was over . Twenty different objects , chosen from our previous report ( Santello et al . , 1998 ) , were used in the current study ( see Supplementary file 1J for a list ) . The experiment was organized in five runs , each composed by twenty trials , in randomized order across participants . Therefore , all the grasp-to-use movements were performed five times . The experiment was preceded by a training session that was performed after the positioning of the markers . Hand posture was measured by an optical motion capture system ( Phase Space , San Leandro , CA , USA ) , composed of ten stereocameras with a sampling frequency of 480 Hz . The cameras recorded the Cartesian positions of the markers and expressed them with reference to a global inertial frame and to a local frame of reference obtained by adding a bracelet equipped with optical markers and fastened to the participants’ forearm . This allowed marker coordinates to be expressed with reference to this local frame . To derive the joint angles of the hand , other markers were placed on each bone ( from metacarpal bones to distal phalanxes ) and on a selected group of joints: thumb carpo-metacarpal ( CMC ) , metacarpophalangeal ( MCP ) and interphalangeal ( IP ) ; index and middle MCPs; and all proximal interphalangeals ( PIPs ) . This protocol is shown in Figure 5—figure supplement 1 and a full list of markerized joints and their locations can be found in Supplementary file 1K and in Gabiccini et al . , 2013 . The placement of the markers was performed according to the model described in Gabiccini et al . , 2013 , which consists of 26 Degrees of Freedom ( DoFs ) , 24 pertaining to the hand and 2 to the wrist . The wrist markers were not used in subsequent analyses . The marker configuration resembles a kinematic tree , with a root node corresponding to the Cartesian reference frame , rigidly fastened to the forearm , and the leaves matching the frames fixed to the distal phalanxes ( PDs ) of the five digits , as depicted in the first report of the protocol ( Gabiccini et al . , 2013 ) . First , the frame rate from the ten stereocameras was downsampled to 15 Hz . After a subject-specific calibration phase , which was performed to extract the geometric parameters of the model and the marker positions on the hand of each participant , movement reconstruction was performed by estimating all joint angles at each sample with an iterative extended Kalman filter ( EKF ) which takes into account both measurements explanation and closeness to the previous reconstructed pose ( see Gabiccini et al . , 2013 for further details ) . Once all trials were reconstructed , the posture representing the final grasping configuration was selected through direct inspection . The final outcome of this procedure was a 24 x 100 matrix for each subject , containing 24 joint angles for 20 objects repeated five times . The kinematic data from each subject were analyzed independently . First , the hand postures were averaged across five repetitions for each object , after which the data matrix was centered by subtracting , from each of the 20 grasping movements , the mean posture calculated across all the motor acts . Two different models were obtained from the centered matrix . The first was a kinematic synergy model , obtained by reducing the dimensionality with a PCA on the 20 ( postures ) by 24 ( joint angles ) matrix and retaining only the first five principal components ( PCs ) . In this way , the postures were projected onto the components space , hence obtaining linear combinations of synergies . To obtain an alternative individual digit model , defined on a somatotopic basis , the displacement of individual digits was also measured ( Kirsch et al . , 2014 ) . Briefly , the displacement of each finger for the twenty single postures was obtained by calculating the sum of the single joint angles within each digit and gesture , again excluding wrist DoFs . The analyses of all the sessions were carried out using MATLAB ( MathWorks , Natick , MA , USA ) , unless stated otherwise The second session consisted of a surface electromyography acquisition ( EMG ) during the execution of grasp-to-use acts performed towards the same imagined objects presented during the kinematic experiment . EMG signals were acquired from five different muscles using self-adhesive surface electrodes . The muscles used for recording were: flexor digitorum superficialis ( FDS ) , extensor digitorum communis ( EDC ) , first dorsal interosseus ( FDI ) , abductor pollicis brevis ( APB ) , and abductor digiti minimi ( ADM ) . The individuation of the sites for the recording of each muscle was performed according to the standard procedures for EMG electrode placement ( Hermens et al . , 1999; Hermens et al . , 2000 ) . The skin was cleaned with alcohol before the placement of electrodes . Participants performed the same tasks and protocol used in the kinematic experiment , i . e . , visual presentation of the target object ( three seconds ) , followed by an inter-stimulus interval ( two seconds ) , an auditory cue to prompt movement , and an inter-trial interval ( seven seconds ) . The experiment was divided into runs that comprised the execution of grasping actions towards all the 20 objects , in randomized order . Participants performed six runs . Each gesture was therefore repeated six times . EMG signals were recorded using two devices ( Biopac MP35 for four muscles; Biopac MP150 for the fifth muscle ) and Kendall ARBO 24-mm surface electrodes , placed on the above-mentioned muscles of the participants’ right arm . EMG signals were sampled at 2 kHz . First , EMG signals were resampled to 1 kHz and filtered with a bandpass ( 30–1000 Hz ) and a notch ( 50 Hz ) filter . For each channel , each trial ( defined as a time window of 2500 samples ) underwent the extraction of 22 primary time-domain features , chosen from those that are most commonly used in EMG-based gesture recognition studies ( Zecca et al . , 2002; Mathiesen et al . , 2010; Phinyomark et al . , 2010; Tkach et al . , 2010; see Chowdhury et al . , 2013 for a review ) . Additional second-order features were obtained from the first features , computing their signal median , mean absolute deviation ( MAD ) , skewness , and kurtosis . A complete list of the EMG features we used can be found in Supplementary file 1L . A muscle model was derived from the chosen features as follows: first , the pool of 410 features ( 82 for 5 channels ) was reduced to its five principal components . The 1 x 5 vectors describing each individual movement were averaged across the six repetitions . This 20 ( movements ) x 5 ( synergies ) matrix represented the muscle synergy model for the subsequent analyses . To verify that the three models ( kinematic synergies , individual digit , and muscle synergies ) were able to accurately describe hand posture , their capability to discriminate between individual gestures was tested . To this purpose , we developed a rank accuracy measure within a leave-one-out cross-validation procedure , as suggested by other authors to solve complex multiclass classification problems ( Mitchell et al . , 2004 ) . For each iteration of the procedure , each repetition of each stimulus was left out ( probe ) , whereas all other repetitions ( test set ) were averaged . Then , we computed PCA on the data from the test set . The PCA transformation parameters were applied to transform the probe data in a leave-one-repetition-out way . Subsequently , we computed the Euclidean distance between the probe element and each element from test dataset . These distances were sorted , generating an ordered list of the potential gestures from the most to the least similar . The rank of the probe element in this sorted list was transformed in a percentage accuracy score . The procedure was iterated for each target gesture and repetition of the same grasping movement . The accuracy values were first averaged across repetitions and then across gestures , resulting in one averaged value for each subject . In this procedure , if an element is not discriminated above chance , it may fall in the middle of the ordered list ( around position #10 ) , which corresponds to an accuracy of 50% . For this reason , the chance level is always 50% , regardless of the number of gestures under consideration , while 100% of accuracy indicated that the correct gesture in the sorted list retained the highest score ( i . e . , the lowest distance , first ranked ) across repetitions and participants . The accuracy values were then tested for significance against the null distribution of ranks obtained from a permutation test . After averaging the four repetitions within the test set , the labels of the elements were shuffled; then , the ranking procedure described above was applied . The procedure was repeated 10 , 000 times , generating a null distribution of accuracies; the single-subject accuracy value was compared against this null distribution ( one-sided rank test ) . This procedure was applied to the three models extracted from kinematic and EMG data , obtaining a measure of noise and stability across repetitions and each posture , as described by the three different approaches . Such validation procedure was therefore a necessary step to measure the information content of these three models before testing their ability to predict the fMRI signal . The extraction of postural or muscle synergies from kinematic and EMG data was based on a PCA applied to the matrices of sensor measures or signal features , respectively . For the analyses performed here , we chose models based on the first five principal components that were shown to explain more than 90% of the variance in previous reports , even if those models were applied on data with lower dimensionality ( Santello et al . , 1998; Weiss and Flanders , 2004; Gentner and Classen , 2006 ) . Moreover , an additional model was obtained from the postural data , thus leading to three different models with the same dimensionality ( five dimensions ) : a kinematic synergy model ( based on PCA applied to joint angles ) , an individual digit model ( based on the average displacement of the digits ) , and a muscle synergy model ( based on PCA applied to EMG features ) . However , to verify that the procedures applied here to reduce data dimensionality yielded the same results of those applied in previous works , we performed PCA by retaining variable numbers of components , from 1 to 10 , and applied the above-described ranking procedure to test the accuracy of all data matrices . The plots of the accuracy values as a function of the number of PCs can be found in Supplementary files 1M and in Figure 6 . The result of this analysis confirmed that the present data are consistent with the previous literature . The same testing procedure was also applied to the individual digit model by computing the rank accuracies for the full model ( five components ) and for the reduced models with 1 to 4 PCs . 10 . 7554/eLife . 13420 . 017Figure 6 . The three graphs display the rank accuracy values as a function of the dimensionality ( i . e . , the number of retained PCs ) of each behavioral model . The two models derived from kinematic and EMG data ( upper and middle graphs , respectively ) have a number of synergies ranging from 1 to 10 while the individual digit model ( lower ) had 1 to 5 retained PCs . Darker bar colors indicate the dimensionality chosen for encoding brain functional data . DOI: http://dx . doi . org/10 . 7554/eLife . 13420 . 017 In the third session , fMRI was used to record the brain activity during the execution of grasp-to-use acts with the objects presented during the previous experiments . Functional data were acquired with a 3 . 0 Tesla GE Signa scanner ( GE , Milwaukee , WI , USA ) , equipped with an 8-channel head-only coil . A Gradient-Echo echo-planar sequence was used , with an acquisition matrix of 128 x 128 , FOV = 240 x 240 mm , Repetition Time ( TR ) = 2 . 5 s , Time of Echo ( TE ) = 40 ms , Flip Angle ( FA ) = 90° . Each volume comprised 43 3 mm-thick slices and the resulting voxel size was 1 . 875 x 1 . 875 x 3 mm . Additional anatomical images were also acquired with a high-resolution T1-weighted Fast Spoiled Gradient Recalled sequence ( FSPGR ) with 1 mm3 isotropic voxels and a 256 x 256 x 0 . 170 mm3 field-of-view; TR = 8 . 16 s , TE = 3 . 18 ms , FA = 12° . Head motion was minimized with foam pads . The task design was identical to that used in previous sessions . Specifically , participants had to shape their hand as if grasping one of the twenty visually-presented objects . In the current session , the subjects were asked to perform only the hand preshaping , limiting the execution of reaching acts with their arm or shoulder , since those movements could easily cause head motion . The day before MRI , all subjects practiced movements in a training session . The paradigm was composed of five runs , each consisting of 20 randomized trials . Each trial consisted of a visual presentation of the target object ( 2 . 5s ) , an inter-stimulus pause ( 5 s ) followed by an auditory cue to prompt movements , and an inter-trial interval ( 12 . 5s ) . The functional runs had two periods of rest ( 15 s ) at their beginning and end to measure baseline activity . The total duration was 6 min and 10 s ( 172 time points ) . The total scanning time was about 40 min . In all sessions , visual stimuli were black and white pictures of the target objects , with a normalized width of 500 pixels . The auditory cue was an 800 Hz sound lasting 150 ms . The experimental paradigm was handled by the software package Presentation® ( Neurobehavioral System , Berkeley , CA , http://www . neurobs . com ) using a MR-compatible visual stimulation device ( VisuaStim , Resonance Technologies , Northridge , CA , USA; dual display system , 5” , 30° of horizontal visual field , 800x600 pixels , 60 Hz ) and a set of MR-compatible headphones for stimuli delivery . The initial steps of fMRI data analysis were performed with the AFNI software package ( Cox , 1996 ) . All volumes within each run were temporally aligned ( 3dTshift ) , corrected for head motion by registering to the fifth volume of the run that was closer in time to the anatomical image ( 3dvolreg ) and underwent a spike removal procedure to correct for scanner-associated noise ( 3dDespike ) . A spatial smoothing with a Gaussian kernel ( 3dmerge , 4 mm , Full Width at Half Maximum ) and a percentage normalization of each time point in the run ( dividing the intensity of each voxel for its mean over the time series ) were subsequently performed . Normalized runs were then concatenated and a multiple regression analysis was performed ( 3dDeconvolve ) . Each trial was modeled by nine tent functions that covered its entire duration from its onset up to 20 s ( beginning of the subsequent trial ) with an interval of 2 . 5 s . The responses associated with each movement were modeled with separate regressors and the five repetitions of the same trial were averaged . Movement parameters and polynomial signal trends were included in the analysis as regressors of no interest . The t-score response images at 2 . 5 , 5 , and 7 . 5 s after the auditory cue were averaged and used as estimate of the BOLD responses to each grasping movement compared to rest . The choice to average three different time points for the evaluation of BOLD response was justified by the fact that such a procedure leads to simpler encoding models for subsequent analyses and that the usage of tent functions is a more explorative procedure that is not linked to an exact time point . For this reason , we could obtain an estimation of brain activity that is more linked to the motor act than to the visual presentation of the target object by concentrating only on a restricted , late time interval . This approach – or similar ones – has also been used by other fMRI studies ( Mitchell et al . , 2008; Connolly et al . , 2012 ) . The coefficients , averaged related to the 20 stimuli of each subject , were transformed to the standard MNI 152 space . First FMRIB Nonlinear Image Registration Tool ( FNIRT ) was applied to the anatomical images to register them in the standard space with a resolution of 1 mm3 ( Andersson et al . , 2007 ) . The matrix of nonlinear coefficients was then applied to the BOLD responses , which were also resampled to a resolution of 2 x 2 x 2 mm . To identify the brain regions whose activity co-varied with the data obtained from the three models – kinematic , EMG synergies , and individual digits – a machine learning algorithm was developed , based on a modified version of the multiple linear regression encoding approach first proposed by Mitchell and colleagues ( Mitchell et al . , 2008 ) . This procedure is aimed at predicting the activation pattern for a stimulus by computing a linear combination of synergy weights obtained from the behavioral models ( i . e . , Principal Components ) with an algorithm previously trained on the activation images of a subset of stimuli ( see Figure 5—figure supplement 1 ) . The procedure consisted in 190 iterations of a leave-two-out cross-validation in which the stimuli were first partitioned in a training set ( 18 stimuli ) and a test set with the two left-out examples . The sample for the analysis was then restricted to the 5000 voxels with the best average BOLD response across the 18 stimuli in the training set ( expressed by the highest t-scores ) . For each iteration , the model was first trained with the vectorized patterns of fMRI coefficients of 18 stimuli associated with their known labels ( i . e . , the target objects ) . The training procedure employed a least-squares multiple linear regression to identify the set of parameters that , if applied to the five synergy weights , minimized the squared error in reconstructing the fMRI images from the training sample . After training the model , only the 1000 voxels that showed the highest R2 ( a measure of fitting between the matrix of synergy weights and the training data ) were retained . A cluster size correction ( nearest neighbor , size = 50 voxels ) was also applied to prune small , isolated clusters of voxels . The performance of the trained model was then assessed in a subsequent decoding stage by providing it with the fMRI images related to the two unseen gestures and their synergy weights , and requiring it to associate an fMRI pattern with the label of one of the left-out stimuli . The procedure was performed within the previously chosen 1000 voxels and accuracy was assessed by considering the correlation distance between the predicted and real fMRI patterns for each of the two unseen stimuli . This pairwise procedure led therefore to a number of correctly predicted fMRI patterns ranging from 0 to 2 with a chance level of 50% . This cross-validation loop was repeated 190 times , leaving out all the possible pairs of stimuli . Therefore , the results consisted of an overall accuracy value – the percentage of fMRI patterns correctly attributed , which is an expression of the success of the model in predicting brain signals – and a map of the voxels that were used in the procedure – i . e . , the voxels whose signal was predictable on the basis of the synergy coefficients . Every voxel had a score ranging from 0 ( if the voxel was never used ) to a possible maximum of 380 ( if the voxel was among the 1000 with the highest R2 and the two left-out patterns could be predicted in all the 190 iterations ) . The encoding analysis was performed in separate procedures for each model – i . e . , kinematic and muscle synergies and individual digit . We obtained , therefore , three sets of accuracy values and three maps of the most used voxels for each subject . These results , which displayed the brain regions whose activity was specifically modulated by the grasping action that was performed inside the scanner , were subsequently used for building the group-level probability maps ( see below ) . The single-subject accuracy was tested for significance against the distribution of accuracies generated with a permutation test within the above-defined encoding procedure . Permutation tests are the most reliable and correct method to assess statistical significance in multivariate fMRI studies ( Schreiber and Krekelberg , 2013; Handjaras et al . , 2015 ) . The null distribution of accuracies was built with a loop in which the model was first trained with five randomly chosen synergy weights that were obtained by picking a random value out of the 18 ( one for each gesture ) in each column of the matrix of synergies . The trained model was subsequently tested on the two left-out images . The procedure was repeated 1000 times , leading to a null distribution of 1000 accuracy values against which we compared the value obtained from the above-described encoding method . Similarly to the encoding analysis , we did not use either the fMRI images or the synergy weights of the two test stimuli for training the model . The left-out examples were therefore tested by an algorithm that had been trained on a completely independent data sample . The weights were shuffled only within column: this procedure yielded vectors of shuffled weights with the same variance as the actual kinematic PCs , even though those vectors were no longer orthogonal . Permutation tests were performed separately for each subject with the three data matrices . Each single-subject accuracy was therefore tested against the null distribution of accuracy values obtained from the same subject data ( one-sided rank test ) . A group map displaying the voxels that were consistently recruited across subjects was obtained for the kinematic synergy model . The single-subject maps achieved from the encoding analysis , which display the voxels recruited by the encoding procedure in each subject , were first binarized by converting non-zero accuracy values to 1 , then summed to obtain an across-subjects overlap image . Moreover , a probability threshold of these maps ( p>0 . 33 ) was applied on the maps to retain voxels in which the encoding procedure was successful in at least four out of the nine subjects ( Figure 1 ) . The accuracies of pairwise discrimination of postures , achieved during the decoding stage of the encoding procedure , were combined across subjects , so to identify the postures that could be discriminated with the highest accuracy based on their associated BOLD activity . The results were displayed as a heat map ( Figure 5 ) , with a threshold corresponding to the chance level of 50% . To evaluate whether the synergies computed on kinematic data from our sample would allow for a reliable reconstruction of hand posture , we needed to verify that these synergies are consistently ranked across individuals . Therefore , we used Metric Pairwise Constrained K-Means ( Bilenko et al . , 2004 ) , a method for semi-unsupervised clustering that integrates distance function and constrained classes . We used the weights of the first three kinematic synergies for the 20 gestures in each subjects as input data and arranged the set of 27 20-items vectors into three classes with nine synergies that showed the higher similarity ( see Supplementary file 1N ) . This analysis was limited to the first three PCs since previous reports ( Santello et al . , 1998; Gentner and Classen , 2006 ) suggest that they may constitute a group of “core synergies” , with a cumulative explained variance greater than 80% . This analysis was performed only on the synergies obtained from the kinematic synergy model , which was able to outperform both the individual digit and muscle synergy models in terms of encoding accuracy percentages on fMRI data . To facilitate the interpretation of the first kinematic PCs as elementary grasps , we plotted the time course of the corresponding hand movements . The plots are 2s-long videos showing three movements from the minimum to the maximum values of PCs 1 , 2 and 3 , respectively , expressed as sets of 24-joint angles averaged across subjects ( Video 1 ) . The three group synergies were studied separately , computing the single correlations between each PC and the fMRI activation coefficient . This correlation estimated the similarity between the activity of every voxel for the 20 grasping acts and the weights of each single synergy . The coefficient of determination ( R2 ) for each synergy was averaged across participants to achieve a measurement of group-level goodness of fit . The overlap image between the group-level probability map and the goodness of fit for each synergy was then obtained and mapped onto a flattened mesh of the cortical surface ( Figure 2 ) . The AFNI SUMA program , the BrainVISA package and the ICBM MNI 152 brain template ( Fonov et al . , 2009 ) were used to render results on the cortical surface ( Figure 1 and 2 ) . To provide a statistical assessment of the orderly mapping of synergies across the regions recruited by the encoding procedure , a comparison between the map space and the feature space was performed ( Goodhill and Sejnowski , 1997; Yarrow et al . , 2014 ) . The correlation of the two spaces is expressed by an index ( C parameter ) that reflects the similarity between the arrangement of voxels in space and the arrangement of their information content: high values indicate that voxels which contain similar information are also spatially close , suggesting a topographical organization . The map space was derived measuring the standardized Euclidean distance between each voxel position in the grid . The feature space was computed using the standardized Euclidean distance between the three synergy weights , as defined by their R2 , for each voxel and averaged across subjects according to the classes described in the sections Assessment of kinematic synergies across subjects and Cortical mapping of the three group synergies . The C parameter was achieved by computing the Pearson correlation between the map space and the feature space ( Yarrow et al . , 2014 ) . An ad-hoc statistical test was developed to assess the existence of the topography . A permutation test was performed generating a null-distribution of C values by correlating the map space with feature spaces obtained by averaging the three synergies across subjects with different random combinations ( 10 , 000 iterations ) . The p-value was calculated by comparing the null-distribution with the C parameter obtained with the cortical mapping ( one-sided rank test ) . Representational content measures ( Kriegeskorte et al . , 2008a; Kriegeskorte and Kievit , 2013 ) were carried out to explore the information that is coded in the regions activated during the execution of finalized motor acts . Representational spaces ( RSs ) are matrices that display the distances between all the possible pairs of neurofunctional or behavioral measures , informing us about the internal similarities and differences that can be evidenced within a stimulus space . By computing a second-order correlation between single model RSs we can evaluate both the similarity between the information carried by the single behavioral models ( kinematic , individual digits and EMG ) and between behavioral data and brain activity as measured by fMRI . RSA was therefore performed within a subset of voxels that were consistently activated by the task . A Region of Interest ( ROI ) was derived from the fMRI data by performing a t-test ( AFNI program 3dttest++ ) that compared the mean brain activity at 2 . 5 , 5 , and 7 . 5 s after the auditory cue and the activity at rest . The results were corrected for False Discovery Rate ( Benjamini and Hochberg , 1995; p<0 . 05 ) ( Figure 4—figure supplement 2 ) . Afterwards , the t-scores relative to each voxel within the ROI were normalized by subtracting the mean across-stimulus activation of all the voxels in the ROI and dividing the value by the standard deviation ( z-score normalization ) . PCA was performed to reduce the BOLD activity of the voxels in the ROI to the first five principal components . Activation pattern RSs were then obtained for each subject by calculating the Euclidean distance between the PCs of all the possible pairs of stimuli ( Edelman et al . , 1998; Kriegeskorte et al . , 2008b; Haxby et al . , 2014 ) . Model RSs were similarly computed for the three types of postural data . This procedure led to a set of brain activity RSs and three sets of model RSs for kinematic synergy , individual digit , and muscle synergy models , respectively . The single subject RSs were averaged to obtain a unique group RS for each model . Since we were interested in identifying the similarities and differences between the information expressed by the behavioral models and the information encoded in the brain , we estimated Pearson correlation separately between the fMRI-based RS and each model RS ( Kriegeskorte et al . , 2008a , 2008b; Devereux et al . , 2013 ) . Moreover , to study the possible specific relations between the behavioral models , additional pairwise correlations between the three model RSs were also performed . These correlations were tested with the Mantel test by randomizing the twenty stimulus labels and computing the correlation . This step was repeated 10 , 000 times , yielding a null distribution of correlation coefficients . Subsequently , we derived the p-value as the percent rank of each correlation within this null distribution ( Kriegeskorte et al . , 2008a ) . The correlations were also estimated between single-subject RSs . In addition , a MDS procedure , using standardized Euclidean distance , metric stress criterion and Procrustes alignment ( Kruskal and Wish , 1978 ) was performed to represent the kinematic synergies and the patterns of BOLD activity across subjects ( Figure 3 ) . Additionally , the fMRI data were used to decode hand postures from stimulus-specific brain activity . This procedure was performed using fMRI coefficients to obtain a set of 24 values , each representing the distances between adjacent hand joints , which could then be used to plot hand configuration . To this purpose , we first run a PCA on the fMRI data , using the voxels within the mask obtained for the RSA and MDS ( see above and Figure 4—figure supplement 2 ) to avoid any possible selection bias; with this procedure , the dimensionality of the data was reduced to the first five dimensions , as previously done for kinematic and EMG data . Then , a multiple linear regression was performed within a leave-one-stimulus-out procedure by using the matrix of postural coefficients as predicted data and the reduced fMRI matrix as predictor . This allowed for the reconstruction of the coefficients of the left-out posture , yielding a matrix with 20 rows ( postures ) and 24 columns ( joint angles ) . Finally , we estimated the goodness of fit ( R2 ) between the reconstructed data and the original postural matrices recorded with the optical tracking system , both subject-wise ( i . e . , computing the correlation of the whole matrices ) and posture-wise ( i . e . , computing the correlation of each posture vector ) . In addition , the decoding performance was assessed using a rank accuracy procedure ( similar to those performed in the behavioral analyses ) in which each reconstructed posture was classified against those originally recorded during the kinematic experiment . The accuracy values were tested against the null distribution generated by a permutation test ( 10 , 000 iterations ) . The reconstructed data were then plotted , using custom code written in MATLAB and Mathematica 9 . 0 ( Wolfram Research , Inc . , Champaign , IL , USA ) ( Figure 4 ) . | The human hand can perform an enormous range of movements with great dexterity . Some common everyday actions , such as grasping a coffee cup , involve the coordinated movement of all four fingers and thumb . Others , such as typing , rely on the ability of individual fingers to move relatively independently of one another . This flexibility is possible in part because of the complex anatomy of the hand , with its 27 bones and their connecting joints and muscles . But with this complexity comes a huge number of possibilities . Any movement-related task – such as picking up a cup – can be achieved via many different combinations of muscle contractions and joint positions . So how does the brain decide which muscles and joints to use ? One theory is that the brain simplifies this problem by encoding particularly useful patterns of joint movements as distinct units or “synergies” . A given task can then be performed by selecting from a small number of synergies , avoiding the need to choose between huge numbers of options every time movement is required . Leo et al . now provide the first direct evidence for the encoding of synergies by the human brain . Volunteers lying inside a brain scanner reached towards virtual objects – from tennis rackets to toothpicks – while activity was recorded from the area of the brain that controls hand movements . As predicted , the scans showed specific and reproducible patterns of activity . Analysing these patterns revealed that each corresponded to a particular combination of joint positions . These activity patterns , or synergies , could even be ‘decoded’ to work out which type of movement a volunteer had just performed . Future experiments should examine how the brain combines synergies with sensory feedback to allow movements to be adjusted as they occur . Such findings could help to develop brain-computer interfaces and systems for controlling the movement of artificial limbs . | [
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] | 2016 | A synergy-based hand control is encoded in human motor cortical areas |
Death domains ( DDs ) mediate assembly of oligomeric complexes for activation of downstream signaling pathways through incompletely understood mechanisms . Here we report structures of complexes formed by the DD of p75 neurotrophin receptor ( p75NTR ) with RhoGDI , for activation of the RhoA pathway , with caspase recruitment domain ( CARD ) of RIP2 kinase , for activation of the NF-kB pathway , and with itself , revealing how DD dimerization controls access of intracellular effectors to the receptor . RIP2 CARD and RhoGDI bind to p75NTR DD at partially overlapping epitopes with over 100-fold difference in affinity , revealing the mechanism by which RIP2 recruitment displaces RhoGDI upon ligand binding . The p75NTR DD forms non-covalent , low-affinity symmetric dimers in solution . The dimer interface overlaps with RIP2 CARD but not RhoGDI binding sites , supporting a model of receptor activation triggered by separation of DDs . These structures reveal how competitive protein-protein interactions orchestrate the hierarchical activation of downstream pathways in non-catalytic receptors .
The death domain ( DD ) is a globular protein module of 80–90 amino acid residues with a characteristic six-helix bundle fold ( Feinstein et al . , 1995; Ferrao and Wu , 2012 ) . DDs are present in a variety of proteins , including several members of the tumor necrosis factor receptor superfamily ( TNFRSF ) and a range of intracellular signaling components , such as caspases and kinases . The DD family includes four subfamilies of structurally related domains , including the canonical DD , the death effector domain ( DED ) , the caspase recruitment domain ( CARD ) , and the pyrin domain ( PYD ) . DD-containing proteins play central roles in apoptotic and inflammatory signaling through the formation of oligomeric protein complexes , and several disease-causing mutations have been mapped to DD interfaces ( Park et al . , 2007 ) . All DD complexes described so far involve homotypic interactions between DDs of the same subfamily ( e . g . , DD with DD , CARD with CARD , etc . ) . All known DD interactions belong to one of three types ( I to III ) , each mediated by conserved asymmetric interfaces in the interacting DDs ( Park , 2011; Park et al . , 2007; Weber and Vincenz , 2001 ) . Heterotypic complexes between DDs from different subfamilies have not yet been described and , aside from a few structures of DDs bound to small polypeptides , no complexes of DDs with proteins outside the DD superfamily have been reported . Thus , type I , II , and III interactions between DDs are thought to represent the predominant mechanism of oligomerization and complex formation for DD-containing proteins . The cytoplasmic domain of the p75 neurotrophin receptor ( p75NTR , also known as NGFR and TNFRSF16 ) contains a C-terminal DD connected to the transmembrane ( TM ) domain by a 60-residue-long linker region ( Liepinsh , 1997 ) . p75NTR is a receptor for members of the neurotrophin family , such as nerve growth factor ( NGF ) and brain-derived neurotrophic factor ( BDNF ) ( Dechant and Barde , 2002; Roux and Barker , 2002 ) . In addition to the neurotrophins , a number of other extracellular ligands can also engage or signal through p75NTR , including the beta-amyloid peptide ( Knowles et al . , 2009; Perini et al . , 2002 ) , the rabies virus glycoprotein ( Tuffereau et al . , 1998 ) , and various myelin-derived polypeptides ( Wang et al . , 2002; Wong et al . , 2002 ) . p75NTR may function alone or in conjunction with other transmembrane proteins to allow ligand binding and intracellular signaling . These proteins include members of the Trk family of receptor tyrosine kinases , members of the Vps10p family of sorting receptors , such as Sortilin , and the Nogo receptor , which promotes binding to myelin-derived ligands ( Underwood and Coulson , 2008 ) . p75NTR can engage different intracellular pathways , of which the best characterized are the RhoA pathway , which regulates axon growth , collapse and degeneration ( Park et al . , 2010; Yamashita et al . , 1999; Yamashita and Tohyama , 2003 ) , the NF-kB pathway , which contributes to cell survival ( Carter et al . , 1996; Khursigara et al . , 2001; Vicario et al . , 2015 ) , and the c-Jun kinase ( JNK ) or caspase pathway , which mediates apoptotic cell death ( Friedman , 2000; Yoon et al . , 1998 ) . p75NTR signaling through any of those three pathways requires a functional DD ( Charalampopoulos et al . , 2012 ) . Expression of p75NTR increases in a number of neurodegenerative diseases and upon injury or stress conditions , where it contributes to neuronal and glial cell damage , axonal degeneration , and synaptic dysfunction ( Ibanez and Simi , 2012 ) . Inhibition of p75NTR signaling has emerged as an attractive strategy for limiting neural damage in neurodegeneration and nerve injury . The mechanism of activation of p75NTR in response to neurotrophins involves a conformational rearrangement of disulfide-linked receptor dimers , resulting in the separation of intracellular DDs ( Vilar et al . , 2009 ) . Fluorescence resonance energy transfer ( FRET ) experiments have shown that the two DDs in the p75NTR dimer are in close proximity to each other ( high FRET state ) and that NGF binding induces a decrease in FRET signal ( Vilar et al . , 2009 ) . Disruption of this conformational change through mutation of a conserved cysteine residue in the TM domain prevents p75NTR signaling in response to neurotrophins ( Vilar et al . , 2009 ) . p75NTR lacks an associated catalytic activity . Similar to other members of the TNFRSF , signaling by p75NTR proceeds via ligand-induced recruitment and release of cytoplasmic effectors to and from its intracellular domain . Ligand-induced separation of p75NTR DDs may allow the recruitment of intracellular components for downstream signal propagation . Although a variety of intracellular proteins have been reported to interact with p75NTR , the molecular mechanisms by which the receptor engages different signaling pathways remain unclear . To begin addressing these questions , our laboratory performed a comprehensive structure–function study of the p75NTR DD that resulted in the identification of three sets of solvent-exposed residues that are critical for p75NTR’s ability to engage the RhoA , NF-kB and JNK/cell death pathways , respectively ( Charalampopoulos et al . , 2012 ) . Receptor mutants that are selectively deficient in one pathway but not others were generated , demonstrating that different signaling outputs can be genetically separated in p75NTR . Understanding how such interfaces relate to each other and to the mechanism of receptor activation has remained an important challenge . In this study , we have undertaken a structural biology approach to elucidate the molecular mechanisms underlying downstream signaling mediated by the DD in p75NTR . We have determined the solution structures of the p75NTR DD in complex with RhoGDI ( Rho guanine nucleotide dissociation inhibitor ) , which links the receptor to the RhoA pathway ( Yamashita et al . , 1999; Yamashita and Tohyama , 2003 ) , or with the CARD domain of RIP2 kinase , which is necessary for p75NTR coupling to the NF-kB pathway ( Charalampopoulos et al . , 2012; Khursigara et al . , 2001 ) . We have also solved the solution structure of the p75NTR DD homodimer , revealing the DD surface that is occluded prior to neurotrophin-mediated receptor activation . These structures uncover novel heterotypic DD interactions , not previously seen in other DD-containing complexes , and reveal the molecular mechanisms underlying the early stages of p75NTR activation and downstream signaling .
RhoGDI interacts constitutively with the DD of unliganded p75NTR , linking the receptor to the RhoA pathway ( Yamashita and Tohyama , 2003 ) . Neurotrophin binding induces the release of RhoGDI from p75NTR and decreases RhoA activity ( Gehler et al . , 2004; Yamashita et al . , 1999; Yamashita and Tohyama , 2003 ) . Using biochemical and cell-based assays , we have previously identified solvent-exposed residues in the p75NTR DD that are critical for its interaction with RhoGDI and RhoA activation , including residues in helices H1 and H6 ( Charalampopoulos et al . , 2012 ) . In order to obtain a molecular understanding of this interaction , we determined the solution structure of the human p75NTR DD:RhoGDI complex by multidimensional nuclear magnetic resonance ( NMR ) spectroscopy ( Figure 1—figure supplement 1 and Table 1 ) . We note that , unless otherwise indicated , all amino acid residue numbering in this study corresponds to the human forms of the respective proteins . Human and rat p75NTR DD share more than 90% sequence identity—including all functionally relevant residues—and an essentially identical three-dimensional structure with an overall RMSD of 1 . 7 Å ( ( Liepinsh , 1997 ) and this study ) . The ensemble of the 10 lowest-energy conformers of the DD:RhoGDI complex and a representative structure are depicted in Figure 1A , B . p75NTR DD in the complex consists of one 310-helix followed by six α-helices and its global fold is very similar to that of our previously described structure of rat p75NTR DD ( Liepinsh , 1997 ) . In the complex , the C-terminal domain of RhoGDI primarily displays an immunoglobulin-like fold similar to previously described structures of this protein ( Longenecker et al . , 1999 ) . Residues Glu40 to Gly57 in the RhoGDI N-terminal domain fold into a long helix , which is not involved in p75NTR DD binding and remains flexible in the complex ( Figure 1A ) . Removal of this N-terminal domain did not affect RhoGDI binding to p75NTR DD ( Figure 1—figure supplement 2 ) . Inspection of the interface in the complex showed that it is mainly formed by α-helices H1 and H6 of the p75NTR DD and β-strands S2 , S3 , S9 and α-helix H2 of RhoGDI , forming a small hydrophobic core surrounded by polar residues ( Figure 1C ) . Charged residues play an important role in the binding interface and high concentration of salt ( i . e . , greater than 200 mM NaCl ) can almost completely disrupt p75NTR DD:RhoGDI interaction in vitro ( Figure 1—figure supplement 1C ) . It is gratifying to note that all functional DD determinants that we have previously identified by site-directed mutagenesis clustered at the DD:RhoGDI interface of the complex structure ( labeled red in Figure 1C , D ) . The structure of the DD:RhoGDI complex offered an opportunity to address the functional importance of a larger set of residues in the p75NTR DD as well as in RhoGDI . Co-immunoprecipitation experiments were performed in cells transfected with constructs of full-length p75NTR and RhoGDI carrying different point mutations in selected residues . Alanine substitution of individual amino acid residues likely uncovers only those side chains making the most critical contribution to the binding interface . In the p75NTR DD , substitution of either Asp412 , Lys343 or Glu420 was found to significantly diminish interaction with RhoGDI ( Figure 1D ) . In RhoGDI , substitution of Lys99 or Lys199 abolished its interaction with p75NTR ( Figure 1E ) . In agreement with this , the structure of the complex shows that these two positively charged side chains make charge interactions with Glu420 and Asp412 , respectively , in the p75NTR DD ( Figure 1C ) . 10 . 7554/eLife . 11692 . 003Table 1 . NMR and refinement statistics for p75NTR DD complexes and RIP2 CARD . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 003NMR distance and dihedral constraintsDD:RhoGDIRIP2 CARDDD:CARDDD:DDDistance constraintsTotal NOE3525210737603344Intra-residue809436798728Inter-residueSequential ( i – j = 1 ) 10546561130986Medium-range ( i – j ≤ 4 ) 6655891016892Long-range ( i – j ≥ 5 ) 945426771706Intermolecular NOE52-4532Total dihedral angle restraints a222132260280Structure StatisticsViolations ( mean and s . d . ) Distance constraints ( Å ) 0 . 36 ± 0 . 020 . 25 ± 0 . 010 . 36 ± 0 . 030 . 28 ± 0 . 01Dihedral angle constraints ( º ) 3 . 50 ± 0 . 462 . 75 ± 0 . 283 . 37 ± 0 . 282 . 86 ± 0 . 59Max . dihedral angle violation ( º ) 4 . 163 . 253 . 774 . 28Max . distance constraint violation ( Å ) 0 . 390 . 260 . 440 . 29Ramachandran Plot ( allowed region ) 99 . 8%99 . 5%99 . 8%99 . 9%Average RMSD ( Å ) bHeavy atoms0 . 91 ± 0 . 120 . 83 ± 0 . 060 . 99 ± 0 . 080 . 77 ± 0 . 06Backbone atoms0 . 55 ± 0 . 130 . 36 ± 0 . 050 . 66 ± 0 . 090 . 43 ± 0 . 02a Dihedral angle constraints were generated by TALOS based on Cα and Cβ chemical shifts . b Average r . m . s . deviation ( RMSD ) to the mean structure was calculated among 10 refined structures . Superimposing residues for DD:RhoGDI , RIP2 CARD , DD:CARD , and DD:DD are 334–421 of DD with 70–204 of RhoGDI , 436–523 of RIP2 CARD , 334–420 of DD with 435–534 of RIP2 CARD and 334–421 of DD respectively . The total AMBER energy for DD:RhoGDI , RIP2 CARD , DD:CARD , and DD:DD are –9884 ± 41 , –4437 ± 32 , –7706 ± 32 and –7161 ± 18 kcal/mol respectively . 10 . 7554/eLife . 11692 . 004Figure 1 . Solution structure of the complex between the p75NTR DD and RhoGDI . ( A ) Superposition of backbone heavy atoms of the 10 lowest-energy structures of the human p75NTR DD:RhoGDI complex . N- and C-termini are indicated . ( B ) Ribbon drawing of the lowest-energy conformer . Light brown , p75NTR DD; Cyan , RhoGDI . N- and C-termini , as well as DD helices H1 and H6 are indicated . ( C ) Details of binding interface in the complex viewed in the same orientations as panel B , respectively . Key residues at the binding interface are labeled and depicted as stick models . Red labels denote interface residues functionally validated in our earlier mutagenesis study ( Charalampopoulos et al . , 2012 ) . ( D ) Co-immunoprecipitation of wild type ( WT ) and DD point mutants of human p75NTR with Myc-tagged RhoGDI in transfected HEK 293 cells . Antibodies used for immunoprecipitation ( IP ) and Western blotting ( WB ) are indicated . WCE , whole cell lysate . The immunoblots shown are representative of three independent experiments . ( E ) Co-immunoprecipitation of WT and point mutants of Myc-tagged human RhoGDI with p75NTR in transfected HEK 293 cells . Antibodies used for immunoprecipitation ( IP ) and Western blotting ( WB ) are indicated . WCE , whole cell extract . The immunoblots shown are representative of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 00410 . 7554/eLife . 11692 . 005Figure 1—figure supplement 1 . NMR spectra of DD:RhoGDI complex in 20 mM HEPES at 28°C and pH 6 . 9 . ( A ) [1H-15N] HSQC spectra of 15N-RhoGDI in the absence ( black ) and presence ( red ) of p75NTR DD . The concentration of RhoGDI and p75NTR DD was 0 . 5 and 2 mM , respectively . ( B ) Representative slices from the 13C , 15N-filtered 3D NOESY spectrum . ( C ) [1H-15N] HSQC spectra of 15N-RhoGDI in complex with p75NTR DD in the absence ( black ) and presence ( red ) of 250 mM NaCl . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 00510 . 7554/eLife . 11692 . 006Figure 1—figure supplement 2 . The N-terminal domain of RhoGDI does not bind to p75NTR DD . [1H-15N] HSQC spectra of p75NTR DD in the presence of RhoGDI ( black ) and RhoGDI without N-terminal domain ( RhoGDI ΔN , red ) at 28°C and pH 6 . 9 . Molar ratio of DD to RhoGDI or to RhoGDI ΔN was 1:4 . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 006 Analysis of the p75NTR DD:RhoGDI complex and a previously described crystal structure of the RhoGDI:RhoA ( GDP ) complex ( Longenecker et al . , 1999 ) indicated that p75NTR DD and RhoA interact with different surfaces in RhoGDI , located at opposite sides of the molecule . The two distant binding sites on RhoGDI suggested that a heterotrimer complex p75NTR DD:RhoGDI:RhoA may be structurally feasible . We investigated this by performing HADDOCK calculations ( Dominguez et al . , 2003 ) using our solution structure of p75NTR DD from its complex with RhoGDI and the crystal structure of the RhoGDI:RhoA ( GDP ) complex . Multiple refinements converged to a mean backbone root mean square deviation ( RMSD ) of 0 . 64 ± 0 . 05 Å ( Figure 2A , B and Table 2 ) . Ramachandran plot analysis of the docking model indicated that the trimer structure , including the two intermolecular interfaces , still occupies the energetically preferred conformation . In the tripartite complex , the N-terminal domain of RhoGDI folded into two helices and bound to RhoA ( GDP ) , similar to its conformation in the RhoGDI:RhoA ( GDP ) complex ( Longenecker et al . , 1999 ) . The DD binding site on RhoGDI remained nearly identical to that in the DD:RhoGDI complex . This analysis shows that interaction of the three proteins can indeed occur simultaneously and explains previous biochemical studies showing that RhoA can be co-immunoprecipitated with p75NTR in the presence of RhoGDI ( Yamashita et al . , 1999 ) . Using the purified proteins , we determined the binding affinity of the RhoGDI:RhoA complex by surface plasmon resonance ( SPR ) . Titration of RhoGDI onto immobilized RhoA , yielded a binding Kd of 0 . 14 ± 0 . 01 μM , which is in agreement with previous measurements ( Tnimov et al . , 2012 ) ( Figure 2C ) . Interestingly , when RhoGDI was precomplexed with purified p75NTR DD , the Kd was 2 . 2 ± 0 . 11 μM ( Figure 2D ) , indicating that binding to the p75NTR DD decreases the affinity of the RhoGDI:RhoA interaction by about 15-fold . We note that , in the absence of RhoGDI , no binding between p75NTR DD and RhoA could be detected in these experiments ( Figure 2E ) . These results suggest that RhoGDI binding to the p75NTR DD weakens its interaction with RhoA , a step which may facilitate RhoA activation . 10 . 7554/eLife . 11692 . 007Figure 2 . Structural model of tripartite complex between p75NTR death domain , RhoGDI and RhoA . ( A ) Superposition of backbone traces of the 10 lowest-energy structures of p75NTR DD:RhoGDI:RhoA tripartite complex . N- and C-termini are indicated . ( B ) Ribbon diagram of a representative structure of p75NTR DD:RhoGDI:RhoA heterotrimer complex . Light brown , p75NTR DD; Cyan , RhoGDI; Blue , RhoA . Mg2+ and GDP appear in ball-and-stick models . p75NTR DD helices H1 , H5 and H6 as well as N- and C-termini are indicated . ( C ) Binding of RhoGDI to immobilized RhoA:GDP:Mg2+ measured by surface plasmon resonance ( SPR ) . Binding affinity was determined by steady-state analysis . One binding site model was used for fitting of SPR data . The sensorgram shown is representative from three independent experiments . ( D ) Binding of RhoGDI complexed with p75NTR DD ( molar ratio 1:2 ) to immobilized RhoA:GDP:Mg2+ measured by SPR . Binding affinity was determined by steady-state analysis . One binding site model was used for fitting of SPR data . The sensorgram shown is representative from three independent experiments . ( E ) Sensorgram showing lack of interaction between p75NTR DD ( tested at 125–500 nM ) and immobilized RhoA:GDP:Mg2+ . The sensorgram shown is representative from three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 00710 . 7554/eLife . 11692 . 008Figure 2—figure supplement 1 . Local structural differences in RhoGDI after interaction with either p75NTR DD or RhoA:GDP . Ribbon diagram of RhoGDI from the complex with p75NTR DD ( light brown ) is shown in cyan , and from the complex with RhoA ( blue ) in red ( from Longenecker et al . , 1999 ) . Main structural differences in RhoGDI are indicated by the dotted line circle . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 00810 . 7554/eLife . 11692 . 009Table 2 . Structural statistics for the 10 lowest-energy structures of p75NTRDD:RhoGDI:RhoA Trimer and Hexamera . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 009TrimerHexamerBackbone RMSD ( Å ) From the mean , full complex0 . 61 ± 0 . 250 . 59 ± 0 . 20From the mean , all interfaces0 . 58 ± 0 . 220 . 48 ± 0 . 13Total energy ( kcal/mol ) -20721 ± 137-37682 ± 210Ramachandran plot ( % ) bResidues in the most favored regions82 . 284 . 7Residues in additional allowed regions14 . 112 . 5Residues in generously allowed regions1 . 91 . 9Residues in disallowed regions1 . 80 . 9aStructural statistics for the 10 lowest-energy conformers were obtained from HADDOCK calculation using NOEs between DD and RhoGDI . bRamachandran analysis was carried out using PROCHECK-NMR . NGF binding to p75NTR elicits the recruitment of RIP2 kinase to the receptor . Recruitment of RIP2 is required for regulation of the NF-kB pathway by p75NTR . Previous biochemical studies established that the interaction between p75NTR and RIP2 is mediated by their DD and CARD domains , respectively ( Khursigara et al . , 2001 ) . The RIP2 CARD consists of 107 amino acids and is located in the C-terminal of the protein . It connects to the N-terminal kinase domain via a linker of 120 amino acids . We determined the NMR structure of human RIP2 CARD in monomeric form ( Figure 3A , B and Table 1 ) . The solution structure of RIP2 CARD comprises an arrangement of six α-helices followed by one short 310-helix , all tightly packed around a hydrophobic core . A C-terminal tail of 17 amino acids ( Leu524-Met540 ) follows the CARD and is unstructured and flexible in solution . A unique segment ( Gln518-Ile523 ) between the C-terminal tail and the 310-helix contains two structural disruptor residues ( i . e . , Pro519 and Pro520 , Figure 3B ) and lacks a secondary structure , but its orientation was well-defined in the NMR structure . A number of hydrophobic residues ( e . g . , Ile523 , Figure 3B ) in this segment closely interact with the first and the last α-helices in the RIP2 CARD . Structural comparison using the DALI server ( Holm and Rosenström , 2010 ) showed that the most similar structure to RIP2 CARD was the CARD of nucleotide-binding oligomerization domain-containing protein 1 ( NOD1 ) , with a Z-score between 9 and 11 . NOD1 and RIP2 have been shown to interact with each other through their CARDs to propagate immune signaling ( Mayle et al . , 2014 ) . The two CARDs share similar structural features , including a similar arrangement of all but the last of the α-helices , which displays different local secondary structures in the two proteins ( Figure 3—figure supplement 1A ) . Despite their folding similarities , the two CARDs exhibit significantly different surface characteristics . Particularly , RIP2 CARD has many more charged residues on its surface than its NOD1 counterpart ( Figure 3—figure supplement 1B , C ) . Different electrostatic surfaces will confer different interaction specificities , a common feature among members of the DD superfamily , including the CARD subfamily . 10 . 7554/eLife . 11692 . 010Figure 3 . Solution structure of RIP2 CARD and its complex with p75NTR DD . ( A ) Superposition of backbone heavy atoms of the 10 lowest-energy structures of human RIP2 CARD . N- and C-termini are indicated . ( B ) Ribbon drawing of the lowest-energy conformer of human RIP2 CARD . N- and C-termini , as well as selected residues in the C-terminal tail are indicated . ( C ) Superposition of backbone heavy atoms of the 10 lowest-energy structures of the human p75NTR DD:RIP2 CARD complex . N- and C-termini are indicated . ( D ) Ribbon drawing of the lowest-energy p75NTR DD:RIP2 CARD conformer . Light brown , p75NTR DD; Green , RIP2 CARD . N- and C-termini , as well as DD helices H2 , H3 , and H5 are indicated . ( E and F ) Details of binding interface in the complex viewed in the same orientations as panel D , respectively . Key residues at the binding interface are labeled and depicted as stick models . Red labels denote interface residues functionally validated in our earlier mutagenesis study ( Charalampopoulos et al . , 2012 ) . ( G ) Co-immunoprecipitation of wild type ( WT ) and point mutants of Flag-tagged human RIP2 with p75NTR in transfected HEK 293 cells . In the overexpression conditions used for this experiment , interaction of RIP2 with p75NTR was constitutive in the absence of ligand . Antibodies used for immunoprecipitation ( IP ) and Western blotting ( WB ) are indicated . WCE , whole cell extract . The immunoblots shown are representative of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01010 . 7554/eLife . 11692 . 011Figure 3—figure supplement 1 . Structure comparison of CARD domains using a sequential structure alignment program ( http://v3-4 . cathdb . info/ ) . ( A ) Overlap of RIP2 CARD ( red ) and NOD1 CARD ( gray , PDB ID: 2B1W ) . ( B ) Surface charge of RIP2 CARD without C-terminal tail . Positive charge surface is colored in blue , negative in red and noncharged in white . ( C ) Surface charge of NOD1 CARD . ( D ) Statistics of pairwise alignment of CARDs from RIP2 and NOD1 . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01110 . 7554/eLife . 11692 . 012Figure 3—figure supplement 2 . NMR Spectra of DD:CARD complex . ( A ) [1H-15N] HSQC spectra of p75NTR DD in water in the absence ( black ) and presence ( red ) of RIP2 CARD . ( B ) Representative slices from the 13C , 15N-filtered 3D NOESY spectra . Asterisk denotes ambiguous NOE peak . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01210 . 7554/eLife . 11692 . 013Figure 3—figure supplement 3 . Structural comparisons of p75NTR DD and RIP2 CARD domains . ( A ) Structural comparison between p75NTR DD from the DD:RhoGDI ( red ) and DD:CARD ( green ) complexes . ( B ) Structural comparison between monomeric RIP2 CARD ( cyan ) and RIP2 CARD from the DD:CARD complex ( light brown ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01310 . 7554/eLife . 11692 . 014Figure 3—figure supplement 4 . The C-terminal tail of RIP2 CARD contributes to its interaction with the p75NTRDD . ( A ) CD spectra of CARD ( orange ) and CARD ΔC mutant lacking the C-terminal tail ( blue ) . ( B ) Sensorgram of binding kinetics of CARD ΔC binding to p75NTR DD at pH 7 . 0 . One binding site model was used for fitting of SPR data . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 014 In order to obtain a molecular understanding of the p75NTR DD:RIP2 CARD interaction , we determined the NMR structure of the complex . Figure 3C , D present the three-dimensional solution structure of this complex obtained from the NMR experimental restraints ( Table 1 and Figure 3—figure supplement 2 ) . The core helical structure of the p75NTR DD in the p75NTR DD:RIP2 CARD complex was similar to that in the p75NTR DD:RhoGDI complex , with a pairwise RMSD of ~1 . 9 Å ( Figure 3—figure supplement 3A ) . The orientation of α-helices H3 and H6 changed by approximately 7°–8° . Similarly , pairwise RMSD between RIP2 CARD in monomeric form and in complex with the p75NTR DD was ~1 . 5 Å ( Figure 3—figure supplement 3B ) . The main interface in the core structure of the p75NTR DD:RIP2 CARD complex involved α-helices H2 , H3 , H5 , H6 , and H5–H6 loop of p75NTR DD and α-helices H1 , H3–H4 , and H5–H6 loops of RIP2 CARD ( Figure 3E , F ) . Both electrostatic and hydrophobic interactions contribute to the p75NTR DD:RIP2 CARD interface . Interestingly , the C-terminal tail of RIP2 CARD was better defined in its complex with the p75NTR DD compared to its monomeric form , although the last six amino acids still remained flexible . The C-terminal tail bound to α-helices H1 , H5 , and H6 of p75NTR DD , through both hydrophobic and charged interactions ( Figure 3D and Figure 3—figure supplement 3B ) . In our previous site-directed mutagenesis studies of the p75NTR DD , we had identified residues in helices H2 ( Asp357 , His361 , and Glu365 ) , H3 ( Gln369 and Glu371 ) , H5 ( Asp399 ) , and H6 ( Asp412 and Glu415 ) as being critical for its interaction with RIP2 ( Charalampopoulos et al . , 2012 ) . We were pleased to note that all these residues mapped to the DD:CARD binding interface defined in our NMR structure of the complex ( labeled red in Figure 3E , F ) . The NMR structure of the DD:CARD complex offered an opportunity to address the functional importance of residues in the RIP2 CARD . Co-immunoprecipitation experiments were performed in cells transfected with constructs of full-length p75NTR and RIP2 , the latter carrying different point mutations in selected residues of the CARD . We found that individual substitution of interface residues Gln437 , Asp467 , Lys471 , Ile496 , Glu500 , or Arg528 , significantly diminished RIP2 interaction with p75NTR ( Figure 3G ) . Comparison of the DD interfaces used for binding to RhoGDI and RIP2 CARD showed that these shared partially overlapping binding sites on p75NTR DD ( Figure 4A ) , indicating that RIP2 and RhoGDI cannot bind to the p75NTR DD simultaneously due to steric hindrance . This is in agreement with our previous biochemical studies that identified overlapping epitopes required for the interaction of p75NTR DD with both RIP2 and RhoGDI ( Charalampopoulos et al . , 2012 ) . In order to better understand the hierarchical relationship of these interactions , we determined the binding affinities of the DD:RhoGDI and DD:CARD complexes by SPR . The Kd of RhoGDI binding to the p75NTR DD was 0 . 82 ± 0 . 3 μM , while the Kd of CARD binding to the DD was 4 . 67 ± 0 . 7 nM ( Figure 4B , C ) , indicating that RIP2 CARD binds with approximately 177-fold higher affinity than RhoGDI to the p75NTR DD . This is in line with the larger buried solvent accessible area in the p75NTR DD:RIP2 CARD complex ( ~1400 Å2 ) compared to that in the p75NTR DD:RhoGDI complex ( ~900 Å2 ) . Kinetic analyses revealed that CARD associates with the p75NTR DD with faster on-rate , and dissociates with slower off-rate , than RhoGDI ( Table 3 ) . The RIP2 CARD could still fold into a typical α-helical structure after deletion of the C-terminal tail ( Figure 3—figure supplement 4A ) . However , the binding affinity of this construct to the p75NTR DD was 58 . 7 ± 8 . 8 nM , that is , approximately 12-fold lower than with the C-terminal tail ( Figure 3—figure supplement 4B ) , indicating a significant contribution of the C-terminal tail to the association of RIP2 with p75NTR . 10 . 7554/eLife . 11692 . 015Figure 4 . Structural basis for competitive interaction between RIP2 and RhoGDI on the p75NTR DD . ( A ) Surface representation of p75NTR DD ( light brown ) with overlapped ribbon drawings of RhoGDI ( cyan ) and RIP2 CARD ( green ) . The expanded view shows detail of the overlapping interfaces demonstrating steric clashes between residues in RhoGDI and CARD ( highlighted as stick models ) . ( B and C ) Binding of RhoGDI ( B ) and RIP2 CARD ( including C-terminal tail ) ( C ) to captured His-tagged p75NTR DD measured by SPR . Colored lines represent experimentally recorded values at different concentrations and black lines are fitting data . Binding affinities were determined by kinetic analysis using one binding site model was used for fitting of SPR data . The sensorgrams shown are representative from three independent experiments . ( D ) [1H-15N] HSQC spectra of 15N-RhoGDI showing the ability of RIP2 to displace RhoGDI from the p75NTR DD . The panels show details of different regions of the spectra for RhoGDI alone ( green ) , RhoGDI in the presence of p75NTR DD ( red ) , and RhoGDI in the presence of both p75NTR DD and RIP2 CARD ( blue ) . Representative RhoGDI residues located in and/or close to the DD:RhoGDI interface are labeled . Arrows denote shifts in the spectra of labeled RhoGDI residues upon addition of p75NTR DD and RIP2 CARD . All the spectra were recorded at pH 6 . 9 and 28°C . The concentrations of RhoGDI , p75NTR DD and RIP2 CARD were 0 . 05 , 0 . 2 , and 0 . 2 mM respectively . ( E ) Analysis of RhoA-GTP levels in lysates of HEK293 cells transfected with p75NTR and RIP2 expression constructs , as indicated . Increasing concentrations of RIP2 construct is indicated . The histogram shows average RhoA-GTP levels ( from triplicate measurements ) normalized to p75NTR alone . Protein expression levels were controlled by Western blotting ( not shown ) . * p<0 . 01 vs . p75NTR alone ( t-test ) . ( F ) Analysis of RhoA-GTP levels in cerebellar extracts prepared from P7 Rip2 knockout mice and wild type littermates ( WT ) . The histogram shows average RhoA-GTP levels in WT ( N = 3 ) and KO ( N = 4 ) animals normalized to WT levels . *p<0 . 05 vs . WT ( t-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01510 . 7554/eLife . 11692 . 016Table 3 . Association and dissociation binding constants of p75NTR DD binding to RhoGDI and RIP2 CARD . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 016ka ( µM-1·s-1 ) kd ( s-1·10-3 ) Kd ( nM ) DD:RhoGDI0 . 06 ± 0 . 0150 ± 9827 ± 338DD:CARD0 . 72 ± 0 . 343 . 32 ± 1 . 54 . 67 ± 0 . 7 The differential binding of RhoGDI and RIP2 CARD to the p75NTR DD was further tested through analysis of 2D NMR spectra of RhoGDI binding to p75NTR DD in competition with RIP2 CARD . Figure 4D shows details of the RhoGDI spectra focusing on representative residues located in and/or close to the DD:RhoGDI interface . Addition of p75NTR DD produced a shift in the cross-peaks of these residues ( red in Figure 4D ) , indicating binding of RhoGDI to the DD . Addition of RIP2 CARD to the RhoGDI:DD complex shifted these cross-peaks back to their initial positions ( arrows in Figure 4D ) , indicating dissociation of RhoGDI from the p75NTR DD . These data demonstrate that RhoGDI and RIP2 CARD compete for binding to the p75NTR DD and that RIP2 CARD can displace RhoGDI from the receptor . In order to test the functional significance of the antagonism between RIP2 and RhoGDI , we assessed the levels of RhoA-GTP , a measure of RhoA activation , in cells transfected with a p75NTR expression construct in the absence or presence of increasing concentrations of a RIP2 construct ( Figure 4E ) . While expression of p75NTR increased RhoA-GTP levels in transfected cells , coexpression of RIP2 decreased RhoA-GTP levels in a concentration-dependent manner ( Figure 4E ) , in agreement with an inhibitory role of RIP2 in p75NTR-mediated activation of the RhoA pathway . In line with this , we found elevated levels of RhoA-GTP in brain extracts from Rip2 knockout mice compared to wild type littermates ( Figure 4F ) , suggesting that RIP2 can also restrict the activation of the RhoA pathway in vivo . The current model of p75NTR activation by neurotrophins predicts that the DDs should be in close proximity to each other to account for the high FRET state of the unliganded receptor . Purified rat p75NTR DD has been shown to exist in either monomeric form or equilibrium between monomeric and dimeric forms depending on pH and counterion ( Vilar et al . , 2014 ) . However , the complete assignment of the DD homodimer was not reported in that study . We also found that human p75NTR DD exists mainly in monomeric form in TRIS or HEPES buffer at pH 6 . 0–7 . 0 , which were the buffer conditions used for structure determination of DD:RhoGDI and DD:CARD complexes . In phosphate buffer , however , we observed a new form of p75NTR DD as revealed by the appearance of a new set of cross peaks in the [1H-15N] HSQC spectrum ( Figure 5—figure supplement 1A , B ) . The set of cross peaks corresponding to monomeric DD was still visible , with nearly identical chemical shift but weaker intensity ( Figure 5—figure supplement 1A ) , suggesting the coexistence of dimeric and monomeric DDs under these conditions . Dynamic lighter scattering ( DLS ) also indicated the formation of dimeric p75NTR DDs in the presence of phosphate ions ( Figure 5—figure supplement 1C , D ) . EGFP-tagged p75NTR DDs showed anisotropic changes due to homodimerization at different DD concentrations . The apparent Kd of dimerization derived from anisotropic change was 49 ± 15 µM ( Figure 5—figure supplement 1E ) . This relatively low-affinity interaction may facilitate DD separation ( low FRET state ) upon receptor activation by neurotrophins . In order to identify the dimerization interface , we determined the NMR structure of the p75NTR DD homodimer . The p75NTR DD homodimer adopted a C2 symmetry ( Figure 5A , B ) . A short C-terminal tail of 7 amino acids ( Ser421-Val427 ) in each monomer remained disordered , similar to the DD:RhoGDI and DD:CARD complexes . The helical bundle , including the 310 helix , did not undergo significant structural change with an RMSD lower than 1 . 5 Å compared to the other complexes . The dimerization interface consisted of α-helices H2 and H3 as well as residues in the H1–H2 and H3–H4 loops . Dimerization involved both charge and hydrophobic interactions . The key residues in the dimer interface included Asp357 , Arg360 , Thr377 , His378 , Glu379 , and Ala380 ( corresponding to Asp354 , Arg358 , Thr375 , His376 , Glu377 , and Ala378 in rat p75NTR DD ) . This is in agreement with previous site-directed mutagenesis and NMR titration studies of the rat p75NTR DD homodimer ( Vilar et al . , 2014 ) ( red-labeled residues in Figure 5C ) . Cys381 ( homologous to Cys379 in rat p75NTR DD ) was also located in the dimerization interface but appeared in reduced form with a Cβ chemical shift of 26 . 5 ppm . The distance between Cys381Sγ from each monomer was 6 . 7 ± 0 . 1 Å , that is , too long for the formation of a disulfide bond . The buried solvent accessible area in the p75NTR DD homodimer was 573 Å2 , in line with the low affinity of the DD:DD interaction . We conclude that the two p75NTR DD protomers form a low-affinity , noncovalent homodimer in our structure . 10 . 7554/eLife . 11692 . 017Figure 5 . Solution structure of the p75NTR DD homodimer . ( A ) Superposition of backbone heavy atoms of the 10 lowest-energy structures of the human p75NTR DD homodimer . N- and C-termini are indicated . ( B ) Ribbon drawing of the lowest-energy conformer viewed perpendicular ( top ) and parallel ( bottom ) to the twofold symmetry axis . DD monomers are colored in light brown and orange . N- and C-termini , as well as DD helices H1 , H2 , H3 , and H4 are indicated . ( C ) Detail of binding interface in the DD homodimer . The top image shows the same view as that in panel B , bottom . Key residues at the binding interface are labeled and depicted as stick models . Red labels denote interface residues functionally validated in a previous mutagenesis study ( Vilar et al . , 2014 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 01710 . 7554/eLife . 11692 . 018Figure 5—figure supplement 1 . Homodimerization of p75NTR DD . ( A ) [1H-15N] HSQC spectra of p75NTR DD in HEPES buffer ( black ) and phosphate buffer ( red ) at pH 6 . 9 and 28°C . ( B ) Representative slices from the 13C , 15N-filtered 3D NOESY spectrum . ( C ) Apparent hydrodynamic radius ( Rh ) distribution of DD from DLS measurement in HEPES ( top ) and phosphate buffer ( bottom ) , respectively . The protein concentrations used are ~0 . 2 mM . ( D ) Average Rh of DD in HEPES and phosphate buffers . The theoretical Rh of DD monomer ( ~10 kDa ) and homodimer ( ~20 kDa ) are ~1 . 6 and ~2 . 2 nm , respectively . ( E ) Determination of monomer-dimer Kd using anisotropy change resulting from FRET . p75NTR DD tagged with EGFP ( A206K ) in its C-terminus was used in these experiments . N= 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 018 The p75NTR DD homodimer interface defined in our NMR studies did not overlap with the RhoGDI binding site ( Figure 6A , B ) . This is in agreement with the constitutive interaction of RhoGDI with the closed conformation of the receptor . On the other hand , the DD homodimer partially occluded the RIP2 CARD interaction surface ( Figure 6C ) , indicating that interaction of RIP2 with p75NTR requires dissociation of the DD homodimer . We investigated whether the recruitment of RIP2 contributes to the separation of DDs triggered after neurotrophin binding to the receptor . This was done by taking real-time homo-FRET anisotropy measurements of DD:DD interaction in response to NGF in cells transfected with EGFP-tagged constructs of full length wild type p75NTR and a DD mutant deficient in RIP2 binding ( Charalampopoulos et al . , 2012 ) as previously described ( Vilar et al . , 2009 ) . Application of NGF to cells expressing wild type p75NTR produced large oscillations of increased anisotropy at the cell membrane ( Figure 6D ) , resulting in a positive net change averaged over a 15-min treatment compared to vehicle ( Figure 6E ) . As anisotropy is inversely related to FRET , this behavior indicates ligand-triggered separation of receptor intracellular domains , as proposed earlier ( Vilar et al . , 2009 ) . We note that the oscillations observed here are unlikely to represent the synchronous behavior of ensembles of receptors , as their average period ( 2–3 min ) would seem too slow to reflect real molecular dynamics . Importantly , the p75NTR construct carrying mutations in the CARD binding site ( CBS mutant ) produced very similar anisotropy changes after NGF treatment ( Figure 6D , E ) , indicating that recruitment of RIP2 is not required for DD separation in response to ligand binding to p75NTR . Finally , we note that the DD homodimer interface also overlapped with several residues involved in neurotrophin-mediated activation of JNK , caspase-3 and the apoptosis pathway ( Figure 6F ) as identified in our previous studies ( Charalampopoulos et al . , 2012 ) . Thus , while the structure and biochemical properties of the p75NTR DD homodimer support the ligand-independent interaction of RhoGDI with the receptor , they also demonstrate that dissociation of the p75NTR DD homodimer is required for recruitment of RIP2 and for activation of the JNK/caspase-3 pathway in response to neurotrophins . 10 . 7554/eLife . 11692 . 019Figure 6 . Relationship between p75NTR DD dimer interface and sites of interaction with downstream effectors . ( A ) Surface presentation of p75NTR DD with homodimer interface colored in blue . N- and C-termini are indicated . ( B , C and F ) Representation of RhoGDI binding site ( yellow in ( B ) RIP2 CARD binding site ( green in ( C ) and JNK/caspase-3 activation sites ( from [Charalampopoulos et al . , 2012] ) ( red in ( F ) on the p75NTR DD surface showing overlap of DD homodimer interface ( blue ) with CARD binding and JNK/caspase-3 activation sites but not with RhoGDI binding site . N- and C-termini are indicated . ( D ) Representative experiment showing traces of average anisotropy change after addition of NGF or vehicle in cells expressing wild type p75NTR or a CARD binding site mutant ( CBS mut ) that is unable to bind RIP2 ( Charalampopoulos et al . , 2012 ) . Addition of NGF , but not vehicle , induced positive anisotropy oscillations above baseline ( horizontal axis at 0 ) in both wild type and mutant receptor constructs . ( E ) Net anisotropy change over 15 min after addition of NGF or vehicle in cells expression wild type p75NTR or the CARD binding site mutant ( CBS mut ) . Results are expressed as average ± SD ( N = 3 experiments; n = 15–17 cells examined per experiment ) . **p < 0 . 001 vs . vehicle . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 019
The main paradigm in signal transduction by DD-containing proteins is oligomerization via homotypic DD interactions . Although p75NTR contains a DD , which is required for downstream signaling , no intracellular p75NTR effectors containing canonical DDs have been identified . The study of DD signaling in p75NTR therefore addresses an unexplored dimension of the repertoire of interactions and activities in the DD superfamily . Homotypic interactions between DDs have been classified as type I , II , and III according to the interfaces involved ( Park , 2011; Park et al . , 2007b; Weber and Vincenz , 2001 ) . Despite what might have been expected of homotypic interactions , all known interactions in the DD superfamily are asymmetric , that is , the interaction is mediated by different interfaces in each of the two interacting domains ( e . g . , Ia and Ib for type I ) . Remarkably , none of the binding surfaces in the p75NTR DD ( or in RIP2 CARD ) identified in this study show a close match to any of the six conserved surfaces that characterize classical type I , II , and III interactions in the DD superfamily . The p75NTR DD surface that binds RhoGDI is formed by residues in helices H1 and H6 . A previous study had proposed helix 5 as a binding site to RhoGDI based on serial deletion analysis of the p75NTR DD ( Yamashita and Tohyama , 2003 ) . This conclusion is not supported by our solution structure of the DD:RhoGDI complex , in which H5 appears at the opposite side of the interface ( Figure 2B ) , nor by previous structure–function studies ( Charalampopoulos et al . , 2012 ) . This discrepancy highlights some of the pitfalls in serial deletion studies that disregard the three-dimensional structures of proteins . The surface in the p75NTR DD that interacts with the CARD of RIP2 includes residues in helices H2 , H3 , and H6 plus residues in the H5–H6 loop . On the other side of this interaction , residues in helix H1 as well as the H3–H4 and H5–H6 loops form the binding surface on the CARD of RIP2 . To the best of our knowledge , this p75NTR DD:RIP2 CARD complex represents the first structural characterization of an heterotypic interaction in the DD superfamily . Our solution structure of this complex also revealed an additional interaction between residues in the C-terminal half of helix H5 of the p75NTR DD and the C-terminal tail of RIP2 , which extends beyond the RIP2 CARD . This additional contact confers approximately fivefold increased binding affinity between the two proteins . Finally , the interface that mediates the p75NTR DD homodimer involves residues located in helix H3 as well as the H1–H2 and H3–H4 loops . This surface is similar , but not identical , to the type IIIb surface , like the one identified in the DD of PIDD for interaction with RAIDD ( Park et al . , 2007b ) . Unlike the classical type IIIb surface , however , the DD:DD interaction in p75NTR makes extensive use of residues in the H3 helix , and the same surface in the two interacting DDs is used to form a symmetric dimer . Further studies will be required to determine whether the interactions identified here for the p75NTR DD are exceptions or else represent new types of interactions that are yet to be identified in other DD-containing proteins . The solution structure of the p75NTR DD homodimer shows it is a symmetric , noncovalent dimer held together by low-affinity interactions involving residues in helix H3 and the H1–H2 and H3–H4 loops . The p75NTR DD dimer interface is in agreement with sites of interaction with downstream effectors identified by the structures reported here and in our previous site-directed mutagenesis studies ( Figure 6 ) . This p75NTR DD dimer structure is also in accordance with a recent mutagenesis study that identified residues important for dimerization of rat p75NTR DD ( Vilar et al . , 2014 ) , many of which are also implicated in our structure . On the other hand , our results do not support two crystallographic structures reported for the rat p75NTR DD homodimer that suggested this to be either a covalent symmetric dimer , held by a disulfide bond between Cys379 residues , or a noncovalent asymmetric dimer ( Qu et al . , 2013 ) . None of the currently available evidence derived from structural , mutagenesis , or functional studies appears to support those crystal structures . Nevertheless , we cannot at present rule out the possibility that p75NTR DDs may under certain circumstances form alternative oligomeric complexes through different interfaces . A recent study has suggested that p75NTR can form trimers in transfected cells based on the apparent molecular weights of p75NTR species in SDS/PAGE ( Anastasia et al . , 2015 ) . Our NMR studies of the p75NTR DD do not support such conclusion as we have not found any evidence for the existence of DD trimers in any of the conditions tested . Another recent study has used solution NMR spectroscopy to investigate the mobility of the transmembrane and intracellular domains of p75NTR incorporated into lipid–protein nanodisks ( Mineev et al . , 2015 ) . These authors found a high level of flexibility in the juxtamembrane domain of p75NTR , an observation that we also reported in our earlier NMR studies of this domain ( Liepinsh , 1997 ) , but they could not detect self-association of intracellular domains . However , it is unclear whether the lipid detergent used to form the lipid–protein nanodisks interacted with the p75NTR DD and prevented its dimerization . A few detergent molecules , too few to affect DD rotational correlation time , would be sufficient to hinder DD:DD interactions . The mechanism underlying ligand-induced dissociation of RhoGDI from p75NTR has remained unclear . As neurotrophins induce the release of RhoGDI and the recruitment of RIP2 , we have speculated that RIP2 may displace RhoGDI from binding sites in the p75NTR DD ( Charalampopoulos et al . , 2012 ) . Our solution structures of the p75NTR DD in complex with RhoGDI and the RIP2 CARD lend experimental support to this notion by showing how steric clashes between the two effector proteins preclude their simultaneous binding to the p75NTR DD . SPR experiments indicated that RIP2 CARD binds with over 100-fold higher affinity to the p75NTR DD than RhoGDI , and 2D-NMR competition studies demonstrated that RIP2 CARD can in fact displace RhoGDI from the receptor . The functional significance of this relationship was evidenced by the ability of RIP2 to decrease p75NTR-mediated RhoA activation in a dose-dependent manner . Furthermore , the enhanced activation of the RhoA pathway observed in brain extracts of Rip2 knockout mice suggests that RIP2 may also restrict activation of this pathway in vivo . These results demonstrate how a hierarchy of binding affinities dictates the differential interaction of downstream effectors with p75NTR and ultimately controls the logic of p75NTR signaling . p75NTR has been postulated to function as a “displacement factor” that releases RhoA from RhoGDI leading to RhoA activation ( Yamashita and Tohyama , 2003 ) . This model has led to the idea that the p75NTR DD and RhoA may compete for binding to RhoGDI . On the other hand , biochemical experiments have shown that RhoA can associate with p75NTR through RhoGDI and the three proteins can be recovered together in co-immunoprecipitation assays ( Yamashita et al . , 1999; Yamashita and Tohyama , 2003 ) , a result that would be incompatible with the displacement concept . Our structural studies show that the p75NTR DD and RhoA bind on opposites sides of the RhoGDI molecule , allowing the formation of a tripartite DD:RhoGDI:RhoA complex . Using a model of this complex and our solution structure of the p75NTR DD homodimer , we have constructed a model of the hexameric complex of these proteins ( Video 1 ) . This model retains the two fold symmetry of the DD homodimer , and accommodates all six components without any steric clashes . How can these interactions lead to RhoA activation ? Our SPR experiments showed that association of RhoGDI with the p75NTR DD reduced its affinity for RhoA by 15-fold . Close comparison of RhoGDI structures in the complexes with either p75NTR DD or RhoA revealed local structural perturbations in RhoGDI ( Figure 2—figure supplement 1 ) , suggesting a potential allosteric mechanism underlying the release and activation of RhoA upon RhoGDI biding to the receptor . Based on the present studies , we propose a model for the early stages of p75NTR engagement with the RhoGDI/RhoA and RIP2/NF-kB pathways based on differential binding affinities and competitive protein–protein interactions ( Figure 7 ) . At the plasma membrane , the p75NTR forms a dimer held together by interactions between its DD and TM domains ( Figure 7A ) . Recruitment of the RhoGDI:RhoA complex brings RhoA close to the plasma membrane ( Figure 7B ) . RhoGDI binding to the p75NTR DD weakens its interaction with RhoA , a lipid-modified protein , allowing it to equilibrate with the plasma membrane where it can be activated by membrane-associated guanine nucleotide exchange factors ( GEFs ) ( Garcia-Mata et al . , 2011 ) . RhoA may thus be in equilibrium between the cell membrane and the RhoGDI:p75NTR complex , and the action of GEFs and GTPase-activating proteins ( GAPs ) may further contribute to the dynamics of this exchange ( Figure 7B ) . Upon dissociation from p75NTR , for example , as a consequence of RIP2 recruitment in response to NGF binding , RhoGDI regains high affinity for RhoA , extracting it from the membrane and holding it back in the cytosol in an inactive state ( Figure 7C ) . This new view of the p75NTR DD in the activation of RhoA is in better agreement with the emerging role of RhoGDI as a general facilitator of small GTPase activity cycles . Recruitment of RIP2 to p75NTR ultimately leads to increased NF-kB activity by as yet unknown mechanisms . Another p75NTR interactor , TRAF6 , is also a known regulator of the NF-kB pathway ( Khursigara et al . , 1999; Ye et al . , 1999 ) . As TRAF6 has been shown to interact with the juxtamembrane region of p75NTR , but not with the DD , RIP2 and TRAF6 may be able to bind simultaneously to the receptor and together contribute to enhance NF-kB activity in response to neurotrophins . 10 . 7554/eLife . 11692 . 020Video 1 . Model of the hexameric complex between p75NTR , RhoGDI and RhoA ( GDP ) . Animation around the two-fold symmetry axis of the hexameric p75NTR DD:RhoGDI:RhoA ( GDP ) complex . p75NTR DD appears in light brown , RhoGDI in cyan and RhoA in blue . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 02010 . 7554/eLife . 11692 . 021Figure 7 . Competitive protein–protein interactions orchestrate coupling of p75NTR to the RhoGDI/RhoA and RIP2/NF-kB pathways . Schematic drawing of a model for the coupling of p75NTR to the RhoGDI/RhoA and RIP2/NF-kB pathways based on the structural and biochemical studies presented above . ( A ) The p75NTR dimer in the cell membrane is held by homotypic interactions of DDs ( light brown ) and TM domains ( blue ) . ( B ) RhoGDI ( cyan ) brings RhoA ( dark purple ) in proximity to the plasma membrane through its interaction with the DD of p75NTR . While the twofold symmetry axis of the DD:RhoGDI:RhoA hexametric complex is likely to be perpendicular to the plasma membrane , its relative orientation is hypothetical . RhoGDI binding to the p75NTR DD decreases its affinity for RhoA by 15-fold , and allows equilibration of RhoA with the plasma membrane , where it can be activated by GEFs . ( C ) Neurotrophin binding induces a conformational change in p75NTR resulting in the separation of its DDs ( Vilar et al . , 2009 ) , exposing binding sites to downstream effectors that couple to the JNK/caspase-3 or NF-kB pathways , including RIP2 . Recruitment of RIP2 to the p75NTR DD is mediated by the interaction of its CARD ( green ) with a binding surface that partially overlaps with that occupied by RhoGDI . As the binding affinity of the RIP2 CARD for the p75NTR DD is 100-fold higher than that of RhoGDI , the recruitment of RIP2 displaces RhoGDI from the receptor . Released from the DD , RhoGDI regains higher affinity for RhoA , extracting it from the membrane and holding it back in the cytosol . DOI: http://dx . doi . org/10 . 7554/eLife . 11692 . 021 The structural studies of DD signaling in p75NTR presented here uncovered novel heterotypic interactions not previously seen in other DD-containing complexes . They represent new ways by which DDs regulate intracellular signaling . NMR , biochemical , and functional studies defined competitive interactions between RhoGDI and RIP2 CARD and between RIP2 CARD and the p75NTR DD homodimer . These interactions give us unique insights into the molecular mechanisms underlying p75NTR activation and signaling , and reveal how overlapping interfaces and differential binding affinities cooperate to orchestrate the hierarchical activation of downstream pathways in noncatalytic receptors .
The cDNAs of human p75NTR DD ( 330–427 ) , RhoGDI ( 2–204 ) , RhoA ( 2–190 ) , and RIP2 CARD ( 434–539 ) were amplified from total human embryonic stem ( ES ) cell cDNA and subcloned into pET32-derived expression vectors between BamH I and Xho I restriction sites . Each recombinant protein contains 16 additional residues ( MHHHHHHSSGLVPRGS ) at the N-terminal , including one 6×His tag . Unlabeled proteins were expressed in E . coli strain SoluBL21 ( DE3 ) in LB or M9 medium . Protein samples were purified using Ni-NTA affinity chromatography , FPLC gel filtration ( Superdex 75 ) , and/or ionic exchange ( MonoQ or MonoS ) . Isotopic labeling was carried out by expressing the proteins in M9 minimal medium containing 15N-NH4Cl and/or 13C-labeled glucose as the sole source of nitrogen and carbon . Protein complexes were prepared by mixing individual purified domains . Due to the weak binding affinities of DD:RhoGDI and DD homodimer complexes , as well as solubility problems of the DD:CARD complex in salt-containing buffers , gel filtration chromatography could not be used to purify these protein complexes . For the p75NTR DD:RhoGDI complex , two double-labeled samples were prepared in 10 mM D18-HEPES , 10 mM D10-DTT , 1 mM EDTA , and 0 . 01% sodium azide at pH 6 . 9: ( 1 ) 0 . 5 mM 13C , 15N-labeled p75NTR DD mixed with 2 mM unlabeled RhoGDI; ( 2 ) 0 . 5 mM 13C , 15N-labeled RhoGDI mixed with 2 mM unlabeled p75NTR DD . For the p75NTR DD:RIP2 CARD complex , two double-labeled samples were made in water with 10 mM D10-DTT: ( 1 ) 0 . 5 mM 13C , 15N-labeled p75NTR DD mixed with 1 mM unlabeled RIP2 CARD; ( 2 ) 0 . 5 mM 13C , 15N-labeled RIP2 CARD mixed with 1 mM unlabeled p75NTR DD . For the RIP2 CARD on its own , 0 . 7 mM 13C , 15N-labeled RIP2 CARD was prepared in 50 mM D10-DTT in water . For the p75NTR DD homodimer , 1 mM 13C , 15N-labeled p75NTR DD was mixed with 1 mM unlabeled p75NTR DD in 50 mM phosphate buffer , 10 mM D10-DTT , 1 mM EDTA and 0 . 01% sodium azide at pH 6 . 9 . NMR experiments were performed on a Bruker 800 MHz NMR spectrometer with a cryogenic probe at 28°C . All spectra were processed with NMRPipe ( Delaglio et al . , 1995 ) and analyzed with NMRView supported by a NOE assignment plugin ( Johnson and Blevins , 1994 ) . Resonance assignments of backbone , aliphatic , and aromatic side chains were obtained using previously described methods ( Lin et al . , 2006; Xu et al . , 2006 ) . Intramolecular NOE restraints were obtained from 4D time-shared 13C , 15N-edited NOESY spectra ( Xu et al . , 2007 ) . Intermolecular NOEs were identified from 13C , 15N-filtered 3D experiments ( Zwahlen et al . , 1997 ) . Ambiguous NOEs were assigned with iterated structure calculations by DYANA ( Herrmann et al . , 2002 ) . Final structure calculation was started from 100 conformers . Energy minimization of the 10 conformers with the lowest final target function values was performed in AMBER force field ( Case et al . , 2002 ) . The mean structure was obtained from the 10 energy-minimized conformers for each domain . PROCHECK-NMR ( Laskowski et al . , 1996 ) was used to assess the quality of the structures . All the structural figures were made using MOLMOL ( Koradi et al . , 1996 ) or Chimera ( Pettersen et al . , 2004 ) . The coordinates of p75NTR DD:RhoGDI , RIP2 CARD , p75NTR DD:RIP2 CARD , and p75NTR DD homodimer have been deposited with the Protein Data Bank with PDB IDs 2n80 , 2n7z , 2n83 , and 2n97 , respectively . The structure of DD:RhoGDI:RhoA was modeled using HADDOCK 2 . 2 ( Dominguez et al . , 2003 ) . The starting structures for the trimer were the DD monomeric structure from the lowest-energy structure of p75NTR DD:RhoGDI complex and the crystal structure of human RhoGDI:RhoA ( GDP ) ( PDB ID: 1CC0 ) . The starting structures used to build the hexameric model were the lowest-energy structure of the p75NTR DD homodimer and the crystal structure of human RhoGDI:RhoA ( GDP ) . NOE data between p75NTR DD and RhoGDI were employed to create interaction restraints for both trimer and hexamer . Totally , 1000 rigid-body docking solutions were first generated by energy minimization . The best 100 structures according to intermolecular energies were subjected to semi-flexible simulated annealing in torsion angle space followed by a final refinement in explicit water . Pairwise structure-based alignment and comparison were carried out using a sequential structure alignment program ( SSAP ) available at the SSAP server ( http://www . cathdb . info/cgi-bin/cath/SsapServer . pl ) . The apparent hydrodynamic radii of p75NTR DD domain in HEPES or phosphate buffer at pH 7 . 0 were examined by DLS ( DynaPro , Protein Solutions Inc . , Lakewood , NJ ) at 22°C . The data were analyzed using Dynamics 5 . 0 software . The CD spectra of all samples were recorded on a Jasco J-810 spectropolarimeter equipped with a thermal controller at 22°C . All sensorgrams were recorded on a BIAcore T200 at 22°C . For experiments with captured p75NTR DD ( ligand ) , purified p75NTR DD-His was captured onto NTA sensor chips via Ni2+/NTA chelation . Protein samples of purified RIP2 CARD or RhoGDI ( analytes ) were sequentially diluted in running buffer ( 10 mM HEPES , 50 mM NaCl , 0 . 005% Surfactant P20 , 0 . 02% protease-free BSA at pH 7 . 0 ) and injected over the surfaces at different concentrations post capture . Regeneration of the NTA surface was performed using 350 mM EDTA . For experiments involving immobilized RhoA , unprenylated RhoA:GDP:Mg2+ was immobilized via amine coupling onto CM5 sensor chips . Unreacted carboxymethyl sites were capped by ethanolamine . Protein samples of analytes were sequentially diluted in running buffer ( 10 mM HEPES , 50 mM NaCl , 0 . 005% Surfactant P20 , pH 7 . 0 ) and injected over the surfaces at different concentrations . To measure the binding of RhoGDI to RhoA , 1 mM MgCl2 and 100 µM GDP were also included in the running buffer . Binding affinities were expressed as equilibrium dissociation constants ( Kd ) determined by steady state ( Figure 2C , D ) or kinetic analyses ( Figures 4B , C and Figure 3—figure supplement 4B ) using the BIA evaluation software . One binding site model was used for fitting of SPR data . For anisotropy measurements of DD homodimerization , a cDNA encoding Enhanced Green Fluorescent Protein ( EGFP ) carrying the A206K mutation ( to prevent its dimerization ) was linked to the C-terminal of the human p75NTR cDNA via DNA ligation and the chimera protein ( p75NTR DD-EGFP ) was expressed in E . coli BL21 ( DE3 ) and purified by FPLC . p75NTR DD-EGFP was sequentially diluted in 50 mM phosphate buffer at pH 7 . 0 . The anisotropy value was obtained from the measurements of fluorescence intensity in both parallel and perpendicular emission modes using a BioTek Cytation Imaging Reader at room temperature . Dimer dissociation constants were obtained by nonlinear fitting of anisotropy measurements to an equation describing a monomer–dimer equilibrium ( Martin and Martin , 1996 ) . Full-length cDNAs encoding human p75NTR , RhoGDI and RIP2 were amplified from human embryonic stem ( ES ) cell cDNA and subcloned in the pCDNA3 vector backbone ( Invitrogen ) for protein expression in mammalian cells . Mutations and epitope tags were introduced using QuickChange Site-Directed Mutagenesis Kit ( Stratagene , United Kingdom ) and verified by DNA sequencing . Normal expression of all constructs was verified by immunoblotting . The origin of antibodies was as follows: ANT-007 anti-p75NTR ( for immunoprecipitation ) from Alomone Labs; ab52987 anti-p75NTR ( for immunoblotting ) and anti-RhoGDI from Abcam; anti-Myc from Cell Signaling Technologies; anti-RIP2 from Enzo Life Sciences; anti-β-actin and anti-βIII-tubulin from Sigma-Aldrich . Rip2 knockout mice were obtained from Koichi Kobayashi and Richard Flavell ( Kobayashi et al . , 2002 ) . HEK293 and COS-7 cells were obtained from ATCC and cultured under standard conditions in DMEM supplemented with 10% fetal calf serum , 100 units/ml penicillin , 100 mg/ml streptomycin , and 2 . 5 mM glutamine . HEK293 cells were transfected with the polyethylenimine ( PEI ) method . Briefly , cells were plated in a 10 cm tissue culture dish at a confluency of 3 × 106 cells/dish in normal growth media . Twenty-four hours after plating , the media was changed to growth media containing 1% v/v FBS . Transfection mix was prepared by mixing 1 μg of plasmid with 3 μg of PEI ( 1 mg/ml ) in DMEM . The transfection mix was left to stand at room temperature followed by addition dropwise into culture plates . 24 hrours after transfection , the transfected cells were returned to normal growth media . After a further 24 hr , cells were placed in sera-free media for 16 hr prior to harvest and lysis in 50 mM Tris/HCl pH 7 . 5 , 1 mM EDTA , 270 mM Sucrose , 1% ( v/v ) Triton X-100 , 1 mM benzamidine , 1 mM PMSF , 0 . 1% ( v/v ) 2-mercaptoethanol , and in the presence of phosSTOP ( Roche ) phosphatase inhibitor cocktail mix as per manufacturer instructions . The cellular extracts were then centrifuged at 4°C top speed on a benchtop centrifuge for 15 min . The supernatant was collected and filtered using a 0 . 2 μM syringe filter . Protein concentration was determined by Bradford Assay . For anisotropy microscopy , COS-7 cells were transfected with Fugene6 ( Promega ) according to manufacturer’s instructions . For immunoprecipitation , cell extracts ( 0 . 5 mg protein ) was incubated for 16 hr at 4°C on a rotating wheel with 0 . 5 μg of anti-p75 antibody ( ANT-007 , Alomone ) attached to Protein G–Sepharose ( 7 . 5 μl packed beads ) . The beads were collected by brief centrifugation ( 2 min , 780× g , 4°C ) , washed three times with 0 . 5 ml of Wash Buffer ( 50 mM Tris/HCl pH 7 . 5 , 1% ( v/v ) Triton X-100 , 0 . 05% ( v/v ) 2-mercaptoethanol , and 0 . 2 M NaCl ) . After the last wash , pelleted beads were aspirated off the wash buffer followed by addition of Laemmli sample buffer and analysis by SDS-PAGE and Western Blot . Immunoblots were developed using the ECL Western Blotting Kit ( Thermo Scientific ) and exposed to Kodak X-Omat AR films . Image analysis and quantification of band intensities were done with ImageJ software ( NIH ) . For RhoA activation assays , mouse cerebella were dissected from postnatal day ( P ) 7 pups . RhoA activity was evaluated in total cerebellar extracts or in lysates of transfected HEK293 cells using the RhoA G-Lisa kit ( Cytoskeleton ) following the manufacturer’s instructions . Equal amount of protein was used from each sample as determined by Bradford Assay . Anisotropy microscopy was done as previously described ( Vilar et al . , 2009 ) in transiently transfected COS-7 cells . Images were acquired 24 hr post-transfection , using a Nikon Eclipse Ti-E motorized inverted microscope ( Nikon , Japan ) equipped with a X-Cite LED illumination system . A linear dichroic polarizer ( Meadowlark Optics ) was placed in the illumination path of the microscope , and two identical polarizers were placed in an external filter wheel at orientations parallel and perpendicular to the polarization of the excitation light . The fluorescence was collected via a CFI Plan Apochromat Lambda 40× , 0 . 95 NA air objective , and parallel and polarized emission images were acquired sequentially on an Orca CCD camera ( Hamamatsu Photonics , Japan ) . Data acquisition was controlled by the Metamorph software ( Molecular Devices , USA ) . NGF ( from Alomone Labs ) or vehicle was added 3 min after the start of the time lapse at a concentration of 100 ng/ml . Anisotropy values were extracted from image stacks of 30 images acquired in both parallel and perpendicular emission modes every 30 s for a time period of 15 min after NGF addition . For each construct , 12–15 ROIs were measured in three independent transfections performed in duplicate . Fluorescence intensity and anisotropy images were calculated as described by Squire et al . ( 2004 ) . Wild type and CARD binding site ( CBS ) mutant cDNA constructs of rat p75NTR were tagged at the C terminus with a monomeric version of EGFP ( Clontech ) carrying the A206K mutation that disrupts EGFP dimerization . The CBS p75NTR mutant corresponded to the triple mutant D355A/H359A/E363A described in our previous study ( Charalampopoulos et al . , 2012 ) . | Cells have proteins called receptors on their surface that can bind to specific molecules on the outside of the cell . Typically , this binding activates the receptor and the activated receptor then triggers some biochemical changes inside the cell . For many receptors , the portion of the receptor inside the cell is essentially an enzyme that can trigger a biochemical change by itself . Some receptors , however , lack any enzymatic activity , and it is often unclear how these ‘non-catalytic receptors’ trigger changes inside a cell . A protein called p75 neurotrophin receptor ( or p75NTR for short ) is a non-catalytic receptor that is expressed when neurons are injured and its activity leads to the death of the neurons and related cells . Inhibiting this non-catalytic receptor is an attractive strategy for limiting the damage caused by diseases of the nervous system . However , the molecular mechanisms behind the activity of p75NTR are not well understood . Previous biochemical studies set out to answer the question of how p75NTR engages with components of the signaling machinery inside the cell , and found several components that interact with this receptor . Now , Lin et al . have tried to gain a more detailed understanding of those interactions at a molecular level . This involved solving the three-dimensional structures of three protein complexes that involve part of p75NTR ( called the “death domain” ) and one of two signaling components ( called RhoGDI and RIP2 ) . Two of the protein complexes showed that RIP2 and RhoGDI bind to the receptor’s death domain at partially overlapping sites , although RIP2 binds about 100 times more strongly than RhoGDI . A third protein complex showed an interaction between two copies of the death domain , which involves a surface of the receptor that overlaps with RIP2’s , but not RhoGDI’s , binding site . These structures , together with the results of other experiments , allowed Lin et al . to propose a model that could explain how p75NTR is activated . First , the two death domains must be separated . Next , RIP2 is recruited to the receptor , and outcompetes and displaces RhoGDI . This change in protein-protein interactions switches the receptor’s signaling from one pathway to the other . Now that these structures are available , they can be used in future experiments to design specific changes in the receptor that would allow researchers to dissect its different activities . | [
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] | 2015 | Structural basis of death domain signaling in the p75 neurotrophin receptor |
Genetic and molecular approaches have been critical for elucidating the mechanism of the mammalian circadian clock . Here , we demonstrate that the ClockΔ19 mutant behavioral phenotype is significantly modified by mouse strain genetic background . We map a suppressor of the ClockΔ19 mutation to a ∼900 kb interval on mouse chromosome 1 and identify the transcription factor , Usf1 , as the responsible gene . A SNP in the promoter of Usf1 causes elevation of its transcript and protein in strains that suppress the Clock mutant phenotype . USF1 competes with the CLOCK:BMAL1 complex for binding to E-box sites in target genes . Saturation binding experiments demonstrate reduced affinity of the CLOCKΔ19:BMAL1 complex for E-box sites , thereby permitting increased USF1 occupancy on a genome-wide basis . We propose that USF1 is an important modulator of molecular and behavioral circadian rhythms in mammals .
To adapt to daily environmental cycles , most organisms have evolved endogenous clocks composed of cell-autonomous , self-sustained oscillators that drive 24-hr rhythms in biochemistry , physiology and behavior ( Bass and Takahashi , 2010; Lowrey and Takahashi , 2011 ) . In mammals , the innate periodicity of the circadian clock is generated by transcriptional/translational feedback loops composed of a core set of clock genes expressed in cells throughout the body ( Reppert and Weaver , 2002; Lowrey and Takahashi , 2011 ) . Two members of the bHLH-PAS transcription factor family , CLOCK ( and its paralog NPAS2 ) and BMAL1 ( ARNTL ) , heterodimerize and initiate transcription of the Period ( Per1 , Per2 ) and Cryptochrome ( Cry1 , Cry2 ) genes through E-box regulatory sequences ( King et al . , 1997; Gekakis et al . , 1998; Hogenesch et al . , 1998; Kume et al . , 1999; Bunger et al . , 2000; Huang et al . , 2012 ) . The resulting PER and CRY proteins form multimeric complexes , translocate to the nucleus , and abrogate their own transcription by repressing CLOCK:BMAL1 ( Lee et al . , 2001; Koike et al . , 2012 ) . As the PER and CRY proteins are degraded , CLOCK:BMAL1 occupancy increases to initiate a new round of transcription ( Lowrey and Takahashi , 2011; Koike et al . , 2012 ) . While this forms the major feedback loop of the mammalian molecular clock , the overall mechanism also depends on additional feedback loops driven by ROR and REV-ERBα/β ( Preitner et al . , 2002; Sato et al . , 2004; Ueda et al . , 2005; Cho et al . , 2012 ) . Significant progress has been made in identifying the core clock genes and the functions of their protein products in the clock mechanism , yet it is clear from other studies , including mutagenesis screens , that additional genes are necessary for a fully functional circadian clock ( Takahashi , 2004 ) . We previously identified at least 13 loci in mice that affect circadian behavior through complex epistatic interactions ( Shimomura et al . , 2001 ) , indicating that there are other clock-relevant genes in the mammalian genome . By extension , it is possible that the variance in circadian behavior observed in the human population results from polymorphisms in non-core circadian clock genes . Thus , identifying these genes is important for both mechanistic understanding and translational application . The quantitative trait locus ( QTL ) approach has been successfully applied to detect loci that associate with phenotypes of interest . An important application of QTL analysis is the identification of loci that modify the function and/or expression of a particular gene ( Nadeau and Topol , 2006 ) . A major obstacle , however , has been the cloning of these genes—particularly those underlying behavioral QTLs ( Nadeau and Frankel , 2000 ) . To overcome this challenge , several approaches have been proposed , yet it remains difficult to obtain a mapping resolution suitable for gene cloning by the positional candidate method ( Darvasi and Soller , 1995; Churchill et al . , 2004; Valdar et al . , 2006 ) . To date there are few examples of behavioral QTLs having been cloned using high-resolution mapping strategies ( Yalcin et al . , 2004; Watanabe et al . , 2007; Tomida et al . , 2009 ) .
Because classical inbred laboratory mouse strains are derived from a limited number of progenitor species and the genomes of inbred strains are an admixture of different domesticated stocks ( Wade et al . , 2002; Frazer et al . , 2007; Yang et al . , 2011 ) , polymorphisms at the Soc locus may be ancestral . If so , other inbred strains that share identity by descent with the Soc locus should also suppress Clock . To test this hypothesis , we characterized 14 additional laboratory inbred strains by crossing to B6 ClockΔ19 mice to create F1 hybrids . Further , because strain background can affect wild-type circadian free-running period , a two-way ANOVA is required to distinguish the effects of the strain background ( evident in wild-type ) from the effects of the strain background on the expression of the Clock mutation ( evident as a strain-by-genotype interaction effect ) . If we detected a significant strain-by-genotype interaction , we accepted this as evidence of suppression of the Clock mutation . Of the 14 inbred strains tested , we identified seven additional suppressor and seven non-suppressor inbred strains ( Figure 2A; Table 1 ) . These results suggest that the Soc phenotype occurs as a consequence of shared ancestral alleles . 10 . 7554/eLife . 00426 . 004Figure 2 . High-resolution mapping of the Soc locus using interval-specific SNP haplotype analysis . ( A ) Identification of ClockΔ19 suppressor and non-suppressor strains . B6 ClockΔ19/+ mice were crossed to 15 different inbred mouse strains . We used a two-way ANOVA to distinguish the effects of the strain background ( evident in the wild-type mice ) from the effects of the strain background on the expression of the ClockΔ19 mutation ( evident as a strain-by-genotype interaction effect ) . Of the 15 inbred strains tested , we identified seven additional suppressor ( A/J , AKR/J , C3H/HeJ , DBA/2J , FVB/NJ , SJL/J and 129S1/SvlmJ ) and seven non-suppressor ( C58/J , BTBR T+tf/J , I/LnJ , MA/MyJ , JF1/Ms , MOLF/EiJ , and CZECHII/EiJ ) strains . A summary of the two-way ANOVA is provided ( Table 2 ) . In each panel the blue and red lines represent wild-type and ClockΔ19/+ animals , respectively; F1 indicates an F1 hybrid between B6 and the specific inbred strain indicated . Each data point represents the mean ± SEM from 4–22 mice . ( B ) Pairwise sequence comparison between B6 and 15 other mouse strains in the 100-kb BALB interval . We used 2714 SNPs within the 160–190 Mb interval of mouse chromosome 1 . Soc should be located in regions of high sequence variation in suppressor strains ( green ) and regions of low sequence variation in non-suppressor strains ( blue ) , relative to B6 . The only region satisfying this criterion is indicated ( green bar ) . ( C ) Physical map of the Soc interval . Soc maps to a 900-kb interval of chromosome 1 . Blue bars in the top panel represent the 22 candidate genes identified within the interval . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 00410 . 7554/eLife . 00426 . 005Figure 2—source data 1 . Single nucleotide polymorphisms ( SNP ) in the Suppressor of Clock ( Soc ) candidate region among 16 mouse strains . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 00510 . 7554/eLife . 00426 . 006Table 1 . Two-way ANOVA for identification of Clock suppressor or non suppressor strains in Figure 2ADOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 006Suppressor strainStrain nameF-ratio ( Clock × strain ) p valuedfBALB/cJ14 . 698<0 . 001df ( 1 , 61 ) A/J13 . 13<0 . 001df ( 1 , 44 ) AKR/J9 . 410 . 004df ( 1 , 44 ) C3H/HeJ56 . 82<0 . 001df ( 1 , 52 ) DBA/2J34 . 92<0 . 001df ( 1 , 46 ) FVB/NJ7 . 240 . 010df ( 1 , 47 ) SJL/J12 . 030 . 001df ( 1 , 55 ) 129S1/SvImJ7 . 0940 . 010df ( 1 , 67 ) Non-suppressor strainC58/J0 . 650 . 423df ( 1 , 46 ) BTBR_T+_tf/J0 . 010 . 911df ( 1 , 53 ) I/LnJ2 . 720 . 107df ( 1 , 40 ) MA/MyJ0 . 020 . 891df ( 1 , 52 ) JF1/Ms0 . 490 . 490df ( 1 , 37 ) MOLF/EiJ0 . 900 . 348df ( 1 , 37 ) CZECHII/EiJ0 . 080 . 780df ( 1 , 46 ) Only the F-ratio for interaction is shown . Two-way ANOVA analyses for circadian period were performed with four groups; C57BL/6J WT , F1 WT , C57BL/6J Clock/+ and F1 Clock/+ . Suppressor strains show a significant interaction between Clock genotype and strain background ( Clock × strain ) . We performed pairwise SNP allele comparisons between B6 and the other 15 inbred strains in the 30-Mb interval from 160 to 190 Mb of mouse chromosome 1 using a total of 2714 SNPs ( Figure 2B ) . Alternating regions of low and high SNP diversity are apparent in which low variation intervals represent strains sharing identity by descent , compared to high variation intervals that represent divergent ancestry . Within the 30-Mb interval , there is a region of high sequence variation between B6 and the suppressor strains , but identical by descent between B6 and the non-suppressor strains ( Figure 2B ) . This region ( green shading ) spans 900 kb and contains 22 transcription units ( Figure 2C; Figure 2—source data 1 ) . Using the Mouse Phylogeny Viewer ( Yang et al . , 2011 ) , the imputed subspecific origin of this 900 kb region reveals two sets of haplotypes that arise from Mus domesticus and M . musculus , respectively ( Table 2 ) . The suppressor strains all carry haplotypes originating from M . domesticus and the non-suppressor strains all carry haplotypes originating from M . musculus–thus confirming our hypothesis of an ancestral allele . 10 . 7554/eLife . 00426 . 007Table 2 . Subspecific origin of the Soc region of mouse chromosome 1DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 007StrainChromosomeStart position ( bp ) End position ( bp ) SubspeciesSup or non-supBALB/cJ1173300000174200000DomSA/J1173300000174200000DomSAKR/J1173300000173302142Mus *SAKR/J1173302461174200000DomSC3H/HeJ1173300000174200000DomSDBA/2J1173300000174200000DomSFVB/NJ1173300000174200000DomSSJL/J1173300000174200000DomS129S1SvlmJ1173300000174200000DomSC57BL/6J1173300000174200000MusNSC58/J1173300000174028368MusNSC58/J1174187607174200000Dom*NSBTBR T<+>tf/J1173300000174200000MusNSI/LnJ1173300000174200000MusNSJF1/Ms1173300000174200000MusNSMOLF/EiJ1173300000174200000MusNSCZECHII/EiJ1173300000174200000MusNSFrom the Mouse Phylogeny Viewer ( Yang et al . , 2011 ) : http://msub . csbio . unc . edu . The AKR/J and C58/J strains contain the proximal and distal breakpoints , respectively , for the Soc locus , and thus there are two entries for these two strains . Dom = M . domesticus , Mus = M . musculus , S = suppressor , NS = non-suppressor . *does not include Soc locus . It is well established that circadian clocks exist in the SCN and throughout the body ( Yoo et al . , 2004 ) . Because the Clock suppressor phenotype occurs in both SCN and pituitary tissue explants ( Figure 1C ) , the gene encoded by the Soc locus should also be expressed in both central and peripheral tissues . To search for co-expression , we profiled the expression of the 22 Soc candidates as well as 9 circadian clock genes in 10 different tissues . Via cluster analysis , we found that all nine clock genes expressed a similar pattern among the tissues examined ( Figure 3A ) , but that only seven Soc candidates ( Vangl2 , Usf1 , Dcaf8 , Copa , Pex19 , Pea15 , and Ncstn ) expressed a similar pattern to the clock genes . We thus prioritized these seven Soc candidate genes for further analysis . 10 . 7554/eLife . 00426 . 008Figure 3 . Usf1 is a candidate for the Soc locus . ( A ) Gene expression profiling of 22 Soc candidates ( gray and light blue ) and nine clock genes ( yellow ) in 10 different tissues from Soc congenic ClockΔ19/+ mice . All tissues were collected at ZT6 . The RNA copy number was calculated by a method described previously ( Uno and Ueda , 2007 ) . Copy number within all tissues for all analyzed genes was normalized ( Z = mean/SD ) . We coded Z scores in seven different colors . Cluster analysis was performed using Systat Software . Only seven of the 22 Soc candidates ( Vangl2 , Usf1 , Dcaf8 , Copa , Pex19 , Pea15 and Ncstn ) show a similar expression profile with that of the clock genes within the 10 tissues examined . ( B ) USF1 activates Per1 and Per2 promoters . The seven Soc candidates were tested for Per1 and Per2 promoter activation ( top left panels ) . Only USF1 activates both promoters . Each data point represents the mean ± SEM from three replicates . Activation of both Per1 and Per2 promoters by USF1 occurs in a dose-dependent manner ( top right panels ) . USF1-mediated transcription is not inhibited by the CRY proteins . While there is strong negative inhibition by CRY of CLOCK:BMAL1-mediated transcription from the Per promoter , Per promoter activation by USF1 is not inhibited by either CRY1 or CRY2 ( bottom panels ) . ( C ) Upregulation of Usf1 mRNA in liver tissue of ClockΔ19 suppressor background animals . Liver tissue was collected every 4 hr following 2 days of DD exposure . Among the seven Soc candidates tested , only Usf1 was increased in liver from F1 ClockΔ19/+ animals as determined by a two-way ANOVA ( p=0 . 006 ) . We did not detect an effect of time on Usf1 expression . In each panel , lines represent B6 ClockΔ19/+ ( blue ) and ( BALB x B6 ) F1 ClockΔ19/+ ( green ) animals . Each data point represents the mean ± SEM from 2–4 mice . Asterisks indicate significant differences between ClockΔ19 suppressor and non-suppressor strain values at each time point ( Tukey's post hoc , p<0 . 05 ) . ( D ) Upregulation of USF1 protein in the liver nuclear extract from BALB/cJ genetic background . The left panel shows western blots for USF1 from B6 , ( BALB x B6 ) F1 and BALB male mouse liver samples . HDAC1 was used as a loading control . Mouse nuclear extract was prepared at ZT8 . The right panel shows the normalized value for USF1 signal against HDAC1 . Each bar represents the mean ± SEM from three replicates . A one-way ANOVA was significant for strain background ( p=0 . 002 ) . ( E ) Upregulation of E-box-containing circadian genes in liver from ClockΔ19 suppressor background animals . We quantified Per1 , Per2 , Cry1 and Cry2 mRNA levels in the same liver samples used in ( C ) . By a two-way ANOVA , we detected significant upregulation in Per1 ( p=1 . 5 × 10−5 ) , Per2 ( p<10−8 ) , Cry1 ( p=1 . 0 × 10−8 ) and Cry2 ( p=6 . 6 × 10−3 ) transcripts . Asterisks indicate significant differences between ClockΔ19 suppressor and non-suppressor strain values at each time point ( Tukey's post hoc , p<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 008 The ClockΔ19 mutation results from an A→T transversion in a splice donor site that causes exon skipping and disruption of the C-terminal transactivation domain of CLOCK ( King et al . , 1997 ) . Consequently , the levels of Per and Cry mRNA are much lower in ClockΔ19 mutants than in wild-type mice ( Lowrey and Takahashi , 2011 ) . Given the nature of the suppressor phenotype , we hypothesized that the product of the gene responsible for the Soc QTL should activate transcription from E-boxes in a manner similar to CLOCK:BMAL1 . We tested E-boxes from Per1 and Per2 for activation by the seven Soc candidates in HEK293T cells . Of these , only Usf1 significantly activated transcription from both Per1 and Per2 E-boxes ( Figure 3B ) . We observed a clear dose response of Per promoter activation by USF1 ( Figure 3B ) . Unlike CLOCK:BMAL1 , activation of E-boxes by USF1 was not inhibited by the CRYs ( Figure 3B ) . Although it has been reported previously that USF1 binds E-box sequences ( Ferré-D'Amaré et al . , 1994 ) , our finding that , of the seven Soc candidates , only USF1 effects significant transactivation from circadian-relevant E-boxes , provided initial molecular evidence that Usf1 is a candidate for the Soc locus . We next tested the circadian expression of the seven candidate genes in liver tissue collected from ClockΔ19/+ ( BALB x B6 ) F1 and B6 animals every 4 hr on the third day of DD exposure . Among the genes tested , only the Usf1 transcript exhibited significant up-regulation in F1 mice ( Figure 3C ) . By western blot analysis , USF1 protein levels in the liver were ∼40% higher in BALB animals ( Figure 3D ) . To test whether the suppressor strain activates E-box-containing circadian clock genes in vivo , we examined the effect of strain background ( B6 or [BALB x B6]F1 ) on expression levels of Per1 , Per2 , Cry1 and Cry2 in liver using quantitative PCR ( qPCR ) . We detected significant up-regulation of all four circadian genes tested in the F1 background ( Figure 3E ) , consistent with elevated expression of an E-box transcription factor such as USF1 . Taken together these results strongly suggest that Usf1 is a prime candidate gene for the Soc locus . Because there are no coding mutations in Usf1 between the B6 and BALB mouse strains ( Figure 2—source data 1 ) , we hypothesized that the elevated expression of Usf1 in the BALB suppressor strain is likely the cause of Clock suppression . Given that Soc is a dominant suppressor and the candidate gene , Usf1 , exhibits up-regulated expression in the BALB Clock suppressor strain , loss-of-function tests for Usf1 are not appropriate . Instead , to test whether Usf1 is the gene responsible for the Soc QTL , we created Usf1 overexpression transgenic mice on an isogenic B6 genetic background . From previous work ( Hong et al . , 2007 ) , we observed that a transgene fused to DNA fragments containing a CMV minimal promoter is induced at low levels , and thus created two independent Usf1 transgenic lines , both of which exhibited a significant behavioral period-shortening effect in ClockΔ19/+ animals , thereby minimizing the possibility of transgene position effects ( Figure 4A ) . Quantitation of Usf1 expression confirmed that the transgenic lines mimic the increase in Usf1 levels observed in the BALB suppressor strain ( Figure 4B ) . Based on these results , we conclude that Usf1 is the gene encoded by the Soc locus . Consistent with the dominant action of Soc , Usf1 knockout animals do not show changes in period length , but do exhibit a reduction in circadian amplitude and locomotor activity levels ( Figure 4C , D ) . 10 . 7554/eLife . 00426 . 009Figure 4 . Transgenic expression of Usf1 suppresses ClockΔ19/+ mice and mimics Soc . ( A ) Representative locomotor activity records of Usf1 transgenic ClockΔ19/+ mice . Records on the left panel are from ClockΔ19/+ mice carrying a Usf1 transgene while those on the right are from ClockΔ19/+ controls . All mice are C57BL/6J isogenic . ( B ) Analysis of circadian behavior in Usf1 transgenic ClockΔ19/+ mice . Significant period shortening was detected in two Usf1 transgenic lines in ClockΔ19/+ ( red ) , but not wild-type ( blue ) mice . Each bar represents 5–35 mice from the C57BL/6J isogenic background ( left ) . The right panel shows level of Usf1 mRNA in hypothalamus at ZT6 . Blue dots represent WT and red dots represent ClockΔ19/+ . A two-way ANOVA was significant for transgene ( Tg ) genotype ( p=0 . 006 ) . The effect of Clock genotype was not significant ( p=0 . 18 ) . There was no significant interaction between Tg and Clock genotype ( p=0 . 616 ) . ( C ) Circadian activity rhythms in Usf1 knockout mice . Representative activity records of two wild type ( left ) and two Usf1 knockout ( right ) mice . ( D ) Circadian period was not different between WT and Usf1 KO mice . However , circadian amplitude and daily activity in constant darkness were significantly lower in Usf1 KO mice . Data represent the mean ± SEM from 26 WT and 11 Usf1 KO mice . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 009 For a gene to be a quantitative trait gene owing to an expression difference , the difference should be in a cis , rather than a trans regulatory element . We further examined the Usf1 allele-specific expression difference between B6 and BALB mice in ( B6 x BALB ) F1 animals , which controls for trans-effects and environmental influences ( Cowles et al . , 2002 ) . Using three independent methods ( Figure 5A , B , C ) , we found that the Usf1 expression difference between B6 and BALB is the result of polymorphisms in a cis-regulatory element causing allele-specific up-regulated expression of the BALB Usf1 transcript . 10 . 7554/eLife . 00426 . 010Figure 5 . Usf1 promoter analysis . ( A ) Allele-specific expression difference between B6 and BALB Usf1 mRNA revealed by DNA sequencing . We chose an SNP at the 3′-UTR of Usf1 as a marker to distinguish between the B6 and BALB alleles . The amplicon size from genomic DNA and cDNA are identical . We PCR-amplified the SNP-containing region in genomic DNA samples from B6 , BALB , ( B6 x BALB ) F1 and three samples containing different mix ratios between B6 and BALB DNA ( 3:1 , 1:1 and 1:3 , respectively ) as shown in the left panel . The panel on the right shows ( B6 x BALB ) F1 Usf1 cDNA sequence from hypothalamus ( top three ) and liver ( lower three ) . The PCR products were sequenced by an ABI3700 machine to compare the fluorescent signal peak ratio at the SNP locus as an indicator of the copy number ratio of the two alleles . This clearly demonstrates that the expression difference of Usf1 between B6 and BALB is the result of polymorphisms in cis regulatory elements . ( B ) Analysis of allele-specific expression differences between B6 and BALB Usf1 mRNA by quantitative PCR . We amplified genomic DNA mixtures described in ( A ) with allele-specific PCR primers and created a standard curve for %BALB genomic DNA vs ΔCt ( black data points ) . The blue data point is from F1 genomic DNA , which is naturally a 1:1 mixture of B6 and BALB . Red data points represent F1 cDNA from hypothalamus . Based on the standard curve generated from genomic DNA , 80% of the F1 cDNA contains the BALB Usf1 allele . ( C ) Analysis of allele-specific expression differences between B6 and BALB Usf1 mRNA by cloning PCR products . We amplified both F1 genomic DNA and cDNA using primers flanking an SNP in exon 10 ( rs31093636 ) of Usf1 . We generated four independent PCR products from a single F1 genomic DNA sample and four PCR products from four different F1 cDNA samples . The exon 10 SNP creates a restriction fragment length polymorphism such that only the BALB allele is cleaved by restriction enzyme TfiI . Each PCR product was cloned into a TA plasmid vector . From each transformation , we picked 24–48 colonies . We isolated 143 colonies containing F1 genomic DNA and 172 colonies containing F1 cDNA . This analysis , like that in ( B ) , demonstrates that the F1 cDNA contains a higher percentage of the BALB Usf1 allele than expected . ( D ) Usf1 promoter analysis between B6 and BALB mouse strains . The putative Usf1 promoter sequence ( ∼2 . 3 kb upstream of exon 1 ) was cloned into the pGL4 luciferase reporter vector and 90 ng of either B6 or BALB construct was transfected into HEK293T cells . Promoter activity from the BALB clone is significantly higher than that from the B6 clone ( p<10−6 ) . Next , we swapped EcoRI-XhoI fragments between the B6 and BALB constructs . The B6 clone containing the BALB EcoRI-XhoI fragment has significantly higher activity than the original B6 clone ( p<10−3 ) . The activity is equivalent to the original BALB clone . On the other hand , the BALB clone containing the B6 EcoRI-XhoI fragment exhibits essentially similar activity as the B6 intact clone . The blue and green bars indicate B6 and BALB DNA fragments , respectively . Each data point represents mean ± SEM of 18–36 samples . ( E ) SNP distribution pattern among 16 mouse strains in the Usf1 promoter candidate region . We detected 14 polymorphisms among 16 mouse strains within the ∼1000 bp candidate region . The top eight strains are ClockΔ19 non-suppressor strains and the bottom eight are ClockΔ19 suppressor strains . There are only seven polymorphisms that perfectly match the phenotype distribution pattern . Blue indicates the B6 allele and green indicates the BALB allele . ( F ) Putative Usf1 promoter SNPs ( containing ∼100 bp of flanking sequence of SNP1 , SNP2 , SNP3 , SNP4 , SNP5&6 and SNP7 ) from B6 and BALB were cloned into the pGL4 luciferase reporter vector . Only SNP3 and SNP7 show elevated promoter activity . In both cases , the BALB allele has significantly higher activity ( p<0 . 001 ) than the B6 allele ( p<0 . 01 ) . In the NCBI RefSNP database , SNP3 corresponds to rs31538551 and SNP7 to rs31538547 . Each data point represents the mean ± SEM of six samples . ( G ) We mutagenized the B6 promoter EcoRI-XhoI fragment to introduce the BALB allele at either SNP3 or SNP7 . We observed a significant increase in the luciferase signal by SNP7 ( p<0 . 05 ) . Although we did not detect significant upregulation by the BALB allele at SNP3 ( p=0 . 18 ) , the level of luciferase was elevated with the BALB allele compared to the B6 allele . Each data point represents the mean ± SEM of 12 samples . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 010 To determine whether the allele-specific expression difference is the result of promoter polymorphisms between B6 and BALB , we tested basal promoter activity of ∼2 . 3 kb upstream of the Usf1 transcription start site using a luciferase reporter assay . Usf1 promoter sequences from both B6 and BALB were cloned into the PGL4 luciferase reporter vector and transfected into HEK293T cells . Although both the B6 and BALB sequences activated the luciferase reporter , the BALB sequence resulted in a significantly higher signal than that from B6 ( Figure 5D ) . By exchanging EcoRI-XhoI fragments between the B6 and BALB reporter clones , we narrowed the critical polymorphisms to the EcoRI-XhoI fragment ( ∼1000 bp ) and identified seven common polymorphisms ( five SNPs and two deletions ) that match the phenotypic distribution of 16 mouse strains ( Figure 5E ) . We next amplified ∼100-bp fragments containing each of the seven polymorphisms from BALB and B6 genomic DNA and assayed their reporter gene activity to test whether the candidate SNPs affect promoter activity . Among the six fragments tested ( SNPs 5 and 6 were contained within the same fragment ) , only two ( SNPs 3 and 7 ) showed strong promoter activity and statistically higher activity from the BALB allele compared to the B6 allele ( Figure 5F ) . SNPs 3 and 7 correspond to rs31538551 and rs31538547 , respectively , in the NCBI SNP database . Next , we used site-directed mutagenesis on the B6 promoter ( EcoRI-XhoI fragment ) to introduce the corresponding BALB polymorphism at each of the two SNP loci and examined promoter activity . We observed a significant increase in luciferase signal by replacing the B6 allele of SNP7 ( rs31538547 ) with the BALB allele ( Figure 5G ) . This strongly suggests that SNP7 from the BALB Usf1 promoter region is a quantitative trait nucleotide ( QTN ) responsible for the Soc phenotype . Because an elevation of Usf1 expression is most likely responsible for suppression of the ClockΔ19/+ phenotype , we sought to determine the mechanism by which USF1 suppresses the Clock phenotype . Like CLOCK and BMAL1 ( Ripperger and Schibler , 2006 ) , USF1 binds to E-box motifs to regulate transcription ( Ferré-D'Amaré et al . , 1994 ) . Using chromatin immunoprecipitation ( ChIP ) followed by qPCR analysis , we verified that CLOCK and USF1 can bind to common E-boxes in three circadian genes ( Dbp , Per2 and Per1 ) ( Figure 6A ) . Although not as robust as CLOCK , we observed USF1 binding to four E-boxes ( Dbp EP , Dbp EI2 , Per1 EP1 and Per1 EP2 ) . In ClockΔ19 mutants , however , USF1 occupancy increases and CLOCKΔ19 occupancy decreases at these sites despite elevated CLOCKΔ19 mutant protein levels ( Figure 6B; Yoshitane et al . , 2009 ) . Further , we observed a time-dependent DNA binding pattern of USF1 with a peak at night that is antiphase to that of CLOCK which peaks at ZT8 ( Figure 6C ) . 10 . 7554/eLife . 00426 . 011Figure 6 . CLOCK:BMAL1 and USF1 binding to the E-box motif . ( A ) CLOCK and USF1 binding at Dbp , Per2 and Per1 E-boxes revealed by ChIP-qPCR . E-box occupancy demonstrated by CLOCK ( top panels ) and USF1 ( bottom panels ) is shown . At all E-boxes examined , CLOCK binding is lower in ClockΔ19/ClockΔ19 animals compared to wild-type controls . In contrast , USF1 binding is elevated in ClockΔ19/ClockΔ19 mice compared to wild-type animals at the Dbp EI2 , Per2 E′ and Per1 EP1 and EP2 E-boxes . USF1 binding is also elevated in Clock and Bmal1 knockout mice . ( B ) Western blot for CLOCK and USF1 in liver nuclear extracts from wild-type and ClockΔ19/ClockΔ19 C57BL/6J isogenic animals . Tissues were harvested at ZT8 . HDAC1 was used as a loading control . ( C ) Reciprocal pattern of CLOCK and USF1 binding at Dbp and Per1 E-boxes with time of day using ChIP-qPCR . ( D ) Antibody validation of CLOCK:BMAL1 and USF1 complexes for EMSA experiments using tandem Dbp EP-box , Dbp EI2-box , Per2 E′-box and Per1 EP1-box , top to bottom , respectively , in liver nuclear extracts from wild-type and ClockΔ19/ClockΔ19 C57BL/6J isogenic animals at ZT8 . CB2 ( CLOCK:BMAL1 tandem heterodimeric complex ) ; CB1 ( CLOCK:BMAL1 heterodimer ) ; USF ( USF1 or USF2 homodimer or USF1:USF2 heterodimer ) . CB1 and CB2 complexes are completely abolished or shifted by CLOCK or BMAL1 antibodies , respectively . USF complexes are reduced significantly by USF1 antibody , with the remaining complex presumed to be USF2 . ( E ) EMSA analysis of E1 and E2 mutants in the tandem Per2 E′-box . Mutation of the E1 site in the Per2 E′-box completely blocks CLOCK:BMAL1 and USF complex binding . Mutation of the E2 site blocks the formation of tandem heterodimeric CB2 complexes in WT liver nuclear extracts and only CB1 complexes are detected in both WT and mutant extracts . This confirms the critical role of the E1 site for binding as well as the requirement for the E2 site for tandem complex formation as reported ( Rey et al . , 2011 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 011 To explore our observation that CLOCKΔ19:BMAL1 occupancy decreases in ClockΔ19/ClockΔ19 mice we determined the relative affinity of CLOCK:BMAL1 , CLOCKΔ19:BMAL1 and USF1 complexes to E-boxes using EMSA . Given our results demonstrating Usf1 expression differences between the B6 ( non-suppressor ) and BALB ( suppressor ) mouse strains ( Figure 3 ) , we used isogenic B6 animals in these experiments to insure that USF1 levels were constant as we examined E-box occupancy of CLOCK , CLOCKΔ19 , and USF1 . We examined the same E-box sites in the Dbp , Per1 , and Per2 genes used for ChIP analysis ( Figure 6A ) , and confirmed the identity of gel shift complexes by supershift using specific antibodies to CLOCK , BMAL1 and USF1 ( Figure 6D ) . At all E-boxes , we detected three types of DNA:protein complexes: CLOCK:BMAL1 tandem heterodimers ( CB2 ) , CLOCK:BMAL1 heterodimers ( CB1 ) , and USF ( consisting of USF1:USF1 and possibly USF1:USF2 or USF2:USF2 dimers ) . As shown previously , these E-box sites consist of tandem E-boxes spaced 6–10 nucleotides apart , and binding to these tandem sites appears to be cooperative ( Figure 6E; Rey et al . , 2011 ) . Interestingly , wild-type CLOCK:BMAL1 complexes bind primarily as a tandem complex ( CB2 ) at all four E-box sites with the Dbp EP and Per2 E′ E-boxes showing the most bias towards tandem binding ( Figure 6D ) . By contrast , the mutant CLOCKΔ19:BMAL1 heterodimer binds primarily as a single complex ( CB1 ) at all four sites . Antibody supershift experiments affected both the CB1 and CB2 complexes , consistent with the hypothesis that these complexes , whether containing mutant or wild-type CLOCK , represent single or tandem CLOCK:BMAL1 binding complexes , respectively ( Figure 6D ) . To measure the affinity of native CLOCK:BMAL1 protein-DNA interactions , we performed saturation binding experiments using EMSA to determine the apparent kd and Bmax values for the different complexes and E-box sites . To estimate the binding affinity of CLOCK:BMAL1 , CLOCKΔ19:BMAL1 , and USF , liver nuclear extracts from ZT8 were incubated with increasing amounts of 32P labeled Dbp EP , Dbp EI2 , Per2 E′ or Per1 EP1 E-box double-stranded oligonucleotide probes ( Figure 7A ) . The binding of each of the complexes at the four E-boxes are presented as both absolute and relative binding plots to show both the relative amount of binding as well as the relative affinity ( Figure 7B ) . At low probe concentrations , the wild-type CLOCK:BMAL1 complex ( CB2 ) binds more strongly than CLOCKΔ19:BMAL1 ( CB1 ) —demonstrating the higher affinity of the wild-type complex for DNA . Although the affinity varies depending on the specific site , Kd values of wild-type CLOCK:BMAL1 range from 0 . 86 nM for Dbp EI2 to 19 nM for Per1 EP1 , while the Kd of CLOCKΔ19:BMAL1 ranges from 9 to 48 nM for Dbp EI2 and Per1 EP1 , respectively ( Table 2 ) . Kd estimates indicate a binding affinity change from 2 . 5- to 11 . 8-fold between wild-type and mutant CLOCK:BMAL1 complexes depending on the E-box site ( Figure 7B; Table 3 ) . Parenthetically , these data also show that the affinity and maximal binding of CLOCK:BMAL1 vary widely among native E-box sites , thus it is important to base interpretations on more than one E-box site . There is no significant difference in USF1 binding affinity between the two genotypes and , in fact , the Kd of USF1 is similar to the mutant CLOCKΔ19:BMAL1 complex ( Figure 7B; Table 3 ) . 10 . 7554/eLife . 00426 . 012Figure 7 . Native CLOCK:BMAL1 and USF1 binding affinity . ( A ) Saturation binding kinetics of CLOCK:BMAL1 and USF at E-boxes . EMSA analysis for the Dbp EP , Dbp EI2 , Per2 E′ , and Per1 EP1 E-boxes , top to bottom , respectively , with different probe concentrations . The left panels are from wild-type and the right panels are from ClockΔ19/ClockΔ19 animals . CB2 ( CLOCK:BMAL1 tandem heterodimeric complex ) ; CB1 ( CLOCK:BMAL1 heterodimer ) ; USF ( USF1:USF1 or perhaps USF1:USF2 , USF1:USF2 ) . Sequences for the tandem E-boxes examined are given ( bottom; E1 sequence , red; E2 sequence , blue ) . ( B ) Analysis of saturation binding kinetics as a function of probe concentration . Left panels represent absolute binding and right panels show binding relative to the maximum . At all sites , CLOCKΔ19:BMAL1 binding affinity is lower than that for CLOCK:BMAL1 and similar to USF binding affinity . Each data point represents the mean ± SEM of three experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 01210 . 7554/eLife . 00426 . 013Table 3 . Apparent binding affinity ( Kd ) and maximal binding ( Bmax ) for CLOCK:BMAL1 and USF at four different E-boxes in Figure 7DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 013CLOCK:BMAL1CLOCKΔ19:BMAL1USF1 WTUSF1 ClockΔ19/ClockΔ19Dbp EP-boxBmax0 . 97 ± 0 . 021 . 25 ± 0 . 05 *0 . 83 ± 0 . 060 . 87 ± 0 . 04Kd2 . 01 ± 0 . 22*32 . 94 ± 3 . 9242 . 8 ± 9 . 3049 . 56 ± 6 . 20Dbp EI2-boxBmax0 . 79 ± 0 . 02*2 . 38 ± 0 . 052 . 25 ± 0 . 052 . 10 ± 0 . 05Kd0 . 71 ± 0 . 10*6 . 18 ± 0 . 546 . 10 ± 0 . 529 . 64 ± 0 . 96Per2 E′-boxBmax0 . 26 ± 0 . 02*0 . 43 ± 0 . 020 . 36 ± 0 . 030 . 41 ± 0 . 02Kd2 . 00 ± 0 . 64*8 . 05 ± 1 . 5717 . 81 ± 5 . 0816 . 71 ± 2 . 46Per1 EP1-boxBmax0 . 74 ± 0 . 12*7 . 38 ± 1 . 183 . 06 ± 0 . 647 . 96 ± 2 . 01Kd16 . 55 ± 9 . 4943 . 69 ± 19 . 4033 . 36 ± 20 . 9958 . 62 ± 37 . 82Values are the mean + SEM derived from four-parameter nonlinear curve-fitting . *indicates that the Bmax or Kd is significantly different from other groups , p<0 . 0001 . In addition to these overall trends , specific E-box sites also provide further insight into the affinity of CLOCK:BMAL1 binding . In the case of the Dbp EI2 site ( Figure 7A ) , where wild-type CLOCK:BMAL1 binds both as single and tandem complexes , the tandem CB2 site has much higher affinity than CB1 , consistent with the prediction of previous work ( Rey et al . , 2011 ) . In contrast , at the Dbp EP site ( Figure 7A ) , where mutant CLOCKΔ19:BMAL1 complex can be seen binding as both single CB1 and tandem CB2 complexes , the affinities of the two mutant complexes are similar and lower than wild-type CLOCK:BMAL1 complex . This suggests that the affinity of the mutant CLOCKΔ19:BMAL1 complex is lower for two reasons: first , it does not preferentially bind as a tandem complex , thus binding may not be cooperative; and second , its affinity , even as a tandem complex , remains lower suggesting that the ClockΔ19 mutation interferes both with cooperativity and with affinity . Thus , our saturation binding experiments clearly demonstrate that the affinity of CLOCKΔ19:BMAL1 is significantly lower relative to the wild-type complex , and that this lower affinity is similar to the affinity of USF1 at the same sites . This suggests that under normal conditions , the wild-type CLOCK:BMAL1 complex binds with much higher affinity than the USF1 complex , but when mutant , the affinities of the CLOCKΔ19:BMAL1 and USF1 complexes are comparable which allows USF1 to bind more effectively to E-box sites . Because USF1 and CLOCK:BMAL1 compete for binding at the same E-boxes , and because CLOCKΔ19:BMAL1 exhibits a lower affinity for these sites , we predicted that there should be a global increase in USF1 occupancy at CLOCK:BMAL1 binding sites in ClockΔ19 mutant mice . To test this hypothesis we performed genome-wide location analysis of USF1 , CLOCK and BMAL1 using ChIP-Seq on liver nuclei collected at ZT8 from wild-type and ClockΔ19/ClockΔ19 mice . Again , we used isogenic B6 mice in order to avoid the complication of different levels of USF1 in suppressor strains . Analysis of USF1 and CLOCK:BMAL1 binding sites reveals extensive overlap in binding in wild-type mice , with 497 of 1885 USF1 peaks binding either CLOCK or BMAL1 ( Figure 8A ) . As predicted for ClockΔ19/ClockΔ19 animals , the overlap between USF1 and CLOCK:BMAL1 binding sites increases significantly to 1916 sites , an almost fourfold increase compared to wild-type controls ( Figure 8A ) . In ClockΔ19/ClockΔ19 mutant animals , USF1 co-occupies 38% of CLOCK:BMAL1 sites ( 1916/5072 ) —a dramatic increase from 14% ( 497/3412 ) observed in wild-type animals . Surprisingly , USF1 binding also increases globally in homozygous ClockΔ19 animals , even at sites that do not bind CLOCK or BMAL1 , from 1885 peaks in wild-type animals to 6091 peaks in ClockΔ19/ClockΔ19 mutant animals . 10 . 7554/eLife . 00426 . 014Figure 8 . Genome-wide location analysis ( Chip-Seq ) reveals extensive overlap between USF1 and CLOCK or BMAL1 binding sites . ( A ) Venn diagram showing a dramatic increase in sites bound by both USF1 and CLOCK:BMAL1 in ClockΔ19 mutant animals ( bottom ) as compared to wild-type animals ( top ) . ( B ) Heat map analysis of the 1916 common sites between USF1 and CLOCK:BMAL1 reveals an increase in the number of USF1 binding sites as well as binding intensity at existing sites in ClockΔ19 mutant animals ( left two heat maps ) . CLOCK binding decreases in ClockΔ19 mutant animals ( middle two heat maps ) , while BMAL1 binding is largely unchanged ( right two heat maps ) . ( C ) Quantification of the top 200 binding sites for each transcription factor is represented as box plots . Similar to the heat map analysis , USF1 binding increases ( left panel ) , CLOCK binding decreases ( middle panel ) , and BMAL1 binding is unaffected in ClockΔ19 mutant mice ( right panel ) . ( D ) Motif analysis of each of the six groups of binding sites reveals a common canonical E-box CACGTG sequence . ( E ) UCSC browser view of binding peaks at four representative genes Per1 , Dbp , Rev-erbα , and Ilf3 . Each horizontal track represents the ChIP-seq binding signal as described previously ( Koike et al . , 2012 ) for either WT ( red ) or ClockΔ19 mutants ( blue ) for USF1 , CLOCK or BMAL1 as indicated on the left . In each case , USF1 binding increases in ClockΔ19 mutants . ( F ) Binding signal of USF1 increases at most reference clock genes in ClockΔ19 mutant animals . ( G ) Averaged histogram view of reference clock genes 1 kb around peak of binding also shows increased binding in ClockΔ19 mutant mice . The ChIP-seq peak lists for USF1 , CLOCK and BMAL1 are available in Figure 8—source data 1 , 2 and 3 respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 01410 . 7554/eLife . 00426 . 015Figure 8—source data 1 . ChIP-seq peak list for USF1 . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 01510 . 7554/eLife . 00426 . 016Figure 8—source data 2 . ChIP-seq peak list for CLOCK . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 01610 . 7554/eLife . 00426 . 017Figure 8—source data 3 . ChIP-seq peak list for BMAL1 . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 017 Heat map analysis of the 1916 sites that are co-occupied by both USF1 and CLOCK:BMAL1 in ClockΔ19/ClockΔ19 mutant animals reveals a dramatic increase in binding intensity of USF1 in mutant mice ( Figure 8B , C ) . Not only do the number of USF1 peaks increase , but also the binding intensity at existing sites increases in mutant animals . Concomitantly , in the mutant animals there is a decline in CLOCK binding intensity at these 1916 common binding sites ( Figure 8B , C ) . We quantitated the binding intensity of the top 10% of the binding sites for all three ChIP-Seq experiments and performed comparisons between wild-type and ClockΔ19/ClockΔ19 animals ( Figure 8C ) . For USF1 and CLOCK there was a significant increase and decrease , respectively , in binding between wild-type and mutant animals , but no change in BMAL1 binding ( Figure 8C ) . Motif analysis of each group of binding sites identified the canonical E-box binding site , CACGTG , as the most significant motif , indicating both USF1 and CLOCK:BMAL1 bind to the same consensus sequence in both wild-type and ClockΔ19/ClockΔ19 mice ( Figure 8D ) . UCSC genome browser profiles are shown for examples of four genes ( Figure 8E ) . Per1 has at least five E-boxes , however ChIP-Seq analysis reveals two major peaks approximately 5 kb upstream of the transcription start site as well as two minor peaks further downstream ( Figure 8E ) . The peak height of USF1 binding increases dramatically in ClockΔ19/ClockΔ19 animals , and there is a slight reduction in CLOCK binding in mutant animals . Within the Dbp gene there are also multiple E-boxes , and USF1 binding increases in ClockΔ19/ClockΔ19 animals on the promoter , and intron two E-boxes . On the Dbp promoter CLOCK and BMAL1 binding decreases in the ClockΔ19/ClockΔ19 animals . A similar pattern is observed for other clock genes such as Rev-erbα which contains multiple E-boxes ( Figure 8E ) and other target genes such as Ilf3 ( Figure 8E ) . We extended this analysis of ChIP-Seq data to 41 E-boxes in 15 ‘reference clock genes' that are thought to be core regulators of the circadian clock ( Rey et al . , 2011 ) . We specifically analyzed these E-box sites in the two genotypes for USF1 binding changes . Of these 41 sites , 23 bound USF1 with a significant increase in USF1 binding in the ClockΔ19/ClockΔ19 animals ( Figure 8F , G ) . Thus , USF1 binds to a majority of reference clock genes and its occupancy on these sites increases in the ClockΔ19/ClockΔ19 mice . These data suggest an intriguing mechanism of rescue of the ClockΔ19 heterozygous phenotype by USF1 . In this model , USF1 expression is higher in BALB than in B6 strain owing to a promoter polymorphism . Our data show that USF1 can competitively bind to the same E-boxes in vivo as CLOCK:BMAL1 . Furthermore quantitative analysis of binding kinetics indicates that the CLOCKΔ19:BMAL1 complex has a much lower binding affinity for E-boxes than its wild-type counterpart . This lowered affinity combined with increased expression of USF1 in BALB mice accounts for the rescue of the ClockΔ19/+ phenotype in this strain . We propose that USF1 acts as a partial agonist whose transcriptional regulation of circadian genes can compensate for the reduction in transcriptional activation by the CLOCKΔ19:BMAL1 complex .
Taken together , we demonstrate that a dominant suppressor of the ClockΔ19 mutation in the BALB genetic background is an ancestral allele of Usf1 which carries a cis-regulatory SNP that enhances Usf1 expression ( Figure 9A ) . USF1 is ubiquitously expressed in both mouse and human and is a member of the evolutionarily conserved bHLH-Zip transcription factor family . USF1 participates in the regulation of several processes including lipid and carbohydrate metabolism , immune responses , and the cell cycle ( Corre and Galibert , 2005 ) . USF1 interacts with the circadian clock gene pathway by binding to E-box regulatory sites in common with those bound by CLOCK:BMAL1 to regulate circadian gene expression ( Figure 9B ) . We find that the mutant CLOCKΔ19:BMAL1 complex binds with much lower affinity than wild-type CLOCK:BMAL1 complexes , arising in part from the absence of cooperativity of binding to tandem E-box sites seen with the wild-type CLOCK:BMAL1 complex ( Figure 9C ) . Because USF1 binds as a dimer , the 40% increase in USF1 levels observed in the Clock suppressor strain could lead to a substantial increase in DNA binding if those levels are below the Kd for USF1 binding . In addition , because the PER:CRY negative feedback complex cannot repress USF1-mediated transactivation ( Figure 3B ) , a modest increase in USF1 could activate target genes more effectively than CLOCKΔ19:BMAL1 . Thus , USF1 can suppress the ClockΔ19 mutant by increased occupancy of CLOCK:BMAL1 E-box sites , acting as a partial agonist for CLOCK:BMAL1-mediated transcription . What might the role of USF1 be in a Clock wild-type background ? We have found that as CLOCK:BMAL1 DNA binding decreases at night ( Rey et al . , 2011; Koike et al . , 2012 ) , USF1 occupancy increases at these sites , exhibiting an antiphase temporal DNA binding pattern ( Figure 6C ) . We speculate that the role of USF1 at CLOCK:BMAL1 sites could be to maintain an open chromatin state to facilitate CLOCK:BMAL1 binding on the following circadian cycle although additional work would be required to test this hypothesis . In addition , in Usf1 knockout mice , we observe a reduction in circadian amplitude and activity levels showing that USF1 plays a role under normal conditions to enhance circadian rhythmicity and robustness . While it is tempting to speculate that these effects of Usf1 might work at the level of the cell autonomous circadian oscillator , additional experiments would be required to determine at what level of organization ( cell , circuit or higher ) these changes in amplitude at the behavioral level originate . 10 . 7554/eLife . 00426 . 018Figure 9 . Model for the USF1 suppression of the ClockΔ19 mutation within the circadian clock mechanism . ( A ) SNP polymorphisms between B6 and BALB in the Usf1 promoter lead to a cis-mediated increase in Usf1 expression levels in suppressor mouse strains . ( B ) In the wild-type state , CLOCK:BMAL1 tandem heterodimers bind to tandem E-box sites in the regulatory regions of the Per and Cry genes and interact cooperatively to enhance binding affinity which is much higher than USF1 affinity . Thus , most tandem E-box sites are preferentially bound by wild-type CLOCK:BMAL1 complex over USF1 . ( C ) In contrast , in the ClockΔ19 mutant , the CLOCKΔ19:BMAL1 complex binds to the E1 site primarily as a single heterodimer with an affinity similar to that of USF1 , yet lower than that of the wild-type CLOCK:BMAL1 tandem heterodimer complex . This allows USF1 to occupy CLOCK:BMAL1 sites more effectively in the mutant state and , as a consequence , drive transcription of CLOCK:BMAL1 target genes under conditions where mutant CLOCKΔ19:BMAL1 is not transcriptionally competent . Because USF1 is not repressed by CRY1 or CRY2 negative feedback , the transcriptional activation by USF1 may be more responsive than that of CLOCK:BMAL1 . However , negative feedback can still occur via CLOCKΔ19:BMAL1 since circadian oscillations can persist in ClockΔ19 mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 00426 . 018 In summary , the global interactions between CLOCK:BMAL1 and USF1 reveal an extensive and previously unknown interface linking these two transcriptional networks . It will be interesting in future work to determine whether the shared targets of these two pathways affect phenotypes beyond the circadian system . Taken together , these results show that USF1 is a significant modulator of molecular and behavioral circadian rhythms in mammals .
All animals used in this study were raised at Northwestern University or the University of Texas Southwestern Medical Center in a 12 hr light/12 hr dark cycle ( LD12:12 ) from birth . All animal care and use procedures were in accordance with guidelines of the Northwestern University and UT Southwestern Institutional Animal Care and Use Committees . At 8–12 weeks of age , mice were transferred to individual cages equipped with running wheels and housed in LD12:12 conditions . After a minimum of 7 days , animals were transferred to DD conditions for 3 weeks . The free running period was calculated using a χ2 periodogram as described previously ( Shimomura et al . , 2001 ) . Per2Luc mice ( Yoo et al . , 2004 ) were euthanized by cervical dislocation between ZT11 and ZT12 . 5 . Tissue was rapidly dissected as described in ( Yamazaki and Takahashi , 2005 ) . 300 μm coronal sections containing the SCN were isolated . Tissues were cultured on Millicell culture membranes ( PICMORG50 , Millipore , Billerica , MA ) and were placed in 35 mm tissue culture dishes containing 2 ml DMEM media ( 90-013-PB , Gibco , Life Technologies , Grand Island , NY ) supplemented with 352 . 5 μg/ml sodium bicarbonate , 10 mM HEPES ( Gibco ) , 2 mM L-Glutamine , 2% B-27 Serum-free supplement ( Invitrogen , Life Technologies , Grand Island , NY ) , 25 units/ml penicillin , 25 μg/ml streptomycin ( Gibco ) , and 0 . 1 mM luciferin potassium salt ( L-8240 , Biosynth AG , Staad , Switzerland ) . Sealed cultures were placed in LumiCycle luminometer machines ( Actimetrics , Wilmette , IL ) and bioluminescence from the tissue was recorded continuously . ( BALB/cJ x C57BL/6J ) F2 ClockΔ19/+ mice were bred by intercrossing ( BALB/cJ x C57BL/6J ) F1 ClockΔ19/+ animals . Only ClockΔ19/+ mice were wheel-tested . Note that because Clock maps to chromosome 5 , and the mutation was originally induced in the B6 background , it was not possible to scan for QTLs on this chromosome in the F2 cross . Genotyping primers used were: forward 5′-TACCAGCTGCTAATGTCCAGTG-3′; reverse 5′-TACATTGGGCTAGCCTTCCTAAG-3′ . PCR conditions were 95°C for 2 min followed by 32 cycles of 95°C for 15 s , 55°C for 30 s , and 72°C for 15 s . Following amplification , PCR products were digested with 2 units of HincII for 2 hr at 37°C . After restriction digestion , PCR products were resolved on 4% agarose gels . The Clock mutant allele was identified as a ∼70-bp product , while wild-type animals were identified by the presence of bands at ∼50 and 20 bp . SSLP markers used in this study are available upon request . The genotyping protocol has been described previously ( Shimomura et al . , 2001 ) . QTL analysis was performed using MapmanagerQTX ( http://www . mapmanager . org/ ) . ( BALB/cJ x C57BL/6J ) F1 ClockΔ19/+ mice were backcrossed to C57BL/6J wild-type mice . Animals were genotyped with five SSLP markers ( D1Mit22 , D1Mit218 , D1Mit33 , D1Mit291 and D1Mit155 ) to identify individuals carrying BALB alleles of Soc . Following the N4 generation , we selected mice for breeding by both phenotype and genotype . After the N6 generation , selection was performed by genotyping with D1Mit452 and D1Mit155 . Mice carrying smaller fragments of the SocBALB region and with suppressed phenotypes were crossed to C57BL/6J wild-type animals . After the 10th generation of phenotypic and genotypic selection , mice were intercrossed to produce the final congenic line . All inbred strains were purchased from Jackson Laboratory ( Bar Harbor , ME ) . These strains were crossed to C57BL/6J ClockΔ19/+ females . For high-resolution mapping of the Soc locus , we used the 150 K SNP set from The Genomics Institute of the Novartis Foundation . RNA was extracted using Trizol reagent ( Invitrogen ) according to the manufacture's protocol . RNA was then reverse-transcribed into cDNA by TaqMan RT kit . qPCRs were performed with SYBR green in an Applied Biosystems 7900HT Fast Real-Time PCR System ( 384 well ) . Primer sequences are described in supplementary file 1 . Copy number estimates were performed as described previously ( Uno and Ueda , 2007 ) . We used three different methods for detecting B6 and BALB allele specific expression in ( BALB x B6 ) F1 ClockΔ19/+ animals . HEK293T cells were cultured at 37°C under a humidified atmosphere containing 5% CO2 in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum . We seeded cells into 24-well poly-D-lysine coated culture plates ( ∼2 × 105 cells/well ) . After 24 hr , plasmid DNAs were transiently transfected into the HEK293T cells using Lipofectamine2000 reagent ( Invitrogen ) according to the manufacturer's protocol . After 24 hr of transfection , the cells were harvested and luciferase assay was performed and the raw numbers were normalized with a beta-actin-LacZ transfection control . Mouse Usf1 cDNA clone ( MGC:59 , 374 ) was obtained from the Mammalian Gene Collection ( Open Biosystems ) and was used to PCR-amplify Usf1 with forward ( 5′-GCCGCCACCATGAAGGGGCAGCAGAAAA-3′ ) and reverse primers ( 5′-TTAAAGAGCGTAATCTGGAACATCGTATGGGTAGTTGCTGTCATTCTTGATGACGACCT-3′ ) . The PCR product containing a modified Usf1 with a Kozak consensus sequence and a 3′ HA tag was inserted into pBluescript II KS- using standard cloning procedures . Site-directed mutagenesis was performed with forward ( 5′- GCAGGGAGGGAGCCAGCGATC-3′ ) and reverse ( 5′- GATCGCTGGCTCCCTCCCTGC -3′ ) primers using Quikchange XL Site-Directed Mutagenesis Kit ( Stratagene , La Jolla , CA ) . The product was reamplified with forward ( 5′-GCCGCCACCATGAAGGGGCAGCAGAAAA-3′ ) and reverse ( 5′-TTAAAGAGCGTAATCTGGAACATCGTATGGGTAGTTGCTGTCATTCTTGATGACGACCT-3′ ) primers and subcloned into the EcoRV restriction site in PMM-400 ( provided by Mark Mayford ) . Transgenic mouse lines were generated by pronuclear injection using standard techniques as described ( Hong et al . , 2007 ) . The linearized DNA fragment was injected at a concentration of 1 ng/μl into fertilized mouse oocytes isolated from crosses of C57BL/6J matings to produce transgenic mice that were isogenic on C57BL/6J to exclude the possibility of contamination by genetic suppressor backgrounds . Transgenic mice were identified by PCR analysis of genomic DNA prepared from tail biopsy samples . Two sets of primers were used for genotyping: USF1-5F: 5′-CCAAAAACGAGGAGGATTTG-3′ and USF1-5R: 5′-GTGGCAGGGTAACCACTGAT-3′ , or USF1-3F: 5′-GCAGGGGTTAGATCAGTTGC-3′ and USF1-3R: 5′-TGCTCCCATTCATCAGTTCC-3′ . PCR conditions were: 95°C for 2 min followed by 32 cycles of 95°C for 15 s , 55°C for 30 s , and 72°C for 30 s . Following amplification , PCR products were resolved on 2% agarose gels . Livers from mice were immediately homogenized in 4 ml per liver of 1× PBS containing 2 mM EGS . The homogenate was incubated for 20 min at room temperature . Next , 110 µl of 36 . 5% formaldehyde was added followed by an 8 min incubation at room temperature . Cross-linking reactions were stopped by the addition of 250 µl of 2 . 5 M glycine and kept on ice . The homogenate was added to 10 ml of ice-cold 2 . 3 M sucrose containing 150 mM glycine , 10 mM HEPES pH 7 . 6 , 15 mM KCl , 2 mM EDTA , 0 . 15 mM spermine , 0 . 5 mM spermidine , 0 . 5 mM DTT and 0 . 5 mM PMSF , and layered on top of a 3 ml cushion of 1 . 85 M sucrose ( containing the same ingredients with 10% glycerol ) and centrifuged for 1 hr at 24 , 000 rpm at 4°C in a Beckman SW32 . 1 rotor . The nuclei were resuspended in 1 ml of 20 mM Tris pH 7 . 5 , 150 mM NaCl , 2 mM EDTA , transferred to a 1 . 5 ml microfuge tube , centrifuged for 3 min at 3000 rpm at 4°C , washed again , and stored –80°C until use . The nuclei were then resuspended in 0 . 8 ml per liver of lysis buffer ( 10 mM Tris pH 7 . 5 , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 2 M NaCl , 0 . 5% N-lauroylsarcosine , 0 . 1% sodium deoxycholate , 1 mM PMSF and EDTA-free protease inhibitor cocktail; Roche , Indianapolis , IN ) , and sonicated 5 s at 4°C 36 times with a Covaris S2 . The fragmented chromatin was then diluted tenfold with IP buffer ( 10 mM Tris pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% triton X-100 , 0 . 1% sodium deoxycholate , 1 mM PMSF , EDTA-free protease inhibitor cocktail ) . Approximately 120 µg of fragmented chromatin was pre-cleared by incubating with 40 µl of protein A-agarose ( Sigma , St . Louis , MO ) for 2 hr at 4°C with rotation . Pre-cleared chromatin was then incubated with antibody overnight at 4°C with rotation , followed by addition of 10 µl of Protein A/G Plus-agarose and incubation for 1 . 5 hr at 4°C . Agarose beads were then washed twice with IP buffer , twice with high salt wash buffer ( 20 mM Tris pH7 . 5 , 500 mM NaCl , 2 mM EDTA , 1% Triton X-100 , 1 mM PMSF ) , twice with LiCl wash buffer ( 20 mM Tris pH 7 . 5 , 250 mM LiCl , 2 mM EDTA , 0 . 5% Igepal CA-630 , 1% sodium deoxycholate , 1 mM PMSF ) , and once with TE . Co-immunoprecipitated DNA fragments were eluted with 100 µl of 20 mM Tris pH 7 . 5 , 5 mM EDTA , 0 . 5% SDS , then reverse crosslinked at 65°C overnight , incubated with 10 µg of RNaseA for 30 min at 37°C , with 160 µg of proteinase K for 30 min at 55°C , and then purified using the QIAquick PCR purification Kit ( Qiagen , Germantown , MD ) . ChIP products were analyzed by qPCR using SYBR green . Primer sequences are described in supplementary file 1 . ChIP-Seq libraries were prepared as described ( Kim et al . , 2010; Koike et al . , 2012 ) . SOLiD sequencing of ChIP-Seq libraries were performed on an ABI SOLiD4 instrument with 35-bp reads according to manufacturer's instructions ( Life Technologies , Grand Island , NY ) by the UTSW McDermott DNA Sequencing Core . Sequence reads were mapped to the mouse genome ( NCBI m37 ) with Applied Biosystems BioScope v1 . 3 . The peaks were identified from uniquely mapped reads without duplicates using MACS ( Zhang et al . , 2008 ) followed by PeakSplitter ( Salmon-Divon et al . , 2010 ) . Sequence reads were checked for abnormal read content in input samples that led to global false positive peak detection that arise primarily from low complexity sequence and contamination from plasmid/cDNA sequences . Global false positive peaks were removed from peak lists derived from MACS and PeakSplitter , and tags derived from plasmids/cDNAs were not analyzed as described previously ( Heinz et al . , 2010 ) . Results were further analyzed using HOMER ( Heinz et al . , 2010 ) . Peak overlaps ( peak summit ± 120 bp ) were determined with HOMER . Genome browser views were normalized as uniquely mapped reads per 10 million reads . Liver nuclear extract was prepared as described previously ( Yoshitane et al . , 2009 ) . Mice were sacrificed by cervical dislocation at ZT8 . Whole liver was quickly removed and briefly washed with ice-cold PBS and homogenized with seven strokes of a dounce tissue homogenizer ( Pestle A only ) at 4°C in 9 ml of ice-cold buffer A ( 10 mM HEPES-NaOH [pH 7 . 8] , 10 mM KCl , 0 . 1 mM EDTA , 1 mM dithiothreitol [DTT] , 1 mM phenylmethylsulfonyl fluoride [PMSF] , 4 mg/ml aprotinin , 4 mg/ml leupeptin , 50 mM NaF , and 1 mM Na3VO4 ) . The homogenate was centrifuged twice ( 5 min each , 700×g ) , and the precipitate was resuspended in 2 ml of ice-cold buffer C ( 20 mM HEPES-NaOH [pH 7 . 8] , 400 mM NaCl , 1 mM EDTA , 5 mM MgCl2 , 2% [v/v] glycerol , 1 mM DTT , 1 mM PMSF , 4 mg/ml aprotinin , 4 mg/ml leupeptin , 50 mM NaF , and 1 mM Na3VO4 ) . After gentle mixing at 4°C for 30 min , the suspension was centrifuged twice ( 30 min each at 21 , 600×g ) , and the final supernatant was used as a nuclear extract . EMSA was performed in a manner similar to that described previously ( Ripperger and Schibler , 2006 ) . For oligonucleotide annealing , 1 nm of each forward and reverse oligonucleotide ( supplementary file 1 ) were mixed with 1× T4 polynucleotide buffer at final volume 50 μl . The reaction mix was heated at 95°C for 10 min then slowly cooled to 25°C ( 1 °C/min ) . Oligonucleotide labeling was performed in 50 μl with 50 pmole of double-stranded oligo , 5 μl of T4 polynucleotide kinase buffer ( NEB , Ipswich , MA ) , 2 . 5 μl of T4 polynucleotide kinase ( NEB ) , 3 . 75 μl ATPγ32P ( Perkin Elmer cat# BLU002Z250UC , Waltham , MA ) , and 36 . 25 μl water . The reaction mix was incubated at 37°C for 30 min followed by 65°C for 10 min . After the kinase reaction , the sample was purified through a G50 spin column . EMSA reactions were performed with 15 μg of nuclear protein in 25 mM HEPES-KOH ( pH 7 . 6 ) , 150 mM NaCl , 0 . 1 mM EDTA , 1 mM DTT , 200 ng/μl sheared salmon sperm DNA , 50 ng/μl poly ( dI-dC ) , and 2 μl of radioactive probe in a volume of 12 μl . Binding buffer and nuclear protein were mixed and incubated at room temperature for 15 min . Then , 2 μl of radioactive probe was added and incubated at 16°C for 30 min . For supershifts , 1 μl of antibody was added immediately before the addition of the radio-labeled probe . The antibodies used were anti-BMAL1 , anti-CLOCK ( a gift from Dr . Choogon Lee ) as described previously ( Koike et al . , 2012 ) , and anti-USF1 ( sc-8983× , Santa Cruz Biotech , Dallas , TX ) . 1 μl of 15% Ficoll was added , and the reaction mixes were loaded on 4% polyacrylamide gels ( 0 . 5× TBE with 2 . 5% glycerol ) . Gels ( 0 . 2 mm thickness , 4% polyacrylamide , Bio-Rad #161–0144 ) were run for 90 min at room temperature ( 7 . 5 V/cm ) and exposed on phosphor imager screens for 18 hr at 4°C . For saturation binding assays , increasing amounts of probe were added to a fixed amount of nuclear extract in triplicate . A four-parameter nonlinear least squares curve fitting method ( Prizm , Graphpad Software , La Jolla , CA ) was used to estimate the Bmax and Kd values and to conduct tests for whether each best-fit parameter differs among the four groups in Table 2 . Mouse embryonic stem cell gene trap line W233A07 and P116C05 were obtained from the German Gene Trap Consortium ( http://www . genetrap . de ) . The ROSAbetageo+1 gene trap vector ( incorporating fused beta galactosidase and neomycin ) was introduced to 129S2/SvPas-derived TBV2 embryonic stem ( ES ) cells to generate mutant cell lines . Both W233A07 and P116C05 cells , carrying a vector insertion into the intron one of Usf1 gene , were used to generate mice which were backcrossed to C57BL/6J for four generations . In order to detect presence of trap vector , Usf1-TrapFwd: 5′-AGTGACAACGTCGAGCACAG-3′ , Usf1-TrapRev: 5′-CGGTCGCTACCATTACCAGT-3′ were used . To discriminate +/− from −/− mouse , we used real time PCR using Usf1-Trap 129Fwd:5′- GGAGGTGGGGATTATAGTCTGAA-3′ , Usf1-Trap B6Fwd:5′- GGAGGTGGGGATTATAGTCTGAG-3′ and Usf1-Trap Rev:5′-TGCTACAAGGAGGGGTTCTG-3′ . Since mutant allele at Usf1 locus is derived from 129S2/SvPas , +/− mouse is 129/B6 heterozygous and +/− mouse is 129/129 homozygous at Usf1 locus . | Circadian rhythms are biochemical , physiological and behavioral processes that follow a 24-hr cycle , responding primarily to the periods of light and dark , and they have been observed in bacteria , fungi , plants and animals . The circadian clock that drives these rhythms—which dictate our sleep patterns and other processes—involves a set of genes and proteins that participate in a collection of positive and negative feedback loops . Previous research has mainly focused on identifying core clock genes—that is , genes that make up the molecular clock—and studying the functions of these genes and the proteins they code for . However , it has become clear that other clock genes are also involved in circadian behavior , and it has been proposed that polymorphisms in these non-core clock genes could contribute to the variations in circadian behavior displayed by different mammals . One important feedback loop in mammals involves two key transcription factors , CLOCK and BMAL1 , that combine to form a complex that initiates the transcription of the negative feedback genes , Period and Cryptochrome . Shimomura et al . discovered that Usf1 , a gene that codes for a transcription factor that is typically involved in lipid and carbohydrate metabolism , as well as other cellular processes , is also important . In particular , this transcription factor is capable of partially rescuing an abnormal circadian rhythm caused by a mutation in the Clock gene in mice . Shimomura et al . showed that the proteins expressed by the mutant Clock gene can bind to the same regulatory sites in the genome as the normal CLOCK:BMAL1 complex , but that gene expression of these targets is reduced because transcriptional activation is lower and binding of the complex is not as strong . However , proteins expressed by the Usf1 gene are able to counter this by binding to the same sites in the genome and compensating for the mutant CLOCK protein . Further experiments are needed to explore how the interactions between the USF1 and CLOCK:BMAL1 transcriptional networks regulate circadian rhythms and , possibly , carbohydrate and lipid metabolism as well . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"neuroscience",
"genetics",
"and",
"genomics"
] | 2013 | Usf1, a suppressor of the circadian Clock mutant, reveals the nature of the DNA-binding of the CLOCK:BMAL1 complex in mice |
Many intergenic long noncoding RNA ( lncRNA ) loci regulate the expression of adjacent protein coding genes . Less clear is whether intergenic lncRNAs commonly regulate transcription by modulating chromatin at genomically distant loci . Here , we report both genomically local and distal RNA-dependent roles of Dali , a conserved central nervous system expressed intergenic lncRNA . Dali is transcribed downstream of the Pou3f3 transcription factor gene and its depletion disrupts the differentiation of neuroblastoma cells . Locally , Dali transcript regulates transcription of the Pou3f3 locus . Distally , it preferentially targets active promoters and regulates expression of neural differentiation genes , in part through physical association with the POU3F3 protein . Dali interacts with the DNMT1 DNA methyltransferase in mouse and human and regulates DNA methylation status of CpG island-associated promoters in trans . These results demonstrate , for the first time , that a single intergenic lncRNA controls the activity and methylation of genomically distal regulatory elements to modulate large-scale transcriptional programmes .
A growing number of nuclear localised long noncoding RNAs ( lncRNA , ≥ 200 nt ) are known to regulate gene transcription and chromatin organisation ( reviewed in ( Vance and Ponting , 2014 ) ) . Many of these transcripts appear to act near to their site of synthesis to regulate the expression of genes locally on the same chromosome ( cis-acting ) . Cis-acting lncRNA regulatory mechanisms have been described in detail for a number of enhancer associated nuclear lncRNAs , as well as lncRNAs involved in the processes of genomic imprinting and X chromosome inactivation ( Tian et al . , 2010; Melo et al . , 2013; Monnier et al . , 2013; Mousavi et al . , 2013; Santoro et al . , 2013; Vallot et al . , 2013 ) . Some cis-acting lncRNAs bind to DNA methyltransferase ( DNMT ) proteins and regulate genomic DNA methylation levels specifically at their sites of transcription ( Mohammad et al . , 2010; Di Ruscio et al . , 2013 ) . Trans-acting lncRNAs that regulate gene expression across multiple chromosomes and on either allele have been documented less frequently . The ability of such lncRNAs to exert widespread effects on gene expression in trans is poorly understood , in large part because direct transcriptional targets for only very few of these transcripts have thus far been identified ( Chu et al . , 2011; Ng et al . , 2013; Simon et al . , 2011; Vance et al . , 2014 ) . Moreover , it is not clear whether these transcripts commonly act directly , or within ribonucleoprotein complexes , and how they might modify their target genes’ regulatory landscape such as by regulating their DNA methylation profiles . Many thousand mammalian intergenic lncRNAs have now been identified . Not all lncRNA transcript models will be functional , however . Single exon models , in particular , can be artefacts arising from genomic DNA contaminating sequencing libraries , and transcripts that are expressed at average levels lower than one copy per cell are less likely to confer function . Highly and broadly expressed , and bona fide monoexonic intergenic lncRNAs , such as Neat1 and Malat1/Neat2 , however , appear not to have essential roles because their knockout mouse models are viable and fertile ( Eissmann et al . , 2012; Zhang et al . , 2012 ) . Transcript sequences and levels are thus not reliable predictors of mechanism . Instead , the significant temporal and spatial co-expression of genomically adjacent intergenic lncRNA and transcription factor genes might suggest that such lncRNAs commonly modulate transcriptional programmes that are initiated by these transcription factors ( Ponjavic et al . , 2009 ) . Indeed , several intergenic lncRNAs have well-documented cis-acting regulatory roles ( Wang et al . , 2011; Zhang et al . , 2012; Berghoff et al . , 2013 ) . Spatiotemporal co-expression of intergenic lncRNA and transcription factor genes is most pronounced during the development of the mouse central nervous system ( CNS ) ( Ponjavic et al . , 2009 ) . To investigate the mechanistic basis of this physical linkage we chose to study a 3 . 5-kb , CNS-expressed , monoexonic , intergenic lncRNA termed Dali ( DNMT1-Associated Long Intergenic ) , owing to its conservation of sequence and transcription across therian mammals and its genomic proximity to a transcription factor gene , Pou3f3 ( also known as Brn1 or Oct8 ) , which encodes a class III POU family transcription factor . Dali is transcribed in the sense orientation , relative to Pou3f3 , from a locus 50 kb downstream of Pou3f3 within the flank of an extended genomic region ( Figure 1A ) that is characterised by near pervasive transcription in neuronal lineages ( Ramos et al . , 2013 ) . Sauvageau et al . recently generated mouse knockout models for two of these intergenic lncRNA loci , linc-Brn1a , and linc-Brn1b ( Figure 1A ) . Genomic deletion of the linc-Brn1b locus resulted in significant ( ∼50% ) down-regulation of the upstream Pou3f3 gene , and linc-Brn1b-/- mice exhibited abnormalities of cortical lamination and barrel cortex organization ( Sauvageau et al . , 2013 ) . These abnormalities may derive from loss of the linc-Brn1b RNA transcript , or from the deletion of DNA functional elements ( Bassett et al . , 2014 ) . The Dali locus is more distally located and does not overlap previously described lncRNA loci or regulatory elements ( Figure 1A ) . 10 . 7554/eLife . 04530 . 003Figure 1 . Conservation and expression within the Dali and Pou3f3 loci . ( A ) Schematic illustration of the mouse Pou3f3 genomic region showing coding and non-coding transcripts , enhancer elements from Vista Enhancer Browser ( Visel et al . , 2007 ) , CpG islands , and published genomic deletions ( Sauvageau et al . , 2013 ) . ( B ) Conservation and relative sizes of Dali transcripts in mouse and human confirmed by RACE . ( C ) Dali and ( D ) Pou3f3 are co-expressed temporally and spatially in the developing mouse brain . DVZ: Dorsal ventricular zone; LVZ: Lateral ventricular zone; DCP: Dorsal cortical plate; LCP: Lateral cortical plate; PP: pre-plate . The levels of Dali , Pou3f3 were measured by qRT-PCR . Results are normalised to Gapdh and presented relative to expression in E9 . 0 sample ( set arbitrarily to 1 ) . Mean ± s . e . , n = 3 ( technical replicates ) . ( E and F ) Similarly to Pou3f3 , Dali is up-regulated during neuronal differentiation of mouse ES cells . Neuronal differentiation of mouse ES cells was induced using RA . The levels of Dali and Pou3f3 were measured by qRT-PCR . Results are presented relative to an Idh1 reference gene which does not change significantly during differentiation . Mean ± s . e . , n = 3 . ( G and H ) Dali is a chromatin associated transcript . The relative amounts of Dali ( G ) and a control mRNA ( Gapdh ) ( H ) in the indicated fractions were measured by qRT-PCR . Mean values ± s . e . of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 00310 . 7554/eLife . 04530 . 004Figure 1—figure supplement 1 . Analysis of the mouse and human Dali loci . ( A ) A detailed view of the mouse Dali locus ( red ) indicating regions of vertebrate DNA sequence conservation . Full-length Dali transcript was mapped using Rapid Amplification of cDNA Ends ( RACE ) in mouse neuroblastoma N2A cells . Promoter region appears to be most conserved . Within the transcript body , highly conserved patches are interspersed with regions or more divergent sequence . ( B ) Schematic illustration of the human POU3F3 genomic region showing coding and non-coding transcripts , enhancer elements ( Vista Enhancer Browser ) and conserved genomic location and transcriptional orientation of DALI relative to POU3F3 . Human DALI ortholog exhibits conserved genomic location and transcriptional orientation relative to POU3F3 . ( C ) A detailed view of the human DALI locus ( red ) confirmed by RACE indicating regions of vertebrate DNA sequence conservation . ( D ) Promoter region of Dali in mouse is associated with DNase I hypersensitivity sites in tissues expressing Dali ( kidney and brain ) but not in ES cells where the Dali locus is silent . ( E ) DALI locus in human is annotated as a poised ( or weak ) enhancer by the ENCODE project 1 . ( F and G ) Dali is a brain-expressed lncRNA . Dali and Pou3f3 expression levels were measured in a panel of adult mouse tissues by quantitative RT-PCR ( qRT-PCR ) . Results were normalized by the average value of Gapdh and Tbp reference genes . Mean values ± standard error ( s . e . ) shown , n = 3 replicates . ( H ) Dali levels in the developing mouse brain even at the earliest stages when it is detected ( E9 . 0 and E10 . 5 ) are much higher than in both proliferating and differentiated N2A cells . LPP = Lateral pre-plate; DPP = Dorsal pre-plate . ( I ) Similar to Dali and Pou3f3 , Dnmt1 is also up-regulated at day 4 of RA-induced neuronal differentiation of ES cells . Mean values ± s . e , n = 3 . ( K ) Dali and Pou3f3 are co-expressed temporally and spa ally in the adult mouse brain ( P56 ) in three regions of adult neurogenesis , that is olfactory bulb , dentate gyrus and sub-ventricular zone . The relative levels of Dali and Pou3f3 were measured in samples obtained by dissecting indicated regions from a single male adult mouse brain per sample using RT-qPCR . Measurements were normalised using Gapdh and presented relative to expression in the olfactory bulb samples ( set arbitrarily to 1 ) . Mean values ± s . e , n = 2 . ( L ) Dali is expressed at an estimated 2 . 0 ± 0 . 4 copies per cell in N2A cells . We constructed a standard curve of known Dali copy number by spiking in vitro transcribed Dali transcripts into RNA from ES cells , which do not express Dali ( left ) . Mean Dali expression per cell was calculated from four independent RT-qPCR experiments using RNA extracted from a defined number of cells . This value was used to estimate Dali copy number form the standard curve . Mean copy number per cell ± s . e . is shown ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 00410 . 7554/eLife . 04530 . 005Figure 1—figure supplement 2 . The Pou3f3 locus occurs in a folded nuclear conformation both prior to and after the onset of the expression of its transcripts . ( A ) Schematic representation of the Pou3f3 locus showing start sites of transcripts in the region and positions of recognition sites of the BglII restriction endonuclease ( small vertical bars ) used for the 3C experiment . Below , the restriction fragments generated by BglII digestion are annotated . Arrows indicate the position of the 3C qPCR primers . Numbers represent the corresponding primer ( small numbers indicate primers not used in the final analysis due to technical reasons ) . Green bars indicate regions found to be in close proximity to the Dali transcription start site used as ‘bait’ . ( B ) Nuclear conformation of the Pou3f3 locus was studied in ES cells where the locus is silent and ES cell derived neuronal precursors ( 4 days retinoic acid differentiation ) where transcripts in the regions are expressed . Mean values ± s . e , n = 3 ( technical replicates ) . ( C and E ) Quantification of genomic interactions between the Dali TSS and the genomic fragments indicated in ( A ) in ES cells ( C ) and ES cell derived neuronal precursors ( E ) . The y axis shows relative cross-linking frequency , the x axis indicates the primer used in combination with primer 21 . ( D and F ) Graphical representation of genomic distances between the Dali TSS , positioned at the centre of the radar , and the indicated BglII genomic fragments in ES cells ( D ) and ES cell derived neuronal precursors ( F ) . Distance was calculated as 1/cross-linking frequency . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 005 Pou3f3 is a single exon gene whose protein binds to DNA in a sequence-specific manner . Pou3f3 contributes to both neuronal and kidney development by regulating the proliferation and differentiation of progenitor cells ( Nakai et al . , 2003 ) . Mouse mutants with homozygous loss of Pou3f3 die of renal failure within 36 hr post partum ( Nakai et al . , 2003 ) , with severe defects of the hippocampus and forebrain among others ( McEvilly et al . , 2002 ) . In the developing neocortex , Pou3f3 is expressed in late neuronal precursors and in migrating neurons and , together with its closely related paralogue Pou3f2 , is required in ventricular zone progenitors for deep-to-upper layer fate transition , sustained neurogenesis and cell migration ( Dominguez et al . , 2013 ) . Our experiments show that Dali is required for the normal differentiation of neural cells in culture . Furthermore , our results indicate that Dali functions by modulating the expression of its neighbouring Pou3f3 gene , as well as by interacting with the POU3F3 protein , and by directly binding and regulating the expression of genes involved in the neuronal differentiation programme in trans . Unexpectedly , Dali associates with the DNMT1 DNA methyltransferase and reduction of Dali levels increases DNA methylation at a subset of Dali-bound and -regulated promoters in trans . Our data therefore provide the first evidence that a lncRNA transcript can regulate multiple genes situated away from its site of synthesis by binding to promoter-proximal regulatory elements and altering their DNA methylation status in trans .
Full-length mouse Dali is approximately 500 nt ( 2 . 6 kb ) longer than a previously identified AK034039 cDNA cloned from the telencephalon ( Figure 1—figure supplement 1A ) . Its locus , downstream of the Pou3f3 gene , contains mammalian conserved sequence both just upstream of its transcriptional start site , which presumably contributes to this locus’ promoter , and within its transcribed sequence . A positionally equivalent and sequence-similar human DALI ( ∼3 . 7 kb ) transcript was identified by RT-PCR and RACE in human foetal brain ( Figure 1B; Figure 1—figure supplement 1B , C ) . Transcriptional evidence also exists for the orthologous locus in rat embryonic , as well as heart and kidney , samples ( data not shown ) . ENCODE data indicate that both mouse and human Dali loci have the properties of a weak ( or poised ) enhancer in both brain and kidney tissues ( Figure 1—figure supplement 1D , E ) . Consistent with this , Dali was most highly expressed in the adult brain and kidney , two of the three tissues displaying highest Pou3f3 expression , when profiled across a panel of adult mouse organs ( Figure 1—figure supplement 1F , G ) . In adult mouse ( P56 ) , Dali and Pou3f3 were expressed in all three regions of adult neurogenesis , the sub-ventricular zone ( SVZ ) , olfactory bulb ( OB ) , and dentate gyrus ( DG ) ( Figure 1—figure supplement 1I ) ( Reviewed in Ming and Song , 2011 ) . Dali was also co-expressed with Pou3f3 temporally and spatially in the developing mouse embryonic brain ( Figure 1C , D ) . Both transcripts were up-regulated with the first appearance of cortical neurons ( E10 . 5 ) , and increased in expression further as the ratio between neurons and progenitors grew ( Figure 1C , D ) . Furthermore , both Dali and Pou3f3 transcripts were undetectable in self-renewing mouse E14 embryonic stem ( ES ) cells , but after 3 days of retinoic acid ( RA ) -induced differentiation , a stage corresponding to the cell cycle exit of neuronal progenitors and their differentiation into neurons , these transcripts were rapidly up-regulated , their levels subsequently peaking at days 7 ( Pou3f3 ) and 8 ( Dali ) ( Figure 1E , F ) . Mouse neuroblastoma N2A cells , which are frequently used as a neuronal progenitor-like cell type and an in vitro model of neuronal differentiation ( Tremblay et al . , 2010 ) , express both Dali ( at a population-average level of 2 copies per cell ( Figure 1—figure supplement 1K ) ) and Pou3f3 . When first detected in neuronal-progenitor-dominated areas of the developing brain ( E10 . 5 ) , Dali is expressed at a level at least two orders of magnitude higher than in N2A cells ( Figure 1—figure supplement 1H ) . However , in N2A cells treated with RA for 72 hr , Dali is up-regulated approximately 4 . 5-fold , similar to the up-regulation observed in embryonic cortical plate ( both dorsal and lateral ) between days E10 . 5 to E18 . 5 ( Figure 1—figure supplement 1H ) . Therefore , despite Dali expression level differences in N2A cells and the in vivo system , N2A cells represent an appropriate model system in which to study Dali function . Furthermore , Dali , but not a control mRNA ( Gapdh ) , was highly enriched in the nucleus of N2A cells , most abundantly in the chromatin fraction ( Figure 1G , H ) . Taken together , the data suggest that Dali may be involved in regulating nuclear function during neuronal development , potentially in coordination with Pou3f3 . We next investigated whether Dali regulates neural differentiation by generating three independent stable Dali knockdown N2A cell lines each showing approximately 50–70% reduction of Dali transcript levels and inducing neural differentiation using RA ( Figure 2A ) . Compared to a stable non-targeting control line , fewer differentiated cells of Dali knockdown lines developed neurites . Those that did exhibited shorter neurites , often with multiple short outgrowths emanating from the same cell , compared to one or two long neurites developed by differentiated control cells ( Figure 2B , C ) indicating that Dali is required for normal differentiation of N2A cells . 10 . 7554/eLife . 04530 . 006Figure 2 . Dali plays a role in regulating genes in neuronal cells . ( A ) qRT-PCR analysis validates reduced levels of Dali in three clonal Dali knockdown cell lines compared to a control line . Mean values ± s . e . , n = 3 . ( B ) Reduced neurite outgrowth in RA-differentiated Dali knockdown cells . Cells were imaged using bright field microscopy . Cells with ≥1 neurites of length greater than twice the cell body diameter were scored as positive . Average values ± s . e . , n = 3 . 500-600 cells were counted in each case across at least three non-overlapping fields . ( C ) Representative images of control and stable Dali knockdown cells differentiated with RA for 72 hr . Scale bar = 200 μm . ( D ) N2A cells were transfected with either a non-targeting control ( scrambled ) or a Dali targeting shRNA expression vector ( shDali ) for 72 hr . Mean values ± s . e . , n = 3 . ( E ) Transient Dali knockdown induces statistically significant changes in the expression of 270 genes in N2A cells ( 10% FDR ) ( Supplementary file 2 ) . ( F ) Gene Ontology ( GO ) categories significantly enriched among Dali regulated genes ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction; Supplementary file 2 ) . ( G ) Decreased Pou3f3 expression upon Dali knockdown . Normalised using Gapdh , shown relative to a non-targeting control ( set at 1 ) . Mean values ± s . e . , n = 3 , one tailed t-Test , unequal variance . ( H ) Reduced Pou3f3 levels in stable Dali knockdown cells ( see panel A ) . qRT-PCR results were normalised using Gapdh and presented relative to expression in control cells ( set arbitrarily to 1 ) . Mean values ± s . e . , n = 3 , one tailed t-Test , unequal variance . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 00610 . 7554/eLife . 04530 . 007Figure 2—figure supplement 1 . Non-coding transcripts in the Pou3f3 locus form a network of regulatory interactions . ( A ) Dali regulates expression of lncRNA AK011913 . N2A cells were transfected with either a non-targeting control ( scrambled ) or three independent Dali targeting shRNA expression vectors . Dali and AK011913 levels were measured by RT-qPCR 72 hr post- transfection . Mean values ± s . e . , n = 3 , one tailed t-Test , unequal variance . ( B ) AK011913 regulates expression of Dali and Pou3f3 positively and linc-Brn1a negatively . N2A cells were transfected with either a non-targeting control ( scrambled ) or a AK011913 targeting shRNA expression vector ( shRNA ) . AK011913 , Dali , linc-Brn1a and Pou3f3 levels were measured by RT-qPCR 72 hr post- transfection . Mean values ± s . e . , n = 3 , one tailed t-Test , unequal variance . ( C ) RT-qPCR validation of transient Dali knockdown microarray results . Good agreement between microarrays and RT-qPCR and across independent shRNA constructs is observed , indicating that the results are unlikely to represent technical artefacts or off-target effects . Mean values ± s . e . , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 007 To investigate the molecular function of Dali , we performed microarray analysis to profile the transcriptome of N2A cells in which Dali transcript levels had been depleted by ∼70% using transient transfection of a specific Dali targeting shRNA expression vector ( Figure 2D; shRNA and RT-qPCR oligo sequences and positions can be found in Supplementary file 1 ) . Dali knockdown resulted in statistically significant changes in expression levels for 270 genes ( False Discovery Rate [FDR] < 10% ) compared to a non-targeting control ( Supplementary file 2; Figure 2E ) . 14 of 15 of these genes were also determined as being differentially expressed , with similar fold changes , using RT-qPCR and two additional independent shRNA expression constructs targeting Dali ( Figure 2—figure supplement 1C ) . Gene expression changes we observed using microarrays were thus unlikely to be dominated by off-target effects of the shRNA used . Gene Ontology ( GO ) analysis revealed that Dali-regulated genes were significantly enriched in cell cycle , DNA repair , cellular response to stimulus , and cell projection assembly functions ( Figure 2F and Supplementary file 2; Benjamini-Hochberg corrected p ≤ 0 . 05 ) . Taken together , these expression and loss of function studies suggest that Dali acts as a pro-differentiation factor in neural development . To investigate whether Dali knockdown affects expression of the adjacent Pou3f3 gene , we reduced its levels by transient transfection of three different shRNA constructs in N2A cells . After 72 hr , reduction of Dali levels by an average of 60–70% was found to reduce Pou3f3 transcript levels by approximately 40% ( Figure 2G ) . Three independent stable Dali knockdown clones in which Dali levels were reduced by 50–60% ( Figure 2A ) also showed ∼15–40% lower Pou3f3 levels ( Figure 2H ) . This suggests that the Dali transcript positively regulates Pou3f3 expression in an RNA-dependent manner . The genome-wide transcriptional response to Dali knockdown thus could be explained , in part , by its effect on Pou3f3 . Levels of another transcript , AK011913 , expressed downstream of Pou3f3 ( Figure 1A ) were reduced by approximately 55% upon Dali knockdown ( Figure 2—figure supplement 1A ) . Reduction of AK011913 levels by approximately 60% using shRNAs resulted in Dali and Pou3f3 levels decreasing by 73% and 82% , respectively ( Figure 2—figure supplement 1B ) . Linc-Brn1a , a lncRNA upstream of and sharing a bi-directional promoter with Pou3f3 , was up-regulated by approximately 90% upon AK011913 depletion ( Figure 2—figure supplement 1B ) . This is reminiscent of the down-regulation of Pou3f3 and up-regulation of lincBrn-1a following knockdown of another lncRNA downstream of Pou3f3 , lincBrn-1b ( Figure 1A ) ( Sauvageau et al . , 2013 ) . Together with previous reports , our data show the opposing regulatory influences of lncRNAs transcribed up- and downstream of Pou3f3 on its expression . Non-coding transcripts expressed from the extended Pou3f3 locus thus contribute to a complex network of regulatory interactions . Furthermore , Chromatin Conformation Capture ( 3C ) showed that the Dali promoter contacted three regions across the Pou3f3 locus ( Figure 1A ) in ES derived neuronal precursors ( Figure 1—figure supplement 2 ) : 1 ) an enhancer element sequence lying upstream of Pou3f3 within the linc-Brn1a locus , 2 ) a region overlapping the 3′ UTR of Pou3f3 and full-length AK53590 ( which are both regulated by Dali ) , as well as parts of TCONS_00000039 and linc-Brn1b , including a differentially methylated region reported to be important in regulating Pou3f3 expression ( Mutai et al . , 2009 ) , and 3 ) a region lying within another non-coding locus ( TCONS_00000040 ) ( Ramos et al . , 2013 ) . Neither Dali nor Pou3f3 appears to play a role in initiating these DNA looping interactions because these contacts were present in E14 ES cells where neither is expressed ( Figure 1—figure supplement 2B ) . Nevertheless , the Dali locus appears to contribute to an extended structurally and transcriptionally complex region centred on the Pou3f3 gene . To examine to what extent the transcriptional response to Dali knockdown can be explained by its effect on Pou3f3 , we reduced the level of Pou3f3 transcript in N2A cells by 35% using transient transfection of a Pou3f3 targeting shRNA vector ( Figure 3A ) and using microarrays observed statistically significant expression changes in 1041 genes ( FDR <10%; Figure 3B ) . Dali transcript levels do not change upon Pou3f3 depletion ( Figure 3A ) . Genes differentially expressed after Pou3f3 knockdown were enriched in categories related to cell division and cell cycle ( Figure 3C ) . The intersection between the sets of genes differentially expressed in Dali or in Pou3f3 knockdown cells was 6 . 2-fold greater than expected by chance ( p-value < 2 . 2 × 10−16 ) , and represented 31% of all genes differentially expressed in Dali knockdown cells ( Figure 3D ) . Approximately equal numbers of genes shared between the two datasets were down- ( 43 genes ) or up-regulated ( 41 genes ) in both experiments ( Supplementary file 3 ) . A strong correlation was observed between the fold-change values of differentially expressed genes in Dali and Pou3f3 knockdown experiments ( R = 0 . 74; Figure 3E ) . Genes that were significantly differentially expressed only when Dali was depleted were enriched in chromatin assembly and MAPKKK signalling functions , whilst genes that were differentially expressed only when Pou3f3 transcripts were depleted were preferentially involved in dendrite development and axon guidance ( Figure 3F ) . Cell cycle , DNA repair , and cellular response to stimulus genes were regulated by Dali in both Pou3f3-dependent and -independent manners . We conclude that Dali and Pou3f3 interact , either genetically or molecularly , to regulate a subset of common targets involved in neural differentiation , and that Dali also likely possesses Pou3f3-independent transcriptional regulatory functions . 10 . 7554/eLife . 04530 . 008Figure 3 . Dali regulates transcription in both Pou3f3-dependent and -independent manners . ( A ) N2A cells were transfected with either a non-targeting control ( scrambled ) or a Pou3f3 targeting shRNA expression vector ( knockdown ) . Pou3f3 and Dali levels were measured by qRT-PCR 72 hr post- transfection . Mean values ± s . e . , n = 3 . ( B ) Pou3f3 knockdown resulted in statistically significant changes in the expression of 1041 genes in N2A cells ( ( 10% FDR , Supplementary file 3 ) . ( C ) GO-analysis of genes differentially expressed upon Pou3f3 analysis ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction; Supplementary file 3 ) . ( D ) Intersection of Pou3f3 and Dali targets shows a significant ( Fisher’s exact test ) overlap approximately 6 . 2 times as large as expected by chance alone . ( E ) Target genes common between Dali and Pou3f3 show correlated expression , with the nearly all being positively or negatively regulated by both factors ( R = 0 . 74; Supplementary file 3 ) . ( F ) Enrichments of Gene Ontology categories of Pou3f3-dependent or -independent Dali targets . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 008 To further investigate the role of Dali in neuronal differentiation we profiled the transcriptomes of proliferating or RA differentiated control and Dali stable knockdown N2A cell lines . In proliferating cells , 733 genes were differentially expressed between Dali knockdown and control cells ( Figure 4A ) , including many genes with functions related to neuronal differentiation , apoptosis , neuronal function ( Figure 4B ) . RA-mediated neuronal differentiation induced expression changes in 958 genes in control cells and 1016 genes in Dali knockdown cells ( Figure 4—figure supplement 1A , B ) . Based on GO category annotations , differentiation of control or Dali knockdown cells was broadly similar , and was associated with altered expression of cell cycle , cell differentiation , energy metabolism , and neuron projection ( Figure 4—figure supplement 1C , D ) . However , 804 genes were differentially expressed between terminally differentiated control and Dali knockdown cells ( Figure 4C ) , of which 376 genes ( 46 . 8% ) also differed in expression between Dali knockdown and control cells prior to their differentiation ( Figure 4E ) . The 428 genes that were significantly altered in expression only between stable Dali and control differentiated cells were enriched in functional categories relating to sterol biosynthesis , energy metabolism , cell cycle , response to chemical stimulus , cell cycle , adhesion and small GTPase signalling ( Figure 4D ) . All 11 ( of 34 known ) sterol biosynthesis genes were down-regulated in Dali knockdown cells . This observation is consistent with the impaired neurite outgrowth of stable Dali knockdown cells because neuritogenesis and neurite outgrowth critically rely on membrane biosynthesis , and therefore , on expression of sterol biosynthesis genes ( Paoletti et al . , 2011 ) . 10 . 7554/eLife . 04530 . 009Figure 4 . Gene expression analysis of stable Dali knockdown cells . ( A ) Stable Dali knockdown resulted in statistically significant changes in the expression of 747 genes in N2A cells ( 1 . 3-fold , 5% FDR , Supplementary file 4 ) . 332 genes were up-regulated , 415 down-regulated . ( B ) GO-analysis of genes differentially expressed upon stable Dali depletion ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction ) . ( C ) Stable Dali knockdown and control cells were differentiated with retinoic acid for 72 hr . 825 genes were differentially expressed between differentiated knockdown and control lines ( ( ≥1 . 3-fold , 5% FDR , Supplementary file 4 ) . 436 genes were up-regulated , 389 down-regulated . ( D ) GO-analysis of genes differentially expressed only between differentiated stable Dali knockdown and control cells ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction ) . ( E ) Intersection of gene sets differentially expressed between stable Dali knockdown and control cells prior to ( undifferentiated ) and after retinoic acid addition ( differentiated ) . ( F ) GO-analysis of genes responding to retinoic acid treatment differently between stable Dali knockdown and control cells ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction ) . ‘Differential responder’ genes were identified using multifactorial analysis of the stable Dali knockdown arrays using limma ( Smyth , 2004 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 00910 . 7554/eLife . 04530 . 010Figure 4—figure supplement 1 . Transcriptomics . ( A and C ) Heatmap displaying expression changes in control ( A ) and stable Dali knockdown ( C ) cells treated with RA for 72 hr . ( B and D ) GO-analysis of genes differentially expressed ( ≥1 . 3-fold , 5% FDR ) upon RA treatment of control ( B ) and stable Dali knockdown ( D ) cells ( 5% FDR , hypergeometric test , Benjamini and Hochberg correction ) . GO categories significantly enriched among genes changing ≥1 . 5-fold are marked with an asterisk ( * ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 010 In addition , several key neuronal differentiation genes , for example Nrcam , Dscam , Dlx1 and Pax3 , were differentially expressed between Dali knockdown and control cells both prior to and after differentiation . Furthermore , multifactorial analysis of RA-induced gene expression changes in control and stable Dali knockdown cells showed that 174 genes responded to RA differently depending on the presence or knockdown of Dali ( FDR 5%; Supplementary file 4 ) . These were significantly enriched in categories relating to neuronal development ( Figure 4F ) , including pro-differentiation factors such as the inhibitor of Wnt signaling Dkk1 ( Cajanek et al . , 2009 ) and Wnt receptor Fzd5 ( Kemp et al . , 2007 ) . In summary , compared to control cells , stable Dali knockdown cells exhibit contrasting alterations in gene expression programmes before and after RA-induced differentiation . In both cases , these programmes are enriched in functional categories related to neural differentiation and function , consistent with the proposed role for Dali in neural development . We next sought to identify and characterise genes that are both bound and regulated by Dali . To do so , we determined the genome-wide binding profile of Dali in N2A cells using Capture Hybridisation Analysis of RNA Targets ( CHART ) -Seq ( Simon et al . , 2011; Simon , 2013 ) ( Figure 5—figure supplement 1A–C ) . We discovered 1427 focal Dali-associated regions genome-wide ( Figure 5A , B; Supplementary file 5 ) , of which all nine selected loci were validated by CHART-qPCR in an independent experiment ( Figure 5—figure supplement 1D ) . 10 . 7554/eLife . 04530 . 011Figure 5 . CHART-Seq analysis of Dali genomic binding sites . ( A ) Peaks were called against control CHART-seq experiments and input DNA . We consider only the 1427 peaks common to both comparisons ( Supplementary File 5 ) . ( B ) Sequencing of Dali bound DNA reveals focal peaks , including those at the promoter of Ache , E2f2 , and Hmgb2 . ( C and D ) Dali peaks are broadly distributed across the mouse genome ( C ) but are particularly enriched in 5′ UTRs and gene promoters ( D ) . Red arrowheads in ( C ) mark the Dali locus . ( E ) A third of Dali peaks are situated within 5 kb of a TSS . ( F ) Dali-bound loci are enriched in active chromatin marks ( H3K4me3 , H3K27ac , PolII ) , DNase I hypersensitivity regions , enhancers and CpG islands annotations ( CGI ) , and CTCF-bound regions , while being depleted of gene body marks ( H3K36me3 ) and repressive chromatin marks ( H3K9me3 and H3K27me3 ) . ( G ) Representative categories from GO analysis of genes associated with Dali binding sites ( within 1 Mb ) include gene expression , cell cycle , signalling , synaptic transmission and cytoskeleton organization among others . Categories marked with an asterisk ( * ) are significantly enriched also among genes associated with peaks within 10 kb of a TSS , with two asterisks ( ** ) —among genes with peaks within 100 kb ( Supplementary File 5 ) . ( H ) The intersection of genes proximal ( <1 Mb ) to Dali peaks , regulated by Dali and changing expression upon Pou3f3 ( 10% FDR ) knockdown identifies those both bound and regulated by Dali , as well as genes regulated by both Dali and Pou3f3 and directly bound by Dali . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 01110 . 7554/eLife . 04530 . 012Figure 5—figure supplement 1 . CHART Analysis . ( A ) CHART-seq was performed using cocktails of capture ( C- ) oligos oligonucleotides complementary to accessible ( violet ) and/or evolutionary conserved ( blue ) regions of Dali and a non-targeting control . ( B ) Specific enrichment of Dali genomic locus ( at position 1250 ) using C-oligos compared to controls was assayed by qPCR . Mean values ± s . e . , n = 3 ( technical replicates ) . ( C ) Specific purification of Dali RNA using C-oligos compared to controls was assayed by RT-qPCR . ( D ) CHART-seq results were validated by performing an independent experiment with the same three cocktails of oligonucleotides , a control sense oligo complementary to the opposite strand of Dali locus and a non-targeting lacZ control oligo . Enrichment of genomic regions identified as peaks was assayed by qPCR . ( E ) Dali binds to chromatin in a focal manner , with most peaks being <1000 bp wide . ( F ) Computational analysis of CHART-seq peak set and Dali showed that DNA sequences under peaks are not more complementary to Dali sequence than control flanking regions , as judged by either length of aligned regions ( left ) or alignment quality score ( right ) . ( G ) DNA sequences under peaks are also not predicted to form RNA:DNA–DNA triplexes with the Dali transcript than control flanking regions . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 012 Dali binding sites were typically limited to less than 1 kb in length ( Figure 5—figure supplement 1E ) and were distributed across the genome with no apparent chromosomal biases other than a depletion on the X chromosome which may reflect the inactivation of one X chromosome copy in these female N2A cells ( Figure 5C ) . These sites were preferentially located at the 5′ end of protein coding genes ( Figure 5D ) : 30 . 5% of peaks were within 5 kb of a transcriptional start site ( TSS ) ( Figure 5E ) . Dali bound sequences were significantly enriched for H3K4me3 , H3K4me1 and H3K27ac modified histones and PolII occupancy , and were depleted for repressive histone marks ( Figure 5F ) . This suggests that Dali preferentially associates with regions of active chromatin . GO category enrichment analysis showed that genes associated with Dali peaks contribute to processes related to neuronal differentiation ( cell cycle ) , neuronal projection development ( cytoskeleton organization and small GTPase mediated signal transduction ) , neuronal function ( synaptic transmission ) , and more general cellular processes , such as gene expression , intracellular signalling , and cellular homeostasis ( Figure 5G ) . 150 genes ( 8 . 6% of all Dali bound genes ) regulated by Dali contained Dali binding sites within their regulatory regions ( Figure 5H ) and presumably represent direct transcriptional targets . To investigate the mechanisms of its genomic targeting , we next performed computational analysis of Dali bound sequences . We discovered that Dali binding sites do not exhibit significant sequence complementarity with the Dali transcript ( Figure 5—figure supplement 1F , see Methods ) , and are not likely to form RNA-DNA:DNA triplex structures ( Figure 5—figure supplement 1G ) , suggesting that Dali does not bind DNA directly . We therefore speculated that Dali may be targeted to the genome indirectly thorough RNA-protein interactions . To identify proteins that interact directly with Dali , we performed a pull down assay in which in vitro transcribed and 5′ end-biotinylated Dali was incubated with nuclear extract prepared from day 4 RA-differentiated ES cells . We identified , using mass spectrometry , 50 proteins that associated with Dali , but not with antisense Dali or a size-matched unrelated control transcript ( Supplementary File 7 ) . Direct interactions between the endogenous Dali transcript and four of these candidate binding proteins , the DNA methyltransferase DNMT1 , the BRG1 core component of the SWI/SNF family chromatin remodelling BAF complex , and the P66beta , and SIN3A transcriptional co-factors , were subsequently validated using UV-crosslinked RNA Immunoprecipitation ( UV-RIP ) in N2A cells ( Figure 6A , B ) . Human DALI was also found , using UV-RIP , to interact with human DNMT1 , yet not with BRG1 , in human neuroblastoma SH-SY5Y cells ( Figure 6B ) . Consequently , in further experiments , we focused on the evolutionarily conserved DNMT1 interaction . 10 . 7554/eLife . 04530 . 013Figure 6 . Dali associates with chromatin and transcriptional regulatory proteins . Dali interacts with BRG1 , SIN3A , and P66beta in mouse N2A cells ( A ) and DNMT1 in mouse N2A and human SH-SY5Y cells ( B ) . Nuclear extracts prepared from UV cross-linked cells were immuno-precipitated using either anti-DNMT1 or control IgG antibodies . Associated RNAs were purified and the levels of Dali and control Gapdh mRNA were quantified using qRT-PCR . Results are expressed as fold enrichment relative to an isotype IgG control antibody . Mean value ± s . e . , n = 3 . ( C ) De novo discovery of a near-perfect match to a CTCF motif in 125/1427 ( 8 . 8% ) Dali CHART-Seq peaks . ( D ) Dali co-occupies several locations shared with CTCF . Control regions are not predicted to be bound by CTCF and are not bound by Dali . ChIP assays were performed in N2A cells using either an antibody against CTCF or an isotype specific control . The indicated DNA fragments were amplified using qPCR . Fold enrichment was calculated as 2-ΔΔCt ( IP/IgG ) . Mean value ± s . e . , n = 3 . ( E ) Dali does not directly interact with CTCF protein in mouse N2A cells . Nuclear extracts were prepared from UV cross-linked cells and immuno-precipitated using either anti-CTCF or control IgG antibodies . Associated RNAs were purified and the levels of Dali and control U1 snoRNA were detected in each UV-RIP using qRT-PCR . Results are expressed as fold enrichment relative to an isotype IgG control antibody . Results are presented as mean value ± s . e . of three independent experiments . ( F ) De novo discovery of a motif for POU III family transcription factors ( which includes POU3F3 ) in 115/1427 ( 8 . 1% ) Dali CHART-Seq peaks . ( G ) UV-RIP in N2A cells: FLAG-tagged POU3F3 protein directly interacts with Dali . Mean value ± s . e . , n = 3 . ( H ) ChIP-qPCR in N2A cells: POU3F3 occupies a subset of loci bound by Dali and regulated by both Pou3f3 and Dali . Loci associated with known ( Dali-independent ) Pou3f3 targets were used as positive control , while loci not regulated by either Pou3f3 or Dali and not bound by Dali were used as negative control . Mean value ± s . e . , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 013 Interestingly , 9 of 58 human transcription factors reported by Hervouet et al . as interacting with the DNMT1 protein ( Hervouet et al . , 2010 ) , including CTCF , but also AP-2 , C-ets-1 , LRH1 , PARP , PAX6 , STAT1 , YY1 , and Sp1 , were found to have binding site motifs that were significantly enriched within our stringent Dali bound CHART-seq peaks ( Supplementary File 6 ) . Motifs for none of 42 transcription factors that do not interact with DNMT1 but interact with DNMT3a and/or DNMT3b ( Hervouet et al . , 2010 ) were enriched in these peaks ( Supplementary File 6 ) . In particular , using a de novo motif discovery approach , we found a highly-enriched CTCF-binding site-like motif in 125 out of 1427 Dali peaks ( 9%; MEME E-value = 3 . 1 × 10−62; Figure 6C ) ( Supplementary File 7 ) . This result was concordant with the greater than expected overlap between Dali-associated regions and known CTCF binding sites in neuronal tissues ( Figure 5F ) ( Shen et al . , 2012 ) . Using Chromatin Immunoprecipitation and qPCR ( ChIP-qPCR ) in N2A cells , we confirmed the CTCF-enrichment of previously-known CTCF-binding sites within 7 Dali-bound and regulated promoters , but not at four control regions ( Figure 6D ) . However , despite CTCF and Dali thus occupying a subset of shared genomic binding sites , UV-RIP provided no evidence of a direct physical interaction ( Figure 6E ) . Consequently , Dali and CTCF may be non-interacting molecular subunits of a larger ribonucleoprotein complex , or alternatively they might independently bind adjacent sequence , or compete for binding to the same region . Taken together , the data suggest that Dali is recruited to chromatin via indirect interactions with several DNA-binding proteins through its direct association with DNMT1 . Increasing numbers of lncRNAs have been shown to direct DNA methylation changes at their sites of synthesis ( Mohammad et al . , 2010; Di Ruscio et al . , 2013 ) . The direct interaction of Dali with DNMT1 , however , suggests that it may be able to regulate DNMT1-mediated CpG methylation at CpG island-associated promoters of Dali-bound and -regulated genes in trans . To investigate this , we performed Combined Bisulfite Restriction Analysis ( COBRA ) ( Xiong and Laird , 1997 ) in parallel at 10 different CpG islands . Selection of these regions was on the basis that they each contained several COBRA-compatible restriction enzyme sites and could be efficiently amplified from bisulfite-converted template . COBRA demonstrated that five of these regions ( corresponding to four genes ) exhibited altered restriction profiles indicative of altered DNA methylation status after Dali depletion depletion ( Figure 7—figure supplement 1 ) . The inability of COBRA to detect changes at all sites may indicate that the DNA methylation status of the remaining regions did not change upon Dali depletion or that changes that occurred were undetected due to technical limitations of the assay . Bisulfite sequencing demonstrating that the Dlgap5 , Hmgb2 , and Nos1 promoters each display increased CpG methylation in two independent stable Dali knockdown lines compared to control further confirmed these results ( Figure 7A ) . Importantly , these data show that methylation changes occur within the core of these CpG islands and are not limited to their shores . Although other unidentified factors are also likely to play a role , our results are consistent with Dali ( or a Dali:POU3F3 complex ) acting in trans , as part of a multi-subunit ribonucleoprotein complex , to reduce DNMT1-mediated CpG methylation at a subset of bound and regulated gene promoters away from its site of transcription . 10 . 7554/eLife . 04530 . 014Figure 7 . Dali modulates DNA methylation at bound and regulated promoters . ( A ) DNA methylation status of three CGI-associated promoters bound and regulated by Dali was assessed using bisulfite sequencing in control and two stable Dali knockdown lines . DNA methylation levels were found to be increased in knockdown lines . The degree of increase was correlated with the degree of Dali knockdown ( see Figure 7—figure supplement 1 ) . ( B ) Nos1 gene has two clusters of alternative TSSs ( Exon 1 and Exon 2 ) . The upstream neuronal tissue-specific cluster ( Exon 1 ) is associated with a CpG island and is bound by Dali . ( C ) Down-regulation of Nos1 observed in stable Dali knockdown lines can be explained by reduced initiation from the Dali-bound TSS ( Exon 1 ) , as the ratio between Exon1 and an internal Exon 3 is diminished , while the ratio between Exon 2 and Exon 3 is not changed . Mean values ± s . e , n = 3 , one tailed t-Test , unequal variance . ( D ) Dali transcript regulates Pou3f3 locally and E2f2 distally in ES mouse cells . Dali is expressed from its endogenous locus in non-expressing mouse E14 ES cells using custom-designed TALE-TF ( left ) . De novo induction of the endogenous Dali locus is sufficient to up-regulate the neighbouring Pou3f3 gene and down-regulate the distally located E2f2 gene ( right ) . Mean value ± s . e . , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 01410 . 7554/eLife . 04530 . 015Figure 7—figure supplement 1 . DNA Methylation analysis . ( A ) DNA methylation status of three Dali-bound and regulated CGI-associated promoters ( Dlgap5 , Nos1 , and Hmgb2; see Figure 7 ) was assayed in a stable control and two independently isolated Dali knockdown lines ( mean value ± s . e . , n = 3 ) . The degree of DNA methylation increase correlated with the degree of Dali depletion observed . ( B , C , D , E ) Combined bisulfite restriction analysis ( COBRA assay ) results for Hmgb2 ( B ) , Fbn1 ( C ) , Dlgap5 ( D ) , Nos1 ( E ) . COBRA was performed by bisulfite-treating genomic DNA of control and stable Dali knockdown cells , proliferating and differentiated with RA ( +RA ) , PCR-amplifying the CpG-island associated promoters of the indicated Dali-bound and regulated genes , and digesting the PCR products with COBRA-compatible or control enzymes . DOI: http://dx . doi . org/10 . 7554/eLife . 04530 . 015 One of these genes , Nos1 , has multiple alternative promoters falling into two distinct regions ( for simplicity referred to here as Exon 1 and Exon 2 ) whose differentiated use is proposed to fine-tune its expression in response to various physiological and developmental stimuli ( Bros et al . , 2006 ) . Only the 5′-most region contains a CpG island and is bound by Dali ( Figure 7B ) . By measuring expression levels of the three 5′-most Nos1 exons in stable Dali knockdown and control lines we observed that the expression level of the 5′ most Dali-bound Exon 1 was reduced , relative to that for Exon 3 , when Dali was depleted , whereas the expression ratio between Exons 2 and 3 was unaffected ( Figure 7C ) . The preferential use of the 5′ most CpG site could reflect a secondary effect of Dali knockdown . Nevertheless , the observation that this site is bound by Dali transcript suggests that Dali may function by promoting the preferential use of a distantly located ( and more rarely used ) alternative promoter potentially through its effect on promoter-associated CpG island methylation . A recognisable binding motif for POU III family transcription factors , such as POU3F3 , was present in 115 out of 1427 Dali CHART-Seq peaks ( 8 . 0%; E-value = 3 . 8 × 10−5; Figure 6F ) . This finding , together with Dali and Pou3f3 regulating a set of common genes ( Figure 3D ) and Dali occupying regulatory regions within 135 ( 13% ) of Pou3f3 targets ( Figure 5H ) , suggested that Dali and POU3F3 protein may interact physically . Indeed , we observed direct RNA-protein interactions between over-expressed FLAG-tagged POU3F3 and co-transfected Dali , using UV-RIP in N2A cells ( Figure 6G ) . Using ChIP-qPCR , we then determined that at least five genes that were regulated by both Dali and Pou3f3 contained regions that were bound both by Dali and by POU3F3 protein ( Figure 6H ) . These results provide further mechanistic insights into Dali's mode of action and indicate that Dali and POU3F3 form a complex that binds to and regulates a subset of genes in trans in N2A cells . Finally , we tested whether de novo expressed Dali transcript can act as a transcriptional regulator in order to further substantiate the observation that Dali functions as a novel regulator of both local and distal gene expression . To achieve this , we induced Dali expression from its endogenous locus in E14 mouse ES cells , which do not express Dali or Pou3f3 to detectable levels , using transient transfection of an artificial Transcription Activator-Like effector ( TALE ) transcription factor . After 72 hr , up-regulation of Dali transcript was shown to significantly increase Pou3f3 expression ( Figure 7D ) . Dali expression from its own locus is thus sufficient to induce the expression of its genomically neighbouring Pou3f3 gene ( Figure 7D ) . We next investigated the expression levels of E2f2 , a gene that we found to be negatively regulated by Dali using shRNA mediated knockdown ( Supplementary file 2 ) , and found that Dali up-regulation reduced E2f2 transcript levels by approximately 40% ( Figure 7D ) . Taken together , these results indicate that Dali can regulate both local and distal target genes when its expression is induced from its endogenous locus .
The ability of nuclear localised lncRNAs to act in trans at distal genomic locations to regulate gene expression programs has been poorly understood . This is in large part because the direct transcriptional targets of only a small number of such transcripts ( for example , Paupar ( mouse ) , HOTAIR , NEAT1 , TERC , RMST ( all human ) , and rox2 ( Drosophila ) ) have been identified thus far ( Chu et al . , 2011; Simon et al . , 2011; Ng et al . , 2013; Vance et al . , 2014 ) . Consequently , it has been unclear how these transcripts are targeted to distal functional elements and whether thereafter they alter chromatin structure in situ . In this study we found evidence that the intergenic lncRNA Dali acts both locally to regulate the expression of its nearest protein-coding gene , Pou3f3 , and distally to regulate both Pou3f3-dependent and -independent target genes in an RNA-dependent manner . 8 . 8% ( 150 ) of all genes whose expression altered following Dali depletion were associated with Dali binding sites within 1 Mb ( although 30% of peaks reside within 5 kb of a TSS , see Figure 5E ) and , therefore , are likely to represent direct regulatory targets . This proportion lies within the range of functional sites observed for transcription factors ( Cusanovich et al . , 2014 ) . Our results are consistent with a model in which mouse or human Dali is recruited to chromatin indirectly via RNA-protein interactions with both sequence-specific transcription factor proteins , such as POU3F3 which is encoded by its neighbouring gene , or non-sequence specific DNA binding cofactors including DNMT1 , which in turn may interact with sequence-specific DNA-binding proteins . In this model , Pou3f3-dependent target genes are regulated by Dali both indirectly , via its transcriptional regulatory effect on the Pou3f3 gene , and directly via its physical interaction with the POU3F3 protein and their co-occupancy at regulatory regions of target genes . Our data show that both human and mouse Dali associate with DNMT1 and that depletion of Dali levels increases CpG methylation at Dali bound and regulated promoters in trans . Whilst a growing body of literature has implicated lncRNAs , such as Kcnq1ot1 and ecCEBPA ( Mohammad et al . , 2010; Di Ruscio et al . , 2013 ) , in modulating CpG methylation in a DNMT1-dependent manner at their sites of synthesis , our findings represent the first evidence that an intergenic lncRNA can regulate DNA methylation in trans at distal genomic locations away from its site of transcription . Our findings suggest that Dali inhibits DNA methylation at a subset of bound and regulated regions , presumably deposited by the DNMT1 DNA methyltransferase , to which it binds . DNMT1 binds structured RNA with higher affinity than its DNA substrate ( Di Ruscio et al . , 2013 ) . It is thus possible that Dali competes for binding to DNMT1 with either protein co-factors such as UHRF1 , which loads and orients the enzyme on the DNA substrate ( Inomata et al . , 2008 ) , or its DNA substrate . Targeting of DNMT1 to specific loci is believed to be mediated by DNMT1-interacting transcription factors . 58 transcriptional factors have been reported as DNMT1 interactors ( Hervouet et al . , 2010 ) , of which 9 have enriched sequence motifs in Dali CHART-Seq peaks . We thus propose a model in which such transcription factors promote the sequence-specificity of Dali-modulated DNA methylation changes . The genomic co-localisation of DNMT1 and transcription factors using ChIP remains unknown owing to the poor performance of the available anti-DNMT1 antibodies in this application . We have shown that Dali regulates genes involved in neural development and function and its depletion disrupts terminal stages of neuronal differentiation , more particularly neurite outgrowth development . Dali RNA binds to and up-regulates the promoters or promoter-proximal regions of key pro-differentiation factors , such as E2f2 ( Persengiev et al . , 2001 ) , Fam5b ( Terashima et al . , 2010 ) , Sparc ( Bhoopathi et al . , 2011 ) and Dkk1 ( Cajanek et al . , 2009 ) ( Watanabe et al . , 2005 ) , as well as binding and negatively regulating genes such as Kif2c and Kif11 which are known to block neurite outgrowth ( Laketa et al . , 2007; Myers and Baas , 2007; Nadar et al . , 2012 ) . Therefore , Dali works as a pro-differentiation factor in neural development by regulating the balance between proliferation and differentiation , as well as processes associated with terminal neuronal differentiation . Cis- or trans-acting modes of action have been proposed for a growing number of lncRNAs ( Fatica and Bozzoni , 2014 ) . Dali is unusual in acting in a transcript-dependent manner to perform both local and distal gene regulatory roles like another such lncRNA , Paupar ( Vance et al . , 2014 ) . Dali is transcribed in the vicinity of a neuronal transcription factor Pou3f3 . Both Dali and Paupar lncRNAs are CNS-expressed and evolutionarily constrained transcripts that are co-expressed with their neighbouring transcription factor genes both spatially and temporally . Moreover , both lncRNAs interact directly with the protein product of their neighbouring genes , POU3F3 and PAX6 , respectively , to regulate a large set of targets in trans . These observations , together with the preferential genomic location of intergenic lncRNA loci adjacent to transcription factor genes ( Ponjavic et al . , 2009 ) imply that lncRNAs may commonly interact with the product of genomically adjacent transcription factor genes to act in trans on distal genes .
We used the Whitehead Institute siRNA selection program to design shRNAs that target multiple regions of Dali or Pou3f3 . To minimise the possibility of off-target effects , we compared candidate sequences against the NCBI RefSeq database and removed those with ≥15 bases in the anti-sense strand that matched a database entry . We then cloned the double stranded DNA oligonucleotides containing sense-loop-antisense targeting sequences downstream of the U6 promoter in pBS-U6-CMVeGFP ( Sarker et al . , 2005 ) by linker ligation . The Dali expression plasmid was constructed by PCR amplifying the full length Dali sequence as an EcoRI-XhoI fragment from mouse N2A cell genomic DNA and inserting it into pcDNA3 . The FLAG-tagged Pou3f3 expression plasmid was constructed by excising the full length Pou3f3 ORF from Pou3f3 ( NM_008900 ) mouse cDNA clone in pCMV Entry vector ( Cambridge Biosciences , UK ) and inserting it into the multiple cloning site ( MCS ) of the N-terminal pFLAG-CMV-6a vector ( Sigma–Aldrich , UK ) between EcoRI and EcoRV sites . The sequences of all oligonucleotides used for cloning are shown in Supplementary file 1 . Cells were plated at a density of approximately 2 × 105 cells per well in a six well plate . 16–24 hr later cells were transfected with 1 . 5 μg shRNA expression construct using FuGENE 6 ( Promega , UK ) following the manufacturer's instructions . Total RNA was extracted from the cells 48–72 hr later using TRIzol-chloroform extraction method . For stable transfections , N2A cells were co-transfected with the pBSU6-shRNA expression vector and pTK-Hyg ( Clontech , Mountain View , CA ) at a 5:1 ratio . 72 hr post-transfection 200 μg/ml Hygromycin B was added to the cells to select individual drug resistant clones that were later isolated and expanded under selective conditions . Dali expression in individual clones was measured by qRT-PCR . Reverse transcription was performed using the QuantiTect Reverse Transcription Kit ( Qiagen , Netherlands ) . SYBR Green quantitative PCR was performed using a Step One Plus Real-Time PCR System ( Applied Biosystems , UK ) . For RACE , GeneRacer Kit ( Invitrogen , UK ) was used according to the manufacturer's instructions . Human foetal brain RNA was purchased from Promega . Primers are listed in Supplementary file 1 . Mouse N2A neuroblastoma and E14 ES cells were cultured as described in ( Vance et al . , 2014 ) . The N2A cell line was chosen because it has been used extensively as a model to study neural differentiation in vitro ( Shea et al . , 1985 ) . Human neuroblastoma ( SH-SY5Y ) cells were grown in DMEM/F12 medium supplemented with 10% FBS , 1% penicillin-streptomycin , and 1% L-glutamine at 37°C in a humidified atmosphere with 5% CO2 . Biochemical fractionation , ChIP and UV-RIP experiments was performed exactly as described in Vance et al . ( 2014 ) . The following antibodies were used: anti-DNMT1 ( ab87656; Abcam , UK ) , anti-BRG1 ( ab4081; Abcam ) , anti-P66beta ( ab76924; Abcam ) , anti-SIN3A ( Active Motif , Belgium , 39 , 865 ) , anti-CTCF ( Abcam , 70 , 303 ) , anti-rabbit IgG control antibodies ( Millipore , Billerica , MA ) and mouse monoclonal anti-FLAG M2 beads ( Sigma–Aldrich ) for FLAG-tagged POU3F3 experiments . All animal experiments were conducted in accordance to schedule one UK Home Office guidelines ( Scientific Procedures Act , 1986 ) . C57BL/6J , postnatal day P56 male and pregnant females were killed by cervical dislocation; whole brains were dissected in ice-cold phosphate-buffered saline ( PBS ) from adult ( n = 2 ) , and intrauterine stages E9 ( n = 6 ) , E10 . 5 ( n = 6 ) , E13 . 5 ( n = 6 ) , E15 . 5 ( n = 6 ) and E18 . 5 ( n = 6 ) mice . Brains were embedded in 5% agarose ( low melting , Bioline ) and sectioned using a vibrating microtome ( Leica , VT1000S ) into 200 μm coronal sections using a chilled solution of 1:1 mixture of RNAlater ( Ambion ) and PBS . Regions of interest ( adult: dentate gyrus , subventricular zone and olfactory bulb; embryos: preplate , proliferative compartmenst combining ventricular and subventricular zones , and cortical plate from lateral and dorsal tiers ) were dissected from individual sections using 27 gauge needles under visual guidance , using transillumination on a dissecting microscope ( MZFLIII , Leica , Switzerland ) . Dissected samples were rinsed in RNAse free PBS/RNAlater 1:1 , submerged in ice-cold RNAlater kept for 24 hr at 4°C and stored at −80°C in RNAlater until processing . Total RNA was isolated using the Qiagen Mini RNeasy kit according to the manufacturers' instructions . RNA integrity was assessed on a BioAnalyzer ( Agilent Technologies , UK ) . 200 ng RNA was used to produce labelled sense single stranded DNA ( ssDNA ) for hybridization with the Ambion WT Expression Kit , the Affymetrix WT Terminal Labelling and Controls Kit and the Affymetrix Hybridization , Wash , and Stain Kit following the manufacturer’s instructions . Sense ssDNA was fragmented and the distribution of fragment lengths was assessed on a BioAnalyzer . Next , fragmented ssDNA was labelled and hybridized to the Affymetrix GeneChip Mouse Gene 1 . 0 ST Array ( Affymetrix , UK ) . Arrays were processed on an Affymetrix GeneChip Fluidics Station 450 and Scanner 3000 . CEL files were analysed using the Limma , oligo , and genefilter R Bioconductor packages ( Smyth , 2004; Carvalho and Irizarry , 2010 ) . Arrays were RMA background corrected and quantile normalised . Summary expression values were calculated at the gene level . Genes whose expression changed upon Dali and Pou3f3 knockdown , as well as upon retinoic acid induced differentiation of control and stable Dali knockdown cells , were filtered to remove genes showing little variation in expression ( variance cut off of 0 . 5 ) before the identification of significant changes . In every case , the Limma Ebayes algorithm was used to identify differential expression between three knockdown and three control samples ( biological replicates ) . 1 . 3-fold change cutoff was applied in every case . GOToolbox was used to perform Gene Ontology analyses ( ( Martin et al . , 2004 ) ; http://genome . crg . es/GOToolBox/ ) . Representative significantly enriched categories were selected from a hypergeometric test with a Benjamini-Hochberg corrected p-value threshold of 0 . 05 . CHART Enrichment and RNase H Mapping experiments were performed as described in ( Simon , 2013 ) . We designed 10 biotinylated DNA capture ( C ) -oligos: 5 oligos complementary to the most accessible regions of Dali , as determined by RNase H mapping , and 5 oligos targeting the most evolutionarily conserved regions of the transcript ( Figure 5A ) . These oligos were used as two cocktails of 5 oligos , and as a pool of all 10 . As controls , we used an oligo designed to target the antisense Dali sequence ( absent from the N2A transcriptome ) . Additionally we require peaks to not overlap with those identified in an analogous CHART-sequence experiment using the E . coli lacZ sequence ( GSE52571 ) ( Vance et al . , 2014 ) . Compared to controls , all three cocktails of Dali oligos showed significant enrichment of the Dali transcript ( 10-fold compared to lacZ ) , but no enrichment of the abundant mRNA Gapdh ( Figure 5B ) . Without any prior information about Dali genomic binding , we considered its endogenous site of synthesis to assess the enrichment of transcript-associated DNA loci . Specific enrichment of Dali at its locus was observed as expected ( Figure 5—figure supplement 1 ) . CHART extract was prepared from approximately 3 × 108 N2A cells per pull down and hybridized overnight with 810 pmol biotinylated oligonucleotide cocktail ( Supplementary File 1 ) at room temperature with rotation . 250 μl MyOneC1 streptavidin beads ( Invitrogen ) were used to capture the complexes overnight at room temperature with rotation . After extensive washes , bound material was eluted using RNase H ( New England Biolabs ( NEB ) , UK ) for 30 min at room temperature . Samples were treated with Proteinase K and cross-links were reversed . RNA was purified from 1/5 total sample volume using the QIAGEN miRNeasy kit . DNA was prepared from the remaining sample using the phenol:chloroform:isoamyl alcohol extraction and ethanol precipitation method . DNA was further sheared to an average fragment size of 150–300 bp using a Bioruptor ( Diagenode , Belgium ) and sequenced on an Illumina HiSeq ( 50 bp paired end ) . CHART-seq was performed with three independent pull down samples ( using two independent cocktails of 5 C-oligos , and one cocktail containing all 10 C-oligos ) and sequenced simultaneously with a matched input sample . 50 bp , paired-end reads were mapped to the mouse genome ( mm9 ) using bowtie with the options ‘–m1 –v2 –best–strata–a’ . For each Dali sample , peaks were called against the matched N2A input sample ( 4208 peaks ) and CHART-seq peaks previously analogously identified in N2A cells using two lacZ controls ( 1928 peaks ) ( Vance et al . , 2014 ) . Peak calls were made using the MACS2 algorithm ( ( Zhang et al . , 2008 ) ; https://github . com/taoliu/MACS/blob/master/README ) with the options ‘–mfold 10 30 –gsize = 2 . 39e9 –qvalue = 0 . 01’ using the CGAT pipeline ‘pipeline_mapping . py’ ( https://github . com/CGATOxford/cgat ) . Peak calls were then filtered such that only peak calls with a −log10 q value >5 were retained ( FDR 0 . 001% ) . We discovered 1427 Dali-associated regions genome-wide called against both input and lacZ control samples ( Figure 5A; Supplementary file 5 ) . The chromosomal distribution of Dali peaks was visualised using the R Bioconductor package ‘ggbio’ ( Yin et al . , 2012 ) . Genome territory enrichments analysis was performed using the Genome Association Tester ( GAT; ( Heger et al . , 2013 ) ) . 10 , 000 simulations were performed using a mappability filtered workspace and an isochore file partitioning the genome into eight bins based on regional GC content . For the chromosomal enrichment analyses , chromosomal territories were proportionally assigned to a single virtual meta-chromosome before using GAT to test for GC and mappability corrected enrichments as above . Gene Ontology categories enriched for Dali binding were identified by intersecting regulatory regions for known coding genes with Dali binding sites . Regulatory regions for genes were defined following the GREAT definition ( McLean et al . , 2010 ) as a basal domain surrounding the TSS ( from −5 kb to +1 kb ) and extending domains upstream and downstream to the nearest gene's basal domain or to a maximum distance of 1 Mb . Enrichments were identified using GOToolbox . Dali peaks were characterised using DNase I hypersensitivity ( HS ) data generated by the Stamatoyannopoulos lab at the University of Washington and chromatin features identified by the Ren lab at the Ludwig Institute for Cancer Research ( ( Shen et al . , 2012 ) ; ENCODE Project Consortium , 2012 ) . Enrichments of DNase I HS and chromatin features overlapping Dali peaks were assessed using GAT to control for mappability and regional GC content as above . Complementarity between Dali sequence and binding locations was assessed using the EMBOSS Water algorithm ( Rice et al . , 2000 ) which performs Smith-Waterman alignment with a range of gap opening and extension penalties . RNA-DNA:DNA triplex formation was assessed using the Triplexator search software suit ( Buske et al . , 2012 ) . The MEME-ChIP ( Machanick and Bailey , 2011 ) algorithm was used to perform de novo motif discovery analysis by examining the unmasked DNA sequence of the central regions of peak locations . MEME-ChIP was run with the options ‘-meme-mod zoops -meme-minw 5 -meme-maxw 30–meme-nmotifs 50’ using a custom background file prepared from regions flanking the peak locations using the command ‘fasta-get-markov -m 2’ . Enrichment of known vertebrate transcription factor binding sites from the TRANSFAC Professional database ( Matys et al . , 2006 ) was assessed using the AME algorithm ( McLeay and Bailey , 2010 ) with the options ‘–method fisher–length-correct’ using the sequence and background file prepared for MEME-ChIP analysis . E14 ES cells or day 4 ES-derived neuronal were cross-linked with 2% formaldehyde . Nuclei were prepared and permeabilized with 0 . 3% SDS in 1 . 2× restriction buffer ( NEB3 for BglII ) for 1 hr at 37°C . Then , SDS was sequestered by adding 1 . 8% Triton X-100 . 1 × 106 nuclei ( ∼15 μg of chromatin ) were digested with 400 units of BglII restriction enzyme overnight , and the enzyme was inactivated . Nuclei were diluted in 1 . 15× T4 DNA ligation buffer ( NEB ) , and SDS sequestered by adding 1% Triton X-100 . The digested chromatin was ligated using 100 Weiss units of T4 DNA ligase for 4 hr at 16°C and treated with Proteinase K to reverse cross-links . Samples were further treated with RNase A , and DNA was phenol-chloroform extracted and ethanol precipitated . A RP23-92N4 ( CHORI; BACPAC ) Bacterial Artificial Chromosome ( BAC ) clone covering the Pou3f3-Dali locus was treated as above and used as a control template for the 3C assay . Ligation products of 3C and BAC samples were quantified by qPCR . PCR reactions consisted of 300 ng 3C sample , 0 . 2 μM test primers and a primer corresponding to Dali promoter and 1× SYBR Green PCR Mastermix ( Life Technologies , UK ) . All reactions were performed in triplicate . The mean threshold cycle ( Ct ) value was calculated and used to calculate relative amounts of PCR products . To normalise for different primer efficiencies , interaction frequencies were calculated by dividing the amount of PCR product obtained from the 3C sample by the amount of DNA obtained from control BAC DNA . Interaction frequencies were also normalised to Gapdh internal controls prepared from genomic DNA in the same manner as the BAC clone sample . All primers used are listed in Supplementary file 1 . We used COBRA to study 9 out of 44 CpG island-containing promoters bound by Dali and associated with genes differentially expressed between stable Dali knockdown and control cell lines prior to or subsequent to the RA-induced differentiation . 80–350 ng of genomic DNA was bisulfite-treated using EZ DNA Methylation Gold kit according to the manufacturer's instruction and used for PCR amplification . Primers for amplifying bisulfite converted template DNA were designed using MethPrimer software accessible at http://www . urogene . org/methprimer/ ( Li and Dahiya , 2002 ) . PCR products were on-column purified with QIAquick PCR Purification Kit . 250 ng to 1 μg of purified products were incubated with appropriate COBRA-compatible ( BstUI ( NEB ) , MspI ( NEB ) , TaqI ( Thermo Scientific ) , HpyCH4IV ( NEB ) ) or control ( Hsp92II ( Promega ) , BfaI ( NEB ) ) restriction enzymes overnight . Restriction products were analysed on 3% low melting point agarose gels . Target regions were selected and TAL effector constructs were designed using software , tools , and information found on the TAL Effector Nucleotide Targeter 2 . 0 website accessible from https://tale-nt . cac . cornell . edu/ . Construction of custom TALE-TFs designed to target promoter-proximal region of Dali to up-regulate transcription from the locus was performed as described by Sanjana et al . ( 2012 ) . The TALE-TF was designed to target the following region lying upstream of the TSS of Dali: chr1 ( mm9 ) : 42807019-42807038 ( "TGTCCCTTGTCCACATATCT" ) . The TAL domain sequence used was as follows: NH NG HD HD HD NG NG NH NG HD HD NI HD NI NG NI NG . Microarray and CHART-Seq data have been deposited in the GEO database under accession number GSE62035 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE62035 ) . | Traditionally genes are considered to contain all the instructions necessary to build proteins . For these instructions to be followed they need to be ‘transcribed’ into molecules called messenger RNA , which are then ‘translated’ to form the protein . Messenger RNAs are not the only type of RNA molecule made in a cell; long non-coding RNAs ( or lncRNAs ) , for example , are transcribed but never translated into proteins . Instead , some lncRNAs control the expression of nearby genes and some alter how the DNA is packaged within the cell . Several lncRNAs have been found to control their neighbouring genes , but it is unclear how many of these molecules can also regulate genes that are much further away , even on other chromosomes . One lncRNA called Dali is made in cells of the nervous system of mammals . In the genome , the gene for Dali is situated next to a gene called Pou3f3 , which encodes a protein that contributes to the growth and development of nerves and the kidneys . Chalei et al . have now shown that artificially reducing the amount of the Dali lncRNA restricts the development of mouse cells called N2A cells , which are commonly used to study the development of nerve cells . Reducing Dali lncRNA levels in these cells caused Pou3f3 messenger RNA levels to also decrease , which demonstrates that Dali is a lncRNA that controls its neighbouring gene . The levels of many other genes were also changed when Dali levels were reduced , including many genes that are needed to grow working nerve cells . Chalei et al . also showed that the Dali lncRNA binds to 1427 different regions of the genome of N2A cells , most often near to the start of active genes; Dali could be carried to these sites by the POU3F3 protein . The DNA sequences with which the Dali lncRNA binds were all different . Chalei et al . found that Dali also binds to an enzyme , called DNMT1 , that chemically modifies DNA to change how it is packaged into a cell , and they predict that this enzyme helps Dali to find its binding sites . Furthermore , when Dali lncRNA levels were artificially reduced , the chemical modifications that affect the packaging of DNA in the cell—and hence the expression of genes encoded by this DNA—were changed for several genes . Some of these genes were located far away from the gene that encodes Dali , indicating that this lncRNA can regulate the packaging and expression of distant genes . Many genes that are regulated by Dali are also regulated by the POU3F3 protein; this suggests that the lncRNA might work together with this protein to affect the expression of some genes . Further work is now needed to uncover how many other lncRNAs act away from their sites of synthesis , and how many also form complexes with DNA-binding and DNA-modifying proteins . | [
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] | 2014 | The long non-coding RNA Dali is an epigenetic regulator of neural differentiation |
The mechanisms through which cancer cells lock in altered transcriptional programs in support of metastasis remain largely unknown . Through integrative analysis of clinical breast cancer gene expression datasets , cell line models of breast cancer progression , and mutation data from cancer genome resequencing studies , we identified RNA binding motif protein 47 ( RBM47 ) as a suppressor of breast cancer progression and metastasis . RBM47 inhibited breast cancer re-initiation and growth in experimental models . Transcriptome-wide HITS-CLIP analysis revealed widespread RBM47 binding to mRNAs , most prominently in introns and 3′UTRs . RBM47 altered splicing and abundance of a subset of its target mRNAs . Some of the mRNAs stabilized by RBM47 , as exemplified by dickkopf WNT signaling pathway inhibitor 1 , inhibit tumor progression downstream of RBM47 . Our work identifies RBM47 as an RNA-binding protein that can suppress breast cancer progression and demonstrates how the inactivation of a broadly targeted RNA chaperone enables selection of a pro-metastatic state .
Cancers arise through an evolutionary process that feeds from stochastic genetic alterations and selection ( Vogelstein et al . , 2013 ) . The identities of the alterations that get selected for are rapidly coming to light through large-scale resequencing efforts . For example , several independent studies have characterized the mutational complement of breast cancer , one of the most common human malignancies ( Shah et al . , 2009; Stephens et al . , 2009; Ding et al . , 2010; Banerji et al . , 2012; Cancer Genome Atlas Network , 2012; Shah et al . , 2012; Stephens et al . , 2012 ) . Besides confirming previously known cancer genes , such as TP53 and PIK3CA , most studies also report a long tail of rarely mutated genes . While many of these mutations are likely to be passenger events , some of them are potential mediators of tumor phenotypes . How to identify such low-frequency driver mutations remains a challenge . In addition to mutations that directly promote tumorigenesis through specific alterations in cell signaling and repair pathways , many aberrations found in cancers do not affect cell signaling pathways directly , but rather , they support the stabilization of altered transcriptomic profiles that facilitate the emergence of pro-tumorigenic and metastatic traits . Mutations in epigenetic regulators fall into this category ( Shen and Laird , 2013 ) . The phenotypic output of such alterations would depend on the activity of already existing signaling processes . Indeed , examples of epigenetic alterations that result in a phenotypic trait in the presence of a specific transcriptional program have been described ( Vanharanta et al . , 2013 ) . Analogously , aberrations in the multi-step mRNA processing and turnover cascade ( Moore and Proudfoot , 2009 ) could also lock in aberrant transcriptomic states . Precise regulation of RNA metabolism is fundamental in the generation of biological complexity in both normal and disease states ( Sharp , 2009; Licatalosi and Darnell , 2010 ) . The concerted action of multiple RNA binding proteins ( RBPs ) regulate the spatial , temporal and functional dynamics of the transcriptome via alternative splicing , alternative polyadenylation and transcript stability ( Moore and Proudfoot , 2009 ) . While malignancy-associated dysregulation of RNA metabolism via aberrant microRNA expression is relatively well established ( Di Leva et al . , 2013; Pencheva and Tavazoie , 2013 ) , a growing body of evidence indicates a prominent role also for RBPs in both the development and progression of cancer . For example , upregulation of the splicing regulator SRSF1 is associated with multiple tumor types ( Karni et al . , 2007 ) , and is necessary for the oncogenic activity of MYC in lung cancer ( Das et al . , 2012 ) . Conversely , RBM5 has been shown to be tumor suppressive in several cancer models ( Oh et al . , 2002; Mourtada-Maarabouni et al . , 2006; Oh et al . , 2006 ) . We hypothesized that combining cancer genome resequencing data with gene expression information from both clinical data sets and experimental model systems of metastasis would allow the identification of rarely mutated cancer genes with potential functional significance . This approach identified RNA binding motif protein 47 ( RBM47 ) as a suppressor of breast cancer progression . By analyzing the transcriptome-wide RBM47 binding patterns we demonstrate that RBM47 , a previously uncharacterized RNA-binding protein , modulates mRNA splicing and stability . Loss of RBM47 function thus provides a specific example of the power of global RNA modulatory events in the selection of pro-metastatic phenotypic traits .
We combined gene expression data from triple negative metastatic breast cancer models ( Minn et al . , 2005; Bos et al . , 2009 ) and a cohort of 368 untreated clinical breast cancer cases ( Minn et al . , 2005; Wang et al . , 2005 ) with mutational data from a brain metastasis that originated from a basal breast cancer ( Ding et al . , 2010; Figure 1A ) . Specifically , we looked for genes that had reduced mRNA expression in functionally metastatic cancer cells , evidence for low mRNA expression associated with poor patient outcome in clinical samples , and an enriched mutation in the brain metastasis sequenced by Ding et al . ( 2010 ) . RBM47 , a gene encoding a previously uncharacterized putative RNA-binding protein was the only one that fulfilled all these criteria ( Baltz et al . , 2012; Castello et al . , 2012; Ray et al . , 2013 ) . We confirmed the lower expression of RBM47 mRNA in the highly metastatic cells ( Figure 1B ) . This translated into a comparable difference at the protein level ( Figure 1C ) . In the clinical data sets , low RBM47 mRNA expression was significantly associated with relapse to brain and lung ( Figure 1D , E ) but not to bone ( Figure 1F ) . In multivariate analysis combining RBM47 expression with estrogen , progesterone and HER2 receptor status ( ER , PR and HER2 ) , the association with brain metastasis remained statistically significant ( Figure 1—figure supplement 1A ) . We further characterized the expression patterns of RBM47 in the TCGA cohort of 748 breast cancer samples studied by RNA-seq ( Cerami et al . , 2012; Cancer Genome Atlas Network , 2012 ) . We found that low RBM47 expression was significantly associated with claudin-low and basal breast cancers ( Figure 1G ) , two subtypes of poor prognosis ( Smid et al . , 2008; Lu et al . , 2013 ) . 10 . 7554/eLife . 02734 . 003Figure 1 . RBM47 expression associated with breast cancer progression . ( A ) A schematic of the analytical approach . Genes identified as mutated in a breast cancer brain metastasis by Ding et al . ( 2010 ) where compared to metastasis-associated gene expression traits in both clinical data sets and experimental model systems . This identified RBM47 as a putative breast cancer suppressor gene . ( B ) RBM47 mRNA expression measured by quantitative real-time RT-PCR in two cell line systems of breast cancer metastasis . In both panels , the data are normalized to the parental cell line ( Par ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . LM , lung metastatic derivative; BoM , bone metastatic derivative; BrM , brain metastatic derivative . ( C ) RBM47 protein expression measured by Western blotting . Samples as in ( B ) . Tubulin used as a loading control . ( D ) Brain metastasis free survival in a cohort of 368 untreated breast cancer patients . Cases classified based on RBM47 mRNA expression , bottom 1/3 in blue , top 2/3 in red . p-value derived from a Cox proportional hazards model using RBM47 expression as a continuous variable . ( E ) Lung metastasis free survival in a cohort of 368 untreated breast cancer patients . Cases classified based on RBM47 mRNA expression , bottom 1/3 in blue , top 2/3 in red . p-value derived from a Cox proportional hazards model using RBM47 expression as a continuous variable . ( F ) Bone metastasis free survival in a cohort of 368 untreated breast cancer patients . Cases classified based on RBM47 mRNA expression , bottom 1/3 in blue , top 2/3 in red . p-value derived from a Cox proportional hazards model using RBM47 expression as a continuous variable . ( G ) RBM47 expression as measured by RNA-seq in the TCGA data set of 748 patients . Samples grouped based on breast cancer molecular subtype: luminal A , luminal B , Her2 positive , claudin low and basal . RBM47 expression is lower in claudin low and basal subtypes , both of which are associated with poor patient outcome . ( H ) A schematic showing the predicted protein structure of RBM47 , its closest homologue A1CF , a known RNA-binding protein , and the predicted structure of RBM47I281fs mutant . Blue diamonds represent RRM motifs , pink rectangle represents a truncated RRM motif . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 00310 . 7554/eLife . 02734 . 004Figure 1—figure supplement 1 . RBM47 expression and genetic alterations in human breast cancer . ( A ) Multivariate Cox proportional hazards models for both brain and lung metastasis free survival in a cohort of 368 untreated breast cancer patients . The model incorporates RBM47 expression as a continuous variable , estrogen receptor ( ER ) expression , progesterone receptor ( PR ) expression and Her2 expression . ( B ) A list of protein-altering RBM47 breast cancer mutations in the Catalogue of Somatic Mutations in Cancer database ( COSMIC , http://cancer . sanger . ac . uk/cancergenome/projects/cosmic/ ) as of June 2013 . ( C ) Percentage of genetic alterations , non-synonymous mutations or homozygous deletions , in RBM47 in the TCGA cohort of 748 breast cancers classified based on molecular subtypes: luminal A , luminal B , Her2 positive , claudin low and basal . A significant enrichment is observed in the basal subtype ( p=0 . 00015 , Fisher's exact test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 004 The RBM47I281fs mutation first reported in a brain metastasis truncates the protein from the third RNA recognition motif ( RRM ) onwards ( Figure 1H ) . As this mutation was already present in a minority subpopulation of the corresponding primary tumor ( Ding et al . , 2010 ) , we looked for additional evidence of genetic RBM47 aberrations in primary breast cancer cohorts . The catalogue of somatic mutations in cancer ( COSMIC ) database ( Forbes et al . , 2010 ) reported 9 non-synonymous mutations in RBM47 , three of which were frameshift mutations truncating one or more of the RRM domains ( Figure 1—figure supplement 1B ) . Furthermore , analysis of the data from the TCGA cohort revealed that in basal breast cancer , RBM47 was targeted by a mutation or homozygous deletion in ∼8% of the cases ( Figure 1—figure supplement 1C ) . Moreover , heterozygous loss of the RBM47 locus was present in 30% of the TCGA cohort ( Cerami et al . , 2012 ) . These correlative analyses of multiple different breast cancer data sets , both experimental systems and large clinical patient cohorts , suggested that reduced expression or function of RBM47 is associated with breast cancer progression already within primary tumors , and that clones with reduced RBM47 function may display enhanced lung and brain metastatic fitness . In order to test the role of RBM47 as a suppressor of breast cancer progression , we stably introduced both wild type RBM47 and RBM47I281fs in the lung metastatic ( LM2 ) and brain metastatic ( BrM2 ) derivatives of the MDA-MB-231 triple negative breast cancer cells ( MDA231 for short ) , respectively ( Figure 2—figure supplement 1A; Minn et al . , 2005; Bos et al . , 2009 ) . Of note , despite robust mRNA expression , the mutant RBM47I281fs protein levels were low , indicating that the mutant protein was unstable and therefore inactive ( Figure 2—figure supplement 1B ) . As determined by in vivo bioluminescence in experimental metastasis assays , wild type RBM47 inhibited lung colonization by the MDA231-LM2 cells , when compared to RBM47I281fs ( Figure 2A–B ) . Similarly , we observed extended brain metastasis free survival in mice inoculated with the MDA231-BrM2 cells expressing RBM47 when compared to those expressing RBM47I281fs ( Figure 2C ) . This difference translated into reduced brain metastatic burden as determined by ex vivo imaging ( Figure 2D , E , Figure 2—figure supplement 1C ) . We then tested the effects of inhibiting endogenous RBM47 in the metastatic colonization of the parental MDA231 cells . With comparable effects of RBM47 re-expression seen in both the lung and brain metastasis models ( Figure 2A , E ) , we chose the lung colonization assay for loss-of-function studies as this allowed the simultaneous analysis of a greater number of tumor re-initiation events . Two RBM47-targeting shRNA constructs significantly shortened the lung metastasis free survival in mice when compared to controls ( Figure 2F , Figure 2—figure supplement 1D ) , as determined by bioluminescence imaging ( Figure 2G , H ) . This result was confirmed with a second more indolent cancer cell clone ( Figure 2—figure supplement 1E–G ) . 10 . 7554/eLife . 02734 . 005Figure 2 . RBM47 suppresses metastatic breast cancer progression . ( A ) Normalized lung photon flux in mice after tail vein inoculation of MDA231-LM2 cells expressing either wild type RBM47 or RBM47I281fs . p-value calculated utilizing repeated measures two-way ANOVA . N = 6 for RBM47I281fs , N = 8 for RBM47 . ( B ) Representative bioluminescence images from the experiment shown in ( A ) . The color scale shows bioluminescence ( photons/second ) . ( C ) Brain metastasis free survival as determined by in vivo bioluminescence imaging in mice after intracardiac inoculation of MDA231-BrM2 cells expressing either wild type RBM47 or RBM47I281fs . p-value calculated using the log-rank test . N = 9 for RBM47I281fs , N = 7 for RBM47 . ( D ) Representative bioluminescence images from the experiment shown in ( C ) . The color scale shows bioluminescence ( photons/second ) . ( E ) Ex vivo quantification of bioluminescence from brain metastases on day 42 of the experiment shown in panel ( C ) . P-value calculated by two-tailed Student's t test . ( F ) Lung metastasis free survival as determined by in vivo bioluminescence imaging in mice after tail vein inoculation of parental MDA231 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . p-values calculated using the log-rank test . N = 8 for Ctrl group , N = 6 for shRNA1 , N = 5 for shRNA2 . ( G ) In vivo bioluminescence imaging on day 36 of the experiment shown in ( F ) demonstrates earlier emergence of detectable lung metastasis for the RBM47 knockdown groups when compared to the control animals . The color scale shows bioluminescence ( photons/second ) . ( H ) Quantification of bioluminescence for the time point shown in ( G ) . Data normalized to signal on day 0 for each animal . p-value calculated using the Wilcoxon rank-sum test . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 00510 . 7554/eLife . 02734 . 006Figure 2—figure supplement 1 . RBM47 suppresses breast cancer progression . ( A ) RBM47 mRNA expression measured by quantitative real-time RT-PCR in 231-BrM2 and 231-LM2 cells transduced with either wild type RBM47 or RBM47I280fs-expressing cDNA constructs , respectively . The data are normalized to the untransduced control cell lines . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( B ) Flag-RBM47 protein expression determined by Western blotting in non-clonal 231BrM2 tet-on Flag-RBM47 cells . No RBM47 or Flag protein is detected in the cells expressing Flag-RBM47I281fs ( predicted size 34 kDa ) treated with the same doxycycline doses . ( C ) Quantification of bioluminescence from the head of mice on day 0 of the experiment shown in Figure 2C . p-value calculated by two-tailed Student's t test . ( D ) RBM47 mRNA and protein expression measured by quantitative real-time RT-PCR and Western blotting , respectively , in parental MDA-231 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . Tubulin used as a protein loading control . ( E ) RBM47 mRNA and protein expression measured by quantitative real-time RT-PCR and Western blotting , respectively , in parental CN34 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . Tubulin used as a protein loading control . ( F ) Quantification of lung metastatic burden on day 0 by in vivo bioluminescence imaging in mice after tail vein inoculation of parental CN34 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . p-values calculated using two-sided Student's t test . N = 5 for all groups . ( G ) Quantification of lung metastatic burden on day 141 by in vivo bioluminescence imaging in mice after tail vein inoculation of parental CN34 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . p-values calculated using two-sided Student's t test . N = 4 for Ctrl group , N = 5 for shRNA1 , N = 4 for shRNA2 . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 006 The initial functional experiments suggested that the tumor suppressive effect of RBM47 on the overall population of metastatic cancer cells was modest . This could reflect either weak tumor suppressive function of RBM47 in general , heterogeneity in the sensitivity to RBM47 among different cancer cell subpopulations , or in the case of RBM47 reintroduction , loss of transgene expression . In order to distinguish between these possibilities , we used immunohistochemistry to assess RBM47 expression in the lung nodules formed either by wild type RBM47 or RBM47I281fs expressing cells . This revealed that some cancer clones were able to form robust lung metastasis even in the presence of RBM47 ( Figure 3A , Figure 3—figure supplement 1A ) , but that many of the metastases formed after the inoculation of wild type RBM47-expressing cells had avoided or suppressed the expression of RBM47 ( Figure 3B , Figure 3A—figure supplement 1A ) . As expected , the RBM47I281fs-expressing tumors contained only weakly staining cancer cells intermingled with small cells with strong RBM47 expression ( Figure 3C ) , similar to those seen in normal lung parenchyma ( Figure 3D ) . The rate of proliferation as determined by Ki67 immunohistochemistry did not correlate with the level of RBM47 expression ( Figure 3—figure supplement 1A ) . This was in line with the idea that some clones were more sensitive to RBM47 than others , but also that a strong selective pressure led metastatic cells to lose RBM47 , a finding consistent with the initial observation of RBM47 loss in metastatic cell populations ( Figure 1C ) . 10 . 7554/eLife . 02734 . 007Figure 3 . Clonal heterogeneity in RBM47 sensitivity . ( A ) A lung metastatic nodule with strong RBM47 expression in a mouse inoculated with RBM47-transduced MDA231-LM2 cells . RBM47 protein expression detected by immunohistochemistry using an antibody against RBM47 . ( B ) A lung metastatic nodule with weak RBM47 expression in a mouse inoculated with RBM47-transduced MDA231-LM2 cells . RBM47 protein expression detected by immunohistochemistry . Note the clearly reduced staining when compared to panel ( A ) . ( C ) A lung metastatic nodule with weak RBM47 expression in a mouse inoculated with RBM47I280fs-transduced MDA231-LM2 cells . RBM47 protein expression detected by immunohistochemistry . Staining intensity similar to that seen in panel ( B ) . ( D ) RBM47 expression in normal mouse lung . RBM47 protein expression detected by immunohistochemistry . Note small cells with strong RBM47 expression , similar to those seen in panels ( B ) and ( C ) . ( E ) Brain metastasis free survival as determined by in vivo bioluminescence imaging in mice after intracardiac inoculation of WT10 cells . The RBM47 group received doxycycline in diet . p-value calculated using the log-rank test . N = 9 for both groups . ( F ) Representative ex vivo bioluminescence images from brain metastasis of the experiment shown in ( E ) . The color scale shows bioluminescence ( photons/second ) . ( G ) Ex vivo quantification of bioluminescence from brain metastases with and without RBM47 reintroduction . WT10 data from the experiment shown in ( E ) . WT6 data from a similar experimental setup . p-value calculated by two-tailed Student's t test . N = 9 for Ctrl group , N = 10 for RBM47 group . ( H ) Brain metastasis free survival as determined by in vivo bioluminescence imaging in mice after intracardiac inoculation of MUT3 cells . The I281fs group received doxycycline in diet . p-value calculated using the log-rank test . N = 9 for Ctrl group , N = 7 for RBM47I281fs group . ( I ) Representative ex vivo bioluminescence images from brain metastases of the experiment shown in ( H ) . The color scale shows bioluminescence ( photons/second ) . ( J ) Ex vivo quantification of bioluminescence from brain metastases of the experiment shown in ( H ) . p-value calculated by two-tailed Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 00710 . 7554/eLife . 02734 . 008Figure 3—figure supplement 1 . RBM47 expression in experimental metastases . ( A ) Examples of lung metastatic nodules in a mouse inoculated with RBM47-transduced 231-LM2 cells . Some metastases have strong expression of RBM47 ( left ) , whereas others show only weak RBM47 expression ( right ) . Staining against human vimentin and Ki67 is shown for the same lesions . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 00810 . 7554/eLife . 02734 . 009Figure 3—figure supplement 2 . Model systems with inducible RBM47 expression . ( A ) RBM47 mRNA expression measured by quantitative real-time RT-PCR in WT6 cells treated with increasing concentrations of doxycycline . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( B ) RBM47 mRNA expression measured by quantitative real-time RT-PCR in WT10 cells treated with increasing concentrations of doxycycline . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( C ) RBM47 mRNA expression measured by quantitative real-time RT-PCR in MUT3 cells treated with increasing concentrations of doxycycline . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( D ) RBM47 protein expression detected by Western blotting in WT6 cells treated with increasing concentrations of doxycycline . ACTB used as a loading control . Quantification of signal shown in the lower panel , normalized to both ACTB loading control and the level of endogenous RBM47 detected in HCC1954 cells . ( E ) RBM47 protein expression detected by Western blotting in WT10 cells treated with increasing concentrations of doxycycline . ACTB used as a loading control . Quantification of signal shown in the lower panel , normalized to both ACTB loading control and the level of endogenous RBM47 detected in HCC1954 cells . ( F ) Proliferation of WT6 cells assessed under standard tissue culture conditions with and without doxycycline ( 2 . 5 ng/ml ) . ( G ) Demonstration of doxycycline-inducible gene induction in brain metastatic lesions formed by 231-BrM2 cells transduced with an inducible RFP construct . Cancer cells express constitutively GFP . Doxycycline treatment started on day 14 after intracardiac cancer cell inoculation . Doxycycline-inducible RFP expression can be seen in cancer cells . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 009 In order to allow better experimental control over RBM47 expression we utilized a conditional expression system . By focusing on the two brain metastatic cell lines that expressed the lowest levels of endogenous RBM47 ( Figure 1C ) we generated single cell-derived clones with doxycycline-inducible expression of either wild type RBM47 ( henceforth WT10 and WT6 , respectively ) or the patient-derived mutant RBM47I281fs ( henceforth MUT3 ) . All clones exhibited dose-dependent RBM47 mRNA upregulation upon doxycycline induction ( Figure 3—figure supplement 2A–C ) that translated into increased RBM47 protein expression in the wild type-expressing clones ( Figure 3—figure supplement 2D , E ) . Cancer cells also tolerated RBM47 in vitro ( Figure 3—figure supplement 2F ) . After confirming the feasibility of doxycycline-mediated conditional gene activation in metastatic brain lesions ( Figure 3—figure supplement 2G ) , we inoculated WT6 , WT10 and MUT3 cells into immunocompromized mice and assessed their brain-metastatic phenotype . All clones formed robust brain metastases under doxycycline-free conditions ( Figure 3E–J ) . The induction of RBM47 expression with doxycycline in the diet inhibited robustly brain colonization of both metastatic cell clones , WT6 and WT10 ( Figure 3E–G ) . However , the patient-derived mutant RBM47I281fs did not show any tumor suppressive effects ( Figure 3H–J ) . Collectively , these results demonstrated that RBM47 was able to strongly inhibit metastatic functions of some cancer clones , whereas other clones were able to form metastasis despite the presence of RBM47 . To determine whether RBM47 can directly bind RNA in vivo , we made use of the ability of UV-irradiation at 254 nm to induce chemical crosslinks between RNA and proteins that are in direct contact ( Darnell , 2010 ) . γ-32P-labeled RNA was detected by autoradiogram at ∼76 kD , the predicted size of Flag-RBM47 , in Flag-immunoprecipitates from UV-irradiated , doxycycline-treated MDA231-BrM2 WT Flag-RBM47 cells , but not doxycycline-treated empty vector control or non-irradiated WT Flag-RBM47 expressing cells ( Figure 4A ) . To identify directly bound RBM47 targets , a modified version of the high throughput sequencing and cross-linking immunoprecipitation ( HITS-CLIP ) protocol ( Licatalosi et al . , 2008; Weyn-Vanhentenryck et al . , 2014 ) was carried out in duplicate on Flag-RBM47 expressing MDA231-BrM2 cells treated with doxycycline . ( Figure 4B , see 'Materials and methods' ) . RBM47-bound HITS-CLIP reads were mapped to the human genome , yielding ∼7 . 7 × 106 and ∼2 . 0 × 106 unique reads ( tags ) per replicate . 75% of the tags mapped to regions corresponding to UCSC/Refseq genes , with a high degree of reproducibility of binding observed between replicates ( Spearmann correlation coefficient R2 = 0 . 998 , total tags per gene , Figure 4C ) . 10 . 7554/eLife . 02734 . 010Figure 4 . HITS-CLIP identifies genome-wide RBM47 binding maps . ( A ) Radiolabelled RNA is detectable in RBM47-expressing 231-BrM2 metastatic cells that have been UV-irradiated indicating in vivo RNA binding ability . No RNA is detected in non-crosslinked cells despite the presence of ample immunoprecipitated Flag-RBM47 protein . No RNA or protein is detected in control 231-BrM2 transduced with empty vector . Samples run in duplicate . ( B ) Schematic of the modified HITS-CLIP protocol showing autoradiogram of duplicate Flag-RBM47 samples used . Purified RBM47-bound RNA fragments ( green ) were polyA tailed and reverse transcribed in the presence of Brd-U using a polydT-NV primer encoding the full sense sequence of the Illumina reverse sequencing primer ( blue ) , an abasic furan that serves as an ApeI cut site ( ξ ) , a partial reverse complement to the Illumina forward sequencing primer ( orange ) , and two hexamer sequences ( purple ) : a known-sequence index for multiplexing and a degenerate barcode used to distinguish unique cDNA clones from PCR duplicates . cDNA were stringently purified , circularized and linearized using ApeI to bring the Illumina sequence into correct orientation with respect to the cloned fragment , and the samples PCR amplified and deep sequenced . ( C ) RBM47 HITS-CLIP is highly reproducible between replicate experiments at the level of unique CLIP tags per transcript . ( D ) Increasing the stringency of biologically reproducible RBM47 binding site definition reveals predominant binding in 3′UTRs and intronic regions of target transcripts , with the most robust binding ( tags per binding site ) evident in 3′UTRs . ( E ) Distribution of tags number per biologically reproducible cluster in coding and non-coding regions of RBM47-targeted transcripts reveals a bimodal binding pattern between 3′UTRs and introns , with the latter having large numbers of reproducible yet less robust binding . ( F ) MEME analysis reveals an enrichment for polyU sequences ( 50 sites , p=2 . 4e−16 ) in the ±10 nt foot print region surrounding reproducible RBM47 deletion CIMS ( 357 sites with ≥5 mutations , FDR ≤0 . 01 , Zhang and Darnell , 2011 ) . ( G ) Widespread RBM47 binding is seen in target transcripts , as exemplified by binding patterns seen in the 3′UTRs of DKK1 and IL8 . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 010 To identify robust and reproducible RBM47-binding sites , tags were clustered to return regions with evidence of binding in both replicates ( biological complexity , BC = 2 ) with increasingly stringent filters ( Figure 4D ) : tags per cluster ( tags ≥2 , 617 , 026 clusters; tags ≥10 , 94 , 966 clusters ) , a previously described significant peak threshold ( significant peak height ≥10 , 29 , 562 clusters [Chi et al . , 2009] ) , and a ranked reproducibility chi-squared score ( p≤0 . 01 , 19 , 433 clusters [Darnell et al . , 2011] ) . As has been previously described for the n-ELAV proteins ( Ince-Dunn et al . , 2012 ) , identification of the most robust binding through increased stringency of cluster definition led to an increase in proportional binding in 3′UTR regions . For RBM47 this occurred due to loss of the majority of reproducible , yet relatively small intronic binding sites ( Figure 4E ) , which may reflect the relative abundance of pre- and mature RNA message . Motif analysis ( MEME , Bailey and Elkan , 1994 ) failed to identify an enriched RBM47-binding sequence in ±10 nt footprints centered on the top 3000 significant peaks ( data not shown ) , but revealed a polyU sequence enriched in RBM47-binding sites containing cross-link induced mutations ( deletion CIMS , Zhang and Darnell , 2011 , Figure 4F ) . The apparent lack of an enriched RBM47-binding motif within robust CLIP-derived binding sites may reflect the broad binding patterns observed , as exemplified by the predominantly 3′UTR binding in DKK1 and IL8 ( Figure 4G ) . Reproducible binding of RBPs in intronic regions has proven to be predictive of a role in pre-mRNA processing for multiple proteins . To explore the relationship between RBM47 intronic binding and alternative splicing , RNA-seq was carried out in triplicate to compare MDA231-BrM2 cells and RBM47-expressing WT10 cells . Reads were mapped to cassette ( CA ) exon junctions as described previously for Mbnl2 ( Charizanis et al . , 2012 ) , and an average inclusion rate ( IR ) calculated for each cell type to allow for the identification of reciprocal splicing changes while normalizing for changes in RNA stability ( Ule et al . , 2005; Figure 5A ) . The average change in inclusion rate ( ΔI ) was then calculated such that positive ΔI indicates RBM47-dependent cassette exon inclusion . This analysis revealed 121 and 140 CA exons with significant RBM47-dependent inclusion and exclusion , respectively . To assess whether RBM47-binding occurred in the vicinity of these splice sites , HITS-CLIP tags in BC2 tags ≥5 clusters ( to account for lower levels of intronic binding seen in Figure 4E ) in the region of the alternative splice were calculated . Forty-eight RBM47-bound included and 49 RBM47-bound excluded CA exons were identified in a total of 84 genes ( Figure 5B; Supplementary file 1 ) . RBM47-dependent splicing changes were confirmed via RT-PCR as shown for SLK , MDM4 , LIMCH1 , MBNL1 and SEC31A ( Figure 5C , Figure 5—figure supplement 1 ) . 10 . 7554/eLife . 02734 . 011Figure 5 . RBM47 regulates alternative splicing . ( A ) Schematic showing the method used to calculate alternative exon inclusion rates from paired-end RNA-seq data . 5′CE–5′ flanking constitutive exon , 3′CE–3′ flanking constitutive exon , 5′FI–5′ Flanking intron , 3′FI–3′ Flanking intron . ( B ) Scatter plot of all expressed alternatively spliced CA exons showing RBM47-dependent change in inclusion ( black , ≥10 RNA-seq reads spanning exon–exon junctions , ΔI ≥|0 . 2| , p≤0 . 05 ) with orange points indicating RBM47-bound and included CA exons , and blue points indicating bound and excluded exons , respectively . CA exons were considered bound given a total of tags ≥10 in BC2 tags ≥5 clusters mapping to the region spanning the start of the 5′CE to the end of the 3′CE . p-values calculated by Fisher's exact test using total isoform 1 and total isoform 2 RNA-seq reads in each condition . ( C ) Left panel shows a section of the SLK transcript ( blue ) that includes a CA exon ( grey box ) . The top two panels show RNA-seq data from WT10 ( green ) and control cells ( red ) , with RBM47 HITS-CLIP tags mapping to the region shown beneath in black . Increased RNA-seq signal corresponding to the CA exon is seen in the presence of RBM47 expression , while robust binding is evident in the 5′FI . Independent RT-PCR validation of this splice is shown in the right panel , with IR calculated using ImageJ analysis of autoradiograms . ( D ) Normalized complexity map of RBM47-dependent alternative splicing of CA exons . Orange and blue peaks represent binding associated with RBM47-dependent exon inclusion and exclusion , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 01110 . 7554/eLife . 02734 . 012Figure 5—figure supplement 1 . RBM47-dependent splicing events . ( A ) RBM47-dependent exclusion of exon 6 of MDM4 , as in Figure 5C . ( B ) RBM47-dependent exclusion of a CA in LIMCH1 , as in Figure 5C . ( C ) RBM47-dependent inclusion of exon 5 in MBNL1 , as in Figure 5C . ( D ) RBM47-dependent inclusion of two CA exons in SEC31A , as in Figure 5C . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 012 By mapping normalized RBM47 CLIP tags associated with RBM47-dependent splicing changes on a composite transcript ( Licatalosi et al . , 2008 ) we generated an RNA binding map of consensus binding sites within 1 kb of exon-intron boundaries , with respect to exon inclusion or exclusion ( Figure 5D ) . The resulting normalized complexity map reveals enriched RBM47 binding in the vicinity of splice acceptor sites of the CA exon and 3′ CE for included alternative isoforms , while relative enrichment of RBM47 binding was seen at the 5′ CE splice donor and 3′CE splice acceptor of excluded isoforms . To further study the functional consequences of RBM47-mRNA binding events , we determined global steady state mRNA levels in the RBM47 expressing brain metastatic cells by RNA-seq . We took advantage of the clonal doxycycline-inducible cell line systems , which facilitated the analysis of RBM47 dose-dependence . First , genome-wide analyses showed that the mRNA level of several more genes correlated significantly with increasing concentrations of doxycycline in both cell lines expressing the wild type RBM47 ( WT6 and WT10 ) , when compared to cells expressing the mutant ( Figure 6A ) . This result indicated that RBM47 elicits dose-dependent changes in mRNA levels . The correlation coefficients followed a pattern that suggested the existence of mRNA species that correlated both positively and negatively with wild type , but not mutant , RBM47 expression , that is few genes in MUT3 cells had correlation coefficients close to 1 or −1 , whereas in WT6 and WT10 cells such genes were abundant ( Figure 6B ) . Encouraged by these observations , we looked for mRNAs that fulfilled the following criteria: ( i ) p-value of correlation less than 0 . 01 in both WT6 and WT10 cells , ( ii ) mRNA expression change detectable already at the lowest levels of RBM47 expression in both cell clones and ( iii ) no significant correlation with RBM47I281fs expression . This revealed a set of 102 mRNAs that were upregulated and 92 that were downregulated , respectively , in cells expressing the wild type RBM47 ( Figure 6C , Figure 6—figure supplement 1A , B; Supplementary file 2 ) . Importantly , these changes were observed already with the lowest expression level of RBM47 that was comparable or lower than those detected in endogenously RBM47 expressing cells ( Figure 3—figure supplement 2D , E ) . 10 . 7554/eLife . 02734 . 013Figure 6 . RBM47-induced changes in mRNA levels . ( A ) Distribution of p-values from correlation analysis of doxycycline concentration and gene expression for all genes in WT6 , WT10 and MUT3 cell lines , respectively . Global gene expression determined by RNA-seq . ( B ) Distribution of correlation coefficients between doxycycline concentration and gene expression in WT6 , WT10 and MUT3 cell lines , respectively . ( C ) Heat maps showing the top 102 positively ( UP ) correlated and 92 negatively ( DOWN ) correlated genes with RBM47 expression in WT6 and WT10 cells . The expression of these genes does not correlate with RBM47I280fs expression in the MUT3 cells . ( D ) RBM47 mRNA expression in the TCGA cohort of breast cancer samples classified by the clusters shown in Figure 6—figure supplement 1C . p-value determined by two-tailed Student's t test . ( E ) Fold change between Cluster 1 and Cluster 2 shown for the 102 positively and 92 negatively correlated RBM47 target genes , respectively . Positive fold change shows higher expression in Cluster 2 , which has lower expression of RBM47 . The genes that are induced upon RBM47 reintroduction tend to have lower expression in Cluster 2 , and the genes that show lower expression upon RBM47 reintroduction tend to have higher expression in Cluster 2 . This is in line with the experimental results shown in panel ( C ) . P-value determined by two-tailed Student's t test . ( F ) Pie charts showing the fraction of target genes with more than 100 RBM47 tags for both the 102 positively and 92 negatively correlated RBM47 target genes . ( G ) Tags per transcript plotted for both the positively and negatively correlated RBM47 target genes that showed more than 1 tag . The only two binding partners with >104 tags represent DKK1 and IL8 , respectively . ( H ) Secreted DKK1 and IL8 protein levels determined by ELISA in WT6 cells treated with the indicated doxycycline concentrations . VEGFA used as a control . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 01310 . 7554/eLife . 02734 . 014Figure 6—figure supplement 1 . Transcriptomic signature of RBM47 reintroduction . ( A ) Normalized average RNA-seq reads per transcript for the 102 positively correlated RBM47 target genes shown for WT6 , WT10 and MUT3 cells treated with the indicated doxycycline concentrations . ( B ) Normalized average counts for the 92 negatively correlated RBM47 target genes shown for WT6 , WT10 and MUT3 cells treated with the indicated doxycycline concentrations . ( C ) A heatmap showing unsupervised hierarchical clustering in the TCGA cohort of 748 samples using the 194 RBM47 target genes identified by RNA-seq . Two main clusters are detected . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 014 To determine whether the 194 RBM47-responsive genes displayed clinically meaningful expression patterns , we conducted an unsupervised hierarchical clustering analysis of the TCGA cohort of breast cancer specimens . Two main clusters emerged , one of which harbored characteristics of the RBM47-low phenotype ( ‘Cluster 2’ , Figure 6—figure supplement 1C ) . First , this subgroup , Cluster 2 , had significantly lower RBM47 expression levels when compared to Cluster 1 ( Figure 6D ) . Second , the genes that were induced upon RBM47 reintroduction tended to have lower expression in Cluster 2 , whereas genes with reduced expression level in the RBM47-expressing cells tended to show higher expression in Cluster 2 ( Figure 6E ) . These observations validated in clinical tumor samples the RBM47-dependent gene expression correlations identified in vitro . To identify directly regulated targets of RBM47 , we combined the mRNA expression data with the HITS-CLIP-derived RBM47 transcriptome-wide binding data . Of the 2498 strongest binding partners with >100 tags per transcript ( total tags in BC2 tags ≥10 clusters; Supplementary file 3 ) , 25 were among the 102 RBM47-upregulated genes and 17 among the 92 RBM47-downregulated genes ( Figure 6F ) , indicating no significant binding preference for either groups . This was reflected in the similar overall RBM47 binding profiles in both the up- and down-regulated genes ( Figure 6G ) . Two of the top-scoring RBM47 binding mRNAs , IL8 and DKK1 ( 17 , 079 and 16 , 208 tags in clusters per gene , respectively , Figure 4G ) , were among the upregulated genes . This increase in mRNA levels was associated with increased protein secretion as determined by ELISA , whereas VEGFA , the mRNA of which was bound but not upregulated by RBM47 , showed no change in protein secretion ( Figure 6H ) . Nuclear RNA binding proteins typically function in large multiprotein complexes that regulate mRNA biogenesis ( Dreyfuss et al . , 2002 ) . Our data from both genome-wide HITS-CLIP and RNA-seq analysis was compatible with RBM47 being a member of these RNA chaperone units . This suggested that RBM47 may not necessarily have a direct tumor suppressive signaling function . Rather , it raised the possibility that loss of RBM47 , leading to subtle changes in multiple mRNAs , either through stabilization , destabilization or alternative splicing , could be selected for if the net effect of both growth-promoting and growth-inhibiting changes would be beneficial for cancer cells under the stress of dissemination to and colonizing distant organs . In line with this , both the up- and down-regulated target genes of RBM47 , as well as the genes that were targets of RBM47-mediated alternative splicing , contain genes that have previously been shown to either promote or inhibit tumor phenotypes ( Supplementary file 1 and Supplementary file 3 ) . For example , DKK1 ( Bafico et al . , 2004; Cowling et al . , 2007; Mikheev et al . , 2008 ) , HTATIP2 ( Zhao et al . , 2007 ) , HBP1 ( Paulson et al . , 2007; Li et al . , 2011 ) , MXI1 ( Lahoz et al . , 1994 ) and CASP7 ( Hudson et al . , 2013 ) , all bound by RBM47 and upregulated upon RBM47 reintroduction , have known tumor suppressive functions . Similarly , RBM47-induced splicing changes were seen in genes such as SLK ( Roovers et al . , 2009 ) , MDM4 ( Wade et al . , 2013 ) and TNC ( Oskarsson et al . , 2011 ) , all of which are genes with known functions in cancer . Focusing on DKK1 , one of the most robustly bound RBM47 target transcripts identified by HITS-CLIP , we investigated the possible role of RBM47 as a modulator of mRNA abundance . As predicted by our results in WT6 and WT10 cells , knockdown of RBM47 in two additional breast cancer cell lines expressing high levels of endogenous RBM47 , SKBR3 and ZR-75-30 , reduced DKK1 mRNA levels ( Figure 7A , Figure 7—figure supplement 1A ) . This validated RBM47 as a modulator of DKK1 mRNA level in breast cancer cells . 10 . 7554/eLife . 02734 . 015Figure 7 . RBM47 modulates DKK1 mRNA stability . ( A ) RBM47 and DKK1 mRNA expression measured by quantitative real-time RT-PCR in SKBR3 cells expressing either control vector ( pGIPZ ) or hairpins against RBM47 ( shRNA1 and shRNA2 ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( B ) DKK1 mRNA stability determined by measuring mRNA levels by quantitative real-time RT-PCR in WT6 , WT10 and MUT3 cells , treated with or without doxycycline , after inhibition of transcription with actinomycin D . Data normalized to time point 0 . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . WT6: T1/2 Ctrl = 2 . 3 hr , T1/2 Dox = 9 . 8 hr; WT10: T1/2 Ctrl = 2 . 4 hr , T1/2 Dox = 5 . 4 hr; MUT3: T1/2 Ctrl = 2 . 1 hr , T1/2 Dox = 2 . 2 hr . ( C ) Schematic showing the locations of different DKK1 miR-constructs in relation to DKK1 genomic locus and RBM47 binding patterns . miRm and miRn target exon 1 that is not bound by RBM47 . miRo , miRp and miRq target DKK1 3′UTR that is strongly bound by RBM47 . ( D ) DKK1 mRNA expression measured by quantitative real-time RT-PCR in WT6 cells expressing either control vector ( pGIPZ ) or the five DKK1-targeting miR constructs shown in panel ( C ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( E ) DKK1 mRNA expression measured by quantitative real-time RT-PCR in WT6 cells expressing either control vector ( pGIPZ ) or the five DKK1-targeting miR constructs , with or without doxycycline treatment . Data normalized to the non-treated control for each cell line separately . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( F ) DKK1 mRNA expression measured by quantitative real-time RT-PCR in WT10 cells expressing either control vector ( pGIPZ ) or the five DKK1-targeting miR constructs shown in panel ( C ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( G ) DKK1 mRNA expression measured by quantitative real-time RT-PCR in WT10 cells expressing either control vector ( pGIPZ ) or the five DKK1-targeting miR constructs , with or without doxycycline treatment . Data normalized to the non-treated control for each cell line separately . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 01510 . 7554/eLife . 02734 . 016Figure 7—figure supplement 1 . Effects of RBM47 knockdown on DKK1 mRNA . ( A ) RBM47 and DKK1 mRNA expression measured by quantitative real-time RT-PCR in ZR-75-30 cells expressing either control vector ( pGIPZ ) or a hairpin against RBM47 ( shRNA2 ) . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 016 The fact that RBM47 bound to DKK1 mRNA 3′UTR and increased DKK1 mRNA levels suggested the possibility that RBM47 had the capability of stabilizing DKK1 mRNA . We tested this by treating cancer cells with actinomycin D , a general inhibitor of transcription , and measuring DKK1 mRNA levels in the following hours . This demonstrated that wild type RBM47 , but not the RBM47I281fs mutant , was able to increase the half-life of DKK1 mRNA by up to fourfold ( Figure 7B ) . As RBM47 binding to DKK1 was concentrated on the 3′UTR , a known region of regulatory activity ( Zhou et al . , 2012 ) , we considered the possibility that RBM47 could compete with microRNAs or other mRNA destabilizing factors that target the 3′UTR ( Bhattacharyya et al . , 2006; Young et al . , 2012 ) . To test this experimentally , we generated miR-30-based shRNA-miR constructs that targeted different regions of the DKK1 transcript , two at the 5′ end with minimal RBM47 binding and three at the 3′ end with abundant RBM47 signal ( Figure 7C; Fellmann et al . , 2011 ) . All constructs knocked the DKK1 transcript level down by 65–80% in the WT6 cells when no doxycycline was present ( Figure 7D ) . The induction of RBM47 expression did not have a significant effect on the efficiency on the 5′-targeting shRNA-miRs ( Figure 7E ) . In contrast , RBM47 inhibited the capability of the 3′-targeting shRNA-miRs to keep the DKK1 mRNA level down ( Figure 7E ) . Similar observations were made with the WT10 cells ( Figure 7F , G ) . Argonaute HITS-CLIP in the MDA231-BrM2 cells ( CBM et al . , unpublished data ) indicates robust binding to the DKK1 3′UTR , supporting the regulatory role of this region . We conclude that the effects of RBM47 on DKK1 mRNA levels may be due to the ability of RBM47 to protect this mRNA from destabilizing factors , possibly through direct interaction with the DKK1 mRNA . DKK1 is an inhibitor of Wnt signaling , a pathway with a well-established role in regulating stem cell characteristics in both normal and malignant cells ( Clevers and Nusse , 2012 ) . Indeed , DKK1 as well as other Wnt antagonists have been shown to inhibit breast cancer progression ( Bafico et al . , 2004; Cowling et al . , 2007; Mikheev et al . , 2008; Matsuda et al . , 2009 ) . The fact that RBM47 was able to increase DKK1 secretion therefore suggested that RBM47 may also inhibit Wnt signaling and consequently reduce the tumorigenic fitness of metastatic breast cancer cells . We first tested the effects of RBM47 on cancer cell Wnt responsiveness by treating WT6 cells with recombinant Wnt3A and subsequently measuring the expression of AXIN2 , a common TCF/β-catenin target gene and a general marker of Wnt activity ( Lustig et al . , 2002; Clevers and Nusse , 2012 ) . Doxycycline-induced expression of RBM47 in WT6 cells led to a dampened Wnt3A-dependent AXIN2 induction ( Figure 8A ) . This inhibition of AXIN2 expression was at least partially dependent on DKK1 ( Figure 8B ) . In agreement with these results , human breast cancers with low RBM47 expression had in general higher levels of Wnt transcriptomic activity when compared to tumors with high RBM47 expression in the TCGA cohort ( Figure 8C ) . 10 . 7554/eLife . 02734 . 017Figure 8 . RBM47 suppresses tumor progression via Wnt inhibition . ( A ) AXIN2 mRNA levels determined by quantitative real-time RT-PCR in WT6 cells treated with recombinant WNT3A in the presence of increasing concentrations of doxycycline . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( B ) Normalized level of AXIN2 mRNA inhibition as determined by quantitative real-time RT-PCR in WT6 cells transduced wither with control vector or shRNAmiR constructs targeting the first exon of DKK1 . The cells were treated with recombinant WNT3A in the presence of increasing concentrations of doxycycline . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( C ) Wnt pathway activity assessed in the TCGA cohort of primary breast tumors , grouped by RBM47 expression tertiles ( L , low; M , medium; H , high ) . Wnt signature value calculated as sum of z-scores for a curated set of 16 Wnt target genes in breast cancer . p-value determined by linear regression analysis . ( D ) Mammary tumor re-initiation assay . 5 , 000 WT6 cells implanted orthotopically in mice . RBM47 induced by doxycycline feed . Tumor growth detected by bioluminescence . p-value determined by the log-rank test . N = 20 tumors for each group . ( E ) Quantification of mammary tumor burden by in vivo bioluminescence imaging on day 33 of the experiment shown in ( D ) . Data normalized to day 0 for each tumor . p-value calculated by the Wilcoxon rank-sum test . ( F ) Quantification of mammary tumor burden by in vivo bioluminescence imaging in mice inoculated with 231-Brm2 cells transduced with either control ( pGIPZ ) or DKK1-targeting shRNAmiR constructs . Data normalized to day 0 for each tumor . p-value calculated by one-tailed Wilcoxon rank-sum test . ( G ) Quantification of ex vivo brain bioluminescence shown for mice inoculated intracardiacly with WT6 cells transduced with either control ( pGIPZ ) or DKK1-targeting shRNAmiR constructs in the presence of RBM47 , that is doxycycline in diet . One out of 9 ( 11% ) control mice developed robust brain metastasis whereas 8/17 ( 47% ) mice in the DKK1 knockdown groups showed metastasis . p-value calculated by one-tailed Student's t test . ( H ) Representative images of coronal brain sections analyzed for GFP immunofluorescence from the experiment shown in panel ( G ) . Lesion contours are marked in white . Arrowheads indicate the lesions shown in higher magnification on the right; a similar brain area is shown for the control group . Scale bar 500 μm . ( I ) At the global level , RBM47 binds to ∼2500 target mRNAs . However , the abundance or alternative splicing of only a fraction of these change depending on RBM47 status . The target genes represent molecules from various signaling pathways . The net effect of growth promoting and inhibiting alterations determine whether RBM47 loss is beneficial for a particular cancer clone . ( J ) At the target mRNA level , the effects of RBM47 are dependent on the presence of other factors that modulate mRNA processing . Hence , the phenotype of RBM47 loss depends on the intracellular molecular milieu on a per transcript basis . This is exemplified by the interaction of RBM47 with DKK1 mRNA . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 01710 . 7554/eLife . 02734 . 018Figure 8—figure supplement 1 . DKK1 as a mediator of RBM47-dependent tumor suppression . ( A ) Quantification of mammary tumor burden by in vivo bioluminescence imaging on day 36 after 5 , 000 WT10 cells were implanted orthotopically in mice . RBM47 induced by doxycycline feed . Data normalized to day 0 for each tumor . p-value calculated by the Wilcoxon rank-sum test . ( B ) DKK1 mRNA measured by quantitative real-time RT-PCR in 231-BrM2 cells transduced with either control ( pGIPZ ) or DKK1-targeting shRNAmiR constructs . Error bars represent 95% confidence intervals obtained from multiple PCR reactions . ( C ) Secreted DKK1 protein levels determined by ELISA in WT6 cells transduced with either control ( pGIPZ ) or DKK1-targeting shRNAmiR constructs , with or without doxycycline treatment . DOI: http://dx . doi . org/10 . 7554/eLife . 02734 . 018 We then tested the possibility that RBM47 would suppress tumorigenesis by implanting low numbers of WT6 cells at the orthotopic site . The induction of RBM47 by doxycycline was able to significantly delay the emergence of mammary tumors ( Figure 8D ) , and the tumors that formed were smaller in size ( Figure 8E ) . This observation was confirmed in WT10 cells ( Figure 8—figure supplement 1A ) . In line with this , inhibition of DKK1 expression in the MDA231-BrM2 cells promoted tumor formation in the orthotopic site ( Figure 8F , Figure 8—figure supplement 1B ) . Finally , we used the two exon 1 DKK1 shRNAmiR constructs ( Figure 7C ) to reduce DKK1 levels in the WT6 cells ( Figure 8—figure supplement 1C ) and tested how this affected the brain metastatic ability of these cells in the presence of RBM47 . Only 1/9 mice ( 16% ) injected with control cells developed brain metastasis , whereas 8/17 mice ( 47% ) inoculated with DKK1 RNAi construct transduced cells developed metastasis ( Figure 8G , H ) . Taken together , these observations suggested that RBM47-dependent suppression of tumor progression was partially mediated by its ability to increase the production of the Wnt antagonist DKK1 , a secreted protein that can inhibit tumor phenotypes in metastatic cancer cells .
Cancer genomes contain numerous genes with low-frequency mutations of unknown functional significance . We have studied one such gene , the previously uncharacterized RBM47 , and demonstrate that it has tumor suppressive functions in breast cancer . RBM47 acts as a multifunctional RBP modulating alternative splicing and the abundance of several mRNAs , which can lead to inhibition of cancer progression ( Figure 8I , J ) . These results highlight the significance of infrequent mutations in cancer , the importance of integrated experimental approaches to identify such functionally relevant mutations , and the role of broadly targeted mRNA chaperones as determinants of cancer progression . RBM47 contains three classical RNA recognition motifs ( RRM domains ) . The closest homologs of RBM47 are Apobec1 complementation factor ( A1CF ) and hnRNP-Q , which regulate RNA editing ( Mehta et al . , 2000 ) and splicing and transcript stability , respectively ( Chen et al . , 2008; Weidensdorfer et al . , 2009 ) . In general , RBPs bind to and influence the function and fate of both pre-mRNAs and mRNAs ( Dreyfuss et al . , 2002 ) . They can operate in large multiprotein complexes that dynamically regulate all the steps of mRNA biogenesis , nuclear export , stability and translation . Individual subunits of these complexes can therefore have diverse phenotypic roles depending on the exact protein complex they are in Chaudhury et al . ( 2010 ) . Our observations on RBM47 are in line with these known general principles of RBP function . Our data demonstrate widespread and reproducible RBM47 binding to target mRNAs predominantly in introns and 3′UTRs , with the most robust binding occurring in 3′UTRs . Recent RNA-compete studies have proposed a binding motif for RBM47 in vitro ( Ray et al . , 2013 ) . Our in vivo HITS-CLIP data does not suggest a clear nucleotide binding specificity for exogenous Flag-tagged RBM47 , although some preference was observed for polyU stretches around CIMS sites . It has been shown that the presence of a canonical motif is neither necessary nor sufficient to predict HITS-CLIP binding sites of FUS in both mouse and human brain ( Lagier-Tourenne et al . , 2012 ) , while specific sites suggested by in vitro RNA selection experiments are not enriched in HITS-CLIP derived FMRP binding sites in vivo ( Darnell et al . , 2011 ) . This would suggest that other factors such as RNA accessibility , secondary structure or protein–protein interactions may modulate RBM47 target choice ( Li et al . , 2014 ) . Further work is therefore needed for a comprehensive understanding of the determinants of RBM47-mRNA interactions . We find that RBM47 binds robustly to ∼2500 gene transcripts in human breast cancer cells , with only a subset showing steady state level change or alternative splicing upon RBM47 reintroduction . Given the stringent criteria used to define RBM47-bound and regulated targets and the generally low level of intronic RNA in a cell , it is likely that this subset is an underestimation of the number of RBM47-regulated transcripts , and does not take into account the potential for regulatory events , such as re-localization , that may not alter steady state transcript levels . The complex interplay of RBPs in agonistic and antagonistic modulation of mRNA is becoming increasingly apparent . For example , the RRM-domain containing protein HuR modulates the destabilizing effects of miRNAs ( Bhattacharyya et al . , 2006; Kim et al . , 2009; Young et al . , 2012 ) and AUF1 ( Zou et al . , 2010 ) on common target transcripts . The data presented here suggest that target-specific RBM47 regulation may arise through modulation of accessibility of other factors to a common mRNA transcript . RBM47 binds and regulates transcripts that encode for proteins of several different biological functions . The effects of reduced RBM47 activity on cancer cell fitness , determined by the sum phenotypic output of all regulated target transcripts , may therefore vary depending on the context . Such pleiotropic effects could target multiple steps of cancer progression . Indeed , even though RBM47 loss was associated with metastatic cancer clones in our model systems , evidence for selection against RBM47 was detected already in primary breast cancers . One of the most highly bound RBM47 mRNA targets , the secreted Wnt inhibitor DKK1 , is stabilized by RBM47 and partially mediates RBM47 tumor suppressive function . Interestingly , rbm47 knockdown in zebra fish embryos leads to a headless phenotype mediated via up-regulation of the wnt8a pathway ( Guan et al . , 2013 ) . In addition to DKK1 , our analysis identified a number of potential mediators of RBM47 effects for future studies . From a general perspective , the present findings illuminate two concepts . First , we show that low-frequency cancer mutations can give rise to tumorigenic phenotypes . Our work highlights the power of orthogonal approaches for the analysis of cancer genome resequencing data . Second , we show that loss of a broadly targeted and multifunctional RBP can increase the fitness of certain cancer cell clones in support of metastasis . This complements previous findings of RNA-binding proteins as mediators of oncogenic phenotypes ( Karni et al . , 2007; Richard et al . , 2008; Sommer et al . , 2011; Das et al . , 2012; Wang et al . , 2013 ) . Deregulation of RNA-binding proteins is thus emerging as a prominent source of complex transcriptomic diversity that can serve as a platform for the selection of metastatic traits during tumor progression .
The metastatic breast cancer cell lines have been previously described ( Kang et al . , 2003; Minn et al . , 2005; Bos et al . , 2009 ) . SKBR3 , ZR-75-30 and HCC1954 cells were obtained from ATCC ( Manassas , VA ) . For retrovirus and lentivirus production , GPG29 and 293T cells , respectively , were utilized . All cell lines were maintained under standard tissue culture conditions . Single cell-derived clones were isolated utilizing fluorescence-activated cell sorting from genetically engineered 231-BrM2 ( WT10 , MUT3 ) and CN34-BrM2 ( WT6 ) cells . For RBM47 restoration , RBM47 was cloned into the pBABE-puro retroviral expression vector . The RBM47I281fs was generated by site-directed mutagenesis . Virus was generated in the GPG29 packaging cells . For the generation of doxycycline-inducible expression constructs , both wild type and mutant FLAG-RBM47 were cloned into the pRetroX-Tight-Pur expression system ( Clontech , Mountain View , CA ) . For RNAi-mediated gene silencing , RBM47 pGIPZ shRNA constructs ( clones V2LHS_176331 and V3LHS_393928 , respectively ) were obtained from Open Biosystems ( Lafayette , CO ) . The DKK1 shRNAmiR constructs were designed based on the rules described by Fellmann et al . ( 2011 ) and cloned into the pGIPZ vector as described ( Dow et al . , 2012 ) using the following oligonucleotide templates:DKK1miRm: TGCTGTTGACAGTGAGCGACGGGTCTTTGTCGCGATGGTATAGTGAAGCCACAGATGTATACCATCGCGACAAAGACCCGGTGCCTACTGCCTCGGADKK1miRn: TGCTGTTGACAGTGAGCGACACCTTGAACTCGGTTCTCAATAGTGAAGCCACAGATGTATTGAGAACCGAGTTCAAGGTGGTGCCTACTGCCTCGGADKK1miRo: TGCTGTTGACAGTGAGCGCCAACTCAATCCTAAGGATATATAGTGAAGCCACAGATGTATATATCCTTAGGATTGAGTTGATGCCTACTGCCTCGGADKK1miRp: TGCTGTTGACAGTGAGCGACAGTAAATTACTGTATTGTAATAGTGAAGCCACAGATGTATTACAATACAGTAATTTACTGCTGCCTACTGCCTCGGADKK1miRq: TGCTGTTGACAGTGAGCGAAACGGAAGTGTGATATGTTTATAGTGAAGCCACAGATGTATAAACATATCACACTTCCGTTCTGCCTACTGCCTCGGA The pGIPZ empty vector was used as a control . All animal experiments were performed in accordance with a protocol approved by MSKCC Institutional Animal Care and Use Committee . Lung metastasis assays were conducted in 5–7 week old female NOD/SCID mice . Brain metastasis and mammary tumor assays were carried out using 5–7 week old female athymic nude mice . In vivo bioluminescence imaging was performed using the IVIS Spectrum Xenogen machine ( Caliper Life Sciences , Hopkinton , MA ) . For orthotopic mammary tumor assays , cells were mixed with Matrigel . Injections were confirmed and tumor growth was followed by bioluminescent imaging . Statistical significance of tumor and metastasis free survival was assessed by the log-rank test . Differences in raw and normalized bioluminescence signal was assessed by the Student's t test and Wilcoxon rank-sum test , respectively . Brain images were acquired with a Leica SP5 up-right confocal microscope and Zeiss AxioVert 200 M using 20X and 5X objectives . Image analysis was performed with Metamorph and ImageJ softwares . Total RNA was extracted using PrepEase RNA spin kit ( USB , Cleveland , OH ) . We used Transcriptor First Strand cDNA Synthesis Kit ( Roche , Indianapolis , IN ) for cDNA synthesis . Quantitative PCR was performed using predesigned Taqman gene expression assays ( Life Technologies , Carlsbad , CA ) and the 7900HT or ViiA 7 real-time PCR systems ( Applied Biosystems/Life Technologies ) . TBP was used as a housekeeping control gene . mRNA stability was assessed by performing quantitative PCR after actinomycin D treatment . For immunoblotting , antibodies recognizing RBM47 ( HPA006347; Sigma , St . Luis , MO ) , α-tubulin ( 11H10; Cell Signaling , Danvers , MA ) and ACTB ( Sigma ) were utilized . Secondary antibodies were HRP ( Pierce , Rockford , IL ) or fluorescence ( LiCor , Lincoln , NE ) conjugated . Immunostaining for RBM47 ( HPA006347; Sigma ) was performed according to standard protocols in the MSKCC Molecular Cytology Core Facility on paraffin embedded tissue blocks . Secreted protein was detected by ELISA ( R&D , Minneapolis , MN ) . 231BrM2 tet-on FLAG-RBM47 cells were treated with 1 ng/ml doxycycline for 3 days before 254 nm UV crosslinking at 400 mJ/cm2 on a bed of ice ( Stratalinker2400; Stratagene , La Jolla , CA ) . Samples were processed for HITS-CLIP as previously described ( Licatalosi et al . , 2008 ) using an anti-Flag antibody ( F3165; Sigma ) , and omitting 3′ linker ligation in favor of direct labeling of protein-bound RNA with 32P-γ-ATP . Non-crosslinked cells were used as a negative control , with IPed Flag-RBM47 protein detected using a second anti-Flag antibody ( F7425; Sigma ) . Purified RBM47-bound RNA fragments were polyA tailed ( E-PAP; NEB ) , and reverse transcribed ( Superscript III; Invitrogen , Carlsbad , CA ) in the presence of Br-dUTP ( Sigma ) . Unique polydT-NV RT-primers were used per replicate , containing Solexa sequences separated by an abasic furan ( that serves as an ApeI cut site ) , a 6 nt degenerate region and a 6 nt index sequence to allow for multiplexing during sequencing . RT Primer 1pGCACTGTTN6GATCGTCGGACTGTAGAACTCT/idSp/CAAGCAGAAGACGGCATACGAT20VNRT Primer 2pGCGAAACTN6GATCGTCGGACTGTAGAACTCT/idSp/CAAGCAGAAGACGGCATACGAT20VN BrdU-cDNA was stringently purified by IP ( sc-32323; Santa Cruz , Dallas , TX ) using ProteinG dynabeads ( Invitrogen ) , eluted from the beads via BrdU competitive elution ( Sigma ) , and re-immunoprecipitated . cDNA was circularized on bead ( CircLigase ssDNA Ligase II , Epicentre , Madison , WI ) , washed and digested with ApeI ( NEB , Ipswich , MA ) to relinearize . cDNAs were eluted from the beads by heating to 98C in Phusion HF Buffer ( NEB ) , then PCR amplified using Phusion DNA polymerase ( NEB ) and SYBR Green I ( Invitrogen ) in an iQ5 real-time PCR machine in order to monitor amplification , with the samples being removed when the RFU signal reached ∼1000 . P5—aatgatacggcgaccaccgacaggttcagagttctacagtccgacg P3—caagcagaagacggcata PCR products were purified using MinElute columns ( Qiagen , Valencia , CA ) as per manufacturer's instructions and quantified using Quant-It ( Invitrogen ) . cDNA was multiplexed and sequenced using Illumina Hi-Seq ( small RNA sequencing primer—cgacaggttcagagttctacagtccgacgatc ) . All data analysis was done using the Galaxy platform ( Hillman-Jackson et al . , 2012 ) , as previously described ( Licatalosi et al . , 2008; Chi et al . , 2009; Darnell et al . , 2011; Zhang and Darnell , 2011 ) . RT-PCR validiation was carried out using total RNA from MDA231-BrM2 ( Control ) and WT10 cells ( iScript , Bio-Rad , Accuprime Pfx Supermix 1 , Life Technologies ) as previously described ( Licatalosi et al . , 2012 ) . In all cases lanes 1–3 contain an equal mixture of control and WT10 cell cDNA amplified at n-1 , n and n+1 cycles , lane 4 contains an absence of reverse transcriptase control ( −RT ) , with all other lanes corresponding to replicate samples of the indicated cell type amplified to n cycles . IR and ΔI calculated using ImageJ ( Schneider et al . , 2012 ) . All analyses were conducted using R . Microarray data from human untreated tumor data sets ( GSE2603 [Minn et al . , 2005] and GSE2034 [Wang et al . , 2005] ) were preprocessed as described ( Zhang et al . , 2009 ) . For the RNA-seq TCGA data set , normalized mRNA z-scores were downloaded from the TCGA cBio portal ( Cerami et al . , 2012 ) . The microarray data from metastatic cell lines ( Minn et al . , 2005; Bos et al . , 2009 ) were processed with GCRMA together with updated probe set definitions using R packages affy , gcrma and hs133ahsentrezgcdf ( version 10 ) . Unsupervised hierarchical clustering was performed using the function heatmap . 2 with Pearson's correlation coefficient as the similarity metric . For survival analysis , a Cox proportional hazards model was utilized as implemented in the coxph function in the R-package survival . For RNA-seq analysis of the cell lines , raw paired-end sequencing data were mapped to human genome ( hg19 build ) with STAR2 . 3 . 0 ( Dobin et al . , 2013 ) using standard options . Reads mapped to each transcript were counted by HTSeq v0 . 5 . 4 ( Anders and Huber , 2010 ) with default settings . The read count table was normalized to library size by DESeq ( Anders and Huber , 2010 ) . Correlation with RBM47 expression was assessed by Pearson's correlation coefficient utilizing the R functions cor and cor . test . Wnt pathway activity in clinical tumors was assessed utilizing a curated list of Wnt target genes in breast cancer ( Matsuda et al . , 2009 ) and calculating sums of z-scores for each tumor . RNA-seq and HITS-CLIP data have been deposited to the Gene Expression Omnibus under the accession numbers GSE53779 and GSE58381 . | Tumors form when mistakes in the genes of a single cell allow it to multiply uncontrollably . Sometimes further mutations in genes allow the cancerous cells to escape from the tumor , enter the bloodstream and start a second cancer elsewhere in the body . However , many of the genetic changes behind this process , which is called metastasis , are poorly understood—especially those changes in genes that occur rarely , but can still help the cancer to spread . Vanharanta , Marney et al . have looked at data on which genes are switched ‘on’ or ‘off’ in metastatic breast cancer cells . A gene called RBM47 was often switched off in these cells , and patients with a low level of RBM47 tended to have a poor clinical outcome . To test the function of the gene , Vanharanta , Marney et al . switched on RBM47 in cancer cells that had spread from the breast to either the lungs or the brain , and then injected these cells into mice . Few of these cells were able to invade lung and brain tissues in the mice . However , switching off the RBM47 gene in breast cancer cells had the opposite effect; these cells invaded the lungs of mice more efficiently . RBM47 encodes a protein that sticks to molecules of messenger RNA: molecules that transport the instructions encoded in DNA to the machinery that builds proteins . Vanharanta , Marney et al . found that the wild-type RBM47 protein increased the levels of 102 different messenger RNA molecules , but decreased the levels of another 92 . Further experiments showed that RBM47 also slows the rate at which messenger RNA molecules are broken down inside cells: this results in the accumulation of proteins that slow down the growth of tumors . Without RBM47 , tumor growth is unleashed . Further work is needed to test if increasing RBM47 activity could be used as a new treatment for some types of cancer . | [
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] | 2014 | Loss of the multifunctional RNA-binding protein RBM47 as a source of selectable metastatic traits in breast cancer |
Aneuploidy causes birth defects and miscarriages , occurs in nearly all cancers and is a hallmark of aging . Individual aneuploid cells can be eliminated from developing tissues by unknown mechanisms . Cells with ribosomal protein ( Rp ) gene mutations are also eliminated , by cell competition with normal cells . Because Rp genes are spread across the genome , their copy number is a potential marker for aneuploidy . We found that elimination of imaginal disc cells with irradiation-induced genome damage often required cell competition genes . Segmentally aneuploid cells derived from targeted chromosome excisions were eliminated by the RpS12-Xrp1 cell competition pathway if they differed from neighboring cells in Rp gene dose , whereas cells with normal doses of the Rp and eIF2γ genes survived and differentiated adult tissues . Thus , cell competition , triggered by differences in Rp gene dose between cells , is a significant mechanism for the elimination of aneuploid somatic cells , likely to contribute to preventing cancer .
Aneuploidy ( gain or loss of whole chromosomes resulting in an abnormal karyotype ) is a hallmark of spontaneous abortions and birth defects and observed in virtually every human tumor ( Hassold and Hunt , 2001; Hanahan and Weinberg , 2011; López-Otín et al . , 2013 ) . It was suggested over 100 years ago that aneuploidy contributes to cancer development ( Boveri , 1914 ) . Aneuploidy can change the copy number of important oncogenes and tumor suppressors , cause stress due to gene expression imbalance , and promote further genetic instability ( Naylor and van Deursen , 2016; Rutledge and Cimini , 2016; Chunduri and Storchová , 2019; Ben-David and Amon , 2020; Zhu et al . , 2018 ) . Mouse models of chromosome instability that result in aneuploidy are oncogenic ( Foijer et al . , 2008; Baker et al . , 2009; Mukherjee et al . , 2014 ) . Drosophila cells with chromosome instability undergo a p53-independent death , but can form tumors if their apoptosis is prevented ( Dekanty et al . , 2012; Gerlach and Herranz , 2020; Morais da Silva et al . , 2013 ) . Because aneuploidy is thought to be detrimental to normal cells , aneuploid cells arising sporadically in vivo should , as a rule , grow poorly ( Sheltzer and Amon , 2011 ) . Studies of yeast carrying extra chromosomes reveal a stress response in these cells , thought to result from the cumulative mismatch in levels of many proteins that interact in the cell , which inhibits growth ( Torres et al . , 2007; Zhu et al . , 2018; Terhorst et al . , 2020 ) . Increasing evidence points to the capacity of normal tissues to recognize and eliminate aneuploid cells ( Hook , 1981; van Echten-Arends et al . , 2011; Bazrgar et al . , 2013; Pfau et al . , 2016; Santaguida et al . , 2017 ) . Array Comparative Genome Hybridization detects mosaic aneuploidy in as many as 60% of normal human embryos , which can nonetheless develop into healthy babies without birth defects or evidence of aneuploid cells , suggesting their elimination ( Greco et al . , 2015 ) . In mice , chimeric embryos can be constructed using both normal diploid cells and cells with a high rate of aneuploidy due to treatment with reversine , an inhibitor of the spindle assembly checkpoint . The reversine-treated cells are actively eliminated from the chimeric embryos , which can develop into morphologically normal adult mice from which reversine-treated cells have been eliminated ( Bolton et al . , 2016 ) . Other observations point to the loss of aneuploid cells in other biological processes . For example , the cortex of normal mouse embryos contains as many as 30% aneuploid cells , but only ~1% are detected by 4 months post-partum , suggesting selective loss of the aneuploid fraction ( Andriani et al . , 2016 ) . The mechanisms of recognition and removal of aneuploid cells are still poorly understood . In mouse tissues , cells with complex karyotypes may be recognized by the immune system ( Santaguida et al . , 2017 ) . In Drosophila , clones of segmentally aneuploid cells ( cells with loss or gain of chromosome segments ) can survive development and differentiate in the adult abdomen , but their representation decreases as more genetic material is lost , whereas cells carrying extra genetic material are less affected ( Ripoll , 1980 ) . Clonal loss of heterozygosity is also tolerated in the abdomens of DNA repair pathway mutants , and studies with genetic markers indicate that this frequently represents loss of substantial chromosome segments ( Baker et al . , 1978 ) . The Drosophila adult abdomen derives from larval histoblasts . In the head and thorax , which develop instead from the larval imaginal discs , there is evidence that aneuploid cells undergo apoptosis . DNA damage following ionizing irradiation rapidly leads to apoptosis but is followed by a smaller amount of delayed apoptosis that is independent of p53 and Chk2 and therefore unlikely to reflect unrepaired DNA damage ( Brodsky et al . , 2004; Wichmann et al . , 2006 ) . A similar biphasic response is seen after mitotic breakage of dicentric chromosomes , and in this case , the delayed , p53-independent cell death only occurs in genotypes likely to lead to aneuploid products ( Titen and Golic , 2008 ) . Accordingly , it is suggested that post-irradiation apoptosis independent from p53 also represents removal of aneuploid cells that arise following DNA repair , and that ‘cell competition’ may provide the p53-independent mechanism ( McNamee and Brodsky , 2009 ) . The term ‘cell competition’ was originally coined to describe the elimination of Drosophila cells heterozygous for mutant alleles of ribosomal protein genes ( Rp genes ) ( Morata and Ripoll , 1975 ) . Most Rp’s are essential , even to the individual cell , so that homozygosity for Rp- mutations is rapidly lethal , whereas Rp+/- heterozygotes are viable and fertile , although slow growing with minor morphological defects such as thin adult bristles ( Marygold et al . , 2007 ) . By contrast to their whole animal viability , individual Rp+/- heterozygous cells or clones are actively eliminated from mosaic Drosophila tissues ( Morata and Ripoll , 1975; Simpson , 1979 ) . This involves apoptosis specific to Rp+/- heterozygous cells near to Rp+/+ cells ( Morata and Ripoll , 1975; Simpson , 1979; Moreno et al . , 2002; Li and Baker , 2007 ) . The defining feature of cell competition is therefore the elimination of cells based on their difference from other neighboring cells rather than based on their intrinsic properties ( Morata and Ripoll , 1975; Baker , 2020 ) . The 80 eukaryotic Rp’s are mostly encoded by single copy genes transcribed by RNA polymerase II , and are dispersed throughout the genome in both humans and in Drosophila ( Uechi et al . , 2001; Marygold et al . , 2007 ) . Accordingly , aneuploidy and other large-scale genetic changes will usually affect Rp gene dose . Since Rp proteins are required stoichiometrically for ribosome assembly , which generally stalls when any one Rp is limiting , imbalanced Rp gene dose can perturb ribosome biogenesis ( de la Cruz et al . , 2015 ) . This provides an almost perfectly suited mechanism to serve as an indicator of unbalanced chromosome content ( McNamee and Brodsky , 2009 ) . Cell competition may thus have evolved to recognize and remove cells with large-scale genetic changes such as aneuploidy , recognized on the basis of their mis-matched Ribosomal protein ( Rp ) gene complements . In this view , cells heterozygous for point mutations in Rp genes are eliminated because they mimic larger genetic changes . A stress response pathway that is activated by Rp mutations in Drosophila has recently been described , and is required for Rp point mutated cells to undergo cell competition ( Baillon et al . , 2018; Kale et al . , 2018; Lee et al . , 2018; Ji et al . , 2019; Blanco et al . , 2020 ) . In Rp+/-genotypes , RpS12 , an essential , eukaryote-specific component of the ribosomal Small Subunit , is required to activate expression of Xrp1 , a rapidly evolving AT-hook , bZip domain transcription factor ( Lee et al . , 2018; Ji et al . , 2019; Blanco et al . , 2020 ) . Although rpS12 null mutations are homozygously lethal , a particular point mutation , rpS12G97D , appears defective only for the cell competition aspect of RpS12 function . Homozygotes for the rpS12G97D mutation are viable , showing only minor effects on morphology and longevity , yet rpS12G97D prevents elimination of Rp+/- cells by cell competition ( Kale et al . , 2018; Ji et al . , 2019 ) . A key target of RpS12 appears to be the putative transcription factor Xrp1 , because Xrp1 protein is barely detected in wild type cells but significantly elevated in Rp+/- wing discs . Xrp1 controls most of the phenotype of Rp+/- cells , including their reduced translation ( Lee et al . , 2018; Ji et al . , 2019; Blanco et al . , 2020 ) . Xrp1 mutants have negligible effect in wild-type backgrounds , and normal lifespan ( Baillon et al . , 2018; Lee et al . , 2018; Mallik et al . , 2018 ) . In response to RpS12 and Xrp1 activities , Rp+/- cells both grow more slowly than surrounding Rp+/+ cells and are also actively eliminated by apoptosis that occurs where Rp+/- cells and Rp+/+ cells meet ( Baillon et al . , 2018; Lee et al . , 2018; Ji et al . , 2019 ) . Why apoptosis occurs at these interfaces in particular is not certain . A role for innate immune pathway components has been proposed ( Meyer et al . , 2014 ) , and different levels of oxidative stress response in Rp+/- cells and Rp+/+ cells ( Kucinski et al . , 2017 ) or local induction of autophagy have also been suggested ( Nagata et al . , 2019 ) . Here , we test the hypothesis that cell competition specifically removes cells with aneuploidies that result in loss of Rp genes in Drosophila imaginal discs . We show that , as hypothesized previously ( McNamee and Brodsky , 2009 ) , most of the p53-independent cell death that follows irradiation resembles cell competition genetically . We then use a targeted recombination method to investigate the fate of somatic cells that acquire large-scale genetic changes directly , and confirm that it is cell competition that removes cells heterozygous for large deletions , when they include ribosomal protein genes . By contrast , when ribosomal protein genes are unaffected , or when the cell competition pathway is inactivated genetically , cells carrying large deletions remain largely un-competed , proliferate , and contribute to adult structures . Thus , cell competition is a highly significant mechanism for elimination of aneuploid somatic cells . We discuss how cell competition to remove aneuploid cells could play a role preventing tumor development in humans .
The idea that cell competition removes aneuploid cells was suggested by studies of p53-independent cell death following chromosome breakage or ionizing irradiation ( Titen and Golic , 2008; McNamee and Brodsky , 2009 ) . Importantly , in Drosophila cell competition of Rp+/- cells does not depend on p53 ( Kale et al . , 2015 ) . If the model was correct , it would be expected that the p53-independent apoptosis that follows irradiation would depend on the genes recently discovered to be required for cell competition . We first confirmed the previous findings ( Wichmann et al . , 2006; McNamee and Brodsky , 2009 ) . Irradiating third-instar larvae resulted in rapid induction of cell death in the wing imaginal disc that was largely p53-dependent ( Figure 1A , C , D , E , G , H ) . The p53-dependent cell death , attributable to the DNA-damage response , was not much affected by the rpS12G97D mutation that interferes with cell competition ( Figure 1B , F , H ) . While total cell death tailed off with time , p53-independent cell death increased around 18–24 hr post-irradiation , as reported previously ( Wichmann et al . , 2006; McNamee and Brodsky , 2009; Figure 1I–L ) . As expected for cell competition , 24 hr after irradiation , p53-independent cell death was reduced by 66% in the rpS12G97D p53 double mutant compared to the p53 mutant alone ( Figure 1K–M ) . To exclude the possibility that other genetic background differences were responsible , rpS12 function was restored to the rpS12G97D p53 double mutant strain using a P element transgene encoding the wild-type rpS12 gene , and this restored p53-independent cell death ( Figure 1M ) . Notably , a genomic transgene encoding the rpS12G97D cell competition-defective allele did not , leading to 86% less p53-independent cell death than the wild-type rpS12 transgene ( Figure 1M ) . Thus , 66–86% of the p53-independent apoptosis was RpS12-dependent and might represent cell competition . Nuclear Xrp1 protein , which is only at low levels in control wing imaginal discs , was detected in scattered cells throughout p53 mutant wing discs 24 hr after irradiation , similar to the distribution of dying cells ( Figure 1N ) . Strikingly , most ( 58% ) of this Xrp1 expression was rpS12-dependent , similar to the rpS12-dependency of p53-independent cell death itself ( Figure 1O–P ) . This is also as expected for cell competition , which is mediated through the induction of Xrp1 expression ( Baillon et al . , 2018; Lee et al . , 2018; Ji et al . , 2019 ) . Radiation-damaged cells that have reduced Rp gene dose would be expected to differentiate short , thin bristles in adults , as reported previously in studies of DNA repair mutants ( Baker et al . , 1978 ) , in studies of ionizing radiation ( McNamee and Brodsky , 2009 ) , and when aneuploidy is induced by mutation of spindle assembly checkpoint genes ( Dekanty et al . , 2012 ) . We found Rp+/--like thoracic bristles at a frequency of ~1/300 following irradiation of either wild type or p53 mutant larvae ( Figure 1Q , R ) . Their frequency increased ~three- to fourfold in rpS12G97D mutants or p53 rpS12G97D double mutants ( Figure 1Q ) . In the absence of irradiation , only 2 Rp+/--like bristles were observed from 500 unirradiated rpS12G97D mutant flies . Since 16 macrochaetae were examined on each fly thorax , this indicated a frequency of ~1/8000 macrochaetae progenitor cells was Rp+/--like . We found none in 1000 unirradiated wild type flies ( 16 , 000 macrochaetae examined ) . While the actual nature of radiation-induced genetic changes in cells forming Minute-like bristles is not directly demonstrated , previous studies of DNA-repair mutants including mei-41 , the Drosophila ATR homolog , demonstrated using multiply-marked chromosomes that the majority of Minute-like bristles reflect loss of heterozygosity for large , contiguous chromosome regions ( Baker et al . , 1978 ) . In a small-scale experiment to compare γ-irradiation to the DNA repair defects studied previously ( Baker et al . , 1978 ) , y+/- rpS12G97D larvae were irradiated ( 1000 Rad ) and 2178 adult flies examined for phenotypically y thoracic bristles representing cells where the y+ allele had been mutated or deleted . Six times more y bristles were recovered in females than in males . Since the y locus is X-linked , this is most easily explained if y bristles generally result from deletions including essential genes linked to y that could not survive in males . In females , 37 . 5% of y bristles were also phenotypically Minute , consistent with loss of chromosome regions extending at least from the y locus to the RpL36 gene 0 . 3 Mb more centromere-proximal that is the nearest Rp locus ( Figure 1S ) . Although this study was small scale , these findings support the conclusion from DNA repair mutant studies that Minute-like bristles seen following irradiation most commonly reflect loss of substantial chromosome segments including Rp genes , ( Baker et al . , 1978 ) . Accordingly , many Minute-like bristles removed by cell competition genes following irradiation could represent such segmentally aneuploid cells . Taken together , these bristle results are also consistent with the notion that cell competition removes ~3/4 of the cells with genetic changes that encompass dose-sensitive Rp loci that arise after irradiation . Having confirmed that cell competition could potentially be important for removing cells following irradiation , we sought to assess the fate of sporadic cells that lose chromosome regions , using an assay where the cell genotypes would be definitively known and the dependence on competition with normal cells could be established . We used the FLP-FRT site-specific recombination system ( Golic and Lindquist , 1989 ) to achieve this , exploiting large collections of transgenic flies that contain FRT sequence insertions at distinct chromosomal locations ( Thibault et al . , 2004 ) . FLP recombination between pairs of FRT elements linked in cis excises intervening sequences to make defined deletions with a single FRT remaining at the recombination site ( Figure 2A , B ) . Insertion elements of the Exelixis collection exist in several configurations , and FLP-mediated excision from paired FRT strains in the FRT w+ … w+ FRT configuration removes both the associated w+ genes , so that affected cells can be identified in the adult eye by loss of pigmentation ( Figure 2A , B ) . Accordingly , we assembled a collection of genetic strains containing linked pairs of appropriate FRT w+ elements , each flanking a distinct genomic region ( Supplementary file 1 ) . It was first necessary to verify FLP recombination between FRT sequences , and investigate any cell-autonomous effects of the resulting segmental-monosomies . We used the eyFlp transgene , which confers continuous FLP expression to the eye and head primordia during larval life , so that FLP-FRT recombination is expected to approach completion ( Newsome et al . , 2000 ) . Excision should result in white adult eyes , and also reveal any cell-autonomous effect of the resulting heterozygous deletion genotype on growth or differentiation of cells contributing to the adult eye . If the recombined genotype was autonomously cell-lethal , we would expect the developing animal to lack head structures and be unable to emerge from the pupa . If FLP-FRT recombination did not occur ( or occurred inefficiently ) , we would expect adult eyes expressing the parental eye color ( or with only scattered white spots that recombine late in development as cell number increases ) . We identified 17 paired FRT strains that were efficient FLP targets in this assay . These 17 strains were completely or substantially white-eyed in the presence of eyFlp , indicating excision between FRT sites in most or all eye cells . We also identified paired FRT strains that were poor substrates for Flp ( Supplementary file 1; Figure 2C–T ) . These either retained the parental eye color in the presence of eyFlp or produce a salt and pepper pattern of very small clones ( Supplementary file 1; Figure 2U–Z ) . No genotype tested was inviable in the presence of eyFlp , so there was no evidence that haploinsufficiency of any of the chromosome segments tested was incompatible with cell viability or severely impacted head development . Instead , most of the 17 genotypes that recombined differentiated heads of remarkably normal external appearance and morphology ( Figure 2C–T ) . Although we did not measure head size , we noticed three genotypes in which eyes and heads were obviously smaller , consistent with a reduced growth rate of the recombined genotypes . The three small eye regions were 48B2-50C1 , 565F16-59B1 , and 87B8-89E5 ( Figure 2E , H , Q ) . Since the Drosophila genome is divided cytologically into 102 band intervals , each with lettered and numbered subdivisions , in this paper we refer to the chromosome P{XP}d09761 pBAC{WH}f00157 , for example , by the cytological locations of the FRT sequences present in the P element and PiggyBac element insertions , which are at 48B2 and 50C1 respectively in this case ( Supplementary file 1 ) . This nomenclature quickly communicates the genome location under study , whether it overlaps or is distinct from that affected in another strain , and also indicates that in this case the FRT elements are likely separated by ~2% of the genome . The full description of each insertion strain is given in Supplementary file 1 . The three excisions that substantially reduce eye size could delete a copy of one or more haploinsufficient genes important for growth during eye development , but there also could be a dominant effect of the novel junction generated by FLP/FRT recombination . Notably , excision between chromosome bands 87B8-93A2 led to eyes of normal size , although this excises all the sequences between 87B8-89E5 which led to reduced eyes . FLP recombination results in a different junctions in 87B8-89E5 and 87B8-93A2 , however ( Figure 2R ) . Overall , these results showed that 17 segmentally aneuploid genotypes were cell-viable , and able to grow and differentiate in the Drosophila eye , although a minority might have an effect on growth in this tissue . Recombination in these 17 strains each deleted 1 . 4 Mb – 8 . 5 Mb of autosomal DNA , representing 1–6% of the sequenced genome each ( Supplementary file 1 , Figure 3A ) . Together these deletions encompass 25 . 3 Mb of DNA , corresponding to 21 . 1% of the Drosophila euchromatin and 17 . 7% of the sequenced genome . 11 of these 17 genotypes deleted one or more Rp loci ( RpS11 , RpS13 , RpS16 , RpS20 , RpS24 , RpS30 , RpL14 , RpL18 , RpL28 or RpL36A ) , whereas six affected no Rp gene ( Supplementary file 1 , Figure 3A ) . In this paper , we use the symbol Rp to indicate a mutation affecting any of the 66 ribosomal protein genes that are dominant through haploinsufficiency , in distinction to 13 Rp encoded by Drosophila loci where heterozygous mutations have no phenotype . The 17 FRT pair strains that were efficient FLP targets were each exposed to a single burst of FLP expression using the heat-shock FLP transgene , intended to stimulate excision in a fraction of cells during early larval life ( see Materials and methods ) . This led to mosaic eyes where segmentally aneuploid cells and diploid cells would be in competition . Clones of excised cells appeared only after heat-shock , confirming strict FLP-dependence . In contrast to eyFlp recombination , mosaic eyes containing sporadic clones of excised cells were only recovered at high frequencies for four segmental aneuploid genotypes , none of which affected Rp loci ( Figure 3 ) . This confirmed that most segmentally aneuploid genotypes were selected against in mosaic eyes where diploid cells were also present , because all had been shown to be intrinsically viable when competing diploid cells were absent ( Figure 2 ) . Importantly , no deletion that included an Rp locus showed more than minimal survival of sporadic clones induced with hsFlp , suggesting Rp loci could be the determinants of cell competition between segmental aneuploid and wild type cells ( Figure 3B–D ) . To test this in a specific case , 5 . 6 kb of genomic DNA encompassing the RpL28 locus was introduced onto the second chromosome using PhiC31-mediated transgenesis . This transgene proved completely sufficient to rescue the survival of cell clones heterozygous for Df ( 3L ) 63A3-65A9 , a deletion of 3 . 2 Mb including the RpL28 locus ( Figure 4A–C ) . Clones of Df ( 3L ) 63A3-65A9 heterozygous cells barely survived alone , with a median contribution of 2% to the eyes of males and 0% to females ( Figure 4H ) . In the presence of the RpL28+ transgene , however , Df ( 3L ) 63A3-65A9/+ clones survived in 49 out of 50 eyes , with median contributions of 37% of the eye in males and 53% in females , not statistically different from clones heterozygous for Df ( 3L ) 63C1-65A9 , an overlapping deletion of 3 . 0 Mb excluding the RpL28 locus ( Figure 4H ) . Thus in this case , RpL28 gene dose alone determined whether a segmentally aneuploid genotype affecting hundreds of genes would be eliminated in competition with diploid cells . Most of the other 10 segmental aneuploid genotypes that deleted one or more Rp loci contributed to adult eyes to a very significantly lower degree that overlapping segmental aneuploidies that spared Rp loci ( Figure 3 ) . This reflected the fact that , in contrast to genotypes affecting Rp genes , 4/6 segmental aneuploidies that spared Rp loci survived in eye clones at high frequencies and large sizes ( Figure 3A–C ) . The two exceptions were Df ( 3R ) 87B8-89B16/+ and Df ( 3R ) 87B8-89E5/+ , for which little eye tissue was recovered ( Figure 3A–C ) . Although neither affected any Rp gene , both deleted a locus mapping to 88E5-6 encoding the translation factor eIF2γ . Since independent studies in our laboratory already identified a role for the eIF2α protein in cell competition ( Kiparaki , Khan , Chuen and Baker , in preparation ) , we tested the possible role of eIF2γ by restoring eIF2γ diploidy to Df87B8-89B16/+ or Df87B8-89E5/+ cells using a 11 . 5 kb genomic transgene including the eIF2γ locus ( Tschiersch et al . , 1994 ) . This completely rescued the growth and differentiation of these cells to the levels typical for aneuploidies not affecting Rp genes ( Figure 4D–G , I ) . Thus , the locus encoding the translation factor eIF2γ behaved similarly to an Rp gene in triggering competitive elimination of heterozygous cells . If these studies , which tested a significant fraction of the Drosophila genome , are representative , they indicate that the normal diploid complement of Rp loci is important for sporadic segmentally aneuploid cells to evade cell competition , and that few other genes are comparably important . The one example of such another gene uncovered in our analysis encoded eIF2γ , another protein affecting translation . If the segmentally aneuploid cells were competed by virtue of their Rp+/- genotypes , the genetic pathways should be similar . Elimination of Rp+/- point-mutant cells by competition depends on apoptosis and is suppressed by a genetic deletion , Df ( 3L ) H99 , that removes three pro-apoptotic genes reaper ( rpr ) , grim , and head-involution defective ( hid ) ( Moreno et al . , 2002; Kale et al . , 2015 ) . These genes are also required for the p53-independent cell death that follows irradiation , much of which resembles cell competition ( Figure 1; McNamee and Brodsky , 2009 ) . Using the Df ( 3L ) 63A3-65A9 , where clone loss was demonstrably due to heterozygosity for the RpL28 gene ( Figure 4H ) , we found that recovery of Df ( 3L ) 63A3-65A9/+ cell clones was enhanced by genetic suppression of apoptosis in the Df ( 3L ) H99/+ background that experiences loss of heterozygosity for rpr , grim , and hid ( Figure 5A , D ) . A similar rescue was obtained with the Df ( 3L ) 63C1-65F5 , which deletes the RpL18 locus ( Figure 5A , D ) . The recoveries of Df ( 3L ) 63A3-65A9/Df ( 3L ) H99 clones and Df ( 3L ) 63C1-65F5/Df ( 3L ) H99 clones approached that of the overlapping genotype Df ( 3L ) 63C1-65A5/Df ( 3L ) H99 , in which no Rp genes were affected ( Figure 5A , D ) . Recovery was quantitatively inferior to that seen for Df ( 3L ) 63A3-65A9/+ p{RpL28+} clones ( Figure 4H ) , but it is to be noted that the Df ( 3L ) H99/+ background unexpectedly reduced recovery of the control Df ( 3L ) 63C1-65A5/+ cells ( Figure 5A ) . Regardless of whether this reflects the recently described role for basal caspase activity in promoting imaginal disc growth in the wild type ( Shinoda et al . , 2019 ) , or some other genetic interaction , it complicates assessment of whether H99 heterozygosity and RpL28+ transgenesis rescue Df ( 3L ) 63A3-65A9/+ clones equally . We attempted to prevent apoptosis more completely using the genetic background hidWRX1/ Df ( 3L ) H99 in which hid is homozygously affected in addition to heterozygosity for rpr and grim . Although hidWRX1/ Df ( 3L ) H99 adult animals were recovered in the absence of other mutations , they became exceptionally rare in heat-shocked combinations with the dual FRT chromosomes: as a result , insufficient data could be obtained to address this question . In any case , it is clear from our results that pro-apoptotic genes contribute significantly to eliminating segmentally aneuploid cells ( Figure 5A ) . Mutations in the rpS12 and Xrp1 genes are more specific for cell competition than mutations in cell death genes . The rpS12 and Xrp1 mutations prevent the elimination of Rp+/- point mutant cells from mosaics , but otherwise lead to seemingly normal flies , and do not affect other cell death processes ( Kale et al . , 2018; Lee et al . , 2018 ) . These genes should be required if segmental aneuploid cells are competed due to reduced Rp gene dose . Our results strongly support this conclusion in nearly all cases . Because the results are too extensive to present together in a single figure , they are presented in groups according to chromosome region ( Figure 5 , Figure 6 , Figure 7 , Figure 8 ) . Beginning with the Df ( 3L ) 63A3-65A9/+ genotype where clone loss was demonstrably due to heterozygosity for the RpL28 gene ( Figure 4H ) , we found that heterozygosity for an Xrp1 mutation greatly restored contribution of Df ( 3L ) 63A3-65A9/+ clones ( RpL28+/- ) to the eye ( Figure 5B , D ) . Similar results were seen for the Df ( 3L ) 63C1-65F5/+ genotype that is heterozygous for RpL18 , but Xrp1 heterozgosity did not affect recovery of clones of the overlapping Df ( 3L ) 63C1-65A9/+ that is Rp+/+ ( Figure 5B , D ) . The Xrp1 mutation even improved the survival of larger segmental-aneuploidies where combinations of the RpL18 , RpL28 , and RpL14 genes were affected ( Figure 6A–C ) . Because the rpS12G97D mutation that affects cell competition recessively maps to the third chromosome , it was simpler to examine in combination with segmental aneuploidies affecting chromosome 2 . These aneuploid eye clones , which like those discussed above were also recovered in the presence of an Xrp1 mutation , included Df ( 2L ) 26A1-29F8/+ , heterozygous for RpL36A and RpS13 , and Df ( 2R ) 56F16-59B1/+ , heterozygous for RpS16 and RpS24 ( Figure 7A , B , D ) . Clones of the Df ( 2L ) 26A1-28C3/+ or Df ( 2R ) 56F16-58E2/+ cells that did not affect any Rp loci were recovered at high rates , independently of Xrp1 genotype ( Figure 7A , B , D ) . As expected , rpS12G97D homozygosity also led to significant recovery of Df ( 2R ) 56F16-59B1/+ clones that were heterozygous for RpS16 and RpS24 , although to a quantitatively lesser degree than Xrp1 ( Figure 7C , D ) The contribution of Df ( 2R ) 56F16-58E2/+ cells , where no Rp gene is affected , was unaltered by the rpS12G97D mutation ( Figure 7C , D ) . Xrp1 also affected other segmentally aneuploid regions . Xrp1 mutations had a minor but statistically significant effect on clones of Df ( 2R ) 48B2-50C1/+ cells , heterozygous for RpS11 ( Figure 8A , D ) , a genotype that also had an autonomous effect on eye growth ( Figure 2E ) . Xrp1 mutations enhanced the contributions of Df ( 3R ) 87B8-89B16/+ and Df ( 3R ) 87B8-89E5/+ cells that were heterozygous for the eIF2γ gene ( Figure 8B–D ) . Crossing to Xrp1 only slightly improved survival of Df ( 3R ) 87B8-93A2/+ clones that were heterozygous for the eIF2γ , RpS20 and RpS30 genes , and did little to enhance recovery of Df ( 3R ) 89B13-93A2/+ genotypes that were heterozygous for RpS20 and RpS30 alone . As these deficiencies already delete the Xrp1 locus itself ( within 91D3-5 region ) , introducing an Xrp1 mutation in trans leads to Xrp1-/- genotypes ( Figure 8B–D ) . Heterozygous mutation of Xrp1 is already sufficient to suppress competition of Rp+/- point mutant cells ( Lee et al . , 2018 ) , probably explaining why Xrp1 homozygosity had little further effect . Further evidence that segmentally aneuploid cells are eliminated by cell competition due to their Rp/+ genotypes came from studies in homotypic Rp mutant backgrounds ( Figure 9 ) . It is known from previous work that cells heterozygous at two Rp loci do not suffer more severe competition than cells heterozygous for only one Rp mutation , and therefore that cells heterozygous for two Rp loci generally cannot be eliminated by cells heterozygous at only one Rp locus ( Simpson and Morata , 1981 ) . Accordingly , clones of segmental aneuploid cells affecting the RpS11 , RpS13 , RpS16 , RpS20 , RpS24 , RpS30 , RpL14 , RpL18 , RpL28 , or RpL36A genes were all recovered significantly better in an RpS3 point mutant background , ie RpS3+/- Df ( Rp ) /+ clones were not eliminated from RpS3+/- tissues ( Figure 9A , B , D ) . This applied to segmental aneuploid clones heterozygous for eIF2γ as well ( Figure 9C , D ) . On the other hand , the RpS3 point mutant background usually had no effect on the survival of clones of genotypes that did not delete other Rp loci ( Figure 9C ) . One exception was Df ( 2L ) 26A1-28C3/+ , for which the RpS3+/- background generated significantly larger clones ( Figure 9C , D ) . Notably , Df ( 2L ) 26A1-28C3/+ cells had previously been recovered less than the other non-Rp segmental aneuploidies ( Figure 3B , C; Figure 9C ) . Although this could also have reflected a lower rate of FLP-recombination between the 26A1 and 28C3 FRT sites , in the RpS3+/- background the recovery of Df2 ( 2L ) 6A1-28C3/+ clones was similar to that of Df ( 2R ) 48F6-50C1/+ , Df ( 2R ) 56F16-58E2/+ or Df ( 3L ) 63C1-65A9/+ , suggesting instead that Df ( 2L ) 26A1-28C3/+ might be subject to a mild cell competition that can be rescued in the RpS3+/- background ( Figure 9C ) . Although suppression of apoptosis , mutation of cell competition genes , or a germline-inherited RpS3 background all restored the growth and survival of cells hemizygous for Rp loci , they may not have done so equally . Suppressing apoptosis was least effective at expanding the contribution of aneuploid cells in the rescued eyes ( Figure 5A ) . Xrp1 , rpS12G97D , and the RpS3+/- background suppressed cell competition to similar extents , although the general RpS3 background often had the greatest effect , comparable to those of RpL28+ and eIF2γ+ transgenes ( see statistical comparisons for the 63A3-65F5 and 56F16-59B1 regions in Figures 5 and 6 legends ) . Although several explanations could justify these differences , it is worth noting that the results correlate with the effects of these genetic backgrounds on translation and growth . Thus Df ( 3L ) H99 , which suppresses apoptosis with no known increase translation or cellular growth , is expected to suppress the competition of Rp+/- cells but not restore their translation . As a consequence , clones of Rp+/- Df ( H99 ) /+ cells , although surviving , are not expected to grow as rapidly or contribute as much to the eye as clones of Rp+/+ cells . By contrast the rpS12G97D and Xrp1 mutations restore the general translation rate of Rp+/- cells , with Xrp1 mutation also restoring more normal rates of cellular and organismal growth ( Lee et al . , 2018; Ji et al . , 2019 ) . A background mutation in RpS3 is not expected to restore translation or growth to segmentally aneuploid cells , but by equally impairing the unrecombined cells , and systemically delaying the growth and developmental rate of the organism as a whole , it equalizes the contributions of aneuploid and control genotypes . In summary , our results strongly support the conclusion that the growth and survival of most segmentally aneuploid regions is determined by cell competition according to Rp gene copy number , and show that the RpS12/Xrp1-dependent process that eliminates Rp+/- point mutated cells also acts on cells with large losses of genetic material that include Rp genes . Experiments using the hsFLP transgene should stimulate recombination and segmental aneuploidy in all tissues , not only in the eye where excision causes loss of pigmentation . To test this , we looked for cells with deletions encompassing Rp loci in the thorax , where Rp haploinsufficiency leads to small , thin thoracic bristles ( Marygold et al . , 2007 ) . This was explored using Df ( 2R ) 56F16-59B1 , which deletes the RpS16 and RpS24 loci . Minute-like bristles were not observed on the thoraces of heat-shocked flies carrying Df ( 2R ) 56F16-58E2 heterozygous clones , which affect no Rp locus , or on the thoraces of heat-shocked 56F16-59B1 flies lacking rpS12 or Xrp1 mutations , but they represented 0 . 5% of the thoracic bristles in the 56F16-59B1/+ rpS12G97D flies and 0 . 25% of the thoracic bristles in the 56F16-59B1/+ Xrp1m2-73/+ background ( Figure 10A , B ) . These findings indicate that Df ( 2R ) 56F16-59B1/+ cells also survive to adulthood in the thorax if cell competition is suppressed , albeit at lower frequency than observed in the eye .
We sought to test the hypothesis that cell competition is a mechanism that can target aneuploid cells based on their altered Rp gene dose ( McNamee and Brodsky , 2009 ) . It was already known that cells carrying point mutations at Rp loci are eliminated from developing imaginal discs by cell competition ( Morata and Ripoll , 1975; Simpson , 1979; Baker , 2020 ) . Here , we tested whether cells with more extensive genetic defects that reduce Rp gene dose also experience cell competition , and if so how significant this is for the removal of cells with damaged genomes . The idea that cell competition eliminates aneuploid cells developed from studies of cellular responses to DNA damage , where a delayed , p53-independent process follows after the acute , p53-dependent DNA damage response ( Wichmann et al . , 2006; Titen and Golic , 2008; McNamee and Brodsky , 2009 ) . We found that a substantial proportion of p53-independent cell death shared genetic requirements with cell competition , consistent with cell competition being responsible ( Figure 1 ) . Accordingly , when the cell competition pathway was inhibited , more Minute-like bristles were recovered on the irradiated flies , an indication that cell competition could be removing cells that experience substantial losses of genetic material ( Figure 1R ) . A proportion of both the p53-independent cell death and Minute-like bristles were independent of rpS12 , however , suggesting that cell competition might not be the only process at work . To measure the role of cell competition on defined genotypes , where the role of surrounding wild type cells could also be assessed , we then used site-specific recombination to excise chromosome segments from isolated cells during imaginal disc development . As expected , segmental aneuploidy prevented cells contributing clones to the adult eye whenever Rp gene dose was reduced ( Figure 11A ) . More significantly , cell competition appears to be the primary mechanism limiting the contribution of segmental aneuploidies in the tested size ranges to adult tissues , because segmental aneuploid cells easily survived and contributed large fractions of the adult tissue when they did not affect Rp loci , when diploidy for Rp loci was restored with a transgene , or when the cell competition pathway that depends on RpS12 and Xrp1 function was mutated . The segmental-aneuploid genotypes examined here were able to form entire heads of aneuploid cells when eyFlp was used to drive recombination in all the cells ( Figure 2 ) . The removal of sporadic aneuploid cells therefore depended on competition with diploid cells . In fact in the cases of Df ( 2R ) 56F16-59B1/+ , heterozygous for the RpS16 and RpS24 genes , and Df ( 3L ) 65A5-65A9/+ , heterozygous for the RpL28 gene , we bred flies that received heat-shock recombination , and recovered non-mosaic , entirely segmentally aneuploid flies in the next generation , derived from FLP-FRT recombination in the germlines of the parents . Thus , these segmentally aneuploid genotypes , which rarely survived in sporadic clones , were viable in all tissues when competing wild type cells were not present . The most effective suppression of Rp+/- segmental aneuploid clones was generally seen when the whole animal was heterozygous for a point mutation in RpS3 ( Figure 9 ) . This is further , compelling evidence that cell competition due to reduced Rp gene dose is the main mechanism eliminating segmentally aneuploid because it shows that the feature of euploid cells that enables them to eliminate aneuploid cells is their Rp+/+ genotype . In contrast to these results , segmental aneuploidy leaving Rp loci unaffected was compatible with clonal growth and differentiation for four of the five genomic regions tested ( Figure 3 ) . In the exception , we identified eIF2γ as the locus responsible for loss Df ( 3R ) 87B8-89B16/+ clones and Df ( 3R ) 87B8-89E5/+ clones ( Figure 4I ) . No point mutant alleles of the eIF2γ gene are known and the locus is believed to be haplo-lethal to Drosophila ( Marygold et al . , 2007 ) . It is cell competition that eliminates eIF2γ+/- aneuploid cells from the eye , however , since they could form apparently normal adult heads when no diploid competitor cells were present ( Figure 2P–R ) . Moreover , clones of the eIF2γ+/- genotypes Df ( 3R ) 87B8-89B16/+ and Df ( 3R ) 87B8-89E5/+ were restored by both the Xrp1 mutant and by the RpS3+/- mutant background , as expected for cell competition ( Figure 8 ) . It is possible that the 26A1-28C3 region might also contain a non-Rp gene whose deletion leads to a cell competition , although much less severe . Out of 63 other translation factor genes examined in a systematic study of whole body , non-mosaic phenotypes , eIF2α and eIF2γ were the only haploinsufficient loci found ( Marygold et al . , 2007 ) . Notably , the eIF2α gene is the only other locus known where point mutants lead to the developmental delay and thin bristle phenotype that is otherwise typical of heterozygous Rp mutants ( Marygold et al . , 2007 ) , suggesting a functional relationship between Rp mutants and the eIF2 complex . Independent studies in our laboratory already indicate that eIF2α is regulated by Xrp1 and contributes directly to the cell competition mechanism ( Kiparaki , Khan , Cheun and Baker , in preparation ) . Previous studies suggested that cells with whole chromosome aneuploidies experience a stress associated with mismatched dose of many proteins ( Torres et al . , 2007; Zhu et al . , 2018; Terhorst et al . , 2020 ) . We cannot measure how such stresses reduced clonal growth of segmental aneuploid cells in our experiments , but the effect must be small compared to cell competition , since without cell competition , segmental aneuploid cells easily contributed half or more of the eye , whereas cell competition drastically reduces this contribution . It seems unlikely that all five independent genomic regions examined here , comprising 21 . 1% of the euchromatic genome , represent exceptional cases . It is plausible , however , that additional stresses increase with more extensive loss of genetic material , eg clones heterozygous for a 6 . 3 Mb deletion removing the RpL14 , RpL18 , and RpL28 loci were recovered less well than smaller deletions ( Figure 6 ) , as if larger monosomies experience other stresses in addition to cell competition . Since extra copies of at least two Rp genes ( RpS12 and RpL36 ) do not trigger cell removal ( Kale et al . , 2018 ) , other mechanisms would also be required to eliminate cells with triploidies , or act in tissues that lack cell competition ( Ripoll , 1980 ) . Finally , preventing apoptosis of Drosophila cells that have chromosome instability leads to invasive tumor growth that can be propagated after transplantation ( Dekanty et al . , 2012; Benhra et al . , 2018 ) . We did not observe invasive growth after blocking cell death of segmentally aneuploid cells , suggesting that chromosome instability may lead to different classes of aneuploidy , or to other additional effects . If cell competition is the main mechanism eliminating cells with segmental monosomies , at least up to a certain size , how important is this ? The segmental aneuploidies we studied were comparable in genetic content to some whole chromosome monosomies in humans . For example , cells heterozygous for Df ( 2R ) 56F16-58E2 were hemizygous for a 2 . 2 Mb region including 1 . 5% of the genome , about as large a region as can be expected to lack any Rp gene , encoding 333 protein coding genes and 55 non-coding RNAs . Human chromosome 21 , which also contains 1 . 5% of the genome that lacks any Rp gene , encodes 234 protein coding genes and 404 non-coding RNAs ( Uechi et al . , 2001 ) . The similarity is not coincidental , because Rp number is conserved and the total gene number is also comparable , so genome segments that lack Rp loci are expected to be similar when measured by gene number or fraction of the genome . Thus , Df ( 2R ) 56F16-58E2 is comparable in genetic terms to loss of a small human chromosome . Some of the segmental aneuploidies we studied in Drosophila were several-fold larger than Df ( 2R ) 56F16-58E2 ( Supplementary file 1; Figure 3A ) , Thus , our studies may best model aneuploidies affecting one or a few human chromosomes . Because ~80% of the Drosophila genome is carried on two autosomes , whole-chromosome aneuploidies in Drosophila , by contrast , better mimic complex karyotypes seen in tumors or in cells with chromosome instability , which affect many chromosomes . Little is known about what aneuploidies arise spontaneously in normal development . Ionizing radiation generates many kinds of chromosome aberration , so if more than half the p53-independent cell death following irradiation resembles cell competition ( Figure 1 ) , this suggests cell competition could be significant for removing many , although not all , the damaged cells that arise . Could cell competition be important in humans ? As in Drosophila , the 80 Rp gene loci are distributed seemingly randomly around the 24 pairs of human chromosomes ( Uechi et al . , 2001 ) . At least 21 human Rp genes have so far been found to be haploinsufficient and are responsible for the dominant syndrome Diamond Blackfan Anemia ( DBA ) ( Ulirsch et al . , 2018 ) . Thus Rp genes could be sensors for aneuploidy in humans . The retention of aneuploid cells in Drosophila that inherit an Rp mutation from the germline ( Figure 9 ) resembles the situation in human DBA patients , the majority of whom are constitutively heterozygous for a Rp gene mutation or deletion ( Ulirsch et al . , 2018 ) . DBA patients experience 4 . 8x higher lifetime incidence of multiple cancers , not limited to the hematopoietic system ( Vlachos et al . , 2018 ) . Current hypotheses for this cancer predisposition include specific alterations to the spectrum of translation due to defective ribosome biogenesis , a loss of translational fidelity due to selection of second-site suppressor mutations , selective pressure for p53 mutations due to the chronic p53 activity in such genotypes , and oxidative stress or metabolic reprogramming in Rp+/-cells ( Sulima et al . , 2019 ) . To these we can now add the possibility that DBA patients experience a diminished capacity to recognize and eliminate aneuploid cells , because their euploid cells are not Rp+/+ ( Figure 11B ) . The nearly fivefold increase in tumor incidence suggests that if this was correct , cell competition might remove as many as 80% of pre-neoplastic cells from normal individuals due to their aneuploidy , This seems comparable to our findings that cell competition removes 58–86% of the cells with radiation-damaged genomes in Drosophila ( Figure 1M , P , R ) .
Flies were reared on standard medium at 25°C unless otherwise noted . The genetic strains used are described in the Key Resources Table . Strains carrying pairs of FRT transgenic elements in cis were obtained after meiotic recombination using appropriate genetic crosses , monitoring recombination frequency to confirm the expected transgene locations . FLP expression was induced by 37°C heat shock for 30 or 60 min at 36 ± 12 hr after egg laying . Adult flies were aged ~1 week to allow eye color to darken fully , then stored at −20°C for later measurement and photography . The fraction of each adult eye populated by unpigmented cells was estimated manually under a dissecting microscope . Samples were blinded for genotype before scoring by an independent investigator . We estimate the clonal composition of the eye by conceptually dividing each eye into segments so as to focus on the composition of the mosaic subregions . For example , an eye that is 56% white might be half white with an additional quarter of the eye that was one quarter white . Estimates are no doubt approximate although we do not think the errors are large . Importantly , the Mann Whitney procedure used to compare results statistically ranks relative clone size between genotypes rather than using the absolute values of the estimates . Many of the genetic backgrounds in which mosaics were generated carried other , distant FRT sites as part of the FRT82B Xrp1m2-73 , FRT82B RpS3 , rpS12G97D FRT80B , Df ( 3L ) H99 FRT80B chromosomes . Accordingly , the control backgrounds in these experiments always included FRT82B or FRT80B , as appropriate , and as described in the figure legends . The RpL28 rescue transgene was obtained by inserting genomic sequences 3L: 3220152–3225729 ( Drosophila genome Release 6 ) into pTL780 , which uses DsRed expression as a transgenic marker ( Blanco-Redondo and Langenhan , 2018 ) . The genomic DNA was amplified from the Drosophila genomic reference strain ( Adams et al . , 2000 ) . The resulting pTL780 ( RpL28+ ) plasmid was used for integration at the VK37 landing site on chromosome 2 ( Venken et al . , 2009 ) . For irradiation , food vials containing larvae were exposed to 500 , 1000 or 4000 rad from a γ-ray source 84 ± 12 hr after egg laying . Dissection , fixation , and immuno-labeling of wing imaginal discs with anti-active Dcp1 and anti-Xrp1 was performed as described previously ( Baker et al . , 2014; Lee et al . , 2018 ) . Frequencies of cell death and of Xrp1 expression were compared pairwise by t-tests ( Figure 1 ) . For multiple comparisons , one-way ANOVA was used with the Holm correction for multiple testing ( Figure 1 , Figure 10 ) . Previous studies indicated that significant results could be obtained from five biological replicates , where a biological replicate is an imaginal disc preparation labeled , imaged , and quantified ( McNamee and Brodsky , 2009 ) . N for each experiment is reported in the figure legends . The extent of white tissue in mosaic eyes was compared using pairwise Mann-Whitney tests with the Benjamini-Hochberg ( BH ) correction for multiple testing , using FDR ≤ 0 . 05 . There are 109 pairwise Mann-Whitney comparisons made in the main text of this paper , their P-values and the BH corrections are summarized in Supplementary file 2 . Where the extent of white tissue in mosaic eyes was compared between multiple genotypes simultaneously , the Kruskal-Wallis test was used with post-hoc follow-up tests using the method of Conover with BH correction using FDR ≤ 0 . 05 . No explicit power analysis was used . All flies obtained were scored in initial experiments , sometimes leading to unequal sample sizes , subsequently we considered 20 eyes of each sex generally sufficient for significant results ( while the number of flies that can be obtained is rarely limiting , blinding and scoring clone sizes is time-consuming ) . N is given in the figures for each experiment . All the figures show experimental and control data obtained from simultaneous parallel experiments in each case , for which all the data scored were included . Some of the genotypes have been generated on multiple occasions with similar results , not all included in the figures . | Aneuploid cells emerge when cellular division goes awry and a cell ends up with the wrong number of chromosomes , the tiny genetic structures carrying the instructions that control life’s processes . Aneuploidy can lead to fatal conditions during development , and to cancer in an adult organism . A safety mechanism may exist that helps the body to detect and remove these cells . Yet , exactly this happens is still poorly understood: in particular , it is unclear how cells manage to ‘count’ their chromosomes . One way they could do so is through the ribosomes , the molecular ‘factories’ that create the building blocks required for life . In a cell , every chromosome carries genes that code for the proteins ( known as Rps ) forming ribosomes . Aneuploidy will alter the number of Rp genes , and in turn the amount and type of Rps the cell produces , so that ribosomes and the genes for Rps could act as a ‘readout’ of aneuploidy . Ji et al set out to test this theory in fruit flies . The first experiment used a genetic manipulation technique called site-specific recombination to remove parts of chromosomes from cells in the developing eye and wing . Cells which retained all their Rp genes survived , while those that were missing some usually died – but only when the surrounding cells were normal . In this situation , healthy cells eliminated their damaged neighbours through a process known as cell competition . A second experiment , using radiation as an alternative method of damaging chromosomes , also gave similar results . The work by Ji et al . reveals how the body can detect and eliminate aneuploid cells , potentially before they can cause harm . If the same mechanism applies in humans , boosting cell competition may , one day , helps to combat diseases like cancer . | [
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] | 2021 | Cell competition removes segmental aneuploid cells from Drosophila imaginal disc-derived tissues based on ribosomal protein gene dose |
Nervous system function requires intracellular transport of channels , receptors , mRNAs , and other cargo throughout complex neuronal morphologies . Local signals such as synaptic input can regulate cargo trafficking , motivating the leading conceptual model of neuron-wide transport , sometimes called the ‘sushi-belt model’ ( Doyle and Kiebler , 2011 ) . Current theories and experiments are based on this model , yet its predictions are not rigorously understood . We formalized the sushi belt model mathematically , and show that it can achieve arbitrarily complex spatial distributions of cargo in reconstructed morphologies . However , the model also predicts an unavoidable , morphology dependent tradeoff between speed , precision and metabolic efficiency of cargo transport . With experimental estimates of trafficking kinetics , the model predicts delays of many hours or days for modestly accurate and efficient cargo delivery throughout a dendritic tree . These findings challenge current understanding of the efficacy of nucleus-to-synapse trafficking and may explain the prevalence of local biosynthesis in neurons .
Dendritic and axonal trees of neurons often have many tens or even thousands of branches that can extend across the entire nervous system . Distributing biomolecular cargo within neuronal morphologies is therefore a considerable logistical task , especially for components that are synthesized in locations distant from their site of use . Nonetheless , molecular transport is important for many neurophysiological processes , such as synaptic plasticity , neurite development and metabolism . For example , long-lasting forms of synaptic plasticity appear to depend on anterograde transport of mRNAs ( Nguyen et al . , 1994; Bading , 2000; Kandel , 2001 ) and specific mRNAs are known to be selectively transported to regions of heightened synaptic activity ( Steward et al . , 1998; Steward and Worley , 2001; Moga et al . , 2004 ) and to developing synaptic contacts ( Lyles et al . , 2006 ) . On the other hand , local biosynthesis and component recycling are known to support dendritic physiology , including some forms of synaptic plasticity ( Kang and Schuman , 1996; Aakalu et al . , 2001; Vickers et al . , 2005; Sutton and Schuman , 2006; Holt and Schuman , 2013 ) and maintenance of cytoskeletal , membrane and signalling pathways ( Park et al . , 2004 , 2006; Grant and Donaldson , 2009; Zheng et al . , 2015 ) . Neurons therefore rely on a mixture of local metabolism and global transport , but the relative contributions of these mechanisms are not understood . Analyzing the performance of global trafficking provides a principled way to understand the division of labor between local and global mechanisms . In this paper , we examine how well trafficking can perform given what we know about active transport and the typical morphologies of neurites . There are two parts to this question . First , how can active transport achieve specific spatial distributions of cargo using only local signals ? Second , how long does it take to distribute cargo to a given degree of accuracy and what factors contribute to delays ? Intracellular trafficking is being characterized in increasing detail ( Buxbaum et al . , 2014b; Hancock , 2014; Wu et al . , 2016 ) . Microscopic cargo movements are stochastic , bidirectional , and inhomogeneous along neurites , leading to to the hypothesis that trafficking is predominantly controlled by local pathways that signal demand for nearby cargo , rather than a centralized addressing system ( Welte , 2004; Bressloff and Newby , 2009; Newby and Bressloff , 2010a; Doyle and Kiebler , 2011; Buxbaum et al . , 2015 ) . These local signals are not fully characterized , but there is evidence for multiple mechanisms including transient elevations in second-messengers like Ca2+ and ADP ( Mironov , 2007; Wang and Schwarz , 2009 ) , glutamate receptor activation ( Kao et al . , 2010; Buxbaum et al . , 2014b ) , and changes in microtubule-associated proteins ( Soundararajan and Bullock , 2014 ) . A leading conceptual model ties together these details by proposing that local signalling and regulation of bidirectional trafficking determines the spatial distribution of cargo in neurons ( Welte , 2004; Buxbaum et al . , 2015 ) . Doyle and Kiebler ( 2011 ) call this the ‘sushi belt model’ . In this analogy , molecular cargoes are represented by sushi plates that move along a conveyor belt , as in certain restaurants . Customers sitting alongside the belt correspond to locations along a dendrite that have specific and potentially time-critical demand for the amount and type of sushi they consume , but they can only choose from nearby plates as they pass . Stated in words , the sushi belt model is an intuitive , plausible account of the molecular basis of cargo distribution . Yet it is unclear whether this model conforms to intuition , and whether it implies unanticipated predictions . Can this trafficking system accurately generate global distributions of cargo using only local signals ? Does the model predict cross-talk , or interference between spatially separated regions of the neuron that require the same kind of cargo ? How quickly and how accurately can cargo be delivered by this model , given what is known about trafficking kinetics , and do these measures of performance depend on morphology or the spatial pattern of demand ? We address these questions using simple mathematical models that capture experimentally measured features of trafficking . We confirm that the sushi-belt model can produce any spatial distribution of cargo in complex morphologies . However , the model also predicts that global trafficking from the soma is severely limited by tradeoffs between the speed , efficiency , robustness , and accuracy of cargo delivery . Versions of the model predict testable interactions between trafficking-dependent processes , while the model as a whole suggests that time-critical processes like synaptic plasticity may be less precise , or less dependent on global transport than is currently assumed .
Transport along microtubules is mediated by kinesin and dynein motors that mediate anterograde and retrograde transport , respectively ( Block et al . , 1990; Hirokawa et al . , 2010; Gagnon and Mowry , 2011 ) . Cargo is often simultaneously bound to both forms of motor protein , resulting in stochastic back-and-forth movements with a net direction determined by the balance of opposing movements ( Welte , 2004; Hancock , 2014; Buxbaum et al . , 2014a , Figure 1A ) . We modelled this process as a biased random walk , which is general enough to accommodate variations in biophysical details ( Bressloff , 2006; Bressloff and Earnshaw , 2007; Müller et al . , 2008; Bressloff and Newby , 2009; Newby and Bressloff , 2010a; Bressloff and Newby , 2013 ) . 10 . 7554/eLife . 20556 . 003Figure 1 . Constructing a coarse-grained model of intracellular transport . ( A ) Cartoon of a single cargo particle on a microtubule attached to opposing motor proteins . ( B ) Three example biased random walks , representing the stochastic movements of individual cargoes . ( Top panel ) A simple random walk with each step independent of previous steps . ( Bottom panel ) Adding history-dependence to the biased random walk results in sustained unidirectional runs and stalls in movement . ( C ) Cartoon of a population of cargo particles being transported along the length of a neurite . ( D ) Concentration profile of a population of cargoes , simulated as 1000 independent random walks along a cable/neurite . ( Top panel ) simulations without runs . ( Bottom panel ) Simulations with runs . ( E ) In the limit of many individual cargo particles , the concentration of particles u is described by a drift diffusion model whose parameters , a and b , map onto the mass action model ( Equation 1 ) . ( F ) The mass-action model provides a good fit to the simulations of bulk cargo movement in ( D ) . ( Top panel ) Fitted trafficking rates for the model with no runs were a ≈ 0 . 42 s−1 , b ≈ 0 . 17 s−1 . ( Bottom panel ) Fitting the model with runs gives a ≈ 0 . 79 s−1 , b ≈ 0 . 54 s−1 . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 00310 . 7554/eLife . 20556 . 004Figure 1—figure supplement 1 . The effect of cargo run length on mass-action model fit and diffusion coefficient . The model of stochastic particle movement ( Equation 7 , Materials and methods ) was simulated with equal transition probabilities ( p-=p0=p+=1/3 ) for various values of k and particle numbers in an infinite cable with 1 µm compartments and 1 s time steps . The expected run length is given by the mean of a negative binomial distribution . For each simulation , a mass-action approximation was fit by matching the first two moments of the cargo distribution , as described in the Materials and methods . In both panels , dots represent simulated triplicates , and lines denote the average outcome with colors denoting the simulated ensemble size ( see legend ) . ( A ) The mass-action model ( Equation 1 , Results ) provides a reasonably accurate fit after 100 s of simulation with moderately long run lengths and low particle numbers . The fit improves for longer simulations and larger particle numbers , since the cargo distribution is better approximated by a normal distribution under these conditions due to the central limit theorem . The coefficient of determination , R2 , reflects the proportion of explained variance by the mass-action model ( equivalent to a Gaussian fit to the concentration profile ) . ( B ) The estimated diffusion coefficient of the mass-action model ( i . e . the variance of the Gaussian fit in panel A ) increases as expected run length increases . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 004 Figure 1 shows this model in a one-dimensional cable , corresponding to a section of neurite . In each unit of time the cargo moves a unit distance forwards or backwards , or remains in the same place , each with different probabilities . In the simplest version of the model , the probabilities of forward and backward jumps are constant for each time step ( Figure 1B , top panel ) . Cargo can also undergo extended unidirectional runs ( Klumpp and Lipowsky , 2005; Müller et al . , 2008; Hancock , 2014 ) . The model can account for these runs with jump probabilities that depend on the previous movement of the particle ( Figure 1B , bottom panel , Materials and methods ) . While the movement of individual cargoes is stochastic , the spatial distribution of a population ( Figure 1C ) changes predictably . This is seen in Figure 1D , which shows the distribution of 1000 molecules over time , without ( top panel ) and with ( bottom panel ) unidirectional runs . The bulk distribution of cargo can therefore be modelled as a deterministic process that describes how cargo concentration spreads out in time . A convenient and flexible formulation of this process is a mass-action model ( Voit et al . , 2015 ) that spatially discretizes the neuron into small compartments . In an unbranched neurite with N compartments , the mass-action model is: ( 1 ) u1⇌b1a1u2⇌b2a2u3⇌b3a3 . . . ⇌bN−1aN−1uN where ui is the amount of cargo in each compartment , and ai and bi denote trafficking rate constants of cargo exchange between adjacent compartments . This model maps onto the well-known drift-diffusion equation when the trafficking rates are spatially homogeneous ( Figure 1E; Smith and Simmons , 2001 ) . We used this to constrain trafficking rate constants based on single-particle tracking experiments ( Dynes and Steward , 2007 ) or estimates of the mean and variance of particle positions from imaging experiments ( Roy et al . , 2012 , see Materials and methods ) . With a compartment length of 1 μm , the simulations in Figure 1D gave mean particle velocities of 15 μm per minute , which is within the range of experimental observations for microtubule transport ( Rogers and Gelfand , 1998; Dynes and Steward , 2007; Müller et al . , 2008 ) . The variances of the particle distributions depended on whether unidirectional runs are assumed , and respectively grew at a rate of ~0 . 58 and ~1 . 33 μm2 per second for the top and bottom panels of Figure 1D . The mass action model provides a good fit to both cases ( Figure 1F ) . In general , the apparent diffusion coefficient of the model increases as run length increases ( Figure 1—figure supplement 1A ) . The accuracy of the mass-action model decreases as the run length increases . However , the model remains a reasonable approximation for many physiological run lengths and particle numbers , even over a relatively short time window of 100 s ( Figure 1—figure supplement 1B ) . The advantage of the mass action model is that it easily extends to complex morphologies with spatially non-uniform trafficking rates , and can accommodate additional processes , including sequestration of cargo . The sushi-belt model ( Doyle and Kiebler , 2011 ) proposes that local mechanisms modify local trafficking rates and capture cargo as it passes . For these local signals to encode the demand for cargo , some feedback mechanism must exist between the local concentration of cargo and the signal itself . There are many biologically plausible mechanisms for locally encoding demand ( see Materials and methods ) . For our main results , we did not focus on these details and simply assumed a perfect demand signal . We have thus addressed the performance of the transport mechanism per se , with the most forgiving assumptions about the reliability of the demand signal . The mass action model of sushi-belt transport is: ( 2 ) u1⇌b1a1u2⇌b2a2u3⇌b3a3u4⇌b4a4 . . . c1↓c2↓c3↓c4↓u1⋆u2⋆u3⋆u4⋆ where u represents the concentration of cargo on the network of microtubules , indexed by the compartment . In each compartment , molecules can irreversibly detach from the microtubules in a reaction ui→ciui⋆ , where ui⋆ denotes the detached cargo . Biologically , cargo will eventually degrade . However , in this study we are concerned with how cargo can be rapidly distributed so that detached cargo can satisfy demand for at least some time . Therefore , for simplicity we assume degradation rates are effectively zero . We first asked whether modifying the trafficking rates alone was sufficient to reliably distribute cargo . Thus , we set all detachment rate constants ( ci ) to zero , and considered a model with trafficking only between compartments , as shown in Figure 2A . Mathematical analysis shows that , for a fixed set of trafficking parameters , the distribution of cargo approaches a unique steady-state distribution over time , regardless of the initial distribution of cargo . The steady-state occurs when the ratio of cargo concentrations between neighboring compartments is balanced by the trafficking rates: ( 3 ) upuc|ss=ba where up is the level in a ‘parent’ compartment ( closer to soma ) , uc is the level in the adjacent ‘child’ compartment ( closer to periphery ) and b and a are the trafficking rate constants between these compartments . 10 . 7554/eLife . 20556 . 005Figure 2 . Local trafficking rates determine the spatial distribution of biomolecules by a simple kinetic relationship . ( A ) The mass action transport model for a simple branched morphology . ( B ) Demonstration of how trafficking rates can be tuned to distribute cargo to match a demand signal . Each pair of rate constants ( {a1 , b1} , {a2 , b2} ) was constrained to sum to one . This constraint , combined with the condition in Equation ( 4 ) , specifies a unique solution to achieve the demand profile . ( C ) A model of a CA1 pyramidal cell with 742 compartments adapted from ( Migliore and Migliore , 2012 ) . Spatial cargo demand was set proportional to the average membrane potential due to excitatory synaptic input applied at the locations marked by red dots . ( D ) Convergence of the cargo concentration in the CA1 model over time , t ( arbitrary units ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 00510 . 7554/eLife . 20556 . 006Figure 2—figure supplement 1 . Equation 4 specifies the relative distribution of cargo , changing the total amount of cargo scales this distribution . ( A ) Inspired by ion channel expression gradients observed in hippocampal cells ( Hoffman et al . , 1997; Magee , 1998 ) , we produced a linear gradient in cargo distribution in an unbranched cable . By Equation 4 , the trafficking rate constants satisfy bi/ai=i/i+1 ( where i indexes on increasing distance to the soma ) . Starting from a uniform distribution of cargo in the cable ( t=0 a . u . ) , the desired linear profile emerges over time . ( B ) Changing the amount of cargo in the cable ( the sum of ui across all compartments , see legend ) does not disrupt the steady-state linear expression profile , but scales its slope . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 006 If u~i represents the local demand signal in compartment i , then Equation ( 3 ) gives the condition for cargo distribution to match demand: ( 4 ) ba=u~pu~c An example demand profile and the corresponding trafficking rate relationships are shown in Figure 2B . This condition ensures that cargo is delivered in proportion to local demand . The absolute concentration at steady-state is determined by the total amount of cargo produced ( Figure 2—figure supplement 1 ) ; in the case of mRNA , this might be controlled at the somatic compartment by transcriptional regulation . In this paper , we focus on the relative accuracy of cargo distribution when some fixed amount of cargo is produced at the soma . To illustrate demand-modulated trafficking in a realistic setting , we used a reconstructed model of a CA1 pyramidal neuron ( Migliore and Migliore , 2012 ) . To provide a demand signal , we modelled excitatory synaptic input at 120 locations within three dendritic regions ( red dots , Figure 2C ) and set demand , ( u~i ) , equal to the average membrane potential in each electrical compartment ( see Materials and methods ) . As expected , cargo was transported selectively to regions of high synaptic activity ( Video 1 ) , matching the demand profile exactly at steady state ( Figure 2D ) . Therefore , local control of trafficking rates ( equivalently , motor protein kinetics ) can deliver cargo to match arbitrarily complex spatial demand . 10 . 7554/eLife . 20556 . 007Video 1 . Distribution of trafficked cargo over logarithmically spaced time points in a CA1 pyramidal cell model adapted from ( Migliore and Migliore , 2012 ) . Cargo was trafficked according to Equation 4 to match a demand signal established by stimulated synaptic inputs ( see Figure 2C ) . Time and cargo concentrations are reported in arbitrary units . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 007 We next investigated the consequences of solely modifying trafficking rates to distribute cargo . A particularly striking prediction of this model is that changes in trafficking ( or , equivalently , demand signals ) in regions close to the soma can strongly affect cargo delivery times to distal sites . As the demand signal u~i approaches zero in a compartment , the trafficking rates into that compartment also approach zero , cutting off the flow of cargo along the neurite ( Figure 3A ) . The smallest demand signal , ϵ , often determines the rate-limiting time constant for cargo delivery to an entire dendritic tree . We refer to this scenario as a ‘transport bottleneck . ’ Figure 3A–C illustrate how decreasing ϵ to zero causes arbitrarily slow delivery of cargo in a simple three-compartment model . 10 . 7554/eLife . 20556 . 008Figure 3 . Transport bottlenecks caused by cargo demand profiles . ( A ) A three-compartment transport model , with the middle compartment generating a bottleneck . The vertical bars represent the desired steady-state concentration of cargo in each compartment . The rate of transport into the middle compartment is small ( ϵ , dashed arrows ) relative to transport out of the middle compartment . ( B ) Convergence of cargo concentration in all compartments of model in ( A ) for decreasing relative bottleneck flow rate , ϵ . ( C ) Simulations ( black dots ) confirm that the time to convergence is given by the smallest non-zero eigenvalue of the system ( solid curve ) . ( D ) Convergence to a uniform demand distribution ( red line ) is faster than a target distribution containing a bottleneck ( blue line ) in the CA1 model . Total error is the sum of the absolute difference in concentration from demand ( L1 norm ) . Neuron morphologies are color-coded according to steady state cargo concentration . ( E ) Transport delay for each compartment in the CA1 model ( time to accumulate 0 . 001 units of cargo ) . ( F ) Bar plot comparison of the convergence times for different spatial demand distributions in the CA1 model ( steady-state indicated in color plots ) . The timescale for all simulations in the CA1 model was normalized by setting ai+bi=1 for each compartment . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 008 To illustrate bottlenecks in a more realistic setting , we imposed a bottleneck in the reconstructed CA1 model by setting demand in the middle third of the apical dendrite to a lower level than the rest of the dendritic tree , which was set uniformly high . As expected , the cargo distribution converged much more quickly for uniform demand than with a bottleneck present ( Figure 3D ) . However , less intuitive effects are seen on the convergence times of cargo in specific compartments . Figure 3E plots convergence time for ui to reach a fraction of the steady state value for each compartment . While distal compartments showed prolonged convergence times , ( Figure 3E , upper right portion of plot ) , the bottleneck shortened the transport delay to proximal compartments ( Figure 3E , lower left portion of plot ) . This occurs because the bottleneck decreases the effective size of proximal part the neuron: cargo spreads efficiently throughout the proximal dendrites , but traverses the bottleneck more slowly . Another counterintuitive effect is seen when demand varies independently at proximal and distal locations , as might occur during selective synaptic stimulation ( see e . g . , Han and Heinemann , 2013 ) . In Figure 3F we simulated demand at proximal and distal portions of the apical dendrite independently and quantified the total convergence time . Proximal demand alone ( Figure 3F ‘proximal’ ) resulted in the fastest convergence time . Convergence was slowest when the demand was restricted to distal dendrites ( Figure 3F , ‘distal’ ) . Interestingly , when both distal and proximal sites signalled demand ( Figure 3F ‘both’ ) , convergence was substantially faster than the distal-only case , even though cargo still needed to reach the distal neurites . Uniform demand across the entire tree ( Figure 3F ‘entire cell’ ) resulted in a similarly short convergence time . Together , these results show that locally modulating trafficking movements will have testable effects on global transport times . The presence and relative contribution of this mechanism can be probed experimentally by characterizing the convergence rate of a cargo that aggregates at recently activated synapses , such as Arc mRNA ( Steward et al . , 1998 ) . This could be achieved using quantitative optical measurements in combination with synaptic stimulation at specific synaptic inputs . We next considered the full sushi-belt model ( Equation 2 ) with local demand signals controlling both trafficking and detachment rate constants ( Figure 4A ) . This provides additional flexibility in how cargo can be distributed , since the model can distribute cargo by locally modulating trafficking rates , detachment rates , or both ( Figure 4B ) . If trafficking is much faster than detachment ( a , b≫c ) , then the previous results ( Figures 2–3 ) remain relevant since the distribution of cargo on the microtubules will approach a quasi-steady state described by equation ( 3 ) ; cargo may then detach at a slow , nonspecific rate ( ci= constant , with c≪a , b ) . Figure 4C shows an example of this scenario , which we call demand-dependent trafficking ( DDT ) . The spatial distribution of cargo is first achieved along the microtubules ( red line , Figure 4C ) , and maintained as cargo detaches ( blue line , Figure 4C ) . 10 . 7554/eLife . 20556 . 009Figure 4 . Multiple strategies for transport with trafficking and cargo detachment controlled by local signals . ( A ) Schematic of microtubular transport model with irreversible detachment in a branched morphology . ( B ) Multiple strategies for trafficking cargo to match local demand ( demand = u~⋆ ) . ( Top ) The demand-dependent trafficking mechanism ( DDT ) . When the timescale of detachment is sufficiently slow , the distribution of cargo on the microtubules approaches a quasi-steady-state that matches u~⋆ spatially . This distribution is then transformed into the distribution of detached cargo , u⋆ . ( Bottom ) The demand dependent detachment ( DDD ) mechanism . Uniform trafficking spreads cargo throughout the dendrites , then demand is matched by slowly detaching cargo according to the local demand signal . An entire family of mixed strategies is achieved by interpolating between DDT and DDD . ( C–E ) Quasi-steady-state distribution of cargo on the microtubules ( u , red ) and steady-state distribution of detached cargo ( u⋆ , blue ) , shown with a demand profile ( u~⋆ , black ) for the various strategies diagrammed in panel B . The demand profile is shown spatially in the color-coded CA1 neuron in the right of panel C . Detached cargo matches demand in all cases . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 009 Alternatively , models can match demand by modulating the detachment process rather than microtuble trafficking . In this case , the trafficking rates are spatially uniform ( ai=bi ) so that cargo spreads evenly , and the detachment rates are set proportionally to the local demand , u~i⋆: ( 5 ) ci∝u~i⋆u~i The result of this strategy , which we call demand-dependent detachment ( DDD ) , is shown in Figure 4D . Unlike DDT , DDD avoids the transport bottlenecks examined in Figure 3 , and can achieve target patterns with u~i⋆ equal to zero in certain compartments by setting ci=0 . Mixed strategies that locally modulate both detachment and trafficking are also able to deliver cargo to match demand . Figure 4E shows the behavior of a model whose parameters are a linear interpolation between pure DDT and DDD ( see Materials and methods ) . Although it is mathematically convenient to separate the timescales of trafficking and detachment in the model , this separation may not exist in biological systems tuned for rapid transport . However , removal of timescale separation in the sushi-belt model results in mistargeted delivery of cargo , as we now show . We returned to the CA1 model of Figures 2–4 and considered a scenario where there is demand for cargo at the distal apical dendrites ( Figure 5A ) . If the detachment rate constants are sufficiently slow , then , as before , delivered cargo matched demand nearly exactly in both the DDT and DDD models ( Figure 5A , left ) . Increasing detachment rates led to faster convergence , but resulted in cargo leaking off the microtubule on the way to its destination ( Figure 5A , right ) . Thus , for a fixed trafficking timescale , there is a tradeoff between the speed and accuracy of cargo delivery . The tradeoff curve shown in Figure 5B shows that both accuracy and convergence time decreased smoothly as the detachment rates were increased . This tradeoff was present regardless of whether the trafficking rates ( Figure 5B , red line ) or detachment rates ( Figure 5B , blue line ) were modified to meet demand ( compare to Figure 4C and D , respectively ) . However , DDD outperformed DDT in this scenario , since the latter caused bottlenecks in proximal dendrites . 10 . 7554/eLife . 20556 . 010Figure 5 . Tradeoffs in the performance of trafficking strategies depends on the spatial pattern of demand . ( A ) Delivery of cargo to the distal dendrites with slow ( left ) and fast detachment rates ( right ) in a reconstructed CA1 neuron . The achieved pattern does not match the target distribution when detachment is fast , since some cargo is erroneously delivered to proximal sites . ( B ) Tradeoff curves between spatial delivery error and convergence rate for the DDT ( red line , see Figure 4C ) and DDD ( blue line , see Figure 4D ) trafficking strategies . ( C–D ) Same as ( A–B ) but with uniform demand throughout proximal and distal locations . The timescale of all simulations was set by imposing the constraint that ai+bi=1 for each compartment , to permit comparison . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 010 We considered a second scenario in which there was a uniform distribution of demand throughout the entire apical tree ( Figure 5C ) . As before , fast detachment led to errors for both transport strategies , this time by occluding cargo delivery to distal synaptic sites ( Figure 5C , right ) . A smooth tradeoff between speed and accuracy was again present , but , in contrast to Figure 5A–B , the DDT model outperformed DDD ( Figure 5D ) . Intuitively , DDT is better in this case because DDD results in cargo being needlessly trafficked to the basal dendrites . Together , these results show that increasing the speed of cargo delivery comes at the cost of accuracy , and that the performance of different trafficking strategies depends on the spatial profile of demand . The balance between demand-dependent trafficking and detachment could be probed experimentally . For example , one could perform an experiment in which distal and proximal synaptic pathways are stimulated independently , while optically monitoring the trafficking of proteins and mRNAs that are known to be selectively distributed at recently activated synapses . Interactions of the kind seen in Figure 5A , C and Figure 3F would allow one to infer whether DDT , DDD or a mixture of both strategies are implemented biologically . We next wanted to understand ( a ) how severe the speed-accuracy tradeoff might be , given experimental estimates of neuron size and trafficking kinetics , and ( b ) whether simple modifications to the sushi-belt model could circumvent this tradeoff . We examined the DDD model in an unbranched cable with a realistic neurite length ( 800 μm ) and an optimistic diffusion coefficient of 10 µm2 s−1 , which we set by inversely scaling the trafficking rate constants with the squared compartment length ( see Materials and methods and Figure 6—figure supplement 1 ) . All cargo began in the leftmost compartment and was delivered to a small number of demand ‘hotspots’ ( black arrows , Figure 6A ) . Similar results were found when the DDT model was examined in this setting ( data not shown ) . 10 . 7554/eLife . 20556 . 011Figure 6 . Tuning the DDD model for speed and specificity results in sensitivity to the target spatial distribution of cargo . ( A ) Cargo begins on the left end of an unbranched cable to be distributed equally amongst several demand ‘hotspots’ . Steady-state cargo profiles ( red ) are shown for three different models ( A1 , A2 , A3 ) and three different spatial patterns of demand ( rows ) . The bottom panel shows an upregulated anterograde trafficking profile introduced to reduce delivery time in A3; soma is at the leftmost point of the cable . ( A1 ) A model with sufficiently slow detachment achieves near-perfect cargo delivery for all demand patterns . ( A2 ) Making detachment faster produces quicker convergence , but errors in cargo distribution . ( A3 ) Transport rate constants , ai and bi , were tuned to optimize the distribution of cargo for the first demand pattern ( top row ) ; detachment rate constants were the same as in model A2 . ( B ) Tradeoff curves between non-specificity and convergence rate for six evenly spaced demand hotspots ( the top row of panel A ) . Tradeoff curves are shown for the DDD model ( blue line ) as well as models that combine DDD with the upregulated anterograde trafficking profile ( as in A , bottom panel ) . Marked points denote where models A1 , A2 , A3 sit on these tradeoff curves . ( C ) Tradeoff curves for randomized demand patterns ( six uniformly placed hotspots ) . Ten simulations are shown for the DDD model with ( red ) and without ( blue ) anterograde trafficking upregulation . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 01110 . 7554/eLife . 20556 . 012Figure 6—figure supplement 1 . Changing compartment size over an order of magnitude leads to insignificant changes in model behavior when trafficking rates are appropriately scaled ( i . e . ai and bi are inversely scaled to the squared compartment length; the diffusion coefficient converges to 10 μm2 s−1 as the compartment size shrinks to zero ) . ( A ) A DDD model with six evenly spaced demand hotspots along a 800 µm cable and a fixed detachment rate constant of 5 × 10−4 s−1 converges to the same qualitative distribution of cargo at steady-state when the number of compartments is varied . ( B ) Number of minutes to deliver 90% of cargo as a function of compartment number . Despite order-of-magnitude changes in compartment size , the convergence time changes much less . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 012 When the detachment timescale was sufficiently slow , the cargo was distributed evenly across the demand hotspots , even when the spatial distribution of the hotspots was changed ( Figure 6A1; Video 2 ) . Increasing the detachment rate caused faster convergence , but erroneous delivery of cargo . In all cases , hotspots closer to the soma received disproportionate high levels of cargo ( Figure 6A2; Video 3 ) . Importantly , the tradeoff between these extreme cases was severe: it took over a day to deliver 95% of cargo with 10% average error , and over a week to achieve 1% average error ( blue line , Figure 6B ) . 10 . 7554/eLife . 20556 . 013Video 2 . A model with slow detachment rate accurately distributes cargo to six demand hotspots in an unbranched cable . The spatial distribution of detached cargo ( bottom subplot ) and cargo on the microtubules ( top subplot ) are shown over logarithmically spaced timepoints . Compare to Figure 6A1 ( top row ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 01310 . 7554/eLife . 20556 . 014Video 3 . A model with a fast detachment rate misallocates cargo to six demand hotspots in an unbranched cable . The spatial distribution of detached cargo ( bottom subplot ) and cargo on the microtubules ( top subplot ) are shown over logarithmically spaced timepoints . Proximal demand hotspots receive too much cargo , while distal regions receive too little . Compare to Figure 6A2 ( top row ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 014 We next attempted to circumvent this tradeoff by two strategies . First , motivated by the observation that too much cargo was delivered to proximal sites in Figure 6A2 , we increased the anterograde trafficking rate of cargo near the soma so that more cargo would reach distal sites . By carefully fine-tuning a linearly decreasing profile of trafficking bias ( illustrated in Figure 6A , bottom panel ) , we obtained a model ( Figure 6A3; Video 4 ) that provided accurate and fast delivery ( within 10% error in 200 min ) for a distribution of six , evenly placed hotspots . 10 . 7554/eLife . 20556 . 015Video 4 . Fine-tuning the trafficking rates in a model with fast detachment produces fast and accurate deliver of cargo to six demand hotspots in an unbranched cable . The spatial distribution of detached cargo ( bottom subplot ) and cargo on the microtubules ( top subplot ) are shown over logarithmically spaced timepoints . Compare to Figure 6A3 ( top row ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 015 However , this model’s performance was very sensitive to changes in the spatial pattern of demand ( Figure 6A3 , middle and bottom; Video 5 ) . Increasing the anterograde trafficking rates produced nonmonotonic speed-accuracy tradeoff curves ( green , red , and cyan curves Figure 6B ) , indicating that the detachment rates needed to be fine-tuned to produce low error . Randomly altering the spatial profile of demand hotspots resulted in variable tradeoff curves for a fine-tuned trafficking model ( red lines , Figure 6C ) ; an untuned model was able to achieve more reliable cargo delivery albeit at the cost of much slower delivery times ( blue lines , Figure 6C ) . 10 . 7554/eLife . 20556 . 016Video 5 . The model fine-tuned for fast and accurate deliver of cargo to six demand hotspots misallocates cargo to three demand hotspots . The spatial distribution of detached cargo ( bottom subplot ) and cargo on the microtubules ( top subplot ) are shown over logarithmically spaced timepoints . Compare to Figure 6A3 ( middle row ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 016 Next , we considered a variant of the sushi-belt model that allowed for the reversible detachment/reattachment of cargo from the microtubules ( Figure 7A ) : ( 6 ) u1⇌b1a1u2⇌b2a2u3⇌b3a3u4⇌b4a4 . . . d1↑↓c1d2↑↓c2d3↑↓c3d4↑↓c4u1⋆u2⋆u3⋆u4⋆10 . 7554/eLife . 20556 . 017Figure 7 . Adding a mechanism for cargo reattachment produces a further tradeoff between rate of delivery and excess cargo . ( A ) Simulations of three models ( A1 , A2 , A3 ) with cargo recycling . As in Figure 6 , cargo is distributed to six demand hotspots ( black arrows ) . The distributions of cargo on the microtubules ( ui , blue ) and detached cargo ( ui⋆ , red ) are shown at three times points for each model . ( B ) Mean percent error in the distribution of detached cargo as a function of time for the three models in panel A . ( C ) Tradeoff curves between excess cargo and time to convergence to steady-state ( within 10% mean error across compartments ) for fixed cargo detachment timescales ( line color ) . For all detachment timescales , varying the reattachment timescale produced a tradeoff between excess cargo ( fast reattachment ) and slow convergence ( slow reattachment ) . Colored squares denote the position of the three models in panel A . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 017 Inspection of this scheme reveals that it is similar in form to the DDT model analyzed in Figure 2 and 3: the reversible detachment step simply adds an additional transient state in each compartment . As we noted in the DDT model , cargo distributions can match demand over time with arbitrarily low error ( see Equation 4 ) . However , transport delays still exist . While releasing cargo to the wrong location is not an irreversible error , it slows delivery by temporarily arresting movement – known as a diffusive trap ( see e . g . Bressloff and Earnshaw , 2007 . We found that cargo recycling creates a new tradeoff between convergence time and excess cargo left on the microtubules . Models that deliver a high percentage of their cargo ( ci>di ) converge more slowly since they either release cargo into the diffusive traps ( Figure 7A1 ) or have a slow detachment process ( Figure 7A2 ) . Models that deliver a low percentage of their cargo ( di>ci ) converge quickly since they release little cargo into diffusive traps , allowing cargo to travel along the microtubules and reach all destinations within the neuron ( Figure 7A3 ) . Figure 7B shows the convergence of the three examples ( A1 , A2 and A3 ) over time . Figure 7C shows that the new tradeoff between cargo utilization and convergence time is similarly severe to the speed-accuracy tradeoff in the sushi-belt model without reattachment . Models with reattachment that utilize cargo efficiently ( for example , Figure 7A2 ) converge on similarly slow timescales to models without reattachment that deliver cargo accurately ( for example , Figure 6A1 ) . Models with less than 10% excess cargo required more than a day to reach steady-state within a tolerance of 10% mean error . On the other hand , models that converged around 103 minutes ( 17 hr ) required more than 90% of cargo to remain in transit at steady-state ( Figure 7C ) . To establish the biological significance of these findings , we examined tradeoffs between speed , precision and excess cargo in reconstructed morphologies of five neuron cell types , spanning size and dendritic complexity ( Figure 8A ) . We simulated trafficking and delivery of cargo to a spatially uniform target distribution in each cell type to reveal morphology-dependent differences . In all cases we used optimistic estimates of transport kinetics , corresponding to a diffusion coefficient of 10 µm2 s−1 ( the rate constants were normalized to compartment size as in Figure 6—figure supplement 1 ) . 10 . 7554/eLife . 20556 . 018Figure 8 . Effect of morphology on trafficking tradeoffs . ( A ) Representative morphologies from four neuron types , drawn to scale . The red dot denotes the position of the soma ( not to scale ) . ( B ) Distribution of cargo on the microtubles ( ui ) and delivered cargo ( ui⋆ ) at four time points for sushi-belt model with irreversible detachment . Cargo originated in the soma and was transported to a uniform distribution ( all ai=bi , normalized to a diffusion coefficient of 10 μm2 s-1 ) ; the detachment rate was spatially uniform and equal to 8 × 10−5 s−1 . ( C ) Tradeoff curves for achieving a uniform distribution of cargo in realistic morphologies ( PV cell = parvalbumin interneuron , morphology not shown ) . The sushi-belt model without reattachment ( as introduced in Figure 4 ) suffers a tradeoff in speed and accuracy , while including reattachment ( as in Figure 7 ) produces a similar tradeoff between speed and excess ‘left-over’ cargo . An optimistic diffusion coefficient of 10 μm2 s−1 was used in both cases . For simulations with reattachment , the detachment rate ( ci ) was set equal to trafficking rates ( ai , bi ) for a one micron compartment . The dashed line denotes the convergence timescale for all simulations in panel B . DOI: http://dx . doi . org/10 . 7554/eLife . 20556 . 018 Figure 8B shows spatial plots of the distribution of cargo on the microtubules ( ui , cyan-to-magenta colormap ) and the distribution of delivered cargo ( ui⋆ , black-to-orange colormap ) for a model with an irreversible detachment rate of 8 × 10−5 s−1 . These parameters produce a relatively slow release of cargo: for each morphology , a sizable fraction of the cargo remains on the microtubules at ~3 hr , and it takes ~1–2 days to release all of the cargo . While the speed of delivery is roughly equivalent , the accuracy varied across the neural morphologies . The hippocampal granule cell converged to very low error ( ~11 . 7% mean error ) , while the larger L5 pyramidal cell converged to ~27 . 7% error . The smaller , but more elaborately branched , Purkinje cell converged to a similarly high average error of ~29 . 1% . As before , faster detachment rates produce faster , but less accurate , delivery; while slower detachment rates produce more accurate , but slower , delivery . These tradeoffs across the entire family of regimes are plotted in Figure 8C ( left ) . Adding a reattachment process largely preserved the effect of morphology on transport tradeoffs ( Figure 8C , right ) . We fixed the detachment rate to be fast , since fast detachment produced the most favorable tradeoff in Figure 7C . Tradeoffs between excess cargo and speed of delivery emerged as the reattachment rate was varied ( Figure 8C , right ) and were more severe for the Purkinje cell and L5 pyramidal cell , and least severe for the Granule cell . Morphology itself therefore influences the relationship between delivery speed and precision , and/or excess cargo required , suggesting that different cell types might benefit from different trafficking strategies .
The molecular motors that drive intracellular transport are remarkably efficient , achieving speeds of approximately 15 µm per minute ( Rogers and Gelfand , 1998; Dynes and Steward , 2007; Müller et al . , 2008 ) . A naïve calculation based on this figure might suggest that subcellular cargo can be delivered precisely within a few hours in most dendritic trees . However , this ignores the stochastic nature of biochemical processes – motors spontaneously change directions and cargo can be randomly delivered to the wrong site . Such chance events are inevitable in molecular systems , and in the case of active transport they lead to diffusion of bulk cargo in addition to directed movement . If this kind of biochemical stochasticity played out in the sushi restaurant analogy , then the waiting time for a dish wouldn’t simply equate to the time taken for the chef to prepare the dish and for the belt to convey it . Instead , the restaurant would be beleaguered by fickle customers who pick up dishes they do not want , either withholding them for an indefinite period , or setting them on another belt destined for the kitchen . Mathematical models provide a rigorous framework to test the plausibility and the inherent relationships in conceptual models . Our study formalized the foremost conceptual model of dendritic transport ( Doyle and Kiebler , 2011 ) to account for trafficking in realistic dendritic morphologies . Over a wide range of assumptions the model exhibits inherent and surprisingly punishing trade-offs between the accuracy of cargo delivery and the time taken to transport it over these morphologies . Using conservative estimates based on experimental data , the canonical sushi-belt model predicts delays of many hours or even days to match demand within 10% . Producing excess cargo and permitting reversible detachment from the microtubules can mitigate this tradeoff , but at a substantial metabolic cost , since a large amount of excess cargo is required . These predictions are unsettling , because nucleus-to-synapse transport appears to play a role in time-critical processes . Elevated synaptic activity can initiate distal metabolic events including transcription ( Kandel , 2001; Deisseroth et al . , 2003; Greer and Greenberg , 2008; Ch'ng et al . , 2011 ) and this has been shown to be an important mechanism of neuronal plasticity ( Nguyen et al . , 1994; Frey and Morris , 1997 , 1998; Bading , 2000; Kandel , 2001; Redondo and Morris , 2011 ) . Moreover , neuronal activity has been observed to influence trafficking directly through second-messengers ( Mironov , 2007; Wang and Schwarz , 2009; Soundararajan and Bullock , 2014 ) , consistent with the hypothesis that trafficking rates are locally controlled . Genes that are transcribed in response to elevated activity can regulate synaptic strengths ( Flavell and Greenberg , 2008; Bloodgood et al . , 2013; Spiegel et al . , 2014 ) , and it has been suggested that nucleus-to-synapse trafficking of Arc directly regulates synaptic plasticity ( Okuno et al . , 2012 ) . None of these findings imply that all kinds of molecular cargo are transported from the soma to distal dendritic locations , since mRNA can be sequestered and locally translated within dendrites ( Kang and Schuman , 1996; Cajigas et al . , 2012; Holt and Schuman , 2013 ) . However , the speed , precision and efficiency tradeoffs revealed in the sushi belt model provide a principled way to understand why some processes might require local biosynthesis , while others operate globally . The different ways that local demand signals can influence trafficking and detachment can impact global performance , sometimes non-intuitively . Many of these effects should be experimentally testable . For example , transport bottlenecks can be induced if demand signals target local trafficking rates along microtubules ( the DDT model ) . Transport to distal compartments will be substantially faster when proximal demand is introduced ( see Figure 3 ) . On the other hand , uniform trafficking combined with locally controlled detachment ( DDD model , Figure 4D ) can avoid bottlenecks , and often leads to faster transport . However , this is not always the case , as was shown in Figure 5D , where uniform trafficking is slower/inaccurate because cargo explores the basal dendritic tree even though there is no demand in that region . Spatial tuning of trafficking speed permitted more efficient cargo delivery in the model ( see Figure 6 ) . However , this has yet to be observed experimentally and would require extremely stereotyped morphology and physiological needs for it to be effective . Intuitively , speed/precision tradeoffs arise because there is a conflict between exploring the dendritic tree and capturing cargo in specific locations . For irreversible cargo detachment , the capture rate needs to be roughly an order of magnitude slower than trafficking , otherwise , compartments proximal to the soma receive disproportionately high levels of cargo . This scaling is unfavorable for achieving high accuracy: if it takes roughly 100 min to distribute cargo throughout the dendrites , it will take roughly 1000 min ( 16–17 hr ) before the cargo dissociates and is delivered to the synapses . If , instead , cargo is able to reattach , then fast reattachment favors exploration at the cost of greater excess ( i . e . non-utilized ) cargo , while slow reattachment hinders transport , since more cargo is detached and thus immobile . Even when the vast majority of cargo is produced as excess , global delivery times of several hours persist . Furthermore , if a neuron needs to rapidly replace a cargo that is already present in high concentrations , the strategy of generating excess cargo will result in large dilution times . Overall , our results show that there are multiple ways that neurons can distribute cargo , but each differs in its speed , accuracy and metabolic cost . Therefore , optimizing for any one of these properties comes at the expense of the others . For example , in the model without reattachment ( Figure 4 ) , the same distribution of cargo can be achieved by: ( a ) location-dependent trafficking followed by uniform release , ( b ) uniform trafficking followed by location-dependent release , or ( c ) a mixture of these two strategies . Experimental findings appear to span these possibilities . ( Kim and Martin , 2015 ) identified three mRNAs that were uniformly distributed in cultured Aplysia sensory neurons , but were targeted to synapses at the level of protein expression by localized translation ( supporting option b ) . In contrast , the expression of Arc mRNA is closely matched to the pattern of Arc protein in granule cells of the dentate gyrus ( possibly supporting option a; Steward et al . , 1998; Farris et al . , 2014; Steward et al . , 2014 ) . Trafficking kinetics do not just differ according to cargo identity – the same type of molecular cargo can exhibit diverse movement statistics in single-particle tracking experiments ( Dynes and Steward , 2007 ) . These differences lead us to speculate that different neuron types and different cargoes have adapted trafficking strategies that match performance tradeoffs to biological needs . It is possible that active transport in biological neurons will be more efficient and flexible than models predict . Real neurons might use unanticipated mechanisms , such as a molecular addressing system , or nonlinear interactions between nearby cargo particles , to circumvent the tradeoffs we observed . For this reason , it is crucial to explore , quantitatively , the behavior of existing conceptual models by replacing words with equations so that we can see where discrepancies with biology might arise . More generally , conceptual models of subcellular processes deserve more quantitative attention because they can reveal non-obvious constraints , relationships and connections to other biological and physical phenomena ( Smith and Simmons , 2001; Bressloff , 2006; Fedotov and Méndez , 2008; Newby and Bressloff , 2010b; Bhalla , 2011; Bressloff and Newby , 2013; Bhalla , 2014 ) . Other modelling studies have focused on the effects of stochasticity and local trapping of cargo on a microscopic scale , particularly in the context of low particle numbers ( Bressloff , 2006; Bressloff and Earnshaw , 2007; Fedotov and Méndez , 2008; Newby and Bressloff , 2010b; Bressloff and Newby , 2013 ) . We opted for a coarse-grained class of models in order to examine transport and delivery across an entire neuron . The model we used is necessarily an approximation: we assumed that cargo can be described as a concentration and that the multiple steps involved in cellular transport can lumped together in a mass action model . By constraining trafficking parameters based on prior experimental measurements , we revealed that a leading conceptual model predicts physiologically important tradeoffs across a variety of assumptions . Experimental falsification would prompt revision of the underlying models as well as our conceptual understanding of intracellular transport . On the other hand , experimental confirmation of these tradeoffs would have fundamental consequences for theories of synaptic plasticity and other physiological processes that are thought to require efficient nucleus-to-synapse trafficking .
Let xn denote the position of a particle along a 1-dimensional cable at timestep n . Let vn denote the velocity of the particle at timestep n; for simplicity , we assume the velocity can take on three discrete values , vn={-1 , 0 , 1} , corresponding to a retrograde movement , pause , or anterograde movement . As a result , xn is constrained to take on integer values . In the memoryless transport model ( top plots in Figure 1B , D and F ) , we assume that vn is drawn with fixed probabilities on each step . The update rule for position is:xn+1=xn+vnvn+1={−1withprobabilityp−0withprobabilityp01withprobabilityp+ We chose p-=0 . 2 , p0=0 . 35 and p+=0 . 45 for the illustration shown in Figure 1 . For the model with history-dependence ( bottom plots in Figure 1B , D and F ) , the movement probabilities at each step depend on the previous movement . For example , if the motor was moving in an anterograde direction on the previous timestep , then it is more likely to continue to moving in that direction in the next time step . In this case the update rule is written in terms of conditional probabilities:vn+1={−1with probabilityp ( −|vn ) 0with probabilityp ( 0|vn ) 1with probabilityp ( +|vn ) In the limiting ( non-stochastic ) case of history-dependence , the particle always steps in the same direction as the previous time step . |vn=−1vn=0vn=1_p ( vn+1=−1 ) p ( vn+1=0 ) p ( vn+1=1 ) |100010001_ We introduce a parameter k∈[0 , 1] to linearly interpolate between this extreme case and the memoryless model . ( 7 ) |vn=−1vn=0vn=1_p ( vn+1=−1 ) p ( vn+1=0 ) p ( vn+1=1 ) |p− ( 1−k ) +kp− ( 1−k ) p− ( 1−k ) p0 ( 1−k ) p0 ( 1−k ) +kp0 ( 1−k ) p+ ( 1−k ) p+ ( 1−k ) p+ ( 1−k ) +k_ The bottom plots of Figure 1B and D were simulated with k=0 . 5 . To estimate the concentration and spatial distribution of cargo in real units , we used a 1 µm/s particle velocity and a 1 s time step to match experimental estimates of kinesin ( Klumpp and Lipowsky , 2005 , and references ) . We assumed a dendritic diameter of 7 . 2705 µm . The mass-action model ( Equation 1 , in the Results ) simulates the bulk movement of cargo across discrete compartments . Cargo transfer is modelled as an elementary chemical reaction obeying mass-action kinetics ( Keener and Sneyd , 1998 ) . For an unbranched cable , the change in cargo in compartment i is given by: ( 8 ) u˙i=aui−1+bui+1− ( a+b ) ui For now , we assume that the anterograde and retrograde trafficking rate constants ( a and b , respectively ) are spatially uniform . The mass-action model can be related to a drift-diffusion partial differential equation ( Figure 1E ) by discretizing into spatial compartments of size Δ and expanding around some position , x: ( 9 ) u˙ ( x ) ≈a[u ( x ) −Δ∂u∂x+Δ22∂2u∂x2]+b[u ( x ) +Δ∂u∂x+Δ22∂2u∂x2]− ( a+b ) u ( x ) ( 10 ) =a[−Δ∂u∂x+Δ22∂2u∂x2]+b[Δ∂u∂x+Δ22∂2u∂x2] We keep terms to second order in Δ , as these are of order dt in the limit Δ→0 ( Gardiner , 2009 ) . This leads to a drift-diffusion equation: ( 11 ) u˙ ( x ) =∂u∂t= ( b−a ) ⏟driftcoefficient∂u∂x+ ( a+b2 ) ⏟diffusioncoefficient∂2u∂x2 Measurements of the mean and mean-squared positions of particles in tracking experiments , or estimates of the average drift rate and dispersion rate of a pulse of labeled particles can thus provide estimates of parameters a and b . How does this equation relate to the model of single-particle transport ( Figure 1A–B ) ? For a memoryless biased random walk , the expected position of a particle after n time steps is E[xn]=n ( p+-p- ) and the variance in position after n steps is n ( p++p-- ( p+-p- ) 2 ) . For large numbers of non-interacting particles the mean and variance calculations for a single particle can be directly related to the ensemble statistics outlined above . We find:a=2p+- ( p+-p- ) 22b=2p-- ( p+-p- ) 22 This analysis changes slightly when the single-particle trajectories contain long , unidirectional runs . The expected position for any particle is the same E[xn]=n ( p+-p- ) ; the variance , in contrast , increases as run lengths increase . However , the mass-action model can often provide a good fit in this regime with appropriately re-fit parameters ( see Figure 1F ) . Introducing run lengths produces a larger effective diffusion coefficient and thus provides faster transport . As long as the single-particles have stochastic and identically distributed behavior , the ensemble will be well-described by a normal distribution by the central limit theorem . This only breaks down in the limit of very long unidirectional runs , as the system is no longer stochastic ( Figure 1—figure supplement 1 ) . An important assumption of the mass-action model is that there are large numbers of transported particles , so that the behavior of the total system is deterministic . Intuitively , when each compartment contains many particles , then small fluctuations in particle number don’t appreciably change concentration . Many types of dendritic cargo are present in high numbers ( Cajigas et al . , 2012 ) . When few cargo particles are present , fluctuations in particle number are more functionally significant . Although we did not model this regime directly , the mass-action model also provides insight into this stochastic regime . Instead of interpreting ui as the amount of cargo in compartment i , this variable ( when appropriately normalized ) can be interpreted as the probability of a particle occupying compartment i . Thus , for a small number of transported cargoes , the mass-action model describes the average , or expected , distribution of the ensemble . In this interpretation , the mass-action model models a spatial probability distribution . Let pi denote the probability of a particle occupying compartment i . If a single particle starts in the somatic compartment at t=0 , and we query this particle’s position after a long period of transport , then the probability ratio between of finding this particle in any parent-child pair of compartments converges to:pppc|ss=ba which is analogous to Equation ( 3 ) in the Results . In the stochastic model , the number of molecules in each compartment converges to a binomial distribution at steady-state; the coefficient of variation in each compartment is given by:1−pi ( ss ) npi ( ss ) This suggests two ways of decreasing noise . First , increasing the total number of transported molecules , n , proportionally decreases the noise by a factor of 1/n . Second , increasing pi decreases the noise in compartment i . However , this second option necessarily comes at the cost of decreasing occupation probability and thus increasing noise in other compartments . The parameters of the mass-action model we study can be experimentally fit by estimating the drift and diffusion coefficients of particles over the length of a neurite . A common approach is to plot the mean displacement and mean squared displacement of particles as a function of time . The slopes of the best-fit lines in these cases respectively estimate the drift and diffusion coefficients . Diffusion might not accurately model particle movements over short time scales because unidirectional cargo runs result in superdiffusive motion , evidenced by superlinear increases in mean squared-displacement with time ( Caspi et al . , 2000 ) . However , over longer timescales , cargoes that stochastically change direction can be modelled as a diffusive process ( Soundararajan and Bullock , 2014 ) . The mass-action model might also be fitted by tracking the positions of a population of particles with photoactivatable GFP ( Roy et al . , 2012 ) . In this case , the distribution of fluorescence at each point in time could be fit by a Gaussian distribution; the drift and diffusion coefficients are respectively proportional to the rate at which the estimated mean and variance evolves over time . These experimental measurements can vary substantially across neuron types , experimental conditions , and cargo identities . Therefore , in order to understand fundamental features and constraints of the sushi belt model across systems , it is more useful to explore relationships within the model across ranges of parameters . Unless otherwise stated , the trafficking kinetics were constrained so that ai+bi=1 for each pair of connected compartments . This is equivalent to having a constant diffusion coefficient of one across all compartments . Given a target expression pattern along the microtubules , this is the only free parameter of the trafficking simulations; increasing the diffusion coefficient will always shorten convergence times , but not qualitatively change our results . In Figures 6–8 we fixed the diffusion coefficient to an optimistic value of 10 µm2 s−1 based on experimental measurements ( Caspi et al . , 2000; Soundararajan and Bullock , 2014 ) and the observation that long run lengths can increase the effective diffusion coefficient ( Figure 1—figure supplement 1 ) . The steady-state ratio of trafficked cargo in neighboring compartments equals the ratio of the trafficking rate constants ( Equation 2 ) . Consider an unbranched neurite with non-uniform anterograde and retrograde rate constants ( Equation 1 ) . It is easy to verify the steady-state relationship in the first two compartments , by setting u˙1=0 and solving:−a1u1+b1u2=0⇒u1u2|ss=b1a1 Successively applying the same logic down the cable confirms the condition in Equation 2 holds globally . The more general condition for branched morphologies can be proven by a similar procedure ( starting at the tips and moving in ) . It is helpful to re-express the mass-action trafficking model as a matrix differential equation , u˙=Au , where u=[u1 , u2 , . . . uN]T is the state vector , and A is the state-transition matrix . For a general branched morphology , A will be nearly tridiagonal , with off-diagonal elements corresponding to branch points; matrices in this form are called Hines matrices ( Hines , 1984 ) . For the simpler case of an unbranched cable , A is tridiagonal:A=[−a1b10 . . . 0a1−b1−a2b200a2−b2−a3b3⋱⋮⋮0a3⋱0⋱−bN−2−aN−1bN−10 . . . 0aN−1−bN−1] For both branched and unbranched morphologies , each column of A sums to zero , which reflects conservation of mass within the system . Assuming nonzero trafficking rates , the rank of A is exactly N-1 ( this can be seen by taking the sum of the first N-1 rows , which results in -1 times the final row ) . Thus , the nullspace of A is one-dimensional . Equation ( 3 ) describes this manifold of solutions: the level of cargo can be scaled by a common multiplier across all compartments without disrupting the relation in ( 2 ) . The steady-state distribution , u~ , is a vector that spans the nullspace of A . It is simple to show that all other eigenvalues A are negative using the Gershgorin circle theorem; thus , the fixed point described by Equation 2 is stable . The convergence rate is determined by the non-zero eigenvalue with the smallest magnitude of A . There are no other fixed points or limit cycles in this system due to the linearity of the model . There are many biochemical mechanisms that could signal demand . Here we briefly explore cytosolic calcium , [Ca]i , as a candidate mechanism since it is modulated by local synaptic activity and [Ca]i transients simultaneously arrest anterograde and retrograde microtubular transport for certain cargoes ( Wang and Schwarz , 2009 ) . We represent the effect of the calcium-dependent pathway by some function of calcium , f ( [Cai] ) . This function could , for example , capture the binding affinity of [Ca]i to enzymes that alter the kinetics of motor proteins; the Hill equation would provide a simple functional form . If all outgoing trafficking rates of a compartment are controlled by cytosolic calcium — i . e . for any parent-child pair of compartments we have a=f ( [Ca]p ) and b=f ( [Ca]c ) — then condition in Equation 4 is satisfied: ( 12 ) ba=f ( [Ca]c ) f ( [Ca]p ) =u~pu~c where u~i=1/f ( [Ca]i ) . We emphasize that other potential signalling pathways could achieve the same effect , so while there is direct evidence for [Ca]i as an important signal , the model can be interpreted broadly , with [Ca]i serving as a placeholder for any local signal identified experimentally . Further , [Ca]i itself may only serve as a demand signal over short timescales , while other , more permanent , signals such as microtubule-associated proteins ( Soundararajan and Bullock , 2014 ) are needed to signal demand over longer timescales . We used a custom-written Python library to generate movies and figures for all simulations in realistic morphologies ( Williams , 2016 ) . We obtained the CA1 pyramidal cell model from the online repository ModelDB ( Hines et al . , 2004 ) , accession number 144541 ( Migliore and Migliore , 2012 ) . We used the default spatial compartments and set the trafficking and dissociation parameters of the mass-action transport model without reference to the geometry of the compartments . Model simulations were exact solutions using the matrix exponential function from the SciPy library at logarithmically spaced timepoints ( Jones et al . , 2001 ) . In Figure 2 we simulated electrical activity of this model with excitatory synaptic input for 5 s using the Python API to NEURON ( Hines et al . , 2009 ) . We used the average membrane potential over this period to set the target demand level . In Figures 3 and 4 , we imposed artificial demand profiles with regions of low-demand and high-demand ( an order-of-magnitude difference ) as depicted in the figures . Time units for simulations of the CA1 model were were normalized by setting trafficking rates ai+bi=1 ( which corresponds to a unit diffusion coefficient ) . In Figure 8 , we obtained representative morphologies of five cell types from neuromorpho . org ( Ascoli et al . , 2007 ) . Specifically , we downloaded a Purkinje cell ( Purkinje-slice-ageP43-6 ) , a parvalbumin-positive interneuron ( AWa80213 ) , a Martinotti cell ( C100501A3 ) , a layer-5 pyramidal cell ( 32-L5pyr-28 ) , and a granule cell from the dentate gyrus ( 041015-vehicle1 ) . In these simulations , we scaled the trafficking parameters inversely proportional to the squared distance between the midpoints of neighboring compartments , which is mathematically appropriate to keep the ( approximated ) diffusion coefficient constant across the neural morphology . We confirmed that compartment size had minimal effects on the convergence rate and steady-state cargo distribution when the trafficking rates were scaled in this way in the reduced cable model ( Figure 6—figure supplement 1 ) . For simulations with reattachment in Figure 8 , we set the detachment rate ( ci ) equal to the trafficking rates ( ai , bi ) for a one micron compartment . We did this based on the observation that a fast detachment rate provided the most favorable tradeoff curve in Figure 7C . For compartment i in a cable , the differential equations with detachment become:u˙i=ai−1ui−1− ( ai+bi−1+ci ) ui+biui+1u˙i⋆=ciui When ai , bi≫ci , then the distribution of cargo on the microtubules ( ui ) approaches a quasi-steady-state that follows Equation 3 . In Figure 4 , we present DDT and DDD models as two strategies that distribute cargo to match a demand signal u~i⋆ . As mentioned in the main text , a spectrum of models that interpolate between these extremes are possible . To interpolate between these strategies , let F be a scalar between 0 and 1 , and let u~⋆ be normalized to sum to one . We choose ai and bi to achieve:u~i=Fu~i⋆+ ( 1−F ) /N along the microtubular network and choose ci to satisfyci∝u~i⋆Fu~i⋆+ ( 1−F ) /N Here , N is the number of compartments in the model . Setting F=1 results in the DDT model ( demand is satisfied purely by demand-modulated trafficking , and non-specific detachment , Figure 4C ) . Setting F=0 results in the DDD model ( demand is satisfied purely by demand-modulated detachment , and uniform/non-specific trafficking , Figure 4D ) . An interpolated strategy is shown in Figure 4E ( F=0 . 3 ) . The mass-action model with reattachment ( Equation 6 ) produces the following system of differential equations for a linear cable , with di denoting the rate constant of reattachment in compartment iu˙i=ai−1ui−1− ( ai+bi−1+ci ) ui+biui+1+diui⋆u˙i⋆=ciui−diui⋆ We examined the DDD model with N=100 compartments and diffusion coefficient of 10 μm2s−1 . The maximal detachment rate constant and the reattachment rates were tunable parameters , while the reattachment rates were spatially uniform . Results were similar when reattachment was modulated according to demand ( data not shown , see supplemental simulations at https://github . com/ahwillia/Williams-etal-Synaptic-Transport ) . In Figure 6 , we explored whether fine-tuning the trafficking rates could provide both fast and precise cargo distribution . We investigated the DDD model with fast detachment rates in an unbranched cable with equally spaced synapses and N=100 compartments . Large detachment rates produced a proximal bias in cargo delivery which we empirically found could be corrected by setting the anterograde and retrograde trafficking rates to be:ai=D2+β⋅N-1-iN-2bi=D2-β⋅N-1-iN-2 where i={1 , 2 , …N-1} indexes the trafficking rates from the soma ( i=1 ) to the other end of the cable ( i=N−1 ) , and D=10μm2/s is the diffusion coefficient . Faster detachment rates require larger values for the parameter β; note that β<D/2 is a constraint to prevent bi from becoming negative . This heuristic qualitatively improved , but did not precisely correct for , fast detachment rates in the DDT model ( data not shown ) . Intuitively , the profile of the proximal delivery bias is roughly exponential ( Figure 6B ) , and therefore the anterograde rates need to be tuned more aggressively near the soma ( where the bias is most pronounced ) , and more gently tuned as the distance to the soma increases . Importantly , tuning the trafficking rates in this manner does not alter the diffusion coefficient along the length of the cable ( since ai+bi is constant by construction ) . These manipulations produce a nonzero drift coefficient to the model , which corrects for the proximal bias in cargo delivery . | Neurons are the workhorses of the nervous system , forming intricate networks to store , process and exchange information . They often connect to many thousands of other cells via intricate branched structures called neurites . This gives neurons their complex tree-like shape , which distinguishes them from many other kinds of cell . However , like all cells , neurons must continually repair and replace their internal components as they become damaged . Neurons also need to be able to produce new components at particular times , for example , when they establish new connections and form memories . But how do neurons ensure that these components are delivered to the right place at the right time ? In some cases neurons simply recycle components or make new ones where they are needed , but experiments suggest that they transport other essential components up and down neurites as though on a conveyor belt . Individual parts of a neuron are believed to select certain components they need from those that pass by . But can this system , which is known as the sushi-belt model , distribute material to all parts of neurons despite their complex shapes ? Using computational and mathematical modeling , Williams et al . show that this model can indeed account for transport within neurons , but that it also predicts certain tradeoffs . To maintain accurate delivery , neurons must be able to tolerate delays of hours to days for components to be distributed . Neurons can reduce these delays , for example , by manufacturing more components than they need . However , such solutions are costly . Tradeoffs between the speed , accuracy and efficiency of delivery thus limit the ability of neurons to adapt and repair themselves , and may constrain the speed and accuracy with which they can form new connections and memories . In the future , experimental work should reveal whether the relationships predicted by this model apply in real cells . In particular , studies should examine whether neurons with different shapes and roles fine-tune the delivery system to suit their particular needs . For example , some neurons may tolerate long delays to ensure components are delivered to the exactly the right locations , while others may prioritize speedy delivery . | [
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During development , biomechanical forces contour the body and provide shape to internal organs . Using genetic and molecular approaches in combination with a FRET-based tension sensor , we characterized a pulling force exerted by the elongating pharynx ( foregut ) on the anterior epidermis during C . elegans embryogenesis . Resistance of the epidermis to this force and to actomyosin-based circumferential constricting forces is mediated by FBN-1 , a ZP domain protein related to vertebrate fibrillins . fbn-1 was required specifically within the epidermis and FBN-1 was expressed in epidermal cells and secreted to the apical surface as a putative component of the embryonic sheath . Tiling array studies indicated that fbn-1 mRNA processing requires the conserved alternative splicing factor MEC-8/RBPMS . The conserved SYM-3/FAM102A and SYM-4/WDR44 proteins , which are linked to protein trafficking , function as additional components of this network . Our studies demonstrate the importance of the apical extracellular matrix in preventing mechanical deformation of the epidermis during development .
In addition to their essential protective , structural and physiological functions , epithelial cells and their closely associated extracellular matrices ( ECMs ) serve as important mediators of embryonic morphogenesis and organogenesis ( Davidson , 2011 , 2012; Heisenberg and Bellaiche , 2013 ) . These developmental functions require epithelial tissues to be appropriately resistant to deformation by a variety of intrinsic and extrinsic mechanical forces that arise during the normal course of development . Accordingly , an improper force balance can lead to morphological abnormalities and birth defects ( Epstein et al . , 2004; Moore et al . , 2013 ) . In Caenorhabditis elegans , the outermost epithelial layer or epidermis ( commonly called the hypodermis in nematodes ) is initially established during early-to-mid embryogenesis ( ∼400 min post fertilization; Sulston et al . , 1983 ) . At this time , future epidermal cells execute stereotypical movements , shape changes and migrations to produce a 1 . 5-fold-stage embryo that is surrounded by an epithelium consisting of a single cell layer ( Sulston et al . , 1983; Chisholm and Hardin , 2005; Chisholm and Hsiao , 2012 ) . Shortly after this stage , ring-shaped actomyosin bundles , which are spaced regularly along the anteroposterior axis of the embryo , undergo coordinated contraction . This contraction leads to the circumferential constriction of the embryo and its conversion to a tapered cylindrical ( fusiform ) shape that is ∼250 µm long ( about five times the length of the egg shell; Costa et al . , 1997; Priess and Hirsh , 1986 ) . As a consequence of constriction at the epidermal surface and contractions by body wall muscles ( Williams and Waterston , 1994; Chisholm and Hardin , 2005 ) , tissues and organs inside the embryo are thought to experience squeezing forces and to elongate in conjunction with the outer layers of the embryo . Notably , the apical ECM ( aECM ) of the embryonic epidermis , termed the embryonic sheath , is required to prevent excessive constriction and deformation of the epidermis by actomyosin ring contraction ( Priess and Hirsh , 1986 ) . Although critical for development , the molecular composition and related physical properties of the embryonic sheath remain poorly characterized . Despite a growing interest in mechanical aspects of development and morphogenesis ( Guillot and Lecuit , 2013; Heisenberg and Bellaiche , 2013 ) , the interplay between mechanical forces and the physical properties and structure of tissues have been difficult to characterize . This is due in part to an incomplete description of mechanical forces in living embryos . In addition , genetic redundancy has likely impeded progress toward fully understanding the molecular control of tissue and organismal morphogenesis ( Thomas , 1993; Pickett and Meeks-Wagner , 1995; Tautz , 2000; Herman and Yochem , 2005; Bussey et al . , 2006 ) . Here we describe a morphological defect that results from the failure of the anterior epidermis to maintain its proper shape while experiencing an inward-directed pulling force exerted by the developing pharynx ( foregut ) as it undergoes elongation . This defect occurs at a low frequency in single mutants of mec-8 , sym-3 and sym-4 , but at a high frequency in mec-8; sym-3 and mec-8; sym-4 double mutants , indicating that this process is redundantly controlled ( Davies et al . , 1999; Yochem et al . , 2004 ) . Whereas sym-3 and sym-4 encode conserved proteins with predicted roles in vesicular trafficking ( Yochem et al . , 2004; also see ‘Discussion’ ) , mec-8 encodes a conserved RNA-binding protein involved in alternative splicing ( Lundquist et al . , 1996; Spike et al . , 2002 ) . We have shown that the contribution of MEC-8 in the resistance to this force arises , at least in part , through its control of FBN-1 , a protein that shares several domains with vertebrate fibrillins and acts in the embryonic sheath . Notably , mutations in human fibrillin genes lead to connective tissue disorders including Marfan syndrome ( Dietz et al . , 2005; Ramirez and Dietz , 2009; Ramirez and Sakai , 2010 ) .
In wild-type embryos at the 1 . 5-fold stage of development , a shallow pit ( ∼2 . 1 µm deep ) , termed the sensory depression , is detected in the region corresponding to the location of the future mouth ( buccal cavity; Figure 1A , Table 1; Sulston et al . , 1983 ) . This morphological feature is relatively short-lived and is no longer visible in threefold-stage embryos ( Figure 1A , Figure 2C ) . In contrast , mec-8; sym-3 and mec-8; sym-4 embryos had a striking keyhole-shaped invagination in this region , which increased in depth between the 1 . 5-fold ( ∼4 . 3 µm ) and 3-fold ( ∼9 . 5 µm ) stages ( Figure 1A , Table 1 ) . In contrast to wild-type L1 larvae , in which the pharynx and associated buccal capsule ( terminal mouth part ) extended to the anterior tip of the worm , mec-8; sym-3 and mec-8; sym-4 L1 larvae displayed what we have termed the ‘Pharynx ingressed’ ( Pin ) phenotype , in which the pharynx and buccal capsule are displaced toward the posterior end of the animal ( Figure 1A ) . In Pin larvae , lateral anterior tissues appeared to fold over and surround the ingressed buccal capsule , thereby preventing double mutants from feeding ( Figure 1A ) . Although these defects were observed at only low frequencies in sym-3 , sym-4 and mec-8 single mutants , they were highly penetrant in mec-8; sym-3 and mec-8; sym-4 double mutants ( Figure 1B , Supplementary file 1 ) . 10 . 7554/eLife . 06565 . 003Figure 1 . mec-8; sym-3 and mec-8; sym-4 mutants exhibit an abnormal ingression of the anterior epidermis . ( A ) Whereas wild-type 1 . 5-fold embryos display only a shallow ingression of the anterior epidermis ( sensory depression ) and little or no ingression by the threefold stage , mec-8; sym-3 and mec-8; sym-4 ( data not shown ) mutants contain a deep keyhole-shaped ingression that increases in depth between the 1 . 5-fold and 3-fold stages . mec-8; sym-3 and mec-8; sym-4 ( data not shown ) L1 larvae also contain an ingressed pharynx ( Pin ) and associated deformities in the head region . Yellow dashed lines indicate lateral pharyngeal borders; orange dashed lines , the sensory depression or keyhole; black arrows , posterior extent of ingression . White scale bars = 10 µm , black bars = 5 µm . ( B ) Quantification of the Pin phenotype in single and double mutants and in mec-8; sym-4 double mutants containing multi-copy extrachromosomal arrays ( fdEx251 and fdEx254 ) that express the fbn-1e cDNA isoform under the control of the native fbn-1 promoter . Error bars represent 95% CIs . For additional details , see Table 1 and Supplementary file 1 . ( C ) Spring-and-cylinder model in which the pharynx exerts an inward-pulling force at the anterior epidermis throughout the mid-to-late stages of embryonic morphogenesis . In embryo representations , pharyngeal borders are indicated by black dashed lines; in cylindrical representations , the pharynx is represented by a spring that is attached to the anterior epidermis at the dark blue dot . Early comma , 1 . 5-fold and 3-fold stages of embryogenesis are depicted . Red arrows indicate the inward-pulling force on the epidermis that results from the resistance of the pharynx to stretching . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 00310 . 7554/eLife . 06565 . 004Table 1 . Ingression depths of the anterior epidermisDOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 004Ingression depth ( μm ) ± 95% CI ( range; n ) Genotype1 . 5-fold3 . 0-foldN22 . 12 ± 0 . 23 ( 1 . 09–3 . 12; 20 ) 0 . 26 ± 0 . 096 ( 0 . 0–0 . 67; 20 ) sym-3 ( mn618 ) 2 . 39 ± 0 . 40 ( 0 . 81–4 . 24; 24 ) 0 . 48 ± 0 . 56 ( 0 . 0–6 . 55; 22 ) sym-4 ( mn619 ) 2 . 74 ± 0 . 64 ( 0 . 72–5 . 67; 21 ) 1 . 28 ± 1 . 06 ( 0 . 0–6 . 74; 22 ) mec-8 ( u74 ) 2 . 33 ± 0 . 46 ( 0 . 72–4 . 34; 20 ) 2 . 42 ± 1 . 50 ( 0 . 0–10 . 43; 26 ) mec-8; sym-3*4 . 25 ± 0 . 89 ( 2 . 77–5 . 72; 18 ) 9 . 82 ± 0 . 68 ( 7 . 84–12 . 00; 15 ) mec-8; sym-44 . 27 ± 1 . 16 ( 2 . 09–6 . 45; 16 ) 9 . 19 ± 0 . 83 ( 7 . 07–10 . 14; 12 ) Pha-1 ( tm3671 ) 0 . 87 ± 0 . 18 ( 0 . 45–1 . 18; 16 ) NAmec-8; pha-1 ( tm3671 ) ; sym-3*0 . 83 ± 0 . 11 ( 0 . 40–1 . 19; 17 ) NApha-1 ( e2123 ) 2 . 15 ± 0 . 27 ( 1 . 04–3 . 34; 19 ) 0 . 10 ± 0 . 07 ( 0 . 0–0 . 59; 16 ) mec-8; pha-1 ( e2123 ) ; sym-3*5 . 27 ± 0 . 53 ( 3 . 89–7 . 46; 14 ) 0 . 60 ± 2 . 35 ( 0 . 0–10 . 29; 19 ) fbn-1 ( ns67 ) 3 . 18 ± 0 . 85 ( 0 . 60–6 . 02; 13 ) 5 . 34 ± 1 . 31 ( 0 . 0–9 . 24; 20 ) fbn-1 ( ns67 ) ; sym-35 . 20 ± 0 . 41 ( 3 . 82–6 . 71; 20 ) 11 . 73 ± 0 . 85 ( 8 . 59–16 . 34; 19 ) fbn-1 ( ns67 ) ; sym-45 . 98 ± 0 . 55 ( 4 . 25–7 . 66; 12 ) 12 . 84 ± 0 . 78 ( 9 . 65–16 . 37; 27 ) mec-8; fbn-1 ( ns67 ) 5 . 03 ± 0 . 47 ( 3 . 76–7 . 19; 19 ) 9 . 84 ± 0 . 55 ( 6 . 94–12 . 24; 21 ) fbn-1 ( tm290 ) 6 . 25 ± 1 . 81 ( 0 . 99–12 . 17; 16 ) 7 . 63 ± 3 . 66 ( 0 . 0–24 . 17; 17 ) fbn-1 ( tm290 ) ; sym-35 . 65 ± 0 . 61 ( 2 . 59–7 . 29; 19 ) 15 . 05 ± 1 . 56 ( 9 . 12–26 . 06; 24 ) fbn-1 ( tm290 ) ; sym-45 . 54 ± 0 . 86 ( 3 . 52–9 . 12; 17 ) 13 . 10 ± 1 . 72 ( 7 . 22–19 . 47; 20 ) mec-8; fbn-1 ( tm290 ) 9 . 84 ± 0 . 55 ( 5 . 47–15 . 18; 31 ) NA*Because these strains give rise to a high frequency of viable mnEx169 ( − ) progeny in the first generation following loss of the array ( F1 escapers ) , next-generation progeny ( F2 ) from mnEx169 ( − ) F1 parents were scored . NA , Non-Applicable; these genotypes led to embryonic arrest prior to the 3-fold stage . 10 . 7554/eLife . 06565 . 005Figure 2 . Genetic and phenotypic analyses support an extension spring model for pharyngeal elongation . ( A–D ) Predicted models and outcomes for testing the hypothesis that the elongating pharynx exerts an inward-pulling force on the anterior epidermis . Black arrows in models show the predicted ( and observed ) outcomes; gray arrows , alternative outcomes . For panels with DIC images , yellow dashed lines indicate lateral pharyngeal borders; orange dashed lines , the sensory depression or keyhole; black arrows , posterior extent of ingression . White scale bars = 10 µm . For additional details , see Table 1 and Supplementary file 1 . ( A ) In mec-8; pha-1 ( tm3671 ) ; sym-3 mutants that fail to establish a connection between the pharynx and epidermis ( 85% ) , deep ingressions or keyholes are not observed , whereas mutants that form an initial attachment ( 15% ) form a stereotypical keyhole . ( B ) Detachment of the pharynx from the epidermis after the twofold stage in mec-8; pha-1 ( e2123 ) ; sym-3 mutants leads to loss of the anterior ingression by the threefold embryonic stage and suppression of Pin in L1 larvae . ( C ) Whereas the depth of the keyhole in mec-8; sym-4 mutants steadily increases between the 2-fold and 3-fold stages of embryogenesis , inhibition of embryonic elongation past the twofold stage by let-502 ( RNAi ) prevents further deepening of the ingression . Error bars indicate 95% CIs , and diagrammed embryos denote the approximate stages of development for each genotype; n = 5 for each genotype at each time point . ( D ) Reversal of embryonic elongation in mec-8; sqt-3 ( e2117ts ) mutants leads to a decrease in keyhole depth . Each line in the plot represents a different embryo; diagrammed embryos denote the approximate stages of development . For these experiments , rare mec-8; sqt-3 ( ts ) mutants that exhibited a keyhole at the twofold stage ( ∼5% ) were analyzed for reasons of experimental convenience . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 005 To account for the defects observed in mec-8; sym-3 and mec-8; sym-4 double mutants , we proposed a testable model for pharyngeal and embryonic elongation . As described above , the embryo acquires an elongated shape through the circumferential constriction of ring-shaped actomyosin bundles arrayed along the surface of the epidermis ( Priess and Hirsh , 1986 ) . During initial stages of embryonic morphogenesis ( ∼350–380 min ) , the primordial pharynx exists as a ball of cells with no connection to the future mouth ( buccal capsule ) or epidermis ( Figure 1C ) . Linkage of the pharynx to the mouth and epidermis is established between the comma and 1 . 5-fold stages ( ∼380–410 min; Figure 1C , data not shown; Sulston et al . , 1983; Portereiko and Mango , 2001 ) . During embryonic development , the pharynx lengthens along its anteroposterior axis , transforming from a blunt conical shape into a bi-lobed structure that is attached to the mouth at the anterior and to the intestine in the mid body ( Figure 1C ) . We hypothesized that lengthening of the pharynx is facilitated in part by an outward-directed pulling force that is exerted by the anterior epidermis as the embryo undergoes elongation . In addition , as the pharynx is stretched , it exerts a counter inward-pulling force on the embryonic epidermis . This inward-pulling force would be greatest in the region where the pharynx attaches to the epidermis , contributing to the formation of the sensory depression ( Figure 1C ) . We liken this situation to that of a spring that is attached ( on the inside ) to the ‘anterior’ end of an elastic-walled cylinder , with the cylinder representing the embryonic epidermis and the spring representing the pharynx ( Figure 1C ) . The ‘posterior’ end of the spring in this model is held in place within the middle of the cylinder through localized contacts , which in the case of the pharynx most likely occur through cell–cell interactions . As the cylinder elongates , it stretches the spring , which then exerts an inward-pulling force at the site of attachment to the cylinder wall ( Figure 1C ) . We hypothesize that in wild-type embryos , one or more means of structural reinforcement prevents the anterior epidermis from undergoing a pronounced invagination or ingression in response to the pharyngeal pulling force . In contrast , the epidermis in mec-8; sym-3 and mec-8; sym-4 mutants is insufficiently reinforced , due to the combined defects in processes controlled by mec-8 and sym-3/4 , resulting in mechanical deformation of the epidermis , the genesis of the keyhole and , ultimately , the Pin phenotype . One prediction of our model is that prevention of a pharyngeal-epidermal attachment should suppress keyhole formation in mec-8; sym-3/4 embryos ( Figure 2A ) . To test this , we used a deletion mutation ( tm3671 ) in pha-1 , which encodes a cytoplasmic protein of unknown function , that prevents initial attachment of the pharynx to the epidermis in ∼85% of embryos ( Fay et al . , 2004 , 2012; Kuzmanov et al . , 2014 ) . As predicted , formation of the keyhole was suppressed in mec-8; pha-1 ( tm3671 ) ; sym-3 triple mutants in which the pharynx failed to attach ( Figure 2A , Table 1 ) . In contrast , in mec-8; pha-1 ( tm3671 ) ; sym-3 embryos in which the pharynx was attached to the epidermis ( ∼15% ) , a keyhole was observed , indicating that the loss of attachment per se , rather than the loss of pha-1 activity , was responsible for the suppression of keyhole formation in the majority of triple mutants ( Figure 2A ) . A second prediction of our model is that maintenance of a pharyngeal-epidermal attachment would be required for persistence of a keyhole in embryos and for progression to a Pin phenotype in larvae ( Figure 2B ) . To test this prediction , we used a hypomorphic allele of pha-1 ( e2123 ) , which establishes a transient connection between the pharynx and epidermis that is severed at later stages of embryogenesis ( Schnabel and Schnabel , 1990; Fay et al . , 2004; Kuzmanov et al . , 2014 ) . In our cylinder-and-spring analogy , loss of the pharyngeal-epidermal attachment in pha-1 ( e2123 ) mutants would be akin to severing the spring near the site of attachment to the tube , leading to the ingressed elastic cylinder tip popping back out and recoil of the spring ( Figure 2B ) . As predicted by our model , early-stage mec-8; pha-1 ( e2123 ) ; sym-3 triple mutants formed a stereotypical keyhole , consistent with the presence of a pharyngeal-epidermal connection . The absence of a keyhole or Pin phenotype in late-stage embryos and L1 larvae , however , indicated that anterior ingression of the epidermis requires a sustained pulling force exerted by the pharynx and that the keyholes are not static once formed ( Figure 2B ) . We also observed that the depth of the keyhole in mec-8; sym-3 and mec-8; sym-4 mutants steadily increased from the comma stage to the threefold stage of embryogenesis ( Figure 1A , Figure 2C , Table 1 ) . We hypothesized that failure to elongate past the 1 . 5-fold or 2 . 0-fold stages would , however , prevent further deepening of the keyhole . In our model , this would be akin to lengthening the cylinder only partway , thereby preventing further ingression of the tip . To test this , we inhibited morphogenesis past the twofold stage in mec-8; sym-4 mutants by RNA interference ( RNAi ) of let-502/ROCK , which encodes an epidermal-expressed Rho-binding kinase required for embryonic elongation ( Wissmann et al . , 1997 , 1999 ) . As expected , keyhole depth in mec-8; sym-4; let-502 ( RNAi ) embryos increased until the twofold stage , reaching an average depth of ∼6 µm , identical to that observed for control RNAi-treated mec-8; sym-4 mutants at the same stage of development ( Figure 2C ) . After morphogenetic arrest , however , keyholes in mec-8; sym-4; let-502 ( RNAi ) embryos failed to deepen , indicating that the progressive increase in keyhole depth is a function of embryonic elongation , rather than the passage of time . We also observed that mec-8; sym-4; let-502 ( RNAi ) embryos took longer to transit from the comma stage to the twofold stage than the control RNAi-treated strain . Consistent with our model , the rate at which keyhole depth increased in mec-8; sym-4; let-502 ( RNAi ) embryos was reduced in proportion with the delay in embryonic elongation ( Figure 2C ) . A final prediction of our model is that a reversal of embryonic elongation should lead to a consequent reduction in the depth of the keyhole in embryos . In our model , this would be analogous to shortening the cylinder and observing a reduction in anterior tip ingression ( Figure 2D ) . To test this , we used a conditional allele of sqt-3 ( e2117ts ) , which undergoes a reversal of elongation from the ∼threefold–∼twofold stages after temperature upshift ( Kusch and Edgar , 1986; Priess and Hirsh , 1986 ) . Consistent with our model , keyholes reached a maximum depth of ∼8–10 µm at around the threefold stage but then shrunk to ∼4–6 µm after a partial reversal of embryonic elongation ( Figure 2D ) . Taken together , our findings provide strong evidence that resistance of the pharynx to stretching or lengthening leads to an inward-pulling force on the anterior epidermis during much of embryogenesis . In the case of wild-type embryos , this force is resisted to an appropriate extent , and a normal morphology is achieved . In contrast , morphological defects in mec-8; sym-3 and mec-8; sym-4 embryos and larvae suggest that the mechanical properties of the epidermis may be compromised in these mutants , leading to the Pin phenotype . To visualize biomechanical forces operating during embryogenesis , we made use of recently developed FRET-based methods for detecting mechanical tension in live cells ( Meng et al . , 2008; Grashoff et al . , 2010; Meng et al . , 2011 ) . Specifically , we used strains expressing a tension sensor module ( TSMod ) inserted into the coding sequence of the unc-70 gene ( Figure 3A; Krieg et al . , 2014 ) . UNC-70 , a β-spectrin ortholog , is expressed widely during embryogenesis and acts together with α-spectrin and actin to form a subcortical cytoskeletal network that is critical for cell shape and mechanics in a variety of cell types in C . elegans ( Bretscher , 1991; Hammarlund et al . , 2000; Moorthy et al . , 2000; Norman and Moerman , 2002 ) . Importantly , the UNC-70 ( TSMod ) fusion protein localized to the cell membrane cortex in a pattern that was seemingly identical to immunostaining of endogenous UNC-70 ( Moorthy et al . , 2000 ) , with a prominent accumulation at future location of the buccal cavity ( Figure 3B ) . Moreover , UNC-70 ( TSMod ) rescued the severely paralyzed locomotion phenotype of unc-70 null mutant animals , indicating that the fusion protein is functional ( Krieg et al . , 2014 ) . 10 . 7554/eLife . 06565 . 006Figure 3 . Pharyngeal attachment leads to increased forces at sensory depression . A FRET-based TSMod inserted into the C . elegans β-spectrin gene ( unc-70 ) was used to assess forces in live embryos . ( A ) Schematic for how UNC-70 ( TSMod ) detects tension . FRET occurs when the donor fluorophore ( mTFP ) transfers energy to a nearby acceptor fluorophore ( Venus ) within the same peptide . When UNC-70 ( TSMod ) experiences mechanical tension , a flexible linker separating mTFP and Venus is lengthened , leading to reduced FRET efficiency . ( B–D ) Representative images of wild-type and ( F-H ) pha-1 ( tm3671 ) strains that express UNC-70 ( TSMod ) . ( J–L ) Representative images of wild-type embryos expressing the no force control UNC-70 ( N-TSMod ) . Panels B , F , J depict 1 . 5-fold embryos after direct excitation of the Venus acceptor fluorophore . Panels C , D , G , H , K , L show FRET measurements where purple pixels indicate regions of highest tension ( low FRET ) . Small white-framed boxes in panels B , C , F , G , J , K indicate the sensory depression region ( SDR ) , which is enlarged in panels D , H , L . Red dashed lines in panels B , C , E , F , J , K outline the embryos . Scale bar in B = 30 µm . ( E , I , M ) FRET indices for the SDR and the region outside the sensory depression ( Out-SDR ) . Individual embryos are represented by red circles , which are connected by lines to indicate values acquired from the same embryo . p-values depicted were calculated using a T-test ( also see Supplementary file 2 ) . Numbers at the bottom indicate the number of embryos that were analyzed for each condition . Each point is an average of ∼3–5 frames from a z-stack encompassing the embryo ( see ‘Materials and methods’ for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 00610 . 7554/eLife . 06565 . 007Figure 3—figure supplement 1 . FRET index of low and high FRET controls . ( A ) Schematic of UNC-70 ( 5aa ) and UNC-70 ( TRAF ) FRET controls . ( B ) Quantification of low ( UNC-70 ( TRAF ) ) and high ( UNC-70 ( 5aa ) ) FRET controls in 1 . 5-fold embryos . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 007 The TSMod sensor consists of a donor ( mTFP ) and acceptor ( Venus ) fluorophore separated by a flexible linker made of 40 residues from the spider-silk flagelliform , which acts as an entropic nanospring suitable for estimating biologically relevant forces ( Figure 3A; Grashoff et al . , 2010 ) . The linker is sensitive to molecular forces in various systems ( Borghi et al . , 2012; Morimatsu et al . , 2013; Cai et al . , 2014; Krieg et al . , 2014; Paszek et al . , 2014 ) . Thus , as stretching forces act on this spring the two FRET fluorophores will be pulled apart and lead to a visible change in energy transfer . Consequently , a low FRET index indicates the application of a stretching force to UNC-70 ( TSMod ) and suggests that actin-spectrin networks in such regions experience high levels of mechanical tension . Conversely , a high FRET index suggests that such regions experience low or no tension across the actin-spectrin network . Importantly , we previously used this same sensor to investigate mechanical tension in C . elegans neurons and extend this robust imaging procedure ( see ‘Materials and methods’ ) to characterize its performance in living animals ( Krieg et al . , 2014 ) . To quantify the extent to which pharyngeal attachment and subsequent pulling forces lead to higher tension near the sensory depression , we compared FRET at the sensory depression region ( SDR ) with areas outside the sensory depression ( non-SDR ) in embryos before and after pharyngeal attachment to the epidermis ( early comma and 1 . 5-fold stages , respectively; see ‘Materials and methods’ ) . This strategy allows us to compare pixels from the SDR and non-SDR that have been measured under exactly the same conditions in a pairwise manner , since both measurements were derived from the same image and analyzed identically . Thus , any changes in FRET efficiency are unlikely to result from differences in expression levels of the sensor or imaging conditions . No significant differences in UNC-70 ( TSMod ) FRET efficiency were observed between the sensory depression region ( SDR ) and non-SDR region prior to the attachment of the pharynx to the epidermis ( early comma stage ) in either wild-type or pha-1 ( 0 ) embryos ( Figure 3E , I ) . In contrast , wild-type 1 . 5-fold embryos had significantly higher tension ( lower FRET ) at the SDR as compared with regions outside the sensory depression ( p = 0 . 0008 ) , consistent with the hypothesis that pharyngeal attachment contributes to the force balance at the sensory depression ( Figure 3C , D , I; Supplementary file 2 ) . In contrast , 1 . 5-fold pha-1 ( 0 ) mutants in which the pharynx failed to attach did not display appreciably lower levels of mechanical tension at the SDR as compared with regions outside the sensory depression ( p = 0 . 2421; Figure 3G , H , I; Supplementary file 2 ) . In addition , tension at the SDR was significantly higher in wild-type embryos as compared with the SDR region in pha-1 ( 0 ) mutants at the 1 . 5-fold ( p < 0 . 0001 ) but not early comma stages ( p = 1 . 000; Supplementary file 2 ) , also consistent with pharyngeal attachment and pulling leading to tension at the anterior epidermis . To interpret UNC-70 ( TSMod ) FRET signals during early stages of embryogenesis , we replaced the flexible linker by a large separator ( TRAF ) or a short linker of only five residues ( 5aa ) to generate UNC-70 ( TRAF ) and UNC-70 ( 5aa ) constructs in which the two FRET fluorophores are separated by a constant distance and , importantly , are insensitive to force . As expected , both control sensors localized in a pattern that was indistinguishable from endogenous UNC-70 ( data not shown; Moorthy et al . , 2000 ) , rescued the paralyzed phenotype of unc-70 adults ( data not shown; see ‘Materials and methods’ ) , and showed FRET values consistent with the distance of the fluorophores ( Figure 3—figure supplement 1 ) . In addition , no significant differences were observed between SDR and non-SDR regions in 1 . 5-fold wild-type embryos using these controls ( Figure 3—figure supplement 1; Supplementary file 2 ) and their FRET efficiency values are similar to those reported previously ( Borghi et al . , 2012; Krieg et al . , 2014 ) . To further confirm that the observed differences in the FRET efficiency of UNC-70 ( TSMod ) reliably report differences in molecular tension in our experimental system , we generated animals that carry an UNC-70 ( N-TSMod ) fusion protein , in which the force sensitive FRET construct has been placed at the N-terminus of full-length unc-70 β-spectrin . In this position , the TSMod is not responsive to force and would be predicted to yield FRET signatures consistent with no-force situations . Similar to the other UNC-70 fusion proteins , UNC-70 ( N-TSMod ) was expressed in a pattern indistinguishable to that of the native UNC-70 protein and the transgene restored locomotion to paralyzed unc-70 adult animals ( data not shown ) . As expected , FRET values were higher in embryos that expressed the force-insensitive UNC-70 ( N-TSMod ) vs UNC-70 ( TSMod ) ( Figure 3; Supplementary file 2 ) , consistent with previous results that a terminal TSMod fusion cannot be pulled apart by cellular forces ( Grashoff et al . , 2010; Borghi et al . , 2012; Conway et al . , 2013; Krieg et al . , 2014 ) . Importantly , we did not see gross variations in FRET across different tissues within the same embryo in N-TSMod expressing animals , consistent with the idea that the variation in UNC-70 ( TSMod ) is due to different forces acting on UNC-70 . We also noted that FRET values were independent of the expression level of the fluorophores , indicating that the FRET signal in each pixel was predominantly coming from intramolecular as opposed to intermolecular energy transfer ( data not shown ) . Taken together , the FRET tension sensor provides strong independent support for our model in which the anterior epidermis experiences a high level of mechanical stress that is due in large part to forces exerted by the pharynx ( Figure 1C ) . We hypothesized that MEC-8 , an RNA-binding protein and known splicing factor ( Lundquist et al . , 1996; Spike et al . , 2002; Calixto et al . , 2010 ) , may regulate the mRNA processing of one or more genes that function to stabilize the epidermis in response to mechanical forces . Because the RNA recognition site for MEC-8 is unknown , we used a non-biased approach to identify candidate MEC-8 targets . mRNAs obtained from wild-type and mec-8 mutant embryos were analyzed using a whole-genome tiling-array approach ( Mockler et al . , 2005; He et al . , 2007 ) . We identified 1106 individual regions within a total of 449 genes that were differentially expressed ( >1 . 5-fold ) between wild-type and mec-8 embryos ( Supplementary file 3 ) . This included 159 genes ( 666 regions ) in which at least one exon was upregulated in mec-8 mutants , 286 genes ( 421 regions ) in which at least one exon was downregulated in mec-8 mutants and 12 genes ( 19 regions ) in which at least one intron was upregulated in mec-8 mutants ( Supplementary file 3 ) . We note that seven genes included in the totals above contained both upregulated introns and exons . Among the 449 identified genes , 135 ( 30% ) are annotated by WormBase as having multiple ( alternatively spliced ) isoforms ( Supplementary file 3 ) . This included 67% ( 8/12 ) of the genes with up-regulated introns , 47% ( 75/159 ) of genes with up-regulated exons and 22% ( 52/286 ) of genes with down-regulated exons . Tiling-array findings were confirmed for several genes within each of the categories described above by PCR analysis ( Figure 4—figure supplement 1; Supplementary file 3 ) . Many of the identified genes , particularly those with only a single identified mRNA isoform , are unlikely to be direct targets of MEC-8 , which regulates alternative splicing ( Spike et al . , 2002; Calixto et al . , 2010 ) . Such genes are more likely to display transcriptional misregulation as an indirect consequence of mec-8 loss of function . Also , a significantly higher proportion of the identified genes containing either up-regulated exons or introns were alternatively spliced , as compared with genes containing down-regulated exons ( p < 0 . 0001 and p < 0 . 005 , respectively ) or in comparison with all annotated C . elegans genes ( p < 0 . 0001 and p < 0 . 005 , respectively; ∼25% of C . elegans genes are thought to be alternatively spliced; Ramani et al . , 2011 ) . Given the established role of MEC-8 in alternative splicing , these genes are more likely to include direct targets of MEC-8 . This is supported by the observation that unc-52 , a known target of MEC-8 ( Spike et al . , 2002 ) , was among the exon-up genes identified by the array study and because a second established target of MEC-8 , mec-2 , requires MEC-8 for the removal of one of its introns ( Calixto et al . , 2010 ) . Given that mec-2 did not , however , meet all of our imposed criteria for designation as a positive outcome from the tiling array , our final gene list is likely to be missing at least some authentic MEC-8 targets . To identify downstream targets of MEC-8 that are relevant to the synthetic phenotype of mec-8; sym-3 and mec-8; sym-4 mutants , we screened ∼200 of the most highly misregulated genes within the dataset for enhancement of the Pin phenotype in single-mutant backgrounds ( i . e . , sym-3 , sym-4 and mec-8 ) using RNAi feeding methods . Although several gene inactivations caused low-to-moderate levels of Pin in one or more of the mutant backgrounds ( data not shown ) , one gene , fbn-1 ( ZK783 . 1 ) , led to strong enhancement of Pin in both non-RNAi-sensitized and RNAi-hypersensitive mutant backgrounds ( see below ) . In addition , several features of fbn-1 made it an attractive candidate as a MEC-8 target . In particular , fbn-1 is notable in that it is one of only 12 genes within the intron-up category , and , based on fold changes , is the third most highly misregulated gene in the tiling array data set ( Supplementary file 3 ) . Based on the tiling array , the region of fbn-1 that is misregulated in mec-8 mutants spans exons 14–19 , which includes the region of fbn-1 that is alternatively spliced ( exons 14–16; Figure 4A , B ) . Most notably , expression of an fbn-1 cDNA ( e isoform ) driven by native fbn-1 promoter sequences partially rescued the synthetic lethality of mec-8; sym-4 mutants in two independent lines ( Figure 1B ) . This latter finding indicates that fbn-1 is a critical target for misregulation in mec-8; sym-4 mutants . 10 . 7554/eLife . 06565 . 008Figure 4 . Splicing between a subset of fbn-1 exons is strongly misregulated in mec-8 mutants . ( A ) A schematic of the fbn-1 genomic locus is shown with alternatively spliced exons ( e14–19 ) indicated by colored blocks and enlarged below . Single-sided arrows indicate PCR primers used in panel B . Lighter-shaded rectangles below exons 14 and 16 indicate alternative 3′ splice sites for these exons . ( B ) PCR of the indicated regions of fbn-1 using wild-type ( N2 ) and mec-8 cDNAs derived from embryos and wild-type genomic DNA ( gDNA ) as templates . White and black arrowheads indicate bands that correspond to known fbn-1 isoforms ( depicted on right ) based on size estimations for PCR products ( in basepairs ) : a/k = 476 , b = 407 , d = 341 , e = 200 , f = 248 , g = 107 , h = 182 . Yellow arrowheads indicate aberrant fbn-1 mRNA products that are present or are strongly enriched only in mec-8 mutants . ( C ) Schematic of FBN-1 ( a isoform ) showing the locations of protein domains and the amino acid positions affected by fbn-1 mutant alleles . For an annotated amino acid sequence , see Figure 4—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 00810 . 7554/eLife . 06565 . 009Figure 4—figure supplement 1 . Examples of gene regions differentially expressed in mec-8 mutants and confirmed by RT-PCR . Total RNA used for tilling arrays was reverse transcribed using oligo-dT primers and amplified by PCR using specific primers for each gene region ( see Supplementary file 3 ) . Each gene region was amplified using 30 cycles . Sizes are indicated in base pairs; arrows indicate bands that correspond to the expected sizes in mec-8 mutants based on tiling array results . For size reference , a 2-log DNA ladder ( NEB ) was used . ( A ) Control PCR with ama-1 . ( B ) A gene ( bed-2 ) with up-regulated introns ( intron up ) and that is not alternatively spliced ( non-AS ) . ( C ) Intron up , AS gene ( ZK180 . 5c and b ) . ( D ) Exon up , non-AS genes ( hsp-16 . 41 , F36A2 . 13 , T15D6 . 8 ) . ( E ) Exon up , AS genes ( ajm-1 , cah-4 , ZK180 . 5a , asns-2 ) . ( F ) Exon down , non-AS genes ( C55B7 . 11 , gcy-6 , dmsr-14 ) . ( G ) Exon down , AS gene ( W05H9 . 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 00910 . 7554/eLife . 06565 . 010Figure 4—figure supplement 2 . Amino acid sequence of FBN-1 . Annotated peptide sequence for FBN-1 ( a isoform ) . Locations of domains and genetic mutations are indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 010 To confirm the tiling array results for fbn-1 , we used PCR to amplify regions of fbn-1 from cDNA pools derived from wild-type and mec-8 mutant embryos . Whereas primers amplifying the region spanning exons 14–19 generated multiple bands of the approximate expected sizes in wild type , these bands were either absent or strongly reduced in mec-8 mutants and were replaced by higher-molecular-weight , or otherwise aberrant , species ( Figure 4A , B ) . Consistent with a reduction in splicing efficiency , splicing between exons 16 and 17 was largely abolished in mec-8 mutants ( Figure 4A , B ) . In contrast , splicing between exons 19 and 20 was unaffected in mec-8 mutants , consistent with both the tiling array findings and the absence of known alternative splicing events between these exons ( Figure 4A , B ) . Thus MEC-8 is required for normal splicing events within the region encompassing exons 14 through 19 of fbn-1 . The observed splicing defects of fbn-1 mRNA in mec-8 mutants should result in a reduction in the abundance of wild-type FBN-1 isoforms and reduced FBN-1 activity . In addition , the presence of stop codons within introns 17 and 18 may lead to the production of abnormal truncated forms of FBN-1 ( Figure 4C ) . It is also possible that some of these aberrant transcripts are targeted for degradation by RNA surveillance systems that recognize abnormally long non-coding regions within mRNAs ( Mango , 2001 ) . FBN-1 is composed of many calcium-binding and non-calcium-binding EGF-like repeats , which are found in many matrix proteins and the extracellular domains of transmembrane proteins ( Figure 4C , Figure 4—figure supplement 2; Davis , 1990 ) . Comparison of the predicted FBN-1 peptide sequence with mammalian sequences revealed greatest sequence similarity with the family of latent TGFβ binding proteins ( LTBPs ) , which include LTBP-1 , -2 and -4 and fibrillins 1–3 ( Rifkin , 2005; Todorovic et al . , 2005; Hynes , 2009 ) . These proteins carry out structural roles in the ECM in association with elastic fibrils , mediate cell-ECM adhesion , and act as sinks or reservoirs for TGFβ ligands , thereby modulating signal transduction . C . elegans FBN-1 differs from the other LTBP proteins by lacking the TGFβ binding domains and by having a zona pellucida ( ZP ) domain . ZP domains are found in many apical ECM proteins and are thought to mediate polymerization , resulting in the formation of protein fibrils ( Plaza et al . , 2010 ) . The presence of a furin cleavage site in FBN-1 immediately after the ZP domain suggests that the extracellular domain of FBN-1 can be secreted ( Figure 4C; also see below ) . FBN-1 also contains an 834-amino-acid region ( 560–1393 ) that is enriched for serine ( 13% ) and threonine ( 13% ) residues as well as a 179-amino-acid region ( 1920–2098 ) that is enriched for serine ( 15% ) , threonine ( 24% ) and proline ( 13% ) residues . More generally , the sequence of FBN-1 , as well as its sequence similarity to vertebrate LTBPs , is consistent with FBN-1 carrying out a structural function in extracellular matrices affiliated with epithelial cells . We note that mis-splicing within the region encompassed by exons 14–19 in mec-8 mutants should not disrupt any known protein motifs ( Figure 4C ) . Nevertheless , this region is well conserved within FBN-1 orthologs in other Caenorhabditis family members and is also present in more distantly related parasitic species . In addition , a failure to splice out intron 17 or 18 would lead to a frameshift in the message and a truncated protein that lacks the ZP domain and transmembrane segment . Three mutant alleles of fbn-1 were obtained for analysis including two point mutations ( ns67 and ns283; M Heiman and S Shaham , unpublished data ) and a deletion mutation ( tm290 ) generated by the C . elegans deletion mutant consortium ( C . elegans Deletion Mutant Consortium , 2012 ) . Both point mutations lead to non-conservative missense mutations , P116L and C148A , within the first and second EGF-like repeats , respectively ( Figure 4A , C , Figure 4—figure supplement 2 ) . The deletion mutation is missing 604 bp within the eighth exon of fbn-1 , which encodes a sequence that is serine and threonine rich ( Figure 4A , C ) . The tm290 mutation should produce a protein containing the first 714 amino acids of FBN-1 followed by 224 novel amino acids before encountering a stop codon; the tm290 transcript may also be targeted for degradation by the non-sense mediated decay pathway ( Mango , 2001 ) . Whereas the ns67 and ns283 alleles were able to be propagated as homozygotes , tm290 homozygotes were not easily propagated and often arrested during the larval molts , consistent with a previous report ( Frand et al . , 2005 ) . We first examined fbn-1 alleles for the presence of the keyhole structure in embryos and the Pin phenotype in L1 larvae . Strikingly , strains containing either missense allele ns67 or ns283 exhibited the Pin phenotype in ∼45% of their progeny , whereas tm290 homozygotes produced by homozygous mothers carrying a rescuing fbn-1 ( + ) extrachromosomal array had a lower percentage of Pin larvae ( ∼20% within the population of array-minus progeny; Figure 5B , C; Table 1 and Supplementary file 1 ) . Consistent with these findings , fbn-1 ( RNAi ) feeding of RNAi-hypersensitive mutants gave rise to ∼30% Pin larvae ( Figure 5A ) . In addition , all three alleles of fbn-1 led to formation of the keyhole in embryos ( Figure 5C , Table 1 , Supplementary file 1 , data not shown ) . Although the penetrance of Pin in fbn-1 single mutants was lower than mec-8; sym-3 or mec-8; sym-4 double mutants , the depth of the keyhole observed in some fbn-1 ( tm290 ) homozygous embryos exceeded that observed in mec-8; sym-3 or mec-8; sym-4 embryos ( Figure 5C , Table 1 ) . Thus inhibition of fbn-1 alone can lead to a compromised embryonic sheath , making the underlying epidermis more susceptible to deformation by mechanical forces including the pulling force exerted by the pharynx . In addition , because mec-8 homozygous animals are viable and showed a relatively low percentage of Pin larvae ( Figure 1; Table 1 and Supplementary file 1 ) , we can infer that fbn-1 function is only partially impaired in mec-8 mutants , consistent with our tiling array and PCR-based analyses ( Figure 4B ) . 10 . 7554/eLife . 06565 . 011Figure 5 . Morphogenesis defects of fbn-1 mutants are strongly enhanced by mutations in sym-3 , sym-4 and mec-8 . ( A ) RNAi feeding of fbn-1 was carried out in the indicated backgrounds including strains hypersensitized to RNAi . Control RNAi strains contained the vector plasmid pPD129 . 36 . ( B ) The Pin phenotype was scored in fbn-1 mutant alleles and in selected double mutants with fbn-1 and mec-8 , sym-3 or sym-4 . Error bars in A and B represent 95% CIs . For additional information , see Table 1 and Supplementary file 1 . ( C ) Representative images for select single and compound mutants . Note the presence of strong head malformations in fbn-1 ( tm290 ) ; sym-3 and fbn-1 ( tm290 ) ; sym-4 larvae . Also note that the strong epidermal malformations observed in fbn-1 ( tm290 ) ; mec-8 mutants are suppressed by let-502 ( RNAi ) . White arrows indicate ingressions or furrows throughout the epidermis; red arrows , detached anterior cells in fbn-1 ( tm290 ) ; mec-8 mutants . Yellow dashed lines indicate lateral pharyngeal borders; orange dashed lines , the sensory depression or keyhole; black arrows , posterior extent of ingression . White scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 011 We next constructed double mutants between fbn-1 and sym-3 , sym-4 and mec-8 using the ns67 and tm290 alleles . The percentage of Pin animals in double mutants ranged from 97–100% , consistent with the enhancement observed for fbn-1 ( RNAi ) in RNAi-hypersensitive backgrounds ( Figure 5A–C , Supplementary file 1 ) . In addition , the average depth of the keyhole in these embryos was typically greater than that observed for mec-8; sym-3 or mec-8; sym-4 mutants as well as for fbn-1 single mutants ( Table 1 ) . Notably , certain double–mutant combinations displayed phenotypes that had not been previously observed in mec-8; sym-3 or mec-8; sym-4 mutants or in fbn-1 single mutants . In the case of fbn-1 ( tm290 ) ; sym-3 and fbn-1 ( tm290 ) ; sym-4 mutants , large lumps or protuberances on the head region were observed in L1 larvae ( Figure 5C ) , which are reminiscent of certain phenotypes observed in integrin pathway mutants ( Baum and Garriga , 1997; Tucker and Han , 2008 ) . Interestingly , mec-8; fbn-1 ( tm290 ) mutants arrested uniformly as embryos and failed to complete embryonic elongation ( Figure 5C ) . These embryos displayed a deep keyhole by the 1 . 5-fold stage ( Table 1 and Supplementary file 1 ) , and by the ∼threefold stage 92% ( n = 73 ) exhibited prominent epidermal ingressions and furrows , which were often regularly spaced ( Figure 5C ) . Notably , this phenotype was previously observed after digestion of the embryonic sheath with trypsin ( Priess and Hirsh , 1986 ) , suggesting that FBN-1 carries out mechanostructural functions throughout the embryonic sheath including a role in stabilizing the epidermis during circumferential constriction . Consistent with this interpretation , inhibition of epidermal actomyosin contraction using let-502 ( RNAi ) reduced the frequency of mec-8; fbn-1 ( tm290 ) embryos that contained deep furrows to 17% ( n = 52; Figure 5C ) . We note that in addition to surface furrows and blobs , some mec-8; fbn-1 ( tm290 ) embryos also showed cell detachment phenotypes ( Figure 5C ) , suggesting that MEC-8 and FBN-1 promote epidermal integrity . Because tm290 is likely to constitute a null mutation in fbn-1 , we interpret the severe phenotype of mec-8; fbn-1 ( tm290 ) mutants to indicate that MEC-8 regulates additional proteins that act redundantly with FBN-1 together to promote normal epidermal structure and morphogenesis . On the basis of the above findings , we hypothesized that FBN-1 is a component of the embryonic sheath , a specialized ECM secreted from the apical surface of epidermal cells that promotes structural stability and resistance to biomechanical forces ( Priess and Hirsh , 1986 ) . A requirement for fbn-1 in the epidermis was first tested by treating wild-type and NR222 strains with fbn-1 ( RNAi ) using standard feeding methods . Whereas wild-type strains can undergo ‘systemic’ RNAi ( throughout the majority of tissues ) , NR222 is engineered to undergo RNAi in the epidermis only ( Qadota et al . , 2007 ) ( Figure 5A , Figure 6A , Supplementary file 1 ) . Although RNAi of fbn-1 failed to produce any visible phenotype in these strains , enhancement of the Pin phenotype was observed in an NR222 derivative that carried a mec-8 mutation ( p < 0 . 01 ) , implicating the epidermis as critical for fbn-1 activity ( Figure 6A and Supplementary file 1 ) . 10 . 7554/eLife . 06565 . 012Figure 6 . The fbn-1 gene is required in the epidermis and specifies a component of the embryonic sheath . ( A ) Systemic and epidermal-specific RNAi of fbn-1 was carried out in wild-type ( N2 ) and strain NR222 , respectively , and in both backgrounds containing the mec-8 ( u74 ) allele . Note that both systemic and epidermal-specific fbn-1 ( RNAi ) led to an increased percentage of Pin animals in the mec-8 background . Error bars indicate 95% CIs; **p < 0 . 01 . ( B ) Schematic of the C . elegans embryonic lineage and findings from the fbn-1 mosaic analysis . Strains used for the analysis were WY1059 , fbn-1 ( tm290 ) ; sym-4 ( mn619 ) ; fdEx249[fbn-1 ( + ) ; sur-5::GFP] , and WY1068 , mec-8 ( u74 ) ; fbn-1 ( tm290 ) ; fdEx249 . Green numbers indicate the number of L4 or adult mosaic animals that were not Pin but contained the fbn-1 ( + ) rescuing array within that lineage only . ( C ) Wild-type and fbn-1 ( ns67 ) embryos expressing Pfbn-1::GFP-PEST ( a convenient marker for embryonic epidermal cells ) . hyp4 cells within the focal plane are indicated and show aberrant morphologies in mutant embryos that contain a keyhole . ( D ) . Expression of Pfbn-1::GFP-PEST and mini-fbn-1::mCherry ( Δfbn-1–49-2418 ) reporters . In the Pfbn-1::GFP-PEST panels , epidermal cells are indicated with white arrows . Black arrows indicate several cells positive for Pttx-3::GFP , which was used as an injection marker and is not expressed in epidermal cells . In the mini-fbn-1::mCherry panels , the apical surface of embryonic epidermal cells ( sheath ) is indicated by white arrows . mini-fbn-1::mCherry is also detected in the extra-embryonic space ( white dashed triangles ) . White scale bar = 10 µm , black bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 012 Genetic mosaics ( Yochem and Herman , 2005 ) were also examined for the focus of fbn-1 activity in the prevention of the Pin phenotype . This non-biased approach used an fbn-1 ( tm290 ) ; sym-4 ( mn619 ) strain carrying a rescuing fbn-1 ( + ) extra-chromosomal array , fdEx249 , that also expresses a fluorescent reporter , sur-5::GFP , to assess mitotic inheritance of the array ( Yochem et al . , 1998 ) . This strain segregated array-minus Pin progeny , array-plus viable progeny and array-plus viable progeny that were mosaic for inheritance of the array; array-minus non-Pin fbn-1 ( tm290 ) ; sym-4 ( mn619 ) animals were not observed . Based on numerous mosaics , fbn-1 activity is focused in hyp6 , the anterior portion of the hyp7 syncytium , or both hyp6 and hyp7 . An exact determination of inheritance was not possible because both the hyp6 and hyp7 syncytia initiate formation through cell fusion near the time the keyhole ( Pin ) becomes apparent and the hyp6 syncytium fuses with the hyp7 syncytium late in the L2 stage ( Yochem et al . , 1998 ) . Nevertheless , two mosaics proved particularly informative in that hyp7 was the only positive tissue . Moreover , SUR-5::GFP was expressed in an anterior-to-posterior gradient in both mosaics , suggesting establishment of the positive clone within the hyp7 syncytium by one or more hyp6 cells , which are anterior , or possibly one or more anterior hyp7 cells . Additional mosaics were consistent with a requirement for fbn-1 ( + ) in anterior epidermal cells . For example , in 16 mosaics , only AB , one of the daughters of the zygote , had established a positive clone ( Figure 6B ) . In contrast , there were no reciprocal mosaics in which P1 , but not AB , had established a positive clone . Although 12 of the hyp7 cells of the embryo descend from P1 , these cells are not located as far anterior in the embryo as are certain hyp7 ( or hyp6 ) cells that descend from AB ( Sulston et al . , 1983 ) . In addition to these 16 AB ( + ) P1 ( − ) mosaics , there were 25 mosaics in which positive clones had been established within the AB sublineage only . In every case , these clones contributed descendants to hyp6 or to the anterior part of hyp7 ( Figure 6B ) . Although the genetic mosaics are consistent with the epidermal-specific RNAi described above , the mosaics cannot eliminate other anterior epidermal cells as important for expression of fbn-1 . For example , although hyp4 cells , which are closer to the sensory depression than hyp6 or hyp7 cells , were not specifically implicated in the analysis , they could still contribute significant FBN-1 for proper function of the sheath in wild-type embryos . For example , a contribution by hyp4 could be obviated in mosaics by over-expression of fbn-1 in hyp6 or anterior hyp7 cells , particularly if it is diffusible following secretion from the apical surface of these cells . In fact , a requirement for fbn-1 in the sheath surrounding hyp4 is consistent with the observed deformation of hyp4 cells in fbn-1 mutants ( Figure 6C ) . Neither sheath nor socket cells associated with the sensory depression were implicated in the mosaic analysis , underscoring the requirement for fbn-1 expression in the epidermis for the prevention of Pin . Also of note , the molting defect associated with fbn-1 ( tm290 ) was rescued in all of the non-Pin mosaics . Thus , the epidermis appears to be the sole focus for both major aspects of the fbn-1 phenotype . To more directly assess fbn-1 expression in live embryos , we used strains that contained one of two fbn-1 fluorescent reporters . Pfbn-1::GFP-PEST is expressed under the control of the native fbn-1 promoter and contains PEST sequences , which reduce the half-life of GFP ( Frand et al . , 2005 ) . Pfbn-1::GFP-PEST expression was first detected in epidermal cells at the onset of embryonic morphogenesis , and expression continued throughout embryogenesis ( Figure 6D ) . The mini-fbn-1::mCherry reporter includes both an N-terminal region ( aa 1–48 ) that contains a predicted signal peptide ( aa 1–26 ) and a portion of the C terminus ( aa 2418–2781 ) that includes the ZP domain ( aa 2438–2674 ) , the furin cleavage site ( aa 2676–2679 ) and the predicted transmembrane segment ( aa 2745–2767 ) . mini-fbn-1::mCherry localized to the apical surface of epidermal cells coincident with the location of the embryonic sheath ( Figure 6D ) . Expression was first detected during early stages of morphogenesis and increased in intensity through the 1 . 5-fold stage , consistent with the timing of embryonic sheath formation ( Figure 6D; Priess and Hirsh , 1986 ) . mini-fbn-1::mCherry was also detected during late stages of embryogenesis and in larvae ( to be described elsewhere ) . Notably , mini-fbn-1::mCherry was detected in the extra-embryonic space of early morphogenetic embryos , consistent with apical secretion of the fusion proteins ( Figure 6D ) . The expression of FBN-1 in epidermal cells and its secretion to the apical surface is consistent with the model that FBN-1 functions as a structural component of the embryonic sheath where it prevents mechanical deformation of the epidermis . Priess and Hirsch ( 1986 ) used laser permeabilization of the eggshell followed by trypsin treatment to induce digestion of the embryonic sheath . Although they reported striking indentations or furrows at the surface of ∼twofold-stage trypsin-treated embryos , similar to what we observed for mec-8; fbn-1 ( tm290 ) mutants ( Figure 5C ) , defects of the sensory depression were not described . We therefore carried out a similar experiment in which we used chitinase to partially or completely digest the eggshell followed by trypsin treatment . Most notably , we detected keyholes in ∼1 . 5-fold to 3 . 0-fold-stage embryos , as well as mild ingressions at the surface of some embryos ( Figure 7A ) . We note that the surface ingressions we observed were less dramatic than those reported in the previous study , which may be due in part to the different methods used to permeabilize the eggshell . Epidermal ingressions induced by trypsin treatment were also less severe than those observed for mec-8; fbn-1 ( tm290 ) mutants ( Figure 5C ) , suggesting that the sheath was more severely compromised in these double mutants . Of interest , keyholes were seen in some embryos that lacked other obvious morphological defects ( data not shown ) , suggesting that the region of the pharyngeal attachment is particularly sensitive to deformation after partial degradation of the sheath . These findings were also consistent with the lack of gross morphological defects seen in fbn-1 single mutant embryos as well as mec-8; sym-3 or mec-8; sym-4 double mutants , which nevertheless had a prominent keyhole and Pin phenotype . We note that the pharyngeal cuticle contains the polysaccharide chitin and thus treatment with chitinase could be expected to preferentially degrade the pharyngeal cuticle as well as the eggshell ( Zhang et al . , 2005 ) . Nevertheless , the penetrance of Pin was ∼10-fold higher in embryos treated with both trypsin and chitinase relative to chitinase alone ( data not shown ) , consistent with a role for sheath proteins in preventing mechanical deformation of cells surrounding the sensory depression . 10 . 7554/eLife . 06565 . 013Figure 7 . The embryonic sheath is critical for resistance to biomechanical forces . ( A ) Wild-type embryos were treated with chitinase to remove part or all of the eggshell and then with trypsin to digest the sheath . Note the presence of a keyhole in both twofold and threefold trypsinized embryos ( black arrows ) and multiple ingressions or furrows in the epidermis of a twofold embryo ( white arrowheads ) . Yellow dashed lines indicate lateral pharyngeal borders; orange dashed lines , the sensory depression or keyhole . White scale bars = 10 µm , black bars = 5 µm . ( B ) Model for the circumferential squeezing force ( red arrows ) and pharyngeal pulling force ( yellow arrow ) that act on the embryonic sheath . When the sheath is moderately weakened , such as when fbn-1 function is partially impaired , a keyhole phenotype is observed , suggesting that the anterior epidermis is particularly sensitive to a reduction in sheath integrity as a result of the pharyngeal pulling force . In cases where the sheath is more severely compromised , the depth of the keyhole may further increase , and the embryonic epidermis develops ingressions or furrows where circumferential constricting forces are acting . DOI: http://dx . doi . org/10 . 7554/eLife . 06565 . 013
Force is essential for shaping the embryo and its internal organs ( Keller et al . , 2003 , 2008; Davidson , 2011; Davidson , 2012; Heisenberg and Bellaiche , 2013 ) , and spatiotemporal application of tightly controlled forces ensures normal morphogenesis . Proper development also requires that cells and tissues that experience forces respond in a consistent and context-appropriate manner . Either too much or too little resistance on the part of targeted tissues can lead to morphogenetic abnormalities and birth defects ( Epstein et al . , 2004; Moore et al . , 2013 ) . Our studies have implicated FBN-1 , along with MEC-8 , SYM-3 and SYM-4 , in promoting correct epidermal morphology and resistance of the C . elegans epidermis to two biomechanical forces . One force is generated by the circumferential constriction of epidermal actomyosin rings and was identified nearly 20 years ago as the major driver of embryonic elongation ( Priess and Hirsh , 1986 ) . We have described here a second force in which the elongating pharynx exerts an inward pull on the anterior epidermis throughout much of embryonic development . Although the potential for a pulling force was suggested by a previous study describing early steps of pharyngeal morphogenesis ( Portereiko and Mango , 2001 ) , this force was not characterized in any detail . Our studies suggest that this force may result from an intrinsic mechanical resistance of the embryonic pharynx to stretching . Moreover , the epidermal constricting force and pharyngeal stretching force are mechanistically linked because the extension of the pharynx requires the elongation of the epidermis . We also note that whereas inhibition of fbn-1 alone led to decreased resistance to the pharyngeal pulling force , deformation of the lateral epidermis by the circumferential constricting force required the simultaneous loss of fbn-1 and mec-8 , indicating that additional targets of MEC-8 likely contribute to epidermal stability . Our studies indicate that FBN-1 , a protein that is related to fibrillin , is critical for biomechanical force resistance by the epidermis during development . FBN-1 was broadly expressed in the embryonic epidermis and was secreted to the apical surface as a putative component of the embryonic sheath . In Pin embryos that lacked wild-type fbn-1 activity , progenitor cells of the hyp4 epidermal syncytium became hyperextended . Although hyp4 cells were not directly implicated as the focus for fbn-1 expression by the mosaic analysis , secreted ECM proteins can rescue defects at a distance or when expressed from cell types that are not normally the source of the protein product ( Heiman and Shaham , 2009 ) . This is particularly true if proteins are overexpressed , as is often the case for mosaic analysis . The lack of identified mosaic animals in which hyp4 was the only positive epidermal clone may be due to their low frequency of occurrence or because expression of fbn-1 in hyp4 is not essential for rescue of Pin if other neighboring cells secrete high levels of FBN-1 . Alternatively , expression of FBN-1 in hyp6/7 progenitors could possibly alter the biophysical properties of the sheath and the tension on hyp4 cells . More generally , our findings implicate fbn-1 expression in the anterior epidermis as critical for suppression of the Pin phenotype . In addition , analysis of mec-8; fbn-1 double mutants indicated a role for FBN-1 throughout the embryonic sheath in resisting or properly distributing forces that arise during circumferential constriction of the epidermis . We failed to detect morphological defects at the attachment site of the intestine to the rectum in any of the mutant backgrounds examined . Although it is possible that stretching of the intestine during embryonic elongation may also lead to forces that act on the posterior epidermis of the worm , such forces may be lower in magnitude than those experienced at the anterior , possibly because of structural differences between the pharynx and intestine . A number of human diseases that affect ECM components can lead to altered mechanical properties of the skin and other connective tissues , which may parallel defects observed in C . elegans fbn-1 mutants ( Judd , 1984; Milewicz et al . , 2000 ) . This includes mutations in human fibrillin 1 , which is mutated in Marfan syndrome ( Dietz et al . , 2005; Ramirez and Dietz , 2009; Ramirez and Sakai , 2010 ) . Although our findings suggest that FBN-1 and human fibrillins may carry out some related functions in the ECM , structural differences between FBN-1 and vertebrate fibrillins suggest significant functional divergence ( Piha-Gossack et al . , 2012 ) . For example , FBN-1 lacks conserved TGFβ binding sites found in human fibrillins and contains a ZP domain not found in LTBP family proteins . Thus , whereas mammalian fibrillins and other members of the LTBP family of proteins have secondary roles in modulating signal transduction , their closest counterparts in nematodes may be limited to structural functions only . In addition , mammalian fibrillins interact with elastins ( Baldwin et al . , 2013 ) , which are not present in C . elegans . A phylogenetic analysis suggested that fibrillins may have been lost or severely disrupted in the nematode lineage as well as in Drosophila , although apparent fibrillin orthologs are present in several insect species including ants and honeybees ( Piha-Gossack et al . , 2012 ) . Our findings in C . elegans suggest that FBN-1 is required in the embryonic sheath to ensure the appropriate level of resistance to mechanical deformation by inward-pulling forces ( Figure 7B ) , a function originally proposed for the sheath by Priess and Hirsch ( 1986 ) . This could be because FBN-1 directly affects the resilience of the embryonic sheath , thereby influencing the response of attached epidermal cells to mechanical forces . Alternatively , FBN-1 may be required for the stable attachment of epidermal cells to the sheath or the efficient transmission and distribution of forces throughout the sheath . For example , FBN-1 in the sheath could interact with other transmembrane proteins expressed on the apical surface of the epidermis , consistent with the presence of putative integrin ( Arg/Gly/Asp; RGD ) binding sites in FBN-1 ( Figure 4—figure supplement 2 ) . In this latter scenario , inward-pulling forces may physically detach epidermal cells from the overlying sheath in mutants with compromised fbn-1 function , leading to excessive or atypical deformation of the unattached epidermis . In addition , it is also possible that FBN-1 could promote attachment of the epidermis to the sheath through its transmembrane domain , although cleavage of FBN-1 by furin proteases , a post-translational processing event conserved in human fibrillins ( Lönnqvist et al . , 1998; Ashworth et al . , 1999 ) , make this mechanism less likely . More generally , our results suggest that mechanical properties of the aECM strongly affect epidermal cell architecture and embryonic morphogenesis . Although originally thought to function exclusively as a protective barrier to the environment , the aECM has recently been recognized to play key roles in epithelial morphogenesis , tube formation and cell junction stability ( Hynes , 2009; Brown , 2011 ) . In C . elegans , several aECM proteins containing extracellular leucine-rich only ( eLLRon ) repeats are required to maintain the integrity of epithelial junctions within the lumen of the excretory system ( Mancuso et al . , 2012 ) and to promote dendrite branching ( Liu and Shen , 2012 ) . Correspondingly , the Drosophila eLLRon protein dALS/convoluted is required to organize the tracheal lumen matrix , and mutations in dALS lead to tracheal tube morphogenetic defects ( Beitel and Krasnow , 2000; Swanson et al . , 2009 ) . A second class of aECM protein , those containing a ZP domain , have been implicated in tubulogenesis and epithelial morphogenesis in Drosophila ( Denholm and Skaer , 2003; Jazwinska et al . , 2003; Roch et al . , 2003; Plaza et al . , 2010; Dong et al . , 2014 ) . The Drosophila ZP-domain protein DPY has been proposed to organize cuticle architecture and to anchor the cuticle to the epidermis . Based on its size , structural motifs and mutant phenotypes , DPY has been proposed to distribute mechanical tension within the cuticle , thereby stabilizing the attachment of the epidermis to the cuticle ( Wilkin et al . , 2000 ) . Notably , dpy is the closest Drosophila relative to C . elegans fbn-1 , and their related mutant phenotypes suggest strong functional conservation . In C . elegans , DYF-7 , a ZP-domain protein , and DEX-1 , which contains a zonadhesin domain , are required to anchor dendrite endings at the nose while the neuronal cell bodies migrate away , stretching the dendrites behind them as they migrate ( Heiman and Shaham , 2009 ) . These neurons may experience mechanical tension during the process of retrograde dendritic extension , and mutations in dex-1 or dyf-7 lead to morphogenetic defects in these neurons and associated glia . In addition to eLLRon and ZP-domain proteins , several other classes of aECM proteins have been implicated in epithelial morphogenesis in C . elegans , Drosophila and other species ( Lane et al . , 1993; von Kalm et al . , 1995; Moussian et al . , 2007; Willenborg and Prekeris , 2011; Labouesse , 2012; Syed et al . , 2012; McLachlan and Heiman , 2013; Luschnig and Uv , 2014 ) . Because our tension sensor records molecular tension only within UNC-70/β-spectrin , it remains unclear how the observed intracellular tension is related to tension in the aECM . Further studies of the mechanical resistance of the embryonic sheath and the forces transmitted through cell-adhesion and matrix-anchoring molecules are needed to elucidate this important aspect of morphogenesis . Although our studies demonstrate that FBN-1 is an important downstream target of MEC-8 in promoting force resistance by the epidermis , it is clearly not the only target of MEC-8 that carries out structural or biomechanical functions . Cytoskeletal and ECM proteins implicated by the mec-8 tiling array studies include AJM-1 , a component of epithelial adherens junctions ( Köppen et al . , 2001 ) ; LET-805 , a fibronectin repeat protein ( Hresko et al . , 1999 ) ; UNC-52/perlecan , a component of basement membranes ( Rogalski et al . , 1993 ) ; VAB-10/plakin a cytoskeletal crosslinker ( Bosher et al . , 2003 ) and UNC-70/β-spectrin ( Hammarlund et al . , 2000 ) . Based on our genetic data , misregulation of fbn-1 may largely account for the role of mec-8 in the context of its synthetic phenotype with sym-3 or sym-4 , although one or more additional MEC-8 targets may contribute to the anterior epidermal defects of mec-8; sym-3 or mec-8; sym-4 double mutants . Furthermore , the synthetic embryonic lethality observed in mec-8; fbn-1 ( tm290 ) double mutants ( Figure 5C ) indicates that MEC-8 regulates the splicing of one or more genes that function redundantly with FBN-1 . Similar to C . elegans mec-8 , mutations in the Drosophila mec-8 ortholog , coach potato ( cpo ) , lead to neuronal and behavioral defects ( Perkins et al . , 1986; Bellen et al . , 1992a , 1992b; Glasscock and Tanouye , 2005 ) , although the splicing targets of Cpo are unknown . In addition , cpo is implicated in diapause regulation and climatic adaptation through an unknown mechanism ( Schmidt et al . , 2008 ) . RBPMS and RBPMS2 , the human orthologs of MEC-8 , are broadly expressed but very little is known about their targets or biological functions ( Shimamoto et al . , 1996 ) . Interestingly , human FBN1 and FBN3 are alternatively spliced and distinct FBN1 isoforms are expressed in a tissue and developmental-specific manner ( Corson et al . , 1993 , 2004; Biery et al . , 1999; Burchett et al . , 2011 ) . Furthermore , alternative splicing of FBN1 has been suggested to modulate the severity of Marfan syndrome ( Burchett et al . , 2011 ) . Although it is tempting to speculate that RBPMS could be a candidate regulator of human fibrillins , it must be noted that the region of fbn-1 that is regulated by MEC-8 ( e14–e19 ) is not conserved outside of nematodes nor is the RNA recognition sequence for MEC-8/Cpo/RBPMS currently known . How SYM-3 and SYM-4 promote epidermal stability or ECM maintenance is at present unresolved . SYM-4 is a predicted β-propeller protein with seven WD-repeats , suggesting a role in coordinating protein interactions . Two independent groups identified mammalian SYM-4 , WDR44 , as a binding partner and candidate effector of the Rab11 GTPase ( Mammoto et al . , 1999; Zeng et al . , 1999 ) . WDR44 associates specifically with the activated GTP-bound form of Rab11 and partially co-localizes with Rab11 ( Mammoto et al . , 1999; Zeng et al . , 1999 ) . Rab11 has been studied in multiple contexts and is primarily associated with the regulation of trafficking to and from the endocytic recycling compartment ( Urbe et al . , 1993; Ren et al . , 1998; Grant and Donaldson , 2009; Horgan et al . , 2010; Kelly et al . , 2012 ) but also functions in exocytosis and in conjunction with the exocyst complex ( Chen et al . , 1998; Satoh et al . , 2005; Ward et al . , 2005; Sato et al . , 2008; Takahashi et al . , 2012; Welz et al . , 2014 ) and in Golgi-endosome transport ( Ullrich et al . , 1996; Wilcke et al . , 2000 ) . In C . elegans , RAB-11 regulates endosomal recycling during mitosis ( Blethrow et al . , 2004; Ai et al . , 2009 ) , cytokinesis ( Bembenek et al . , 2010 ) and meiosis ( Cheng et al . , 2008 ) and , most notably , promotes secretion and ECM formation in embryos ( Sakagami et al . , 2008; Wehman et al . , 2011 ) . Based on a high-throughput screen , the Drosophila SYM-4 ortholog , CG34133 , physically interacts with Amph/Amphiphysin ( Guruharsha et al . , 2011 ) , a BAR-domain protein that promotes endocytosis through membrane bending and vesicle fission ( Peter et al . , 2004; Campelo and Malhotra , 2012; Cowling et al . , 2012 ) , suggesting that SYM-4 may interact directly with components of the vesicular trafficking machinery . SYM-3 contains an N-terminal C2 domain ( NT-C2/EEIG1/EHBP1 ) , which suggests an association with the cytoplasmic surface of cell membranes ( Zhang and Aravind , 2010 ) . Intriguingly , the physical interaction between WDR44 and Rab11 was previously proposed to require an unidentified membrane-associated factor ( Zeng et al . , 1999 ) . The only other C . elegans NT-C2 protein , EHBP-1 , is a co-partner of RAB-10 in endocytic recycling ( Shi et al . , 2010 ) and an NT-C2 domain is present in the mammalian Rab11 interactor , Rab11-FIP2 ( Hales et al . , 2002; Welz et al . , 2014 ) . The Drosophila SYM-3 ortholog , CG8671 , is required for efficient dsRNA uptake , a process that requires receptor-mediated endocytosis ( Saleh et al . , 2006 ) . Correspondingly , sym-3 inhibition may lead to a modest reduction in the sensitivity of C . elegans to RNAi feeding ( Saleh et al . , 2006 ) . Thus , although their specific molecular functions are largely uncharacterized , available evidence points to a role for both SYM-3 and SYM-4 in vesicular trafficking and endocytosis and/or endocytic recycling . We propose that SYM-3 and SYM-4 may co-regulate the cell-surface trafficking of one or more proteins that regulate epidermal stability . Loss of sym-3 or sym-4 activity could potentially result in the mislocalization of one or more integral membrane proteins or ECM components required for normal resistance to mechanical stress . Correspondingly , the combined loss of both mec-8 and either sym-3 or sym-4 activity most likely lead to a synergistic effect on the epidermis and the observed synthetic phenotype . SYM-3 and SYM-4 may regulate the secretion of aECM proteins , such as FBN-1 , or may control the trafficking of integral membrane proteins required for the adhesion of epidermal cells to the aECM or possibly other cell types . We note that the lack of any molting defect in mec-8; sym-3 and mec-8; sym-4 mutants , a phenotype observed following strong loss of function of fbn-1 , is perhaps most consistent with SYM-3 and SYM-4 acting on a target distinct from FBN-1 . In any case , the roles of MEC-8 , SYM-3 and SYM-4 in morphogenesis are revealed only under genetic conditions in which overlapping or redundant functions are inhibited . Further studies to fully understand the basis for morphogenesis and the role of the aECM in development are also likely to require approaches that address and overcome limitations imposed by genetic redundancy .
All strains were cultured on nematode growth medium ( NGM ) supplemented with Escherichia coli OP50 as a food source according to standard protocols ( Stiernagle , 2006 ) and were maintained at 20°C except for strains containing the sqt-3 ( e2117 ) allele . Strains used in this study included N2 , SP2231 [sym-3 ( mn618 ) X] , SP2232 [sym-4 ( mn619 ) X] , WY893 [mec-8 ( u74 ) I; sym-3 ( mn618 ) X; mnEx169 ( sym-3 ( + ) ;sur-5::GFP ) ] , WY969 [mec-8 ( u74 ) I; sym-4 ( mn619 ) X; fdEx226 ( sym-4 ( + ) ; sur-5:GFP; Phsp-16::peel-1 ) ] , SP1750 [mec-8 ( u74 ) I; mnEx2 ( mec-8 ( + ) ; pRF4rol-6 ( su1006d ) ) ] , WY870 [mec-8 ( u74 ) I; pha-1 ( e2123ts ) III; sym-3 ( mn618 ) X; mnEx169] , WY873 [mec-8 ( u74 ) I; pha-1 ( tm3671 ) III; sym-3 ( mn618 ) X; mnEx169; fdEx201 ( PBX ( pha-1 ( + ) ; sur-5::RFP ) ) ] , WY965 [lin-35 ( n745 ) I; sym-3 ( mn618 ) X] , WY964 [lin-35 ( n745 ) I; sym-4 ( mn619 ) X] , GE24 [pha-1 ( e2123ts ) III] , WY849 [pha-1 ( tm3671 ) III; fdEx183 ( pBX; sur-5::GFP ) ] , CHB11 [fbn-1 ( ns67 ) III; oyIs44 [odr-1::RFP] V] , CHB31 [fbn-1 ( ns283 ) unc-32 ( e189 ) III; kyIs136 ( str-2pro::GFP ) X] , WY1034 [fbn-1 ( tm290 ) III; fdEx249 ( sur-5::GFP; fbn-1 ( + ) -fosmid wrm0635cH08 ) ] , WY1048 [fbn-1 ( ns67 ) III; oyIs44 V; sym-3 ( mn618 ) X; mnEx169] , WY1049 [fbn-1 ( ns67 ) III; oyIs44 V; sym-4 ( mn619 ) ; fdEx225 ( sym-4 ( + ) ; sur-5::GFP ) ] , WY1056 [mec-8 ( u74 ) I; fbn-1 ( ns67 ) III; oyIs44; fdEx249] , WY1057 [fbn-1 ( tm290 ) III; sym-3 ( mn618 ) X; fdEx249] , WY1058 [fbn-1 ( tm290 ) III; oyIs44 V; sym-3 ( mn618 ) X; fdEx249] , WY1068 [mec-8 ( u74 ) I; fbn-1 ( tm290 ) III; fdEx249] , CB4121 [sqt-3 ( e2117 ) V] , mec-8 ( u74 ) I; sqt-3 ( e2117 ) V] , ARF256 [aaaEx32 ( Pfbn-1::gfp-pest + Pttx-3::GFP ) ] , ARF262 [aaaEx33 ( fbn-1Δ49-2418::mCherry + Pmyo-2::GFP ) ] , WY1082 [fbn-1 ( ns67 ) ; aaaEx32] , GN517 [pgEx116 ( unc-70-TSmod; myo3::mCherry ) ] , GN519 [pgEx131 ( unc-70 ( 5aa ) punc-122::RFP ) ] , GN518 [pgEx126 ( unc-70 ( TRAF ) ; punc-122::RFP ) ] , GN600 [pgIs22 ( unc-70 ( N-TSMod ) ) , oxIs95 ( myo2::gfp; pdi-2::unc-70 ) V] , WY1047 [pha-1 ( tm3671 ) III; fdEx182; pgEx116] , GN486 [unc-70 ( s1502 ) V; oxIs95 IV; pgEx126] , GN491 [unc-70 ( s1502 ) V; oxIs95IV; pgEx131] , GN601 [unc-70 ( s1502 ) V; oxIs95 IV; pgIs22] , NR222 [rde-1 ( ne219 ) V; kzIs9 ( pKK1260 ( lin-26p::nls::GFP ) ) ; pKK1253 ( lin-26p::rde-1 ) ; pRF6 ( rol-6 ( su1006 ) ) ] , WY1033 [mec-8 ( u74 ) X; rde-1 ( ne219 ) V; kzIs9] . Wild-type C . elegans ( N2 ) and mec-8 ( u314 ) animals were grown at 20°C on high peptone plates until gravid . Embryos were extracted with a solution that contained 1 M NaOH and 30% bleach , in water . Total RNA from embryos was extracted using TriReagent ( Sigma ) and cleaned using RNeasy columns ( Qiagen ) according to the manufacturer's protocol . Purified RNA was then treated with 10 U DNase I ( Roche ) for 30 min in 100 μl 1× One-Phor-All buffer ( Amersham ) . The RNA was then re-purified with RNeasy columns ( QIAGEN ) and 1 µl random hexamers ( 3 µg/µl ) was added to 15 µg purified total RNA together with reverse transcriptase . The ( ds ) cDNA was then purified using QIAGEN PCT purification columns , and 17 µg of ( ds ) cDNA was digested and labeled using standard Affymetrix methods The hybridization cocktail was injected into an Affymetrix GeneChip C . elegans Tiling 1 . 0R Array . Hybridized microarrays were washed and scanned according to chapter 5 of the GeneChip Whole Transcript ( WT ) Double Stranded Target Assay Manual ( https://www . affymetrix . com/support/downloads/manuals/wt_dble_strand_target_assay_manual . pdf ) . For reverse transcriptase PCR studies of select MEC-8 targets identified by the tiling array ( Figure 4—figure supplement 1 ) , total RNA used for the tilling array experiments was reverse transcribed using oligo-dT primers and amplified using specific primers for each gene region ( Supplementary file 3 ) for 30 cycles . Total RNA from N2 and mec-8 ( u74 ) embryos was isolated using Trizol and purified on RNeasy minicolumns ( Qiagen ) . cDNA was prepared from 1 μg RNA using a SuperScript II first-strand synthesis system ( Invitrogen ) and analyzed by PCR ( 30 and 35 cycles ) using the following primers: 5′-CAACAGAGTCATCCGAAGCT-3′ and 5′-TGCAGTTGTGGTGGTGGTAGGT-3′ ( which anneal to exon 16 and exon 17 of fbn-1 , respectively ) , 5′-GACAGGAAAAACCAACTACTAAA-3′ and 5′-TGTGACTGTGGAGCAAAGAGATG-3′ ( which anneal to exon 14 and exon 19 of fbn-1 , respectively ) and 5′-TGTCTTCCAGGATTTACTGGAG-3′ and 5′-TACATACTGCGTTCGGGTG-3′ ( which anneal to exon 19 and exon 20 of fbn-1 , respectively ) . RNAi-feeding was done with strains from the Geneservice Library , using the standard feeding protocol ( Ahringer , 2006 ) . Control RNAi-feeding assays were carried out using a bacterial strain carrying the RNAi vector pDF129 . 36 , which produces an ∼200-bp dsRNA that is not homologous to any C . elegans gene ( Timmons et al . , 2001 ) . RNAi hypersensitive mutations used included lin-35 ( n745 ) ( Wang et al . , 2005; Lehner et al . , 2006 ) and rrf-3 ( pk1426 ) ( Simmer et al . , 2002 ) . For let-502 ( RNAi ) of mec-8; fbn-1 mutants , dsRNA targeting let-502 exon 4 was injected into P0s at a concentration of ∼500 ng/µl and F1s laid between 24–48 hr post injection were scored . Mosaic analysis was carried out using strains WY1059 , fbn-1 ( tm290 ) ; sym-4 ( mn619 ) ; fdEx249[fbn-1 ( + ) ; sur-5::GFP] , and WY1068 , mec-8 ( u74 ) ; fbn-1 ( tm290 ) ; fdEx249 , following established protocols ( Yochem et al . , 2000; Yochem , 2006 ) . To generate Pfbn-1::GFP-PEST , nucleotides 7621743–7625323 of Chromosome III were amplified from N2 and spliced to the gfp-pest cassette from pAF207 ( Frand et al . , 2005 ) using PCR methods . We note that strains expressing Pfbn-1::GFP-PEST also contain a Pttx-3::GFP marker , which is expressed in AIY neurons but not in epidermal cells ( Hobert et al . , 1997 ) . To generate the mini-fbn-1::mCherry ( fbn-1Δ49-2418::mCherry ) fusion gene , nucleotides 7621652–7626214 of Chromosome III , which contain the presumptive fbn-1 promoter and first 48 codons , were amplified from N2 DNA and cloned into pUC19 . Then nucleotides 7638794–7641181 of Chromosome III , which contain the last 362 codons and native 3′ UTR of fbn-1 , were amplified from N2 and inserted downstream of the former fragment , producing plasmid pVM61 . The mCherry cassette from KP1272 was then inserted in-frame between the two genomic fragments using an engineered NotI site . Injection of DNA constructs or PCR products to generate extrachromosomal arrays was carried out using standard procedures ( Mello and Fire , 1995 ) . A rescuing fbn-1 cDNA sequence was PCR amplified as three overlapping fragments ( atgtctac . . . gaaaattg , 2049 bp; ggaaaagt . . . gtacctgc , 3581 bp; gtatggct . . . gattctag , 2758 bp ) from a cDNA library ( gift of Carl Procko ) and the plasmid clone yk670d9 ( gift of Yuji Kohara ) , which were then assembled into a single 8065-bp cDNA sequence using internal PstI and SalI sites ( at position 1985 bp and 5431 bp , respectively ) . A 4406-bp fbn-1 promoter sequence capable of driving embryonic GFP expression was isolated ( tcgaggag . . . ttgcagga ) and assembled with the fbn-1 cDNA as a SbfI-AgeI promoter fragment and an AgeI-NheI cDNA fragment in a modified pPD95 . 69 vector bearing an NheI-SpeI unc-54 3′ UTR fragment , to create the plasmid pMH281 . With the exception of FRET studies , micrographs were taken with a Nikon Eclipse microscope , using a 100× objective . Percent Pin was calculated by counting 1 . 5-fold or older embryos or L1-stage larvae . Fluorescent confocal images were acquired using a 100× objective on an Olympus IX-71 inverted microscope . Image acquisition and microscope control were carried out with Metamorph software ( Molecular Devices ) . Keyhole/sensory depression depth was quantified using Openlab software . Keyhole depth for mec-8; sym-3 embryos grown on let-502 RNAi plates was quantified every 15 min starting at the late comma stage through the threefold stage . mec-8; sqt-3 embryo keyhole depth was measured by growing embryos at 25°C starting at the twofold stage and quantified every 60 min for 5 hr . Embryos were obtained by bleaching N2 adults , using standard methods . Embryos were permeabilized by treatment with 2 mg/ml chitinase for 5–10 min at room temperature ( Bianchi and Driscoll , 2006 ) . Permeabilized mixed-stage embryos were treated with trypsin ( Sigma Aldrich ) at a concentration of 5 μg/ml for 15 min at room temperature followed by trypsin inhibitor ( Sigma Aldrich ) , which was added to a concentration of 50 μg/ml and incubated for 2 min ( Priess and Hirsh , 1986 ) . Embryos were rinsed with M9 and examined immediately using DIC microscopy . | For an animal embryo to develop , its cells must organize themselves into tissues and organs . For example , skin and the lining of internal organs—such as the lungs and gut—are made from cells called epithelial cells , which are tightly linked to form flat sheets . In a microscopic worm called Caenorhabditis elegans , the outermost layer of epithelial cells ( called the epidermis ) forms over the surface of the embryo early on in embryonic development . Shortly afterwards , the embryonic epidermis experiences powerful contractions along the surface of the embryo . The force generated by these contractions converts the embryo from an oval shape to a roughly cylindrical form . These contractions also squeeze the internal tissues and organs , which correspondingly elongate along with the epidermis . It has been known for decades that such ‘mechanical’ forces are important for the normal development of embryos . However , it remains poorly understood how these forces generate tissues and organs of the proper shape—partly because it is difficult to measure forces in living embryos . It is also not clear how the mechanical properties of specific tissues are controlled . Now , Kelley , Yochem , Krieg et al . have analyzed the development of C . elegans' embryos and discovered a novel mechanical interplay between the feeding organ ( called the pharynx ) and the worm's epidermis . The experiments involved studying several mutant worms that perturb epidermal contractions and disrupt the attachment of the pharynx to the epidermis . These studies suggested that the pharynx exerts a strong inward pulling force on the epidermis during development . Using recently developed methods , Kelley , Yochem , Krieg et al . then measured mechanical forces within intact worm embryos and demonstrated that greater forces were experienced in cells that were being pulled by the pharynx . Kelley , Yochem , Krieg et al . further analyzed how the epidermis normally resists this pulling force from the pharynx and implicated a protein called FBN-1 . This worm protein is structurally related to a human protein that is affected in people with a disorder called Marfan Syndrome . Worm embryos without the FBN-1 protein become severely deformed because they are unable to withstand mechanical forces at the epidermis . FBN-1 is normally synthesized and then transported to the outside of the worm embryo by epidermal cells , where it is thought to assemble into a meshwork of long fibers . This provides a strong scaffold that attaches to the epidermis to prevent the epidermis from undergoing excessive deformation while it experiences mechanical forces . The work of Kelley , Yochem , Krieg et al . provides an opportunity to understand how FBN-1 and other fiber-forming proteins are produced and transported to the cell surface . Moreover , these findings may have implications for human diseases and birth defects that result from an inability of tissues to respond appropriately to mechanical forces . | [
"Abstract",
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] | 2015 | FBN-1, a fibrillin-related protein, is required for resistance of the epidermis to mechanical deformation during C. elegans embryogenesis |
Asymmetric disassembly of the synaptonemal complex ( SC ) is crucial for proper meiotic chromosome segregation . However , the signaling mechanisms that directly regulate this process are poorly understood . Here we show that the mammalian Rho GEF homolog , ECT-2 , functions through the conserved RAS/ERK MAP kinase signaling pathway in the C . elegans germline to regulate the disassembly of SC proteins . We find that SYP-2 , a SC central region component , is a potential target for MPK-1-mediated phosphorylation and that constitutively phosphorylated SYP-2 impairs the disassembly of SC proteins from chromosomal domains referred to as the long arms of the bivalents . Inactivation of MAP kinase at late pachytene is critical for timely disassembly of the SC proteins from the long arms , and is dependent on the crossover ( CO ) promoting factors ZHP-3/RNF212/Zip3 and COSA-1/CNTD1 . We propose that the conserved MAP kinase pathway coordinates CO designation with the disassembly of SC proteins to ensure accurate chromosome segregation .
Accurate chromosome segregation during meiosis is critical for sexually reproducing organisms . Meiosis is the specialized cell division program by which diploid germ cells generate haploid gametes that during fertilization will form a diploid zygote . This is accomplished by following a single round of DNA replication with two consecutive rounds of cell division ( meiosis I and II ) in which , first pairs of homologous chromosomes ( bivalents ) , and then sister chromatids , separate away from each other . At center stage during prophase I of meiosis from yeast to humans is a tripartite proteinaceous structure known as the synaptonemal complex ( SC ) ( Colaiácovo , 2006 ) . The SC is a ladder-like structure comprised of lateral element proteins running along the axes of the homologs and central region proteins connecting these axes ( Colaiácovo , 2006; Page and Hawley , 2003 ) . The SC is required to stabilize the interactions between pairs of homologous chromosomes and for interhomolog crossover ( CO ) formation in budding yeast , worms , flies , and mice ( Nag et al . , 1995; Storlazzi et al . , 1996; Page and Hawley , 2001; MacQueen et al . , 2002; Colaiácovo et al . , 2003; de Vries et al . , 2005; Smolikov et al . , 2007a; 2007b; 2009 ) , all of which are prerequisite steps for achieving accurate chromosome segregation at meiosis I . Importantly , the SC must be disassembled prior to the end of prophase I to ensure timely and proper chromosome segregation ( Sourirajan and Lichten , 2008; Jordan et al . , 2009; Dix et al . , 1997 ) . Several proteins have been recently implicated in regulating the disassembly of SC proteins , including polo-like kinase in mammals and budding yeast , Ipl1/Aurora B kinase in budding yeast , the condensin complex component dcap-g in flies and both AKIR-1 ( a member of the akirin protein family ) and ZHP-3 ( ortholog of budding yeast Zip3 ) in worms ( Sourirajan and Lichten , 2008; Jordan et al . , 2009; Resnick et al . , 2009; Jordan et al . , 2012; Clemons et al . , 2013; Bhalla et al . , 2008 ) . However , whether these factors directly regulate the SC in vivo and the mechanisms by which they promote the disassembly of SC proteins are not fully understood . We previously showed that the SC disassembles asymmetrically in C . elegans , progressing from being localized along the full length of the interface between homologous chromosomes in early prophase to persisting at discrete chromosome locations , termed the short arm domains , corresponding to one end of every pair of homologs at late prophase I ( Figure 1A ) ( Nabeshima et al . , 2005 ) . Importantly , factors required for CO formation are necessary for the asymmetric disassembly of the SC and localized retention of SC central region proteins . Since in C . elegans , a single CO occurs at the terminal third of every pair of homologous chromosomes , we proposed that chromosomes remodel around the single off-centered CO event at the pachytene-diplotene transition . This results in bivalents with a cruciform configuration comprised of two perpendicular chromosomal axes ( namely the long and short arm domains ) intersecting at the chiasma , where the long arms face the poles and the short arms occupy an equatorial position on the metaphase plate ( Figure 1A ) ( Nabeshima et al . , 2005; Riddle et al . , 1997; Maddox et al . , 2004 ) . This remodeling includes changes in chromosome compaction as well as changes in both the localization and the types of proteins associated with the long and short arm domains ( Figure 1A ) ( Nabeshima et al . , 2005; Chan et al . , 2004; de Carvalho et al . , 2008; Martinez-Perez and Villeneuve , 2005 ) . Subsequent studies in yeast , flies and mice ( Newnham et al . , 2010; Qiao et al . , 2012; Bisig et al . , 2012; Takeo et al . , 2011; Gladstone et al . , 2009 ) also observed an asymmetric disassembly of the SC , with a residual localized retention of the SC at centromeres . However , the mechanism linking CO recombination sites to asymmetric SC disassembly remained unknown . 10 . 7554/eLife . 12039 . 003Figure 1 . ECT-2 regulates AIR-2 localization and SC dynamics in meiotic prophase I . ( A ) Schematic representation of SC dynamics and chromosome remodeling during prophase I of meiosis . A single pair of homologous chromosomes ( bivalent ) is shown for simplicity . Upon entrance into pachytene , the SC is present along the full length of the pairs of homologous chromosomes . CO formation is completed within the context of fully synapsed chromosomes , and in worms , a single CO is formed per homolog pair usually at an off-centered position . Chromosome remodeling has been proposed to take place around the off-centered CO ( or CO precursor ) resulting in a cruciform configuration comprised of two intersecting perpendicular chromosomal axes of different lengths ( long and short arms of the bivalent , corresponding to the longest and shortest distances from the off-centered CO/CO precursor site to opposite ends of the chromosomes ) . This remodeling involves disassembly of central region components of the SC ( SYP-1/2/3/4 ) along the long arms of the bivalents starting during late pachytene and diplotene resulting in the restricted localization of these proteins to the short arms . During diplotene and diakinesis , chromosomes undergo condensation as evidenced by a coiling of the arms and increased bivalent compaction . In late diakinesis , the SC proteins located on the short arms are replaced by AIR-2 , which promotes the separation of the homologs at the end of meiosis I . CO – crossover , S – short arm and L – long arm . ( B ) Immunolocalization of HTP-3 and AIR-2 on -1 oocytes at diakinesis in wild type , ect-2 ( gf ) and ect-2 ( rf ) gonads . AIR-2 is localized to the short arm of the bivalents in wild type and ect-2 ( gf ) mutants , but fails to localize onto chromosomes in ect-2 ( rf ) mutants . Diagrams on the right illustrate the cruciform structure of the bivalents at this stage consisting of long ( L ) and short ( S ) arms and the localization of AIR-2 ( red ) and HTP-3 ( green ) in wild type and ect-2 ( rf ) mutant . White box indicates the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . ( C ) Immunolocalization of HTP-3 and SYP-1 on leptotene/zygotene nuclei from gonads of the indicated genotypes . SYP-1 aggregates ( polycomplexes ) are detected in ect-2 ( gf ) mutants . ( D ) SYP-1 and HTP-3 localize throughout the full length of the synapsed homologous chromosomes during pachytene in wild type and in most pachytene nuclei in ect-2 ( rf ) mutants . Arrowhead indicates a nucleus where chromosomes persist in the DAPI-bright and tighter clustered configuration characteristic of the leptotene/zygotene stage in ect-2 ( rf ) . In ect-2 ( gf ) mutants , meiotic nuclei that progressed through the leptotene/zygotene stage before the shift to the non-permissive temperature show wild type-like SYP-1 localization . Worms from all the indicated genotypes , including wild type , were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . n>26 gonads were examined for each genotype in ( B ) and n>15 in ( C ) and ( D ) . Bars , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 00310 . 7554/eLife . 12039 . 004Figure 1—figure supplement 1 . ect-2 ( RNAi ) affects AIR-2::GFP loading on the chromosomes . AIR-2::GFP localization in -1 oocytes at diakinesis of control ( RNAi ) and ect-2 ( RNAi ) gonads . AIR-2::GFP localization was visualized using anti-GFP antibody . n>35 gonad arms were analyzed for each . 100% showed a defect in AIR-2::GFP loading on the short arms of the bivalent in oocytes at diakinesis in ect-2 ( RNAi ) . Diagrams on the right illustrate the cruciform structure of the bivalents at this stage consisting of long ( L ) and short ( S ) arms and the localization of AIR-2 ( green ) in control ( RNAi ) and ect-2 ( RNAi ) gonads . Bar , 3 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 00410 . 7554/eLife . 12039 . 005Figure 1—figure supplement 2 . ect-2 ( e1778 ) null mutant exhibits disorganized germline with fewer germ cells . Low-magnification DAPI stained whole gonad arm images of age-matched ( A ) wild type and ( B ) ect-2 ( e1778 ) . ect-2 ( 21778 ) null mutants exhibit an extremely disorganized germ line with fewer germ cells . White arrow points to abnormal large DAPI bodies in the premeiotic tip indicative of defects in mitotic division . Mitotic defects could mask the role of ect-2 in meiotic progression making it impossible to use the ect-2 null mutant to understand the role of ect-2 in meiosis . n>10 gonads were examined for each genotype . Bar , 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 00510 . 7554/eLife . 12039 . 006Figure 1—figure supplement 3 . Rescue of ect-2 ( ax751rf ) phenotypes by expression of functional ECT-2::GFP in the germline . Histogram showing brood size , embryonic lethality and Him ( High Incidence of Males ) phenotype in wild type , ect-2 ( ax751rf ) , and ect-2 ( ax751rf ) mutant animals expressing ECT-2::GFP . ECT-2::GFP is able to rescue brood size , embryonic lethality and the Him phenotype of ect-2 ( ax751rf ) mutants at the non-permissive temperature . **p<0 . 0001 ( unpaired student’s t-test ) . 20 , 30 and 20 gonad arms were analyzed for wild type , ect-2 ( ax751 ) and ect-2 ( ax751 ) ; ECT-2::GFP animals , respectively . ECT-2::GFP has been shown to rescue the sterility phenotype of ect-2 ( gk44 ) null mutants in Chan and Nance ( 2013 ) . Brood size indicates the total number of eggs ( non-hatched and hatched ) laid per hermaphrodite . All worms from the indicated genotypes , except ect-2 ( ax751 ) ;ECT-2::GFP , were grown at 15°C and shifted to 25°C as L4 worms . ect-2 ( ax751 ) animals expressing ECT-2::GFP were grown at 20°C to prevent silencing of the transgene and shifted as L4 animals to 25°C . All genotypes were analyzed 18–24 hr post-L4 at 25°C . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 00610 . 7554/eLife . 12039 . 007Figure 1—figure supplement 4 . Co-localization of ECT-2::GFP and RHO-1 in the germline . Low-magnification image of a whole mounted gonad from a wild type animal expressing ECT-2::GFP stained with antibody against GFP ( green ) , RHO-1 ( red ) and DAPI ( blue ) . ECT-2::GFP and RHO-1 are expressed throughout the germline on the germ cell membrane giving a honeycomb pattern . In addition , ECT-2::GFP and RHO-1 show nuclear localization in late pachytene , diplotene and diakinesis stage nuclei . ECT-2::GFP and RHO-1 co-localize throughout the germ line . A whole mounted gonad from a wild type animal that does not express ECT-2::GFP was stained with anti-GFP antibody as a negative control . Insets show higher-magnification images of indicated regions . 15 gonad arms were analyzed to verify expression pattern . Worms from all the indicated genotypes were grown and analyzed at 20°C . Bar , 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 007 Recently , a two-step CO specification process has been described to take place following SC assembly as prospective CO sites progressively differentiate during C . elegans and mouse meiosis ( Yokoo et al . , 2012; Holloway et al . , 2014 ) . This consists of CO licensing during mid-pachytene followed by CO designation at or just prior to the mid-to-late pachytene transition in a manner dependent of the pro-CO factor COSA-1/CNTD1 . Since DSBs outnumber COs in most species ( Martinez-Perez and Colaiácovo , 2009 ) this has been proposed as a strategy to pare down the number of early recombination sites that will become CO sites thus both ensuring and limiting the number of COs . These and other features of meiosis in C . elegans , including the existence of various markers that distinguish the short and long arm subdomains and the CO precursor sites , therefore provide an ideal scenario to understand the regulation of the disassembly of the SC proteins from distinct chromosome subdomains and late prophase I chromosome remodeling . Here , we report that regulation of the disassembly of the SC proteins from the long arms of the bivalents in C . elegans requires the mammalian Rho GEF homolog , ECT-2 . We show that ECT-2 functions through the conserved MAP kinase pathway to regulate the asymmetric disassembly of SC proteins during prophase I of meiosis . We show that MPK-1 potentially directly phosphorylates SYP-2 , a central region component of the SC , and that constitutively phosphorylated SYP-2 impairs the disassembly of SC proteins from the long arms . Moreover , inactivation of MPK-1 takes place in late pachytene in a manner dependent on pro-CO factors ZHP-3/RNF212/Zip3 and COSA-1/CNTD1 and concomitant with the initiation of SC disassembly . Therefore , we propose a model in which MPK-1 is inactivated in response to CO designation resulting in either de novo loading of unphosphorylated SYP-2 or dephosphorylation of chromatin-associated SYP-2 , which triggers disassembly of SC proteins from along the long arms . Thus , coordination between CO designation and the disassembly of SC proteins executed via a conserved MAP kinase pathway is critical for ensuring accurate chromosome segregation during meiosis .
We identified ect-2 , the homolog of mammalian Rho GEF , in a targeted RNAi screen for novel components regulating chromosome remodeling and short/long arm identity by using the mislocalization of Aurora B kinase , AIR-2 , which localizes to the short arms of diakinesis bivalents in wild type oocytes as a readout ( see Materials and methods ) . ECT-2 ( Epithelial Cell Transforming sequence 2 ) is a highly conserved protein , initially identified as a proto-oncogene in cell culture ( Miki et al . , 1993 ) . It encodes a Guanine nucleotide Exchange Factor ( GEF ) that belongs to the Dbl family and functions as a key activator of Rho GTPase mediated signaling with roles during cytokinesis , DNA damage-induced cell death , cell polarity establishment during embryogenesis , vulval development , and epidermal P cell migration ( Prokopenko et al . , 1999; Saito et al . , 2003; Morita et al . , 2005; Srougi and Burridge , 2011; Canevascini et al . , 2005; Motegi and Sugimoto , 2006 ) . Although ECT2 is expressed in testis and ovaries in humans ( Hirata et al . , 2009; Fields and Justilien , 2010 ) , understanding its role during mammalian meiosis is challenging due to its earlier roles in development . The availability of conditional mutants , such as temperature-sensitive mutants in C . elegans , therefore provided a unique opportunity to discover a novel role for ECT-2 in meiosis . Analysis of ect-2 ( ax751rf ) temperature-sensitive reduction-of-function mutants and of worms depleted of ect-2 by RNAi revealed a failure of AIR-2 to load on the chromosomes in late diakinesis even though AIR-2 is present inside the nucleus ( Figure 1B and Figure 1—figure supplement 1 ) . Importantly , we shifted ect-2 ( ax751rf ) worms to the non-permissive temperature at the L4 larval stage to bypass the requirements for ECT-2 during somatic development and germ cell mitotic proliferation such as seen in ect-2 ( e1778 ) null mutants , which are sterile and exhibit fewer germ cells with abnormal nuclei ( Figure 1—figure supplement 2 ) . ect-2 ( ax751rf ) mutants also exhibited a reduced brood size , increased embryonic lethality and a High Incidence of Males ( Him ) at the non-permissive temperature , all phenotypes indicative of increased meiotic chromosome nondisjunction ( Table 1 ) . 10 . 7554/eLife . 12039 . 008Table 1 . Worms were maintained at 15°C and then shifted to 25°C at the L4 larval stage . All the analyses were conducted at 25°C for all the genotypes indicated above . The 'Eggs Laid' column indicates the average number of eggs laid ( including both hatched and non-hatched embryos ) per P0 hermaphrodite ± standard deviation . % Embryonic lethality was calculated by dividing the number of non-hatched embryos by the total number of hatched and non-hatched embryos laid . % Males was calculated by dividing the total number of males observed by the total number of hatched ( viable ) progeny scored . N = total number of P0 worms for which entire broods were scored . N . A . = not applicable . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 008GENOTYPEEGGS LAID% EMBRYONIC LETHALITY% MALESNWild type192 ± 32 . 30020ect-2 ( ax751 ) 109 ± 38 . 676 . 43 . 2530ect-2 ( zh8 ) 4 . 5 ± 6 . 793 . 4032let-60 ( ga89 ) 3 . 1 ± 5 . 683 . 8020mpk-1 ( ga111 ) 5 . 8 ± 7 . 656020ect-2 ( ax751 ) ; let-60 ( ga89 ) 0 . 7 ± 0 . 7100020ect-2 ( zh8 ) ; mpk-1 ( ga111 ) 0N . A . N . A . 30 To investigate the localization of ECT-2 in the germline , we utilized a ECT-2::GFP transgene driven by the ect-2 promoter ( Chan and Nance , 2013 ) . ECT-2::GFP is able to rescue the reduced brood size , embryonic lethality and Him phenotypes observed in the ect-2 ( ax751rf ) mutants ( Figure 1—figure supplement 3 ) , confirming that mutation of ect-2 impairs fertility . We found that ECT-2::GFP localizes throughout the germline from the mitotically dividing nuclei at the premeiotic tip , where it is enriched at the germ cell membrane , to the end of diakinesis , where it exhibits a stronger nuclear signal ( Figure 1—figure supplement 4 ) . Importantly , we discovered a novel role for ECT-2 in regulating SC dynamics , a term we will use herein to refer to SC assembly and disassembly . In ect-2 ( ax751rf ) mutants , SC formation is indistinguishable from wild type in most of the meiotic nuclei except for a few pachytene nuclei ( 11 . 1% , n=120/1080 pachytene nuclei ) which display reduced SC assembly ( Figures 1C and D ) . SC disassembly occurred as in wild type , initiating in late pachytene and resulting in the restricted localization of the central region proteins of the SC , as exemplified by SYP-1 , to the short arm of the bivalents ( Figure 2 and Figure 2—figure supplement 1 ) . In contrast , ect-2 ( zh8gf ) gain-of-function mutants , in which a mutation in an auto-inhibitory BRCT domain retains ECT-2 in its constitutively active configuration ( Canevascini et al . , 2005 ) , exhibited defects in both SC assembly and disassembly at the non-permissive temperature . Specifically , the SC failed to assemble on the chromosomes and instead SYP-1 formed aggregates referred to as polycomplexes ( Figure 1C ) . Those nuclei that had already gone through the leptotene/zygotene stage before the temperature shift showed normal synapsis during early and mid pachytene stages , but the central region components of the SC remained localized along the long arms of the bivalents and failed to become restricted to the short arms during late prophase ( a defect herein referred to as impaired disassembly of the SC proteins ) ( Figure 2 ) . Importantly , the HORMA domain containing lateral element protein HTP-3 , exhibits a wild type pattern of localization in ect-2 mutants ( Figures 1C , D and 2 ) . This suggests that ECT-2 acts in a specific manner to regulate SC dynamics , which involves regulating central region components of the SC . 10 . 7554/eLife . 12039 . 009Figure 2 . ECT-2 regulates the disassembly of SC proteins from the long arms of the bivalents through the MPK-1 pathway . Co-staining with HTP-3 ( green ) , SYP-1 ( red ) and DAPI ( blue ) of diplotene and early diakinesis nuclei from the indicated genotypes . At diplotene and early diakinesis , SYP-1 localization is restricted to the short arm in both wild type and ect-2 ( rf ) mutants . In contrast , SYP-1 fails to disassemble from the long arm and become restricted to the short arm of the bivalents in ect-2 ( gf ) , let-60 ( gf ) and lip-1 ( rf ) mutants . mpk-1 ( lf ) mutants suppress the defect in disassembly of SC proteins from the long arms observed in ect-2 ( gf ) mutants whereas ect-2 ( rf ) ; let-60 ( gf ) double mutants exhibit the phenotype of let-60 ( gf ) mutants . Illustrations depict the bivalent configuration at this stage . White box indicates the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Worms from all the indicated genotypes , including wild type , were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . Histograms on the right indicate the percentage of diplotene and diakinesis stage nuclei with SYP-1 either only on the short arm ( S , blue ) or on both long and short arms ( red , L&S ) of the bivalents . All the bivalents were examined in every nucleus and the bivalents in the same nucleus either all exhibited SYP-1 staining on both the long and short arms or all exhibited staining only on the short arms . Numbers of nuclei scored are shown . ( B ) Schematic representation shows the crosstalk between the conserved ECT-2 and RAS/MAPK pathways and restriction of the SC to the short ( S ) arm of the bivalent at diakinesis in wild type . Remaining schematic shows epistasis analysis in ect-2 ( gf ) ; mpk-1 ( ga111lf ) and ect-2 ( rf ) ; let-60 ( gf ) double mutants . S indicates short arm and L indicates long arm . n>15 gonads arms were analyzed for each genotype . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 00910 . 7554/eLife . 12039 . 010Figure 2—figure supplement 1 . SYP-1 and HTP-3 localization at diakinesis in ect-2 mutants . Higher-magnification images of -1 oocytes at diakinesis show that HTP-3 and SYP-1 immunolocalization is indistinguishable between wild type , ect-2 ( ax751rf ) and ect-2 ( zh8gf ) mutants . HTP-3 is present along both short and long arms of the bivalents whereas the SC is fully disassembled by the end of diakinesis and SYP-1 is no longer observed on either short or long arms . White boxes indicate the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Illustrations depict the bivalent configuration at this stage . S indicates short arm , L indicates long arm , localization of HTP-3 is in green and DNA is shown in blue . Worms from all the indicated genotypes , including wild type , were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . 15 , 21 and 26 gonad arms were analyzed for wild type , ect-2 ( rf ) and ect-2 ( gf ) genotypes , respectively . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01010 . 7554/eLife . 12039 . 011Figure 2—figure supplement 2 . SYP-1 localization in unc-4 and unc-32 mutants . Higher-magnification images of wild type , unc-4 , and unc-32 mutants stained with anti-SYP-1 ( red ) and DAPI ( blue ) . ( A ) leptotene/zygotene , ( B ) pachytene and ( C ) diplotene stage nuclei were analyzed for SYP-1 localization in wild type , unc-4 and unc-32 mutants . SYP-1 localizes between the homologs at the leptotene/zygotene stage , is observed as continuous tracks between completely synapsed chromosomes at pachytene , and is lost from the long arms and retained at the short arms of the bivalents , as shown in diplotene , in a manner indistinguishable between wild type and unc-4 and unc-32 mutants . 10 , 12 and 11 gonad arms were analyzed for wild type , unc-4 , and unc-32 mutants , respectively . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01110 . 7554/eLife . 12039 . 012Figure 2—figure supplement 3 . SYP-1 localization in MAP kinase mutants . Immunolocalization of SYP-1 in leptotene/zygotene and pachytene stage nuclei for the indicated genotypes . Instead of the continuous tracks of SYP-1 observed between homologs in wild type ( A ) , SYP-1 aggregates ( polycomplexes ) are observed at leptotene/zygotene in ( B ) mpk-1 ( ga117 ) , ( C ) mpk-1 ( ku1rf ) , ( D ) mpk-1 ( ga111lf ) , ( E ) let-60 ( ga89gf ) mutants , ( G ) ect-2 ( ax751rf ) ; let-60 ( ga89gf ) , and ( H ) ect-2 ( zh8gf ) ; mpk-1 ( ga111lf ) mutants . SYP-1 localization is indistinguishable from wild type at this stage in ( F ) lip-1 ( zh15rf ) mutants . In all these mutants , nuclei that have passed the leptotene/zygotene stage before the shift to the non-permissive temperature exhibit a wild type-like SYP-1 localization pattern at pachytene . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 for phenotype . n>15 gonad arms were analyzed for each genotype . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01210 . 7554/eLife . 12039 . 013Figure 2—figure supplement 4 . LAB-1 localization in let-60 ( ga89gf ) and lip-1 ( zh15rf ) mutants . Immunolocalization of LAB-1 and HTP-3 in diakinesis oocytes of the indicated genotypes . LAB-1 is lost from the short arms and restricted to the long arms of the bivalents in a manner indistinguishable from wild type in both let-60 ( ga89gf ) and lip-1 ( zh15rf ) mutants . n>14 gonad arms were analyzed for each genotype . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 for phenotype . White boxes indicate the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Illustration on the right depicts bivalent at this stage . LAB-1 is indicated in red , HTP-3 in green , DNA in blue , S indicates short arm and L indicates long arm . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01310 . 7554/eLife . 12039 . 014Figure 2—figure supplement 5 . Immunolocalization of dpMPK-1 in the germlines of ect-2 mutants . ( A ) Low-magnification images of whole gonad arms showing the immunolocalization of activated MPK-1 ( dpMPK-1 ) . ect-2 ( ax751rf ) mutants exhibit reduced dpMPK-1 signal in the pachytene zone compared to wild type . Meanwhile , ect-2 ( zh8gf ) mutants exhibit increased dpMPK-1 signal compared to wild type and it persists from late pachytene through early diakinesis where it is turned off in wild type . Arrow indicates intestine . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 for phenotype . ( B ) Graph showing the quantification of dpMPK-1 fluorescence signal intensity in different regions of the gonad arm in wild type , ect-2 ( ax751rf ) and ect-2 ( zh8gf ) mutants . **p<0 . 005 , *p<0 . 05 ( unpaired student’s t-test ) . Regions where dpMPK-1 signal was quantified are indicated on the diagram depicting the hermaphrodite germline . Progression from mitosis into meiosis is displayed from left to right and the last three oocytes at diakinesis ( -3 to -1 ) are indicated . TZ stand for transition zone ( leptotene/zygotene ) . E , M and L stand for early , mid and late pachytene , respectively . Number on the X-axis of the graph corresponds to the number depicted in the gonad arm where fluorescence intensity was measured . >14 gonads arms were analyzed for each genotype . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01410 . 7554/eLife . 12039 . 015Figure 2—figure supplement 6 . Active dpMPK-1 level is reduced upon rho-1 RNAi in the germline . Low-magnification images of whole gonad arms image for ( A ) gfp ( RNAi ) ( control ) and ( B ) rho-1 ( RNAi ) stained with anti-dpMPK-1 ( red ) , anti-SYN-4 ( green ) , and DAPI ( blue ) . SYN-4 marks the germ cell membrane . RNAi was performed in the rrf-1 mutant to specifically knock down rho-1 in the germline . Depletion of rho-1 in the germline reduces active dpMPK-1 signal in the germline compared to the control RNAi . Bars , 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 015 Since the SC is no longer restricted to the short arms of the bivalents in ect-2 ( zh8gf ) mutants , we tested whether late prophase chromosome remodeling is altered in this mutant . First , we observed that AIR-2 localization is still successfully restricted to the short arms of the bivalents at diakinesis in ect-2 ( zh8gf ) mutants ( Figure 1B ) . Second , LAB-1 , a proposed functional ortholog of Shugoshin , is still lost from the short arms and restricted to the long arms of the bivalents as in wild type ( de Carvalho et al . , 2008; Schvarzstein et al . , 2010; Tzur et al . , 2012 ) ( Figure 3 ) . These data indicate that key aspects of late prophase chromosome remodeling , namely LAB-1 and AIR-2 restricted localizations , are not altered in ect-2 ( zh8gf ) mutants and that ECT-2 specifically regulates the disassembly of the SC proteins during prophase I of meiosis . 10 . 7554/eLife . 12039 . 016Figure 3 . ECT-2 does not alter LAB-1 localization . Immunolocalization of LAB-1 and HTP-3 in -1 oocytes at diakinesis indicates that LAB-1 is restricted to the long arm of the bivalents in ect-2 ( rf ) and ect-2 ( gf ) mutants similar to wild type . Illustration depicts the cruciform structure of the bivalents at this stage and the localization of LAB-1 ( red ) to the long arm ( L ) and HTP-3 ( green ) to both long and short ( S ) arms in wild type . White box indicates the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Worms from all the indicated genotypes , including wild type , were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . n>15 gonads were analyzed for each genotype . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 016 ECT-2 has been shown to activate the RAS/MAP kinase signaling cascade to promote primary vulval cell fate specification during vulval development in C . elegans ( Canevascini et al . , 2005 ) . The RAS/ERK ( Extracellular signal-regulated kinase ) MAP Kinase signaling regulates various aspects of the cell cycle and components of the signaling cascade are highly conserved between C . elegans and mammals ( Sundaram , 2013 ) . In C . elegans , the ERK MAP kinase , MPK-1 , is expressed in both somatic tissues as well as in the germline where it controls several aspects of germ line development including pachytene progression and germ cell survival ( Church et al . , 1995; Kritikou et al . , 2006; Lee et al . , 2007 ) . However , the role of MPK-1 in SC dynamics has never been explored . We therefore tested whether the ERK MAP kinase pathway regulates SC dynamics in the germline . Analysis of mpk-1 ( ga117 ) null , mpk-1 ( ga111lf ) and mpk-1 ( ku1lf ) temperature-sensitive loss-of-function mutants revealed defects in SC assembly as indicated by the formation of polycomplexes , similar to ect-2 ( zh8gf ) mutants at the non-permissive temperature ( Figure 1C and Figure 2—figure supplement 3 ) . However , germline nuclei that had already passed through the leptotene/zygotene stage before the temperature shift exhibited normal SC tracts along chromosomes at the pachytene stage ( Figure 2—figure supplement 3 ) . Since nuclei fail to progress from early/mid-pachytene to late-pachytene in mpk-1 ( ga117 ) null mutants ( Lee et al . , 2007 ) , we analyzed the disassembly of SC proteins in mpk-1 ( ga111lf ) mutants . The mpk-1 ( ga111 ) temperature sensitive loss-of-function mutant has been shown to still have a low level of dpMPK-1 activity and an incompletely penetrant pachytene arrest phenotype ( Lee et al . , 2007 ) . Nuclei that were able to progress from pachytene to diplotene exhibited normal SC disassembly similar to the ect-2 ( ax751rf ) mutant ( Figure 2 ) . To further examine the role of the ERK MAP kinase pathway in the disassembly of SC proteins , we analyzed let-60 , which encodes for the RAS protein that functions upstream of the MAP kinase cascade to activate the MAP kinase pathway . A let-60 ( ga89gf ) temperature-sensitive gain-of-function mutant leads to constitutive activation of MAP kinase both in somatic tissues and the germline ( Lee et al . , 2007 ) . Similar to the ect-2 ( zh8gf ) mutant , let-60 ( ga89gf ) mutants exhibit defects in both SC assembly , as evidenced by the presence of polycomplexes , and the disassembly of the SC proteins from the long arms of the bivalents , where they persisted at the non-permissive temperature ( Figure 2 and Figure 2—figure supplement 3 ) . Similar defects in the disassembly of SC proteins are also observed in the lip-1 ( zh15rf ) reduction-of-function mutant ( Figure 2 ) , where the LIP-1 phosphatase fails to inactivate , MPK-1 in late pachytene , which therefore persists throughout pachytene , diplotene and diakinesis ( Hajnal and Berset , 2002; Rutkowski et al . , 2011 ) , suggesting that the constitutive presence of active MPK-1 impairs the disassembly of SC proteins from the long arms of the bivalents . In contrast , we did not observe any defects in SC assembly in lip-1 ( zh15rf ) ( Figure 2—figure supplement 3 ) . Further , we found that LAB-1 localization was not altered in either let-60 ( ga89gf ) or lip-1 ( zh15rf ) mutants suggesting that MAP kinase specifically regulates the disassembly of SC proteins from the long arms of the bivalents and not other aspects of late prophase chromosome remodeling ( Figure 2—figure supplement 4 ) . Altogether these data suggest that the MAP kinase pathway plays an essential role in regulating the disassembly of SC proteins whereas the defects we see in SC assembly might be a secondary consequence of the role of MAP kinase in mitosis or due to several other germline functions of MPK-1 . To examine the connection between ECT-2 and the ERK MAP kinase pathway , we took advantage of the tightly regulated windows where the activated diphosphorylated form of MPK-1 ( dpMPK-1 ) can be detected in the germline . In the ect-2 ( ax751rf ) mutant , we see reduced dpMPK-1 signal in the mid-late pachytene region compared to wild type ( Figure 2—figure supplement 5A and B ) . We next tested whether the Rho GTPase , RHO-1 , activates ERK MAP kinase signaling in the germline , similar to its role in the regulation of vulval development ( Canevascini et al . , 2005 ) . Given the highly abnormal gonads observed following strong loss of rho-1 function , partial RNAi knockdown of rho-1 was performed to obtain germlines with essentially wildtype morphology/organization ( see Materials and Methods ) . Similarly to the ect-2 ( ax751rf ) mutant , partial depletion of rho-1 also results in reduced dpMPK-1 signal in the mid-late pachytene region ( Figure 2—figure supplement 6 ) . In addition , the expression pattern of RHO-1 is similar to that observed for ECT-2 in the germline ( Figure 1—figure supplement 4 ) . In contrast to ect-2 ( ax751rf ) mutants and to rho-1 partial knock down , dpMPK-1 expression is not turned off at late pachytene and during diplotene in the ect-2 ( zh8gf ) mutant . Instead , dpMPK-1 persists from pachytene to diakinesis similar to let-60/RAS gain-of-function mutants ( Figure 2—figure supplements 5A and B; [Lee et al . , 2007] ) . Taken together , these data indicate that ECT-2 and RHO-1 either promote MPK-1 activation or block MPK-1 inactivation in the germline . Next , we performed epistasis analysis to test whether ECT-2 functions through the MAP kinase pathway to regulate the disassembly of SC proteins . First , we analyzed the brood size in ect-2 ( ax751rf ) ; let-60 ( ga89gf ) double mutants ( Table 1 ) . let-60 ( ga89gf ) mutants exhibit a severely reduced brood size compared to ect-2 ( ax751rf ) . Interestingly , ect-2 ( ax751rf ) ; let-60 ( ga89gf ) double mutants exhibit a severely reduced brood size similar to let-60 ( ga89gf ) . Further , ect-2 ( ax751rf ) ; let-60 ( ga89gf ) double mutants exhibit defects in the disassembly of SC proteins similar to let-60 ( ga89gf ) mutants ( Figure 2A and B ) . These data show that LET-60 functions downstream of ECT-2 in the germline . We also analyzed ect-2 ( zh8gf ) ; mpk-1 ( ga11lf1 ) double mutants for defects in the disassembly of SC proteins . We found that mpk-1 ( ga111lf ) is able to suppress the SC disassembly defect of ect-2 ( zh8gf ) mutants ( Figure 2A and B ) . Thus , our data demonstrates that ECT-2 functions through the MAP Kinase pathway to regulate the disassembly of the SC proteins on the long arms of the bivalents uncovering a novel mode of regulation for this structure . To determine how the disassembly of the SC proteins is regulated by MPK-1 , we tested whether SC components might be a direct phosphorylation target of MPK-1 . First we examined the central region components of the SC , SYP-1 , SYP-2 , SYP-3 and SYP-4 , for the presence of potential MAP kinase phosphorylation sites using the phosphorylation site predictor programs GPS 2 . 1 and KinasePhos 2 . 0 ( Wong et al . , 2007; Xue et al . , 2008 ) . This identified potential MAP kinase phosphorylation sites on SYP-1 , SYP-2 , SYP-4 and none on SYP-3 ( Figure 4A ) . Next , to verify the predicted MAP kinase phosphorylation sites in the SYP proteins , we immunoprecipitated the SYP-1/2/3 proteins from lysates of adult worms expressing either SYP-2::GFP or SYP-3::GFP and performed extensive mass spectrometry analysis to look for post-translational modifications ( a similar analysis was not possible for SYP-4 given that neither functional antibodies or tagged transgenic lines are currently available ) . In addition , a phospho-proteomics approach was applied to wild type lysates to further confirm the MAP kinase phosphorylation sites in the SYP proteins ( see Materials and methods ) . These combined approaches confirmed that SYP-2 is phosphorylated at S25 , a potential MAP kinase phosphorylation site ( Figure 4A and B ) . 10 . 7554/eLife . 12039 . 017Figure 4 . Phosphorylation of SYP-2 is dependent on the MAP kinase pathway . ( A ) Schematic representation of SYP-1 , SYP-2 , and SYP-4 proteins with predicted MAP kinase phosphorylation sites indicated in black and site confirmed by mass spectrometry indicated in red . CC indicates the coiled-coil domains . ( B ) MS/MS fragmentation spectrum for SYP-2 phosphopeptide VSFASPVSSSQK in the range 100–1300m/z . The annotated spectrum shows fragment ion species matched between theoretical and measured values . 'b ions' are generated through fragmentation of the peptide bond from the N-terminus , whereas 'y ions' are generated through fragmentation from the C-terminus . Ion species detected with a mass loss of 98 ( phosphoric acid ) are indicated in yellow; those ions without phospho-loss are annotated in red . Analysis of 'y' and 'b' ions with and without phospho-loss is consistent with phosphorylation of the second serine ( VSFApSPVSSSQK ) , corresponding to S25 of the SYP-2 protein . ( C ) Western blots showing immunoprecipitation of SYP-2 from SYP-2::GFP whole worm lysates with a GFP antibody and dpMPK-1 and IgG immunoprecipitation from wild type whole worm lysates with dpMPK-1 and IgG antibodies , respectively . Standard SDS-PAGE was used and western blots were probed with the indicated antibodies . Black arrow ( upper band ) indicates germline-specific and red arrow ( lower band ) indicates soma-specific isoforms of MPK-1 . The germline-specific isoform of MPK-1 co-immunoprecipitates with SYP-2 confirming the in vivo interaction detected between dpMPK-1 and SYP-2 . ( D ) Calf intestinal phosphatase assay ( CIP ) showing SYP-2 is phosphorylated in vivo . After CIP treatment , only a faster migrating band of SYP-2 is present whereas in the presence of the phosphatase inhibitor , both faster and slower migrating bands are present . In the mpk-1 ( ga117 ) and ect-2 ( e1778 ) null mutant lysates , the upper band is no longer present whereas in let-60 ( ga89 ) and ect-2 ( zh8 ) gain-of-function mutants , as well as in ect-2 ( ax751rf ) reduction-of-function mutants , both upper and lower bands are present . In the syp-2 ( S25A ) phosphodead mutant , only the lower migrating band is present . α-tubulin is used as a loading control . Worms from all indicated genotypes were grown at 15°C and shifted to 25°C at the L4 stage . Worm lysates were prepared from 18–24 post-L4 worms . Phos-tag SDS-PAGE was used for better separation and detection of phosphorylated proteins . ( E ) Western blot showing that in the presence of CIP , dpMPK-1 is not present , whereas in the absence of CIP activity dpMPK-1 is present , indicating that the CIP assay is working . There is no mobility shift detected for the SYP-3 protein in either presence or absence of CIP activity . α-tubulin is used as a loading control . Phos-tag SDS-PAGE was used as for ( D ) . ( F ) In vitro kinase assay showing SYP-2 as a potential direct phosphorylation substrate of ERK MPK kinase . Tandem mass ( MS/MS ) spectrum of the SYP-2 protein subjected to the in vitro kinase reaction . A SYP-2 peptide containing a phosphorylated serine residue ( Ser-25 ) , VSFApSPVSSSQK , was identified in this spectrum . The neutral loss from the doubly charged precursor ion is indicative of the phosphorylated peptide . The b and y product ions are indicated in blue and red , respectively . The spectrum is representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01710 . 7554/eLife . 12039 . 018Figure 4—figure supplement 1 . SYP-3 does not exhibit a detectable mobility shift in vivo . Calf intestinal phosphatase assay ( CIP ) showing that SYP-3 may not be phosphorylated in vivo . There is no difference in the migration of the SYP-3 band with and without CIP treatment as well as in the indicated mutant backgrounds . Overexposed blots are shown to indicate that there is no other detectable additional band . Phos-tag SDS-PAGE was used for better separation and detection of phosphorylated proteins . α-tubulin was used as a loading control . Worms from all indicated genotypes were grown at 15°C and shifted to 25°C at the L4 stage . Worm lysates were prepared from 18–24 post-L4 worms . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 018 We next determined that SYP-2 interacts with the germline-specific isoform of MPK-1 in vivo by immunoprecipitating SYP-2 from the lysates of adult worms expressing endogenous SYP-2 as well as SYP-2::GFP and probing with an MPK-1 antibody on western blots ( Figure 4C ) . The reciprocal experiment showed that the SYP-2 protein co-immunoprecipitates with dpMPK-1 in pull downs from adult worm lysates ( Figure 4C ) . This in vivo interaction was further confirmed by mass spectrometry analysis where we identified two MPK-1 peptides in the SYP-2 pull down ( data not shown ) . To further confirm whether SYP-2 is phosphorylated in vivo , we incubated worm lysates with calf intestinal phosphatase ( CIP ) either in the presence or absence of the phosphatase inhibitor EDTA , and ran these samples on Phos-tag gels for a better separation of phosphorylated from non-phosphorylated bands . CIP treatment results in phosphate removal and a faster running band for SYP-2 , whereas when CIP activity is inhibited using EDTA two bands are detected corresponding to the phosphorylated and unphosphorylated forms of SYP-2 ( Figure 4D ) . This shows that SYP-2 is phosphorylated in vivo and that SYP-2 exists in both phosphorylated as well as unphosphorylated forms . Further , in mpk-1 ( ga117 ) null mutants , the upper band corresponding to phosphorylated SYP-2 is not present , suggesting that phosphorylation of SYP-2 is dependent on MAP kinase . In let-60 ( ga89gf ) /RAS gain-of-function mutants , we detect two bands corresponding to the phosphorylated and unphosphorylated SYP-2 protein , instead of only the phosphorylated band . This could be due to only a subset of the SYP-2 protein being phosphorylated by MPK-1 . This is consistent with the expression pattern of MPK-1 in the germline where the active form of dpMPK-1 is expressed only from mid to late pachytene . Meanwhile , SYP-2 is expressed from the leptotene/zygotene stage to late diakinesis . Therefore , SYP-2 could be phosphorylated by dpMPK-1 at the mid to late-pachytene region thereby regulating the localization of SYP-2 in a spatio-temporal manner . Further , we found that the SYP-2 ( S25A ) phosphodead mutant protein runs at the same size of the unphosphorylated SYP-2 band , suggesting that there are no additional phosphorylation sites in the SYP-2 protein ( Figure 4D ) . We used SYP-3 as a negative control , since our analyses indicate it lacks phosphorylation sites , and we did not detect any shift in band size ( Figure 4E and Figure 4—figure supplement 1 ) . While this suggests that there may only be an unphosphorylated form of SYP-3 , we cannot rule out the possibility that either a small fraction of SYP-3 in the extract tested is phosphorylated or that phosphorylation may not lead to a mobility shift under the conditions utilized in this analysis . We used MPK-1 as a positive control , where the band corresponding to dpMPK-1 ( phosphorylated form of MPK-1 ) is not present after CIP treatment whereas it is present when CIP activity is inhibited ( Figure 4E ) . These data show that SYP-2 is phosphorylated in vivo and that its phosphorylation is dependent on the MAP kinase pathway . Finally , to determine whether SYP-2 is a potential direct target for phosphorylation by MPK-1 , we performed an in vitro kinase assay . Recombinant SYP-2 protein expressed and purified from E . coli was incubated with and without the MPK-1 mouse homolog ERK MAP kinase and examined by mass spectrometry to verify phosphorylation . We found that SYP-2 was phosphorylated by MAP kinase at the S25 site in vitro in the presence of ERK MAP kinase ( Figure 4F ) . The phosphorylated peptide was not observed in the absence of the kinase or when the kinase reaction was subsequently incubated with lambda phosphatase . Altogether , our data indicate that in vivo phosphorylation of SYP-2 is dependent on the MAP kinase pathway and that SYP-2 is an in vitro phosphorylation substrate of the ERK MAP kinase . To determine the role for the MPK-1-mediated phosphorylation of SYP-2 , we generated phosphodead and phosphomimetic SYP-2 mutants using CRISPR-Cas9 technology ( Tzur et al . , 2013 ) . We generated lines where the S25 residue was mutated to either an alanine to generate a phosphodead mutant or to aspartic acid to generate a phosphomimetic mutant . We did not observe defects in either SC assembly or disassembly in the syp-2 phosphodead mutant ( Figure 5 and Figure 5—figure supplement 1 ) . This result was not surprising since the mpk-1 ( ga111lf ) mutant did not show any defects in the disassembly of SC proteins at the non-permissive temperature and we already hypothesized that the SC assembly defects we see in both mpk-1 loss- and gain-of-function mutants could be due to other roles played by MPK-1 in germline development or through its effects on different target proteins . However , in the syp-2 phosphomimetic mutant , while SC assembly was normal , the disassembly of SC proteins was impaired as exemplified by SYP-1 , SYP-2 , SYP-3 and SYP-4 persisting on the long arms and failing to become restricted to the short arms of the bivalents ( Figure 5 , Figure 5—figure supplement 2 and Figure 5—figure supplement 3 ) . This is consistent with the observation that constitutive activation of dpMPK-1 leads to failure in disassembly of SC proteins from the long arm of the bivalents ( this study ) , and that all four SYP proteins are interdependent on each other for their localization ( Colaiácovo et al . , 2003; Smolikov et al . , 2007a; 2009 ) . Other aspects of late prophase chromosome remodeling , such as AIR-2 and LAB-1 restricted localizations , are not altered in either the syp-2 phosphodead or phosphomimetic mutants ( Figure 5—figure supplement 3 and 4 ) . However , we see a reduction in brood size ( 109 ± 32 ) , accompanied by 14 . 3% embryonic lethality and 2 . 1% males , in syp-2 phosphomimetic mutants indicating that impaired disassembly of the SC proteins results in defects in chromosome segregation . Taken together , our data show that phosphorylation of SYP-2 protein at the S25 site prevents the disassembly of the SC proteins from the long arm of the bivalents during late prophase . 10 . 7554/eLife . 12039 . 019Figure 5 . Phosphorylation of SYP-2 at S25 is required for normal SC dynamics . High-magnification images of wild type , syp-2 ( S25A ) phosphodead , and syp-2 ( S25D ) phosphomimetic mutants co-stained with HTP-3 ( green ) , SYP-1 ( red ) and DAPI ( blue ) . In the syp-2 ( S25D ) phosphomimetic mutant the SC fails to disassemble from the long arms of the bivalents in 54% of the nuclei scored . All images are of diakinesis nuclei . White box indicates the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Worms from all the indicated genotypes were grown at 20°C and analyzed 18–24 hr post-L4 . Histograms on the right indicate the percentage of diplotene and diakinesis stage nuclei with SYP-1 either only on the short arm ( S , blue ) or on both long and short arms ( red , L&S ) of the bivalents . All the bivalents were examined in every nucleus and the bivalents in the same nucleus either all exhibited SYP-1 staining on both the long and short arms or all exhibited staining only on the short arms . Numbers of nuclei scored are shown . n>15 gonads were analyzed for each genotype . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 01910 . 7554/eLife . 12039 . 020Figure 5—figure supplement 1 . SYP-1 and HTP-3 localization in syp-2 phosphodead and phosphomimetic mutants . Immunolocalization of HTP-3 and SYP-1 in leptotene/zygotene ( A ) and mid-pachytene ( B ) stage nuclei of the indicated genotypes . HTP-3 and SYP-1 localization is indistinguishable between syp-2 phosphodead and phosphomimetic mutants and wild type at these stages . n>15 gonad arms were analyzed for each . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 02010 . 7554/eLife . 12039 . 021Figure 5—figure supplement 2 . SYP-2 , SYP-3 and SYP-4 localization in ect-2 ( gf ) , let-60 ( gf ) , and syp-2 phosphomimetic mutants . Higher-magnification images of diakinesis oocytes from the indicated genotypes stained with ( A ) HTP-3 ( green ) , SYP-2 ( red ) , and DAPI ( blue ) , ( B ) HTP-3 ( green ) , SYP-3 ( red ) , and DAPI ( blue ) , and ( C ) HTP-3 ( green ) , SYP-4 ( red ) , and DAPI ( blue ) . White boxes indicate the bivalents shown at a higher magnification on the right . Illustrations depict bivalent at this stage . S indicates short arm and L indicates long arm . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 02110 . 7554/eLife . 12039 . 022Figure 5—figure supplement 3 . SYP-1 and LAB-1 localization in ect-2 ( gf ) , let-60 ( gf ) , and syp-2 phosphomimetic mutants . Higher-magnification images of diakinesis oocytes from the indicated genotypes stained with SYP-1 ( green ) , LAB-1 ( red ) , and DAPI ( blue ) . White boxes indicate the bivalents shown at a higher magnification on the right . Illustrations depict bivalents at this stage . SYP-1 is indicated in green , LAB-1 in red , S indicates short arm and L indicates long arm . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 02210 . 7554/eLife . 12039 . 023Figure 5—figure supplement 4 . AIR-2 and LAB-1 localization in syp-2 phosphodead and phosphomimetic mutants . Higher-magnification images of oocytes at diakinesis from the indicated genotypes stained with ( A ) HTP-3 ( green ) , LAB-1 ( red ) , and DAPI ( blue ) , and ( B ) HTP-3 ( green ) , AIR-2 ( red ) , and DAPI ( blue ) . White boxes indicate the bivalents shown at a higher magnification on the right . Illustrations depict bivalents at this stage . Worms from all the indicated genotypes were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . Bar , 2 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 023 Disassembly of SC proteins is impaired and the SYP proteins fail to be properly restricted to a chromosome subdomain during late prophase in crossover-defective mutants such as spo-11 , msh-5 , msh-4 , zhp-3 and cosa-1 , which instead often continue to retain the SYP proteins along the full lengths of the chromosome axes ( Bhalla et al . , 2008; Nabeshima et al . , 2005; Yokoo et al . , 2012; Martinez-Perez et al . , 2008 ) . Our study of HIM-18/Slx4 , which is involved in the resolution of Holliday junction intermediates into COs , indicated that the symmetry-breaking event detected at late pachytene is likely a CO precursor and not a completed CO event ( Saito et al . , 2009 ) . Specifically , analysis of him-18 null mutants showed six ZHP-3 foci per oocyte marking the six CO precursor sites also observed in wild type during late pachytene , and the establishment of long and short arm domains as indicated by LAB-1 , SYP-1 and AIR-2 localization . Since our current study indicates that the MPK-1 pathway regulates the disassembly of SC proteins we tested the hypothesis that MPK-1 coordinates recognition of a CO precursor/intermediate with disassembly of SC proteins from the long arms of the bivalents . First , active dpMPK-1 and foci for the pro-CO factor COSA-1 are both observed at the same time in pachytene stage nuclei suggesting a correlation between CO precursors and active MPK-1 ( Figure 6A ) . Second , we observed a significant persistence of dpMPK-1 signal in zhp-3 and cosa-1 mutants compared to wild type suggesting that inactivation of MPK-1 is linked to formation of a CO precursor ( Figure 6B and C ) . Third , the defect in disassembly of SC proteins observed in cosa-1 mutants is fully rescued in a cosa-1 mpk-1 ( lf ) double mutant and partly rescued in ect-2 ( rf ) ;cosa-1 and cosa-1;syp-2 ( S25A ) double mutants ( 11 . 5% and 40 . 9% suppression of the cosa-1 disassembly defect phenotype , respectively ) ( Figure 6D ) . The partial suppression observed in ect-2 ( rf ) ;cosa-1 mutants can be ascribed to the fact that ect-2 ( rf ) is a reduction-of-function mutant and still has dpMPK-1 activity as indicated in Figure 2—figure supplement 5 as well as by the presence of phoshorylated SYP-2 detected on Westerns ( Figure 4D ) . The cosa-1; syp-2 ( S25A ) partial rescue leads us to hypothesize that there may be additional kinases regulating the other SYP proteins in an MPK-1-dependent manner . Indeed , while we did not find evidence for additional MPK-1 phosphorylation sites on SYP-2 , there are additional phosphorylation sites for other kinases , such as the polo-like kinase , on SYP-1 and SYP-4 , verified by mass spectrometry ( Nadarajan and Colaiacovo , unpublished and PHOSIDA ( http://www . phosida . com ) and Polo-like kinase itself was identified as a target of MPK-1 in C . elegans ( Arur et al . , 2009 ) . While the roles exerted by these additional kinases on the SC remain to be investigated , our observations support a role for MPK-1 in coordinating recognition of a CO precursor with asymmetric disassembly of SC proteins . 10 . 7554/eLife . 12039 . 024Figure 6 . MPK-1 links CO designation with the disassembly of SC proteins . ( A ) Low-magnification images of whole mounted gonads depicting COSA-1::GFP and dpMPK-1 localization in wild type . Signals for the pro-crossover marker COSA-1::GFP and dpMPK-1 are both observed at the mid to-late pachytene region . Insets show higher magnification images of different regions in the germline: ( green inset ) Early to mid-pachytene , no COSA-1 localization and dpMPK-1 is off; ( red inset ) mid to late-pachytene , COSA-1::GFP starts to localize on the chromosomes concomitant with the appearance of the dpMPK-1 signal; ( yellow inset ) late pachytene , 6 COSA-1::GFP foci/nucleus are observed and strong dpMPK-1 signal is detected . 20 gonads were analyzed . Bars , 20μm for whole gonad and 2μm for insets . ( B ) Low-magnification images of wild type , cosa-1 and zhp-3 gonads co-stained with dpMPK-1 ( red ) , and DAPI ( blue ) . dpMPK-1 expression level is not turned off in the cosa-1 and zhp-3 mutants in late pachytene and diplotene in contrast to wild type . n>18 gonads were analyzed for each . Bar , 20 μm . ( C ) Graph showing the quantification of dpMPK-1 fluorescence signal intensity in different regions of the gonad arm in wild type , zhp-3 and cosa-1 mutants . **p<0 . 005 , *p<0 . 02 ( unpaired student’s t-test; n>18 , 17 and 15 gonad arm were analyzed for wild type , zhp-3 and cosa-1 mutants , respectively ) . Regions where dpMPK-1 signal was quantified are indicated on the diagram depicting the hermaphrodite germline . TZ stands for transition zone ( leptotene/zygotene ) . E , M and L stand for early , mid and late pachytene , respectively . Progression from mitosis into meiosis is displayed from left to right and the last three oocytes at diakinesis ( -3 to -1 ) are indicated . ( D ) Co-staining with HTP-3 ( green ) , SYP-1 ( red ) and DAPI ( blue ) of diplotene nuclei from the indicated genotypes . Illustrations depict the bivalent configuration at this stage . S indicates short arm and L indicates long arm . White boxes indicate the bivalent shown at a higher magnification on the right . Bivalents with both long and short arms clearly displayed were chosen for higher magnification . Histograms on the right indicate the percentage of diplotene and diakinesis stage nuclei with SYP-1 either only on the short arm ( S , blue ) or on both long and short arms ( red , L&S ) of the bivalents . All the bivalents were examined in every nucleus and the bivalents in the same nucleus either all exhibited SYP-1 staining on both the long and short arms or all exhibited staining only on the short arms . Numbers of nuclei scored are shown . Bar , 2 μm . Worms from all the genotypes indicated in ( A–C ) were grown at 20°C and analyzed 18–24 hr post-L4 . Worms from all the genotypes indicated in ( D ) were grown at 15°C , shifted to 25°C at the L4 stage , and analyzed 18–24 hr post-L4 . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 024
The central region component of the SC in budding yeast , Zip1 , is continuously added to the SC during meiotic prophase and exhibits differential dynamics near the recombination site ( Voelkel-Meiman et al . , 2012 ) . Similarly , the C . elegans SC has been proposed to be a dynamic structure that can disassemble and reassemble locally in response to exogenous DNA damage induced by irradiation ( Couteau and Zetka , 2011 ) . Here , we identify a regulatory mechanism underlying the important step of disassembly of the SC proteins from the long arms of the bivalents . Our studies have revealed the existence of both a phosphorylated and an unphosphorylated pool of SYP-2 protein . We propose that SYP-2 is phosphorylated by MPK-1 at the S25 site and that this must take place during the mid to late pachytene stage when activated MPK-1 is expressed . Our studies revealed that a constitutively phosphorylated SYP-2 resulted in an inability to remove the SYP proteins from the long arms of the bivalents in a timely manner . However , an inability to phosphorylate SYP-2 at S25 does not affect either SC assembly or disassembly , and we did not detect residual or novel phosphorylation in syp-2 ( S25A ) mutants in our CIP assays . These observations lead us to suggest that phosphorylation of SYP-2 at S25 may 'prime' this protein for subsequent dephosphorylation once crossover designation is detected . This would ensure that homologous interactions are not prematurely dissolved until CO specification takes place , thus guaranteeing that homologs are locked and ensuring subsequent accurate chromosome segregation . The CO precursor markers and dpMPK-1 are expressed during the same time in the germline ( Figure 6A ) , suggesting that the timing when CO designation is completed coincides with when dpMPK-1 is turned off ( germ cells progress from pachytene to diplotene ) . Our model is that MPK-1 functions as part of a surveillance mechanism in the pachytene region that recognizes the CO precursor and triggers the disassembly of SC proteins from along the long arms of the bivalents upon CO designation ( Figure 7 ) . Our model is further supported by the impaired disassembly of SC proteins detected in the absence of CO designation in zhp-3 and cosa-1 mutants ( Bhalla et al . , 2008; Yokoo et al . , 2012 ) and our observation that dpMPK-1 persists into late pachytene and diplotene in zhp-3 and cosa-1 mutants ( Figure 6B and C ) . 10 . 7554/eLife . 12039 . 025Figure 7 . Model for MPK-1-mediated coordination between CO designation and the disassembly of SC proteins during meiosis . Proposed model for MPK-1 function in coordinating CO designation and the disassembly of SC proteins from the long arms of the bivalents . In mid-pachytene , dpMPK-1 phosphorylates the SYP-2 protein at the S25 site and CO specification is progressing . When CO designation is detected , MAP kinase is turned off , SYP-2 is either dephosphorylated along the long arms of the bivalents by a yet unknown phosphatase or , given the dynamic nature of the SC , phosphorylated SYP-2 is replaced by unphosphorylated SYP-2 in this region and the SC starts to disassemble from the long arms in late pachytene/diplotene . In pro-CO mutants , such as cosa-1 and zhp-3 , CO designation is impaired and dpMPK-1 persists at the late pachytene and diplotene regions preventing disassembly of the SC proteins . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 02510 . 7554/eLife . 12039 . 026Figure 7—figure supplement 1 . Conserved MAP kinase phosphorylation sites present in central region components of the mammalian SC . Schematic representation of human and mouse SYCE1 , SYCE2 and TEX12 proteins with indicated MAP kinase phosphorylation sites . MAP kinase phosphorylation sites predicted by GSP 2 . 1 are indicated in black and site verified by mass spectrometry is indicated in blue ( Huttlin et al . , 2010 ) . CC indicates coiled-coil domains . DOI: http://dx . doi . org/10 . 7554/eLife . 12039 . 026 The SYP proteins are removed from the chromosomes in two steps , first SYPs are lost from the long arms of the bivalents in the late pachytene to early diplotene stage nuclei , but are selectively maintained on the short arms of the bivalents until late diakinesis ( -3 oocyte; Figure 6C ) . In the second step , the SYP proteins are lost from the short arms of the bivalents . This second round of removal might be regulated in a manner independent of the MAP kinase pathway . This is supported by our observation that the SYP proteins are able to disassemble from the short arms of the bivalents in fog-2 ( oz40 ) female animals where MPK-1 is not active in the proximal oocytes , in a manner indistinguishable from wild type ( Nadarajan and Colaiacovo , unpublished results ) . How then are the SYP proteins selectively lost from the long arms but not from the short arms of the bivalents during the diplotene stage ? There are several possible ways this could be carried out: 1 ) There could be proteins localizing to the short arms of the bivalents in late pachytene to prevent loss of the SYP proteins specifically from the short arms; 2 ) a phosphatase could specifically localize to the long arms of the bivalents and dephosphorylate the SYP-2 protein located only on that chromosome subdomain thereby leading to loss of SYP proteins from the long arms while localization of this phosphatase to the short arms of the bivalents could be blocked by other proteins; and 3 ) there might be factors preventing the addition of unphosphorylated SYP-2 protein on the short arms of the bivalents . In fact , there are precedents for such modes of regulation acting on the short/long arms of the bivalents , as exemplified by LAB-1 , the ortholog of Shughoshin in C . elegans , and the HORMA domain containing proteins HTP-1/HTP-2 . LAB-1 localizes exclusively to the long arm of the bivalents in late prophase where it prevents Aurora B Kinase , AIR-2 , from loading on the long arm of the bivalents and restricts its localization to the short arm by recruiting the PP1/Glc7 phosphatases GSP-1 and GSP-2 , thereby ensuring homologous chromosome segregation during meiosis I and sister chromosome segregation during meiosis II ( de Carvalho et al . , 2008; Tzur et al . , 2012 ) . Similar to LAB-1 , the HORMA domain containing HTP-1/2 proteins relocalize from being associated along the full length of the chromosomes to being exclusively localized to the long arm of the bivalents during chromosome remodeling in late prophase I ( Martinez-Perez et al . , 2008 ) . It has been suggested that differentially regulated pools of HTP-1/2 near the chromosome axes and/or post-translational modifications such as phosphorylation within the closure motifs of HTP-3 , which remains associated with both long and short arms and with whom HTP-1/2 interact , could contribute to their distinct localization ( Kim et al . , 2014 ) . How conserved is the asymmetric disassembly of SC proteins ? This process occurs not only in C . elegans , but also in budding yeast , flies and mice . In both yeast and flies , the SC disassembles asymmetrically and persists associated with the centromere until late prophase ( Newnham et al . , 2010; Takeo et al . , 2011; Gladstone et al . , 2009 ) . Similarly in mouse spermatocytes , the SC is lost along the length of the chromosome but is retained at paired centromeres ( Qiao et al . , 2012; Bisig et al . , 2012 ) . Studies in yeast show that the SC that persists at centromeres , after the SC disassembles throughout other regions of the chromosomes , promotes centromere bi-orientation thereby ensuring homologous chromosome segregation at meiosis I ( Gladstone et al . , 2009 ) . In mouse spermatocytes , the SC that persists between the chiasmata , after SC disassembly in the pachytene region , is proposed to regulate local remodeling of homologous chromosome axes thereby promoting centromere and chiasmata functions , which are to ensure proper homolog segregation at meiosis I ( Qiao et al . , 2012 ) . While chromosomes in C . elegans are holocentric , and therefore lack a localized centromere , we propose that the CO designation-triggered asymmetric disassembly of SC proteins , and concomitant chromosome remodeling , are set in place to ensure that the short arm of the bivalents can function in a manner orthologous to centromeres to promote bi-orientation of the homologs thereby ensuring accurate segregation of homologous chromosomes during meiosis I suggesting the existence of a conserved regulatory mechanism . Further support for this stems from the demonstration that chromosome segregation at meiosis I in C . elegans occurs by outward pushing forces from microtubules assembling at the interface between the short arms of the separating homologs ( Dumont et al . , 2012 ) . Lastly , both mammalian ECT2 and the ERK/MAP kinase are expressed in testis and ovaries ( Hirata et al . , 2009; Fields and Justilien , 2010; Inselman and Handel , 2004; Nissan et al . , 2013; Uhlén et al . , 2015 ) . We have found that some of the central region components of the mammalian SC , namely SYCE1 , SYCE2 and TEX12 , contain putative MAP kinase phosphorylation sites predicted by GSP 2 . 0 ( [Wong et al . , 2007; Xue et al . , 2008]; Figure 7—figure supplement 1 ) . Moreover , the predicted ERK/MPK kinase phosphorylation sites in SYCE1 ( S308 ) and TEX12 ( S27 ) are conserved between mice and humans , and the S308 site in SYCE1 has been confirmed by mass spectrometry analysis as being phosphorylated in vivo in mice ( Huttlin et al . , 2010 ) , raising the interesting possibility that we uncovered a conserved mode of regulation for asymmetric disassembly of SC proteins shared between worms and mammals .
The C . elegans N2 Bristol strain was used as the wild-type background and worms were cultured under standard conditions as described in ( Brenner , 1974 ) . Temperature sensitive strains ( ts ) were grown at the permissive temperature of 15°C and transferred to the restrictive condition of 25°C at the L4 stage . 24 hr post-L4 worms were then analyzed for the mutant phenotypes . The wild type worms used for comparisons with these mutants were all subjected to the same temperature shifts and examined at the same times as the mutants . The following mutations and chromosome rearrangements were used: LG II: ect-2 ( e1778 ) /dyp-10 ( e128 ) II , ect-2 ( ax751 ) II , unc-4 ( e120 ) ect-2 ( zh8 ) II/mIn1 [dpy-10 ( e128 ) mIs14] , unc-4 ( e120 ) II , ect-2 ( gk44 ) II; unc-119 ( ed3 ) III; xnIs162 [ect-2::GFP + unc-119 ( + ) ] , ect-2 ( ax751 ) II; unc-119 ( ed3 ) III; xnIs162 [ect-2::GFP + unc-119 ( + ) ] , ect-2 ( zh8 ) II/mIn1 [dpy-10 ( e128 ) mIs14]; mpk-1 ( ga111 ) III , ect-2 ( ax751 ) II; let-60 ( ga89 ) IV; ect-2 ( ax751 ) II; cosa-1 ( me13 ) III; LG III: mpk-1 ( ga117 ) /dpy-17 ( e164 ) unc-79 ( e1068 ) III , mpk-1 ( ku1 ) unc-32 ( e189 ) III , unc-32 ( e189 ) III , mpk-1 ( ga111 ) III , mels9 ( unc-119 ( + ) pie-1promoter::gfp::syp-3 ) ; unc-119 ( ed3 ) III; cosa-1 ( me13 ) mpk-1 ( ga111 ) III; cosa-1 ( me13 ) III; syp-2 ( rj16 ( S25A ) IV; LG IV: lip-1 ( zh15 ) IV , let-60 ( ga89 ) IV; and LG V: syp-2 ( ok307 ) V/nT1 [unc- ? ( n754 ) let- ? ( m435 ) ] ( IV;V ) , wgIs227 [syp-2::TY1::EGFP::3xFLAG ( 92C12 ) + unc-119 , syp-2 ( rj16 ( S25A ) V , and SYP-2 ( rj17 ( S25D ) . ect-2 was identified in a targeted RNAi screen on 168 germline-enriched genes designed to identify novel components regulating chromosome remodeling and short/long arm identity on bivalents . Specifically , we utilized a GFP-tagged AIR-2 containing line ( ojIs50 ( pie-1p::GFP::AIR-2 + unc-119 ( + ) ] ) to screen for candidates that when depleted resulted in the mislocalization of the Aurora B kinase , AIR-2 , which localizes to the short arms of the bivalents during late prophase I of meiosis to ensure accurate chromosome segregation . Criteria for the selection of these genes are detailed in ( Colaiácovo et al . , 2002 ) and were applied to germline-enriched genes identified by microarray analysis in ( Reinke et al . , 2004 ) . ect-2 ( ax751rf ) mutants phenocopied the ect-2 ( RNAi ) phenotype at the non-permissive temperature with AIR-2 failing to load onto the chromosomes ( Figure 1A ) . However , unlike ect-2 ( RNAi ) , where 100% of animals ( N=44/44 ) showed failure in AIR-2 loading on the chromosomes , only 52% of the ect-2 ( ax751rf ) mutants ( N=13/25 ) exhibited this defect , which could be due to the fact that ect-2 ( ax751 ) is not a null mutant . Since the ect-2 ( zh8gf ) and the mpk-1 ( ku1 ) mutants are in the unc-4 and unc-32 mutant backgrounds , respectively , we analyzed SC dynamics in unc-4 and unc-32 mutants . We did not find any defects in either SC assembly or disassembly in unc-4 and unc-32 mutants ( Figure 2—figure supplement 2 ) . Analysis of mpk-1 ( ga117 ) null mutants revealed defects in SC assembly , indicated by the formation of polycomplexes at 20°C ( 15% , n=20 , where n is the number of gonads scored ) and 25°C ( 66% , n=16 ) . Primary antibodies were used at the following dilutions for immunofluorescence: chicken α-GFP ( 1:400; Abcam , Cambridge , MA ) , rabbit α-SYP-1 ( 1:200; ( MacQueen et al . , 2002 ) , guinea pig α-HTP-3 ( 1:400; ( Goodyer et al . , 2008 ) , rabbit α-AIR-2 ( 1:100;[de Carvalho et al . , 2008] ) , rabbit α-LAB-1 ( 1:300;[de Carvalho et al . , 2008] ) , mouse α-dpMPK-1 ( 1:500; Sigma , St . Louis , MO ) and rabbit anti-RhoA ( 1:200; Santa Cruz ) . The following secondary antibodies from Jackson ImmunoResearch ( Jackson ImmunoResearch , WestGrove , PA ) were used at a 1:200 dilution: α-chicken FITC , α-rabbit Cy5 , α-mouse FITC , and α-guinea pig FITC . Vectashield containing 1μg/μl of DAPI from Vector Laboratories was used as a mounting media and anti-fading agent . Primary antibodies were used in the following dilutions for western blot analysis: chicken α-GFP ( 1:2000; Abcam ) , rabbit α-SYP-2 ( 1:200;[Colaiácovo et al . , 2003] ) , rabbit α-SYP-3 ( 1:200; [Smolikov et al . , 2007b] ) , mouse α-dpMPK-1 ( 1:500; Sigma ) , mouse α-tubulin ( 1:2000; Sigma ) and rabbit α-MPK-1 ( 1:2000; Santa Cruz , Dallas , TX ) . HRP-conjugated secondary antibodies , donkey anti-chicken , rabbit anti-mouse , and mouse anti-rabbit from Jackson ImmunoResearch were used at a 1:10 , 000 dilution . Whole mount preparation of dissected gonads and immunostaining procedures were performed as in Colaiácovo et al . ( 2003 ) . Immunofluorescence images were captured with an IX-70 microscope ( Olympus , Waltham , MA ) fitted with a cooled CCD camera ( CH350; Roper Scientific , Tuscon , AZ ) driven by the Delta Vision system ( Applied Precision , Pittsburgh , PA ) . Images were deconvolved using the SoftWorx 3 . 0 deconvolution software from Applied Precision . Flourescent images showing RHO-1 expression and dpMPK-1 expression were captured with a Zeiss Axioskope microscope fitted with a Hamamatsu digital CCD camera . Mouse recombinant p42 MAPK ( Erk2 ) was purchased from New England Biosciences ( Cat #- P6080S; Ipswich , MA ) . Full-length recombinant SYP-2 , expressed and purified from E . coli , was used for in vitro kinase assays . syp-2 cDNA from C . elegans was cloned into the pET30a vector and expressed and purified from E . coli following the protocol published in Jambhekar et al . ( 2014 ) . Kinase reactions were performed as described previously ( Arur et al . , 2011 ) . Briefly , 20 µM SYP-2 was incubated with 20 U p42 MAPK in the presence of 250 µM ATP and 1X kinase buffer ( NEB , Cat #- B6022S ) for 30 min at 30°C . In the control reaction , p42 MAPK was not included . Reactions were performed in duplicate . For the phosphatase treated reaction , the kinase reaction was carried out as described above . Following completion of the reaction , the kinase activity was inhibited with 400 µM ERK Inhibitor II , FR180204 ( Santa Cruz , Cat . #- sc-203945 ) . 100 U lambda phosphatase ( NEB , Cat #- P0753S ) , supplemented with 1 mM MnCl2 was then added and the sample incubated for an additional 30 min at 30°C . All reactions were terminated by adding 2X SDS sample buffer and boiling for 3 min . The samples were resolved by SDS-PAGE electrophoresis , stained with Coomassie blue , the bands corresponding to the molecular weight of SYP-2 excised , and subjected to in-gel digestion with Trypsin as previously described ( Shevchenko et al . , 2006 ) . Following overnight digestion at 37°C , peptides were extracted with 5% formic acid/50% acetonitrile , purified over Empore C18 extraction media ( 3M ) , and analyzed by liquid chromatography- tandem mass spectrometry ( LC-MS/MS ) with a LTQ-Velos linear ion trap mass spectrometer ( Thermo Scientific , Cambridge , Mass ) with an 18 cm3 125 µm ( ID ) C18 column and a 50 min 10–35% acetonitrile gradient . MS/MS spectra searches were performed using Sequest ( Eng et al . , 1994 ) . The CRISPR-Cas9 genome editing technology was used to engineer syp-2 phosphodead and phosphomimetic mutations at the endogenous locus ( Tzur et al . , 2013 ) . To generate a phosphodead syp-2 mutant , serine 25 ( S25A ) was mutated to alanine . To generate a phosphomimetic mutant , serine 25 ( S25D ) was mutated to aspartic acid . We used the sgRNA recognition site ( gaaaacagctgcagtaactgtgg ) 139 base pairs downstream of the start codon . To express the sgRNA , we replaced the unc-119 recognition sequence ( gaattttctgaaattaaaga ) in the pU6::unc-119_sgRNA plasmid ( Friedland et al . , 2013 ) with the SYP-2 sequence ( gaaaacagctgcagtaactg ) . The donor sequence containing the genomic sequence of syp-2 extending from 1380 bp upstream to 1821 bp downstream of the start codon with the following changes: TC to GA change at position 1192 to generate the phosphomimetic mutant and a T to G change at position 1192 to generate the phosphodead mutant and a G to C change at position 160 ( resulting in a silent mutation that is expected to prevent re-cutting by the Cas9 ) , was cloned into the BglII site of the pCFJ104 vector expressing Pmyo-3::mCherry::unc-54 ( Frøkjaer-Jensen et al . , 2008 ) . A cocktail consisting of a plasmid expressing the sgRNA ( 200 ng/μl ) , a plasmid expressing the donor sequence ( 97 . 5 ng/μl ) , Cas9 ( 200 ng/μl ) and the co-injection marker pCFJ 90 ( Pmyo-2::mCherry::unc-54utr; 2 . 5 ng/μl ) was microinjected into the gonad arms of the worms ( P0s ) . F1 animals expressing the co-injection marker were sequenced to identify mutants and homozygous animals were picked from among the F2 generation and confirmed by sequencing . Wild type animals expressing SYP-2::GFP and GFP::SYP-3 were grown in large quantity using 8-times peptone-enriched plates seeded with NA22 bacteria . 24 hr post-L4 worms were collected and washed 3 times with M9 and then one time with lysis buffer ( 50 mM Hepes buffer , pH 7 . 5 , 1 mM MgCl2 , 300 mM KCl , 10% glycerol , 0 . 05% NP-40 , 5 mM 2-me and protease inhibitor [protease inhibitor cocktail tablet , catalog # 11836153001; from Roche , Branford , CT] ) . Worms were frozen in liquid nitrogen followed by grinding using mortar and pestle . Frozen ground worms were mixed with an equal volume of lysis buffer and sonicated in a Diagenode Bioruptor sonication water bath ( Bioruptor Sonication , VCD300 ) alternating for 15 s on and 45 s off for a total of 20 min . Crude worm lysate was spun down at 14000 rpm and filtered with a 0 . 45um syringe filter ( Millex-HP filter unit , Catalog # SLHP033RS ) . 500 μl of worm lysate was incubated with 30 μl anti-GFP conjugated agarose beads ( MBL , Catalog # D153-8 ) overnight at 4°C on a nutator . Beads were washed 3 times with 1XPBS and boiled in 2xSample buffer for 5 min . For SYP-2 interaction with dpMPK-1 , supernatant was run on a 4–15% gradient gel and protein-protein interaction was verified on western blots with the appropriate antibody . Anti-IgG conjugated agarose beads were used as control . To verify the interaction by mass spectrometry , protein in the supernatant was precipitated using the proteoExtract protein precipitation kit ( CALBIOCHEM , catalog # 539180 ) followed by mass spectrometry analysis . To identify phosphorylation sites on the SYP proteins , the supernatants were run on 4–15% gradient gel , stained with a Pierce silver stain kit ( catalog # 24612 ) and bands of the appropriate size were analyzed by mass spectrometry . Immunoprecipitation was prepared as mentioned above . Thirty microliters of immunoprecipitate were incubated with 20 units of calf intestinal phosphatase ( NEB catalog # M0290S ) for 1 hr at 37°C . 50 mM EDTA was used as phosphatase inhibitor . The immunoprecipitate was purified after CIP treatment by using a Pierce SDS-PAGE sample prep kit ( catalog # 89888 ) . The sample was boiled for 5 min and run on a SuperSep phos-tag 12 . 5% precast gel ( catalog # 195–16391 from Wako ) to get better separation of phosphorylated from non-phosphorylated protein . To improve the efficiency of protein transfer to PVDF membrane , gel was soaked in transfer buffer with 5 mmol/L EDTA for 10 min with gentle agitation then washed with transfer buffer for 10 min . Western blot was developed with Pierce ECL plus Western blotting substrate ( Catalog number # 32132 ) . RNAi was performed as in Govindan et al . ( 2006 ) with the following modifications: three L4-stage animal were placed on each RNAi plate and next generation 24 hr post-L4 animals were screened for phenotype . HT115 bacteria expressing empty pL4440 vector was used as the control RNAi . Strong RNAi knockdown of rho-1 leads to severe cytokinesis defects , large nuclei in pachytene , a disrupted plasma membrane organization , a small oogenic germline and an abnormal oocyte progression . Strong RNAi knockdown of rho-1 also leads to little or no activated MPK-1 ( dpMPK-1 ) staining . However , it is possible that severe reduction of dpMPK-1 staining is an indirect effect of highly abnormal germline following strong loss of rho-1 function . Therefore , partial RNAi knockdown of rho-1 was performed resulting in germlines with wild type morphology/organization . Partial rho-1 depletion was achieved by feeding RNAi started at the mid/late-L4 stage for 24 hrs at 20°C followed by gonad dissection and staining . Wild-type worms were grown and nuclei isolated as described in ( Silva et al . , 2014 ) . Subsequent phosphopeptide preparation and identification are described in detail in Supplemental Information . | Most plants and animals , including humans , have cells that contain two copies of every chromosome , with one set inherited from each parent . However , reproductive cells ( such as eggs and sperm ) contain just one copy of every chromosome so that when they fuse together at fertilization , the resulting cell will have the usual two copies of each chromosome . Embryos that have incorrect numbers of chromosome copies either fail to survive or develop disorders such as Down syndrome . Therefore , it is important that when cells divide to form new reproductive cells , their chromosomes are correctly segregated . To end up with one copy of each chromosome , reproductive cells undergo a form of cell division called meiosis . During meiosis , pairs of chromosomes are held together by a zipper-like structure called the synaptonemal complex . While held together like this , each chromosome in the pair exchanges DNA with the other by forming junctions called crossovers . Once DNA exchange is completed , the synaptonemal complex disappears from certain regions of the chromosome . Using a range of genetic , biochemical and cell biological approaches , Nadarajan et al . have now investigated how crossover formation and the disassembly of the synaptonemal complex are coordinated in the reproductive cells of a roundworm called Caenorhabditis elegans . This revealed that a signaling pathway called the MAP kinase pathway regulates the removal of synaptonemal complex proteins from particular sites between the paired chromosomes . Turning off this pathway’s activity is required for the timely disassembly of this complex , and depends on proteins that are involved in crossover formation . This regulatory mechanism likely ensures that the synaptonemal complex starts to disassemble only after the physical attachments between the paired chromosomes are “locked in” , thus ensuring that reproductive cells receive the correct number of chromosomes . Given that the MAP kinase pathway regulates cell processes in many different organisms , a future challenge is to determine whether this pathway regulates the synaptonemal complex in other species as well . | [
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] | 2016 | The MAP kinase pathway coordinates crossover designation with disassembly of synaptonemal complex proteins during meiosis |
The segregation of eukaryotic chromosomes during mitosis requires their extensive folding into units of manageable size for the mitotic spindle . Here , we report on how phosphorylation at serine 10 of histone H3 ( H3 S10 ) contributes to this process . Using a fluorescence-based assay to study local compaction of the chromatin fiber in living yeast cells , we show that chromosome condensation entails two temporally and mechanistically distinct processes . Initially , nucleosome-nucleosome interaction triggered by H3 S10 phosphorylation and deacetylation of histone H4 promote short-range compaction of chromatin during early anaphase . Independently , condensin mediates the axial contraction of chromosome arms , a process peaking later in anaphase . Whereas defects in chromatin compaction have no observable effect on axial contraction and condensin inactivation does not affect short-range chromatin compaction , inactivation of both pathways causes synergistic defects in chromosome segregation and cell viability . Furthermore , both pathways rely at least partially on the deacetylase Hst2 , suggesting that this protein helps coordinating chromatin compaction and axial contraction to properly shape mitotic chromosomes .
The DNA molecule at the core of any eukaryotic chromosome is a hundred to million times longer than the average diameter of the cell that hosts it . Thus , cells need to fold their genetic material in order to fit it in the interphase nucleus; they need to pack it further during mitosis , in order to move sister-chromatids safely and symmetrically apart . Furthermore , chromatin folding must be dynamic to allow transcription and replication during interphase , and such that exceptionally large chromosomes can hyper-condense during anaphase in order to fit the size of the spindle and prevent chromosome missegregation ( Neurohr et al . , 2011; Titos et al . , 2014 ) . Moreover , mitotic condensation also facilitates the decatenation of sister chromatids during their separation ( Charbin et al . , 2014 ) , and might help to ‘cleanse’ chromosomes from transcription , replication and cohesion factors ( Yanagida , 2009 ) . This is thought to ‘reset’ the transcriptional state of genes , and prevent displaced factors from interfering with chromosome segregation . However , despite their importance for chromosome segregation , the events ensuring the mitotic condensation of chromosomes are still only partially understood . Early studies made evident that nucleosomes play a critical role in DNA packaging . In favor of the idea that they play specific roles in chromatin condensation , histone H3 is phosphorylated by aurora B throughout mitosis on a serine at position 10 in most , if not all , eukaryotes . Furthermore , aurora B inactivation leads to chromosome condensation and segregation defects in budding yeast ( Lavoie et al . , 2004 ) , fission yeast ( Petrova et al . , 2013; Tada et al . , 2011 ) , HeLa cells ( Tada et al . , 2011 ) and roundworms ( Hagstrom et al . , 2002 ) . However , the precise role of H3 S10 phosphorylation has remained unclear ( Ajiro and Nishimoto , 1985 ) . Recent data demonstrated that H3 S10 phosphorylation promotes the recruitment of the sirtuin-related deacetylase Hst2 , which in turn deacetylates , at least , lysine 16 of histone H4 ( Wilkins et al . , 2014 ) . This unmasks a basic patch , allowing H4 to interact with the acidic patch on H2A , most probably on an adjacent nucleosome ( Robinson et al . , 2008; Gordon et al . , 2005 ) . Thus , this cascade of events initiated by H3 phosphorylation is thought to tighten the interaction between neighboring nucleosomes . However , all studies carried out so far have failed to reveal strong phenotypes for H3 serine 10 to alanine mutations in a plethora of model organisms ( de la Barre et al . , 2001; Afonso et al . , 2014; Ditchfield et al . , 2003 ) . Furthermore , mutation of this residue in budding yeast did not affect axial contraction of chromosomes and the condensation of the rDNA during regular mitoses ( Neurohr et al . , 2011; Lavoie et al . , 2004; Lavoie et al . , 2002 ) . Indeed , the only phenotype identified upon replacement of H3 S10 with alanine in yeast so far is limited to the reduced ability to hyper-condense artificially long chromosomes in order to fit them in the spindle ( Neurohr et al . , 2011 ) . Thus , it remains unclear whether H3 phosphorylation and H4 deacetylation play any general role in mitotic chromosome condensation . The discovery that mitotic extracts of frog eggs lacking any one of the subunits of a protein complex called condensin largely failed to condense chromosomes ( Hirano and Mitchison , 1994 ) opened new perspectives for understanding chromosome condensation ( Piazza et al . , 2013; Thadani et al . , 2012 ) . Condensin is a ring-shaped pentameric protein complex . The core of the ring is formed by two structural maintenance of chromosome ( SMC ) subunits , Smc2 and Smc4 . Three non-SMC proteins ( Brn1 , Ycg1 and Ycs4 in budding yeast ) close the ring . The mechanism of condensin loading on chromatin is not understood , but seems to depend on the activity of the kinase aurora B ( Lavoie et al . , 2004; Tada et al . , 2011 ) . Furthermore , the non-SMC subunits were recently shown to directly bind DNA ( Piazza et al . , 2014 ) , potentially followed by topological entrapment of chromatin inside the condensin ring ( Cuylen et al . , 2011 ) . How condensin performs its functions in chromosome condensation is unclear , but it has been proposed that condensin’s role might be structural , by inducing loops within the same DNA strand ( Cuylen et al . , 2011; Cuylen and Haering , 2011 ) or might be enzymatic by promoting positive DNA supercoiling ( Baxter and Aragón , 2012 ) , both assisting in a decrease in length of mitotic chromatids . Mitigating the central role of condensin in chromosome condensation , however , were observations in model organisms as diverse as fission yeast , fly , chicken and mammalian cells that indicate that chromosomes can still , at least partially , condense in the absence of condensin ( Petrova et al . , 2013; Coelho et al . , 2003; Vagnarelli et al . , 2006; Gerlich et al . , 2006 ) . Thus , although condensin was established as a key player in chromosome condensation , it cannot be the sole factor shaping mitotic chromosomes . In order to gather insights into whether and how H2A-H4 interaction contributes to the organization of mitotic chromosomes , we sought for a method to assay the condensation state of chromatin in vivo . Here , we use a fluorescence-based assay to investigate short-range chromatin compaction and use it to study the relationships between condensin and histone modifications during chromosome condensation in mitotic cells .
In order to develop a chromatin condensation assay , we reasoned that increased nucleosome-nucleosome interaction might render chromatin less accessible to DNA-binding proteins . To test this idea directly , we asked whether chromatin condensation restricted access for heterologous reporter proteins to their binding sites when those are introduced at a chosen chromosomal locus . Therefore , we used a yeast strain in which a set of Tet operator ( TetO ) repeats are inserted at the TRP1 locus on chromosome IV , 15 kb from CEN4 , and constitutively expressing the TetR-mCherry fusion protein , which efficiently binds the TetO repeat . As a consequence , these cells exhibit a red dot in their nucleus throughout the cell cycle ( Figure 1A ) . To test whether the intensity of TetR-mCherry fluorescence possibly varied over the cell cycle , we measured the fluorescence intensity of this dot in G1 cells ( unbudded ) , when the chromatid is decondensed , but not replicated yet , and in late anaphase mother cells , when the chromatid is separated from its sister and has reached full condensation ( Figures 1B and 3C; ( Neurohr et al . , 2011; Sullivan et al . , 2004; D'Amours et al . , 2004 ) and see below ) . After subtracting background fluorescence , we noticed a highly significant ( p<0 . 0001 ) , 2–2 . 5-fold decrease in mCherry fluorescence intensity at the TetO repeats on the anaphase compared with the G1-phase chromosomes ( Figure 1A , B ) . 10 . 7554/eLife . 10396 . 003Figure 1 . Fluorescence intensity of TetO/TetR-mCherry as a read-out for chromatin compaction . ( A ) Representative images of a cell in G1 and anaphase , containing a TetO array at the TRP1 locus and expressing TetR-mCherry ( red ) . Fluorescence intensity of a focus is measured by determining the total fluorescence and subtracting the background , giving the corrected fluorescence intensity . Scale bar is 2 µm . ( B ) TetR-mCherry intensities for the indicated wild type ( WT ) and mutant strains in G1 and anaphase mother cells . One way Analysis Of Variance ( ANOVA ) was performed to test significance . ( C ) Fluorescence intensity for a wild type strain containing LYS4:LacO and expressing LacI-GFP . Student’s t-test was performed to determine significance . ( D ) Anaphase TetR-mCherry intensities for the indicated strains , synchronized in G1 by alpha-factor treatment and released at the indicated temperatures . Intensities for G1 were determined 5 min after release from alpha-factor induced arrest . All data are means and standard deviation for n>30 cells . **** p<0 . 0001 and n . s . not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 003 We next asked whether the variations of fluorescence intensity at the TetO repeats reflected changes in H2A/H4 interaction . Supporting this view , mutating key residues in the H3 phosphorylation and H4 deacetylation pathway established by ( Wilkins et al . , 2014 ) affected these variations ( Figure 1B , C ) . Strikingly , mutations that abrogate the mitotic interaction between H2A and H4 , such as H3 S10A , hst2∆ and the H4 ∆9–16 mutations , all abolished the reduction in brightness of the TetR-mCherry focus normally observed in anaphase cells ( Figure 1B ) . In reverse , mutations that promote constitutive H2A/H4 interaction , such as H3 S10D and H4 K16R , caused the TetR-mCherry focus to constitutively show , that is , even in G1 cells , the low fluorescence intensity normally specific of anaphase cells . The effect of the H3 S10D mutation was indeed mediated by the recruitment of Hst2 , since the hst2Δ mutation suppressed it; the H3 S10D hst2∆ double mutant cells showed constitutive high brightness , similar to hst2∆ single mutant cells . Interestingly , however , introducing the H4 K16R mutation in the hst2∆ mutant cells did not restore the intensity drop normally observed during anaphase , suggesting that H4 K16 is not the sole residue that Hst2 deacetylates to promote nucleosome-nucleosome interaction ( Figure 1B ) . To test whether the observed fluctuations in fluorescence intensity were specific for the TRP1 locus or TetO/TetR-mCherry , we also measured the fluorescence intensity at the LYS4 locus , in the middle of the right arm of chromosome IV , where we integrated LacO repeats in cells expressing LacI fused to Green Fluorescent Protein ( GFP ) . Although the effect was slightly less pronounced , we observed a similar , and significant , decrease in reporter brightness in anaphase compared with G1 cells at this locus ( Figure 1C ) . As for the TRP1 locus , mutants in the H3 S10 pathway also affected fluctuations in intensity at the CEN distal locus: hst2∆ , H3 S10A and H3S10D hst2∆ showed a continuously higher fluorescent intensity on the LacO repeats near the LYS4 gene and the H3 S10D mutation also resulted in a continuously lower fluorescent signal . These data indicated that changes in chromatin organization during mitosis indeed affected either the recruitment or the fluorescence intensity of TetR-mCherry and LacI-GFP on two distant chromatin loci , one close to the centromere and the second in the middle of the second longest yeast chromosome arm . Since chromatin condensation is regulated by the kinase aurora B ( Ipl1 in budding yeast ) , we last asked whether Ipl1 activity is required for the intensity decrease of TetR-mCherry at the TRP1 locus in mitotic cells . We arrested wild type yeast cells and cells containing the temperature sensitive ipl1-321 allele in G1 with alpha-factor , released them at the restrictive temperature of 35ºC and determined TetO/TetR-mCherry fluorescence intensity in the same G1 or following anaphase ( Figure 1D ) . Whereas wild type cells showed no significant difference in G1 and anaphase TetO/TetR-mCherry fluorescence intensity , the ipl1-321 strain showed a significantly brighter dot when undergoing anaphase at the restrictive temperature . Compaction in G1 of ipl1-321 cells at the restrictive temperature was not affected , presumably due to the fact that this protein has no activity in G1 , even in wild type cells ( Buvelot et al . , 2003 ) . Thus , we conclude that the enhanced H2A-H4 interaction triggered by aurora B-dependent recruitment of the deacetylase Hst2 onto chromatin indeed affects the intensity of the TetR-mCherry signal on the chromosome . We next wanted to better understand the molecular processes and structural changes of chromatin that were underlying the fluorescence variation at the TetO array over the cell cycle . Assuming enhanced nucleosome-nucleosome interaction promotes chromatin compaction , three models may explain the observed decrease of fluorescence in mitosis . First , chromatin compaction might reduce access of DNA-binding proteins , such as TetR-mCherry , to their binding site on DNA and cause their removal , as postulated by the chromosome cleansing hypothesis . Second , chromatin compaction might increase the local packing of TetR-mCherry , leading to quenching of the fluorophore ( Lakowicz , 2013 ) ; these two first models are depicted in Figure 2A . Third , the changed local environment of mitotic chromatin might reduce the intrinsic fluorescence of mCherry and GFP . 10 . 7554/eLife . 10396 . 004Figure 2 . Fluorophore quenching causes changes of TetO/TetR intensity over the cell cycle . ( A ) Two models to explain anaphase-specific decrease in fluorescence brightness ( cleansing and quenching , see text for explanations ) . Shown are the consequences of each model in G1 and anaphase , in the case of cells carrying TRP1:TetOs and either expressing only TetR-mCherry or TetR-mCherry and TetR-GFP . ( B ) G1 and anaphase TetO/TetR-mCherry intensities in cells carrying TetO and expressing only TetR-mCherry ( left ) or TetR-mCherry and TetR-GFP ( right ) . Data are means and standard deviations , unpaired Student’s t tests were performed to test significance , **** p<0 . 0001 and n . s . not significant . ( C ) FRET values for indicated strains . Plotted are mean values and standard deviation . Unpaired Student’s t tests were performed to test significance , ** p<0 . 01 and n . s . not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 004 In order to better distinguish between these models , we rationalized that coexpressing TetR-GFP with TetR-mCherry would not protect mCherry from a cleansing effect ( model 1 ) or a change in local environment ( model 3 ) , but should strongly reduce any quenching , due to intercalation of a second fluorophore with a different excitation spectrum . Furthermore , in this context , quenching might be replaced by Förster Resonance Energy Transfer ( FRET ) between the TetR-GFP and TetR-mCherry molecules . Remarkably , unlike the cells expressing only TetR-mCherry , cells expressing both versions of TetR failed to show significant variation of the fluorescence signal for either mCherry or GFP at the TetO array between anaphase and G1 ( Figure 2B ) . Thus , cleansing and a general change in the local environment of the fluorophores are unlikely to explain the fluorescence drop observed at the TetO array during anaphase in the cells expressing solely TetR-mCherry . Supporting the idea that the intensity drop was due to a quenching effect , FRET was indeed observed upon exciting in the GFP and recording emission in the red channel in cells expressing both TetR-mCherry and TetR-GFP , but not in cells expressing TetR-mCherry alone ( Figure 2C ) . Moreover , FRET was significantly increased during anaphase compared with G1-phase ( Figure 2B , C ) , indicating that the fluorophores are indeed brought in closer proximity during anaphase compared with interphase . We conclude that increased H2A/H4 interaction results in a tighter packing of fluorophores and their quenching , establishing that H2A/H4 interaction leads to compaction of mitotic chromatin in vivo . Furthermore , cell cycle dependent changes of TetR-mCherry or TetR-GFP signals on TetO arrays is a reliable measure of short-range compaction of the underlying chromatin . Next , we investigated the dynamics of chromatin compaction during the cell cycle . To this end , we visualized both the TetO/TetR-mCherry ( at TRP1 ) and LacO/LacI-GFP ( at LYS4 ) loci simultaneously . This presence of two labeled loci on the same chromosome allowed measuring the physical distanceseparating them and hence the long-range contraction of the chromosome arm along its longitudinal axis during anaphase . Using this strain , we first recorded time-lapse movies ( Figure 3A ) in which we measured the intensity of the LacI-GFP fluorescence at the LYS4 locus in cells progressing through mitosis ( Figure 3B ) . Upon averaging the signal of at least 15 ( t = -18 minutes ) to maximum 31 ( t = 0 minutes ) such traces , we observed that the intensity of the signal was indeed lowest during the first 12 minutes of anaphase , while starting to increase as soon as the cells started to exit mitosis ( Figure 3B , blue line indicates the formation of the first bud in the population ) . We also noticed that fluorescence intensity at the LacO locus was highly variable throughout every single movie , leading to high standard deviations . This variation was lowest during anaphase and started to increase as soon as chromatin was decondensing , consistent with the idea that chromatin is more constrained when it is most compacted and fluorophore quenching is highest . The source of this fluorescence variation is not known , but might reflect breathing movements of the underlying chromatin or complex photochemistry effects . In either case , this intrinsic cell-to-cell variability precludes drawing conclusions at the single cell level and emphasizes the fact that the quenching assay introduced here is statistical in nature . 10 . 7554/eLife . 10396 . 005Figure 3 . Dynamics of chromatin compaction and chromosome arm contraction . ( A ) Example of a cell going from metaphase to the next G1 phase with TRP1 and LYS4 loci marked with TetR-mCherry and LacI-GFP , respectively . ( B ) Background normalized , mean GFP-intensity values of mitotic cells , aligned at mid-anaphase ( red dashed line: GFP dot split ) . Blue line indicates formation of the first bud . Standard deviations are shown . ( C ) Upper panel: normalized ( to G1 ) intensity of TetR-mCherry and LacI-GFP foci in mother cells in the indicated cell cycle stages . Lower panel: mother TRP1:TetO - LYS4:LacO distances in indicated cell cycle stages . Shown are mean and standard deviation for n>30 cells . ( D ) Nuclear diameter of G1 and late anaphase cells in wild type and hst2∆ cells containing Nup170-GFP . Box shows median value , whiskers all data points n>50 cells . Scale bars are 2 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 005 Next , we wanted to determine whether the dynamics of chromatin compaction could be related to the contraction of the chromosome arm measured using the TRP1-LYS4 distance , as described previously by us and others ( Neurohr et al . , 2011; Petrova et al . , 2013; Guacci et al . , 1994; Vas et al . , 2007 ) . To avoid variations due to photobleaching , we used snapshot images of cells at precise and representative time points in mitosis ( Figure 3C ) : metaphase ( large buds but neither the TRP1 nor the LYS4 loci were separated ) , early anaphase ( sister TRP1 loci – in red – are separated , but the two LYS4 loci are not ) , mid-anaphase ( both loci have undergone separation but the LYS4 locus still lags behind ) , late anaphase ( all loci are separated and moved to the opposite poles of the cell ) and G1 ( unbudded cell ) . For each of these stages ( >25 cells each ) , we measured both the intensity of the fluorescence on the two arrays and the distance between them . In this study , we focused specifically on the loci segregated to the mother cell , as we showed before that mother and bud are not directly comparable ( Neurohr et al . , 2011 ) . Analysis of this data set indicated that the two marked loci underwent compaction and decompaction with slightly different kinetics ( Figure 3C , upper panel ) . During early anaphase , both the TetO and LacO array seemed to be compacted to some extent already . As cells progressed to mid anaphase , the CEN4 proximal TetO arrays seemed slightly more compacted than the distal LacO arrays . In late anaphase , the TetO array was already starting to unpack , whereas the LacO remained compacted . Both dots had recovered their full intensity in the G1 cells , demonstrating that the intensity decrease in anaphase was not solely the consequence of sister-chromatid separation , which is expected to reduce fluorescence intensity by half , down to its G1 level until the next S-phase . In the same cells , measuring the TetO-LacO distance ( Figure 3C , lower panel ) indicated that the chromosome first stretched out upon anaphase onset , to subsequently contract , reaching their shortest length in late anaphase , as reported ( Guacci et al . , 1994; Harrison et al . , 2009 ) . The TetO-LacO distance re-extended then to its steady state average in G1 cells . The changes in distance between the two loci could be due to changes in nuclear diameter , which would constrain the maximal distance that the loci can move apart by random motion . However , we did not observe any significant changes in nuclear diameter ( as determined by GFP-tagging the nucleoporin Nup170 ) when comparing late anaphase and the G1 phase in wild type and hst2Δ cells ( Figure 3D ) . Thus , the shortening of the TRP1-LYS4 distance in late anaphase cells truly reflects the effect of chromatin condensation by axial contraction of the chromosomes . Furthermore , our results established that short-range chromatin compaction was not strictly concomitant with long-range axial contraction of the chromosome , but rather preceded it . These observations suggested that chromatin compaction and axial contraction of mitotic chromosomes might be distinct processes . Thus , we asked whether condensin , which is essential for axial chromosome contraction , contributed to short-range compaction of chromatin . We analyzed the brightness of the TetR-mCherry focus in yeast cells carrying the smc2-8 allele , a temperature sensitive mutation in the condensin subunit Smc2 ( Figure 4A ) . Remarkably , condensin inactivation for 90 min at the restrictive temperature had no effect on the changes in mCherry brightness between the anaphase and G1-phase of the cell cycle , whereas it indeed abrogated shortening of the TetO-LacO distance during anaphase ( see below ) . We therefore conclude that condensin , unlike histone 3 phosphorylation and histone 4 deacetylation , does not promote nucleosome-nucleosome interaction . To test this idea further , we directly probed H2A/H4 interaction by using genetically encoded Ultraviolet ( UV ) inducible crosslinking ( Wilkins et al . , 2014 ) . We arrested cells in G1 with alpha-factor and released them in the presence of nocadozole under wild type and smc2-8 conditions at 37ºC . Fluorescence-Activated Cell Sorting ( FACS ) analysis showed that the release and arrest was equally efficient in both cells ( Figure 4—figure supplement 1 ) . In wild type cells , as reported before , H4/H2A crosslinking is observed in mitosis and correlated strongly with H4 K16 deacetylation ( Figure 4B ) . In fitting with the microscopy data ( Figure 4A ) , the crosslinking between H4 and H2A showed no difference in kinetics in the condensin inactivated and in the wild type cells ( Figure 4B ) . Thus , condensin function is not required for proper , short-range compaction of mitotic chromatin . 10 . 7554/eLife . 10396 . 006Figure 4 . Condensin does not impact chromatin compaction . ( A ) TetR-mCherry intensities in the mother cell for the indicated strains and cell cycle stages . To inactivate Smc2 , cells were shifted to 37ºC for 90 min . One way ANOVA was performed to test significance , **** p<0 . 0001 and n>40 . ( B ) Yeast cells producing H2A Y58BPA were synchronized with alpha-factor at permissive temperature and then released into medium containing nocodazole at restrictive temperature . Samples were taken at indicated times , irradiated with UV and histones extracted with acid from isolated nuclei . Western blot against H4 detects the H2A-H4 crosslink ( upper row ) , bulk H4 ( lower row ) and blotting against H4 K16Ac shows cell cycle progression . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 006 10 . 7554/eLife . 10396 . 007Figure 4—figure supplement 1 . FACS analysis of alpha-factor synchronized cells released into nocodazole ( wild type or smc2-8 ) . The same samples were analysed by western blot in Figure 4B . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 007 In order to investigate in more detail how the phosphorylation of H3 S10 and the subsequent activation of Hst2 contributed to chromosome condensation , we next characterized if these events promoted axial contraction of chromosome IV , using the LacO-TetO distance as a readout ( see Figure 3D ) . Confirming the role of aurora B/Ipl1 in chromosome condensation , the ipl1-321 mutant cells failed to undergo chromosome contraction when shifted to the restrictive temperature prior to mitosis , compared with wild type cells at these temperatures ( Figure 5A ) . As expected from previous studies ( Neurohr et al . , 2011; Lavoie et al . , 2002 ) , the function of Ipl1 in the contraction of regular chromosomes was unlikely to require H3 S10 phosphorylation and H2/H4 interaction , since the mutations H3 S10A and H4 Δ9–16 did not impair anaphase contraction ( Figure 5B , C ) . Thus , Ipl1 promotes the axial contraction of chromosomes independently of phosphorylating H3 S10 and of promoting H4/H2A interaction , but possibly by promoting condensin function ( see discussion ) . 10 . 7554/eLife . 10396 . 008Figure 5 . Chromatin compaction does not influence axial chromosome contraction . ( A ) TRP1-LYS4 distances for the indicated strains , synchronized in G1 by alpha-factor treatment and released at the indicated temperatures . ( B ) TRP1-LYS4 distances were determined in the mother cell for the indicated strains and cell cycle stages . Box shows median value , whiskers all data points n>30 cells . One way ANOVA was performed to test significances , ** p<0 . 01 , **** p<0 . 0001 , n . s . not significant . ( C ) Example cells containing the indicated mutations and their impact on chromosome length as determined by the TRP1 ( red ) to LYS4 ( green ) distance . ( D ) TRP1-LYS4 distances were determined for the indicated strains in anaphase . Box shows median value , whiskers all data points n>45 . One way ANOVA was performed to test significances , *** p<0 . 001 , * p<0 . 05 , n . s . not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 008 In contrast , the hst2∆ mutation did abrogate the proper contraction of the chromosome during mitosis , implying that Hst2 acts in both axial chromosome contraction and short-range chromatin compaction ( Figures 5C and 1B ) . Even more remarkably , the H3 S10D phospho-mimicking allele caused chromosome IV to remain in a constitutive state of axial contraction throughout the cell cycle ( Figure 5B , C ) . This most probably reflected constitutive recruitment of Hst2 to nucleosomes , since inactivation of the HST2 gene in these cells abolished contraction ( Figure 5B , C ) . Thus , boosting the recruitment of Hst2 to chromatin through mimicking constitutive and ubiquitous phosphorylation of H3 promoted both chromatin compaction and axial chromosome contraction , despite the fact that the H3-dependent pathway of Hst2 recruitment is normally dispensable for Hst2 function in axial chromosome contraction during regular mitoses ( though , it is essential for adaptive hyper-condensation of an artificially long chromosome ) . We next sought to determine the role of H4 K16 deacetylation in anaphase chromosome contraction . In accordance with the idea that H4 deacetylation solely plays a role in mitotic chromatin compaction , the H4 K16R allele did not show any significant differences in chromosome contraction compared with wild type cells ( Figure 5D ) . When we deleted HST2 on top of H4 K16R , we observed a significant decrease in chromosome contraction as compared with wild type and H4 K16R cells , but no significant difference with hst2∆ cells ( Figure 5D ) . Based on this we conclude that H4 deacetylation has no evident role in chromosome contraction . Furthermore , our data suggest that HST2 , when deleted , does not have its defects on chromosome contraction through the chromatin compaction pathway , but rather by acting on another factor . Together , in agreement with the fact that they follow different kinetics ( Figure 3 ) , our data reveal that axial contraction of chromosomes and compaction of chromatin are two independent processes . They depend on distinct molecular pathways that are at least in part coordinated by the same regulatory input , namely the activity of the kinase aurora B and the deacetylase Hst2 . The data above demonstrated that hst2∆ mutant cells have strong defects in axial chromosome contraction , while other components of the chromatin compaction pathway ( H3 S10 and H4 ) do not . Thus , we rationalized that Hst2 might not only promote chromosome compaction , but also the function of factors required for the axial contraction of chromatids , such as condensin . To test this notion , we measured the effect of combining the smc2-8 mutation with H3 and hst2∆ mutations on axial chromosome contraction during anaphase . As expected , the smc2-8 single mutant cells grown at the restrictive temperature failed to axially contract their mitotic chromosomes to wild type levels ( Figure 6A ) . Deletion of Hst2 in these cells did not exacerbate their contraction phenotype ( Figure 6A ) , suggesting that condensin and Hst2 might act in the same genetic pathway . In contrast , contraction was restored in the H3 S10D smc2-8 double mutant cells . This effect was likely due to boosting Hst2 recruitment and activity since it disappeared in the H3 S10D hst2∆ smc2-8 triple mutant cells . The effect of H3 most likely depended on the phosphorylation state of S10 , since the S10A mutation did not promote contraction . Thus , enhanced activation of Hst2 ( H3 S10D ) suppressed the effect of the smc2-8 mutation on chromatin contraction . Hst2 might mediate the suppression of the contraction defect due to the smc2-8 mutation either through activation of a still unknown , alternative contraction pathway or through stimulation of condensin activity such as to restore at least part of its function . 10 . 7554/eLife . 10396 . 009Figure 6 . Condensin and HST2 are in the same genetic pathway that ensures proper chromosome segregation . ( A ) TRP1-LYS4 distances were determined for the indicated strains in anaphase after shifting cells for 90 min to 37ºC . Box shows median value , whiskers all data points n>30 . One way ANOVA was performed to test significance between G1 in smc2-8 at 25ºC and other strains , **** p<0 . 0001 and n . s . not significant . ( B ) Spotting assay of indicated strains on YPD plates at the indicated temperatures . ( C ) Percentage of anaphase cells containing anaphase bridges for the indicated strains after 90 min at 25ºC or 90 min at 33ºC . DAPI , 4' , 6-diamidino-2-phenylindole; YPD , yeast extract peptone dextrose . n>240 , scale bar is 2 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 009 10 . 7554/eLife . 10396 . 010Figure 6—figure supplement 1 . Plate growing spotting assay on YPD plates of wild type , H3 S10A , H3S10D and hst2Δ strains at different temperatures . YPD , yeast extract peptone dextrose . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 010 In order to establish the physiological relevance of our phenotypic observations , we next asked whether combining compaction and contraction defects cell growth and viability . Consistent with condensin and H3 S10 phosphorylation pathways acting synergistically to promote proper chromosome condensation , the smc2-8 H3-S10A double mutant cells were growing slower than the smc2-8 single mutant cells , and were unable to grow at 33°C , unlike the single mutant cells ( Figure 6B and Figure 6—figure supplement 1 ) . Accordingly , the smc2-8 hst2∆ double mutants showed the same phenotype . Thus , when the condensin complex was only partially active , cells depended on Hst2 and phosphorylation of histone H3 on S10 for growth . Remarkably , however , the smc2-8 H3 S10D double mutant did not show any altered or improved growth or viability phenotype compared with the smc2-8 single mutant cells . It grew reasonably well at 33°C , while being non-viable at 37°C . Thus , restoring axial contraction of the chromosomes using the H3 S10D mutation was not sufficient to restore the viability of the cells lacking condensin function . These results are consistent with condensin playing more roles than simply promoting the axial contraction of chromosomes . To further test the requirement of condensin and H3 S10 pathways on chromosome segregation , we imaged anaphase cells stained with 4' , 6-diamidino-2-phenylindole ( DAPI ) to visualize the frequency with which the single and double mutant strains produced lagging chromatin . The cells were grown at 25°C , the permissive temperature for the smc2-8 mutation or shifted for 90 min to 33°C , a semi-permissive temperature for smc2-8 mutant cells . At 25°C , the smc2-8 single and the smc2-8 H3 S10A , smc2-8 hst2∆ and smc2-8 H3 S10D double mutants cells showed no strong differences ( 6 . 3–8 . 0% ) in the frequency at which lagging chromatin was observed in the center of the spindle ( Figure 6C ) . In contrast , the smc2-8 H3 S10A and the smc2-8 hst2∆ double mutants grown at 33°C showed a marked increase in the frequency ( 18 . 9% and 16 . 9% , respectively ) of lagging chromatin compared with the smc2-8 single mutant cells . The smc2-8 H3 S10D mutant cells did not show such an additive phenotype ( 10 . 0%; Figure 6C ) . These data indicate that the pathways promoting the short-range compaction of chromatin and the axial contraction of chromosomes contribute synergistically to shaping mitotic chromosomes in order to ensure their correct segregation prior to cell division .
Mitotic chromosome condensation is the process through which a relatively relaxed interphase chromosome condenses into two relatively short , compact sister chromatids that the spindle can symmetrically segregate . The kinase aurora B has long been implicated in this process ( Lavoie et al . , 2004; Tada et al . , 2011; Lavoie et al . , 2002 ) . Through activation of condensin and phosphorylation of S10 on histone H3 it is thought to promote the formation of a mitotic chromosome . However , how these pathways interacted with each other to shape mitotic chromosomes was poorly understood . Here , we monitored chromosome condensation in living yeast cells by using two microscopy assays , allowing us to distinguish long-range contraction of chromosomes along their longitudinal axis from short-range chromatin compaction . The first assay has been used before ( Petrova et al . , 2013; Guacci et al . , 1994; Vas et al . , 2007 ) , but did not detect a role for histone phosphorylation during regular mitoses ( Neurohr et al . , 2011 ) . The second assay , introduced here , uses fluorophore quenching to show that nucleosome-nucleosome interaction via H3 phosphorylation and in an Hst2-dependent manner promotes short-range compaction of mitotic chromatin in vivo . This enhanced packing is evidenced by the increased quenching of fluorophores when the chromosome locus is decorated by multiple copies of a fluorescent reporter . The quenching effect was observed similarly well whether we used GFP or mCherry as a fluorophore , and independently of whether it was fused to TetR or to LacI . Though we cannot exclude any local effects of the repetitive nature of the TetO and LacO repeats on chromatin compaction , this novel fluorescence-based assay allows for a simple detection of chromatin compaction states . Beyond promoting chromosome segregation , the exact function of this compaction process will be interesting to address . Our studies establish that chromatin compaction does not displace DNA-associated proteins such as TetR or LacI , as a strong cleansing model would predict . Although they do not exclude that other DNA-binding factors might be removed from the DNA during this process , these data suggest that compaction might serve other functions , such as to change the biophysical properties of the chromatin fiber during segregation . Chromatin compaction might for example affect the local stiffness , elasticity and mechanical resistance of chromosomes to facilitate their decatenation , protection and movement during anaphase . Related to this , it is remarkable that the H3 S10D mutation is able to establish an Hst2-dependent and constitutive state of compaction and contraction , as evidenced by both assays , without affecting much growth and viability . We propose that this mutation strongly boosts the recruitment and activation of Hst2 all along chromosomes by mimicking the phosphorylated state of histone H3 . Two models may explain how H3 S10D elicits its effects . In the first , H3 S10D might lead to hyper-activation of chromatin compaction by over-recruitment of Hst2 , such that it might shorten the chromosome axis , independently of the contraction machinery . However , the following observations speak against this idea: I . crosslinking studies do not indicate that H3 S10D increases nucleosome-nucleosome interaction ( Wilkins et al . , 2014 ) , II . fluorescence quenching of TetR-mCherry and LacI-GFP is not enhanced in the H3 S10D mutant ( Figure 1B , C ) compared with WT anaphase cells , and III . phosphorylation of H3 S10 does promote the condensin-dependent and Hst2-dependent contraction of artificially long chromosomes ( Neurohr et al . , 2011; Wilkins et al . , 2014 ) . Thus , these data support an alternative model: Hst2 may stimulate condensin function , and hyper-activation of Hst2 by H3 S10D may promote this effect and thereby chromosome arm contraction . To fully distinguish between these two models it will be important to investigate the effect of the H3 S10D mutation on condensin loading and activity . Furthermore , it is important to note that the phenotype of the H3 S10D mutant cells does not seem to reflect physiological conditions taking place during regular mitoses , indicating that the fraction of Hst2 driving wild type chromosome contraction does not depend on H3 S10 phosphorylation ( dashed arrow Figure 7 , left panel ) . Rather , it might reflect what happens when both chromosome condensation pathways are hyper-activated , for example under the presence of an artificially long chromosome or in small cells , such as cells grown on a poor carbon source ( see below ) . In any case , it is striking that the H3 S10D strain does not show any obvious defects in growth and viability despite causing chromatin compaction and contraction throughout the cell cycle . Thus , this constitutively condensed state does not impair access and remodeling of chromatin by the transcription and replication machineries during interphase in any major manner . 10 . 7554/eLife . 10396 . 011Figure 7 . Model of chromosome condensation . H3 S10 phosphorylation leads to chromatin fiber closure , whereas condensin is required for axial shortening . Hst2 is needed for both of these levels of condensation . DOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 011 Our kinetic data suggest that chromatin undergoes a number of successive structural changes during anaphase and that compaction precedes contraction . In this respect , two models could explain the extended TRP1-LYS4 distance observed in early anaphase cells . First , this might simply be due to the fact that the anaphase nucleus is more elongated , allowing for the chromosome arm to reach its real extension without being confined in the normally spherical morphology of late anaphase or G1 nuclei . In this scenario , the apparent stretching of the chromosome in early anaphase does not correspond to any rearrangement of the chromatin or changes in contraction , and the impression that contraction follows compaction is an illusion . However , previous work has established that residual cohesion between sister chromatids persists during early anaphase and that this actually causes chromosome stretching during early segregation stages ( Renshaw et al . , 2010; Harrison et al . , 2009 ) . Thus , a second model would be that maximum contraction is only reached when cohesion is fully resolved , such as to not interfere with cohesion removal . This scenario suggests that chromosome contraction indeed needs to be a late anaphase event to complete chromosome segregation , and that perhaps the compaction events that precede play an important role in facilitating cohesion resolution . This model is attractive because it might more thoroughly account for the molecular events that are actually taking place during anaphase . Understanding how these processes are regulated during early anaphase will be necessary in order to distinguish between the two models . However , one definite conclusion that we can already draw is that arm contraction and chromatin compaction undergo relaxation between late anaphase and G1 , since these nuclei are not significantly different in size , and hence the effects observed are not due to confinement alone . Based on our data on chromosome contraction and compaction in Figure 3C , we propose that mitotic chromosome condensation entails at least three processes ( Figure 7 ) . First , a histone 3-dependent and histone 4-dependent process , which we term chromatin compaction , ensures the short-range tightening of DNA into a smaller volume via nucleosome-nucleosome interaction ( Wilkins et al . , 2014 ) . This process strictly depends on the phosphorylation of histone H3 on serine 10 , and the subsequent deacetylation of lysine 16 of histone H4 . Because this event has little impact on the length of condensed chromosomes , it has escaped attention until now . However , our data indicate that it is an important event for proper chromosome architecture and is an early event of every yeast mitosis . Second , a condensin-dependent process , termed here axial chromosome contraction , ensures the long-range contraction of the chromosome , probably by facilitating the formation of higher order chromatin structures along the chromosome axis ( Cuylen and Haering , 2011; Baxter and Aragón , 2012 ) . This process is completed after chromatin compaction , in late anaphase , but does not strictly require it in order to proceed . Certainly , it will be interesting to clarify this order of event , to dissect how it is controlled , and to determine whether it facilitates the ordered condensation of well-separated chromosome arms . Moreover , the observation that Hst2 contributes to both processes suggests that this enzyme might be pivotal for coordinating compaction and contraction with each other . In this respect , it will be interesting to determine how Hst2 promotes the shortening of the chromosome axis , and whether it does so by directly modulating condensin function . Third , our data also indicate the existence of an additional , also condensin-dependent process devoted to some other aspect of chromosome organization , beyond compaction and contraction . Indeed , restoring the contraction of the chromosome along its axis in the smc2-8 condensin mutant cells by mimicking constitutive H3 phosphorylation did not alleviate the temperature-dependent lethality caused by the smc2-8 mutation . Furthermore , although inactivation of the deacetylase Hst2 largely abolished the axial contraction of chromosomes , HST2 is not an essential gene . Hence , we suggest that the essential function of condensin is not to mediate long-range contraction . Rather , the essential function of condensin most likely relates to its roles in orchestrating the decatenation of intertwined sister-chromatids ( Charbin et al . , 2014; Baxter and Aragón , 2012; Baxter et al . , 2011 ) and the re-annealing of single stranded DNAs ( Sakai et al . , 2003 ) . In an earlier study we demonstrated that yeast cells challenged by the presence of an exceptionally long chromosome or by being themselves exceptionally small ( for example , whi3 mutant cells and cells growing on a poor carbon source ) possess the ability to condense chromosomes beyond wild type levels to adjust the axial contraction of long chromosomes and adapt their length to the length of the spindle , a process we termed adaptive hyper-condensation ( Neurohr et al . , 2011 ) , and which we would like to rename adaptive hyper-contraction . This process depended on condensin and phosphorylation of H3 S10 , this last requirement being in contrast to what is observed for the contraction of most wild type chromosomes during regular mitoses . Accordingly , single smc2-8 and H3 S10A mutants failed to hyper-contract an exceptionally long chromosome ( Neurohr et al . , 2011 ) . Since the H3 S10A mutation had no defect on the axial contraction of wild type chromosomes , these results indicated that adaptive hyper-contraction depended more heavily on Ipl1-dependent phosphorylation of S10 on histone H3 than regular chromosome condensation does . Thus , we propose that H3 S10 is hyper-phosphorylated on long chromosomes by Ipl1 located in the center of the spindle ( midzone ) , boosting Hst2 and condensin activity ( dotted arrow , Figure 7 ) . Our observation that mimicking S10 phosphorylation , using the H3 S10D allele , promotes axial shortening in an Hst2-dependent manner is indeed consistent with high levels of S10 phosphorylation promoting the crosstalk between compaction and contraction , via Hst2 . It will be interesting to test how the principles laid out here function in eukaryotes with a more complex chromosome structure , such as humans . A good starting point for such investigations would be to test the effects of H3 S10 mutation and/or removal of the Hst2 homologues on such cells . How important is a tight regulation between short-range chromatin compaction and long-range chromosome contraction ? How does that impact chromosome structure ? And how do such mutations affect the segregation of the genetic material ? Together , our work demonstrates that yeast is a powerful system to dissect the mechanisms underlying chromosome condensation and how the disruption of such mechanisms affects chromosome segregation and cell viability .
All yeast strains used in this study were derived from the S288c background and are described in Table 1 . Histone mutants were amplified from the synthetic non-essential histone collection from Thermo Scientific and transformed in S288c-derived strains . HST2 was deleted by using standard methods ( Janke et al . , 2004 ) . smc2-8 temperature sensitive strains were obtained by crossing and tetrad dissection . Spotting assay in Figure 5B was done by aligning cells at OD600 = 0 . 1 and diluting 1:5 in subsequent steps . 2 . 5 μl drops were plated on yeast extract peptone dextrose ( YPD ) medium and grown for 2 . 5 days . 10 . 7554/eLife . 10396 . 012Table 1 . Yeast strains used in this studyDOI: http://dx . doi . org/10 . 7554/eLife . 10396 . 012 yYB number Mating type genotype N/A a his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 smc2::smc2-8:kanMX N/A a his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 3476 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP 3477 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP ipl1-321 4699 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Spc42-GFP:hph hht1::hht1-S10A:KanMXloxP hht2::hht2-S10A:bleloxP ura3 ade1 leu2 8080 a Nup170-GFP:HIS3 ura3 ade1 leu2 9101 a his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHTS-HHFS]-URA3 9332 a his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHT-S10D-HHFS]-URA3 10818 a his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHTS10AS]-URA3 11676 a smc2-8 hhf1-hht1::NatMX hst2::hphNT1 ura3 ade1 leu2 12002 a hst2::KanMX his3∆1 leu2∆0 ura3∆0 met15∆0 10116 , 10117 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Hhf1-Hht1::NatMX hht2-hhf2::[HHT-HHF∆9-16]-URA3 ura3 ade1 leu2 10122 , 10123 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Hhf1-Hht1::NatMX hht2-hhf2::[HHT-HHFK16R]-URA3 ura3 ade1 leu2 10331 a Nup170-GFP:HIS3 hst2::NatMX ura3 ade1 leu2 11518 , 11519 diploid trp1::TetO:TRP1/TRP1 lys4::LacO:LEU2/LYS4 TetR-mRFP leu2/leu2 ura3/ura3 his3/his3 11520 , 11521 diploid trp1::TetO:TRP1/TRP1 TetR-mRFP/ TetR-GFP:LEU2 leu2/leu2 ura3/ura3 his3/his3 11173 , 11989 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP hht1::hht1-S10A:KanMXloxP hht2::hht2-S10A:bleloxP smc2-8 ura3 ade leu2 9782 , 9783 , 9784 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Hhf1-Hht1::NatMX ura3 ade1 leu2 10006 , 10007 , 10008 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Hhf1-Hht1::NatMX hst2::hphNT1 ura3 ade1 leu2 10274 , 10276 , 10277 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP smc2-8 10450 , 10451 , 10452 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP HHT2S10D:URA3 ura3 ade1 leu2 10584 , 10585 , 10586 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP HHT2S10D:URA3 smc2-8 10858 , 10859 , 10860 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP HHT2S10D:URA3 hst2::NatMX ura3 ade1 leu2 10861 , 10862 , 10863 a trp1::TetO:TRP1lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP HHT2S10D:URA3 smc2-8 hst2::NatMX 11566 , 11567 , 11568 alpha his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHTS10AS]-URA3 smc2-8 11767 , 11768 , 11769 a , alpha his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHTS-HHFS]-URA3 smc2-8 12155 , 12156 , 12157 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP hst2::KanMX 12731 , 12732 , 12733 a his3Δ200 leu2Δ0 lys2Δ0 trp1Δ63 ura3Δ0 met15Δ0 can1::MFA1pr-HIS3 hht1-hhf1::NatMX4 hht2-hhf2::[HHT-S10D-HHFS]-URA3 smc2-8 13100 , 13102 a trp1::TetO:TRP1 lys4::LacO:LEU2 his3::LacR-GFP:HIS3 TetR-mRFP Hhf1-Hht1::NatMX hht2-hhf2::[HHT-HHFK16R]-URA3 hst2::hphNT1 ura3 ade1 leu2 All strains were grown at 30ºC on YPD medium . Condensin temperature-sensitive strains were grown in liquid YPD until OD600 = 0 . 4 and then transferred to 25ºC or 37ºC for 90 min prior to imaging . For imaging , cells were resuspended in non-fluorescent medium ( Synthetic Defined ( SD ) minus tryptophan ( – TRP ) ) and put on an agar pad . All microscopy was done using a Deltavision microscope ( Applied Precision ) with a CCD HQ2 camera ( Roper ) , 250W Xenon lamps , Softworx software ( Applied Precision ) , and a temperature chamber set to the desired temperature . For both chromosome contraction and chromatin compaction assays , still images were taken using 750 ms exposure in the Tetramethylrhodamine ( TRITC ( red ) ) channel and 500 ms exposure time for the Fluorescein ( FITC ( green ) ) over ten Z-slices that were 0 . 5 μm apart . Transmission images were taken as a reference . Analysis was done using Fiji image processing software . We defined G1 cells as round cells with no bud and anaphase cells as a budded cell where the red focus leads and the green focus followed . All measurements were done in mother cells . Sum intensity projections were used and a line was drawn between the green and red foci to determine chromosome contraction . For fluorescence intensity , a Region Of Interest ( ROI ) was drawn around a focus and the integrated density was determined . An identically sized ROI was put in the nucleus to determine the background signal . Background intensity was subtracted from the focus intensity to yield the fluorescent intensity for a given focus . For the example cell in Figure 3A , 1-hr long movies were taken using slightly modified conditions: 300 ms FITC exposure , 500ms TRITC exposure and eight Z-stacks that were 0 . 5μm apart . FRET was done using the same microscope described above . First the FRET channel was recorded using 500 ms exposure in the FITC channel while capturing emission in the TRITC channel . Then , excitation and emission in TRITC was recorded using 500 ms of exposure time . Analysis was done by drawing a ROI around the fluorescent focus in both channels , subtracting the background and determining the ratio between them . At least 50 cells were measured for all conditions . For Ipl1 inactivation , exponentially growing cells were arrested with alpha-factor according to standard protocols and released at 25°C or 35°C . To obtain G1 condensation , cells were released for 5 min , to obtain anaphase condensation cells were released for approximately 2 hr at the indicated temperatures before imaging . Nuclear diameters were determined by imaging Nup170-GFP as described above and by using Fiji image processing software . DAPI staining was performed by growing cells of a given strain to OD600 < 1 . 0 in YPD at 25°C , then two new diluted cultures were made at OD600 = 0 . 2 . One of them was again put at 25°C , while the other was kept at 33°C for 90 min . Cells were harvested by 90 s centrifugation in a 1 . 5 ml Eppendorf tube at 650 RCF . Cells were resuspended in 1 ml 70% ethanol , left to fix for 7 min , spun down again and washed in 1 ml sterile water , before being resuspended in 3–5 µl Phosphate Buffered Saline ( PBS ) containing 0 . 5 µg/ml DAPI . Cells were prepared on a glass slide and imaged by using the DAPI channel with 300 ms exposure . Transmission images were taken for reference . At least 240 cells were counted for each condition . Crosslinking and acid extraction of histone H2A was performed and analyzed as previously described ( Wilkins et al . , 2014 ) . In brief , the histone H2A Y58TAG expression vector , under control of its native promoter , was cotransformed into yeast cells with the plasmid pESC-BPARS , which harbors the evolved E . coli Tyrosine tRNACUA/amino-acyl tRNA synthetase ( Tyr-tRNACUA/AARS ) pair , under control of constitutive promoters . Cells were cultured as described below and then subjected to irradiation at 365 nm UV light at a distance of ~5 cm for 7 min on ice ( Vilber Lourmat lamp , 2 X 8W , 365 nm tubes , 32 W , 230 V #VL-208 . BL ) . The nuclei of the crosslinked samples were isolated and then the histones further purified by acid extraction . The histone crosslinked products , or H4 K16ac levels , were visualized by western blot chemoluminescence after being decorated with H4 ( Abcam , ab7311 ) or H4 K16ac ( Active Motif , 39167 ) antibodies . Synchronization of yeast cells was performed in either BY4741 ( WT ) or smc2-8 temperature sensitive cells ( Li et al . , 2011 ) . Both cell types were cultured in the same manner except that the smc2-8 cells were grown at a permissive temperature of 25°C rather than 30°C . Cells were initially inoculated into an overnight culture of standard synthetic complete medium ( 1 . 7 g/L Difco Yeast nitrogen base without amino acids , 5 g/L ammonium sulfate , 2% glucose and 2 g/L amino acid dropout mixture ) supplemented with p-benzoyl-L-phenylalanine ( pBPA ) at a final concentration of 1 mM . The cultures were then diluted to an OD600 = 0 . 25 in YPD , also supplemented with pBPA . Cells were allowed to double once at permissive temperatures and then alpha-factor was added to a final concentration of 5 μg/mL ( Sigma T6901 ) . Cells were again grown at permissive temperature for 2 hr and the arrest efficiency was monitored by microscopy ( arrest stopped when >95% of the cells entered G1 based on morphology ) . Cells were washed with YPD and then expanded into YPD ( without pBPA ) . At release time zero , nocodazole was added to a final concentration of 1 . 5 μg/mL and shifted to the restrictive temperature of 37°C . Proper cell arrest and synchronization was monitored by FACS . FACS samples were prepared as previously described ( Haase , 2004 ) . Cells were grown in liquid YPD at 30ºC and fixed for 15 min in 4% paraformaldehyde ( Sigma-Aldrich ) and 3 . 4% sucrose . Fixed cells were spun and the pellet was washed twice with a potassium phosphate buffer ( pH 7 . 5 ) containing 1 . 2 M sorbitol . For imaging , cells were spun and resuspended in Vectashield ( Vector laboratories ) mounting medium for fluorescence . Cells were placed between an unfrosted slide and a number 1 . 5 high precision coverslip ( Marienfeld Superior ) sealed with nail polish . Acquisitions were made with an Applied Precision OMX Blaze ( GE Healthcare ) equipped with a 60X 1 . 42 NA Plan Apo oil objective and a sCMOS OMX V4 camera . The oil ( Applied Precision ) had a refractive index of 1 . 514 . For GFP imaging a 488 nm laser line was used for excitation with a 504–552 nm emission filter . With 10% of the light intensity 300 ms exposures were performed . Stacks span 1 . 75 µm with a spacing of 125 nm between each focal plane . Image reconstruction was performed with the Softworx software ( Applied Precision ) with a Wiener filter of 0 . 002 and a channel specifically measured optical transfer function . | DNA in humans , yeast and other eukaryotic organisms is packaged in structures called chromosomes . When a cell divides these chromosomes are copied and then the matching pairs are separated so that each daughter cell has a full set of its genome . To enable these events to take place , the DNA must become more tightly packed so that the chromosomes become rigid units with projections called arms . Any failure in this chromosome “condensation” leads to the loss of chromosomes during cell division . Within a chromosome , sections of DNA are wrapped around groups of proteins to make a series of linked units called nucleosomes , which resemble beads on a string . These units and other scaffold proteins together make a structure called chromatin and establish the overall shape of the chromosome . However , it is not exactly clear how the nucleosomes and scaffold proteins are rearranged during condensation . Kruitwagen et al . used microscopy to study chromosome condensation in budding yeast . The experiments reveal that condensation involves two separate processes . First , modifications to the nucleosomes result in these units becoming more tightly packed in a process called short-range compaction . Second , a group of proteins called condensin is responsible for rearranging the compacted chromatin to enforce higher-order structure on the arms of the condensed chromosome ( long-range contraction ) . Further experiments suggest that an enzyme called Hst2 may help to co-ordinate these processes to ensure that chromosomes adopt the right shape before the cell divides . For example , Hst2 ensures that longer chromosomes condense more than shorter ones . A future challenge will be to find out whether chromosome condensation works in a similar way in humans and other large eukaryotes , which form much larger chromosomes with more complicated structures than yeast . | [
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] | 2015 | Axial contraction and short-range compaction of chromatin synergistically promote mitotic chromosome condensation |
Patterning is a critical step during organogenesis and is closely associated with the physiological function of organs . Tooth root shapes are finely tuned to provide precise occlusal support to facilitate the function of each tooth type . However , the mechanism regulating tooth root patterning and development is largely unknown . In this study , we provide the first in vivo evidence demonstrating that Ezh2 in the dental mesenchyme determines patterning and furcation formation during dental root development in mouse molars . Mechanistically , an antagonistic interaction between epigenetic regulators Ezh2 and Arid1a controls Cdkn2a expression in the dental mesenchyme to regulate dental root patterning and development . These findings indicate the importance of balanced epigenetic regulation in determining the tooth root pattern and the integration of roots with the jaw bones to achieve physiological function . Collectively , our study provides important clues about the regulation of organogenesis and has general implications for tooth regeneration in the future .
Control of organ patterning is crucial for organ function and is a fundamental aspect of biology . Teeth are important for a number of physiological functions , such as mastication and speech . The tooth root is essential for these functions because it anchors the tooth to the jaw bone . During mastication , the root transmits and balances occlusal forces to the jaw bone through the periodontal ligament ( PDL ) . The neurovascular bundle , which supplies blood flow , nutrition , and sensation to our teeth , also runs through the tooth root ( Li et al . , 2017 ) . The loss of a functional root therefore reduces bone support to the tooth and adversely affects function of the dentition . Understanding how this organ patterning is determined will also provide information about organismic fitness through evolution ( Irvine and Harvey , 2015 ) . Teeth are the most common primate fossil remains due to their resistance to degradation; thus , morphological analysis of the tooth root can also provide critical clues about hominid evolution ( Emonet et al . , 2014 ) . For example , a reduction in the number of premolar roots can be observed already in early hominins ( Emonet and Kullmer , 2014 ) . Neanderthal molars show elongated root trunks and apically positioned root furcations ( Kupczik and Hublin , 2010; Macchiarelli et al . , 2006 ) . Therefore , investigating root development offers unique insights into organogenesis and human evolution . Epithelial-mesenchymal interactions are required for root development and integration with the jawbone . The tooth root begins to develop with the guidance of a bilayered structure called Hertwig’s epithelial root sheath ( HERS ) . The cranial neural crest cell ( CNCC ) -derived mesenchyme forms the dental papilla and dental follicle . The mesenchyme of the apical papilla interacts with the inner layer of HERS and differentiates into odontoblasts that form dentin . The dental follicle also interacts with HERS and eventually produces the cementum , PDL , and adjacent alveolar bone ( Li et al . , 2017 ) . Disruption of the interaction between HERS and the dental papilla or dental follicle leads to root development defects . For instance , if there is a disturbance to the developing HERS , differentiation of root odontoblasts will be compromised ( Kim et al . , 2013 ) . HERS has been considered critical for determination of the tooth root number . It develops tongue-shaped epithelial protrusions ( known as the epithelial diaphragm ) that join horizontally to form a bridge , called the furcation , which constitutes the base of the pulp cavity and divides the roots . After the furcation forms , the apical growth of HERS drives root development in multi-rooted teeth , just as it does in single-rooted teeth . The different orientations of HERS in different types of teeth contribute to the formation of two-rooted lower molars , three-rooted upper molars , and single-rooted incisors ( Li et al . , 2017 ) . However , the mechanisms involved in HERS regulation of furcation development remain unknown . Although previous studies have shown that changes in cell proliferation activity in the dental mesenchyme can lead to furcation defects ( Fons Romero et al . , 2017; Sohn et al . , 2014 ) , it is not clear whether the instructions that ultimately determine root furcation development and number reside in the dental mesenchyme or epithelium . Recently , multiple signaling pathways have been implicated in the processes of root initiation and elongation ( Alfaqeeh et al . , 2015; Kim et al . , 2015; Li et al . , 2015; Ono et al . , 2016 ) , but how the number of tooth roots is determined remains unknown . Ezh2 is the enzymatic subunit of Polycomb repressive complex 2 ( PRC2 ) , a complex that methylates lysine 27 of histone H3 ( H3K27 ) to promote transcriptional silencing . Polycomb proteins are an evolutionarily conserved family of chromatin regulators that serve to establish and maintain epigenetic memory during development ( Margueron and Reinberg , 2011 ) . Previous reports have indicated that craniofacial bone and cartilage formation are not detectable after loss of Ezh2 in neural crest cells , indicating a critical role for Ezh2 in the determination of the osteochondrogenic lineage during craniofacial development ( Schwarz et al . , 2014 ) . The function of Ezh2 has recently been reported to be antagonized by other epigenetic factors . SWI/SNF chromatin remodeling complexes remodel nucleosomes and modulate gene transcription . In Drosophila , antagonism between polycomb and SWI/SNF complexes has been shown to regulate gene expression during development ( Kennison and Tamkun , 1988 ) . In humans , PRC2 and SWI/SNF complexes also antagonize each other during tumor formation ( Kadoch et al . , 2016; Kadoch et al . , 2017 ) . For example , Ezh2 inhibition leads to regression of ovarian tumors with mutations in Arid1a ( a subunit of the SWI/SNF complex ) ( Bitler et al . , 2015 ) . However , whether and how these two opposing epigenetic regulating complexes regulate developmental patterning and morphogenesis in mammals still remains unknown . In this study , we found that loss of Ezh2 in the tooth mesenchyme dramatically affects root patterning by transforming multi-rooted mouse molars into single-rooted ones , indicating a critical role for Ezh2 in determination of the molar root number via regulation of furcation development . In contrast , root furcation development was delayed after loss of Ezh2 in the epithelium and was unaffected after loss of Ezh2 in odontoblasts , suggesting that regulation of furcation development is determined through a mesenchymal signal . Significantly , Ezh2 and Arid1a work antagonistically to control Cdkn2a expression to coordinate furcation development and determine the root number . Our results have shown for the first time that the antagonistic interaction between Ezh2 and Arid1a plays a key role in regulating organogenesis in mammals . These findings provide a new understanding of the mechanism governing molar root number determination and may lead to applications for tooth regeneration in the future . Given that Neanderthal molars have long root trunks and delayed furcation formation in comparison to those of modern humans , our study highlights the significance of epigenetic regulation for the patterning of organs during human evolution .
Ezh2 is a key enzyme of the PRC2 complex that is responsible for trimethylation of histone 3 lysine 27 ( H3K27Me3 ) . In order to investigate the role of Ezh2 in epigenetically regulating root patterning during tooth morphogenesis , we first analyzed the expression pattern of Ezh2 in developing molars . We found that Ezh2 is widely expressed in the dental epithelium , dental follicle , and dental papilla of control mice prior to root development initiation at the newborn stage ( Figure 1C ) . H3K27Me3 was detectable in a similar pattern to that of Ezh2 in control mice , consistent with Ezh2’s execution of a PRC2-dependent function during molar development ( Figure 1E ) . In order to test the functional significance of Ezh2-mediated root patterning and development , we generated Osr2-Cre;Ezh2fl/fl mice , in which Ezh2 is specifically ablated in the dental mesenchyme . Osr2-Cre genetically targets the dental mesenchyme and alveolar bone but not the tooth epithelium; thus , we expected Ezh2 expression to be lost from the mesenchyme of Osr2-Cre;Ezh2fl/fl teeth , but to persist in the epithelium . Indeed , Ezh2 and H3K27Me3 were undetectable in the molar mesenchyme of Osr2-Cre;Ezh2fl/fl mice at the newborn stage ( Figure 1D and F ) , indicating efficient tissue-specific deletion of Ezh2 in the dental mesenchyme . There were no morphological differences between the crowns of Osr2-Cre;Ezh2fl/fl and control molars at the newborn stage ( Figure 1A–1B ) , prior to root development . At one week after birth , tooth crown formation is almost complete and root formation is yet to start . Tooth crown formation was similar in Osr2-Cre;Ezh2fl/fl and control mice at one week of age ( Figure 2—figure supplement 1A–1F ) , indicating that Ezh2 is dispensable for crown patterning . In control mice , at two weeks after birth the root furcation was well formed , resulting in two roots in the mandibular first molars ( Figure 2A–2E ) . Interestingly , only one root trunk with no furcation was observed in Osr2-Cre;Ezh2fl/fl mandibular first molars ( Figure 2F–2J ) . The absence of furcation persisted in Osr2-Cre;Ezh2fl/fl mice at postnatal 4 weeks ( Figure 2P–2T ) , by which time the tooth root had completed development in the control group ( Figure 2K–2O ) . Moreover , the alveolar bone underneath the molar was undetectable throughout all developmental stages in Osr2-Cre;Ezh2fl/fl mice . Interestingly , Dspp expression was not affected in Osr2-Cre;Ezh2fl/fl mice , indicating that loss of Ezh2 in the dental mesenchyme has no effect on odontoblast differentiation ( Figure 2—figure supplement 2 ) . In order to investigate whether mandibular and maxillary tooth furcations develop similarly , we also analyzed maxillary molars from two-week-old mice , which have three roots rather than the two of mandibular molars in controls ( Figure 2—figure supplement 1G–1J ) . We found that maxillary molars were also single-rooted with no furcation formation in Osr2-Cre;Ezh2fl/fl mice ( Figure 2—figure supplement 1K–1N ) , suggesting that the mechanisms regulating root patterning and furcation development are similar for maxillary and mandibular molars . At the beginning of root formation , the root sheath forms the epithelial diaphragm . Previous studies highlighted the importance of differential growth of the epithelial diaphragm as the crucial step in forming multi-rooted molars ( Li et al . , 2017 ) . In order to test whether formation of the epithelial diaphragm was affected in Osr2-Cre;Ezh2fl/fl mice , we investigated its development at earlier time points . At PN 1 week , the epithelial diaphragm was not fused at the furcation region in control mice , as evidenced by a lack of continuous Krt14 staining in the apical region of the molar ( Figure 3A–3B ) . One day later , a fused epithelial diaphragm was detectable in control mice ( Figure 3G–3H ) . In contrast , we did not detect epithelial diaphragms in Osr2-Cre;Ezh2fl/fl mice at any time point ( Figure 3D–3E and J–K ) . Moreover , cell proliferation activity in the mesenchyme of the apical region was compromised in molars of Osr2-Cre;Ezh2fl/fl mice just prior to the time point at which epithelial diaphragm formation would normally be expected ( Figure 3C , F and I ) . However , no apoptotic cells were detectable in the molars of control or Osr2-Cre;Ezh2fl/fl mice at PN 1 week or PN 3 weeks of age , indicating that loss of Ezh2 in the tooth mesenchyme has no impact on cell survival ( Figure 3—figure supplement 1 ) . In addition , formation of the alveolar bone and PDL was abnormal in Osr2-Cre;Ezh2fl/fl mice . At PN 2 weeks , alveolar bone was already formed between and underneath the molars of control mice . However , alveolar bone was undetectable in Osr2-Cre;Ezh2fl/fl mice until PN 4 weeks ( Figure 4A–4B , D–E and G–J ) . Similarly , expression of PDL marker periostin was detectable in two-week-old control mice , but its expression was undetectable in Osr2-Cre;Ezh2fl/fl mice ( Figure 4C and F ) , indicating defective PDL formation due to loss of Ezh2 in the dental follicle . Collectively , our studies show that Ezh2 in the dental mesenchyme is crucial for the development of alveolar bone and PDL . Ezh2 is also expressed in the epithelium of the mouse molar . In order to investigate whether Ezh2 in the epithelium is crucial for root patterning and furcation development , we generated Krt14-Cre;Ezh2fl/fl mice . At PN 2 weeks , the furcation was already formed in control molars , but it was not detectable in molars of Krt14-Cre;Ezh2fl/fl mice ( Figure 5A–5J ) . Interestingly , alveolar bone formation was delayed in Krt14-Cre;Ezh2fl/fl mice at PN 2 weeks ( Figure 5—figure supplement 1A–1B ) . However , by PN 3 weeks , the alveolar bone and molar root furcation had formed in Krt14-Cre;Ezh2fl/fl mice ( Figure 5K–5T and Figure 5—figure supplement 1C–1F ) , indicating delayed furcation development due to loss of Ezh2 in the dental epithelium . Next , we investigated whether Ezh2 in odontoblasts has a role in root patterning and furcation development by generating Dmp1-Cre;Ezh2fl/fl mice . We found no distinguishable differences between the tooth roots of Dmp1-Cre;Ezh2fl/fl and control mice at PN 3 weeks , based on CT images and H&E staining ( Figure 5—figure supplement 2 ) . Alveolar bone and PDL formation were also normal in Dmp1-Cre;Ezh2fl/fl mice when compared to control samples . Collectively , our results indicate that Ezh2 in odontoblasts is not required for root patterning and furcation development . Arid1a is part of the SWI/SNF chromatin remodeling complex and has an antagonistic relationship with Ezh2 of the PRC2 complex in cancer development ( Bitler et al . , 2015; Wu et al . , 2018 ) . Arid1a has a similar expression pattern to that of Ezh2 during root development . Interestingly , it appears that the expression of Arid1a was not affected in the molars of Osr2-Cre;Ezh2fl/fl mice ( Figure 5—figure supplement 3 ) , indicating Arid1a is not a downstream target of Ezh2 . However , previous studies have shown that the antagonism between Ezh2 and Arid1a may occur on the functional level ( Bitler et al . , 2015 ) . In order to investigate whether there is an antagonistic interaction between Ezh2 and Arid1a in regulating furcation development , we generated Osr2-Cre;Ezh2fl/fl;Arid1afl/+ mice . Indeed , the abnormal root patterning and furcation development seen in Osr2-Cre;Ezh2fl/fl mice were completely rescued in Osr2-Cre;Ezh2fl/fl;Arid1afl/+ molars , based on microCT images ( Figure 6A–6L ) , indicating that Arid1a and Ezh2 may work antagonistically to control furcation development . To examine whether monoallelic deletion of Arid1a affects furcation development , we generated Osr2-Cre;Arid1afl/+ mice and found that their root furcations were identical to those of control mice ( n = 4 ) , suggesting that haploinsufficiency of Arid1a did not affect root patterning or development ( Figure 6—figure supplement 1 ) . We also analyzed alveolar bone and PDL development at PN 3 weeks and found that there was no difference between the molars of Osr2-Cre;Ezh2fl/fl;Arid1afl/+ and control mice , suggesting that alveolar bone and PDL devlopment was also rescued in Osr2-Cre;Ezh2fl/fl;Arid1afl/+ mice ( Figure 6 ) . In order to identify downstream mediators that control furcation development , mesenchymal tissue from PN day three mouse molars was isolated for RNA-seq analysis . We found that more genes were upregulated than downregulated in Osr2-Cre;Ezh2fl/fl molars ( Figure 7A ) , consistent with the gene repression function of Ezh2 . Patterning genes such as Hox family members and Hand2 were highly enriched and among the top twenty upregulated genes , indicating their potential role in root furcation development . The proliferation of dental mesenchymal cells has been shown to regulate tooth root furcation formation ( Sohn et al . , 2014 ) . Interestingly , we found that Cdkn2a , a cell cycle inhibitor , was also upregulated in the root-forming dental mesenchyme in Osr2-Cre;Ezh2fl/fl molars , consistent with the observed reduction in cell proliferation activity . Therefore , we hypothesized that Cdkn2a may be a downstream target of Ezh2 involved in root furcation development . In order to test our hypothesis , we first examined the expression of Cdkn2a in the molar root-forming region and found that it was upregulated in Osr2-Cre;Ezh2fl/fl mice ( Figure 7B and D ) , whereas the level of Cdkn2a expression was restored to the level in control samples in Osr2-Cre;Ezh2fl/fl;Arid1afl/+ molars ( Figure 7F ) . Based on this finding , we further examined cell proliferation activity in the root-forming region and found that proliferation was also restored in Osr2-Cre;Ezh2fl/fl;Arid1afl/+ molars ( Figure 7C , E , G and H ) . Furthermore we performed the CHIP sequencing of H3K27Me3 in root mesenchyme of the control molars . Interestingly , our data have shown that the Hox genes and Cdkn2a are in the H3K27Me3 binding sites ( Figure 7—figure supplement 1 ) , which is consistent with our RNA sequencing data . Collectively , our data highlight a critical role for the antagnistic interaction between Ezh2 and Arid1a in controlling Cdkn2a expression in regulating cell proliferation during root patterning and furcation development .
Epithelial-mesenchymal interaction is crucial for organ patterning and morphogenesis . During the formation of branched organs , the mesenchyme can instruct the epithelium to form branching patterns . For example , various types of signaling in the mesenchyme , including WNT , hedgehog ( HH ) and bone morphogenetic protein ( BMP ) , play important roles in regulating branch patterning and morphogenesis in the salivary gland , kidney and lung ( Lu and Werb , 2008 ) . Similarly , epithelial-mesenchymal interaction is also crucial for tooth root patterning and morphogenesis . Previous studies have suggested that the pattern of HERS growth may correlate with the number , length , and shape of roots ( Kumakami-Sakano et al . , 2014 ) . Furthermore , HERS provides instructive signals that contribute to the induction of dental mesenchyme differentiation , suggesting that it functions as a signaling center to guide root formation ( Huang et al . , 2009; Li et al . , 2017 ) . For example , HERS-derived TGFβ/BMP signaling regulates root dentin formation through Nfic expression in the dental mesenchyme ( Huang et al . , 2010 ) . Previous studies have suggested that the pattern of the cervical epithelial diaphragm may guide furcation formation , and signals from HERS may have a critical impact on determination of the root number . For instance , the Eda pathway is specifically active in HERS in mouse molars . Cell proliferation activity is altered in the dental mesenchyme in Eda mutant molars with delayed furcation formation , suggesting that epithelial-derived signals may regulate furcation development through epithelial-mesenchymal interaction ( Fons Romero et al . , 2017 ) . Recent studies have begun to explore the role of the dental mesenchyme in regulating root patterning and furcation development . The directionality of HERS growth may be regulated by differential proliferation of mesenchymal cells in furcation-forming and root-forming regions , which in turn determines root number ( Sohn et al . , 2014 ) . However , the key determinant for root patterning remained unknown . In this study , we found that loss of Ezh2 in the tooth mesenchyme transformed multi-rooted molars into single-rooted ones in the mouse , suggesting the significance of the dental mesenchyme in regulating root pattern and furcation development . In contrast , loss of Ezh2 in the dental epithelium resulted in delayed furcation development without affecting root patterning . These data suggest that signals from the mesenchyme , rather than the epithelium , are the driving force behind tooth patterning . It is possible that loss of Ezh2 in the dental mesenchyme affects the growth of HERS through mesenchymal-epithelial interaction . This is highlighted by the phenotype in which the epithelial diaphragm fails to fuse at the future furcation site in Osr2-Cre;Ezh2fl/fl mice . Previous studies have also highlighted that root defects are mainly related to signaling alterations in the mesenchyme rather than the epithelium ( Li et al . , 2017 ) . Moreover , in humans , mutations adversely affecting the dental mesenchyme are closely associated with tooth root defects . For example , dentinogenesis imperfecta type I ( attributed to mutations in COL1A1 and COL1A2 ) and dentinogenesis imperfecta type II ( caused by mutations in DSPP ) both involve root defects . Furthermore , X-linked hypophosphatemia ( linked to mutations in PHEX ) , which results in hypomineralized dentin and enlarged pulp cavities , results in a phenotype similar to taurodontism without apical displacement of the furcation ( Fong et al . , 2009; Li et al . , 2017 ) . Taking all these lines of evidence together , we conclude that mesenchyme-derived signals are the key determinant of root patterning . It is interesting to note that dental cusp and root patterning can be regulated independently because loss of Ezh2 in the dental mesenchyme does not adversely affect dental cusp patterning though it results in a root patterning defect . Importantly , alveolar bone formation is intimately linked to root patterning and development . It is well known that proper integration between the dental root and alveolar bone is of paramount importance for our dentition . Future study will allow us to investigate whether there are common or predetermined progenitor cells that contribute both to root and alveolar bone formation . Osr2-Cre is active in the dental mesenchyme including both the dental follicle and dental papilla . The dental follicle gives rise to periodontal tissues such as alveolar bone and PDL , which have a critical impact on tooth root development and tooth eruption ( Takahashi et al . , 2019 ) , implying the interaction between periodontal tissue and tooth root development . In our study , loss of alveolar bone and PDL in the mouse molar correlated with the observed root furcation defect . However , whether the alveolar bone and PDL defects are primary malformations or the result of root furcation defect still needs to be further investigated . Although various signaling pathways have been reported to play crucial roles in organ patterning and morphogenesis , such as BMP , TGFβ , WNT , FGF , and HH , the function of epigenetic regulation in organ patterning is largely unknown . The antagonism between PRCs and SWI/SNF complexes is crucial in both development and disease . For example , SWI/SNF antagonizes Polycomb-mediated transcriptional repression and suppresses Cyclin E transcription , arresting the cell division of myogenic precursors during muscle differentiation ( Ruijtenberg and van den Heuvel , 2015 ) . In human malignant rhabdoid tumors , loss of SMARCB1 ( a subunit of SWI/SNF ) leads to Polycomb-mediated repression of genes that suppress proliferation; when SMARCB1 is re-expressed , Polycomb is removed from the chromatin and DNA methylation is lost ( Kadoch et al . , 2016 ) . However , there is virtually no information on how PRCs and SWI/SNF exert epigenetic control over organ patterning and development in mammals . In this study , we found that antagonistic interaction between Ezh2 and Arid1a is indispensable for tooth root furcation patterning . Interestingly , Ezh2 represses the Hox gene family , and many Hox genes can suppress osteochondrogenesis ( Creuzet et al . , 2002 ) . In particular , cells in pharyngeal arch one and the anterior domains of neural crest cells ( NCCs ) do not express Hox genes , thus enabling the cartilage and bony elements of the face to form ( Minoux et al . , 2017 ) . Although migration of NCCs and their localization to target structures are not impaired by loss of Ezh2 , craniofacial osteochondrogenesis is suppressed in Wnt1-Cre;Ezh2fl/fl mice ( Schwarz et al . , 2014 ) . In our study , we found that loss of Ezh2 in the dental mesenchyme also affected dental follicle-derived tooth root-supporting tissue including PDL and alveolar bone , likely through overactivation of Hox genes , indicating that the differentiation of dental follicle-derived cells is also Ezh2-dependent . A previous study has shown that inhibition of Ezh2 methyltransferase activity can inhibit tumor cells in an Arid1a-mutated model , highlighting the antagonism of Ezh2 and Arid1a in tumor formation . Interestingly , Arid1a knockdown does not affect Ezh2 expression , and the antagonism between Ezh2 and Arid1a occurs on the functional level ( Bitler et al . , 2015 ) . Similarly , in our study , we found that loss of Ezh2 does not affect Arid1a expression but instead works antagonistically with Ezh2 to control the furcation pattern , possibly via regulation of Cdkn2a , suggesting Ezh2 may antagonize Arid1a on the functional level in this domain as well . Cdkn2a is a well-known cell cycle inhibitor . Previous studies have shown that Cdkn2a is involved in cell cycle regulation in various physiological process as a downstream target of PRC2 . For example , Cdkn2a serves as a cell cycle regulator downstream of Ezh2 in a variety of cancers ( Kim and Roberts , 2016 ) . In our study , Cdkn2a is likely the master cell cycle regulator that controls the root furcation development via regulating the cell proliferation activity in the root apical region . Taken together , our results highlight the importance of the fine-tuned balance between antagonistic epigenetic regulators Ezh2 and Arid1a in tooth root patterning and development . From an evolutionary perspective , our results clearly demonstrate that epigenetic regulation plays a key role in dental root patterning and development . Neanderthal molars have a taurodont phenotype with a longer root trunk than the ones seen in anatomically modern humans and show late bifurcation or trifurcation of the roots ( Macchiarelli et al . , 2006 ) . In our study , loss of Ezh2 in the tooth epithelium recaptures the taurodont phenotype , indicating the potential role of epithelial Ezh2 in human evolution . Importantly , it has been suggested that the root attachment area is adaptively linked to the differing occlusal loads and mechanical resistance levels of foods eaten by mammals . For instance , primates that eat hard substances exhibit larger root surface areas than those that feed on less mechanically resistant foods ( Kupczik and Hublin , 2010 ) . Some of the ways by which selective mechanisms may have operated to maximize root surface area are to increase the number of roots or lengthen the root , thus stabilizing the dentition . The well-separated dental roots offer improved stability for our dentition within the jawbones in modern humans . Collectively , a better understanding of the mechanisms involved in determination of tooth root patterning and development can therefore provide critical clues about human evolution , as well as potential therapeutic approaches to tooth regeneration .
Arid1afl/fl ( Gao et al . , 2008 ) , Dmp1-Cre ( Lu et al . , 2007 ) , Ezh2fl/fl ( Shen et al . , 2008 ) , Krt14-Cre ( Fell et al . , 2014 ) , and Osr2-Cre ( gift from Rulang Jiang , Cincinnati Children’s Hospital , Tian et al . , 2017 ) mouse lines were used and cross-bred as needed in this study . All mouse experiments were conducted in accordance with protocols approved by the Department of Animal Resources and the Institutional Animal Care and Use Committee of the University of Southern California . All mice were housed in pathogen-free conditions and analyzed in a mixed background . Mice were identified by ear tags . Genotyping was conducted on tail samples . Tail biopsies were lysed through incubation at 55°C overnight in DirectPCR tail solution ( Viagen 102 T ) followed by 85°C heat inactivation for 30 min and PCR-based genotyping ( GoTaq Green MasterMix , Promega , and C1000 Touch Cycler , Bio-rad ) . Mice were euthanized by carbon dioxide overdose followed by cervical dislocation . All mice were used for analysis regardless of sex . For immunofluorescence analysis , mouse mandibles were dissected , fixed in 4% PFA overnight , and decalcified with 10% EDTA for 4 weeks . Then , the tissues were incubated with 15% sucrose for 2 hr and 30% sucrose overnight , followed by embedding in OCT . Frozen tissue blocks were sectioned at 10 mm on a cryostat ( Leica ) and mounted on SuperFrost Plus slides ( Fisher ) . The tissue sections were blocked for 1 hr at room temperature in blocking solution ( Vector Laboratories ) . Sections were then incubated with primary antibodies diluted in blocking solution at 4°C overnight . After washing three times with PBS , sections were incubated with secondary antibodies in blocking solution at room temperature for 1 hr . DAPI was used for nuclear staining and all images were acquired using a Keyence microscope ( Carl Zeiss ) . In situ hybridization was performed using RNAscope multiplex fluorescent assay ( Advanced Cell Diagnostics ) . Briefly , tissues were fixed in 4% PFA overnight at room temperature before cryosectioning . ISH was performed on 10 μm sections according to the manufacturer’s instructions . MicroCT analysis was performed using a SCANCO μCT50 device at the University of Southern California Molecular Imaging Center . The microCT images were acquired with the x-ray source at 70 kVp and 114 μA . The data were collected at a resolution of 10 μm . Three-dimensional ( 3D ) reconstruction was done with AVIZO 7 . 1 ( Visualization Sciences Group ) . Specimens were harvested , fixed overnight in 4% PFA , and decalcified in 10% EDTA for four weeks . Tissues were embedded in OCT compound ( Sakura Tissue-Tek 4583 ) , frozen , and sectioned at 8–10 µm thickness . Apoptotic cells were detected with the In Situ Cell Death Detection Kit ( Roche Life Science 11684795910 ) following the recommended protocol . Molar samples from three-day-old Ezh2fl/fl ( control ) and Osr2-Cre;Ezh2fl/fl mice ( n = 4 per group ) were collected for RNA isolation with RNeasy Micro Kit ( QIAGEN ) . The quality of RNA samples was determined using an Agilent 2100 Bioanalyzer and all samples for sequencing had RNA integrity ( RIN ) numbers > 7 . 0 . cDNA library preparation and sequencing were performed at the Epigenome Center of the University of Southern California . Single-end reads with 75 cycles were performed on Illumina Hiseq 4000 equipment for three pairs of samples . Raw reads were trimmed , aligned using TopHat ( version 2 . 0 . 8 ) with the mm10 genome , and normalized using RPKM . Differential expression was calculated by selecting transcripts that changed with a significance of p<0 . 05 . Molar samples from three-day-old wildtype mice were collected to performed ChIP-sequencing using H3K27me3 antibody ( Cell signaling ) and Chromatrap Enzymatic Shearing Kit ( Chromatrap ) . ChIP DNA was quantified by Bioanalyzer and sequencing libraries construction were prepared using the standard Illumina ChIP-seq protocol . Technology Center for Genomic and Bioinformatics , University of California , Los Angeles constructed the library and sequenced the ChIPseq libraries on Illumina Nextseq 500 platform . Reads were mapped to NCBI mouse reference genome ( Genome Reference Consortium Mouse Build 38 , Jan 2012 ) using Burrows-Wheeler Alignment ( BWA ) tool . The uniquely mapped reads were used to identify the regions in the genome with significant enrichment of H3K27me3 modification . The aligned bam files were sorted using SAMtools followed by peak calling by MACS2-2 . 1 . 1 using broad calling with p<0 . 005 . GraphPad Prism was used for statistical analysis . All bar graphs display mean ± SD ( standard deviation ) . Significance was assessed by independent two-tailed Student’s t test or analysis of variance . p<0 . 05 was considered statistically significant . ImageJ was used to determine the percentage of the immunostained area . Positive immunofluorescence signals in molar apical regions were first converted to 8-bit binary images and measured using the ‘Analyze Particles’ function . The derived area was then divided by the total area of apical regions to calculate the percentage of positive immunostaining . The GEO accession number for the RNA sequencing and ChIP sequencing data reported in this paper is GSE131684 . | Different teeth have different numbers of roots . Incisors and canines each have one , and molars have two or three . Roots anchor the teeth to the jawbone , and provide a route for blood and nerves to reach the tooth . Getting the shape and number of the roots right during development is important to make sure that each tooth has proper support and function . A protein called Ezh2 helps the bones of the face to develop , but it was not known how it affects how the roots of teeth grow . Teeth form from two layers of tissue; epithelium on the outside and mesenchyme on the inside . Jing et al . have now looked at what happens when Ezh2 is not present in these tissues in the molar teeth of developing mice . The teeth of the mice were affected in different ways depending on which tissue Ezh2 was missing from . When the mesenchyme lacked Ezh2 , the roots of the molar teeth did not form properly: the teeth formed too few roots , and the ‘bridge’ region between the roots did not develop correctly . When the epithelium lacked Ezh2 , molar teeth formed the correct number of roots but the bridges between the roots developed later than normal . This suggests that signals from the mesenchyme determine how many roots each tooth grows . Further investigation revealed that Ezh2 opposes the activity of another protein called Arid1a , and together they regulate the production of a protein that influences when cells divide . A balanced interaction between Ezh2 and Arid1a is important for tooth root development . This controls how bridges form between tooth roots , and ultimately determines how many roots a tooth grows . Neanderthal teeth show evidence of forming bridges between roots later than modern human teeth , suggesting that similar regulation mechanisms have been important throughout human evolution . In the future , understanding how the roots of teeth form could help researchers to develop ways to regenerate teeth . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"methods"
] | [
"developmental",
"biology"
] | 2019 | Antagonistic interaction between Ezh2 and Arid1a coordinates root patterning and development via Cdkn2a in mouse molars |
Rod and cone photoreceptors are highly similar in many respects but they have important functional and molecular differences . Here , we investigate genome-wide patterns of DNA methylation and chromatin accessibility in mouse rods and cones and correlate differences in these features with gene expression , histone marks , transcription factor binding , and DNA sequence motifs . Loss of NR2E3 in rods shifts their epigenomes to a more cone-like state . The data further reveal wide differences in DNA methylation between retinal photoreceptors and brain neurons . Surprisingly , we also find a substantial fraction of DNA hypo-methylated regions in adult rods that are not in active chromatin . Many of these regions exhibit hallmarks of regulatory regions that were active earlier in neuronal development , suggesting that these regions could remain undermethylated due to the highly compact chromatin in mature rods . This work defines the epigenomic landscapes of rods and cones , revealing features relevant to photoreceptor development and function .
The retina is the starting point of vision . It originates from the embryonic diencephalon and contains three layers of neurons: an outer nuclear layer with rods and cones; an inner nuclear layer with bipolar , horizontal , and amacrine cells; and a ganglion cell layer ( Swaroop et al . , 2010 ) . Rods can respond to a single photon and mediate vision in dim light . Cones are less sensitive to light and mediate color vision . Photoreceptor specialization results from well-defined rod- and cone-specific patterns of gene expression ( Kefalov , 2012; Siegert et al . , 2012 ) , which are in part controlled by retinal transcription factors ( TFs ) OTX2 , CRX , NRL , and NR2E3 . In both rods and cones , OTX2 determines photoreceptor cell fate ( Nishida et al . , 2003 ) , and CRX regulates expression of terminal photoreceptor genes ( Furukawa et al . , 1997 ) . Rod photoreceptor fate and gene activation are induced by NRL and its downstream target NR2E3 ( Mears et al . , 2001 ) . Loss of NR2E3 leads to enhanced S-cone syndrome , an autosomal recessive human retinal disease ( Haider et al . , 2000 ) that is recapitulated in retinal degeneration 7 ( rd7 ) mice ( Haider et al . , 2001 ) . rd7 rods show a partial conversion of photoreceptor identity because they retain expression of rod-specific genes but also de-repress a subset of cone-specific genes ( Chen et al . , 2005; Corbo and Cepko , 2005; Peng et al . , 2005 ) . Regulatory regions such as enhancers and promoters control functional differences between rods and cones . Although these regions are beginning to be defined , current studies have limitations . ChIP-seq can identify TF binding sites but requires high-quality antibodies and can only interrogate one TF at a time . TF binding sites are typically marked by increased chromatin accessibility ( Thurman et al . , 2012 ) . However , existing datasets measuring chromatin accessibility are limited to whole retina from wild-type mice ( Wilken et al . , 2015 ) . Because rods make up 70–80% of all mouse retinal cells and outnumber cones by 35:1 ( Jeon et al . , 1998 ) , whole retina studies can approximate features of rods but mask differences between rods and cones that contribute to their unique identities . Therefore , the current understanding of photoreceptor gene regulation also remains limited by the lack of cell type-specific information . Of special interest is the high positive correlation between accessible chromatin and local regions of low DNA methylation that has been observed in various cell types ( Stadler et al . , 2011; Hon et al . , 2013; Ziller et al . , 2013; Mo et al . , 2015 ) . TF binding can result in local regions of low DNA methylation , leading to strong overlaps between regions identified as DNA hypo-methylated and as accessible chromatin . At present , genome-wide , single-base resolution DNA methylation profiles have not been reported for either rods or cones , precluding a large-scale analysis of this phenomenon in either photoreceptor type . Also of interest is the small size of the rod nucleus ( ~5 μm; Solovei et al . , 2009 ) and its highly condensed chromatin ( Kizilyaprak et al . , 2010 ) , which may potentially impact how chromatin accessibility correlates with DNA methylation . In addition , rods are the only known cell type in mice with nuclei that have heterochromatin centers surrounded by peripheral euchromatin ( Carter-Dawson and LaVail , 1979 ) . This inverted organization is thought to facilitate nocturnal vision ( Solovei et al . , 2009 ) . By contrast , cone nuclei are larger and exhibit the conventional arrangement of central euchromatin and peripheral heterochromatin . Here , we explore the epigenomic differences that contribute to rod and cone photoreceptor identity . Unexpectedly , most rod-specific regions of low DNA methylation are not located in accessible chromatin in adult rods . Instead , our evidence suggests that these regions are potential active regulatory sites in fetal neural tissue and , despite loss of active chromatin marks , remain hypo-methylated in adult rods due to the barrier to cytosine methyltransferases posed by chromatin condensation . In addition , we identify rod- and cone-enriched regions of accessible chromatin that may play gene regulatory functions and carry distinct DNA sequence motifs . Integrated analysis of rd7 rods , together with normal rods and cones , shows that NR2E3 function is necessary for rods to gain their complete ensemble of epigenomic features . We further examine epigenomic patterns in retinal photoreceptors versus brain neurons . Overall , our findings highlight both global and local epigenomic differences between retinal rods and cones that reflect unique aspects of their biology .
To characterize putative regulatory DNA in adult rod and cone photoreceptors , we purified their nuclei using either affinity purification ( INTACT; Mo et al . , 2015 ) or flow cytometry ( Figure 1—figure supplement 1 ) . We then applied ATAC-seq to map sites of enhanced chromatin accessibility that include putative sites of TF binding ( Buenrostro et al . , 2013 ) , and we applied MethylC-seq to measure DNA methylation levels at single-base resolution ( Lister et al . , 2008 ) ( Figure 1A; Supplementary file 1 ) . All samples were analyzed using independent pairs of biological replicates . 10 . 7554/eLife . 11613 . 003Figure 1 . Relationship of DNA methylation and accessible chromatin in retinal rods and cones . ( A ) Browser image showing accessible chromatin ( top ) and DNA methylation ( bottom ) near Abca4 , a photoreceptor gene expressed by both rods and cones . Enlarged images of ATAC-seq signals in the highlighted area are shown for one replicate of each cell or tissue type . For ATAC-seq , <100 bp ATAC-seq reads are shown . For DNA methylation , mCG/CG is shown . Methylated CG positions are indicated by upward ( plus strand ) and downward ( minus strand ) ticks , with the height of each tick representing the fraction of methylation at the site ranging from 0 to 1 . Bars below the raw data show locations identified as ATAC-seq peaks , UMRs , and LMRs . Fetal , fetal E13 cerebral cortex from Lister et al . , 2013 . Biological replicates ( R1 , R2 ) . ( B ) Line plot showing lower mean CG methylation at rod and cone ATAC-seq peaks relative to size-matched random genomic regions ( repeated 10 times ) . ( C ) Line plot showing higher mean ATAC-seq signals at rod and cone UMRs and LMRs relative to size-matched random genomic regions ( repeated 10 times ) . ( D ) Barplot showing that the percentage of cone LMRs that overlap ATAC-seq peaks ( 38% ) is two-fold higher than the percentage of rod LMRs that overlap ATAC-seq peaks ( 19% ) . ( E ) Violin plot showing that rod LMRs have a bimodal distribution of <100 bp ATAC-seq signals . The median ( white dot ) and interquartile range ( black bar ) are indicated . CGI: CpG islands; Random: size-matched random genomic regions ( repeated 10 times ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 00310 . 7554/eLife . 11613 . 004Figure 1—figure supplement 1 . Genetic labeling of mouse rod and cone photoreceptor nuclei . ( A–B ) Immunohistochemistry for GFP ( green ) showing labeling of rod photoreceptors in adult Lmopc1-Cre; R26-CAG-LSL-Sun1-sfGFP-myc retina . Lmopc1-Cre is a rod-specific Cre transgene controlled by the mouse opsin promoter ( Le et al . , 2006 ) . GFP is restricted predominantly to rods in the outer nuclear layer ( ONL ) . Scale bar ( B ) 20 µm . ( C ) Immunohistochemistry for GFAP ( red ) in adult Lmopc1-Cre; R26-CAG-LSL-Sun1-sfGFP-myc retina . GFAP is normally expressed by retinal astrocytes and is upregulated in reactive Müller glia during retinal stress . Although astrocytes in this retina are strongly GFAP+ , there is no detectable GFAP labeling in Müller glia . OS , outer segments; INL , inner nuclear layer; GCL , ganglion cell layer . Native GFP fluorescence is shown ( green ) . Scale bar: 50 µm . ( D–E ) Immunohistochemistry for GFP ( green ) and labeling with peanut agglutinin ( PNA , red ) , a marker for cones , in adult HRGP-Cre; R26-CAG-LSL-Sun1-sfGFP-myc mice . HRGP-Cre is a cone-specific Cre transgene controlled by the human red/green pigment promoter ( Le et al . , 2004 ) . HRGP-Cre predominantly recombines cones , as seen by their location , labeling with PNA , and DAPI staining ( Solovei et al . , 2009 ) . Scale bars: 50 µm ( D ) and 10 µm ( E ) . ( F ) A representative flow cytometry profile of nuclei sorted from HRGP-Cre; R26-CAG-LSL-Sun1-sfGFP-myc retinas . The thresholds used to define singlet nuclei ( left ) and GFP+ nuclei ( right ) are outlined in black . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 00410 . 7554/eLife . 11613 . 005Figure 1—figure supplement 2 . Accessible chromatin in whole retina versus DNA methylation . Barplot showing the percentage of hypo-methylated features in WT rods , rd7 rods , and cones that overlap with ATAC-seq peaks in whole WT retina , rd7 retina , and Nrl KO retina , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 005 We first assessed the genome-wide relationship between DNA methylation and chromatin accessibility in rod and cone photoreceptors . Using previously defined criteria ( Stadler et al . , 2011; Burger et al . , 2013 ) , we identified two types of regions that are depleted for DNA methylation: ( 1 ) 16617 rod and 15888 cone discrete ( <5 kb ) un-methylated regions ( UMRs; median mCG = 6% ) , which tend to be at promoters , and ( 2 ) 85992 rod and 65791 cone low-methylated regions ( LMRs; median mCG = 24% ) , which are likely associated with distal regulatory regions ( Supplementary file 2 ) . We identified a set of 55366 regions in rods and 75650 regions in cones with increased ATAC-seq densities that mark accessible chromatin ( Supplementary file 3 ) . In both rods and cones , ATAC-seq peaks exhibit low levels of DNA methylation ( Figure 1B ) . On average , hypo-methylated regions also have elevated ATAC-seq signals ( Figure 1C ) , and UMRs show strong overlap with ATAC-seq peaks ( Figure 1D ) . However , we were surprised to find substantial differences between rods and cones in the fraction of LMRs that are located within accessible chromatin ( Figure 1D ) . 38% ( 24830 ) of cone LMRs overlap with ATAC-seq peaks , a percentage close to previous reports in embryonic stem cells and cortical neurons ( Xie et al . , 2013; Mo et al . , 2015 ) . Yet , only 19% ( 15984 ) of rod LMRs overlap with ATAC-seq peaks . In fact , 31% of rod LMRs show no sign of chromatin accessibility ( <0 . 1 ATAC-seq FPKM ) , whereas this fraction is only 7% for cones . This analysis shows that rods harbor a substantial compartment of demethylated , but inaccessible , DNA that is largely absent in cones . To further explore this observation , we applied ATAC-seq to an independent set of retinal samples that did not require nuclear purification . Approximately 70–80% of cells in the WT mouse retina are rods ( Jeon et al . , 1998 ) and , in the absence of NRL , cells fated to become rods are converted en masse to S-cones ( Mears et al . , 2001 ) . Therefore , the sites of accessible chromatin in unfractionated nuclei from WT and Nrl KO retinas would be expected to largely mirror those in rods and cones , respectively . Similar to our results using purified nuclei , a greater fraction of cone LMRs overlap Nrl KO ATAC-seq peaks ( 49%; 31952 ) compared to rod LMRs that overlap WT ATAC-seq peaks ( 26%; 22590 ) ( Figure 1—figure supplement 2 ) . A previous study demonstrated that a subset of active enhancers in embryonic tissue have low levels of DNA methylation in adult tissues in the absence of ongoing enhancer activity ( Hon et al . , 2013 ) . Referred to as vestigial enhancers , these hypo-methylated regions are not enriched for active histone marks and DNaseI hypersensitivity in adult cells . When we examine the densities of sub-nucleosomal-length ATAC-seq reads , a subset of reads that may better capture sites of TF binding ( He et al . , 2014 ) , we observe a bimodal distribution of rod ATAC-seq signals at LMRs ( Figure 1E ) , with the lower peak at nearly zero signal . This distribution is significantly different than that of cone ATAC-seq signals at LMRs ( bootstrap Kolmogorov-Smirnov p<2 . 2 x 10–16 ) and potentially reflects a greater number of vestigial enhancers in rod , compared to cone , LMRs . If this hypothesis were correct , we would expect these regions to show differential methylation between rods and cones , with rods retaining low levels of DNA methylation and cones gaining methylation . We would further expect these regions to be enriched for epigenomic marks associated with active regulatory regions in neural progenitor tissue . To explore this idea , we first identified differentially methylated regions ( DMRs ) between rods and cones ( Feng et al . , 2014 ) ( Figure 2A; Supplementary file 4 ) . We find a greater number of DMRs that have lower methylation levels ( hypo-DMRs ) in rods than in cones ( 10784 rod hypo-DMRs versus 6693 cone hypo-DMRs ) . As expected from previous studies ( Schultz et al . , 2015 ) , DNA methylation levels at DMRs around gene transcription start sites ( TSSs ) show a strong negative correlation with gene expression , with a trough Pearson correlation of -0 . 8 4 kb downstream of the TSS ( Figure 2—figure supplement 1 ) . 10 . 7554/eLife . 11613 . 006Figure 2 . Rod hypo-DMRs show active chromatin marks in early neural development . ( A ) Browser images showing examples of rod hypo-DMRs that are enriched for active enhancer histone marks in fetal E14 . 5 brain ( from Shen et al . , 2012 ) but not in adult rods . These rod hypo-DMRs also display low levels of DNA methylation in both WT rods and fetal E13 cerebral cortex ( from Lister et al . , 2013 ) , but not in cones or in most adult cortical neuron types ( Exc , PV , VIP; from Mo et al . , 2015 ) . In addition , rd7 rods show higher levels of methylation than WT rods but lower levels than cones . ( B ) Cone hypo-DMRs show a six-fold higher overlap with ATAC-seq peaks , compared to rod hypo-DMRs with rod ATAC-seq peaks . ( C–G ) Heatmap showing CG methylation levels in a 3 kb window centered at rod and cone hypo-DMRs ( C ) . At the same genomic regions , the following were plotted: ATAC-seq signal ( D ) , ChIP-seq signals for histone modifications in adult rods ( this study ) or in E14 . 5 fetal brain ( from Shen et al . , 2012 ) ( E ) , and ChIP-seq signals for retinal TFs ( from Corbo et al . , 2010; Hao et al . , 2012; Samuel et al . , 2014 ) ( F ) . The density of DMRs relative to their closest TSS is shown in ( G ) for a 100 kb window around the TSS . For ( C–G ) , the rows are ordered by decreasing rank of the absolute signals of rod and cone ATAC-seq data at rod and cone hypo-DMRs , respectively . ( H ) The fetal cortex shares low CG DNA methylation with rods at a substantial fraction of rod hypo-DMRs ( top ) , but shows high methylation at the majority of cone hypo-DMRs ( bottom ) . Furthermore , methylation levels in rd7 rods are generally intermediate between WT rods and cones , particularly at rod hypo-DMRs . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 00610 . 7554/eLife . 11613 . 007Figure 2—figure supplement 1 . Rod versus cone DNA methylation levels at DMRs are strongly anti-correlated with relative gene expression . Relative gene expression ( log2 ( Rod/Cone ) RNA TPM ) has a stronger magnitude of correlation with DNA methylation levels at DMRs ( black line ) , than with either ATAC-seq signals at differential ATAC-seq peaks ( red line ) or mean mCG levels in 1 kb genomic bins ( blue line ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 00710 . 7554/eLife . 11613 . 008Figure 2—figure supplement 2 . Relationship of rod and cone hypo-DMRs to gene promoters . ( A ) Cone hypo-DMRs are distributed closer to the TSS than rod hypo-DMRs . ( B ) Barplot showing the percentage of rod and cone hypo-DMRs that fall proximal ( <10 kb ) or distal ( 10–100 kb and >100 kb ) to a TSS . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 008 Similar to our results using LMRs , we find that rod hypo-DMRs are especially discordant with accessible chromatin: only 307 ( 3% ) of rod hypo-DMRs overlap rod ATAC-seq peaks , compared with 1475 ( 22% ) of cone hypo-DMRs that overlap cone ATAC-seq peaks ( Figure 2B ) . In rods the large majority of hypo-DMRs are depleted for ATAC-seq signal ( Figures 2C–D ) , active histone modifications H3K4me1 and H3K27ac ( Figure 2E ) , and retinal photoreceptor TF binding ( Figure 2F ) , and they are located distally from promoters ( Figure 2G; Figure 2—figure supplement 2 ) . In contrast , the minority of rod hypo-DMRs that show strong ATAC-seq signals are located at relatively closer distances to the TSS ( Figure 2G ) . We next evaluated the levels of DNA methylation ( Lister et al . , 2013 ) and histone modifications ( Shen et al . , 2012 ) in fetal cerebral cortex and brain , respectively , which are rich sources of neural progenitors and immature neurons . Supporting the idea that rod hypo-DMRs may be enriched for enhancers that function earlier in neural development , most rod hypo-DMRs have low levels of DNA methylation in fetal cortex ( Figure 2C , H ) and are enriched for active histone modifications in fetal brain ( Figure 2E ) . Could the greater discrepancy between accessible chromatin and DNA hypo-methylation in rods compared to cones be a result of greater chromatin compaction in rods ? If chromatin compaction were to limit the access of cytosine methyltransferases to DNA , vestigial enhancers could remain undermethylated in adult rods . To address this question , we took advantage of the observation that disruption of NR2E3 in rd7 rods preserves the inverted chromatin arrangement , but reduces chromatin condensation ( Corbo and Cepko , 2005 ) . Therefore , rd7 rods provide a natural model to explore the relationship between chromatin condensation and the global DNA methylation pattern . We find that whereas rd7 rods are also hypo-methylated at rod hypo-DMRs , including those that may encompass vestigial enhancers , they show higher methylation levels than WT rods ( Figure 2C , H ) . Taken together , these data are consistent with a role for chromatin condensation in limiting the methylation of vestigial enhancers . To identify putative regulatory regions in rod and cone photoreceptors , we analyzed their patterns of chromatin accessibility . We first evaluated the cellular specificity of accessible chromatin , reasoning that differences in accessibility would help pinpoint regulatory regions important for unique aspects of rod and cone identity . For this analysis , we focused on comparisons between WT and Nrl KO retinas , rather than between purified rod and cone nuclei , because the complete absence of rods in the Nrl KO retina provides a degree of purity that cannot be obtained by physical separation . We note , however , that the data obtained from purified rod and cone nuclei closely match those obtained from WT and Nrl KO retinas ( Figure 3—figure supplement 1 ) . Furthermore , ATAC-seq signals between biological replicates are highly similar at accessible chromatin ( Pearson r >0 . 99; Figure 3—figure supplement 2 ) . In comparing WT and Nrl KO retinas , 22520 ATAC-seq peaks show >2-fold increased accessibility in Nrl KO retina , but only about a third as many ( 7916 peaks ) have greater accessibility in WT retina ( Figure 3A ) . WT-enriched regions of accessible chromatin cluster near the promoters of rod-specific genes ( Supplementary file 5 ) and have high levels of H3K27ac , H3K4me1 , and H3K4me3 ( Figure 3B–D ) . In contrast , Nrl KO-enriched accessible chromatin cluster near promoters of cone-specific genes ( Figure 3B , C ) . We further examined previously published ChIP-seq data for OTX2 ( Samuel et al . , 2014 ) , NRL ( Hao et al . , 2012 ) , and CRX ( from both WT and Nrl KO retina; Corbo et al . , 2010 ) . A higher percentage of binding sites for NRL and CRX in WT retina overlap WT-enriched , rather than Nrl KO-enriched , peaks ( Figure 3E ) . However , CRX binding sites in Nrl KO retina show higher overlap with Nrl KO-enriched peaks . 10 . 7554/eLife . 11613 . 009Figure 3 . Distinctive features of WT-enriched versus Nrl KO-enriched accessible chromatin . ( A ) Histogram showing that the WT retina has nearly three-fold fewer number of enriched ATAC-seq peaks compared to the Nrl KO retina . ( B ) Browser images showing histone modification ChIP-seq signals ( rods , top ) , ATAC-seq signals ( middle ) , and TF ChIP-seq signals ( bottom ) near Rho , a rod-specific gene ( left ) and near Cngb3 , a cone-specific gene ( right ) . Enlarged images of the ATAC-seq signals in the highlighted area are shown for one replicate of each cell or tissue type . Bars below the raw data indicate locations identified as ATAC-seq peaks or TF ChIP-seq peaks . ( C ) Peaks near rod genes ( green; e . g . , Nrl , Gnat1 ) generally show higher ATAC-seq signals in WT than in Nrl KO retina . Peaks near cone genes ( purple; e . g . , Pde6h , Pde6c ) generally show higher ATAC-seq signals in Nrl KO than in WT retina . Colored points show all ATAC-seq peaks that fall within 2 . 5 kb ( triangle ) or 10 kb ( circle ) of the TSS . Selected peaks are labeled by their associated gene . r , Pearson correlation . ( D ) Line plots showing that WT-enriched ATAC-seq peaks have higher mean levels of active rod histone modifications ( H3K27ac , H3K4me1 , and H3K4me3 ) compared to Nrl KO-enriched peaks . ( E ) Barplot showing the percentage of TF ChIP-seq peaks that overlap each category of ATAC-seq peak . ( F ) WT-enriched ATAC-seq peaks are distributed closer to the TSS than Nrl KO-enriched and shared ATAC-seq peaks . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 00910 . 7554/eLife . 11613 . 010Figure 3—figure supplement 1 . Comparisons of ATAC-seq signals between purified rod and cone nuclei . ( A ) Histogram showing that rods have ~2 . 5-fold fewer number of enriched peaks compared to cones . ( B ) Similar to Figure 3C , peaks near rod genes ( green; e . g . , Nrl , Gnat1 ) generally show higher ATAC-seq signals in rods than in cones . In contrast , peaks near cone genes ( purple; e . g . , Pde6h , Pde6c ) generally show higher ATAC-seq signals in cones than in rods . Colored points show ATAC-seq peaks which fall within 2 . 5 kb ( triangle ) or 10 kb ( circle ) of the TSS . Selected peaks are labeled by their associated gene . r , Pearson correlation . ( C ) Barplot showing the percentage of TF ChIP-seq peaks that overlap rod-enriched , cone-enriched , and shared ATAC-seq peaks . ( D–E ) Rod-enriched ATAC-seq peaks are distributed closer to the TSS than cone-enriched and shared ATAC-seq peaks . The majority of cone-enriched ATAC-seq peaks fall >10 kb from a TSS , whereas the majority of rod-enriched ATAC-seq peaks are TSS-proximal . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01010 . 7554/eLife . 11613 . 011Figure 3—figure supplement 2 . ATAC-seq signals between biological replicates . ATAC-seq signals are well-correlated between biological replicates at ATAC-seq peaks . Biological replicates ( R1 , R2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01110 . 7554/eLife . 11613 . 012Figure 3—figure supplement 3 . Cell type-specific differences in ATAC-seq peak distribution are not reflected by gene expression . ( A–B ) Barplot showing the percentage of all ATAC-seq peaks in each sample that fall proximal ( <10 kb ) or distal ( 10–100 kb and >100 kb ) to a TSS ( A ) . For cell type-specific peaks ( B ) , WT-enriched ATAC-seq peaks are distributed closer to the TSS than Nrl KO-enriched peaks . ( C ) Density plot showing that all samples have similar distributions of gene expression levels . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 012 WT and Nrl KO retina also share regions of common accessible chromatin , including 43104 accessible chromatin regions with <2-fold difference in ATAC-seq signal ( Figure 3A ) . Furthermore , the WT retina shows lower , but non-zero , levels of accessibility compared to the Nrl KO retina at many ATAC-seq peaks near cone-specific genes ( Figure 3C ) . The analogous result is seen at rod-specific genes , with lower-amplitude Nrl KO retina ATAC-seq signals at sites of higher-amplitude WT retina ATAC-seq peaks . Importantly , low levels of ATAC-seq signals near rod-specific genes in the Nrl KO retina could not have originated from sample contamination by rods . These observations are in line with previous studies showing that rod-specific TFs NR2E3 and NRL bind to regulatory elements near both rod- and cone-specific genes ( Peng et al . , 2005; Peng and Chen 2005; Oh et al . , 2007; Onishi et al . , 2009 ) , a phenomenon that presumably reflects a shared photoreceptor identity . Our data generalize previous results by showing shared chromatin accessibility , regardless of any particular TF , around photoreceptor genes . Our data further highlight large differences in the magnitudes of chromatin accessibility between WT and Nrl KO retinas at these regions . Interestingly , ATAC-seq peaks in WT retina are distributed closer to promoters than peaks in Nrl KO retina ( Figure 3—figure supplement 3A ) . WT retina-enriched peaks are also depleted in distal intergenic regions: only 4% ( 324 ) of WT-enriched peaks are >100 kb from a TSS , compared with 27% ( 5997 ) of Nrl KO-enriched peaks ( Figure 3F; Figure 3—figure supplement 3B ) . These differences in chromatin accessibility do not appear to be associated with large differences in the overall distributions of gene expression levels ( Figure 3—figure supplement 3C ) . Instead , these results raise the possibility that gene expression is regulated by more promoter-proximal sequences in rods compared with cones . To test experimentally whether putative regulatory regions showed cell type-specific activity , we used in vivo retinal electroporation to ask whether discrete DNA segments that overlap ATAC-seq peaks could induce reporter activity in WT or Nrl KO retinas ( Figure 4; Figure 4—figure supplement 1 ) . Electroporation at postnatal day 0 ( P0 ) into WT retina , where rod progenitors are actively proliferating , evaluates whether a DNA segment is active in rods; similarly , P0 electroporation into Nrl KO retina evaluates activity in S-cones ( Matsuda and Cepko , 2004; Swaroop et al . , 2010 ) . We cloned DNA segments ( mean length 552 bp; Supplementary file 6 ) located near rod genes ( 9 regions ) or cone genes ( 16 regions ) upstream of a minimal promoter and a GFP reporter ( Billings et al . , 2010 ) , co-electroporated each construct together with a constitutively active tdTomato ( TdT ) control , and evaluated the native GFP fluorescence at TdT+ regions . 10 . 7554/eLife . 11613 . 013Figure 4 . In vivo retinal electroporation of putative regulatory elements . Cryosections of C57Bl/6J or Nrl heterozygote retinas ( left ) and Nrl KO retinas ( right ) from 3–4 week old mice after in vivo retinal electroporation at P0 of a putative rod regulatory element near Nr2e3 ( top row ) or putative cone regulatory elements near cone-specific genes ( bottom rows ) . The element near Nr2e3 induces GFP reporter expression only in WT retina but not in Nrl KO retina . Elements near Gnat2 and Pde6h induce GFP reporter expression in Nrl KO retina but not in WT retina . The TdT signal is a control for electroporation efficiency . The average % of electroporated ( TdT+ ) cells that are GFP+ is shown . Coordinates of electroporated elements are listed in Supplementary file 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01310 . 7554/eLife . 11613 . 014Figure 4—figure supplement 1 . Additional examples of in vivo retinal electroporation of putative cone regulatory elements . Cryosections of C57Bl/6J or Nrl heterozygote retinas ( left ) and Nrl KO retinas ( right ) from 3–4 week old mice after in vivo retinal electroporation at P0 of putative cone regulatory elements near cone-specific genes . Elements near Pde6h and Opn1sw induce GFP reporter expression in Nrl KO retina but not in WT retina . An element near Thrb induces GFP reporter expression in both WT and Nrl KO retinas . The TdT signal is a control for electroporation efficiency . The average % of electroporated ( TdT+ ) cells that are GFP+ is shown . Coordinates of electroporated elements are listed in Supplementary file 6 . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 014 Six regions showed reporter activity whereas no signal was detected in the remaining regions . Lack of reporter signal may be due to the limited sensitivity of the native GFP fluorescence; furthermore , regulatory regions that function in a combinatorial manner may be missed ( Corbo et al . , 2010 ) . Within the sensitivity limits of the assay , one previously identified enhancer near Nr2e3 ( Hsiau et al . , 2007 ) induced reporter activity in WT but not in Nrl KO retinas ( Figure 4 , top ) , and four out of five regions near cone-specific genes induced reporter activity in Nrl KO but not in WT retinas ( Figure 4 , bottom; Figure 4—figure supplement 1 ) . The fifth region ( near Thrb ) induced activity in both retina types . Although the electroporation experiments tested only a limited number of regions , the majority ( 5/6 ) of regions that had detectable reporter expression showed cell type-specificity . To further explore how DNA sequences could reflect rod versus cone identity , we asked whether particular sequence features were preferentially found in regions with cell type-specific accessible chromatin . We first set stringent thresholds for both cell type-specific and shared accessible chromatin and defined 88 WT-specific , 1493 Nrl KO-specific , and 2463 shared 500 bp , non-promoter peaks . Consistent with our previous analysis ( Figure 3C ) , WT-specific and Nrl KO-specific ATAC-seq peaks are preferentially located near rod-enriched and cone-enriched genes , respectively , and show the expected pattern of CRX , NRL , and OTX2 binding ( Figure 5—figure supplement 1A , B ) . We used MotifSpec ( Karnik and Beer , 2015 ) in order to detect single de novo motifs that can discriminate between two sequence sets . Consistent with CRX binding data ( Figure 5—figure supplement 1B ) , a motif matching the canonical CRX motif ( p<1 x 10–10; Gupta et al . , 2007 ) is enriched in all three peak sets ( WT-specific , Nrl KO-specific , and shared ) relative to random sequences ( Figure 5A , left ) and is the top motif for both Nrl KO retina and shared peaks . A motif matching CTCF ( p<1 x 10–5 ) shows the second strongest enrichment in shared peaks against random sequences ( Figure 5A , middle ) . Shared peaks with the strongest CTCF binding sites appear to be located in accessible chromatin in a broad range of cell types: 87% of ATAC-seq peaks with the strongest ( top 20% ) CTCF binding sites show DNaseI hypersensitivity in >30% of mouse tissues surveyed by ENCODE ( Stamatoyannopoulos et al . , 2012 ) , compared to only 35% of peaks in the remainder ( bottom 80% ) that lack strong CTCF binding sites . For WT-specific peaks , we report a novel motif ( Figure 5A , right ) that can distinguish this set from random sequences ( area under receiver operating characteristic curve , auROC = 0 . 70 ) . Although the biological significance of this motif remains unclear , we find that about half ( 33/72 ) of WT-specific peaks with strong CRX scores also contain high scores for this motif . Conversely , the majority of peaks ( 39 peaks; 75% ) with strong scores for this novel motif also contain strong scores for CRX , but a sizeable minority ( 13 peaks; 25% ) do not appear to be sites of CRX binding . 10 . 7554/eLife . 11613 . 015Figure 5 . Machine learning identifies DNA sequence features of photoreceptor accessible chromatin . ( A ) Barplots showing score distributions of the strongest single motifs detected by a discriminative algorithm ( MotifSpec ) used to identify differentially enriched motifs . CRX binding sites are enriched in all sets of peaks relative to GC-matched random genomic sequences ( left ) . CTCF is enriched at shared peaks ( middle ) , and a novel motif is enriched at WT-specific peaks ( right ) . In each peak set , the distribution of motif scores which are predictive above AUC = 0 . 6 versus random sequence is shown in blue . ( B–C ) ROC curves showing that gapped k-mer SVM can classify ATAC-seq peaks using regulatory sequence features ( B ) . When trained versus GC-matched random genomic sequences , gkm-SVM auROC is high ( in parentheses ) . Distinguishing Nrl KO-specific ATAC-seq peaks from shared peaks is more challenging ( C , right ) than distinguishing between Nrl KO-specific ( C , top left ) and shared ( C , bottom left ) peaks from random regions . Nevertheless , the sequence-based SVM score , a weighted sum of k-mer counts , is still able to distinguish Nrl KO-specific peaks from shared peaks based on sequence features . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01510 . 7554/eLife . 11613 . 016Figure 5—figure supplement 1 . Retinal k-mers . ( A ) For regions used in the k-mer analysis , WT-specific ATAC-seq peaks ( left ) are distributed near the TSS of rod-enriched ( >2 fold ) genes , whereas Nrl KO-specific ATAC-seq peaks ( right ) are closer to the TSS of cone-enriched genes . ( B ) Boxplots showing the log-transformed , normalized ATAC-seq signal ( left two columns ) and TF ChIP-seq signal ( right four columns ) in different classes of ATAC-seq peaks . ( C ) Lists of the most predictive sequence features for distinguishing ATAC-seq peaks . The largest gkm-SVM weights for Nrl KO-specific peaks ( + ) versus random regions ( - ) ( left ) , shared peaks ( + ) versus random regions ( - ) ( middle ) , and Nrl KO-specific ( + ) versus shared peaks ( - ) ( right ) are listed . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01610 . 7554/eLife . 11613 . 017Figure 5—figure supplement 2 . DNA regulatory sequences inferred from retinal chromatin accessibility yield gkm-SVM scores which predict enhancer activity in a massively parallel reporter assay . ( A ) A scatterplot showing that retinal expression levels of >3000 candidate retina , brain , heart , and liver CREs ( Shen et al . , 2016 ) are strongly correlated with the number of replicates in which each candidate CRE barcode was detected in the RNA sample . In addition , candidate CREs with high expression have higher scores using the regulatory vocabulary trained on the WT retina ATAC-seq dataset . Error bars show mean +/- 1 S . D . for each of the four sets of data points . ( B ) Relative to all candidate CREs , the top-scoring 10% of 36 , 005 candidate CRE constructs are strongly enriched in highly expressed sequences ( i . e . , those detected in all three replicates ) when gkm-SVM is trained on retinal ATAC-seq peaks or DHS . There is no enrichment when training is performed with chromatin features from non-retinal cell types: p300-bound enhancers in melanocytes ( Gorkin et al . , 2012 ) , GATA1-bound enhancers in megakaryocytes ( Pimkin et al . , 2014 ) , and DHSs in lymphoblasts ( Lee et al . , 2015 ) . ( C ) For retinal ATAC-seq or DHS , the mean gkm-SVM score is higher for sequences detected in all three replicates , than for sequences detected in zero , one , or two replicates , or when gkm-SVM is trained on chromatin features from non-retinal cell types . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 017 Because individual motifs are known to have relatively weak predictive power ( Ghandi et al . , 2014 ) , state-of-the-art regulatory sequence prediction methods incorporate combinations of motifs for improved accuracy . Therefore , we next classified WT-specific , Nrl KO-specific , and shared peak sets against each other and against random sequences using a gapped k-mer support vector machine ( gkm-SVM; Ghandi et al . , 2014 ) , which models TF binding specificity with a complete set of k-mer features ( i . e . , words of length k ) . Although the WT-specific set was too small for the gkm-SVM analysis , the Nrl KO-specific ( auROC = 0 . 91 ) and shared ( auROC = 0 . 86 ) regions can be distinguished from random regions based on regulatory sequences contained within the peaks ( Figure 5B , C ) . We find that both Nrl KO-specific and shared classes have large SVM weights for k-mers matching the CRX binding site ( GATTA ) ( Figure 5—figure supplement 1C ) . Therefore , the relatively lower accuracy in classifying Nrl KO-specific peaks versus shared peaks ( auROC = 0 . 74 ) may reflect the close developmental paths of rods and cones , which include their usage of common photoreceptor TFs . Even in this case , however , we find sequence features that allow the SVM score to separate many of the Nrl KO-specific peaks ( TTAA-enriched homeodomain binding sites ) from shared peaks ( CTCF binding sites ) . Further supporting the ability of our gkm-SVM model to predict enhancer activity , we find that a gkm-SVM trained on WT retina ATAC-seq could predict retinal enhancer activity as assessed by a massively parallel reporter assay ( Shen et al . , 2016 ) . In this assay , candidate cis-regulatory elements ( CREs ) from retina , brain , heart , and liver were joined to a minimal promoter and a barcoded transcription unit . Enhancer activity was tested in three independent experiments by introducing the CRE library ( ~45 , 000 barcodes covering >3000 CREs ) into neonatal mouse retina , composed of primarily rods , followed by quantification of barcode abundances in the resulting RNA population . Using the number of replicates in which RNA was detected to discretize the expression level ( Figure 5—figure supplement 2A ) , we find that gkm-SVM trained on WT retina ATAC-seq is a strong predictor of expression level . Among the 12858 constructs with RNA detected in at least one replicate , the Pearson correlation between the gkm-SVM WT retina ATAC-seq score and the average retinal CRE expression level is 0 . 427 ( p<10–320 ) . Relative to all candidate CREs , those that score in the top 10% of gkm-SVM scores are 4-fold enriched for high-level expression ( Figure 5—figure supplement 2B ) . Gkm-SVM trained on retina ATAC-seq performed slightly better than gkm-SVM trained on retina DNaseI hypersensitive sites ( Yue et al . , 2014 ) , whereas training on chromatin features from unrelated cell types produced no enrichment . Conversely , candidate CREs that confer high-level expression have , on average , the highest gkm-SVM score when the model is trained on WT retina ATAC-seq regions ( Figure 5—figure supplement 2C ) . We next asked how perturbing rod development through loss of NR2E3 impacts the rod epigenome . Pearson correlations of ATAC-seq signal between rd7 and WT retina ( r = 0 . 91; Figure 6A ) and between rd7 and Nrl KO retina ( r = 0 . 88; Figure 6B ) are both higher than the correlation between WT and Nrl KO retina ( r = 0 . 78; Figure 3C ) . These correlations indicate that the rd7 rod chromatin shows a hybrid rod/cone phenotype , recapitulating previous observations from gene expression studies ( Chen et al . , 2005; Corbo and Cepko , 2005; Peng et al . , 2005 ) . Notably , 44 ATAC-seq peaks within 10 kb of rod-specific genes show greater than two-fold higher signal in WT retina compared to rd7 retina , whereas only one peak displays the opposite pattern . Reciprocally , peaks near cone genes that are normally repressed in rods by NR2E3 ( e . g . , Gnat2 , Pde6c , Pde6h , Gnb3 ) show higher chromatin accessibility in rd7 retina compared to WT retina . 10 . 7554/eLife . 11613 . 018Figure 6 . rd7 rods show intermediate epigenomic profiles compared to WT rods and cones . ( A–B ) ( A ) Peaks near rod-specific genes ( green ) generally show equivalent ATAC-seq signals in WT and rd7 retinas . A subset of peaks near cone-specific genes ( purple ) have higher signals in rd7 retina than in WT retina . ( B ) Peaks near rod-specific genes ( green ) generally show higher ATAC-seq signal in rd7 retinas than in Nrl KO retinas . Peaks near cone-specific genes ( purple ) show either similar ATAC-seq signals in rd7 retina and Nrl KO retina or higher ATAC-seq signal in Nrl KO retina . For both ( A ) and ( B ) , colored points show ATAC-seq peaks that fall within 2 . 5 kb ( triangle ) or 10 kb ( circle ) of a rod-specific gene ( green ) or a cone-specific gene ( purple ) . Selected peaks are labeled by their associated gene . r , Pearson correlation . ( C ) Genes that are up-regulated in rods ( left ) and cones ( middle ) show lower levels of CG DNA methylation in rods and cones , respectively . SEM , standard error of the mean . ( D–E ) At individual rod-specific ( D ) and cone-specific ( E ) genes , line plots showing CG DNA methylation levels in a region between -5 kb and +20 kb around the TSS . Biological replicates are shown as separate lines ( WT rods , green; rd7 rods , blue; cones , purple ) . Pairwise DMRs are indicated with black lines . R , WT rods; C , cones; rd7 , rd7 rods . The gene body is indicated with a red line . Barplots showing RNA abundances . All genes are differentially expressed between WT rods and cones and between WT retina and Nrl KO retina . Asterisks indicate differentially expressed genes between rd7 rods and WT rods or between rd7 rods and cones . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01810 . 7554/eLife . 11613 . 019Figure 6—figure supplement 1 . Epigenomic patterns of WT rods , rd7 rods , and cones near photoreceptor genes . Browser images showing ATAC-seq signals ( top ) , CG DNA methylation ( middle ) , and TF ChIP-seq signals ( bottom ) at examples of rod-specific ( Nrl , Nr2e3 , Pde6b ) and cone-specific ( Pde6c , Cnga3 ) genes . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 01910 . 7554/eLife . 11613 . 020Figure 6—figure supplement 2 . CG DNA methylation around rod-specific genes . Line plots showing CG DNA methylation levels in a region between -5 kb and +20 kb around the TSS of rod-specific genes . Biological replicates are shown as separate lines . Hypo-DMRs are indicated with black lines . R , WT rods; C , cones; rd7 , rd7 rods . The gene body is indicated with a red line . Barplots showing RNA abundances . All genes are differentially expressed between WT rods and cones and between WT retina and Nrl KO retina . Asterisks indicate differentially expressed genes between rd7 rods and WT rods or between rd7 rods and cones . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 02010 . 7554/eLife . 11613 . 021Figure 6—figure supplement 3 . CG DNA methylation around cone-specific genes . Line plots showing CG DNA methylation levels in a region between -5 kb and +20 kb around the TSS of cone-specific genes . Biological replicates are shown as separate lines . Hypo-DMRs are indicated with black lines . R , WT rods; C , cones; rd7 , rd7 rods . The gene body is indicated with a red line . Barplots showing RNA abundances . All genes are differentially expressed between WT rods and cones and between WT retina and Nrl KO retina . Asterisks indicate differentially expressed genes between rd7 rods and WT rods or between rd7 rods and cones . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 021 With respect to their DNA methylation , very few regions show differences between rd7 rods and WT rods: 287 regions are hypo-DMRs in rd7 rods and 1385 regions are hypo-DMRs in WT rods . A comparison of rd7 rod versus cone methylomes reveals more differences: 1981 regions are hypo-DMRs in rd7 rods and 4929 regions are hypo-DMRs in cones . Consistent with previous studies in the retina at targeted genes ( Merbs et al . , 2012 ) or using enrichment-based methylation assays ( Oliver et al . , 2013 ) , intragenic and promoter levels of DNA methylation are lower in rods at rod-specific genes and in cones at cone-specific genes ( Figure 6C ) . At rod-specific genes ( e . g . , Nrl , Esrrb , Cnga1 ) , rd7 rod methylation levels generally resemble those seen in WT rods or lie midway between WT rods and cones ( Figure 6C , D; Figure 6—figure supplement 1–2 ) . A rare exception to this pattern occurs at Aqp1 ( Figure 6D ) , a gene coding for an aquaporin water channel ( Papadopoulos and Verkman , 2013 ) . Both rd7 rods and cones have low Aqp1 RNA abundance and high DNA methylation at the Aqp1 gene , whereas this gene is expressed and de-methylated in WT rods . In rd7 rods , most cone-specific genes are methylated , except those that are de-repressed as a result of NR2E3 loss ( Figure 6C , E; Figure 6—figure supplement 1 , 3 ) . The intermediate methylation levels at adult photoreceptor genes in rd7 rods prompted us to ask whether these hybrid photoreceptors show evidence of a partial cell fate conversion at other types of genomic regions . In particular , we examined multi-kilobase , hypo-methylated domains termed DNA methylation valleys ( DMVs ) that are strongly enriched for TF genes ( Xie et al . , 2013 ) ( Figure 7A ) . We categorized 782 , 635 , and 816 long ( ≥5 kb ) UMRs as DMVs in WT rods , rd7 rods , and cones , respectively . The large majority of these regions are located within 2 . 5 kb of the TSS ( 89–90% ) and overlap at least one gene body ( 93–94% ) . DMV-associated genes are also , consistent with previous studies ( Xie et al . , 2013; Mo et al . , 2015 ) , highly enriched for DNA-binding factors ( Figure 7—figure supplement 1A ) . 10 . 7554/eLife . 11613 . 022Figure 7 . Retinal photoreceptors show distinct methylation patterns at DNA methylation valleys . ( A ) Browser images showing rod H3K27me3 ( top track , blue ) and CG methylation levels in retinal and cortical methylomes ( bottom tracks , green ) . A variety of cell type-specific mCG patterns are shown at these regions , including hyper-methylation in all retinal samples compared to all cortical samples ( e . g . , Vax2/Vax2os1/2 ) and hyper-methylation in a subset of retinal and cortical samples ( e . g . , Islr2 ) . Rod H3K27me3+ DMVs overlapping Islr2 , Vax2os1/2 , Lhx4 , and Onecut1 show higher levels of methylation in cones compared to rods . In contrast , rod H3K27me3+ DMVs overlapping Dll1 and Six6 show higher levels of methylation in rods compared to cones . Black lines indicate DNA methylation valleys identified in each cell type . ( B ) Barplots showing the levels of CG methylation in rods , rd7 rods , and cones at DMVs overlapping individual TF genes . Asterisks indicate significance at FDR <1 x 10–10 ( Fisher’s Exact Test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 02210 . 7554/eLife . 11613 . 023Figure 7—figure supplement 1 . CG DNA methylation at DNA methylation valleys . ( A ) GO analysis from GREAT ( McLean et al . , 2010 ) showing that genes associated with DMVs are highly enriched for DNA-binding factors . The top ten terms from GO Molecular Function are displayed . ( B ) A schematic showing how DMV coordinates were merged across WT rod , rd7 rod , and cone methylomes . ( C ) Barplots showing the mean levels of WT rod , rd7 rod , and cone CG methylation at DMVs with higher methylation in cones than in WT rods ( left ) or at DMVs with higher methylation in WT rods than in cones ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 023 We further merged DMV coordinates to form a union of 996 regions ( Figure 7—figure supplement 1B ) ; of these regions , 425 overlap rod Polycomb-repressed ( H3K27me3+; H3K4me3- ) regions , and 483 overlap rod active ( H3K27me3-; H3K4me3+ ) regions ( Supplementary files 7 and 8 ) . About half of the merged DMVs ( 508 ) show equal or higher methylation in cones ( mean mCG = 12 . 0% ) than in WT rods ( 6 . 7% ) , with rd7 rods showing an intermediate methylation level ( 9 . 9% ) ( Figure 7—figure supplement 1C , left ) . This category includes H3K4me3+ DMVs overlapping Nrl and Nr2e3 and H3K27me3+ DMVs overlapping Islr2 , Vax2os1/2 , Lhx4 , and Onecut1 ( Figure 7B , top two rows ) . Interestingly , ONECUT1 is an early cone marker that drives cone genesis , acts upstream of NRL , and becomes silenced during cone maturation ( Emerson et al . , 2013; Sapkota et al . , 2014 ) . Onecut1 overlaps a merged DMV that is 37% methylated in cones , 10% methylated in rods , and 20% methylated in rd7 rods . This intermediate methylation level in rd7 rods suggests that Onecut1 may also have developmentally dynamic epigenomic or gene expression patterns in these hybrid photoreceptors . The remaining 488 DMVs have higher mCG in WT rods than in cones; here , the mean methylation level is slightly higher in rd7 rods ( 14 . 7% ) than in WT rods ( 13 . 5% ) ( Figure 7—figure supplement 1C , right ) , which could potentially reflect a non-specific , genome-wide increase in DNA methylation in rd7 rods ( see Figure 8A–C ) . Certain individual DMVs , such as those overlapping Hr , Six6 , and Otx2 , show pronounced , stepwise decreases in DNA methylation across WT rods , rd7 rods , and cones ( Figure 7B , bottom row ) . 10 . 7554/eLife . 11613 . 024Figure 8 . DNA methylation at retinal photoreceptors versus cortical neurons . ( A–B ) The levels of CH ( A ) and CG ( B ) DNA methylation for retinal and cortical cell types . FC , E13 fetal cerebral cortex . ( C ) The total level ( CG and CH ) of DNA methylation . The percentage of all methylcytosines that are in the CH context ( top ) is shown . ( D ) Heatmap showing the pairwise Pearson correlation ( r ) of CG methylation levels in 500 bp genomic bins among retinal and cortical samples . The dendrogram shows hierarchical clustering using 1-r as the distance measure . Biological replicates ( R1 , R2 ) . ( E ) The fetal cortex shows a lower distribution of mCG/CG at pan-retinal hypo-DMRs ( top ) compared to pan-cortical hypo-DMRs ( bottom ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 02410 . 7554/eLife . 11613 . 025Figure 8—figure supplement 1 . Correlations of accessible chromatin among retinal and cortical samples . ( A ) Heatmap showing pairwise Pearson correlation ( r ) for ATAC-seq and DNaseI-seq ( D ) read densities in 500 bp genomic bins among retinal and cortical samples . The dendrogram shows hierarchical clustering using 1 - r as the distance measure . ( B ) Heatmap showing the Jaccard index for ATAC-seq and DNaseI-seq ( D ) peaks among retinal and cortical samples . The dendrogram shows hierarchical clustering using 1 - Jaccard index as the distance measure . Biological replicates ( R1 , R2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 02510 . 7554/eLife . 11613 . 026Figure 8—figure supplement 2 . Retinal versus cortical hypo-DMRs . ( A–D ) Heatmap showing CG DNA methylation levels at retinal versus cortical hypo-DMRs ( A ) . At the same genomic regions , the following were plotted: ATAC-seq signals ( B ) , histone ChIP-seq signals ( C ) , and retinal TF ChIP-seq signals ( D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 026 In summary , epigenomic patterns in rd7 rods generally resemble those of WT rods , but may not attain the full magnitudes of rod chromatin accessibility or DNA hypo-methylation . At the same time , rd7 rods also do not match the epigenomic patterns of cones . This hybrid epigenome highlights the importance of NR2E3 in activating and maintaining rod-specific photoreceptor identity as well as in repressing cone-specific attributes . Lastly , we compared the epigenomic landscapes of retinal photoreceptors with brain neurons . Non-CG methylation--referred to as mCH ( where H=A , C , or T ) --is a defining feature of brain neurons but is rare outside of neurons and pluripotent stem cells ( Xie et al . , 2012; Lister et al . , 2013; Schultz et al . , 2015 ) . As retinal rods and cones are specialized sensory neurons , we asked whether they also have a high level of mCH . Compared to cerebral cortical neurons ( Mo et al . , 2015 ) , rods and cones have similar levels of mCG but up to 29- and 7-fold lower levels of mCH , respectively ( Figure 8A–C ) . However , rods and cones have higher mCH levels compared to most other non-brain tissue types ( <0 . 05% mCH; Schultz et al . , 2015 ) . WT rods have lower mCH ( 0 . 11–0 . 13% ) compared to cones ( 0 . 42–0 . 45% ) , whereas rd7 rods show an intermediate mCH level ( 0 . 21–0 . 25% ) . Because NR2E3 is expressed after terminal mitoses in rods , our data also suggests that mCH accumulates post-mitotically in photoreceptors , as it does in the brain . To further explore similarities and differences between retinal photoreceptors and cortical neurons , we quantified the epigenomic distance between samples by calculating the genome-wide Pearson correlation of DNA methylation at CG sites between all pairwise sample combinations ( Figure 8D ) . Hierarchical clustering shows that retinal photoreceptors are tightly clustered , whereas cortical neurons cluster separately . In addition , cortical neurons show greater epigenomic distance between neuron subtypes compared to the rod-cone distance . A similar pattern is observed when epigenomic distance is calculated using chromatin accessibility ( Figure 8—figure supplement 1 ) . Based on their DNA methylation patterns , the fetal cortex clusters more closely to mature retinal photoreceptors than to mature cortical neurons ( Figure 8D ) . This clustering organization exists despite the anatomical difference between the cerebral cortex and retina and the development of many fetal cortical cells into mature cortical neurons . One interpretation of this clustering pattern is that cortical neurons may acquire more extensive cell type-specific modifications than photoreceptors during their developmental maturation . We therefore defined DMRs across all retinal and cortical samples and found twice as many regions that showed hypo-methylation only in retinal photoreceptors ( 63384 ) compared to those that showed hypo-methylation only in cortical neurons ( 31483; Figure 8—figure supplement 2 , Supplementary file 4 ) . Also consistent with our interpretation , a larger proportion of retinal hypo-DMRs , compared to cortical hypo-DMRs , display low-to-moderate levels of DNA methylation in fetal cortex ( Figure 8E ) .
In comparing rod and cone landscapes of DNA methylation and chromatin accessibility , we have uncovered several unusual features of rod photoreceptors . First , rods have relatively fewer regions of high chromatin accessibility , and on average , rod-enriched accessible chromatin sites are located closer to promoters . Second , rods have more un-methylated and low-methylated regions compared to cones . Third , compared with cone hypo-DMRs , rod hypo-DMRs are located at greater distances from the TSS and show seven-fold lower overlap with ATAC-seq peaks . Furthermore , rod hypo-DMRs are enriched for regions that are both hypo-methylated and marked by active histone modifications in fetal neural tissue . These findings suggest that many hypo-methylated regions in rods may mark previously active fetal enhancers that have retained their hypo-methylation despite loss of enhancer activity ( Figure 9 ) . Such regions have previously been described as vestigial enhancers ( Hon et al . , 2013 ) , but the factors that contribute to continuing hypo-methylation and to variation in the number of vestigial enhancers across different cell types remained unclear . Mammalian cells show a spectrum of nuclear size , and rod nuclei fall near one extreme of this spectrum ( Solovei et al . , 2009 ) . Mouse rod nuclei are exceptionally small , and increased chromatin condensation in rods could potentially pose a barrier to DNA methylation by limiting the accessibility of DNA methyltransferases . In support of this hypothesis , we find that rd7 rods , a hybrid rod/cone cell with slightly increased nuclear size and lower levels of chromatin condensation compared to rods ( Corbo and Cepko , 2005 ) , show higher levels of DNA methylation at putative rod vestigial enhancers . rd7 rods also show intermediate levels of mCH relative to normal rods and cones . A previous study showed that dense heterochromatin regions surrounding immunoglobulins or olfactory receptor clusters in brain neurons coincide with regions of low mCH ( Lister et al . , 2013 ) , consistent with the idea that the highly condensed chromatin in rods could be a global limiting factor to mCH accumulation . Taken together , our results suggest that high chromatin compaction could provide a unifying explanation of some of the unique epigenomic features of rod photoreceptors . These observations may be relevant to other cell types with different nuclear sizes and extents of chromatin condensation . For example , cells in the oligodendrocyte lineage undergo chromatin compaction as they progress from progenitors to mature myelinating oligodendrocytes ( Shen et al . , 2005 ) . 10 . 7554/eLife . 11613 . 027Figure 9 . Epigenomic model of rod and cone photoreceptor development . Enhancers that are active only in progenitor cells ( termed 'fetal-only' , as the fetal brain was used as a rich source of generic neural progenitors ) have low levels of DNA methylation and are enriched for H3K27ac and H3K4me1 histone modifications . In mature cones , histones near fetal-only enhancers lose H3K27ac and H3K4me1 and there is a gain of DNA methylcytosines . In contrast , in mature rods , fetal-only enhancers lose H3K27ac and H3K4me1 but the DNA remains unmethylated , potentially due to the barrier to cytosine methyltransferases posed by their high level of chromatin condensation . In both rods and cones , expressed genes , including rod- and cone-specific photoreceptor genes , have promoters marked by low DNA methylation , high chromatin accessibility , and enrichment for H3K27ac and H3K4me3 . Active enhancers are marked by low DNA methylation , high chromatin accessibility , and enrichment for H3K27ac and H3K4me1 ( not shown ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11613 . 027 A progressive restriction of the accessible chromatin landscape is a hallmark of differentiating cells and can be conceptualized with a genome-centric version of the Waddington landscape of cell differentiation ( Waddington , 1940; Stergachis et al . , 2013 ) . In this landscape , a ball rolls down a succession of narrowing valleys . The ball represents a differentiating cell traveling along a trajectory that leads to a progressively restricted fate . Our finding that rods show fewer accessible chromatin regions than cones suggests that , at the epigenomic level , a rod could represent a more developmentally restricted cell type than a cone . This idea is consistent with current models of photoreceptor genesis , which propose that NRL , perhaps in combination with NR2E3 , is necessary to direct photoreceptor precursors to turn off the default S-cone fate and differentiate along the rod lineage ( Swaroop et al . , 2010 ) . We find that perturbing normal rod development by inactivating NR2E3 converts the rod epigenome to a state that is intermediate between that of rods and cones . rd7 rods fail to attain WT rod levels of chromatin accessibility and DNA hypo-methylation at rod-specific regions , and they exhibit increased accessibility and hypo-methylation at multiple cone-specific regions . Furthermore , rd7 rods show intermediate patterns of DNA methylation at large hypo-methylated domains that overlap key developmental TF genes . Future studies at multiple developmental timepoints will be necessary to explore how chromatin accessibility and DNA methylation evolve differentially in WT rods , rd7 rods , and cones , particularly at the point of photoreceptor fate commitment and early differentiation . In addition to enriching our understanding of photoreceptor gene regulation , mapping the epigenomic landscape of rods and cones could be informative for understanding the clinical variability of human retinal diseases . In inherited retinal diseases such as retinitis pigmentosa and Stargardt macular dystrophy , patients with the same coding region mutation can differ markedly in the age of onset , rate , and pattern of photoreceptor degeneration ( Shastry , 1994; Fishman et al . , 1999; Oh et al . , 2004 ) . Although part of this phenotypic heterogeneity may be a result of environmental factors such as light exposure ( Wright et al . , 2010 ) , another contributing factor could be differences in regulatory DNA sequences that would affect the binding of TFs . Therefore , characterizing genome-wide accessible chromatin in rods and cones could identify regions that influence the clinical course of retinal disease . The identification of rod and cone regulatory regions could also be useful in the quest to develop cell-based therapies to treat retinal disease . Recent advances in cellular reprogramming have led to the production of stem cell-derived retinal photoreceptors that might be transplanted into the diseased retina to restore vision ( Lamba et al . , 2009; Gonzalez-Cordero et al . , 2013 ) . Our datasets and integrative analyses could help in refining the reprogramming approaches in current use by identifying key regulatory regions that drive photoreceptor identity , as well as by providing benchmarks for assessing the extent to which the reprogramming process has faithfully recapitulated a normal rod or cone identity .
We crossed Lmopc1-Cre ( Le et al . , 2006 ) and HRGP-Cre mice ( Le et al . , 2004 ) with INTACT mice ( R26-CAG-LSL-Sun1-sfGFP-myc; Mo et al . , 2015 ) to generate progeny with tagged rod and cone nuclei , respectively . Lmopc1-Cre; R26-CAG-LSL-Sun1-sfGFP-myc and HRGP-Cre; R26-CAG-LSL-Sun1-sfGFP-myc retinas appeared morphologically normal with no retinal degeneration . For purified rods and cones , we used mice between 8–11 weeks of age . We also used WT C57BL/6J mice ( JAX 000664 ) , rd7 mice ( JAX 004643 ) , and Nrl KO mice ( Mears et al . , 2001 ) . Both rd7 and Nrl KO mice were congenic on a C57BL/6J background . For ATAC-seq , we used WT , rd7 , and Nrl KO mice aged between P21 and P25 . Relative to the mice used for INTACT , a younger set of mice were used in the whole retina ATAC-seq experiments in order to minimize effects due to low-level retinal degeneration in the Nrl KO retina ( Mears et al . , 2001 ) and rd7 retina ( Haider et al . , 2001 ) . For all sequencing experiments , only male mice were used . Standard procedures for immunohistochemistry were used . Retinas were sectioned at 10 µm thickness using a cryostat . Whole-mount retinas were prepared by fixing eyes with 1 . 5% PFA for 1 hr at room temperature before dissection and immunohistochemistry . The following reagents were used: DAPI , rabbit anti-GFP ( 1:400 , A11122 , Life Technologies , Carlsbad , CA ) ; rhodamine labeled Peanut Agglutinin ( 1:1000 , RL-1072 , Vector Laboratories , Burlingame , CA ) ; rabbit anti-GFAP ( 1:200 , RB-087-A , NeoMarkers , Fremont , CA ) ; and Alexa Fluor-conjugated IgG secondary antibodies ( 1:400 , Life Technologies ) . Confocal images were taken with an LSM700 ( Zeiss , Jena , Germany ) microscope . Image processing was performed using Adobe Photoshop ( Adobe Systems Inc . , San Jose , CA ) , and included adjustments of brightness , contrast , and levels in individual channels for merged color images . For electroporation images , identical settings were maintained between WT and Nrl KO retinas . For each experiment , retinas from two to four HRGP-Cre; R26-CAG-LSL-Sun1-sfGFP-myc mice were homogenized using a loose pestle in a Dounce homogenizer in ice-cold homogenization buffer ( 0 . 25 M sucrose , 25 mM KCl , 5 mM MgCl2 , 20 mM Tricine-KOH , 1 mM DTT , 0 . 15 mM spermine , 0 . 5 mM spermidine ) with EDTA-free protease inhibitor ( 11 836 170 001 , Roche , Basel , Switzerland ) . After addition of 5% IGEPAL-630 to bring the sample to 0 . 3% IGEPAL-630 , the sample was further homogenized using a tight pestle . The sample was filtered using a 40 µm strainer ( 08-771-1 , Fisher Scientific , Waltham , MA ) , mixed with 1 . 5 ml of 50% iodixanol density medium ( D1556 , Sigma , St . Louis , MO ) , and pelleted by centrifugation at 10 , 000g for 18 min in a swinging bucket centrifuge at 4°C . Nuclei were sorted using a MoFlo MLS high-speed cell sorter ( Beckman Coulter , Brea , CA ) . Nuclei were sorted into either Buffer RLT for RNA preparation or PBS for DNA preparation ( see below ) . An aliquot of nuclei sorted using the same parameters was placed into an additional tube . After sorting , this aliquot was inspected using a Zeiss Imager Z1 and Apotome system in order to verify the purity of the sorted sample . INTACT purification of rod and cone nuclei was performed as previously described ( Mo et al . , 2015 ) with the modification that retinas were homogenized in 1 . 5 ml of homogenization buffer , and 1 . 5 ml ( instead of 5 ml ) of 50% iodixanol density gradient was added to the sample . In contrast to brain homogenates , which consisted predominantly of singlet nuclei , retinal homogenates showed a mixture of singlet , doublet , and higher order nuclear aggregates . The inability of Dounce homogenization to fully dissociate retinal nuclei into a suspension of single nuclei was presumably due to the tight packing of retinal photoreceptors and the small size of photoreceptor cell bodies . Based on fluorescence microscopy of purified nuclei ( >400 nuclei/experiment ) , INTACT purification of rod photoreceptors was 97 . 5% specific ( 96 . 7–98 . 2%; n = 5 ) , with non-rod nuclei nearly exclusively arising from non-singlet aggregates . Candidate rod- and cone-specific regulatory elements and controls ( Supplementary file 6 ) were cloned into the Stagia3 plasmid ( Billings et al . , 2010 ) , which consists of a minimal promoter upstream of eGFP-IRES-PLAP . All DNA segments overlapped ATAC-seq peaks . DNA segments were selected based solely on their location relative to known photoreceptor genes and not by measures of mammalian sequence conservation , ATAC-seq peak intensity , or differential ATAC-seq signal . Stagia3 plasmids ( 5 µg/µl ) were co-electroporated together with a CMV-driven MARCKS-TdTomato plasmid ( 1 µg/µl ) into C57Bl/6J , Nrl heterozygous , and Nrl KO retinas . In vivo electroporation into P0 mouse retina was performed as previously described ( Matsuda and Cepko , 2004 ) . Eyes were harvested at 3–4 weeks and immersion-fixed for 1 hr in 1 . 5% PFA at room temperature . Dissected retinas were equilibrated in 30% sucrose , embedded in OCT , and sectioned at 10 µm thickness using a cryostat . Slides were stained with DAPI and mounted with Fluoromount-G ( 0100–01 , SouthernBiotech , Birmingham , AL ) . The native GFP and TdT fluorescence was viewed with a Zeiss LSM700 confocal microscope . Native fluorescence was chosen because of the relative linearity of the signal compared to alkaline phosphatase staining or immunostaining; however , due to its lower sensitivity , weak reporter activity was not detected . If a construct showed enhancer activity in either WT retina or Nrl KO retina , enhancer strength was quantified by counting the number of TdT+ nuclei that were also positive for GFP signal . For each construct and genotype ( i . e . , WT or Nrl KO ) , over 100 nuclei were counted across three experimental replicates , except for the Opn1sw -1 bp construct in WT retina , where only two replicates were used . RNA , DNA , and nucleosomes from INTACT-purified nuclei were prepared as described in Mo et al . , 2015 . Briefly , whole RNA was prepared using the RNeasy Micro kit ( 74004 , Qiagen , Venlo , Netherlands ) with on-column DNase digestion . For RNA purification from FACS-sorted cone nuclei , nuclei were directly sorted into a tube containing Buffer RLT ( Qiagen 74004 ) . DNA was prepared using the DNeasy Blood and Tissue kit ( Qiagen 69504 ) . For DNA purification from FACS-sorted cone nuclei , nuclei were sorted into a tube containing PBS . Nucleosomes for native ChIP-seq were prepared by digesting 1–2 million nuclei with 0 . 025 units/µl micrococcal nuclease ( LS004798 , Worthington , Lakewood , NJ ) at 37°C for 15 min . Libraries for RNA-seq , MethylC-seq , ChIP-seq , and ATAC-seq were prepared as previously described , with slight modifications ( Garber et al . , 2012; Buenrostro et al . , 2013; Lister et al . , 2013; Mo et al . , 2015 ) . Briefly , total RNA was converted to cDNA and amplified ( Ovation RNA-seq System V2 , #7102 , NuGEN Technologies Inc . , San Carlos , CA ) . After adding a spike-in of ERCC RNA ( 4456740 , Life Technologies ) , amplified cDNA was fragmented , end-repaired , linker-adapted , and sequenced for 50 cycles on a HiSeq 2500 ( Illumina Inc . , San Diego , CA ) . MethylC-seq libraries were PCR amplified with KAPA HiFi HotStart Uracil+ ReadyMix ( KK2802 , Kapa Biosystems , Wilmington , MA ) and sequenced on an Illumina HiSeq 2000 up to 101 cycles . Histone ChIP-seq was performed by scaling down the HT ChIP-seq protocol ( Garber et al . , 2012 ) . Each ChIP-seq reaction used chromatin prepared from 0 . 5–1 million nuclei , 25 µl Protein G Dynabeads ( 10004D , Life Technologies ) , and 1 µg of the following antibodies: rabbit anti-H3K27me3 ( 07–449 , Millipore , Billerica , MA ) , rabbit anti-H3K27ac ( ab4729 , Abcam , Cambridge , UK ) , rabbit anti-H3K4me3 ( ab8580 , Abcam ) , and rabbit anti-H3K4me1 ( ab8895 , Abcam ) . Input and ChIP-enriched DNA was end-repaired , linker-adapted , amplified , and sequenced on an Illumina HiSeq 2500 for 50 cycles . ATAC-seq on 50 , 000 INTACT-purified nuclei was performed as in Buenrostro et al . ( 2013 ) with modifications as in Mo et al . ( 2015 ) . For each ATAC-seq sample using whole WT , rd7 , and Nrl KO retinas , both retinas from one mouse were homogenized in 1 . 5 ml of homogenization buffer , as described above . The homogenate was filtered through a 10 µm filter ( 04-0042-2314 , Sysmex Partec , Kobe , Japan ) into a 15 ml glass tube ( Corning , Corning , NY ) . The homogenate was brought up to 5–6 ml with homogenization buffer and pelleted at 400 g for 10 min at 4°C . After rinsing the pellet once with 1 ml of homogenization buffer , the pellet was incubated with 50 µl of homogenization buffer on ice for 10 min with gentle pipetting to resuspend the nuclei . After quantifying the nuclei concentration using a hemocytometer , approximately 50 , 000 nuclei ( up to 2 . 5 µl of the concentrated suspension ) were used in a 50 µl Tn5 transposition reaction . | Vision in humans is made possible by a light-sensing sheet of cells at the back of the eye called the retina . The surface of the retina is populated by specialized sensory cells , known as rods and cones . The rod cells detect very dim light , while the cones are less sensitive to light but are used to detect color . Together , the rods and cones gather the information needed to create a picture that is then transmitted to the brain . Rods and cones have been studied for decades , and genetic analyses have revealed the patterns of gene expression that lead a cell to develop into either a rod or a cone . Researchers have also identified several key regulatory genes that control these patterns , but less is known about the role of other factors that control the expression of genes . Chemical modifications to DNA or modifications to the proteins associated with DNA – which are collectively called epigenetic modifications – can either promote or inhibit the activation of nearby genes . Now , Mo et al . have shown that rods and cones from mice have very different patterns of epigenetic modifications . The experiments also revealed that many sections of DNA that are marked to promote gene activation contain known rod-specific or cone-specific genes; and that rod cells need a known regulatory gene to develop their specific pattern of epigenetic modifications . Finally , Mo et al . showed that epigenetic regulation differed between brain cells and rods and cones . These insights into epigenetic regulation of rod and cone genes may help explain why some people with eye diseases caused by the same genetic mutation may develop symptoms at different ages or lose vision at different rates . The new information about gene regulation may also help scientists to reprogram stem cells to become healthy rods or cones that could be transplanted into people with eye disease to restore their vision . | [
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How the immune system affects tissue regeneration is not well understood . In this study , we used an emerging mammalian model of epimorphic regeneration , the African spiny mouse , to examine cell-based inflammation and tested the hypothesis that macrophages are necessary for regeneration . By directly comparing inflammatory cell activation in a 4 mm ear injury during regeneration ( Acomys cahirinus ) and scarring ( Mus musculus ) , we found that both species exhibited an acute inflammatory response , with scarring characterized by stronger myeloperoxidase activity . In contrast , ROS production was stronger and more persistent during regeneration . By depleting macrophages during injury , we demonstrate a functional requirement for these cells to stimulate regeneration . Importantly , the spatial distribution of activated macrophage subtypes was unique during regeneration with pro-inflammatory macrophages failing to infiltrate the regeneration blastema . Together , our results demonstrate an essential role for inflammatory cells to regulate a regenerative response .
Over the past three decades , regenerative biology has merged its rich historical practice with new genetic tools to discover how animals are capable of regenerating tissue and organs . Regeneration biologists commonly investigate organ regeneration in a range of metazoans including hydra , planarians , crickets , zebrafish , salamanders , newts , lizards and even some mammals . Conventional studies perturb cellular functions or genetic pathways to inhibit the normal regenerative response and thus seek to identify key cellular and molecular mechanisms underlying the regenerative response to injury . Alternatively , some investigators have employed a comparative approach to discover key mechanisms underlying regeneration . In this framework , two related species undergo different responses to injury in identical tissues and exhibit either a regenerative or a scarring response ( Gawriluk et al . , 2016; Sánchez Alvarado , 2000; Sikes and Bely , 2010; Wagner and Misof , 1992 ) . This comparative approach may be particularly useful for unraveling complex interactions such as how inflammation and immunity permit , instruct or inhibit local cells to initiate and undergo functional regeneration in lieu of scarring . Exactly how cellular inflammation and immunity affect regeneration remains controversial . One perspective posits that inflammation impedes regeneration ( Harty et al . , 2003; Mescher et al . , 2017 ) , a view supported by reports of less robust immune responses in animals and tissue that regenerate when compared to those that cannot ( Brant et al . , 2016; Mak et al . , 2009; Mescher et al . , 2013; Redd et al . , 2004 ) . Similarly , chronic inflammation leads to compromised healing and fibrotic disease ( Martin and Leibovich , 2005; Riches , 1988; Wynn , 2004 ) . However , physical injury elicits inflammation during regeneration and scarring . Specifically , cytokines and chemokines produced by neutrophils , macrophages and T-cells recruit fibroblasts , promote granulation tissue formation , activate myofibroblasts , and promote collagen production and deposition ( Aliprantis et al . , 2007; Lakos et al . , 2006; Mori et al . , 2008; Ong et al . , 1999; Smith et al . , 1995 ) . Dampening the inflammatory response by depleting leukocytes creates better healing outcomes following damage to skin , skeletal muscle , and liver ( Dovi et al . , 2003; Duffield et al . , 2005; Martin et al . , 2003; Novak et al . , 2014 ) . Thus , when one considers that injury-mediated inflammation and immunity is an ancient process shared by animals ( and plants ) that can and cannot regenerate , a more nuanced relationship between regeneration and immunity emerges . Mounting evidence suggests that certain immune cells may be necessary to induce and sustain regeneration . Depletion of phagocytic cells ( e . g . macrophages and dendritic cells ) inhibits regeneration in axolotl limbs , zebrafish fins , and neonatal mouse hearts ( Aurora et al . , 2014; Godwin et al . , 2013; Petrie et al . , 2014 ) . Furthermore , the timing of leukocyte depletion has a major impact on regenerative outcomes ( Arnold et al . , 2007; Duffield et al . , 2005; Varga et al . , 2016 ) supporting an important role for changing immune cell phenotypes ( Gensel and Zhang , 2015; Koh and DiPietro , 2011; Mantovani et al . , 2013 ) . Although these findings support a positive function of certain immune cells on regeneration , they also simplify important differences across species . For instance , salamanders lack important T-cell phenotypes and utilize primarily IgM rather than IgG antibodies while mounting an adaptive immune response ( Chen and Robert , 2011; Cotter et al . , 2008 ) . While this diversity is of interest to biologists , it may obscure the goal of regenerative medicine -- to induce regeneration in humans . This makes mammalian models of tissue regeneration especially relevant to questions regarding what role immune cells play during regeneration . Since first described by Markelova ( cited in Vorontsova and Liosner , 1960 ) , ear pinna regeneration has remained an interesting example of musculoskeletal regeneration in mammals ( Gawriluk et al . , 2016; Goss and Grimes , 1975; Joseph and Dyson , 1966; Matias Santos et al . , 2016; Seifert et al . , 2012a; Williams-Boyce and Daniel , 1980 ) . Recent work in African spiny mice species ( Acomys cahirinus , A . kempi and A . percivali ) supports ear pinna regeneration as an epimorphic process ( Gawriluk et al . , 2016 ) aligning it with appendage regeneration in other vertebrate regenerators such as salamanders , newts , zebrafish and lizards . Importantly , not all mammals can regenerate ear tissue providing variation to compare regeneration and scarring in identical tissue ( Gawriluk et al . , 2016; Williams-Boyce and Daniel , 1986 ) . Spiny mice are able to regenerate full-thickness skin , blood vessels , nerves , cartilage , adipose tissue and some muscle , whereas the same injury in Mus musculus ( outbred and inbred strains ) leads to incomplete ear hole closure and scar formation ( Gawriluk et al . , 2016; Matias Santos et al . , 2016; Seifert et al . , 2012a ) . Here , we report how the two main orchestrators of inflammation , neutrophils and macrophages , respond to injury during epimorphic regeneration in Acomys cahirinus compared to scarring in Mus musculus . Acomys and Mus exhibit the same circulating leukocyte profiles , and we demonstrate a robust acute inflammatory response in both species . We demonstrate higher neutrophil activity in the scarring system compared to higher ROS activity in the regenerative system . We show that macrophages between the two species display similar in vitro properties providing a comparable baseline prior to and following injury . We also observed distinct differences in the spatiotemporal distribution of macrophage subtypes during regeneration and scarring . Finally , depletion of macrophages , prior to and during injury , inhibited blastema formation and regeneration , thus demonstrating a necessity for these cells .
We first set out to test if baseline differences in circulating peripheral white blood cell ( WBC ) profiles existed prior to injury in Acomys and Mus . Using a Sudan Black B modified Giemsa-Wright stain , we quantified monocytes , lymphocytes , neutrophils and eosinophils from Acomys and Mus whole blood ( Figure 1A–D ) . Both species exhibited similar profiles and typical morphologies for all four cell types ( Figure 1A–E ) . For instance , monocytes were distinguishable by their kidney-shaped nucleus and diffuse cytoplasmic stain ( Figure 1A ) , while lymphocytes were similar in size to RBCs and their compact nucleus filled the entire cell ( Figure 1B ) . Polymorphonuclear neutrophils stained strongly with Sudan-Black B and displayed multi-lobed nuclei ( Figure 1C ) . In contrast , while eosinophils displayed multi-lobed nuclei and dark pink granules in the cytoplasm they contained few if any Sudan-Black-stained granules ( Figure 1D ) . In Mus and Acomys , the percentage of circulating lymphocytes was significantly higher than other leukocyte populations , and eosinophils comprised the smallest population of circulating leukocytes ( Figure 1E ) ( Tukey’s Multiple comparison , simple effect p<0 . 05 , Figure 1—source data 1 ) . These data are consistent with other leukocyte profiles from outbred CD1 mice showing that lymphocytes comprise the highest percentage of circulating WBCs ( Hedrich , 2004 ) . Importantly , while we identified differences in the percentage of leukocyte subtypes within each species , leukocyte profiles were the same between Acomys and Mus ( two-way ANOVA , species effect F = 0 . 01 , p=0 . 92 , and leukocyte subtype effect F = 97 . 04 , p<0 . 0001 , n = 8 Acomys; n = 4 Mus ) . Our data demonstrate these two rodent species possess the same circulating leukocyte profiles prior to injury and provide a baseline to ask if local differences arise following injury . 10 . 7554/eLife . 24623 . 003Figure 1 . Circulating leukocyte profiles from uninjured animals are the same in A . cahirinus and M . musculus . ( A–D ) Sudan-Black B modified Giemsa Wright stain helps identify leukocyte subtypes based on morphology and stain in blood smears of A . cahirinus . Monocytes ( A ) show kidney shaped nucleus and diffuse cytoplasmic stain; lymphocytes ( B ) show round nuclei encompassing most of the cell with very little cytoplasm; polymorphonuclear neutrophils ( C ) show dense black staining of cytoplasmic granules and a banded , multi-lobed nucleus; and eosinophils ( D ) show dense pink staining of cytoplasmic granules and multi-lobed nucleus . Scale bar = 10 μm . ( E ) Counts of white blood cell subtypes as a percentage of total white blood cells ( two-way ANOVA for main effects species F = 0 . 01 , p=0 . 92; and leukocyte subtype effect F = 97 . 04 , p<0 . 0001 . *Tukey's multiple comparison test for simple effect leukocyte subtype p<0 . 05 indicating significant differences when comparing neutrophils versus lymphocytes , neutrophils versus eosinophils , lymphocytes versus monocytes , and lymphocytes versus eosinophils within each species; S . E . M . ; n = 8 Acomys; n = 4 Mus ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 00310 . 7554/eLife . 24623 . 004Figure 1—source data 1 . Statistical values are reported for comparing circulating leukocytes between and within species . The percent of circulating monocytes , neutrophils , lymphocytes , and mast cells were compared across species using a two-way ANOVA with main effects cell subtype and species . We found no significant effect between species . We found a significant effect between cell subtypes . Tukey's multiple comparison test was carried out to compare the means of each cell subtype and determine statistical significance with p<0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 004 Building on our observation that circulating leukocyte populations are similar in Acomys and Mus , we next assessed the acute inflammatory reaction to injury during epimorphic regeneration and scarring using our 4 mm punch assay through the ear pinna ( Gawriluk et al . , 2016; Seifert et al . , 2012a ) ( Figure 2—figure supplement 1A–C ) . To quantify the influx of myeloid cells into the injured ear tissue , we performed fluorescence-activated cell sorting ( FACS ) using CD11b ( Figure 2A–C ) . CD11b ( aka ITGAM ) is a broad-spectrum marker used to isolate mammalian macrophages and neutrophils across a range of species from mouse to humans ( Figueiredo et al . , 2013; Sawano et al . , 2001; Tamatani et al . , 1991; Venneri et al . , 2007; Venosa et al . , 2015 ) . Using our recently published regeneration transcriptome for Acomys , we found Cd11b was upregulated after injury ( Gawriluk et al . , 2016 ) . Alignment of Acomys and Mus Cd11b revealed 88% nucleotide identity compared to a 79% identity between Mus and Human ( Table 1 ) . FACS analysis using CD11b isolated a specific cell population in Acomys and Mus ( Figure 2A–B ) . While we observed a significant increase in CD11b+ cells in response to injury in both species ( two-way ANOVA with main effect time F = 31 . 86 , p<0 . 0001 and species F = 17 . 06 , p=0 . 0002 ) , the acute increase at D3 was significantly greater in Mus than Acomys ( Sidak's multiple comparison test p<0 . 05 ) ( Figure 2C ) . 10 . 7554/eLife . 24623 . 005Figure 2 . Acute infiltration of neutrophils and macrophages is a hallmark of regeneration and scarring . ( A–B ) . Single-cell suspensions of whole tissue isolates from injured ears at D5 subjected to flow cytometry using CD11b show two distinct populations of cells , one CD11b- and one CD11b+ ( red boxes ) in Acomys ( A ) and Mus ( B ) . ( C ) Quantifying cells over time using flow cytometry shows a peak increase of CD11b+ cells in Mus at D3 and a broader but smaller peak of CD11b+ cells in Acomys between D3 and 5 ( two-way ANOVA main effect time F = 31 . 86 , p<0 . 0001 , main effect species , F = 17 . 02 , p=0 . 0002 , *Sidak's multiple comparison test p<0 . 05 , n = 4 animals combined left and right ear/species per timepoint ) . ( D ) Representative images of immunohistochemistry for myeloperoxidase ( brown ) in M . musculus ( top panel ) and A . cahirinus ( bottom panel ) 24 hr post injury . Nuclei ( blue ) were counterstained with Mayer’s Hematoxylin . Magnification 200x , Scale bars = 50 μm . Inset images highlight polymorphonuclear appearance of positively stained cells ( red arrows ) . Scale bars = 20 μm . ( E ) Cell counts of polymorphonuclear/MPO+ cells in healing tissue per field of view ( FOV ) ( n = 5 animals/species , D1; n = 6 animals/species , D3 , D5; n = 4 animals/species , D10 , two-way ANOVA , main effect time and species F = 11 . 12 , p<0 . 0001 , F = 8 . 229 , p=0 . 007 respectively , *p<0 . 05 Sidak’s multiple comparisons test at time points indicated ) . ( F ) Myeloid protein IBA1 ( red ) reactivity in Acomys ear tissue at D5 showing distinct positive cells ( yellow arrow ) and negative cells with multi-lobed nuclei characteristic of neutrophils ( green arrows ) . DAPI = grey , IBA1 = red . ( G ) Quantification of the total IBA1+ area in Acomys and Mus ears at D5 , D10 and D20 normalized to total DAPI+ area ( n = 3/species , two-way ANOVA , main effect time and species F = 0 . 132 , p=0 . 723 , F = 0 . 438 , p=0 . 655 , respectively ) ( H–K ) Representative images of the IBA-1+ area quantified in ( G ) at D15 . IBA-1+ cells localize proximal and distal to the injury site in Acomys and Mus and within the blastema in Acomys . IBA-1 = red , DAPI = grey , autofluorescent red blood cells ( RBC ) = orange . Scale bars ( H , J ) = 100 μm . Scale bar ( I , K ) = 50 μm Distal = left , Dorsal = top of image . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 00510 . 7554/eLife . 24623 . 006Figure 2—figure supplement 1 . Timeline of regeneration following 4 mm ear punch injury in Acomys compared to scar-formation in Mus . ( A ) Schematized graph showing ear hole area over time adapted from Gawriluk et al . ( 2016 ) . Mus = blue dotted line , Acomys = red solid line . ( B ) Whole mount ear pinna photographs during regeneration ( Acomys - top panel ) and scarring ( Mus - bottom panel ) at indicated timepoints post injury . Scale bar = 2 mm . Colored bars align key processes ( e . g . inflammation , re-epithelialization , blastema formation , etc . ) as a function of days post injury . Data represented here from Gawriluk et al . ( 2016 ) , Seifert et al . ( 2012a ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 00610 . 7554/eLife . 24623 . 007Figure 2—figure supplement 2 . Isolation of monocytes using flow cytometry . ( A ) Flow cytometric analysis using Ly6G antibody to detect positive cells from whole tissue isolates of the injured ear at D5 . Mus shows two distinct populations of cells , one Ly6G negative and one Ly6G positive . Acomys did not show two distinct populations of cells as expected due to lack of the Ly6G gene ( B ) Tracking Ly6G+ cells over time in Mus using flow cytometry for total cell count shows an acute peak of positive cells at D3 . ( n = 4 Mus , 8 ears/timepoint . ANOVA main effect time , F = 21 . 14 p<0 . 0001 ) . ( C ) FACS analysis for neutrophils versus monocytes in Mus using Ly6G antibody . Gating for CD11b+/Ly6G+ neutrophils ( upper right quadrant ) and CD11b+/Ly6G– monocytes ( lower right quandrant ) at D3 ( blue dots ) and D7 ( red dots ) shows a shift in cell populations over time post injury with neutrophils being more abundant at D3 and almost completely absent by D7 . ( D ) Quantitation of FACS analysis of total CD11b+/Ly6G- cell counts over time in Mus ( n = 4 Mus , 8 ears/timepoint ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 00710 . 7554/eLife . 24623 . 008Table 1 . Nucleotide comparisons for protein targets used in this study . Comparison is between Mus and Acomys and Mus and Human . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 008Mus gene nameAcomysHumanLy6g ( lymphocyte antigen 6 complex , locus G ) No homologNo homologLy6e ( lymphocyte antigen 6 complex , locus E ) 100%69%Cd11b ( integrin alpha M ) 88%79%Iba1/Aif1 ( allograft inflammatory factor 1 ) 86%85%Cd86 ( CD86 antigen ) 78%76%Cd206 ( mannose receptor , C type 1 ) 90%81%Arg1 ( arginase 1 ) 81%78%Cd3e ( CD3 antigen , epsilon polypeptide ) 80%74%Mpo ( myeloperoxidase ) 94%85%Adgre1 ( adhesion G protein-coupled receptor E1 ) , ( F4/80 ) 86%79% Because CD11b isolates macrophages and neutrophils , we next sought to individually quantify neutrophil and macrophage influx at the injury site in Acomys and Mus . We first used the well-characterized cell surface Ly6G antigen to separate neutrophil and macrophage populations in Mus ( Bain et al . , 2014; Mirza et al . , 2009; Rose et al . , 2012 ) and detected a clear population of Ly6G+ cells ( Figure 2—figure supplement 2A ) . This population of Ly6G+ cells was significantly elevated in healing tissue between D1-D7 , with peak numbers occurring at D3 ( Figure 2—figure supplement 2B ) . Whereas Ly6G+/CD11b+ cells dominated the injury site at D3 in Mus ( Figure 2—figure supplement 1C -blue dots ) , these cells were replaced by a Ly6G-/CD11b+ population at D7 ( Figure 2—figure supplement 2C - red dots ) . Thus , during the initial wave of leukocyte recruitment in Mus ( D1-5 ) , CD11b+ cells are primarily neutrophils and at later stages of inflammation ( D7-D15 ) CD11b+ cells are primarily macrophages ( Figure 2—figure supplement 2D ) . Interestingly , we did not detect a clear Ly6G+ population of CD11b+ cells from either tissue or circulating blood isolated from Acomys ( Figure 2—figure supplement 2A ) . The failure to resolve a discrete population of Ly6G+ cells in Acomys suggested neutrophils might more closely resemble rat and human neutrophils which lack Ly6G ( Lee et al . , 2013 ) . The mouse Ly6 gene complex is composed of 11 known Ly6 genes . Of these , the subcluster Ly6b , c , g and e are most highly expressed by mouse neutrophils , whereas rat and human lack all these genes except Ly6e ( Lee et al . , 2013 ) . While we identified the genomic sequence and expressed transcript for the Acomys homolog of Ly6e ( Table 1 ) , examination of the Ly6 gene complex using our preliminary A . cahirinus genome revealed no homologs for Ly6b , Ly6c or Ly6g ( Table 1 ) . These data reveal that the genomic structure for the Ly6 complex in Acomys is similar to rat and human suggesting that Acomys neutrophils more closely resemble rat and human neutrophils which also lack Ly6g ( Lee et al . , 2013 ) . Given these results , we turned to the neutrophilic marker MPO which reliably detects polymorphonuclear cells in tissue sections across species ( Bradley et al . , 1982; Petrie et al . , 2014; Seifert et al . , 2012b ) . We assessed MPO reactivity at D1 , D3 , D5 and D10 to compare neutrophil accumulation and clearance in Mus and Acomys ( Figure 2D , E ) . Neutrophils were readily identified as MPO+ with multi-lobed nuclei ( Figure 2D ) , and Mus displayed a significantly higher number of neutrophils 24 hr after injury when compared to Acomys ( two-way ANOVA with main effects , species F = 8 . 229 , p=0 . 007 and time F = 11 . 12 , p<0 . 0001; Sidak’s multiple comparison test for simple effect between species , p<0 . 05 at D1 ) ( Figure 2E ) . After D1 , Mus and Acomys showed comparable numbers of neutrophils at the site of injury with a return to baseline levels by D10 ( Figure 2E , Sidak’s multiple comparison test for simple effect between species , p>0 . 05 at D3-10 ) . In both species , neutrophils initially accumulated distal to the cut in the dermis and periodically exhibited signs of cell death ( e . g . pyknotic nuclei and reduction in cell size ) . We also observed neutrophils within the scab at all timepoints analyzed ( Figure 2D ) . Taken together , our results show that both species exhibit neutrophil infiltration , accumulation and clearance in response to injury , although neutrophils accumulate faster , at higher levels and appear to be more active 24 hr after injury in Mus . Concomitant with neutrophil invasion , circulating and tissue-specific macrophages are activated in response to injury and are recruited to the injury site at the outset of regenerative and scarring responses ( Godwin et al . , 2013; Li et al . , 2012; Nguyen-Chi et al . , 2015; reviewed in Novak and Koh , 2013; Petrie et al . , 2014; Varga et al . , 2016 ) . To ascertain total macrophage abundance in healing tissue , we employed the pan-macrophage marker , ionized calcium-binding adaptor molecule 1 ( IBA-1 ) , to quantify the spatiotemporal pattern of macrophage infiltration in Acomys and Mus . IBA-1 is an actin-binding protein active in macrophages and microglia and has been used to label cells in mouse , dog , cat , human and other primates ( Imai et al . , 1996; Köhler , 2007; Pierezan et al . , 2014; Sasaki et al . , 2001; Schmidt et al . , 2016 ) . In Acomys , we found IBA-1+ cells in the mesenchyme and the epidermis , but IBA-1 was specifically absent from polymorphonuclear neutrophils ( Figure 2F , arrows ) . We found no significant difference in the amount of IBA-1+ cells between Mus and Acomys at D5 , D10 or D15 ( Figure 2G , two-way ANOVA main effect species F = 0 . 132 p=0 . 723 , and main effect time F = 0 . 438 p=0 . 655 ) . Accumulation of IBA1+ cells occurred at similar levels in both species within 200 μM proximal to and distal to the site of injury ( Figure 2H–K ) . Notably , IBA-1+ cells were present within the blastemal region of D15 ears in Acomys ( Figure 2H , I ) as well as within the central granulation tissue region of D15 ears in Mus ( Figure 2J , K ) . These data demonstrate that macrophages persist at the injury site during regeneration and scarring up to 2 weeks after injury . To determine the extent of the inflammatory reaction in vivo , we measured myeloperoxidase ( MPO ) activity and reactive oxygen species ( ROS ) production ( Tseng and Kung , 2012 ) . To track MPO activity in vivo we used luminol , a chemiluminescent compound that exhibits specific and high sensitivity for phagocyte-mediated MPO activity from neutrophils ( Gross et al . , 2009 ) . Due to its larger size and reduced cell permeability ( compared to luminol ) , we used lucigenin to measure ROS production by NADPH oxidase . Lucigenin chemiluminescence is primarily attributed to macrophage activation , and to a lesser extent from endothelial cell and neutrophil populations ( Tseng and Kung , 2012 ) . Tracking luminol chemiluminescence , Mus displayed peak MPO activity between 24–48 hr after injury ( Figure 3A , repeated measures ANOVA F = 5 . 095 , p<0 . 001 ) . This finding was congruent with our immunohistochemical cell count data using MPO ( Figure 2E ) . While Acomys displayed a similar peak in MPO activity , the magnitude was significantly muted compared to Mus ( Figure 3A , *Sidak's multiple comparison test p<0 . 05 for time points indicated ) . Conversely , Acomys exhibited a much more robust production of ROS compared to Mus during the entire inflammatory phase ( Figure 3B , repeated measures ANOVA F = 4 . 536 , p<0 . 001 ) . Lucigenin chemiluminescence peaked 24 hr after injury in Acomys and remained significantly elevated compared to Mus through D5 ( Figure 3B , *Sidak's multiple comparison test p≤0 . 05 for the time indicated ) . Throughout the inflammatory phase , lucigenin chemiluminescence was significantly muted in Mus ( Figure 3B ) . Together , these data suggest a bias toward strong neutrophilic MPO activity during scarring and early and prolonged macrophage-produced ROS during regeneration . 10 . 7554/eLife . 24623 . 009Figure 3 . Acute myeloperoxidase activity is elevated during scarring , while reactive oxygen species production is elevated during regeneration . ( A–B ) In vivo imaging of the chemiluminescent compounds luminol and lucigenin , showing myeloperoxidase activity ( A ) or ROS production ( B ) in the injured ears of Mus ( red bars ) and Acomys ( blue bars ) . Images below graphs are representative for each timepoint . Chemiluminescence is measured in radiance [photons ( p ) per second ( s ) emitted from a square centimeter of tissue ( cm2 ) and radiating into a solid angle of one steradian ( sr ) ] . For luminol experiments: n = 7 Mus ( 14 ears ) and n = 6 Acomys ( 12 ears ) repeated measures ANOVA , F = 5 . 095 , p<0 . 001 and for lucigenin experiments: n = 8 Mus ( 16 ears ) and n = 6 Acomys , ( 12 ears ) , repeated measures ANOVA F = 4 . 536 , p<0 . 001 , *p<0 . 05 Sidak's multiple comparison test between species at the time indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 009 Persistence of macrophages at the injury site , coupled with stark differences in ROS production suggested that macrophages might positively affect regeneration consistent with observations during Xenopus tail regeneration ( Love et al . , 2013 ) . In vivo depletion of macrophages using clodronate liposomes during axolotl limb regeneration and via genetic ablation in zebrafish caudal fins supports a requirement for these cells to stimulate a regenerative response ( Godwin et al . , 2013; Petrie et al . , 2014 ) . Clodronate liposomes have been widely used to deplete systemic and local populations of phagocytes , the majority of which are macrophages ( reviewed in van Rooijen and Hendrikx , 2010 ) . In order to test if macrophages also regulate epimorphic regeneration in spiny mice , we depleted these cells by injecting clodronate liposomes ( Clo-Lipo ) at the base of the ear immediately prior to injury ( D0 ) , at D2 , and at D5 ( Figure 4A ) . Control animals received similar injections of PBS liposomes ( control ) ( Figure 4A ) . Whereas control ear holes initiated a regenerative response and began closing at D5 , Clo-Lipo injected animals did not initiate ear hole closure until D20 ( Figure 4B–C ) . In contrast to control ears , which showed complete ear hole closure by D34 , all Clo-Lipo injected ears remained open past D44 ( Figure 4B , C ) . Complete hole closure and regeneration among Clo-Lipo-treated ears was delayed , with 3/12 closed by D53 , 8/12 ears closed by D60 and 11/12 ears closed by D70 ( Figure 4B–C ) . Although blastema formation and expansion occurred in PBS-Lipo-treated ears , examination of Clo-Lipo-treated ears showed a sharp reduction in cell accumulation past the original cut site anda delay in blastema formation at D20 ( Figure 4D–E ) . The early stages of blastema formation were evident in 50% of macrophage-depleted ears ( 4/8 ) beginning at D20 ( Figure 4D–E , dotted line denotes original plane of biopsy ) . 10 . 7554/eLife . 24623 . 010Figure 4 . Macrophage depletion with clodronate liposomes inhibits regeneration . ( A ) Ears were injected with clodronate liposomes ( Clo-Lipo ) or PBS liposome controls ( PBS-Lipo ) at D0 immediately before injury , D2 after injury and at D5 . Ears were allowed to regenerate and tissue collected at later time points . ( B ) Wound size was measured over time . PBS-lipo ears close completely by D34 ( black line , graph , bottom panel images ) . Clo-Lipo ears remain open until D70 ( grey dotted line , graph , top panel images , n = 6 animals , 12 ears per treatment ) . ( C ) Representative ear from Clo-Lipo ( top panel ) and PBS-Lipo ( bottom panel ) followed over time . ( D–E ) H & E stained Clo-Lipo ear at D20 shows variable reduction in cell accumulation past the injury site ( black dotted line ) with relatively little new growth compared to PBS-Lipo ears . Scale bar = 100 μm . ( F–I ) H & E stained Clo-Lipo ear at D5 ( F ) shows a delay in epidermal closure ( blue arrows ) and loss cartilage plate histolysis ( green arrow ) compared to PBS-Lipo ears ( H ) . Scale bar = 200 μm . ( G ) Boxed region in ( F ) showing accumulation of polymorphonuclear cells ( yellow arrowheads ) . ( I ) Boxed region in ( H ) with monocytic cells evident ( green arrowheads ) and few polymorphonuclear cells present ( yellow arrowhead ) . Scale bar = 10 μm . ( J–N ) IBA-1 immuno-positive area following macrophage depletion compared to control . Total IBA-1+ area normalized to total DAPI+ area at D5 after the final treatment ( J , n = 3 animals per treatment , *unpaired Student's t-test p<0 . 05 ) . IBA-1+ cells in PBS-lipo tissue at D5 ( K ) compared to Clo-lipo tissue at D5 ( L ) . IBA1+ cells in PBS-Lipo tissue at D20 ( M ) and Clo-lipo treated tissue at D20 ( N ) show a return of positive cells . . Scale bar = 100 μm . Box delineates area of high-magnification images , Scale bar = 50 μm . IBA-1 = red , DAPI = grey , autofluorescent red blood cells ( RBC ) = orange . Distal = left , Dorsal = top . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 010 In Clo-Lipo-treated ears , we noted a slight expansion of ear hole area and residual scabbing at D10 suggesting defects in re-epithelialization following macrophage depletion ( Figure 4B–C ) . Indeed , examining macrophage-depleted ear pinna at D5 we observed a lack of cartilage histolysis ( Figure 4F , green arrow ) and a delay in re-epithelialization ( Figure 4F , blue arrows ) . This delay in re-epithelialization was coincident with an accumulation of neutrophils ( Figure 4G yellow arrowheads ) when compared to control ears ( Figure 4H–I ) . Monocytic cells were apparent throughout the tissue in control ears ( Figure 4H green arrowheads ) but were absent in Clo-Lipo treated ears ( Figure 4F ) . We confirmed effective depletion of macrophages from the injury site by staining for the pan macrophage marker IBA-1 ( Figure 4J–L ) . Macrophage populations can be restored within 2 weeks of the final Clo-Lipo injection ( Ames et al . , 2016; Li et al . , 2013; Summan et al . , 2006; Sunderkötter et al . , 2004 ) , and IBA-1+ cells at D20 reveals a return of macrophages in Clo-Lipo-treated ears , concurrent with re-epithelialization and initiation of blastema formation ( Figure 4M–N ) . Together , these data support the important activity of macrophages to facilitate histolysis and re-epithelialization during the early phase of regeneration . Furthermore , our data suggests that macrophages directly or indirectly are necessary for blastema formation during regeneration in spiny mice . Macrophages maintain an interesting duality as professional phagocytes and as coordinators of the local immune response . Because macrophages were present and persistent during regeneration and scarring , it was possible that macrophage phenotype might contribute to the different healing outcomes . First , we asked whether spiny mice macrophages could undergo classical ( M1 ) and alternative ( M2 ) activation under stereotypical conditions ( Figure 5 ) . We isolated spiny mouse bone marrow as previously described for Mus ( Edwards et al . , 2006 ) and activated these cells using Macrophage-Colony Stimulating Factor ( M-CSF ) to produce activated bone-marrow-derived macrophages ( BMDM ) ( Figure 5 ) . After 1 week in culture , all BMDM were CD11b+ ( Figure 5A ) . 10 . 7554/eLife . 24623 . 011Figure 5 . In vitro activation assays shows Acomys macrophages can be polarized to express different markers . ( A–I ) Bone-marrow-derived macrophages isolated from Acomys femurs are cultured with no cytokines ( unstimulated , A , D , G ) with IFNγ+LPS ( M1 , B , E , H ) or with IL-4 ( M2 , C , F , I ) . Immunocytochemistry for the pan-macrophage marker CD11b ( green ) ( A–C ) , for the M1 macrophage marker CD86 ( green ) and the M2 macrophage marker Arginase 1 ( red ) ( D–F ) , or CD206 ( red ) ( G–I ) . ( J–R ) . Bone-marrow-derived macrophages were isolated from Mus femurs and cultured with no cytokines ( J , M , P ) with IFNγ and LPS ( K , N , Q ) or with IL4 ( L , O , R ) as above . Immunocytochemistry was performed for CD11b ( green ) ( J–K ) , for CD86 ( green ) and Arginase 1 ( red ) ( M–O ) , and CD206 ( red ) ( P–R ) . Nuclei were counterstained with DAPI ( grey ) in all panels . Scale bars = 50 μm . Images are representative of n = 3 technical replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 01110 . 7554/eLife . 24623 . 012Figure 5—figure supplement 1 . Immunofluorescent staining for macrophage marker F4/80 in Acomys and Mus . ( A–C ) Bone-marrow-derived cells isolated from Acomys and stained for F4/80 ( green ) . ( A ) unstimulated cells , ( B ) cells stimulated with IFNγ and LPS , ( C ) cells stimulated with IL-4 . ( D–F ) Bone-marrow-derived cells isolated from Mus and stained for F4/80 ( green ) . ( D ) unstimulated cells , ( E ) cells stimulated with IFNγ and LPS , and ( F ) cells stimulated with IL-4 . Scale bar = 50 μm . ( G ) Acomys ear tissue at D15 after injury stained for F4/80 ( green ) , CD206 ( red ) and DAPI ( grey ) . ( H ) Mus ear tissue at D7 after injury stained for F4/80 ( green ) , CD206 ( red ) and DAPI ( grey ) . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 012 Next , we tested if Acomys macrophages could undergo classical and alternative activation ( polarization ) toward an M1 or M2 phenotype , respectively . Classic activation assays stimulate BMDMs with the pro-inflammatory molecules interferon gamma ( IFNγ ) and lipopolysaccharide ( LPS ) which specifically increase the expression of CD86 among other pro-inflammatory cell surface markers and cytokines ( Edwards et al . , 2006; Hathcock et al . , 1994; Inaba et al . , 1994 ) . In response to IFNγ and LPS , Acomys BMDMs remained CD11b+ and upregulated CD86 compared to un-stimulated and M2 stimulated macrophages ( Figure 5B–F ) . Alternative activation with interleukin 4 ( IL4 ) increases the expression of CD206 ( Stein et al . , 1992 ) and Arginase 1 among other pro-reparative , cytoprotective genes . In response to IL-4 , Acomys BMDMs remained CD11b+ and expressed CD206 ( Figure 5C , I ) . In contrast , most unstimulated and classically activated BMDMs were CD206- ( Figure 5G–H ) . In addition to CD206 , stimulation with IL-4 also elicited Arginase 1 reactivity in BMDMs ( Figure 5F ) . We also isolated and activated BMDMs from Mus and confirmed they display a similar pattern of protein regulation in response to polarization ( Figure 5J–R ) . In addition to these phenotypic markers , we also tested the mouse pan macrophage marker F4/80 ( Austyn and Gordon , 1981 ) for its reactivity to Acomys macrophages ( Figure 5—figure supplement 1 ) . Using unstimulated BMDMs , we only found a small subset of F4/80+ cells in Acomys compared to ubiquitous labeling in Mus ( Figure 5—figure supplement 1A , D ) . Following IFNγ+LPS stimulation , we observed an increase in F4/80+ Acomys macrophages , whereas IL-4 stimulation did not increase F4/80 labeling compared to unstimulated BMDMs ( Figure 5—figure supplement 1B–C ) . Examining F4/80 staining in vivo , we found that F4/80+ cells were distinct from CD206+ cells in Acomys ( Figure 5—figure supplement 1G ) . In contrast , we found that F4/80+ cells were mostly CD206+ in Mus supporting its localization as a pan macrophage marker in Mus ( Figure 5—figure supplement 1H ) . Thus , our in vitro and in vivo findings suggest that F4/80 only marks a subset of macrophages in Acomys ( most likely M1 ) . Despite this difference in F4/80 reactivity , our in vitro results show intrinsic similarities between Acomys and Mus macrophages supporting a general capacity to activate and change phenotype given specific wound contexts and stimuli . Next , we addressed whether differences in the spatiotemporal distribution of macrophage subtypes might promote regeneration in lieu of scarring . First , we quantified accumulation of classically activated macrophages in healing tissue by staining for CD86 ( Figure 5E and Figure 6A–B ) . Using immunohistochemistry to localize CD86+ cells in Acomys and Mus , we determined these cells were rare in uninjured ear tissue ( Figure 6—figure supplement 1A–F ) . Although rare , in Mus , we did find CD86+-positive cells in the epidermal layer reminiscent of human Langerhan’s cells ( Boltjes and van Wijk , 2014 ) and in the perichondrium ( Figure 6—figure supplement 1A–F ) , whereas in uninjured Acomys tissue CD86+ cells were restricted to the dermis ( Figure 6—figure supplement 1D–E ) . These cells had long , thin projections and a spindle-like shape characteristic of dermal dendritic cells found in humans ( Boltjes and van Wijk , 2014 ) . 10 . 7554/eLife . 24623 . 013Figure 6 . Activated CD86+ macrophages are restricted from the blastema in Acomys . ( A ) Immunofluorescent staining for the cell surface marker CD86 in Acomys and Mus at specific time points after injury as a percent of total area analyzed ( n = 4 per time point; two-way ANOVA main effect time and species F = 131 . 8 , p<0 . 0001 , F = 220 . 0 , p<0 . 0001 *Sidak's multiple comparison test p<0 . 05 at time point indicated ) . ( B ) Left panels 10x magnification , CD86 ( red ) , DAPI ( grey ) . Scale bar = 100 μm . White lines delineate cartilage . Middle panel , 40x magnification , CD86 ( red ) , DAPI ( grey ) . Scale bar = 20 μm . Right panel , diagram with the general overview of CD86+ localization at D15 and delineating high-magnification area ( box ) . ( C ) Immunofluorescent staining for the cell surface marker CD206 in Acomys and Mus as a percent of total area analyzed ( n = 4 per time point per species , two-way ANOVA main effect time and species , F = 2 . 33 , p=0 . 125 , F = 1 . 54 , p=0 . 230 respectively ) . ( D ) Left panel magnification 10x . CD206 ( red ) , DAPI ( grey ) . Scale bar = 100 μm . White lines delineate cartilage . Middle panel 20x magnification . CD206 ( red ) , DAPI ( grey ) . Scale bar = 50 μm . Right panel , Schematic depicting the general trends for CD206+ cell localization in Acomys and Mus at D15 . Box delineates the location of the high-magnification images . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 01310 . 7554/eLife . 24623 . 014Figure 6—figure supplement 1 . Immunofluorescent staining for CD86+ cells at D0 , D3 and D7 post injury in Mus and Acomys . ( A–C ) CD86+ cells in Mus ear before injury ( D0 ) . ( A ) 10x magnification image of the Mus ear before injury . Scale bar = 100 μm . ( B ) High magnification of ( A ) reveals CD86+ cells in the epidermis before injury and ( C ) in the perichondrium and surrounding dermis ( yellow arrows ) . Scale bars = 20 μm . ( D–F ) CD86+ cells in Acomys ear before injury . ( D ) 10x magnification of Acomys ear for reference . Scale bar = 100 μm ( E ) High magnification of ( D ) showing CD86+ cells in the dermis underlying the epidermis at D0 ( yellow arrow ) . ( F ) CD86+ cells ( yellow arrows ) located in the adipose tissue layer of the ear . Scale bar = 20 μm . ( G ) Representative image of Mus ear at D3 showing CD86+ cell localization . ( H ) Representative image of Acomys ear at D3 showing few CD86+ cells in the injury site . ( I ) CD86+ staining in the Mus ear at D7 showing positive cells distal to the injury site . ( J ) CD86+ cells in the Acomys ear at D7 showing very few positive stained cells distal to the injury site . Scale bar = 50 μm CD86 = red , CD3 = green , DAPI = grey . Epi = epidermis . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 01410 . 7554/eLife . 24623 . 015Figure 6—figure supplement 2 . Immunofluorescent staining for CD206+ cells at D0 , D3 , D7 post injury in Mus and Acomys . ( A–H ) CD206 staining in Mus ( A , C , E , G ) and Acomys ( B , D , F , H ) . ( A ) CD206+ cells in ear before injury in Mus and ( B ) in Acomys . Scale bar = 100 μm . ( C ) High magnification of ( A ) showing CD206+ cells in dermis of Mus ear before injury . ( D ) High magnification of ( B ) showing CD206+ cells in dermis of Acomys ear before injury . Scale bar = 20 μm . ( E ) CD206+ cells in Mus ear and ( F ) Acomys ear at 3 days post injury . ( G ) CD206+ cells in Mus ear and ( H ) Acomys ear at 7 days post injury . Scale bar = 50 μm . CD206 = red , DAPI = grey and Epi = epidermis . DOI: http://dx . doi . org/10 . 7554/eLife . 24623 . 015 We next assessed the distribution of CD86+ cells during regeneration and scarring . Comparing CD86+ cells distal to the injury between species , we found a significant effect of time ( two-way ANOVA F = 131 . 8 , p<0 . 0001 ) and species ( two-way ANOVA F = 220 . 0 , p<0 . 0001 ) whereby injury elicited a strong increase in CD86+ cells per unit area in Mus , but not in Acomys at D3 ( Sidak's multiple comparison test p<0 . 05 ) ( Figure 6A ) . Examining the spatial distribution of CD86+ cells in Mus , we found that they accumulated in the connective tissue distal to the cut cartilage ( Figure 6B ) . Co-staining with the T-cell surface marker CD3 revealed interactions between CD86+ and CD3+ cells in this region as well as in the epidermis , consistent with the role of CD86 as a co-stimulatory molecule for T-cell activation ( Figure 6B ) . In contrast to Mus , CD86+ macrophages behaved very differently in Acomys during regeneration . Following injury , we did not observe CD86+ cells accumulating in the blastema ( Figure 6B and Figure 6—figure supplement 1J ) . Instead , at D15 we observed clusters of CD86+ cells located almost exclusively lateral to the cut cartilage ( Figure 6B ) . Thus , while CD86+ cells are present in Acomys , they remain proximally restricted and do not infiltrate the blastema , whereas these same cells in Mus accumulate in newly deposited connective tissue ( granulation tissue ) . Although alternatively activated ( M2 ) macrophages are usually associated with pro-regenerative outcomes , this concept remains largely untested for epimorphic regeneration ( Campbell et al . , 2013; Kigerl et al . , 2009; Wang et al . , 2014 ) . Using our comparative system , we tested for an association between CD206+ ( M2 ) macrophages and a regenerative response . Prior to injury , CD206+ cells were spatially distributed throughout the ear in similar numbers in both species ( Figure 6—figure supplement 2A–D ) . These cells were present in the dermis between the epidermis and elastic cartilage of the ear ( Figure 6—figure supplement 2A–D ) . We did not detect CD206+ cells in the epidermis in either species . Quantifying CD206+ cells distal to the injury in Mus and Acomys , we observed no significant difference across time ( two-way ANOVA , F = 2 . 330 , p=0 . 125 ) or between species ( two-way ANOVA , F = 1 . 540 , p=0 . 23 ) ( Figure 6C ) . After D3 , CD206+ cell numbers remained constant in Acomys and Mus and showed no significant change distal to the injury site ( Figure 6C ) . Unlike CD86+ cells that localize proximal to the injury in Acomys , CD206+ cells were observed distal to the injury site in Acomys ( and Mus ) ( Figure 6D and Figure 6—figure supplement 2E–H ) . However , the distal distribution of these cells in Acomys appeared regionalized with a CD206+ dense region directly beneath the epidermis and a CD206+ sparse region in the central blastema region of the injury ( Figure 6D ) . In Mus , CD206+ cells were evenly distributed throughout the connective tissue distal to the injury ( Figure 6D ) . Together with our data for IBA-1 , our results show that while macrophages infiltrate the injury area during regeneration , the blastema is relatively free of classically activated macrophages .
A popular hypothesis to explain why most mammals heal injuries with scar tissue is that they evolved a strong inflammatory and adaptive immune response that induces intense fibrosis in lieu of regeneration ( Godwin , 2014; Mescher et al . , 2017 ) . Yet , the fact that some mammals exhibit epimorphic regeneration ( e . g . rodent and primate digit tips , rabbit and spiny mice ear punches and skin ) ( Borgens , 1982; Gawriluk et al . , 2016; Goss and Grimes , 1975; Han et al . , 2008; Joseph and Dyson , 1966; Neufeld and Zhao , 1993; Seifert et al . , 2012a; Singer et al . , 1987 ) suggests that regeneration can occur despite a complex adaptive immune system . Different immune system components and inherent physiological differences between mammals and traditional regeneration models like salamanders , newts and zebrafish ( e . g . homeothermy versus poikilothermy , high versus low metabolic rates , etc . ) make it difficult to extrapolate how inflammation and immunity might act to affect regeneration in mammals . There have been few studies detailing how the immune system responds during epimorphic regeneration and thus how it compares to immune mediated fibrosis . In this study , we have begun to address how inflammatory cells behave during complex tissue regeneration in African spiny mice ( Acomys ) . We identified key differences in the spatiotemporal infiltration of inflammatory cells in regenerating and scar-forming systems and demonstrated that reactive oxygen species ( ROS ) catalyzed through NADPH oxidation were significantly increased at the injury site during regeneration . Importantly , we showed that macrophages were required for regeneration to proceed . These results support a role for inflammatory cell signals polarizing the injury response toward two very different outcomes . Our characterization of circulating leukocyte profiles in Acomys and Mus , demonstrate that inherent differences in myeloid cell numbers do not explain intrinsic differences in regenerative ability . This contradicts a recent report suggesting that spiny mouse blood is neutropenic and that lower numbers of circulating neutrophils are responsible for a muted inflammatory response to injury ( Brant et al . , 2016 ) . In fact , our immunohistochemical data comparing neutrophil infiltration in Acomys and Mus show that while neutrophils accumulate faster during scarring , peak neutrophil numbers are equivalent 3 days after injury in both species . In general agreement with this data , we observed differences in the intensity of myeloperoxidase activity via luminol chemilumenscence persisting until D4 after which time there was no difference across species . While neutrophil invasion in response to injury is apparent across all regenerating vertebrates ( Jordan and Speidel , 1924; Li et al . , 2012; Seifert et al . , 2012b ) , our results suggest that precise activity levels could lead to different injury outcomes . While prolonged inflammation can antagonize regeneration ( Margalit et al . , 2005; Mescher et al . , 2013 ) , our data supports an initial wave of cell-based inflammation as a shared feature of any injury response , including regeneration . Along with neutrophils , monocytes also infiltrate local tissue after injury , and converging evidence suggests that macrophages are required in some capacity for epimorphic regeneration ( Godwin et al . , 2013; Petrie et al . , 2014 ) . Our results extend these vertebrate studies by demonstrating a similar requirement during epimorphic regeneration in mammals . Acute depletion of phagocytic monocytes with clodronate liposomes delayed the initiation of ear hole closure and blastema formation by up to 2 weeks . Importantly , re-commencement of blastema formation was concurrent with the return of macrophages into the injured tissue . Examination of ear tissue at D5 revealed that phagocyte depletion delayed re-epithelialization and histolysis , two key events that are themselves required for regeneration . It is possible that macrophages provide an initiating signal for regeneration or remove subpopulations of local cells secreting inhibitory signals ( e . g . senescent cells ) . In support of the first idea , ROS production has been suggested as an essential early signal for regeneration based on studies in Xenopus and zebrafish tail models of regeneration ( Gauron et al . , 2013; Love et al . , 2013 ) . Macrophages are a major source of ROS after injury , and we observed significantly stronger and prolonged ROS production during regeneration compared to scarring ( Weber et al . , 2016 ) . Understanding the functional consequences of balanced ROS production through NADPH oxidation versus myeloperoxidase activity will require a more complete understanding of what cell types are responsible for ROS production and how ROS more specifically can affect local cellular phenotypes . In support of the idea that macrophages may limit inhibitory signals through selective removal of senescent cells , recent work in salamanders suggested that clearance of senescent cells is important for limb regeneration ( Yun et al . , 2015 ) and persistence of senescent cells during liver regeneration leads to excessive fibrosis ( Krizhanovsky et al . , 2008 ) . Furthermore , the accumulation of senescent cells with age has been suggested to shorten lifespan , degrade tissue function , and increase the expression of pro-inflammatory cytokines in mammals ( Baker et al . , 2016 , 2011 ) . These and other studies suggest that proper clearance of senescent cells from damaged tissues may promote regenerative outcomes . Interestingly , as the induction of cellular senescence occurs during normal wound healing when a scar forms , it is possible that clearance of senescent cells is less important than the secretory phenotype of these cells which in some contexts can promote regeneration . For instance , short exposure to factors secreted by senescent cells in response to injury decreases fibrosis and promotes stem cell gene expression ( Chiche et al . , 2017; Jun and Lau , 2010; Ritschka et al . , 2017 ) . These studies underscore the importance of analyzing how senescent cells regulate regeneration and scarring and provide evidence that the phenotype of senescent cells and their timely removal by macrophages could be an important factor in Acomys ear regeneration . While our data demonstrates that macrophages as a total population are required for regeneration in mammals , a requirement for macrophage subtypes and how these cells interact with other immune cells during epimorphic regeneration is not known . Because the activation of macrophages is generally associated with collagen production and fibrotic disease ( Duffield et al . , 2005; Gibbons et al . , 2011; Wynn , 2008 ) , the question remains as to how these immune cells orchestrate both regeneration and scar-formation in different species . Previous studies suggest a temporal component to macrophage function as a major factor for determining the outcome to skin , liver and bone injury ( Alexander et al . , 2011; Arnold et al . , 2007; Duffield et al . , 2005; Mirza et al . , 2009 ) . While we did not observe temporal differences in total macrophage accumulation between Acomys and Mus in the ear pinna , we did observe distinct differences in the spatial distribution of macrophage subtypes . For instance , while M2 ( CD206+ ) macrophages were present at comparable levels in Acomys and Mus , we observed a zone dense in CD206+ macrophages in Acomys that were associated with de novo hair follicle development and a zone sparse in CD206+ cells that were associated with an area of decreased collagen production and blastema formation . Whereas CD206+ cells were absent from the blastema in Acomys , in Mus , we observed CD206+ cells throughout the collagen-rich granulation tissue , a situation similar to fibrosis observed during skin wound repair ( Mirza and Koh , 2011; Song et al . , 2000; Willenborg et al . , 2012 ) . The spatial restriction of CD206+ cells suggests unique interactions between macrophages and surrounding cells may drive differences in a pro-regenerative or pro-fibrotic environment . On the other hand , it is possible that the local environment ( cells and/or ECM ) in regenerating systems drives immune cell profile instead of immune cells driving injury outcomes . We noted an accumulation of M1 macrophages ( CD86+ cells ) in injured Mus tissue , and the ability of CD86+ cells to interact with CD3+ T-cells is consistent with the role of CD86 as a co-stimulatory molecule that promotes T-cell activation and production of pro-inflammatory cytokines ( Hathcock et al . , 1994; Lanier et al . , 1995; Peng et al . , 2013 ) . Conversely , the Acomys blastema was mostly devoid of M1 macrophages and we did not observe CD86+/CD3+ interacting cells . This was not due to an inability to detect M1 macrophages in Acomys , as we readily observed these cells at the boundary between injured and uninjured tissue . Moreover , we found that Acomys macrophages up-regulated CD86 in vitro when classically activated in response to IFNγ and LPS stimulation ( Ding et al . , 1993; Freedman et al . , 1991 ) . This shows that Acomys macrophages possess the intrinsic ability to express M1 markers ( e . g . CD86 ) in response to pro-inflammatory stimuli , but may be inhibited from doing so within the blastema in vivo . A recent report suggested that spiny mice regenerate skin because they lack a robust inflammatory response ( Brant et al . , 2016 ) . While our findings contradict their primary conclusions , careful re-interpretation of their data supports the idea that inflammation occurs during regeneration and scarring , and that different inflammatory cell phenotypes polarize the response to injury . Brant et al . based their conclusions on three primary findings . First , they presented circulating leukocyte profiles showing spiny mice with lower neutrophils and higher lymphocytes in circulation when compared to CD1 mice and suggested this would lead to fewer neutrophils in spiny mice wounds . As we have shown , neutrophils indeed arrive at the wound bed and numbers are not significantly different three days post injury . Interestingly , our data for myeloperoxidase activity suggests that differences in neutrophil activity , rather than simple numerical differences , likely play a more important role during the injury response . Second , they were unable to detect macrophages in spiny mice wounds using F4/80 and concluded no macrophages infiltrated regenerating skin wounds . Using a cross-species marker for total macrophages ( IBA1 ) , we show that macrophages indeed infiltrate an injury site . More importantly , we demonstrate that macrophages are required for regeneration . Instead , differential infiltration of macrophage subtypes exists between regeneration and scarring . While F4/80 may be a pan-macrophage marker in mice , studies have shown that this is not the case for humans ( Hamann et al . , 2007 ) , and it remains unclear if this is the case for other mammals . Our data shows that F4/80 marks a subset of macrophages with pro-inflammatory characteristics in Acomys ( i . e . F4/80+ cells are increased with M1 stimulation in vitro and F4/80+ cells do not colocalize with the M2 marker , CD206 , in vivo ) . When re-interpreted with this knowledge , their results align with our findings that reduced numbers of pro-inflammatory macrophages enter the wound site during regeneration or are present , but not activated toward a pro-inflammatory phenotype . Thirdly , using a cytokine array designed against mouse antigens they detected 30 cytokines during acute inflammation ( D0-D7 post injury ) in Mus , but only detected 12 of these cytokines in spiny mice during the same period . They interpreted the absence of 18 cytokines as an absence of these signals in spiny mice wounds . However , without validation of epitopes , failure to detect particular spiny mouse cytokines on a Mus-specific cytokine array does not reflect an absence of spiny mouse antigen . Moreover , of the 12 spiny mouse cytokines they did detect , 5 are classic pro-inflammatory markers ( e . g . IL-1α , IL-1ß , IL-1ra , CXCL13 and IFNγ ) that were upregulated in response to injury and present at similar levels to Mus . Thus , their spiny mouse cytokine data demonstrates strong acute inflammation in response to injury . Although nuances between regenerating dorsal skin wounds and complex tissue of the ear pinna are likely to exist , fine-tuning inflammatory responses is key to promoting scar-free outcomes . Of the mammalian models of epimorphic regeneration that exist , very few have investigated how inflammatory cells affect blastema formation and regeneration . A forthcoming study on digit tip regeneration lends support to our conclusion that macrophages are required to help initiate regeneration ( Muneoka et al . , 2017 ) . Autoimmune-prone MRL mouse strains and their parent strain LgJ have been promoted as a mammalian regeneration model ( Clark et al . , 1998 ) . Paradoxically , while these strains possess enhanced rates of healing , published reports across most injury models have demonstrated that they do not regenerate ( Colwell et al . , 2006; Gawriluk et al . , 2016; Moseley et al . , 2011; Smiley et al . , 2014 ) . Studies have documented that the healing response of the MRL mouse differs depending upon the type of injury , be it the ear pinna or other tissues ( Beare et al . , 2006; Colwell et al . , 2006; Davis et al . , 2007; Kench et al . , 1999; Rajnoch et al . , 2003; Tolba et al . , 2010 ) . In the ear specifically , although 2 mm biopsy punches close and form new cartilage nodules ( Clark et al . , 1998 ) , larger holes and a 2 mm crush injury heal with excessive collagen deposition producing a scar ( Gawriluk et al . , 2016; Rajnoch et al . , 2003 ) . In our view , the use of MRL strains as purported regeneration models has obscured their potential utility for studying inflammation and fibrosis . Of interest , MRL mice have been extensively studied for their aberrant macrophage profiles ( Dang-Vu et al . , 1987; Donnelly et al . , 1990; Santoro et al . , 1988 ) . Bone marrow and peritoneal macrophages proliferate without CSF stimulation ( Hamilton et al . , 1998 ) , a rare observation among other strains of mice , and accumulation of macrophages is commonly observed in MRL tissues ( Bloom et al . , 1993; Davis and Lennon , 2005; Yui et al . , 1991 ) . Macrophages in strains of MRL mice show higher production of H2O2 suggesting higher pro-inflammatory activity ( Dang-Vu et al . , 1987 ) and show increased expression of Tgfβ1 , a growth factor implicated during increased fibrosis ( reviewed in Border and Noble , 1994; Kench et al . , 1999 ) . Thus , MRL macrophages and their response to injury are unique in many respects . A detailed study of the immune response in MRL mice could instruct how inflammation guides fibrotic repair based on intrinsic and environmental tissue differences . Macrophage phenotypes change based on environmental cues ( Stout et al . , 2005 ) and macrophages that infiltrate injured tissue exhibit temporal changes in gene and protein expression ( Arnold et al . , 2007; Varga et al . , 2016 ) . In addition , cancer and mesenchymal stem cells ( MSCs ) can drive macrophage phenotypes dampening the production of pro-inflammatory cytokines ( reviewed in Gao et al . , 2016 ) . These observations underscore the fluid nature of macrophage phenotypes in response to injury and disease and support a model where different subtypes differentially affect healing outcomes . Although further work is needed to clarify macrophage activation phenotypes during regeneration , our results support the initial inflammatory environment as a potential source of pro-regenerative signals . Future studies in spiny mice will need to determine how specific immune cell types signal to local cells , whether macrophages induce change in the ECM and if these cells can be manipulated in a non-regenerative system . Future studies into the role of specific macrophage and other immune cell phenotypes will resolve the role of these cells in scar formation and regeneration .
Acomys cahirinus and Mus musculus ( Swiss Webster Envigro_Harlan Hsd:ND4 ) were housed at the University of Kentucky , Lexington , KY . A . cahirinus were housed at a density of 10–15 individuals in metal wire cages ( 24 in . x 18 in . x 16 in . , height x width x depth ) ( Quality Cage Company , Portland , OR ) and fed a 3:1 mixture by volume of 14% protein mouse chow ( Teklad Global 2014 , Harlan Laboratories , Indianapolis , IN ) and black-oil sunflower seeds ( Pennington Seed Inc . , Madison , GA ) 1x/day ( Haughton et al . , 2016 ) . Mus were fed mouse chow only . Acomys and Mus mice were exposed to natural light , and all animals used were sexually mature . Experiments used a combination of male and female animals matched between species . For ear punch , animals were anesthetized with 3% vaporized isoflurane ( v/v ) ( Henry Schein Animal Health , Dublin , OH ) at 1 psi oxygen flow rate . A 4 mm biopsy punch ( Sklar Instruments , West Chester , PA ) was used to create a through-and-through hole in the center of the right and left ear pinna . Ear tissue was collected at specified time points with an 8 mm biopsy punch ( Sklar Instruments , West Chester , PA ) circumscribing the original injury . All animal procedures were approved by the University of Kentucky Institutional Animal Care and Use Committee ( IACUC ) under protocol 2013–1119 . Healing ear tissue was harvested as outline above at D0 , 1 , 3 , 5 , 7 , 10 and 15 . To create a single-cell suspension , both ears were combined into one tube for each animal , and we used enzymatic and mechanical digestion as previously described with modifications ( Jensen et al . , 2010 ) . Briefly , tissue was digested with a 1:1 trypsin , dispase solution for 1 hr at 37°C allowing for subsequent mechanical separation of the epidermis . Separated epidermis and dermis were incubated with a solution of collagenase ( 1 mg/mL , VWR RLMB120-0100 ) , hyaluronidase ( 0 . 5 mg/mL , VWR IC1512780 ) , and elastase ( 0 . 015 U/μL , VWR IB1753-MC ) in HBSS ( VWR 45000–456 ) for 1 hr at 37°C . Following digestion , cell suspensions were washed with PBS and filtered through a 70 μm cell strainer . Single-cell suspensions were incubated with an FcγR block ( CD16/32 block , 20 μg/mL , BD Pharmingen Cat# 553141 ) followed by incubation with directly conjugated primary antibodies at 4°C for 1 hr . Antibodies included APC-conjugated Ly6G ( BD Pharmingen Cat# 580599 , 3 μg/mL ) , PE-conjugated CD11b ( BD Pharmingen , Cat# 557397 , 3 μg/mL ) , diluted in FBS-staining buffer ( BD Pharmingen , Cat# 554656 ) . Fluorescent-activated cell sorting ( FACS ) was carried out by trained experts in the University of Kentucky Flow Cytometry Core using the iCyt Synergy sorter system ( Sony Biotechnology Inc . , San Jose , CA ) . Laser calibration and compensation was performed for each experiment using unstained , single fluorescent , and fluorescent minus one ( FMO ) control samples . Dot plots were created using FloJo ( version 10 ) ( n = 4 animals per timepoint ) . Blood was collected from the submandibular venous bed of age and gender matched Acomys ( n = 8 ) and Mus ( n = 4 ) . Individuals were anesthetized with 4% ( v/v ) isoflurane and gently scruffed so that the skin covering the submandibular venous bed was taut . A 5 mm lancet ( Medipont Inc . , Mineola , NY ) was inserted quickly into the venous bed and one drop of blood was collected per slide , with a total of 3 drops per animal . Slides were allowed to air dry and were prepared for a Sudan Black B modified Giemsa-Wright staining as described by ( Sheehan and Storey , 1947 ) using formaldehyde vapor fixation for 10 min . Slides were incubated for 1 hr in a filtered Sudan Black B ( 3 mg/mL , Sigma Aldrich , St . Louis , MO ) disodium hydrogen phosphate solution . Slides were incubated with Giemsa-Wright ( Sigma Aldrich , St . Louis , MO ) stain followed by a 1 min wash in 0 . 5% acetic acid . Slides were allowed to air dry and coverslipped with CytoSeal XYL . Images were collected on an Olympus BX51 upright microscope at 40x magnification . Ten fields of view were acquired per slide for an average 9224 total cells per animal and an average 42 total white blood cells per animal . White blood cells were hand counted based on granular staining and nuclear morphology . Cell subtype is reported as a percent of total WBC per animal ( n = 4 Mus , n = 8 Acomys ) . On the days indicated , animals with 4 mm wounds were anesthetized using 2 . 5% ( v/v ) isoflurane and injected I . P . with lucigenin ( 5 mg/kg in PBS; M8010 Sigma-Aldrich , St . Louis , MO ) or luminol ( 100 mg/kg in PBS; A4685 Sigma-Aldrich ) . After approximately 10 min of incubation , the animals were imaged with a chemiluminescent , in vivo imaging-system ( IVIS 200 Spectrum; Perkin Elmer , Waltham , MA ) . We determined that the peak activity occurs within 20 min post injection , and thus , measured luminescence during the first 25 min post injection . We acquired 15 images with a 60 s exposure , f-stop equal to 1 , binning factor equal to 8 , and a 21 . 6 cm field of view . To analyze the radiance emitted from the wounds , a circular region of interest with a diameter of 6 mm was placed around each ear wound and the total flux in the regions was measured . The maximum value for each wound over the 15 images was used for subsequent analyses . Harvested tissue was placed into 10% ( v/v ) neutral buffered formalin ( American Master Tech Scientific Inc . , Lodi , CA ) and incubated at 4°C overnight . Tissue was washed three times with PBS , three times with 70% ( v/v ) ethanol and stored at 4°C in 70% ( v/v ) ethanol . All tissue processings were completed using a rapid microwave histoprocessor ( Micron Instruments , Inc . Carlsbad , CA ) . Tissues were embedded in paraffin ( Leica Biosystems , Buffalo Grove , IL ) and 5-µm sections were placed onto Superfrost Plus slides ( Fisher Scientific ) . Immunohistochemical and H & E staining were performed on deparaffinized and rehydrated sections . Immunohistochemical staining for rabbit anti-human MPO ( Dako , Cat #A0398 ) was carried out as previously described ( Gawriluk et al . , 2016 ) using heat-mediated antigen retrieval in a Tris-EDTA buffer at pH9 . 0 . For , secondary detection of the primary antibody , we used biotin-conjugated goat anti-rabbit antibody followed by HRP-conjugated streptavidin for 3 , 3’-diaminobenzidine conversion according to Vector Elite ABC staining kit ( Vector , Burlingame , CA ) . Nuclei were counterstained with Mayer’s hematoxylin for brightfield visualization and coverslips mounted with Cytoseal XYL ( ThermoFischer , Waltham , MA ) . Immunofluorescent staining was performed on flash frozen tissue collected in Tissue-Tek OCT ( Sakura , Torrance , CA ) and frozen on dry ice . 8 μm sections were collected on a Leica CM1900 cryostat at 20°C . Tissue was subsequently fixed to slides with ice-cold acetone for 15 min and washed in PBS . Slides were incubated with primary antibodies overnight: rat anti-mouse CD86 ( 1:100 , BD Biosciences , Cat #553698 ) , goat anti-mouse CD206 ( 1:1000 , R and D Systems , Cat #AF2535 ) , rabbit anti-human CD3 ( 1:400 , Dako , Cat #A0452 ) , rabbit anti-mouse IBA1 ( Wako , Cat #019–19741 ) . Secondary detection of antibodies was carried out using donkey antibodies conjugated to Alexa Fluor 488 , 594 ( 1:500 , Invitrogen , Carlsbad , CA ) Nuclei were counterstained with 10 µg/ml DAPI and coverslips were mounted using ProLong Gold mounting medium ( Invitrogen , Carlsbad , CA ) for fluorescence . To quantify the total area of positive signal for fluorescent images , three photomicrographs within the center of the injury were obtained at 40x magnification using an Olympus BX53 fluorescent deconvolution microscope ( Olympus America Inc ) . Quantification of positive signal was performed on four separate samples ( one ear per animal ) per time point ( unless otherwise noted ) by thresholding fluorescent signal and mask subsampling with Metamorph Imaging software ( Molecular Devices , Sunnyvale , CA ) . The ratio of total immuno-positive area per total area of the region of interest was then calculated . To quantify total number of DAB-positive cells , two photomicrographs within the center of the injury were obtained at 20x magnification using an Olympus BX53 microscope . Counts were calculated using the Cell Counter plugin for ImageJ ( version 1 . 51d ) . Cells included in counts were based on both nuclear morphology and immuno-positive staining . Total positive cells were reported as a percent of total area ( pixels ) in a region of interest that excluded scab , epidermis , and cartilage plate . The ID of the samples being run was blinded from the user until the end of the analysis . Macrophage progenitors were isolated from femur and tibia of Acomys and Mus . After sacrifice , femur and tibia were surgically removed , and mechanically cleared of all skin , muscle and tendon . Marrow was aspirated from bones by flushing the marrow with 10 mL of RPMI +10% FBS through a 28 gauge syringe . Red blood cells were lysed with a hypotonic solution and remaining cells were plated at a density of 1 × 106 cells/mL in T-75 culture flasks . For the first 7 days , bone marrow cells were grown in complete RPMI media supplemented with 20% L929 media containing M-CSF , 10% FBS , 1% PenStrep . After 7 days in culture , BMDM were split using cold PBS and a cell scraper , plated onto coverslips in 24-well plates at a density of 5 × 105 cells/mL and allowed to settle for 24 hr in complete RPMI media supplemented with 10% FBS and 1% PenStrep . For macrophage activation , cells were stimulated with either 500 μL of IFNγ ( 20 pg/mL ) and LPS ( 200 ng/mL ) in cRPMI media 10% FBS or 500 uL of IL-4 ( 20 ng/mL ) in cRPMI media 10% FBS . 24 hr after activation , media was removed , cells washed once with cold PBS and fixed with 10% neutral buffered formalin for 10 min at room temperature . After three washes with PBS to remove excess formalin , cells were permeabilized with 1% Triton x100 in PBS for 10 min . Immunocytochemistry was performed by first blocking non-specific binding sites using 5% goat serum in PBS block . Cells were washed once with PBS and incubated with primary antibody overnight at 4°C . Primary antibodies include rabbit anti mouse Arginase 1 ( 1:500 GeneTex , Cat #113131 ) , goat anti mouse CD206 ( 1:1000 , R and D Systems , Cat #AF2535 ) , rat anti-mouse CD86 ( 1:100 , BD Biosciences , Cat #553689 ) , rat anti-mouse Cd11b ( 1:500 , Abd Serotec , Cat #MCA74G ) , rabbit anti-mouse IBA1 ( 1:1000 , Wako , Cat #019–19741 ) , rat anti-mouse F4/80 ( 1:400 , clone BM8 , eBiosciences , Cat #14-4801-82 ) . Detection of primary antibodies was accomplished by incubating cells with secondary antibody conjugated to Alexa Fluor fluorophores ( 1:1000 , Invitrogen , Carlsbad , CA ) as follows: Donkey anti-Goat AF546 , Goat anti-Chicken AF488 , Donkey anti-Rat 488 , and Donkey anti-Rabbit 546 . Cell nuclei were counterstained with DAPI and after air-drying , coverslips were mounted to slides using ImmunoMount ( Invitrogen , Carlsbad , CA ) . Following previously reported protocols ( Barrera et al . , 2000; Li et al . , 2013; van Rooijen and Hendrikx , 2010 ) , we inject 20 μL of 50 μg/mL clodronate liposomes- or PBS liposomes ( www . clodronate-liposomes . org , ( van Rooijen and Hendrikx , 2010 ) ) as vehicle control . Injections were performed using a Hamilton syringe ( Hamilton Company , Reno Nevada ) at the base of each ear at D0 , 2 and 5 after injury . Tissue was collected at D5 ( n = 3 Acomys/treatment ) and D20 ( n = 4 Acomys/treatment ) for immunohistochemical and histological examination . Ear hole closure was measured over time using a digital micrometer ( n = 6 Acomys , 12 ears per treatment ) . For analyzing immuno-positive cells , observations within the same mouse were averaged so analysis could be on the level of the experimental unit . For each marker , we analyzed the percentage of immune-positive signal as a fraction of total DAPI positive area using a two-way ANOVA with species , day and the species*day interaction ) . When noted , Sidak's or Tukey's multiple comparison tests were conducted where appropriate using Prism 6 Data analysis software ( Graphpad Software Inc , La Jolla , CA ) . Sample size was determined by calculating power from previous immunohistochemical studies . These calculations show with appropriate transformation of percentages and a standard deviation of 0 . 07 , groups of n = 4 animals should be sufficient to detect a species effect with a size of 0 . 16395 with 80% power and alpha = 0 . 05 . To calculate sample size for repeated measures , we used previous wounding studies to determine maximum standard deviation of 2 . 21 . These tests show groups of n = 5 animals , 10 ears should be sufficient to detect a treatment effect of 2 . 77 with 80% power and alpha = 0 . 05 . To analyze the chemiluminescent data , we performed a repeated-measure , two-way ANOVA with time and species as main effects and Sidak's post-hoc tests to compare the species x time effect . Graphs were created using Prism and annotated in Illustrator ( Adobe Creative Suite 6 , San Jose , CA ) . Graphs display standard error of mean ( S . E . M . ) with statistical significance values indicated in figure legends . Sample size ( n ) is stated in each figure legend and refers to biological replication size ( n = number of distinct animals ) with the exception of in vitro studies in which n = number of technical replicates for BMDM activation . | The cells of the immune system are essential to defend an organism from disease . In addition , some of them are also thought to play an important role in helping injured tissues heal or even regrow . For example , when an animal is injured , immune cells such as macrophages rush to the wounded site to clear debris and help repair the damage . Macrophages come in different forms and subtypes , and express different protein markers on their surface , depending on where in the body they reside . Few mammals can completely renew or regrow a damaged tissue – a process known as tissue regeneration . Instead , humans and most other mammals repair injuries by producing scar tissue , which has different properties compared to the original tissue it replaces . One exception is the African spiny mouse , which , unlike other rodents studied , can regrow skin and fur , nerves , muscles , and even cartilage . It has been shown that in highly regenerative animals such as salamanders and zebrafish , macrophages are necessary to initiate tissue regeneration . Documented cases of tissue regeneration in mammals are rare and therefore less understood . Until now , it was not clear why two species as closely related as spiny mice and house mice would heal identical injuries in different ways . To better understand how new tissue regenerates , Simkin et al . compared the healing abilities of spiny mice and house mice after they received an injury to their ear and showed that macrophages appeared to be important for both the regeneration of new tissue and the formation of scar tissue . When Simkin et al . removed all macrophages in the ear of spiny mice , their ear tissue could not heal and regrow . When the macrophages were allowed to re-invade the injured site , the tissue in the ear regenerated . Further experiments showed that during tissue regeneration and scarring , different subtypes of macrophages appeared to be active . The findings suggest that specific subtypes of macrophages could be a key element in helping tissue to regenerate . An important next step will be to further explore the different types of macrophages and whether the injury site determines what types of cells are active . A deeper understanding of how tissues can regrow in mammals will be essential to advancing our ability to stimulate tissue regeneration in humans . | [
"Abstract",
"Introduction",
"Results",
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] | [
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"biology",
"cell",
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] | 2017 | Macrophages are necessary for epimorphic regeneration in African spiny mice |
The chloroplast proteome contains thousands of different proteins that are encoded by the nuclear genome . These proteins are imported into the chloroplast via the action of the TOC translocase and associated downstream systems . Our recent work has revealed that the stability of the TOC complex is dynamically regulated by the ubiquitin-dependent chloroplast-associated protein degradation pathway . Here , we demonstrate that the TOC complex is also regulated by the small ubiquitin-like modifier ( SUMO ) system . Arabidopsis mutants representing almost the entire SUMO conjugation pathway can partially suppress the phenotype of ppi1 , a pale-yellow mutant lacking the Toc33 protein . This suppression is linked to increased abundance of TOC proteins and improvements in chloroplast development . Moreover , data from molecular and biochemical experiments support a model in which the SUMO system directly regulates TOC protein stability . Thus , we have identified a regulatory link between the SUMO system and the chloroplast protein import machinery .
The chloroplast is a membrane-bound organelle that houses photosynthesis in all green plants ( Jarvis and Lopez-Juez , 2013 ) . Chloroplasts have an unusual evolutionary history – they are the integrated descendants of a cyanobacterial ancestor that entered the eukaryotic lineage via endosymbiosis . Although chloroplasts retain small genomes , almost all of the proteins required for chloroplast development and function are now encoded by the central , nuclear genome ( Jarvis , 2008 ) . These proteins must be imported into the organelle after synthesis in the cytosol , and this import is mediated by the coordinate action of the TOC and TIC complexes ( the translocons at the outer and inner envelope membranes of chloroplasts; Jarvis , 2008 ) . The TOC complex contains three major components: the Omp85 ( outer membrane protein , 85 kDa ) -related protein , Toc75 , which serves as a membrane channel ( Schnell et al . , 1994; Tranel et al . , 1995 ) , and two GTPase-domain receptor proteins , Toc33 and Toc159 ( Hirsch et al . , 1994; Kessler et al . , 1994; Perry and Keegstra , 1994; Jarvis et al . , 1998; Jarvis , 2008 ) . Toc33 and Toc159 project into the cytosol and bind incoming preproteins . The key components of the TOC complex were identified more than two decades ago ( Hirsch et al . , 1994; Kessler et al . , 1994; Schnell et al . , 1994; Tranel et al . , 1995; Jarvis , 2008 ) . However , the regulation of the activity and stability of the complex was , until recently , poorly understood . Major insights came from a forward genetic screen for suppressors of the pale-yellow Toc33 mutant , ppi1 ( Ling et al . , 2012 ) . In this screen , SP1 ( SUPPRESSOR OF PPI1 LOCUS 1 ) , a novel RING-type E3 ubiquitin ligase , was identified . A series of sp1 mutations were shown to partially suppress the phenotypic defects of ppi1 with respect to chlorosis , chloroplast development , and chloroplast protein import . In addition , SP1 function was shown to promote plastid interconversion events ( e . g . , the development of the chloroplast from its precursor organelle , the etioplast ) . Later work demonstrated that SP1 function is also important for abiotic stress tolerance by enabling optimization of the organellar proteome via protein import regulation ( Ling and Jarvis , 2015 ) . Thus , through SP1 , the ubiquitin-proteasome system promotes TOC complex degradation and reconfiguration in response to developmental and/or environmental stimuli . Ubiquitinated TOC proteins are extracted from the chloroplast outer envelope membrane and degraded in the cytosol . Recent work identified two proteins that physically associate with SP1 and promote the membrane extraction of TOC proteins ( Ling et al . , 2019 ) . These are SP2 , an Omp85-type β-barrel channel protein that was identified in the same genetic screen as SP1 , and Cdc48 , a well-characterized cytosolic AAA+ chaperone ATPase that provides the motive force for the extraction of proteins from the chloroplast outer envelope . The three proteins – SP1 , SP2 , and Cdc48 – together define a new pathway for the ubiquitination , membrane extraction , and degradation of chloroplast outer envelope proteins , which has been named chloroplast-associated protein degradation ( CHLORAD ) . In addition to CHLORAD , there exist cytosolic ubiquitin-dependent systems that also contribute to chloroplast biogenesis by regulating the levels of unimported preproteins ( Lee et al . , 2009; Grimmer et al . , 2020 ) and by controlling the stability of the Toc159 receptor prior to its integration into the outer envelope membrane ( Shanmugabalaji et al . , 2018 ) . The discovery of SP1 and the CHLORAD pathway demonstrated that the TOC complex is not static but , instead , can be rapidly ubiquitinated and degraded in response to developmental and environmental stimuli . To complement this work , we decided to explore whether the TOC complex is also regulated by the small ubiquitin-like modifier ( SUMO ) system . This work was motivated in part by the results of a high-throughput screen for SUMO substrates in Arabidopsis ( Elrouby and Coupland , 2010 ) . This screen suggested that Toc159 , a key component of the TOC complex , is a SUMO substrate . SUMOylation is intricately involved in plant development and stress adaptation , and so we were interested to determine whether the TOC complex is targeted by the SUMO system , and whether any such SUMOylation is functionally important . As crosstalk between the SUMO system and the ubiquitin-proteasome system is common , we reasoned that answering these questions might provide insights into the regulation of SP1 and the CHLORAD pathway . To explore the relationship between the chloroplast protein import and SUMO systems , we carried out a comprehensive series of genetic , molecular , and biochemical experiments . Mutants representing most components of the Arabidopsis SUMO pathway were found to partially suppress the phenotype of the chlorotic Toc33 null mutant , ppi1 , with respect to leaf chlorophyll accumulation , chloroplast development , and TOC protein abundance . Conversely , overexpression of either SUMO1 or SUMO3 enhanced the severity of the ppi1 phenotype . Moreover , the E2 SUMO conjugating enzyme , SCE1 , was found to physically interact with the TOC complex in bimolecular fluorescence complementation ( BiFC ) experiments; and TOC proteins were seen to physically associate with SUMO proteins in immunoprecipitation ( IP ) assays . In combination , our data conclusively demonstrate significant crosstalk between the SUMO system and the chloroplast protein import apparatus , and emphasize the complexity of the regulation of the TOC translocase .
Two key components of the CHLORAD pathway , SP1 and SP2 , were identified in a forward genetic screen for suppressors of ppi1 , an Arabidopsis Toc33 null mutant ( Ling et al . , 2012; Ling et al . , 2019 ) . Both sp1 and sp2 mutants can partially suppress the ppi1 phenotype with respect to chlorophyll accumulation , chloroplast development , and TOC protein abundance . To investigate whether the TOC complex is targeted by the SUMO system , we obtained several Arabidopsis SUMO system mutants , crossed them with ppi1 , and carefully examined the phenotypes of the resulting double mutants . This reverse genetic approach was possible because the basic architecture of the Arabidopsis SUMO system is remarkably simple . In the SUMO pathway , the ubiquitin-like SUMO modifier protein is conjugated to substrates by the coordinated action of E1 activating enzymes , E2 conjugating enzymes , and E3 SUMO ligases . Although thousands of proteins are SUMOylated in Arabidopsis , there is just one known E1 SUMO activating enzyme , one known E2 SUMO conjugating enzyme , and only two known E3 SUMO ligases of canonical function ( Saracco et al . , 2007; Ishida et al . , 2009 ) . First , we analyzed sce1-4 , a weak mutant allele of the sole E2 SUMO conjugating enzyme gene in Arabidopsis , which is an essential gene ( Saracco et al . , 2007 ) . The sce1-4 mutant shows a moderate reduction in the expression of SCE1 and in global levels of SUMOylation , but it displays no obvious visible phenotypic defects under steady-state conditions ( Saracco et al . , 2007 ) . The ppi1 sce1-4 double mutant was phenotypically characterized , and , intriguingly , it appeared greener than the ppi1 single mutant ( Figure 1A , Figure 1—figure supplement 1A ) . This was linked to a moderate increase in leaf chlorophyll concentration ( Figure 1B , Figure 1—figure supplement 1B ) . Next , we asked whether the phenotypic suppression observed in ppi1 sce1-4 was linked to changes in the development of chloroplasts . The chloroplasts of ppi1 sce1-4 were visualized via transmission electron microscopy . Interestingly , the chloroplasts of the ppi1 sce1-4 double mutant appeared larger and better developed than those of the ppi1 control ( Figure 1C ) . The transmission electron micrographs were quantitatively analyzed , and the ppi1 sce1-4 chloroplasts were indeed found to be significantly larger than those of ppi1 ( Figure 1D ) , with larger , more interconnected thylakoidal granal stacks ( Figure 1E and F ) . SUMO conjugation is usually dependent on the action of E3 SUMO ligases . In Arabidopsis , the best characterized E3 SUMO ligase is SIZ1 ( Kurepa et al . , 2003; Miura et al . , 2005; Saracco et al . , 2007 ) . SIZ1 is not essential , but null mutants display severely dwarfed phenotypes . In order to include SIZ1 in our genetic analysis , we obtained two new T-DNA insertion alleles and named them siz1-4 and siz1-5 . While both mutants were visibly similar to the published mutants ( Miura et al . , 2005; Liu et al . , 2019 ) , siz1-4 showed a milder phenotype with only moderate growth retardation when grown to maturity . We mapped the integration sites of the T-DNA insertions in these two mutants ( Figure 1—figure supplement 2A ) and showed that both display a strong reduction in SIZ1 transcript by RT-PCR analysis ( Figure 1—figure supplement 2B ) . In addition , both mutants displayed defects in global SUMOylation in response to heat shock , similar to the published alleles ( Figure 1—figure supplement 3 ) . The two new siz1 mutants were crossed with ppi1 and the resulting double mutants were phenotypically characterized . Both the ppi1 siz1-4 and the ppi1 siz1-5 double mutants appeared greener than the ppi1 control ( Figure 1G , Figure 1—figure supplement 1C ) . In addition , the double mutants showed dramatic increases in leaf chlorophyll concentration relative to ppi1 ( Figure 1H , Figure 1—figure supplement 1D ) . Next , we asked whether the phenotypic suppression observed in ppi1 siz1-4 and ppi1 siz1-5 was linked to changes in the abundance of TOC proteins . To this end , protein samples were taken from the two double mutants and relevant control plants and resolved via immunoblotting . Both double mutants displayed clear increases in the abundance of Toc159 and Toc75 , two core components of the TOC complex , relative to ppi1 ( Figure 1I , J and K ) . As discussed in the previous section , the SUMO system is encoded by a remarkably small number of genes in Arabidopsis . As a consequence , SUMO system mutants have highly pleiotropic molecular and physiological phenotypes . We therefore asked whether the partial suppression of ppi1 by SUMO system mutants was specific to the ppi1 background . We crossed sce1-4 with tic40-4 and hsp93-V-1 , two TIC-complex-associated mutants . These mutants are chlorotic , due to defects in protein import across the chloroplast inner membrane , and in this respect are highly similar to ppi1 ( Kovacheva et al . , 2005 ) . Significantly , the resulting double mutants , tic40-4 sce1-4 and hsp93-V-I sce1-4 , were indistinguishable from tic40-4 and hsp93-V-1 , their respective single mutant controls ( Figure 2A and C ) . Moreover , the double mutants did not display changes in leaf chlorophyll accumulation relative to the single mutant controls ( Figure 2B and D ) . We therefore concluded that the suppression effects observed in ppi1 sce1-4 plants were background-specific and associated with the TOC complex . Next , we asked whether the sce1-4 and siz1-4 single mutants display an increase in chlorophyll concentration even in the wild-type background . However , neither mutant appeared greener than wild-type plants ( Figure 2E and G , Figure 1—figure supplement 1A and C ) or displayed an increase in leaf chlorophyll concentration ( Figure 2F and H , Figure 1—figure supplement 1B and D ) . We therefore concluded that the suppression effects mediated by the SUMO mutants were synthetic phenotypes specific to the ppi1 background . Our reverse genetic experiments revealed a genetic interaction between the E2 SUMO conjugating enzyme , SCE1 , and the protein import machinery at the chloroplast outer membrane . To determine whether SCE1 directly interacts with the TOC complex , we carried out BiFC experiments in Arabidopsis protoplasts . The SCE1 coding sequence was cloned into a vector that C-terminally appends the C-terminal half of YFP ( cYFP ) to its insert . This construct was co-expressed with various other constructs encoding TOC proteins bearing the complementary , N-terminal moiety of the YFP protein ( nYFP ) , appended N-terminally; or with a negative control construct encoding ΔOEP7 bearing the nYFP moiety appended C-terminally . In this system , protein-protein interactions are inferred via the detection of a YFP signal , caused by the nYFP and cYFP fragments coming together to reconstitute a functional YFP protein . Strikingly , SCE1-cYFP was found to physically associate with all tested TOC proteins – nYFP-Toc159 , nYFP-Toc132 , nYFP-Toc34 , and nYFP-Toc33 ( Figure 3 ) . Moreover , these interactions were concentrated at the periphery of the chloroplasts , placing them in an appropriate subcellular context for the in situ regulation of the chloroplast protein import machinery . Conversely , SCE1-cYFP was not found to physically associate with the negative control protein ΔOEP7-nYFP . The ΔOEP7 protein comprises the transmembrane domain of plastid protein OEP7 , which is sufficient to efficiently target the full-length YFP protein to the chloroplast outer membrane ( Lee et al . , 2001 ) ; thus , cYFP-ΔOEP7 serves as a location-specific negative control . To further explore the genetic interaction between the chloroplast protein import and SUMO systems , we crossed ppi1 with several SUMO protein mutants . There are three major SUMO isoforms in Arabidopsis – SUMO1 , SUMO2 , and SUMO3 . The SUMO1 and SUMO2 genes are expressed at a relatively high level throughout the plant and are largely functionally redundant ( Saracco et al . , 2007; van den Burg et al . , 2010 ) . In addition , they are highly similar to each other in terms of amino acid sequence ( Saracco et al . , 2007 ) . In contrast , at steady state , SUMO3 is expressed at a relatively low level throughout the plant , while the SUMO3 amino acid sequence is significantly divergent with respect to the other two SUMO isoforms ( van den Burg et al . , 2010 ) . First , we analyzed SUMO1 and SUMO2 . We obtained sum1-1 and sum2-1 , two previously characterized null mutants ( Saracco et al . , 2007 ) , and crossed them with ppi1 . To account for the functional redundancy between these two genes , we also sought a ppi1 sum1-1 sum2-1 triple mutant . However , as SUMO1 and SUMO2 are collectively essential , ppi1 sum1-1 sum2-1 plants that were homozygous with respect to ppi1 and sum2-1 , but heterozygous with respect to the sum1-1 mutation , were selected from a segregating population . The double and triple mutants were phenotypically characterized , and all three appeared larger and greener than the ppi1 control plants ( Figure 4A ) . Moreover , the double and triple mutants showed corresponding increases in leaf chlorophyll concentration , with the triple mutant showing a larger increase than the double mutants ( Figure 4B ) . These were synthetic effects as the sum1-1 , sum2-1 , and sum1-1 sum2-1 single and double mutants did not appear greener than wild-type plants or show increases in chlorophyll accumulation ( Figure 4—figure supplement 1 ) . We therefore concluded that the sum1-1 and sum2-1 mutants can additively suppress the phenotype of ppi1 . To complement the preceding experiment , we generated transgenic plants overexpressing SUMO1 in the ppi1 background . The SUMO1 coding sequence was cloned into a vector carrying a strong , constitutive promotor ( cauliflower mosaic virus 35S ) upstream of the cloning site . The resulting construct was stably introduced into the ppi1 background via Agrobacterium-mediated transformation . Two lines carrying a single , homozygous transgene insert were identified and taken forward for analysis . The overexpression of SUMO1 was confirmed in both lines by RT-PCR ( Figure 4—figure supplement 2A ) . Significantly , both lines displayed an accentuation of the ppi1 phenotype: the plants were significantly smaller and paler than the ppi1 control plants ( Figure 4C ) , and showed decreases in leaf chlorophyll concentration ( Figure 4D ) . Next , we turned our attention to SUMO3 . We obtained sum3-1 , a previously characterized null mutant ( van den Burg et al . , 2010 ) , and crossed it with ppi1 . The resulting double mutant was phenotypically characterized , but it did not appear obviously different from the ppi1 control ( Figure 4E ) . Correspondingly , it did not display any clear increase in leaf chlorophyll concentration relative to ppi1 ( Figure 4F ) . To complement this experiment , we generated transgenic plants overexpressing SUMO3 in the ppi1 background using the approach described above , and a line carrying a single , homozygous insert was identified and taken forward for analysis . The overexpression of SUMO3 was confirmed by RT-PCR ( Figure 4—figure supplement 2B ) . Interestingly , the transgenic plants showed a striking increase in the severity of the ppi1 phenotype: the plants were severely dwarfed and paler than the ppi1 control ( Figure 4G ) , and displayed a significant decrease in leaf chlorophyll accumulation ( Figure 4H ) . These findings are particularly noteworthy when considered alongside a previous report , which explored the consequences of overexpressing SUMO3 in wild-type plants ( van den Burg et al . , 2010 ) . In that study , SUMO3 overexpression was not found to alter the appearance of the transgenic plants , which implies a degree of specificity in the phenotypic accentuation observed here . The genetic and molecular experiments described thus far strongly suggested that TOC proteins are SUMOylated . However , to our knowledge , conclusive evidence that chloroplast-resident proteins are SUMOylated is currently lacking . To investigate whether chloroplast proteins may be SUMOylated , we isolated chloroplasts from seedlings by cell fractionation and analyzed them by anti-SUMO immunoblotting . For this analysis , we employed a proven commercial antibody against SUMO1 , which is one of the most abundant SUMO isoforms in Arabidopsis making it more tractable for analysis , and which furthermore is known to accumulate in response to heat and other stresses ( Kurepa et al . , 2003; van den Burg et al . , 2010 ) . To enhance detection of SUMOylated proteins in our samples , we subjected some of the seedlings to heat shock before chloroplast isolation and/or treatment with 10 mM N-ethylmaleimide ( NEM ) during chloroplast isolation . NEM is a potent inhibitor of SUMO-specific proteases ( Hilgarth and Sarge , 2005 ) . Importantly , we detected protein SUMOylation in the isolated chloroplast samples , and this SUMOylation was increased by NEM treatment ( Figure 5—figure supplement 1 ) . Next , we carried out a series of biochemical experiments to investigate whether TOC proteins may be SUMOylated . In the first of these , the SCE1 coding sequence was cloned into a vector that appends a C-terminal YFP tag ( Karimi et al . , 2002 ) . We confirmed that the resulting SCE1-YFP construct delivers good expression and the expected nucleocytoplasmic fluorescence pattern when transiently expressed in protoplasts ( Figure 5—figure supplement 2A ) . Then , we transfected a large volume of protoplasts with the SCE1-YFP construct ( or with a YFP-HA negative control construct ) and performed IP using YFP-Trap magnetic beads . The samples were analyzed by immunoblotting . The YFP-HA and SCE1-YFP fusion proteins both showed robust expression and strong recovery in the IP elutions ( Figure 5A ) . Remarkably , the SCE1-YFP fusion protein was found to be associated with native Toc159 and Toc132 , but not with the negative control proteins Tic110 or Tic40 ( Figure 5A; Kovacheva et al . , 2005; Inaba et al . , 2005 ) . Conversely , YFP-HA did not associate with any of the tested proteins . Given that SCE1 is a promiscuous enzyme that associates with thousands of proteins ( Elrouby and Coupland , 2010 ) , and that these interactions are likely to be transient , it is remarkable that TOC co-elution was detectable in this experiment . In the second experiment , we cloned the SUMO1 , SUMO2 , and SUMO3 coding sequences into a vector that appends an N-terminal YFP tag to its insert ( Karimi et al . , 2002 ) , a modification that previous studies have shown to be tolerated ( Ayaydin and Dasso , 2004 ) . All three constructs expressed well and showed the expected nucleocytoplasmic fluorescence pattern when transiently expressed in protoplasts ( Figure 5—figure supplement 2B ) . The three constructs were expressed in parallel in protoplasts alongside the YFP-HA negative control construct . As in the previous experiment , the protoplasts were subjected to YFP-Trap IP , and the samples were subsequently analyzed by immunoblotting . Remarkably , all three YFP-SUMO proteins were found to physically associate with Toc159 , although YFP-SUMO3 clearly bound Toc159 with the greatest affinity ( Figure 5B , Figure 5—figure supplement 3 ) . Moreover , inspection of an extended exposure of the anti-YFP blot revealed a number of higher molecular weight bands that we interpret to be SUMO adducts and indicative of the functionality of the fusions ( Figure 5—figure supplement 4 ) . In contrast with the SUMO fusions , the YFP-HA negative control did not associate with Toc159 , and none of the four YFP fusion proteins physically associated with Tic40 , a negative control protein ( Figure 5B ) . The IP experiment described above identified SUMO3 as having the highest affinity for Toc159 . To extend our analysis of SUMO3 to include another TOC protein , and to more rigorously investigate the possibility of TOC protein SUMOylation , the experiment was repeated with modifications , as follows . Protoplasts were co-transfected with YFP-SUMO3 and Toc33-HA , or YFP-HA and Toc33-HA; in each case , Toc33 was transiently overexpressed to aid detection of this component and its adducts . Upon co-expression of these construct pairs , the protoplast samples were subjected to YFP-Trap IP analysis , as described earlier . In accordance with the Toc159 result ( Figure 5B ) , YFP-SUMO3 , but not YFP-HA , was found to physically associate with Toc33-HA ( Figure 5C ) . Moreover , bands of the exact expected molecular weight for Toc33-HA bearing one or two YFP-SUMO3 moieties ( 75 and 114 kDa ) were also detected . These bands were accompanied by a high molecular weight smear at the top of the immunoblot , which is indicative of complex , multisite or chain SUMOylation .
This work has revealed a genetic and molecular link between the SUMO system and the chloroplast protein import apparatus . The genetic experiments demonstrated that SUMO system mutations can suppress the phenotype of the Toc33 mutant , ppi1 , while the molecular and biochemical experiments indicated that TOC proteins associate with key SUMO system proteins and are likely SUMOylated . Visible suppression effects observed in the ppi1 / SUMO system double mutants were linked to improvements in chloroplast development and enhanced accumulation of key TOC proteins . Thus , our results suggest that SUMOylation acts to destabilize the TOC complex , and that when such SUMOylation is perturbed the TOC proteins are stabilized . We interpret that TOC complexes containing Toc34 , Toc75 , and Toc159 accumulate at higher levels in ppi1 / SUMO system double mutants , and that this synthetically improves the double mutant phenotypes relative to the ppi1 control . Importantly , each core TOC protein , including all of those analyzed in this study , was predicted with high probability to have one or more SUMOylation sites ( Table 1; Zhao et al . , 2014; Beauclair et al . , 2015 ) . The ppi1 suppression effects described here are remarkably similar to those mediated by the sp1 and sp2 mutations ( Ling et al . , 2012; Ling et al . , 2019 ) . Like sp1 and sp2 , SUMO system mutations can partially suppress ppi1 with respect to chlorophyll concentration , TOC protein accumulation , and chloroplast development . This similarity suggests that SUMOylation may regulate the activity of the CHLORAD pathway . This is an attractive hypothesis as both SUMOylation and the CHLORAD pathway are activated by various forms of environmental stress ( Kurepa et al . , 2003; Ling and Jarvis , 2015; Ling et al . , 2019 ) . One possibility is that the SUMOylation of TOC proteins promotes their CHLORAD-mediated degradation . Indeed , as already noted , the ability to carry out SUMOylation is negatively correlated with the stability of TOC proteins in the context of the developed plants studied here . However , it should be kept in mind that SUMOylation can both promote and antagonize the effects of ubiquitination in different situations ( Desterro et al . , 1998; Ahner et al . , 2013; Liebelt and Vertegaal , 2016 ) ; and so our results do not preclude the possibility that SUMOylation may have different consequences for chloroplast biogenesis in other contexts . The Toc159 receptor is regulated by SP1 when integrated into the outer envelope membrane ( Ling et al . , 2012; Ling et al . , 2019 ) , but by a different E3 ligase when it exists as a cytosolic precursor during the earliest stages of development before germination ( Shanmugabalaji et al . , 2018 ) . Thus , regulation by SUMOylation might be similarly different in these two distinct developmental contexts . The precise mechanisms underpinning the observed negative regulation of the TOC apparatus by SUMOylation are currently unknown . One possibility is that the SUMOylation of TOC proteins promotes their association with SP1 . SUMOylation can modify protein-protein interactions , and some RING-type E3 ubiquitin ligases specifically recognize SUMOylated substrates ( Sriramachandran and Dohmen , 2014 ) . However , these SUMO-targeted ubiquitin ligases ( STUbLs ) typically contain SUMO-interacting motifs ( SIMs ) , which guide the ligases to SUMO proteins conjugated to their substrates , and these are not apparent in SP1 ( data not shown; Zhao et al . , 2014 ) . However , SP1 forms a complex with SP2 and very likely additional cofactors , and these could hypothetically provide a SUMO binding interface . Another possibility is that SUMOylation could be involved in the recruitment of Cdc48 from the cytosol . Two important Cdc48 cofactors are Ufd1 and Npl4 , and the former contains a SIM , which can guide Cdc48 to SUMOylated proteins ( Nie et al . , 2012; Baek et al . , 2013 ) . Moreover , the SUMO-mediated recruitment of Cdc48 has important roles in the maintenance of genome stability in yeast ( Bergink et al . , 2013 ) . The biochemical experiments described in this article indicate that , of the three SUMO isoforms tested , SUMO3 binds TOC proteins with the highest affinity . However , there is an apparent incongruence between the results of these experiments and the results of the genetic experiments . While the sum1-1 and sum2-1 mutants were found to additively suppress ppi1 , the sum3-1 mutant did not suppress ppi1 . At face value , this seems puzzling; however , it can be explained by the relative abundance of the three SUMO proteins in planta . SUMO1 and SUMO2 are highly abundant relative to SUMO3 , which is , at steady state , very weakly abundant ( van den Burg et al . , 2010 ) . The IP data shown in Figure 5B indicated that SUMO1 and SUMO2 can weakly interact with Toc159 , and so it is likely that these two isoforms can compensate for the loss of SUMO3 in the sum3-1 mutant . Although SUMO3 associates with TOC proteins with the highest affinity , the higher abundance of the other two SUMO proteins may facilitate such compensation . It is also noteworthy that , when overexpressed , SUMO3 accentuates the ppi1 phenotype to a far greater extent than does SUMO1 . It is now well established that the regulation of chloroplast protein import has critical roles in plant development and stress acclimation ( Sowden et al . , 2018; Watson et al . , 2018 ) . Here , we demonstrate regulatory crosstalk between the SUMO system and the chloroplast protein import machinery , and present results that are consistent with a model in which SUMOylation modulates the activity or effects of the CHLORAD pathway . The precise nature of the links between these two critically important control systems will be the subject of future investigation .
All Arabidopsis thaliana plants used in this work were of the Columbia-0 ( Col-0 ) ecotype . The mutants used in most of the analyses ( ppi1 , sce1-4 , sum1-1 , sum2-1 , sum3-1 , hsp93-V-I , tic40-4 ) have been described previously ( Jarvis et al . , 1998; Kovacheva et al . , 2005; Saracco et al . , 2007; van den Burg et al . , 2010 ) . The siz1-4 ( SAIL_805_A10 ) and siz1-5 ( SALK_111280 ) mutants were obtained from the Salk Institute Genomic Analysis Laboratory ( SIGnAL ) ( Alonso et al . , 2003 ) via the Nottingham Arabidopsis Stock Centre ( NASC ) . Each line was verified via PCR genotyping ( see Table 2 for primer sequences ) and phenotypic analysis ( including the double and triple mutants ) . The positions of the T-DNA insertions were mapped via PCR . The following primer pairs were used to generate diagnostic amplicons from genomic DNA: LB1 and Siz1-Seq-1R ( for mapping siz1-4 ) , and LBb1 and Siz1-Seq-3R ( for mapping siz1-5 ) ( see Table 2D and E ) . The amplicons were sequenced and the positions of the T-DNA insertions inferred from the sequence data . In most experiments , plants were grown on soil ( 80% [v/v] compost [Levington M2] , 20% [v/v] vermiculite [Sinclair Pro , medium particle size] ) . However , where plants were grown for selection of transformants or for chloroplast isolation , seeds were surface sterilized and sown on petri plates containing Murashige–Skoog ( MS ) agar medium . The plates were stored at 4°C for 48 hr before being transferred to a growth chamber . Both soil-grown and plate-grown plants were kept in a growth chamber ( Percival Scientific ) under long-day conditions ( 16 hr light , 8 hr dark ) . The light intensity was approximately 120 µE m–2 s–1 , the temperature was held constant at 20°C , and the humidity was held constant at approximately 60% ( relative humidity ) . Chlorophyll measurements were taken from mature rosette leaves in each instance . A handheld Konica-Minolta SPAD-502 meter was used to take each measurement , and the raw values were converted into chlorophyll concentration values ( nmol/mg tissue ) via published calibration equations ( Ling et al . , 2011 ) . Chloroplasts were isolated from 14-day-old , plate-grown seedlings as described previously ( Flores-Pérez and Jarvis , 2017 ) . Some of the seedlings were heat-shocked immediately prior to chloroplast isolation . To do this , the plates containing the seedlings were wrapped in clingfilm and placed into a water bath ( 42°C for 1 hr , followed by a 1 hr recovery period at 22°C ) . Protein samples were prepared from the isolated chloroplasts by extraction using SDS-PAGE sample buffer , as well as from whole 14-day-old seedlings as previously described ( Kovacheva et al . , 2005 ) . In some cases , the samples were treated with 10 mM NEM ( Hilgarth and Sarge , 2005 ) ; this was added directly to the protein extraction buffer ( whole seedling samples ) or to the chloroplast isolation buffer following polytron homogenization and all subsequent buffers ( chloroplast samples ) . The constructs used in the BiFC experiments were generated as follows . The coding sequences of SCE1 , SIZ1 , TOC159 , TOC132 , TOC34 , and TOC33 were PCR amplified from wild-type cDNA ( see Table 2 for primer sequences ) . In the case of ΔOEP7 , the first 105 base pairs of the OEP7 coding sequence were amplified; this encodes a truncated sequence , which is sufficient to efficiently target the full-length YFP protein to the chloroplast outer envelope membrane ( Lee et al . , 2001 ) . The inserts were cloned into one of the following complementary vectors: pSAT4 ( A ) -cEYFP-N1 ( SCE1 ) , pSAT4-nEYFP-C1 ( TOC159 , TOC132 , TOC34 , TOC33 ) , or pSAT4 ( A ) -nEYFP-N1 ( ΔOEP7 ) , which were described previously ( Tzfira et al . , 2005; Citovsky et al . , 2006 ) . The constructs used in the IP experiments were generated as follows . The coding sequences of SCE1 , SUMO1 , SUMO2 , and SUMO3 were PCR amplified from wild-type cDNA using primers bearing 5′ attB1 and attB2 adaptor sequences ( see Table 2 for primer sequences ) . The amplicons were then cloned into pDONR207 ( Invitrogen ) , a Gateway entry vector . The inserts from the resulting entry clones were then transferred to one of two destination vectors: p2GWY7 ( SCE1 ) or p2YGW7 ( SUMO1 , SUMO2 , SUMO3 ) ; the former appends a C-terminal YFP tag to its insert , and the latter appends an N-terminal YFP tag to its insert ( Karimi et al . , 2002; Karimi et al . , 2005 ) . The Toc33-HA and YFP-HA constructs have been described previously ( Ling et al . , 2019 ) . The constructs used to generate transgenic plants were generated as follows . The coding sequences of SUMO1 and SUMO3 were PCR amplified from wild-type cDNA using primers bearing 5′ attB1 and attB2 adaptor sequences ( see Table 2 for primer sequences ) . The inserts were then cloned into pDONR201 ( Invitrogen ) , a Gateway entry vector . The inserts from the resulting entry clones were then transferred to the pH2GW7 binary destination vector ( Karimi et al . , 2002; Karimi et al . , 2005 ) . Protoplasts were isolated from mature rosette leaves of wild-type Arabidopsis plants and transfected in accordance with an established method ( Wu et al . , 2009; Ling et al . , 2012 ) . In the BiFC experiments , 100 µL protoplast suspension ( containing approximately 105 protoplasts ) was transfected with 5 µg plasmid DNA; in the IP experiments , 600 µL protoplast suspension ( containing approximately 6 × 105 protoplasts ) was transfected with 30 µg plasmid DNA . In both cases , the samples were analyzed after 15–18 hr . Transgenic lines carrying the SUMO1 OX or SUMO3 OX constructs were generated via Agrobacterium-mediated floral dip transformation ( Clough and Bent , 1998 ) . Transformed plants ( T1 generation ) were selected on MS medium containing phosphinothricin . Multiple T2 families were analyzed in each case , and lines bearing a single T-DNA insertion were taken forward for further analysis . Transgene expression was analyzed by semi-quantitative RT-PCR as described previously ( Kasmati et al . , 2011; see Table 2 for primer sequences ) . Transmission electron micrographs were recorded using mature rosette leaves as previously described ( Huang et al . , 2011 ) . Images were taken from three biological replicates ( different leaves from different individual plants ) , and at least 10 images were taken per replicate . The images were analyzed using ImageJ ( Schneider et al . , 2012 ) . The freehand tool was used to measure the plan area of the chloroplasts . For this , between 9 and 28 chloroplasts were analyzed for each biological replicate ( i . e . , for each plant ) , and then an average value for each replicate was calculated and used for statistical comparisons . The analysis of chloroplast ultrastructure was performed as in previous work ( Huang et al . , 2011 ) . For this , between 3 and 8 chloroplasts were analyzed per biological replicate , and the data were processed as above . The BiFC experiments were carried out as described previously ( Ling et al . , 2019 ) . Protoplasts were co-transfected with two constructs encoding fusion proteins bearing complementary fragments of the YFP protein ( nYFP and cYFP; Citovsky et al . , 2006 ) . After transfection and overnight incubation , the protoplasts were imaged using a Leica TCS SP5 laser scanning confocal microscope equipped with a Leica HC Plan Apochromat CS2 63 . 0× UV water immersion lens with a numerical aperture ( N . A . ) of 1 . 2 . YFP was excited with an argon-ion laser at 514 nm , selected using an acousto-optic tuneable filter ( AOTF ) , and was detected using a 525–600 nm bandpass filter and a photomultiplier . Chlorophyll fluorescence was simultaneously excited with 514 nm excitation and detected with a 680–700 nm bandpass filter using a photomultiplier . Images were collected in 8-bit resolution with the pinhole set at 111 . 5 µm ( 1 Airy Unit ) , using 16-line averaging and a scan speed of 400 Hz . The image size was 512 × 512 pixels , with an ( x , y ) pixel size of 0 . 239 µm . Images were processed in the Leica Application Suite ( LAS ) software . Protein extraction and immunoblotting were performed as described previously ( Kovacheva et al . , 2005 ) . Total protein samples were extracted from 50 mg of intact , pooled seedlings after 2 weeks of growth . To detect proteins , we used an anti-SUMO1 antibody ( Ab5316 , Abcam ) , an anti-Toc75-III antibody ( Kasmati et al . , 2011 ) , an anti-Toc159 antibody ( Bauer et al . , 2000 ) , an anti-Toc132 antibody ( Ling et al . , 2012 ) , an anti-Toc33 antibody ( Kasmati et al . , 2011 ) , an anti-Tic110 antibody ( Aronsson et al . , 2010 ) , an anti-Tic40 antibody ( Kasmati et al . , 2011 ) , and an anti-green fluorescent protein antibody ( Sigma , SAB4301138 ) . In most cases , the secondary antibody used was goat anti-rabbit immunoglobulin G ( IgG ) conjugated with horseradish peroxidase ( Sigma , 12-348 ) ; and protein bands were visualized via chemiluminescence using an ECL Plus western blotting detection kit ( GE Healthcare ) and an LAS-4000 imager ( GE Healthcare ) . However , in the case of Figure 5—figure supplement 1 , the secondary antibody was goat anti-rabbit IgG conjugated with alkaline phosphatase ( Sigma , A3687 ) , and the membrane was incubated with BCIP/NBT chromogenic substrate ( Sigma , B3679 ) . The IP experiments were carried out as described previously ( Ling et al . , 2019 ) . Constructs encoding YFP-conjugated fusion proteins ( YFP-HA , SCE1-YFP , YFP-SUMO1 , YFP-SUMO2 , YFP-SUMO3 ) were transiently expressed in protoplasts . In some cases , the constructs were co-expressed with a construct encoding Toc33-HA . The protoplasts were solubilized using IP buffer containing 1% Triton X-100 , and the resulting lysates were incubated with GFP-Trap beads ( Chromotek ) . After four washes in IP buffer , the protein samples were eluted by boiling in SDS-PAGE loading buffer , and then analyzed by immunoblotting . The data from each experiment were analyzed in R . In most cases , two-tailed t-tests were performed . However , in one case , a one-way ANOVA was performed in conjunction with a Tukey HSD test ( as indicated in the figure legend ) . The figures are annotated to indicate the level of significance , as follows: ns: not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001; *****p<0 . 00001 . The amino acid sequences of Toc159 , Toc132 , Toc120 , Toc90 , Toc75 , Toc33 , and Toc34 were retrieved from The Arabidopsis Information Resource ( TAIR ) website ( Berardini et al . , 2015 ) . The GPS-SUMO algorithm was applied to all seven sequences ( http://sumosp . biocuckoo . org/online . php; Zhao et al . , 2014 ) . The ‘high stringency’ setting was applied . The p-values were generated by the GPS-SUMO algorithm , and hits that were accompanied by p-values exceeding >0 . 05 were manually removed . The JASSA algorithm was also applied to all seven amino acid sequences ( http://www . jassa . fr/index . php; Beauclair et al . , 2015 ) . In this case , the ‘high cutoff’ setting was applied . The GPS-SUMO and JASSA algorithms use fundamentally different methodologies ( Chang et al . , 2018 ) . | All green plants grow by converting light energy into chemical energy . They do this using a process called photosynthesis , which happens inside compartments in plant cells called chloroplasts . Chloroplasts use thousands of different proteins to make chemical energy . Some of these proteins allow the chloroplasts to absorb light energy using chlorophyll , the pigment that makes leaves green . The vast majority of these proteins are transported into the chloroplasts through a protein machine called the TOC complex . When plants lack parts of the TOC complex , their chloroplasts develop abnormally , and their leaves turn yellow . Photosynthesis can make toxic by-products , so cells need a way to turn it off when they are under stress; for example , by lowering the number of TOC complexes on the chloroplasts . This is achieved by tagging TOC complexes with a molecule called ubiquitin , which will lead to their removal from chloroplasts , slowing photosynthesis down . It is unknown whether another , similar , molecular tag called SUMO aids in this destruction process . To find out , Watson et al . examined a mutant of the plant Arabidopsis thaliana . This mutant had low levels of the TOC complex , turning its leaves pale yellow . A combination of genetic , molecular , and biochemical experiments showed that SUMO molecular tags control the levels of TOC complex on chloroplasts . Increasing the amount of SUMO in the mutant plants made their leaves turn yellower , while interfering with the genes responsible for depositing SUMO tags turned the leaves green . This implies that in plants with less SUMO tags , cells stopped destroying their TOC complexes , allowing the chloroplasts to develop better , and changing the colour of the leaves . The SUMO tagging of TOC complexes shares a lot of genetic similarities with the ubiquitin tag system . It is possible that SUMO tags may help to control the CHLORAD pathway , which destroys TOC complexes marked with ubiquitin . Understanding this relationship , and how to influence it , could help to improve the performance of crops . The next step is to understand exactly how SUMO tags promote the destruction of the TOC complex . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways . It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits . Here , we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons . We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns . Based on their in vivo preynaptic population statistics ( firing rates , membrane potential fluctuations , and correlations due to ensemble dynamics ) , our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging . These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits .
The dendritic tree of a cortical neuron performs a highly nonlinear transformation on the thousands of inputs it receives from other neurons , sometimes resulting in a markedly sublinear ( Longordo et al . , 2013 ) and often in strongly superlinear integration of synaptic inputs ( Losonczy and Magee , 2006; Nevian et al . , 2007; Branco and Häusser , 2011; Makara and Magee , 2013 ) . These nonlinearities have been traditionally studied from the perspective of single-neuron computations , using a few well-controlled synaptic stimuli , revealing a remarkable repertoire of arithmetic operations that the dendrites of cortical neurons carry out ( Poirazi and Mel , 2001; London and Häusser , 2005; Branco et al . , 2010 ) including additive , multiplicative and divisive ways of combining individual synaptic inputs in the cell’s response ( Silver , 2010 ) . More recently , the role of nonlinear dendritic integration in actively shaping responses of single neurons under in vivo conditions has been demonstrated in several cortical areas including the hippocampus ( Grienberger et al . , 2014 ) , as well as visual ( Smith et al . , 2013 ) and somatosensory cortices ( Xu et al . , 2012; Lavzin et al . , 2012; Palmer et al . , 2014 ) . However , while many of the basic biophysical mechanisms underlying these nonlinearities are well understood ( Stuart et al . , 2007 ) , it has proven a daunting task to include all these mechanisms in larger scale network models to understand their interplay at the level of the circuit ( Herz et al . , 2006 ) . Conversely , studies of cortical computation and dynamics have largely ignored the complex and highly nonlinear information processing capabilities of the dendritic tree and concentrated on circuit-level computations emerging from interactions between point-like neurons with single , somatic nonlinearities ( Hopfield , 1984; Seung and Sompolinsky , 1993; Gerstner and Kistler , 2002; Vogels et al . , 2011 ) . Therefore , it is unknown how dendritic nonlinearities in individual cells contribute to computations at the level of a neural circuit . A limitation of most theories of nonlinear dendritic integration is that they focus on highly simplified input regimes ( Mel et al . , 1998; Poirazi et al . , 2003; Archie and Mel , 2000; Poirazi and Mel , 2001; Ujfalussy et al . , 2009 ) , essentially requiring both the inputs and the output of a cell to have stationary firing rates . This approach thus ignores the effects and consequences of temporal variations in neural activities at the time scale of inter-spike intervals characteristic of in vivo states in cortical populations ( Crochet et al . , 2011; Haider et al . , 2013 ) . In contrast , we propose an approach which is specifically centered on these naturally occurring statistical patterns – in analogy to the principle of ‘adaptation to natural input statistics’ which has been highly successful in accounting for the input-output relationships of cells in a number of sensory areas at the systems level ( Simoncelli and Olshausen , 2001 ) . We pursued this principle in understanding the integrative properties of individual cortical neurons , for which the relevant statistical input patterns are those characterising the spatio-temporal dynamics of their presynaptic spike trains . Thus , rather than modelling specific biophysical properties of a neuron directly , our goal was to predict the phenomenological input integration properties that result from those biophysical properties and are matched to the statistics of the presynaptic activities . Our theory is based on the observation that cortical neurons mainly communicate by action potentials , which are temporally punctate all-or-none events . In contrast , the computations cortical circuits perform are commonly assumed to involve the transformations of analog activities varying continuously in time , such as firing rates or membrane potentials ( Rumelhart et al . , 1986; Hopfield , 1984; Dayan and Abbott , 2001; Archie and Mel , 2000; London et al . , 2010 ) . This implies a fundamental bottleneck in cortical computations: the discrete and stochastic firing of spikes by neurons conveys only a limited amount of information about their rapidly fluctuating activities ( Pfister et al . , 2010; Sengupta et al . , 2014 ) . Formalising the implications of this bottleneck mathematically reveals that the robust operation of a circuit requires its neurons to integrate their inputs in highly nonlinear ways that specifically depend on two complementary factors: the computation performed by the neuron and the long-term statistics of the inputs it receives from its presynaptic partners . To critically evaluate our theory , we first illustrate qualitatively the nonlinearities that most efficiently overcome the spiking bottleneck for different classes of presynaptic correlation structures . Next , to provide biophysical insight , we demonstrate that the form of optimal input integration for these presynaptic correlations can be efficiently approximated by a canonical , biophysically-motivated model of dendritic integration . Finally , we test the prediction that cortical dendrites are optimally tuned to their input statistics in in vitro experiments . For this , we use available in vivo data to characterize the presynaptic population activity of two different types of cortical pyramidal cells . Based on these input statistics , our theory accurately predicts the integrative properties of the postsynaptic dendrites measured in two-photon glutamate uncaging experiments . We also show that NMDA receptor activation is necessary for dendritic integration to approximate the optimal response . These results suggest a novel functional role for dendritic nonlinearities in allowing postsynaptic neurons to integrate their richly structured synaptic inputs near-optimally , thus making a key contribution to dynamically unfolding cortical computations .
To introduce our theory , we consider a postsynaptic neuron computing some function , f , of the activity of its presynaptic partners , u ( Figure 1A , top ) : ( 1 ) v˙=f ( u ) where v˙ is the resultant temporal change of the activity of the postsynaptic neuron . We chose u and v to be analog variables , rather than for example digital spike trains , in line with the vast bulk of theories of network computations ( Hopfield , 1984; Dayan and Abbott , 2001; Pouget et al . , 2003 ) and experimental results suggesting analog coding in the cortex ( London et al . , 2010; Shadlen and Newsome , 1998 ) . In particular , we considered these variables to correspond to the coarse-grained ( low-pass filtered ) somatic membrane potentials of neurons ( in particular , excluding the action potentials themselves , as often reported in experimental data; Carandini and Ferster , 2000 ) , although the theory can equally be formalized in terms of instantaneous firing rates ( Materials and methods , Figure 1-figure supplement 1 ) . 10 . 7554/eLife . 10056 . 003Figure 1 . Spike-based implementation of analogue computations in neural circuits . ( A ) Computation ( top ) is formalized as a mapping , f , from presynaptic activities , u1 , …uN ( left ) , to the postsynaptic activity , v ( right ) . As neurons communicate with spikes , the implementation ( bottom ) of any computation must be based on the spikes the presynaptic neurons emit , s1 , …sN ( middle ) . Optimal input integration in the postsynaptic cells requires that the output of g is close to that of f . ( B ) The logic and plan of the paper . Grey box in the center shows theoretical framework , blue boxes around it show steps necessary to apply the framework to neural data . To compute the transformation from stimulation patterns ( bottom left ) to the optimal response ( bottom right ) we assumed linear computation ( top right ) and specified the presynaptic statistics based on cortical population activity patterns observed in vivo ( top left ) . To demonstrate the validity of the approach , we studied the fundamental qualitative properties of the optimal response ( Figure 2 ) , compared it to biophysical models ( Figures 3–4 ) and tested it in in vitro experiments ( Figure 5 ) . ( C ) The optimal postsynaptic response ( purple line , bottom ) linearly integrates spikes from different presynaptic neurons ( top: rasters in shades of green; middle: membrane potential of one presynaptic cell ) if their activities are statistically independent . ( D ) Optimal input integration becomes nonlinear ( purple line , bottom ) if the activities of the presynaptic neurons are correlated ( rasters in shades of green , top ) , even though the long-term statistics and spiking nonlinearity of individual neurons remains the same as in ( C ) . In this case , the best linear response ( black line , bottom ) is unable to follow the fluctuations in the signal . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 00310 . 7554/eLife . 10056 . 004Figure 1—figure supplement 1 . An example of supralinear input integration with firing rate-based rather than membrane potential-based computations . ( A ) Optimal response ( dark red to pink ) and linear predictions ( black ) to an increasing number of input spikes ( 1→30 ) arriving with a short delay ( 1 ms ) . Throughout the paper , we assumed that the variables relevant for the postsynaptic neurons were the sub-threshold membrane potential values of their presynaptic partners , and for consistency , that the function the postsynaptic neuron computed was also represented by its sub-threshold membrane potential . We chose membrane potentials as the time-dependent representational substrate because they can be directly measured and are well defined at all times in individual trials in electrophysiological experiments ( in contrast to firing rates which require averaging over trials or time ) . Here , we demonstrate that our basic results apply equally if computations are instead based on firing rates . We used the same model of presynaptic statistics as before but defined the intended computation as a linear mapping between pre- and postsynaptic firing rates ( cf . Equation 5 ) : τpostr˙post ( t ) =-rpost ( t ) +∑i=1Nwiri ( t ) where ri ( t ) =geβui ( t ) is the firing rate of presynaptic neuron i as defined previously . This means that the optimal spike-based implementation is ( cf . Equation 6 ) : τpostr~˙post ( t ) =-r~post ( t ) +P ( u ( t ) |s0:t ) ∑i=1Nwi geβui ( t ) du ( t ) . Therefore , based on this equation , the equations of assumed density filtering ( Equations 20–22 ) remained unchanged but the optimal response was computed as τpostr~˙post ( t ) =-r~post ( t ) +∑iwi∑zζz ( t ) γi ( z ) ( t ) where γi ( z ) ( t ) =geβμi ( z ) ( t ) +12β2Σii ( z ) ( t ) is the posterior mean firing rate of neuron i in population state z , as before . Here we plot r~post ( t ) as the optimal response . ( B ) The amplitude of the measured response as a function of the linear expectations . ( C ) Nonlinearity of the response amplitude as a function of the number of input spikes . The nonlinearity in this case is comparable to that obtained in the case of membrane potential-based computations ( cf . Figures 2 and 5 of the main text ) . In fact , it is even stronger because the firing rate is a highly supra-linear ( here: exponential ) function of the membrane potential . Parameters were tuned to hippocampal sharp wave dynamics ( Table 1 , HP ) with N=500 , τpost=5ms , and wi=w=1/N . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 00410 . 7554/eLife . 10056 . 005Figure 1—figure supplement 2 . The range of total dendritic inputs in vitro and in vivo . ( A ) Input ranges to a single dendritic branch in our in vitro experiments ( green histogram ) are similar to the input ranges expected during natural , in vivo stimulus conditions ( black curve ) . Throughout the paper , we assumed that the dynamics of the postsynaptic cell is governed by the total input arriving to the cell , g ( s ) ( cf . Equation 6 ) : τpostv~˙ ( t ) =-v~ ( t ) +g ( s ) where g ( s ) =1N∫P ( u ( t ) |s0:t ) ∑i=1N ui ( t ) du ( t ) is the average of the estimated presynaptic membrane potentials . To illustrate the input distribution in our in vitro cortical experiment ( green histogram ) , we computed the values of the total input g ( s ) for s that were the stimuli given to the cell shown in Figure 5B–C and , just as in Figure 5C–E , we used neocortical input statistics ( Table 1 , NC ) to determine P ( ut|s0:t ) . Next , to compute the input distribution to a dendritic branch relevant in vivo ( black curve ) , we simulated 100 s of activity from a presynaptic population of N=120 neurons undergoing dynamics with the same neocortical statistics and estimated g ( s ) in a similar way , but now with s being the spikes generated using these in vivo statistics . We observed an excellent overlap between the ranges spanned by the two distributions suggesting that the relatively simple stimuli used in our in vitro experiments were appropriate to probe the physiologically relevant integrative properties of a single dendritic branch . However , note that the range of the input received in vivo by a whole neuron ( orange curve ) , rather than a single branch , can be substantially wider than that probed in our experiments . We modelled the neuron as possessing 10 branches receiving statistically independent inputs , with the total input being an average across all synapses and branches . ( B ) Examples for single neuron computation , f ( u ) . Note , that although f ( u ) can be highly nonlinear over the whole-neuron input range ( orange distribution in A ) , in many cases it can be reasonably well approximated by a linear function over the single-branch input range used in our in vitro experiments ( green box ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 00510 . 7554/eLife . 10056 . 006Figure 1—figure supplement 3 . Nonlinear computation . ( A ) To demonstrate that the theory applies to the case when the computation is nonlinear , we assumed that the postsynaptic neuron computes a sigmoidal function of the weighted sum ( specifically the average ) of the presynaptic membrane potentials ( cf . Equation 5 ) : f ( u ) =11+eβf∑i=1Nwiui-θf where θf and βf are the threshold and the slope of the sigmoidal nonlinearity , respectively ( see inset , which also shows the marginal distribution of the inputs ) . To demonstrate the need for nonlinear dendritic integration , we compared the optimal response ( black , Equation 3 , where f ( u ) is defined above , and the posterior P ( u|s ) is approximated by Equations 20–22 ) with a response of a linear model ( grey ) , a model with nonlinear soma but linear dendrites ( somatic , orange; Equation 31 ) and the clustered dendrite model with nonlinear dendrites and nonlinear soma ( clustered , red ) . For the somatic nonlinearity , we chose a sigmoidal function to match the form of the required computation and fitted its parameters to training data . For the clustered dendrite model , the dendritic nonlinearities were fitted to the data but the somatic nonlinearity was assumed to be identical to the nonlinearity used for the computation , f ( u ) . We cross-validated the quality of the fits on a separate set of test data . Similar test and training errors confirmed that the parameter optimisation found good , locally near-optimal solutions without significant overfitting ( not shown ) . ( B ) The error ( mean ± sd , Equation 33 ) of the clustered dendrite model ( red ) is similar to the error of the optimal response ( black ) and is substantially smaller than the error of the models with linear dendritic integration ( grey and orange ) . Moreover , having a global nonlinearity does not provide substantial improvement over the purely linear response , further emphasising the importance of nonlinear dendritic integration . Parameters were Ω-=10 Hz , Ω+=4 Hz , u¯=2 . 3 mV , τ=20 ms , Σii=1 mV2 , Σij=0 mV2 , g=10 Hz , β=0 . 4 mV-1 , τrefr=1 ms , prel=1 , βf=1 and θf=1 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 006 The standard description of neural circuit dynamics in Equation 1 hides an important informational bottleneck intrinsic to the operation of cortical circuits . While according to Equation 1 , the postsynaptic neuron’s analog activity , v , is required to depend directly on the analog activities of its presynaptic partners , u , in reality it only accesses these presynaptic activities through the spikes the presynaptic population transmits , s , incurring a substantial loss of information ( Alenda et al . , 2010; Sengupta et al . , 2014 ) . Therefore , the function g a neuron actually implements on its inputs can only depend directly on the presynaptic spikes , not the underlying activities ( Figure 1A , bottom ) : ( 2 ) v˙=g ( s ) Importantly , while f ( u ) is dictated by the computational function of the circuit , the actual transformation of the synaptic input to the postsynaptic response , expressed by g ( s ) , is determined by the morphological and biophysical properties of the cell . ( For these purposes , we regard the presynaptic side of synapses , transforming presynaptic spike trains to synaptic transmission events , as conceptually being part of the postsynaptic cell’s g ( s ) function . ) How can then the neuron integrate the incoming presynaptic spikes , as formalized by g ( s ) , such that the resulting postsynaptic response best matches the required computational function , f ( u ) , thereby alleviating the fundamental informational bottleneck of spiking-based communication ? Determining the best g ( s ) is nontrivial because the same presynaptic spike train may be the result of many different underlying presynaptic activities ( Paninski , 2006 ) , each potentially implying a different output of the computational function . This ambiguity is formalized mathematically as a posterior probability distribution , P ( u|s ) , expressing the probability that the analog activities of the presynaptic cells might currently be u given their spike trains , s ( Pfister et al . , 2010; Ujfalussy et al . , 2011 ) . The optimal response , i . e . the g ( s ) that minimizes the average squared error relative to f ( u ) , is the expectation of f ( u ) under the posterior: ( 3 ) g ( s ) =∫f ( u ) P ( u|s ) du Crucially , the expression for the posterior , given by Bayes’ rule , is: ( 4 ) P ( u|s ) ∝P ( s|u ) P ( u ) Note that while Equations 3–4 do not reveal directly the specific biophysical properties the postsynaptic cell should have , they tell us phenomenologically what signal integration properties should result from its biophysical properties . In particular , they make it explicit that the optimal g ( s ) depends fundamentally on two factors ( Figure 1B , top ) : In the following , we show that the outcome of the integration of presynaptic spike trains in cortical neurons approximates very closely the optimal response , and that dendritic nonlinearities are crucial for achieving this near-optimality . For this , 1 ) we make an assumption about the computational function of the postsynaptic cell , f ( u ) ( Figure 1B , top right ) ; 2 ) we constrain presynaptic statistics , P ( u ) and P ( s|u ) , by in vivo data about cortical population activity patterns ( Figure 1B , top left ) ; and with these 3 ) we compute the optimal response they jointly determine for various stimulation patterns ( Figure 1B , bottom left and right ) . To specify our model , we considered the case when f ( u ) itself is linear . Although networks with purely linear dynamics can perform non-trivial computations already ( Dayan and Abbott , 2001; Hennequin et al . , 2014 ) , in the general case , we do expect f ( u ) to be nonlinear , e . g . sigmoidal ( Hopfield , 1984 ) . Nevertheless , in typical electrophysiological experiments only a small fraction of the full dynamic range of a neuron’s total input is stimulated ( Figure 1—figure supplement 2 ) , and so we approximate the computational function , f ( u ) , as being linear on this limited input range without loss of generality . ( See Figure 1—figure supplement 3 for the application of the theory to the case of nonlinear f . ) Yet , as we show below , for physiologically realistic statistics of presynaptic activity patterns , the optimal response combines input spike trains in highly nonlinear ways even in the case of linear computation , predicting experimentally characterized nonlinearities in dendritic input integration . In particular , second- and higher-order prior presynaptic correlations , represented by P ( u ) , will have a major role in determining the form of the corresponding optimal response . The likelihood , P ( s|u ) , also influences the optimal response , but only in its quantitative details , as it does not involve correlations across neurons: each neuron’s firing is independent from the others’ , given its own somatic membrane potential ( Materials and methods ) . Previous suggestions for how postsynaptic neurons achieve reliable computation despite the substantial ambiguity about the individual presynaptic activities relied on the linear averaging of inputs arriving from a sufficiently large pool of presynaptic neurons ( Dayan and Abbott , 2001; Pfister et al . , 2010 ) . However , linear averaging is only guaranteed to produce the correct output , as dictated by Equations 3-4 , if the activities of presynaptic neurons are statistically independent under the prior distribution , i . e . P ( u ) =∏iP ( ui ) ( Materials and methods ) . In contrast , the membrane potential ( Crochet et al . , 2011 ) and spiking ( Cohen and Kohn , 2011 ) of cortical neural populations often show complex patterns of correlations , which include both ‘spatial’ ( cross-correlations between different neurons ) and temporal components ( auto-correlations , i . e . the correlation of the activity of the same cell with itself at different moments in time ) . Thus , in this more general case , we expect the optimal response to involve a nonlinear integration of spike trains . While temporal correlations alone do not require nonlinear dendritic integration across synapses , only local nonlinearities within each synapse , as brought about e . g . by short term synaptic plasticity ( Pfister et al . , 2010 ) , spatial correlations require the non-linear integration of spikes emitted by different presynaptic neurons . To illustrate that presynaptic spatial correlations require nonlinear integration across synapses , we compared the best linear response to a given presynaptic spike pattern with the optimal response ( Equation 3 , as approximated by Equation 23 ) for two different input statistics that differed only in the correlations between the presynaptic cells but not in the activity dynamics or spiking of individual neurons ( temporal correlations ) . To compute the postsynaptic response , we assumed that dendritic integration in the postsynaptic cell was linear but , in order to dissect the role of dendritic integration across synapses from the effects of nonlinearities in individual synapses , we allowed spikes from the same presynaptic neuron still to be integrated nonlinearly ( Pfister et al . , 2010 ) . In the first case ( Figure 1C ) , when the presynaptic neurons were independent , the best linear response was identical to the optimal response . However , if presynaptic neurons became correlated , the optimal response became nonlinear and the best linear response was unable to accurately follow the fluctuations in the input ( Figure 1D ) . 10 . 7554/eLife . 10056 . 007Table 1 . Parameters used in Figures 1–5 of the main paper ( see also Tables 2–3 ) . Ω- ( Ω+ ) is the rate of switching from the active to the quiescent ( from the quiescent to the active ) state . The resting potential corresponding to the active and quiescent states is u¯ and -u¯ , respectively . Σ¯ii ( Σ¯ij ) is the posterior variance ( covariance ) of the presynaptic membrane potential fluctuations in a given state where Σ¯=Qτ2 . τrefr is the length of the refractory period and prel is the baseline transmission probability in these synapses ( 13 , 49 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 007Figure 1Figure 2Figure 3Figure 4Figure 5parameterunitB , CA , BC , DABA-Dindcor2NCHPΩ-Hz10–1010–10––1010Ω+Hz10–0 . 270 . 67–0 . 67––40 . 027u¯mV2 . 402 . 32 . 302 . 300102 . 3τms20202020202020202020Σ¯iimV2116411111101Σ¯ij ∀i≠jmV200 . 50 . 500 . 500*50 . 5gHz1115 . 30 . 550 . 50 . 512βmV-110 . 40 . 40 . 50 . 40 . 4120 . 10 . 6τrefrms3331313333prel–111111110 . 50 . 2N–207020101010+0†+20†**τpostms01010000****wi–1/N1/N1/N1/N1/N1/N**** *These parameters were fitted to experimentally recorded dendritic responses , see Figure 5—figure supplement 1 . †The numbers 0 and 20 indicated here are in addition to the number of stimulated synaptic sites in the experiment . For the ind model , this number does not affect the predictions , for the cor2 model its effects could phenomenologically be incorporated into which we chose to fit instead . 10 . 7554/eLife . 10056 . 008Table 2 . Features of neocortical population activity during quiet wakefulness . Parameters of the model are given in column NC of Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 008DataModel ( NC ) Referenceduration of active states130 ms100 msGentet et al . ( 2010 ) duration of quiescent states200 ms250 msGentet et al . ( 2010 ) r+ , firing rate during active states2 . 5 Hz2 . 86 HzGentet et al . ( 2010 ) r- , firing rate during quiescent states≤1/3 Hz0 . 39 HzGentet et al . ( 2010 ) 2u¯ , depolarisation during active states20 mV20 mVGentet et al . ( 2010 ) time constant20 ms20 msPoulet and Petersen ( 2008 ) 10 . 7554/eLife . 10056 . 009Table 3 . Features of hippocampal population activity during sharp wave-ripple states . Parameters of the model are given in column HP of Table 1A recent intracellular study ( English et al . , 2014 ) recording from CA1 neurons in awake mice found parameters similar to our previous estimates . Using the parameters found in that study – r+=12 . 8 Hz , r-=2 . 85 Hz ( Table 1 of English et al . , 2014 ) , 2u¯=5 mV and Σ¯ii=4 mV2 ( Figure 3A of English et al . , 2014 ) yielding g=5 Hz and β=0 . 3 mV-1 – did not influence our results ( not shown ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 009DataModel ( HP ) Referenceactivation rate of an ensemble≪ 0 . 25 Hz0 . 027 HzGrosmark et al . ( 2012 ) ; Pfeiffer and Foster ( 2013 ) duration of SPWs105 ms100 msCsicsvari et al . ( 2000 ) r+ , firing rate during SPW10 Hz9 . 5 HzCsicsvari et al . ( 2000 ) r- , firing rate between SPWs0 . 5 Hz0 . 6 HzGrosmark et al . ( 2012 ) ; Csicsvari et al . ( 2000 ) 2u¯ , depolarisation during SPWs0–10 mV4 . 6 mVYlinen et al . ( 1995 ) time constant8–22 ms20 msEpsztein et al . ( 2011 ) Thus , inputs from presynaptic neurons whose activity tends to be correlated need to be nonlinearly integrated , while inputs from uncorrelated sources need to be integrated linearly . This could be naturally achieved in the same dendritic tree by clustering synapses of correlated inputs to efficiently engage dendritic nonlinearities , while distributing the synapses of uncorrelated inputs on different dendritic branches ( Larkum and Nevian , 2008 ) . Crucially , for correlated inputs it is also necessary that the dendritic nonlinearities have just the appropriate characteristics for the particular pattern of correlations in presynaptic activities . In order to systematically study the nonlinearities in the optimal response in the face of naturalistic input patterns , we derived and analyzed its behavior for a flexible class of richly structured , correlated inputs . Our statistical model for presynaptic activities , specifying the parametric forms of P ( u ) and P ( s|u ) ( Materials and methods and Figure 2—figure supplement 1 ) , was able to generate a variety of multi-neural activity patterns resembling the statistical properties described in in vitro and in vivo multielectrode recordings of neuronal population activities ( Figure 2A and D show two representative examples ) . Once we have specified the statistical model of presynaptic activities , it uniquely determined the optimal response to any given input pattern ( Equations 3–4 ) . Thus , we used the same statistical model in two fundamentally different ways: first , to generate “naturalistic” in vivo-like patterns of presynaptic membrane potential traces and spike trains; and second , to compute the optimal response pattern to any stimulation pattern , be it “naturalistic” or parametrically varying “artificial” as used in typical in vitro experiments . 10 . 7554/eLife . 10056 . 010Figure 2 . Nonlinearities in the optimal response . ( A–C ) Second order correlations between presynaptic neurons ( A ) imply sublinear integration ( B–C ) . ( A ) Membrane potentials and spikes of two presynaptic neurons with correlated membrane potential fluctuations . ( B ) The optimal response ( solid line ) to a single spike ( left ) and to a train of six presynaptic spikes ( right , green colors correspond to different presynaptic cells , two of which are shown in A ) when the long-run statistics of presynaptic neurons are like those shown in ( A ) . Shaded areas highlight how response magnitudes to a single spike from the same presynaptic neuron differ in the two cases: the response to the sixth spike in the train ( right , light blue shading ) is smaller than the response to a solitary spike ( left , gray shading ) implying sublinear integration . Dashed line shows linear response . ( C ) Response amplitudes for 1–12 input spikes versus linear expectations . ( D–F ) Same as ( A–C ) but for presynaptic neurons exhibiting synchronized switches between a quiescent and an active state , introducing higher order correlations between the neurons ( D , bottom ) . In this case , the optimal response shows supralinear integration ( E–F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01010 . 7554/eLife . 10056 . 011Figure 2—figure supplement 1 . Definition of the statistical model describing presynaptic activities and illustration of the inference process in the model . ( A ) Graphical model showing statistical dependencies between variables ( quantities changing in time , circles ) , and the parameters ( quantities constant on the time scale of our interest , above and beside arrows ) controlling those dependencies . ( B ) Table showing the variables and parameters of the model . See Materials and methods for further details . ( C ) Validating assumed density filtering ( Equations 20–22 ) with one presynaptic cell and two states . Black shows the true state variable ( top ) , and membrane potential trace with the spikes indicated by vertical lines ( bottom ) . Cyan shows the posterior mean state variable ( top ) and membrane potential ( bottom ) obtained by assumed density filtering , red shows the corresponding posterior means estimated by particle filtering . Parameters were N=1 , β=0 . 33 mV-1 , τ=20 ms , g=10 Hz , Σii=4 mV2 , u¯=5 mV and Ω+=Ω-=5 Hz . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 011 The optimal response determined by this statistical model , for essentially any setting of parameters , was inherently nonlinear because the additional effect of a presynaptic spike depended on the pattern of spikes that had been previously received from the presynaptic population . Temporal correlations in the presynaptic population caused the optimal response to depend on the spiking history of the same cell ( Pfister et al . , 2010 ) , while crucially , the additional presence of spatial correlations introduced a dependency on the past spikes of other cells . Thus , the integrated effect of multiple spikes could not be computed as a simple linear sum of their individual effects in isolation . Specifically , a spike that was consistent with the information already gained from recent presynaptic spikes had only a small effect on the response ( Figure 2B ) . Conversely , a spike that was unexpected based on the recent spiking history caused a larger change ( Figure 2E ) . As could be anticipated based on Equations 3-4 , whether a spike counted as expected or unexpected relative to recently received spikes , and hence whether it had a small or large postsynaptic effect , depended on the long-run prior distribution of presynaptic activities , P ( u ) . As a result , the same pattern of presynaptic spikes led to qualitatively different responses under different prior distributions . In particular , sublinear integration was optimal when presynaptic activities exhibited Gaussian random walks and thus they did not contain statistical dependencies beyond second order correlations ( Figure 2A-C ) , as seen in the retina and cortical cultures ( Schneidman et al . , 2006 ) . This was because with the activities of presynaptic neurons being positively correlated , successive spikes conveyed progressively less information about the presynaptic signal resulting in sublinear integration ( Figure 2C ) and the strength of the sublinearity depended on the magnitude of correlations ( Ujfalussy and Lengyel , 2011 ) . In contrast , supralinear integration was optimal when the presynaptic population exhibited coordinated switches between distinct states associated with large differences in the activity levels compared to activity-fluctuations within each state ( Figure 2D–F ) . These switches led to higher order statistical dependencies as seen in the cortex in vivo , either due to population-wide modulation by cortical state ( Gentet et al . , 2010; Crochet et al . , 2011 ) , or due to stimulus-driven activation of particular cell ensembles ( Harris et al . , 2003; Ohiorhenuan et al . , 2010; Miller et al . , 2014 ) . In this case , while observing a few spikes was consistent with random membrane potential fluctuations within the quiescent state , thus only warranting a small response , further spikes suggested that the presynaptic population was in the active state now and thus the response should be larger , leading to supralinear integration ( Figure 2F ) . Note , that nonlinearities in the optimal postsynaptic response needed not simply compensate for the nonlinearities in the presynaptic spike generation process , as captured by P ( s|u ) , but they critically depended on the presynaptic correlations , as captured by P ( u ) . Indeed , in Figures 1C , D and 2A–F , the same spiking nonlinearity was used and yet very different input integration was required depending on the form of the presynaptic statistics: linear integration for uncorrelated inputs ( Figure 1B ) and nonlinear integration for correlated inputs ( Figure 1C ) , with sub- or supralinear integration being optimal depending on whether only second order ( Figure 2A–C ) or also higher order correlations were present in the presynaptic population ( Figure 2D–F ) . Moreover , optimal input integration remained nonlinear even if the postsynaptic neuron computed a function of the presynaptic firing rates ( rather than membrane potentials ) which were linearly related to spikes ( Figure 1—figure supplement 1 ) . The nonlinear input integration seen in the optimal response strongly resembled dendritic nonlinearities . Indeed , the basic biophysical mechanisms present in dendrites naturally yield nonlinearities that are qualitatively similar to those of the optimal response: purely passive properties lead to sublinear integration ( Koch , 1999 ) , whereas locally generated dendritic spikes endow dendrites with strong supralinearities ( Nevian et al . , 2007; Branco and Häusser , 2011 ) . However , the full mathematical implementation of the optimal response is excessively complex ( Materials and methods ) and thus , there is unlikely to be a one-to-one mapping between the variables necessary for implementing it and the biophysical quantities available in dendrites . Therefore , we sought to establish a formal proof that dendritic-like dynamics can implement , even if approximately , the optimal response . For this , we considered two limiting cases of our statistical model of presynaptic activities , P ( u ) and P ( s|u ) , and compared the properties of the corresponding optimal response to a well-established simplified model of nonlinear dendritic integration , using a combination of analytical and numerical techniques . First , we considered a limiting case in which the statistics of a large presynaptic population were strongly dominated by the simultaneous switching of presynaptic neurons between a quiescent and an active state ( as shown in Figure 2D ) . In this limiting case we were able to show mathematically ( see Materials and methods ) that a simple , biophysically-motivated , canonical model of nonlinear dendritic integration ( Poirazi and Mel , 2001 ) is able to produce responses that are near-identical to the optimal response for any sequence of presynaptic spikes ( Figure 3A , see also Figures 4C ) . In this simple dendritic model ( Figure 3A , inset; Equations 24–25 ) , inputs within a branch are integrated linearly and the local dendritic response is then obtained by transforming this linear combination through a sigmoidal nonlinearity , which is a hallmark of supralinear behavior in dendrites ( Poirazi et al . , 2003b ) . 10 . 7554/eLife . 10056 . 012Figure 3 . A canonical model of dendritic integration approximates the optimal response . ( A ) The optimal response ( black ) and the response of a canonical model of a dendritic branch , v ( inset ) , with a sigmoidal nonlinearity ( red , Equation 25 ) as functions of the linearly integrated input , vlin ( inset , Equation 24 ) , when the presynaptic population exhibits synchronized switches between a quiescent and an active state , as in Figure 2D . Black dots show optimal vs . linear postsynaptic response sampled at regular 2 . 5 ms intervals during a 3 s-long simulation of the presynaptic spike trains . ( B ) Optimal response ( black ) approximated by the saturating part of the sigmoidal nonlinearity ( blue ) when the presynaptic population is fully characterized by second-order correlations , as in Figure 2A . Inset shows the same data on a larger scale to reveal the sigmoidal nature of the underlying nonlinearity ( gray box indicates area shown in the main plot ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01210 . 7554/eLife . 10056 . 013Figure 3—figure supplement 1 . Reducing the optimal response with second order correlations to a canonical model of dendritic integration . ( A–C ) Comparing the full ( Equations 20–23 ) and the reduced model Equations A99-A100 ) of the optimal response . The estimates of the mean presynaptic membrane potential by the full model ( A , grey ) and the reduced model ( A , black ) are nearly identical . The error of the reduced model ( quantified as the mean squared difference between the two models normalized by the variance of the full model ) decreases monotonically with increasing correlations in the presynaptic population ( B ) and remains bounded as the number of neurons increases ( C ) . ( D ) Steady state posterior variance , σ¯∞2 , as a function of the posterior mean , μ¯ , in the reduced model ( Equation A100 ) . ( E ) Comparing the linear-nonlinear model and the optimal response . Black dots: the optimal response against the output of the linear model , vlin ( Equation 24 ) . Blue line: sigmoidal nonlinearity operating in the linear-nonlinear model at the arrival of spikes , h ( vlin ) ( Equation A103 ) . Orange line: the result of numerically fitting a sigmoidal nonlinearity in the canonical model ( Equation 25 ) to the optimal response . Parameters were N=10 , g=2 Hz ( A–C ) , or N=1 , g=20 Hz ( D–E ) , and β=2 mV-1 , τ=20 ms , Σii=1 mV2 ( A–E ) , and ρ=0 . 5 ( A ) or as indicated on the x-axis ( B ) or in the legend ( C ) . For details , see Appendix B . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01310 . 7554/eLife . 10056 . 014Figure 3—figure supplement 2 . Adaptation without parameter change . ( A ) We simulated a presynaptic population with two different global activity states , a synchronized and a desynchronized state and first determined the optimal response in the two states separately ( black ) . Next , we trained a linear ( grey ) and a nonlinear ( red ) dendrite to approximate the optimal response in both the synchronized and the desynchronized state ( grey ) . ( B ) Green and black dots indicate the optimal response as a function of the best linear response respectively during the desynchronized and synchronized states . The same single dendritic nonlinearity ( red line ) can efficiently approximate the optimal response in both states simply because each state uses a different part of the input range of this nonlinearity: during the synchronized state the expansive supralinearity of the upstroke is being predominantly used , while during the desynchronized state the saturating sublinear-linear regime is dominating the response . ( C ) The error of the dendritic response is slightly larger than that of the optimal response but still substantially smaller than the error of the linear response . Parameters of the synchronized state were Ω-=10 Hz , Ω+=0 . 7 Hz , u¯=2 . 3 mV , τ=20 ms , Σii=1 mV2 , Σij=0 . 5 mV2 , g=5 . 3 Hz , β=0 . 5 mV-1 , τrefr=1 ms and prel=1 and the desynchronized state was identical to a persistent active phase of the synchronized state . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01410 . 7554/eLife . 10056 . 015Figure 4 . A simple nonlinear dendritic model closely approximates the optimal response for realistic input patterns . ( A ) Presynaptic spiking activity matching the statistics observed during hippocampal sharp waves . Spike trains ( rows ) belonging to four different assemblies are shown ( colors ) , gray shading indicates assembly activations . ( B ) Different variants of the dendritic model , parts colored in yellow , orange , and red highlight the differences between successive variants ( see text for details ) . ( C ) Estimating the mean of the presynaptic membrane potentials based on the observed spiking pattern ( shown in A ) by the optimal response ( black ) compared to the linear ( dotted ) , somatic ( yellow ) , random ( orange ) and clustered ( red ) models . ( D ) Performance of the four model variants compared to that of the optimal response . Gray lines show individual runs , squares show mean ± s . d . Performance is normalized such that 0 is obtained by predicting only the time-average of the signal , and 1 means perfect prediction attainable only with infinitely high presynaptic rates ( Materials and methods ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01510 . 7554/eLife . 10056 . 016Figure 4—figure supplement 1 . Responses of different variants of the dendritic model compared to the true signal . Responses of different variants of the dendritic model to the spiking pattern shown in Figure 4A of the main paper compared to the true signal ( green ) , that is the mean of the presynaptic membrane potentials . The models shown are the optimal response ( A , black ) and the linear ( B , dotted ) , somatic ( C , yellow ) , random dendrite ( D , orange ) and clustered dendrite ( E , red ) models ( see also Figure 4B–D of the main paper ) . The error ( gray areas , measured relative to the signal , cf . Figure 4C where it is measured relative to the optimal response ) of the linear , somatic and random models is substantially larger than the error of the optimal response , whereas the response of the clustered model is nearly identical to the optimal response . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01610 . 7554/eLife . 10056 . 017Figure 4—figure supplement 2 . Performance of different neuron models over a wide range of input statistics . ( A ) Performance of the optimal ( opt ) linear ( lin ) and clustered dendritic ( clust ) model for presyanaptic statistics with only second-order correlations ( Table 1 , cor2 ) with different numbers of presynaptic neurons ( N ) , while keeping firing rates fixed ( g=0 . 5 Hz ) . ( B ) Same as ( A ) , but changing firing rates ( g ) while keeping the number of presynaptic neurons fixed ( at N=10 ) . The performance of the dendritic neuron is always close to the performance of the optimal response . ( With N=10 and g=20 Hz the dendritic model seems to be slightly better than the optimal response . This is because we derived the optimal response with the assumption that there were no more than one spike in each time bin and we did not enforce this condition in these simulations . ) ( C ) Performance of the models using neocortical input statistics ( Table 1 , NC ) with different numbers of presynaptic neurons . ( D ) Performance of the models using hippocampal-like input ( Table 1 , HP; with Σ¯ij=0 and τrefr=1 ms in D–F ) with different numbers of presynaptic neurons . ( E ) The dendritic model performs significantly better than the linear model and nearly as well as the optimal response for different values of N and g . ( F ) The performance of the models depends mostly on the firing rate of the population , N×g , and not on N or g individually . ( Range of N values used is shown in the legend , g was 1 , 2 , 4 , or 10 . ) This can be useful because although the computational complexity of the optimal response scales as N2 , and thus computing it for large N can be prohibitive , this scaling suggests that , for practical purposes , the large N limit can be studied by scaling g rather than N . Error bars show s . d . and were smaller than the symbols for the means in some cases . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 017 Second , we considered another limiting case in which the statistics of the presynaptic population were fully characterized by second-order correlations ( as shown in Figure 2A ) . In this case , the same type of dendritic model , but with a sublinear input-output mapping , was able to approximate the optimal response very closely . Although a closed-form solution for the optimal nonlinear mapping could not be obtained in this case , it could be shown to be sublinear ( Appendix ) , and was well approximated by a sigmoidal nonlinearity parametrized to be dominantly saturating ( Figures 3B and Figure 3—figure supplement 1 ) . We also noted that it was the same type of sigmoidal nonlinearity which could implement supralinear and sublinear integration depending on the input regime ( low background , synchronous spikes: supralinear; high background , asynchronous spikes: sublinear integration , compare Figure 3A and B , inset ) . This suggests that dendritic integration may adapt to systematic changes in presynaptic statistics , such as those brought about by transitioning between the desynchronized and synchronized states of the neocortex , or sharp waves and theta activity in the hippocampus , without having to change the parameters of its nonlinearity ( Borst et al . , 2005 ) ( Figure 3—figure supplement 2 ) . Indeed , Gasparini and Magee ( 2006 ) demonstrated that dendritic integration in hippocampal pyramidal cells was supralinear when inputs were highly synchronized ( as they are during sharp waves ) , while integration was linear if the input was asynchronous ( such as during theta activity ) . While the foregoing analyzes proved that dendritic-like nonlinearities can closely approximate the optimal response in certain limiting cases , they do not address directly whether having such nonlinearities in input integration is crucial for attaining near-optimal computational performance for more realistic input statistics , or simpler forms of input integration could achieve similar computational power . To study this , we considered a scenario in which the presynaptic population consisted of four ensembles , such that neurons belonging to each ensemble underwent synchronized switches in their activity levels which were independent across the four ensembles , while there were also independent fluctuations in the activity of individual presynaptic neurons which were comparable in magnitude to those caused by these synchronized activity switches ( Figure 4A ) . We then assessed the performance of four different variants of a simple dendritic model relative to that of the optimal response ( Figure 4B ) : a model with linear dendrites and soma; a model in which only the soma was nonlinear , and two models in which nonlinearities resided in the dendrites with either random or clustered connectivity between the presynaptic assemblies and the dendritic branches . We quantified the performance of each of the models based on how closely their output approximated the linear average of the analog presynaptic activities giving rise to the spike trains they were integrating ( Figure 4—figure supplement 1 , Materials and methods ) . For a fair comparison , we tuned the parameters of each variant of the dendritic model to obtain the best possible performance with it ( Figure 4C ) . The model with nonlinear dendrites and clustered connectivity had near-optimal cross-validated performance ( Figure 4D ) while all other models performed significantly worse ( n = 20 runs , t = 51 , t = 35 , t = 20 , and P<10-15 , P<10-15 , P<10-13; respectively from left to right as shown in Figure 4D ) . This remained true when we varied the number and firing rate of presynaptic neurons over a wide range , and under a diverse set of qualitatively different population-level statistics , determining the dynamics of assembly switchings and within-assembly membrane potential correlations ( Figure 4—figure supplement 2 ) . Taken together , these results demonstrate that the clustering of correlated inputs together with nonlinearities akin to those found in dendrites is necessary to achieve optimal estimation performance in the face of presynaptic correlations . However , in order to be tractable , our dendritic model was mathematically simplified and , as a result , only qualitatively reproduced the nonlinearities of real dendrites . Thus , we directly compared experimentally recorded responses in dendrites to the optimal response . A crucial prediction of our theory is that dendritic nonlinearities act to achieve near-optimal responses in a way that the form of the nonlinearity is specifically matched to the long-run statistics of the presynaptic population . We tested this prediction in experiments in which two different types of cortical pyramidal neurons , from layer 2/3 of the neocortex ( Figure 5A–E ) and from area CA3 of the hippocampus ( Figure 5F–J ) , received patterned dendritic stimulation using two-photon glutamate uncaging , and compared their subthreshold somatic responses with the optimal responses predicted by the theory . 10 . 7554/eLife . 10056 . 018Figure 5 . Nonlinear dendritic integration is matched to presynaptic input statistics . ( A ) Sample membrane potential fluctuations ( left , adapted from Gentet et al . , 2010 ) and multineuron spiking patterns ( right , adapted from Ji and Wilson , 2007 ) recorded from the neocortex ( top ) , and matched in the model ( bottom , see also Tables 1–3 ) . ( B ) Two-photon image of a neocortical layer 2/3 pyramidal cell , numbers indicate individual dendritic spines stimulated in the experiment . ( C ) Responses to trains of seven stimuli using different inter-stimulus intervals ( ISI , shown below traces ) recorded in the cell shown in ( B ) ( black; mean ± s . d . ) and predicted by the optimal response tuned to the presynaptic statistics shown in A ( red ) . Parameters related to postsynaptic dendritic filtering were tuned for the specific dendrite ( Figure 5—figure supplement 1B–C ) . ( D ) Dependence of response amplitudes on ISI in the same dendrite shown in B-C ( squares ) , and as predicted by the optimal response ( filled circles ) or linear integration ( empty circles ) . ( E ) Average error of fitting dendritic recordings across all dendrites and conditions using the optimal response tuned to different presynaptic statistics ( NC , HP , cor2 , ind; see text for details ) compared to within-data variability ( var ) . Gray lines show individual dendrites . Rightmost bar ( NC-AP5 ) shows fit using NC presynaptic statistics to responses obtained after pharmacological blockade of NMDA receptor activation . ( F–J ) Same as ( A–E ) for presynaptic patterns characterized by hippocampal sharp waves ( F ) and recordings from hippocampal CA3 pyramidal cells ( H–J ) when stimulating synapses on its basal dendrites ( G ) . In vivo data in ( F ) was adapted from ( Ylinen et al . , 1995 ) ( left , membrane potential traces , not simultaneously recorded ) and ( O'Neill et al . , 2006 ) ( right , multineuron spike trains ) . Error bars show s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01810 . 7554/eLife . 10056 . 019Figure 5—figure supplement 1 . Best fit parameters for fitting dendritic responses . ( A–B ) The parameters of the HP and NC models describing the activity of the presynaptic population were fitted to in vivo recordings from the corresponding presynaptic populations ( see Materials and methods ) . When using these presynaptic models to fit individual hippocampal ( orange ) and neocortical ( red ) dendritic responses , respectively , we only tuned parameters that characterized the postsynaptic neuron for the optimal response: its membrane time constant , τpost ( A ) , the response amplitude for a single spike ( not shown ) , and the size of the dendritic sububit , i . e . the number of presynaptic neurons innervating a single dendritic branch , N ( B ) . ( C ) When using the cor2 presynaptic model to fit hippocampal ( hipp ) , neocortical ( cort ) and cerebellar ( cbl ) dendritic responses , we tuned the correlations between presynaptic neurons , ρ , instead of the size of the dendritic subunit ( for an explanation , see also Table 1 ) . Box plots show the range of the data ( whiskers ) , the quartiles ( box ) , and the median ( center line ) . Green lines in ( C ) show mathematically possible largest negative correlations ( -1N-1 ) , where N is the number of presynaptic cells . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 01910 . 7554/eLife . 10056 . 020Figure 5—figure supplement 2 . Dendritic integration in cerebellar stellate cells is not predicted by cortical presynaptic statistics . We used data recorded from cerebellar stellate cells in which input integration is sublinear ( Abrahamsson et al . , 2012 ) to test our predictions with a qualitatively different form of dendritic integration . Stellate cells receive input from cerebellar granule cells ( Shepherd , 2004 ) . ( A ) Top: Presynaptic membrane potential trace recorded intracellularly in a cerebellar granule cell ( adapted from Duguid et al . , 2012 ) . Bottom: presynaptic membrane potentials in two model neurons ( left ) and spiking in the model population ( right ) with simple second-order correlations across cells . ( No simultaneously recorded multineural experimental data was available . ) Although it is clear from in vivo recordings from cerebellar granule cells that these neurons do not show state-switching dynamics under anesthesia ( Duguid et al . , 2012 ) , the limited experimental data about their natural population activity made it unfeasible to fit our model quantitatively to data . ( B ) Responses to increasing number of stimuli ( 2 → 10 ) recorded in a cerebellar stellate cell ( black lines , adapted from Abrahamsson et al . , 2012 ) and predicted by the optimal response ( thick colored lines ) assuming second-order correlations ( left ) or independence ( right ) between the membrane potentials of presynaptic neurons . ( C ) Dependence of response amplitudes on the number of stimuli in the same dendrite shown in ( B ) ( squares , adapted from Abrahamsson et al . , 2012 ) , and as predicted by the optimal response ( assuming second order correlations , filled circles ) or linear integration ( empty circles ) . ( D ) Average error of fitting dendritic recordings across conditions using the optimal response tuned to different presynaptic statistics ( HP , NC , cor2 , ind; see main text , and Table 1 for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10056 . 020 For generating our predictions of the optimal response in these two cell types , we fitted the parameters describing presynaptic statistics in our model , P ( u ) and P ( s|u ) , to the statistical patterns in the activity of their respective presynaptic populations . For neocortical pyramidal cells , we fitted in vivo data available on the membrane potential fluctuations of layer 2/3 pyramidal cell-pairs in the barrel cortex during quiet wakefulness ( Gentet et al . , 2010; Crochet et al . , 2011 ) ( NC , Figure 5A , Table 2 ) . For hippocampal pyramidal cells , we fitted presynaptic statistics to membrane potential fluctuations ( Ylinen et al . , 1995; English et al . , 2014 ) and to multineuron spiking patterns of hippocampal pyramidal cells during sharp wave activity ( Csicsvari et al . , 1999; 2000 ) ( HP , Figure 5F , Table 3 ) . Due to the limitations of available hippocampal data sets , extracellular rather than intracellular data was used for fitting correlations . The motivation for our choice of the particular neocortical and hippocampal states used for fitting presynaptic statistics was two-fold . First , the general network state of the slice preparations in which we tested dendritic integration was likely most analogous to these states ( A Gulyás , personal communication; see also Karlocai et al . , 2014; Schlingloff et al . , 2014 ) , characterized by relatively suppressed neural excitability due to low levels of cholinergic modulation ( Harris and Thiele , 2011; Eggermann et al . , 2014 ) . Second , the stimulation protocol used in our study ( short bursts of synaptic stimuli following longer silent periods ) was also most consistent with population activity during hippocampal sharp waves and quiet wakefulness in the cortex . In order to capture variability across the cells we recorded from , the parameters related to postsynaptic dendritic filtering ( amplitude and decay of the response to a single stimulation , and the size of the dendritic subunit , Figure 5—figure supplement 1B-C ) were tuned for the individual dendrites . Importantly , the parameters describing presynaptic statistics were fitted without regard to our dendritic experimental data , thus allowing a strong test of our predictions about dendritic integration ( see Materials and methods ) . We found that the non-linear integration of individual spike patterns in cortical neurons was remarkably well fit by the optimal response when it was tuned to the correct presynaptic statistics ( Figure 5C , H ) . The systematic dependence of response amplitudes on the inter-stimulus interval ( ISI ) in individual cells ( Figure 5D , I ) was also well predicted by the optimal response . We quantified the quality of match between the predicted and experimentally recorded time course of responses across a population of n = 6 ( neocortex ) and n = 6 ( hippocampus ) dendrites under a range of conditions varying ISI or the number of stimuli , and found that the precision of our predictions was not statistically different from that expected from the inherent variability of responses in individual dendrites ( Figure 5E , J; neocortex: t = 0 . 2 , P = 0 . 85; hippocampus: t = 1 . 85 , P = 0 . 12 ) . In contrast , when the optimal response was tuned to unrealistic presynaptic statistics characterized purely by second-order correlations ( cor2 ) , or by a lack of any correlations implying statistically independent presynaptic firing ( ind ) , the quality of fits became significantly worse ( Figure 5E , J; neocortex: t = −4 . 6 , P = 0 . 006 for cor2 , and t = −4 . 9 , P = 0 . 004 for ind; hippocampus: t = −4 , P = 0 . 01 for cor2 , and t = −4 . 9 , P = 0 . 004 for ind ) . Moreover , using realistic presynaptic statistics , but matching hippocampal rather than neocortical activities , also resulted in significantly worse fits for neocortical responses ( Figures 5E; t= −3 . 6 , P = 0 . 02 ) . The converse was not observed in the case of hippocampal neurons ( Figures 5J; t= 0 . 43 , P = 0 . 68 ) . This might be because hippocampal neurons also receive neocortical inputs ( albeit on their apical not basal dendrites ) that show similar population activity patterns to the ones we matched here for the neocortical cells ( Isomura et al . , 2006 ) , while the primary sensory cortical pyramidal cells we recorded from do not receive direct input from the hippocampus . Nevertheless , when we analyzed the quality of fit between our predictions and recorded responses in hippocampal and neocortical data together , we found a small , but significant interaction between the source of the input statistics ( neocortex or hippocampus ) and the location of the postsynaptic neurons ( ANOVA F = 5 , P < 0 . 05 ) . This suggests that dendritic nonlinearities in cortical pyramidal neurons are specifically tuned to the dynamics of their presynaptic cortical ensembles . Furthermore , the blockade of NMDA receptor activation by AP5 resulted in dendritic responses that afforded substantially poorer fits by the model , even after refitting the postsynaptic parameters ( Figure 5E , J , AP5 ) . This indicated that the fine tuning of dendritic nonlinearities to input statistics relied on the action of NMDA receptors . As dendrites in both of our cortical cell types integrated inputs supralinearly , as a further control , we analyzed similar data available from cerebellar stellate cell dendrites , which are known to integrate their inputs sublinearly ( Abrahamsson et al . , 2012 ) ( Figure 5—figure supplement 2 ) . In this case , we fitted the statistics of individual presynaptic cells to those of cerebellar granule cells . The correlations between these cells are less known , but we found that assuming simple second-order correlations made the optimal response a close match to dendritic responses . In contrast , the hippocampal- or neocortical-like statistics that were crucial for matching responses in cortical dendrites ( Figure 5D , H ) resulted in a substantially poorer fit in this cerebellar cell type . This demonstrates a double dissociation in the matching of cortical and subcortical neuron types to cortical and non-cortical input statistics .
The central insight of our theory is the relationship between presynaptic statistics and postsynaptic response , formalized as the optimal response . The optimal response can be expressed as a set of nonlinear differential equations that requires storing and continuously updating ~N2 variables within the dendritic tree , where N is the number of synapses ( Materials and methods ) . Thus , it is unlikely to be implemented by the postsynaptic neuron as such . Consequently , to demonstrate the biophysical feasibility of our theory , we derived a simple approximation to the optimal response that performs about equally well using just a few postsynaptic variables and that corresponds to a canonical descriptive model of dendritic integration ( Poirazi et al . , 2003; Poirazi et al . , 2003b ) . We found that simple second order correlations between presynaptic neurons imply sublinear integration which can be implemented by the saturating nonlinearity characteristic of passive dendrites . Conversely , the biophysical substrate for the type of supralinear integration that was optimal for state-switching dynamics likely involves NMDA receptors because the particular dendritic nonlinearites observed in the cortical cells in which we tested our theory are known to be mediated primarily through NMDA receptor activation ( Branco et al . , 2010; Makara and Magee , 2013; Major et al . , 2013 ) . Indeed , we found that pharmacological inactivation of NMDA receptors abolished the precise match between dendritic integration and presynaptic statistics in these neurons ( Figure 5 ) . Moreover , the local plateau potentials generated by NMDA currents have been shown to have graded response durations ( Major et al . , 2008 ) , and the resulting nonlinearities could be continuously tuned between weaker and stronger forms ( boosting and bistability , Major et al . , 2013 ) . These properties make NMDA receptor mediated dendritic nonlinearities ideally suited for being matched to presynaptic statistics , as the optimal response involves sustained dendritic depolarisations of varying duration ( Figure 4 ) that depend parametrically on those statistics . A central prediction of our theory that awaits confirmation is the existence of a tight relationship between the structure of correlations in the activity of presynaptic cells and the morphological clustering of their synapses on the postsynaptic dendrite . This is because our theory requires nonlinear integration of inputs from neurons with statistically dependent activity , while spikes from independent neurons need to be integrated linearly . Biophysical considerations suggest ( Koch , 1999 ) and experimental data supports ( Polsky et al . , 2004; Losonczy and Magee , 2006 ) that , when synchronous , nearby inputs on the same dendritic branch are summed nonlinearly , whereas widely separated inputs are combined linearly . Consequently , our theory predicts that the correlation structure of the inputs will be mapped to the dendritic tree in a way that presynaptic neurons with strongly correlated activities target nearby locations while independent neurons innervate distinct dendritic subunits . According to our theory , the kind of correlation relevant for determining synaptic clustering is the ‘marginal’ correlations between the membrane potentials of presynaptic neurons . Marginal correlations include both signal and noise correlations ( Averbeck et al . , 2006 ) and thus can reach substantial magnitudes even when noise correlations alone are small , as e . g . during desynchronized cortical states ( Renart et al . , 2010 ) , especially for neurons with overlapping receptive fields ( Froudarakis et al . , 2014 ) , and when measured between the membrane potentials of neurons rather than their spike counts ( Dorn and Ringach , 2003; de la Rocha et al . , 2007; Poulet and Petersen , 2008 ) . At the level of different dendritic regions , the segregation of different input pathways along the dendritic tree of hippocampal neurons supports this prediction ( Witter et al . , 1989; Druckmann et al . , 2014 ) . At the level of individual synapses , the degree and the existence of clustering among inputs showing correlated activity is currently debated . High resolution imaging revealed subcellular topography of sensory inputs in the tadpole visual system ( Bollmann and Engert , 2009 ) , clustered patterns of axonal activity in the parallel fibres that provide input to cerebellar Purkinje cells ( Wilms and Häusser , 2015 ) , and experience-driven synaptic clustering in the barn owl auditory localization pathway ( McBride et al . , 2008 ) . Furthermore , it has been demonstrated that neighboring synapses are more likely to be coactive than synapses that are further away from each other in developing hippocampal pyramidal cells ( Kleindienst et al . , 2011 ) as well as in hippocampal cultures and in vivo in the barrel cortex during spontaneous activity ( Takahashi et al . , 2012 ) . These results thus suggest clustering of correlated inputs . In contrast , an interspersion of differently tuned orientation- , frequency- or whisker-specific synaptic inputs on the same dendritic segments was found in the mouse visual , auditory or somatosensory cortex , respectively , thus challenging the prevalence of synaptic clustering ( Jia et al . , 2010; Chen et al . , 2011; Varga et al . , 2011 ) . However , in all these studies the stimuli used were non-naturalistic and varied along a single stimulus dimension only ( direction of drifting gratings , pitch of pure tones , or the identity of the single whisker being stimulated ) , which may account for the apparent lack of clustering . In particular , our theory predicts clustering based on the long-term statistical dependencies between the responses of the presynaptic neurons for naturalistic inputs , which can be quite poorly predicted from their tuning properties for single stimulus dimensions ( Harris et al . , 2003; Fiser et al . , 2004 ) . In contrast , the statistical dependencies relevant for our theory are well represented by those found during spontaneous activity ( Berkes et al . , 2011 ) . Indeed , studies finding evidence in favor of synaptic clustering analyzed the structure of synaptic input to dendritic branches during spontaneous network activity ( McBride et al . , 2008; Kleindienst et al . , 2011; Makino and Malinow , 2011; Takahashi et al . , 2012 ) . Thus , presynaptic correlations for naturalistic stimulus sets may be predictive of synaptic clustering and providing more direct evidence for or against such clustering will offer a crucial test of our theory . Although , in general , we expect single-neuron computations to be nonlinear ( Zador , 2000 ) , and our theory indeed applies to nonlinear computations ( Figure 1—figure supplement 3 ) , we assumed the postsynaptic computation to be linear for matching experimental data . This choice was justified by two reasons . First , it is difficult to determine , without making strong prior assumptions , what kind of nonlinear function the neuron actually computes; and so the choice of any particular such function would have been arbitrary . Note that even in relatively well-characterized cortical areas ( such as the visual cortex ) it is unknown how much of the computationally relevant output of individual neurons ( such as orientation or direction selectivity ) is brought about by specific nonlinearities in the input-output transformations of these neurons , or by multiple steps of feed-forward and recurrent processing carried out at various stages of the visual pathway between the retina and those neurons . Moreover , in some cases , even networks with linear dynamics can provide a remarkably close fit to experimentally observed cortical population dynamics ( Hennequin et al . , 2014 ) . This issue may be best addressed in systems that are more specialized than the cortex so that there are well-supported hypotheses about the particular nonlinear computations individual neurons need to perform , such as the fly visual system ( Single and Borst , 1998 ) or the mammalian and avian auditory brain stem ( Agmon-Snir et al . , 1998 ) . In order to test our theory in these systems , in vivo multineural data will need to be collected from the afferent brain areas , preferably in the unanesthetized animal , for characterising the relevant statistical properties of the presynaptic population to which dendritic nonlinearities are adapted according to our prediction . Second , any nonlinear function can be approximated to high precision by a linear function over a sufficiently limited input range . Currently available experimental techniques for systematically probing dendritic nonlinearities , including those used in our study , only provide data over such a very limited range ( ∼0 . 1% of the number of excitatory inputs impinging a neuron , Megías et al . , 2001 ) . Inputs in this small range do not sufficiently engage global nonlinearities brought about by active somatic conductances or global events such as Ca2+ spikes . Thus , we could assume linear computation over this range without loss of generality ( Figure 1—figure supplement 2 ) . In fact , from this perspective , it is a non-trivial phenomenon to account for on its own right that stimulating such a small fraction of inputs already leads to observable nonlinearities in the postsynaptic dendrite . By defining the computation to be linear , we could demonstrate that such strong dendritic nonlinearities arise naturally in our theory , entirely due to the correlations in the prior input statistics , thus providing a functional account for this remarkable phenomenon . Once patterned dendritic stimulation over a broader and more realistic range of inputs becomes feasible , our theory will provide a principled method for dissecting the roles of presynaptic correlations vs . genuine nonlinear computations in shaping dendritic nonlinearities . A sufficiently rich set of such data will allow the fitting of presynaptic parameters , as we did here , followed by fitting postsynaptic transfer functions to dendritic responses without having to make strong prior assumptions about their ( linear ) nature . Our formalism was based on the assumption that cortical neurons only influence each other’s membrane potentials via the action potentials they emit . While there exist other , more analog forms of communication , such as the modulation of the effects of action potentials by subthreshold potentials ( Clark and Häusser , 2006 ) , the propagation of voltage signals through gap junctions ( Vervaeke et al . , 2012 ) , and ephaptic interactions between nearby cells ( Anastassiou et al . , 2011 ) , these either require slow membrane potential dynamics , small distances between interacting cells , or large degrees of population synchrony , and are thus generally believed to have a supplementary role beside spike-based communication ( Sengupta et al . , 2014 ) . Note that our theory is self-consistent even though it considers spiking only in the presynaptic population and not in the postsynaptic neuron . This is because we assumed that the computationally relevant mapping is that between the membrane potentials of the presynaptic neurons and the postsynaptic cell ( Figure 1A , Equation 1 ) , and so , by induction , the spikes of the postsynaptic neuron will effect the mapping from its membrane potential to those of its postsynaptic partners . We also assumed that presynaptic spikes deterministically and uniformly impact the postsynaptic response , and thus apparently neglected the stochasticity in synaptic transmission , and in particular systematic variations in synaptic efficacy brought about by short-term synaptic plasticity . Nevertheless , these presynaptic features are compatible with our theory . The stochasticity of synaptic transmission , due to a baseline level of synaptic failures , is straight-forward to incorporate in the model by reducing the effective presynaptic firing rate , which can thus be interpreted as a ‘transmission rate’ instead . In fact , we have already done this while matching hippocampal and neocortical presynaptic statistics ( Table 1 ) . Short-term synaptic plasticity , resulting in dynamical changes in synaptic efficacy as a function of the recent spiking history of the presynaptic neuron , is not only a constraint in our framework , but as we have shown in related work , it can act itself as an optimal estimator of the membrane potentials of individual presynaptic neurons ( Pfister et al . , 2010 ) . Thus , the effects of short-term plasticity can be regarded as a special case of what can be expected from our optimal response: when presynaptic neurons are statistically independent , spikes arriving at different synapses are integrated linearly , and local nonlinearities acting at individual synapses suffice ( Figure 1C , see also Materials and methods ) . However , the importance of nonlinear interactions between inputs from different presynaptic neurons , brought about by dendritic nonlinearities , rapidly increases with the magnitude of presynaptic correlations , especially in large populations ( Figure 1D , see also Ujfalussy et al . , 2011 ) . These considerations suggest that short-term synaptic plasticity and dendritic nonlinearities have complementary roles in tuning the postsynaptic response to the statistics of the presynaptic population along the orthogonal dimensions of time and space . The former is useful in the face of temporal correlations private to individual presynaptic neurons ( auto-correlations , e . g . , brought about by spike frequency adaptation , Pfister and Surace , 2014 ) , while the latter is matched to spatio-temporal correlation patterns present across the presynaptic population . We focused on the nonlinear integration of excitatory inputs in the dendritic tree of cortical neurons that have been extensively studied and described over the past decades , giving rise to a strong body of converging evidence as to their characteristics and mechanisms ( Spruston , 2008 ) . Recent work studying the nonlinear interaction between inhibitory and excitatory inputs in active dendrites ( Gidon and Segev , 2012; Jadi et al . , 2012; Müller et al . , 2012; Wilson et al . , 2012; Lovett-Barron et al . , 2012 ) demonstrated that local inhibition has a powerful control over the excitability of the dendritic tree . However , it is not yet clear whether these inhibitory inputs are directly involved in the computation performed by the circuit , just as excitatory neurons but with negative signs ( Koch et al . , 1982 ) , or , alternatively , they may have a more ancillary role in supporting computations carried out primarily by excitatory neurons ( Vogels et al . , 2011 ) . Our theory can be extended to include both possibilities , by allowing inhibitory inputs to contribute to the computational function , f ( u ) , with negative weights , or by considering them as providing auxiliary information about the common state of the excitatory presynaptic ensemble , especially when this state is in the more suppressed regime . Indeed , our preliminary results suggest that such an extension of our theory ( Ujfalussy and Lengyel , 2013 ) successfully accounts for the interaction of ( excitatory ) Schaffer collateral inputs with the feedforward inhibitory effects of the temporo-ammonic pathway ( Remondes and Schuman , 2002 ) , likely mediated by interneurons delivering dendritic inhibition ( Dvorak-Carbone and Schuman , 1999 ) . In the present paper we focused on dendritic integration in pyramidal neurons because dendritic nonlinearities have traditionally been more extensively characterized in this cell type , but our theory equally applies to synaptic integration in other types of neurons , including inhibitory interneurons . Therefore , our theory predicts a qualitative similarity of dendritic integration in different neuron types ( i . e . interneurons versus principal cells ) when they receive inputs from overlapping presynaptic populations . Indeed , it has been recently found that inhibitory interneurons can exhibit dendritic NMDA spikes under certain experimental circumstances ( Katona et al . , 2011; Chiovini et al . , 2014 ) in addition to standard sublinear integration . The differences between dendritic integration in excitatory and inhibitory neurons could be attributed to their different computational function , f ( u ) , or differences in the specific presynaptic populations innervating them . According to our theory , the optimal response depends on prior information about the input statistics . Consequently , for dendritic processing to approximate the optimal response , this prior information needs to be implicitly encoded in the form of the particular nonlinearity a dendrite expresses . Therefore , our theory predicts an ongoing adaptation of dendritic nonlinearities to presynaptic firing statistics across several time-scales . First , there is a simple yet potent mechanism implicit in our theory that can ensure that a match of dendritic integration to presynaptic statistics is maintained as those statistics are changing over time . This is based on the observation that essentially instantaneous , albeit probably incomplete , adaptation can occur even without specific changes in the integrative properties of dendrites per se , simply due to the fact that a critical level of input synchrony is required to elicit dendritic spikes , and so the same sigmoid-looking dendritic transfer function can be used as superlinear , linear , or sublinear , depending on which part of its input range is being used ( Figure 3—figure supplement 2 ) . Second , to match the more specific modulation of the statistics of presynaptic activities by global cortical states ( Crochet et al . , 2011; Mizuseki and Buzsaki , 2014 ) , dendritic integration may also be modulated by these states . As different cortical states are typically accompanied by changes in the neuromodulatory milieu ( Hasselmo , 1995; Harris and Thiele , 2011 ) , neuromodulators may be the ideal substrates to ensure that dendritic integration also changes according to the current cortical activity mode . This may provide a functional account of changes in the excitability of the dendritic tree as dynamically regulated by acetylcholine and monoamines ( Sjöström et al . , 2008 ) . Third , experience-dependent synaptic plasticity can gradually change the statistics of the presynaptic population activity implying that the optimal form of input integration should also change as a function of experience . We propose that branch-specific forms of plasticity of dendritic excitability ( Losonczy et al . , 2008; Makara et al . , 2009; Müller et al . , 2012 ) may have a functional role in enabling dendrites to adjust the form of input integration to such slowly developing and long-lasting changes in the statistics of their inputs . Finally , whether inputs from two presynaptic cells are integrated linearly or nonlinearly in a dendrite depends critically on the distance between their synapses within the dendritic tree ( Polsky et al . , 2004; Losonczy and Magee , 2006 ) . Our theory requires nonlinear integration of inputs from neurons with statistically dependent activity , predicting a mapping of presynaptic correlations on the postsynaptic dendritic tree . Local electrical and biochemical signals can drive synaptic plasticity ( Larkum and Nevian , 2008; Govindarajan et al . , 2011; Winnubst et al . , 2015 ) and rewiring ( DeBello , 2008 ) leading to synaptic clustering of correlated inputs along the dendritic tree ( Kleindienst et al . , 2011; Takahashi et al . , 2012 ) . A combination of all these mechanisms may be crucial for achieving and dynamically maintaining , at the level of individual neurons , a detailed matching of dendritic nonlinearities to presynaptic statistics . Thus , our theory provides a novel framework for studying a range of phenomena regarding the dynamical regulation of dendritic nonlinearities from the perspective of circuit-level computations .
In order to study the optimal form of input integration with realistic input statistics , we need to make two important assumptions . First , we need to assume a particular algebraic form for the computation that a neuron performs . Second , we need to define what the relevant presynaptic statistics are , that is , the membrane potential and spiking dynamics of the presynaptic population under naturalistic in vivo circumstances . Given these two assumptions , the theory uniquely defines the optimal response of a neuron to any input pattern . The optimal response has qualitatively similar properties whether computations are defined as mappings between pre- or postsynaptic membrane potentials or firing rates ( Figure 1—figure supplement 1 ) . Throughout the paper the term input refers to the spatio-temporal spiking pattern impinging the neuron while the response of a neuron refers to its ( subthreshold ) somatic membrane potential ( or firing rate , see below ) . All parameters used in the paper are given in Table 1 or in the caption of the corresponding Figure . We assumed the postsynaptic computation to be linear , i . e . the dynamics of the postsynaptic membrane potential v ( t ) evolves according to a weighted sum of the presynaptic membrane potential values , u ( t ) ( cf . Equation 1 ) : ( 5 ) τpostv˙ ( t ) =-v ( t ) +∑i=1Nwiui ( t ) where τpost is the time constant of the postsynaptic neuron , N is the number of presynaptic neurons , and wi is the computational weight assigned to presynaptic neuron i . As the postsynaptic neuron cannot access presynaptic membrane potentials , u , directly only the spikes the presynaptic cells emit , s , ( Figure 1 , Equation 2 ) , the optimal response ( that minimizes mean squared error ) is the expectation of Equation 5 under the posterior distribution of the presynaptic membrane potential at time t , u ( t ) given the history of presynaptic spiking up to that time , s ( 0:t ) ( cf . Equation 3 ) : ( 6 ) τpostv~˙ ( t ) =-v~ ( t ) +∫P ( u ( t ) |s ( 0:t ) ) ∑i=1Nwiui ( t ) du ( t ) Throughout the paper we call the output of Equation 6 the optimal response and compare its behavior to input integration in the dendrites of cortical pyramidal cells . Table 1 shows the values of the parameters in Equation 5 ( N , τpost , and wi ) used in the simulations . In short , to illustrate the contributions of inference to Equation 6 ( the term including the integral ) , we used τpost=0 in Figures 1 , 3 and 4 as well as in all Supplemental Figures , unless otherwise stated . We used τpost=10 ms in Figure 2 to aid comparison with experimental data and fitted τpost to data for Figure 5 . Throughout the paper we used wi=1/N , except in Figure 5 where we fit N and wi=w jointly to the data . Computing the posterior , P ( u ( t ) |s ( 0:t ) ) in Equation 6 requires a model for the joint membrane potential and spiking statistics of the presynaptic population , P ( u , s ) ( see also Equation 4 ) . For mathematical convenience , we present some of our results below in discrete time with time step size δt , which we will eventually take to zero to derive time-continuous equations . We distinguish discrete and continuous time results by using time as an index versus as an argument of the corresponding time-dependent quantities , e . g . ut vs . u ( t ) . We describe the joint statistics of presynaptic membrane potentials and spikes by a hierarchical generative model that has three layers of variables ( Figure 2—figure supplement 1A , B ) . The global state of the system is described by a single binary variable , z that switches between a quiescent ( - ) and an active ( + ) state following first-order Markovian dynamics ( see Appendix for the extension to an arbitrary number of states ) . The transition rates to the active and quiescent states are given by Ω+ and Ω- , respectively . The dynamics of ( subthreshold ) membrane potentials u are modeled as a multivariate Ornstein-Uhlenbeck ( mOU ) process , which yields random walk-like behavior that ( unlike simple Brownian motion ) decays exponentially towards a baseline defined by the resting potential u¯ , which in turn depends on the momentary global state of the system , zt: ( 7 ) P ( ut|ut-δt , zt ) =△𝒩 ( ut; ( 1-δtτ ) ut-δt+δtτ u¯ ( zt ) , δt Q ) where τ is the presynaptic time constant of the exponential decay , and Q is the ‘process noise’ covariance matrix determining the variance of individual membrane potentials ( together with τ ) and , importantly , also the correlations between presynaptic neurons . It is straightforward to extend the model by also making these parameters state- ( or in fact , neuron- ) dependent . Note that both the state switching and mOU components of this model introduce both spatial and temporal statistical dependencies in the membrane potentials and spike trains of presynaptic cells . In the rest of this paper , we informally refer to any statistical dependency ( second or higher order ) as ‘correlation’ , and we write ‘auto-correlation’ when we refer to the correlations between the membrane potential ( firing rate ) values of the same neuron at different times , and ‘cross-correlation’ when referring to the correlation between the activities of two different cells ( at the same time , or at different times ) . Also note that temporal and spatial correlations can not be studied in complete isolation in the case of smoothly varying signals , such as membrane potentials , as the cross-correlation between the activity of two presynaptic neurons always has a characteristic temporal profile . While it is possible to consider a presynaptic neuronal population completely lacking spatial correlations ( i . e . independent presynaptic neurons , as in Figure 1C ) , having a population with only spatial but not temporal correlations would require the membrane potentials of the individual neurons to be temporally white noise – which is so far removed from reality that we did not consider this case worth pursuing . More specifically , the timescale of temporal correlations ( auto-correlations ) in the model depend on the transition rates of the switching component , Ω+ and Ω- , and the presynaptic time constant of the mOU component , τ , such that cells are auto-correlated as long as τ , Ω+-1 , and Ω--1>0 . Spatial correlation ( cross-correlations between different presynaptic neurons ) also emerge from both components . First , the pairs of presynaptic neurons corresponding to the non-zero off-diagonal elements of Q matrix of the mOU component become correlated . Second , synchronous state transitions during state switching in the presynaptic ensemble introduce positive correlations . Importantly , in both the temporal and spatial domains , while the mOU process can only introduce second-order correlations ( i . e . it makes membrane potentials be distributed according to a multivariate normal ) , the switching process introduces higher order correlations ( such that membrane potentials are not normally distributed any more ) . These higher order correlations are stronger when the effect of state-switching is large relative to the membrane potential fluctuations within a single state . Finally , instead of modeling the detailed dynamics of action potential generation , we model spiking phenomenologically by introducing a single discrete variable , si , t , for each presynaptic neuron that represents the number of spikes neuron i fires in time step t . ( Note that in the limit δt→0 this variable becomes binary , i . e . there can never be more than one spike fired in a δt time window . ) Spiking in each cell only depends on the membrane potential of that cell , and follows an inhomogeneous Poisson process with the firing rate , r , being an exponential function of the membrane potential ( Gerstner and Kistler , 2002 ) : ( 8 ) P ( st|ut ) =△∏i Poisson ( si , t;δt ri , t ) , with ri , t=geβui , t where β describes how deterministically the cell switches to firing at threshold ( u=0 ) and g is the firing rate at that threshold . We modeled the absolute refractory period by not allowing the generation of spikes ( i . e . setting ri , t=0 ) within a time window of length τrefr following each spike in a cell , regardless of its membrane potential . The parameters of the presynaptic statistics used in the paper are given in Table 1 . Examples of neural dynamics generated by the model are shown in Figures 1 , 2 , 4 , and 5 . Our goal was to infer the posterior distribution of the membrane potential based on the spiking pattern observed up to time t , P ( ut|s0:t ) . We first show that linear dendritic integration is sufficient when presynaptic neurons are statistically independent . We start by noting that by marginalising out the past membrane potential history of the presynaptic cells and using Bayes’ rule , the posterior can always be written as ( 9 ) P ( ut|s0:t ) =∫P ( u0:t|s0:t ) du0:t-δt ∝∫P ( s0:t|u0:t ) P ( u0:t ) du0:t-δt and as the spikes of each neuron are independent from all other neurons conditioned on its own membrane potential history ( Equation 8 ) , this can be rewritten as ( 10 ) P ( ut|s0:t ) ∝∫∏iP ( si , 0:t|ui , 0:t ) P ( u0:t ) du0:t−δt In the special case when we assume that presynaptic neurons are statistically independent , i . e . their prior factorizes P ( u0:t ) =∏iP ( ui , 0:t ) , the posterior also becomes factorised ( 11 ) P ( ut|s0:t ) ∝∏i∫P ( si , 0:t|ui , 0:t ) P ( ui , 0:t ) dui , 0:t−δt ( 12 ) =∏iP ( ui , t|si , 0:t ) which in continuous time reads simply as ( 13 ) P ( u ( t ) |s ( 0:t ) ) = ∏iP ( ui ( t ) |si ( 0:t ) ) Thus , taking our usual assumption that the postsynaptic computation is linear ( Equation 5 ) , the optimal response in Equation 6 can be written as ( 14 ) τpostv~˙ ( t ) =-v~ ( t ) +∑i=1Nwi∫ui ( t ) P ( ui ( t ) |si ( 0:t ) ) dui ( t ) indicating that integration of inputs from different neurons is linear in this case ( it is a weighted sum of terms each depending on just a single presynaptic neuron ) . However , even in this case , note that integration of input spikes from the same presynaptic neuron , i . e . the result of the integral over each ui ( t ) as a function of si ( 0:t ) , is still nonlinear in general ( Pfister et al . , 2010 ) . Indeed , Equation 14 including these local nonlinearties was used to compute the linear response in Figure 1C–D . In the general case inference can be performed using filtering such that in each step we update the inferred state of the hidden variables , zt and ut , using information from two different sources: the likelihood of emitting a particular spiking pattern ( observation ) and the dynamics of the hidden variables combined with the previous estimate ( innovation ) : ( 15 ) P ( ut=u , zt=z|s0:t ) ∝P ( st=s|ut=u ) ⋅==⋅∑z′P ( zt=z|zt−δt=z′ ) ∫du′P ( ut=u|ut−δt=u′ , zt=z ) ⋅⋅ P ( ut−δt=u′ , zt−δt=z′|s0:t−δt ) where the likelihood P ( st|ut ) is defined by Equation 8 , the dynamics of the global state variable P ( zt|zt-δt ) is first order , Markovian ( see above ) and the state-dependent membrane potential dynamics P ( ut|ut-δt , zt ) is given by Equation 7 . Equation 15 thus defines a mapping between the posterior distribution of the hidden variables in the previous time step ( last term on RHS ) and their current distribution ( LHS ) . The posterior over membrane potentials can then be obtained by simply marginalising out the state variable: ( 16 ) P ( ut|s0:t ) =∑z P ( ut , zt=z|s0:t ) For the following , it is useful to represent the posterior as a product of two terms: ( 17 ) P ( ut=u , zt=z|s0:t ) =P ( zt=z|s0:t ) P ( ut=u|zt=z , s0:t ) As the state variable is binary , its posterior is a Bernoulli distribution which we parametrize by ζ , without loss of generality: ( 18 ) P ( zt=+|s0:t ) =△ζt However , in general , the posterior of the membrane potentials conditioned on the current state , zt , can be arbitrarily complex . To allow an analytical reduction of the inference process , we adopted an assumed density filtering approach in which this distribution is moment-matched in each time step by a multivariate normal distribution which is thus described by two ( sets of ) parameters , its mean , μt ( z ) , and covariance , Σt ( z ) : ( 19 ) P ( ut=u|zt=+ , s0:t ) ≃△𝒩 ( u;μt+ , Σt+ ) with the analogous equation for the posterior of ut conditioned on zt being in the - state . One advantage of this parametric approach is that inference ( filtering ) can be implemented by updating only the parameters describing the ( approximate ) posterior distribution ( Equations 18–19 ) : ζt , μt ( z ) , and Σt ( z ) . In the Appendix we derive an analytical form for these parameter updates resulting in the following set of differential equations: ( 20 ) ζ˙=−ζ ( 1−ζ ) ( γ¯+−γ¯− ) +ζ ( 1−ζ ) s ( t ) T⟨Γ⟩−1 ( γ+−γ− ) + ( 1−ζ ) Ω+−ζΩ− ( 21 ) μ˙+=u¯+−μ+τ+βΣ+ ( s ( t ) −γ+ ) +1−ζζΩ+ ( μ−−μ+ ) ( 22 ) Σ˙+=2τ ( τ2Q−Σ+ ) −β2Σ+Γ+Σ++1−ζζΩ+[ ( Σ−−Σ+ ) + ( μ−−μ+ ) ( μ−−μ+ ) T] where γ ( z ) ( Γ ( z ) ) is a state-dependent vector ( diagonal matrix ) of which the elements γi ( z ) =Γii ( z ) =geβμi ( z ) +12β2Σii ( z ) are the expected firing rates of the neurons in a given state , γ¯ ( z ) =∑iγi ( z ) is the expected total population firing rate in state z , and ⟨Γ⟩=ζ Γ++ ( 1-ζ ) Γ- is the expected firing rate of the cells averaged across states . In these equations , the spike trains of the presynaptic neurons are represented by the sum of Dirac-delta functions in continuous time and are denoted by s ( t ) , to be distinguished from its discrete time analog , st , such that s ( t ) =limδt→0st/δt ( see Equation A62 in the Appendix ) . The differential equations for the conditional mean and variance in the - state , μ˙- and Σ˙- , are analogous to Equations 21–22 . The absolute refractory period is taken into account by setting γi=Γii=0 after each observed spike for the duration of the refractory period , τrefr , thus omitting the effect of the likelihood ( terms containing γ ( z ) or Γ ( z ) ) from Equations 20–22 . The first term in Equation 20 captures the decay in ζt that is proportional to the difference in the state conditional firing rates in the absence of presynaptic spikes; the second term expresses the instantaneous change in ζt after observing a spike , proportional to both the state estimation uncertainty , ζ ( 1-ζ ) , and the differences in the conditional firing rates ( γ+-γ- ) ; and the last term captures the decay of ζt to its steady state in the absence of observations . The filtering equations for the conditional mean and covariance ( Equations 21–22 ) are each composed of three terms: the first term expresses the decay of the variable towards its baseline in the absence of observations; the second term captures the effect of the current observation ( i . e . the presence or absence of a spike ) on the variable; and the third term describes the changes in the variable caused by potential state transitions . This can be viewed an extension and generalization of earlier work deriving the equivalents of Equations 21–22 for the special case of a single neuron without state-switching dynamics ( Pfister et al . , 2010 ) . Another advantage of the parametrization of the posterior we chose is that computing the optimal response , i . e . the posterior expectation of the simple linear functions that we consider in this paper , becomes straightforward ( cf . Equation 6 ) : ( 23 ) τpostv~˙ ( t ) =−v~ ( t ) +∑iwi ( ζ ( t ) μi+ ( t ) + ( 1−ζ ( t ) ) μi− ( t ) ) In order to verify the assumed density filtering approximations used above we numerically integrated the system of differential equations ( Equations 20–22 ) using the software package R ( R Core Team , 2012; Soetaert et al . , 2010 ) and compared the results with those obtained using standard particle filters ( Doucet et al . , 2001 ) . In these simulations we used 500 , 000 particles to evaluate Equation 15 with 1 neuron and 2 states . Figure 2—figure supplement 1C shows that the results of assumed density filtering are essentially identical to those of particle filtering , confirming that the approximations we used were valid . Here we first describe a simple canonical model of dendritic integration following Poirazi and Mel ( Poirazi and Mel , 2001 ) , and then show that it provides an approximation to the optimal response ( Equations 20–23 ) in the limiting case in which presynaptic dynamics are dominated by simultaneous switching between a quiescent and an active state . In this simple dendritic model , inputs within a branch are integrated linearly: ( 24 ) v˙lin =-𝒜vlin+ℬs ( t ) -𝒞 where vlin is the variable linearly integrating inputs with weight ℬ , dendritic time constant 1/𝒜 and steady state value -𝒞/𝒜 ( in the absence of spikes ) , and s ( t ) =∑isi ( t ) denotes the spike train of presynaptic neurons , collecting all spikes from the presynaptic population . The actual dendritic response , vden , is then given by mapping this linear response through a sigmoidal nonlinearity , scaled to be between vmin and vmax ( Figure 3A , inset ) : ( 25 ) vden ( t ) =vmin+ ( vmax-vmin ) 11+e-vlin ( t ) To demonstrate that this reduced model of dendritic integration closely approximates the optimal response , we first note that under appropriate conditions ( τpost is small , N is large , Q is diagonal , and β is small relative to the diagonal elements of Q ) the dynamics of the optimal response are dominated by the state switching process ( Equation 20; see Appendix ) . Thus , the optimal response essentially follows the inference about the global state variable , ζ , up to linear rescaling and filtering: ( 26 ) v~ ( t ) ≈ u¯-+ ( u¯+-u¯- ) ζ ( t ) with u¯+ and u¯- respectively denoting the resting membrane potential in the active and quiescent states . As Equation 26 is linear , all nonlinear interactions , corresponding to dendritic nonlinearities , must be contained in the temporal dynamics of the posterior probability of this global state variable being in the active state , ζ ( t ) , which can be expressed as ( 27 ) ζ ˙≈ ζ ( 1-ζ ) [B s ( t ) -C] where constants B and C depend on the parameters of the presynaptic statistics ( see Appendix ) . Note that the fact that ζ ( 1-ζ ) multiplies Equation 27 expresses the simple intuition that the size of the update to ζ ( the posterior probability of z=+ ) in response to incoming information ( presence , B term , or absence of a spike , C term ) should be proportional to our current ( posterior ) uncertainty about z; and since the posterior is a Bernoulli distribution , the uncertainty associated with it is simply ζ ( 1-ζ ) . The solution of Equation 27 can be expressed in a form that is similar , albeit not identical ( see below ) , to the canonical model for dendritic integration ( Equations 24–25 ) . This form requires the linear integration of incoming spikes ( 28 ) ν˙=B s ( t ) -C and the temporal evolution of ζ is expressed as a sigmoidal function of the linearly integrated inputs ν: ( 29 ) ζ ( t ) =11+e-ν ( t ) Thus , dendrites with sigmoidal nonlinearity are near-optimal when their synaptic inputs switch between a quiescent and an active state . The main difference between the dendritic integrator and the optimal response is that the dynamics of spike integration imply exponential decay towards a finite baseline in the former ( there is a negative term in Equation 24 which is scaled by vlin ) and steady decrease towards negative infinity in the latter ( the only negative term in Equation 28 is a constant , independent of ν ) . This is because the approximations we used for deriving Equation 27 were accurate only in the quasi-static case , when the state switching dynamics are infinitely slow . In this case , remote and more recent observations should have identical effects on the current value of ζ as they all correspond to the same underlying state . In the more general case , when state switching occurs with non-zero probability , more remote observations likely correspond to a state which has changed in the meantime , and should thus count for less , such that their effect on the current value of ζ should decay with time – leading to leaky integration of incoming spikes , similar to that in Equation 24 . To compare quantitatively the response of the linear and nonlinear dendrites to the optimal response in a computational task using realistic input statistics , we divided the presynaptic population into four groups ( cell assemblies ) , where neurons within each group were statistically dependent ( either through simple second-order correlations , Figure 4—figure supplement 2 , or through sharing a common state variable , Figure 4 and Figure 4—figure supplement 2 ) while neurons from different groups were independent ( Figure 4A ) . In this task we used 4 different versions of the simplified neuron model ( Figure 4B ) : ( 30 ) v~˙ℓ ( t ) =v¯ℓ-v~ℓτℓ+wℓ s ( t ) where s ( t ) is the total incoming spike train ( as before ) . This model had three parameters . ( 31 ) v~g ( t ) =ag1+e-βg ( v~ℓ ( t ) -θg ) -v¯g where we computed v~ℓ ( t ) as defined in Equation 30 above with wℓ=1 and v¯ℓ=0 ( as these parameters were interchangeable with βg and θg ) . This model had five free parameters . To fit the models we generated 240 s-long samples of presynaptic activity and optimized the parameters of the models to minimize the squared error between the signal ( v , the true average of the stimulated presynaptic potentials , cf . Equation 5 with wi=1N ) and their estimates ( v^ , the outputs of the models ) , averaged over the duration of the sample: ( 32 ) ϵestimation=1T∑t=1T ( v^t-vt ) 2 After training , we tested the different models in cross-validation , on a novel 120 s-long input sequence , and quantified their performance by the fraction of variance unexplained , i . e . the temporally averaged squared error , ϵestimation , normalized by the variance of the signal ( as a sensible upper limit on the error – achievable by an estimator that predicts the prior mean , ignoring incoming spikes altogether ) : ( 33 ) ϵ¯estimation=ϵestimationVar[v] where Var[v]=1T-1∑t ( vt-E[v] ) 2 and E[v]=1T∑tvt . ( Normalization was unnecessary during training because the parameters of the models that we were optimizing obviously did not influence the variance of the signal . ) Figure 4 shows 1-ϵ¯estimation , i . e . the fraction of variance explained as ‘performance’ , and Figure 1—figure supplement 3 , Figure 3—figure supplement 2 , and Figure 4—figure supplement 2 show ϵ¯estimation as ‘estimation error’ . To predict dendritic integration in hippocampal and neocortical neurons we fitted the parameters describing presynaptic statistics in our model , P ( u ) and P ( s|u ) , to the statistical patterns in the activity of their respective presynaptic populations . The basal dendrites of neocortical layer 2/3 pyramidal cells are targeted by neighbouring pyramidal neurons as well as by neurons from layer 4 ( Douglas and Martin , 2004 ) . We used in vivo intracellular paired recordings from layer 2/3 pyramidal neurons in the barrel cortex ( Poulet and Petersen , 2008; Gentet et al . , 2010; Crochet et al . , 2011 ) to set the parameters of our model to reproduce the salient features of the presynaptic population dynamics during quiet wakefulness ( Tables 1–2 ) . In the hippocampal experiments we stimulated synapses on the proximal dendrites of CA3 neurons targeted by recurrent collaterals of neighbouring pyramidal cells ( Andersen et al . , 2007 ) . We fitted the presynaptic statistics to in vivo population activity patterns recorded from the hippocampus during quiet wakefulness , characterised by sharp wave ( SPW ) activity ( Csicsvari et al . , 2000 ) . As intracellular recordings from CA3 pyramidal neurons during SPW activity in the awake animal are not available , we fitted the presynaptic statistics to awake extracellular data from CA3 ( Csicsvari et al . , 2000; Grosmark et al . , 2012 ) and intracellular ( Ylinen et al . , 1995; English et al . , 2014 ) data from CA1 pyramidal neurons ( Tables 1 and 3 ) . We used four different parameter sets ( models ) to describe the activity of the presynaptic population ( Figure 1 ) . The parameters of the HP and NC models were fitted to in vivo recordings from the corresponding presynaptic populations as described above . As a control , we used two simpler models with no state switching dynamics . The cor2 model had correlated membrane potential fluctuations with all cross-correlations between presynaptic neurons being the same , -1N-1≤ρ≤1 . Neurons in the last model , ind , had independent membrane potential fluctuations . The HP , NC , and ind models had no free parameters , while parameter ρ of the cor2 model was left free and later tuned to fit dendritic data . Note that to fit supralinear cortical responses , ρ had to be tuned to unnaturally large negative values in this model ( Figure 5—figure supplement 1A ) – and it still produced significantly poorer fits than the HP and NC models ( Figure 5 ) . After setting the parameters of the presynaptic population , we computed the optimal response by numerically integrating Equations 20–22 in the software package R ( R Core Team , 2012; Soetaert et al . , 2010 ) . When comparing the optimal response to experimental data , we assumed that each uncaging event corresponded to a single spike at the presynaptic axon terminal , and spines not showing measurable gluEPSP were considered to be non-stimulated . For all four presynaptic parameter sets , we varied two postsynaptic parameters to fit the responses of the optimal estimator to dendritic integration data , i . e . the somatic membrane potential traces recorded in our in vitro experiments . These two parameters were the weight w of the presynaptic neurons and the time constant of the postsynaptic filtering , τpost ( Equation 5 ) . To avoid overfitting , we assumed that all presynaptic neurons had equal weight , i . e . ∀i wi=w . A final free parameter that we had to consider was the number of synapses that were in the same functional cluster as the synapses we stimulated in our experiments – where the term ‘functional cluster’ refers to a set of synapses for which the presynaptic cells are correlated . This parameter was irrelevant for the ind model ( by definition ) , it was fixed at 20 for the cor2 model ( because its effects on the optimal response were largely indistinguishable from that of varying ρ , see above ) , and it was tuned to fit dendritic integration for the state-switching models ( NC and HP ) . In sum , the number of free parameters used to fit dendritic integration data was 2 for the ind model and 3 for the cor2 , NC , and HP models . We confirmed that the higher number of free parameters in the latter models did not result in an unfair advantage in fitting performance by using Bayesian Information Criterion ( BIC ) , rather than squared error ( see below ) , as our measure of performance . BIC includes an explicit term penalizing the number of parameters , and our results were not qualitatively affected by it: fitting the model using the relevant in vivo statistics resulted in 3500 ± 940 ( NC , mean ± s . d . ) and 2000 ± 1100 ( HP ) higher BIC scores than when using independent statistics ( where each unit of BIC difference corresponds to a likelihood that is higher by a factor of e≃2 . 71 ) . We fitted each recorded neuron independently using these parameters by minimizing the mean squared error between the predicted , v~ , and the recorded postsynaptic membrane potential ( averaged across repetitions of the same stimulation in the same cell ) , v¯*: ( 34 ) ϵfitting=1T∑t=1T ( v~t-v¯t* ) 2 To be able to compare results across different neurons and different stimulation protocols , we normalized the error by the total variance of the data: ( 35 ) ϵ¯fitting=ϵfittingVar[v¯*] where Var[v¯*]=1T-1∑t ( v¯t*-E[v¯*] ) 2 and E[v¯*]=1T∑tv¯t* . A natural lower bound of our fitting error was the intrinsic variability of the data , so we computed the mean of the variance of the experimental data across repetitions , normalized by the total variance of the data: ( 36 ) ϵ¯min=1L-1∑l=1L1T∑t ( vt , l*-v¯t* ) 2Var[v¯*] where vt , l* is the raw data before averaging across repetitions , L is the number of repetitions using the same stimulation protocol in the same cell and v¯t*=1L∑Lvt , l* . Figures 5D , H and Figure 5—figure supplement 1G show ϵ¯fitting as the ‘fitting error’ and ϵ¯min as ‘var’ . The best fitting parameter values for the postsynaptic time constant , τpost , and total number of neurons in a functional cluster , N , are shown in Figure 5—figure supplement 1B , C . | Imagine that you are in the habit of checking three different weather forecasts each day , and then one day in early September the first forecast suddenly predicts snow . If you live in an area where it doesn’t normally snow in September , your initial reaction is likely to be surprise . However , you will not be quite so surprised to see a prediction of snow in the second forecast , and by the third forecast you will hardly be surprised at all . In these three cases , you have responded to the same piece of information in a different way . In mathematics , this type of response is referred to as “nonlinear” because the output ( varying degrees of surprise ) is not directly proportional to the input ( identical predictions of snow ) . In the case of the weather forecasts , the source of the nonlinearity was the fact that the three predictions were not truly independent . Instead , they corresponded with one another , or “correlated” , because all three depended on the weather itself . In the brain , a single neuron can receive thousands of inputs from other cells . These are received via junctions called synapses that form between the cells . In many cases , the synapses form on the receiving neuron’s dendrites – the short branches that protrude from its cell body . Each dendrite can receive signals from hundreds of other neurons , and must combine these inputs to produce a single neuronal response . How dendrites do this is not clear . Ujfalussy et al . have now developed a computational model that predicts the optimal response of dendrites to complex and realistic inputs from other neurons . The model shows that when dendrites receive inputs from neurons that independently respond to different stimuli , the optimal response is for the dendrites to average the inputs . This is a form of linear processing . By contrast , when the inputs are correlated – for example , because they come from neurons responding to the same stimulus – the optimal response is nonlinear processing . In this and other cases , the optimal response predicted by the model is similar to the response observed in real dendrites . The model also makes a number of testable predictions; for example , that neurons with correlated activities will tend to form clusters of synapses close together on the dendrites of a target neuron , whereas neurons with unrelated activities will tend to form synapses that are further apart . Somewhat unexpectedly , Ujfalussy et al . show that compensating for input correlations accounts for almost all the nonlinearities that can be found in real neurons' dendrites – at least in response to relatively simple input patterns . Thus , it remains to be shown whether nonlinear dendritic responses to more complex input patterns can also be explained by this single principle . Further studies are also required to understand how different plasticity mechanisms enable neurons to achieve this close match between input correlations and dendritic processing . | [
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] | 2015 | Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits |
Neuronal ELAV-like ( nELAVL ) RNA binding proteins have been linked to numerous neurological disorders . We performed crosslinking-immunoprecipitation and RNAseq on human brain , and identified nELAVL binding sites on 8681 transcripts . Using knockout mice and RNAi in human neuroblastoma cells , we showed that nELAVL intronic and 3' UTR binding regulates human RNA splicing and abundance . We validated hundreds of nELAVL targets among which were important neuronal and disease-associated transcripts , including Alzheimer's disease ( AD ) transcripts . We therefore investigated RNA regulation in AD brain , and observed differential splicing of 150 transcripts , which in some cases correlated with differential nELAVL binding . Unexpectedly , the most significant change of nELAVL binding was evident on non-coding Y RNAs . nELAVL/Y RNA complexes were specifically remodeled in AD and after acute UV stress in neuroblastoma cells . We propose that the increased nELAVL/Y RNA association during stress may lead to nELAVL sequestration , redistribution of nELAVL target binding , and altered neuronal RNA splicing .
RNA binding proteins ( RBPs ) associate with RNAs throughout their life cycle , regulating all aspects of RNA metabolism and function . More than 800 RBPs have been described in human cells ( Castello et al . , 2012 ) . The unique structure and function of neurons , and the need to rapidly adapt RNA regulation in the brain both within and at sites distant from the nucleus , are consistent with specialized roles for RBPs in the brain . Indeed , mammalian neurons have developed their own system of RNA regulation ( Darnell , 2013 ) , and RBP:mRNA interactions are thought to regulate local protein translation at synapses , perhaps underlying learning and long-term memory ( McKee et al . , 2005 ) . Numerous RBPs have been linked to human neurological disorders ( reviewed in Richter and Klann ( 2009 ) ) . For example , FUS , TDP-43 and ATXN2 mutations have been found in familial amyotrophic lateral sclerosis patients ( Elden et al . , 2010; Vance et al . , 2009; Sreedharan et al . , 2008 ) , TDP-43 has additionally been associated with frontotemporal lobar degeneration , Alzheimer’s Disease ( AD ) and Parkinson’s Disease ( PD ) ( Baloh , 2011 ) , STEX has been linked to amyotrophic lateral sclerosis 4 ( Chen et al . , 2004 ) , and spinal muscular atrophy can be caused by mutations in SMN ( Clermont et al . , 1995 ) . The neuronal ELAV-like ( ELAVL ) and NOVA RBPs are targeted by the immune system in paraneoplastic neurodegenerative disorders ( Buckanovich et al . , 1996; Szabo et al . , 1991 ) . Mammalian ELAVL proteins include the ubiquitously expressed paralog ELAVL1 ( also termed HUA or HUR ) and the three neuron-specific paralogs , ELAVL2 , 3 and 4 ( also termed HUB , C , and D , and collectively referred to as nELAVL; Ince-Dunn et al . , 2012 ) . nELAVL proteins are expressed exclusively in neurons in mice ( Okano and Darnell , 1997 ) , and they are important for neuronal differentiation and neurite outgrowth in cultured neurons ( Akamatsu et al . , 1999; Kasashima et al . , 1999; Mobarak et al . , 2000; Anderson et al . , 2000; Antic et al . , 1999; Aranda-Abreu et al . , 1999 ) . Redundancy between the three nELAVL isoforms complicates in vivo studies of their individual functions . Nevertheless , even haploinsuffiency of Elavl3 is sufficient to trigger cortical hypersynchronization , and Elavl3 and Elavl4 null mice display defects in motor function and neuronal maturation , respectively ( Akamatsu et al . , 2005; Ince-Dunn et al . , 2012 ) . ELAVL proteins have been shown to regulate several aspects of RNA metabolism . In vitro and in tissue culture cells , nELAVL proteins have been implicated in the regulation of stabilization and/or translation of specific mRNAs , as well as in the regulation of splicing and polyadenylation of select transcripts [reviewed in Pascale et al . ( 2004 ) ] . A more comprehensive approach was taken by immunoprecipitating an overexpressed isoform of ELAVL4 in mice , although such RNA immunoprecipitation experiments cannot distinguish between direct and indirect targets ( Bolognani et al . , 2010 ) . Recently , direct binding of nELAVL to target RNAs in mouse brain was demonstrated by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation ( HITS-CLIP; Ince-Dunn et al . , 2012 ) ; these data , coupled with transcriptome profiling of Elavl3/4 KO mice , demonstrated that nELAVL directly regulates neuronal mRNA abundance and alternative splicing by binding to U-rich elements with interspersed purine residues in 3’UTRs and introns in mouse brain ( Ince-Dunn et al . , 2012 ) . While genome-wide approaches have been applied to studying nELAVL proteins in mice , the targets of nELAVL in the human brain remain largely unknown . This is of particular importance , as nELAVL proteins have been implicated in neurological disorders such as AD ( Amadio et al . , 2009; Kang et al . , 2014 ) and PD ( DeStefano et al . , 2008; Noureddine et al . , 2005 ) . Hence , to advance our understanding of the function of nELAVL in humans and its link to human disease , we set out to investigate nELAVL:RNA interactions in the human brain . To globally identify transcripts directly bound by nELAVL in human neurons , we generated a genome wide RNA binding map of nELAVL in human brain using CLIP . CLIP allows the identification of functional RNA-protein interactions in vivo by using UV-irradiation of intact tissues to covalently crosslink and then purify RNA-protein complexes present in vivo ( Licatalosi and Darnell , 2010; Ule et al . , 2003 ) . This method has been adopted for a variety of RBPs ( Darnell , 2010; 2013; Moore et al . , 2014 ) . Here , we systemically identified tens of thousands of reproducible nELAVL binding sites in human brain and showed that nELAVL binds transcripts that are important for neurological function and that have been linked to neurological diseases such as AD . We validated the functional consequences of nELAVL binding in mice and cultured human neuroblastoma cells and showed that the loss of nELAVL affected mRNA abundance and alternative splicing of hundreds of transcripts . We further investigated RNA regulation in AD brains , and found that numerous transcripts were differentially spliced in AD , which correlated with differential nELAVL binding in some cases . Remarkably , we observed the most significant increase in nELAVL binding in AD on a class of non-coding RNAs , Y RNAs . We recapitulated these findings in human neuroblastoma cells , showing that nELAVL binding is linked to Y ribonucleoprotein ( RNP ) remodeling acutely during UV-induced stress , and chronically in AD .
To gain insight into nELAVL-mediated RNA regulation in human brain we performed CLIP on postmortem brain samples of eight human subjects ( Supplementary file 1A ) . Tissue samples were derived from BA9 , which is part of the dorsolateral prefrontal cortex ( Figure 1A ) , a brain area that is damaged in later stages of AD and that is important for executive functions such as working memory , cognitive flexibility , planning , inhibition , and abstract reasoning ( O'Reilly , 2010 ) . Antibodies that specifically recognize individual ELAVL paralogs and that can be used for CLIP are currently not available . We therefore purified nELAVL-RNP complexes with antiserum reactive to all three nELAVL proteins ( Figure 1B ) . 32P-labeled nELAVL-RNP complexes were not recovered with control serum or in the absence of UV-irradiation ( Figure 1B ) . 10 . 7554/eLife . 10421 . 003Figure 1 . Identification of nELAVL targets in human brain . ( A ) Illustration depicting the brain area analyzed by CLIP and RNAseq . The image was generated using BodyParts3D/Anatomography service by DBCLS , Japan . ( B ) SDS-PAGE separation of radiolabeled nELAVL-RNP complexes . nELAVL-RNP complexes from 40 mg of human brain were specifically immunoprecipitated with Hu-antiserum , compared to control serum ( compare lane #4 to #1 ) , which is dependent on UV irradiation ( compare lane #4 to #2 ) . Wide-range nELAVL-RNP complexes collapse to a single band in the presence of high RNAse concentration ( lane #3 ) . RNAse dilutions: + 19 . 23 Units/µl; +++ 3846 Units/µl . As in studies of mouse nELAVL ( Ince-Dunn et al . , 2012 ) , higher molecular weight bands were present in nELAVL CLIP autoradiograms , which correspond at least in part to nELAVL multimers . ( C ) Shown is the most enriched motif in the top 500 nELAVL peaks , determined with MEME-ChiP . ( D ) Pie chart of the genomic peak distribution of 75 , 592 nELAVL peaks ( p < 0 . 01; present in at least 5 individuals ) . ( E ) nELAVL binding correlates with mRNA abundance . nELAVL binding ( CLIP tags within binding sites per transcript ) was compared to mRNA abundance ( RNAseq tags per transcript ) . Only expressed genes with peaks are shown and the correlation coefficient is indicated . The top 1000 targets were identified as genes with highest normalized nELAVL binding ( binding sites were normalized for mRNA abundance and summarized per gene ) . ( F ) Subnetwork of direct protein-protein interactions of top nELAVL targets . The 1000 top nELAVL target genes and six additional genes highly associated with AD ( APP , BACE1 , MAPT , PICALM , PSEN1 and PSEN2 ) were clustered using the organic layout algorithm in yEd . Genes with no direct interactions with other target genes were excluded , leaving 172 nodes from the top nELAVL target list ( green ) and 5 AD associated genes ( blue ) in this subnetwork . The size of the nodes is proportional to the connectivity degree . Six clusters ( gray circles ) containing at least 10 nodes were identified , and subjected to enrichment analysis ( see Supplementary file 1F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00310 . 7554/eLife . 10421 . 004Figure 1—figure supplement 1 . Cross-correlation plot comparing nELAVL peak binding between eight individuals ( n = 75 , 592 ) . Shown are R values . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00410 . 7554/eLife . 10421 . 005Figure 1—figure supplement 2 . Shown is the most enriched motif in the top 500 nELAVL peaks , determined with HOMER . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00510 . 7554/eLife . 10421 . 006Figure 1—figure supplement 3 . Pie chart of the genomic peak distribution of 75 , 592 nELAVL peaks ( p < 0 . 01; present in at least 5 individuals ) , normalized for region length . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00610 . 7554/eLife . 10421 . 007Figure 1—figure supplement 4 . nELAVL peaks within 3’UTRs are higher than intronic binding sites . The genomic distribution of nELAVL binding sites is plotted as a function of nELAVL peak binding . The number of nELAVL binding sites ( n ) within each category is indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00710 . 7554/eLife . 10421 . 008Figure 1—figure supplement 5 . Cross-correlation plot comparing the mRNA abundance of all transcripts between eight individuals ( n = 19 , 185 ) . R values are depicted . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 00810 . 7554/eLife . 10421 . 009Figure 1—figure supplement 6 . Correlations between mRNA abundance and nELAVL binding . ( A , B ) mRNA abundance shows a higher correlation with nELAVL 3’UTR ( A ) than intronic binding ( B ) . nELAVL binding was defined as CLIP tags within binding sites per transcript . Shown are genes with exclusively 3’UTR ( A ) or intronic ( B ) binding and R values are indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 009 nELAVL-crosslinked RNA tags were sequenced and mapped to the hg18 build of the human genome . We searched for and identified nELAVL RNA binding sites ( peaks ) that were significant ( p<0 . 01 ) and that were present in at least five out of 8 individuals ( n = 75 , 592 ) . nELAVL binding at these sites correlated between individuals ( Figure 1—figure supplement 1 ) , and these peaks were further investigated . We determined the nELAVL binding motif by analyzing the top 500 nELAVL peaks ( +/- 25nt ) using MEME ChIP ( Machanick and Bailey , 2011 ) and HOMER ( Heinz et al . , 2010 ) . This revealed that nELAVL binds polyU RNA stretches in human brain , particularly when interrupted by a G ( Figure 1C and Figure 1—figure supplement 2 ) . This is in excellent agreement with the nELAVL binding motif identified in mouse brain using CLIP and in vitro binding assays ( Ince-Dunn et al . , 2012 ) . Because nELAVL has been shown to regulate alternative splicing and mRNA abundance in mouse brain by binding to introns and 3’UTRs , respectively , we analyzed the genomic distribution of the peaks defined here . As previously reported for mouse nElavl ( Ince-Dunn et al . , 2012 ) , nELAVL binding sites are found in 3’UTRs and introns ( Figure 1D ) , with far higher per-nucleotide density in 3’UTR regions ( Figure 1—figure supplement 3 ) . Consistently we observed that nELAVL peaks in 3’UTRs were higher than intronic peaks ( Figure 1—figure supplement 4 ) . These results suggest that nELAVL could regulate splicing and mRNA abundance in the human brain . To relate nELAVL binding to mRNA abundance , we performed RNAseq on the same brain samples used for CLIP analysis ( Figure 1—figure supplement 5 and Supplementary file 1A/B ) . 74 , 423 nELAVL peaks mapped to 8681 expressed genes ( Supplementary file 1C ) , referred to as nELAVL targets ( Supplementary file 1D ) hereafter , which are shown in Figure 1E . We observed that nELAVL binding correlated with mRNA abundance ( Figure 1E ) . This was not unexpected , as nELAVL 3’ UTR binding has previously been shown to increase mRNA abundance , due to its role in mRNA stabilization . The correlation between nELAVL binding and mRNA abundance might therefore not only reflect the dependence of nELAVL binding on mRNA abundance , but also a role of nELAVL in mRNA stabilization . Consistently , we observed that intronic nELAVL binding correlated less with mRNA abundance than 3’UTR binding ( Figure 1—figure supplement 6 ) . To identify genes most likely to be impacted by nELAVL , we defined the top 1000 nELAVL targets ( colored in green in Figure 1E; Supplementary file 1E ) . Top targets were identified based on normalized nELAVL binding ( binding sites were normalized for mRNA abundance and summarized per gene ) . Thirty-seven percent of nELAVL peaks ( n = 27 , 581 ) mapped to these top targets . We constructed a subnetwork that connects top nELAVL target gene products based on a literature-based network of protein-protein interactions ( PPI ) created from multiple online databases ( Chen et al . , 2012 ) . Six clusters were identified within the resulting network ( Figure 1F ) and gene set enrichment analyses were performed for top nELAVL targets found in the different clusters with Enrichr ( Chen et al . , 2013 ) . Each cluster was examined for enrichment of Biological Processes ( BP ) , Molecular Functions ( MF ) , Cellular Components , and Mammalian Phenotypes ( MP ) terms ( Supplementary file 1F ) . Enriched terms included RNA processing and transcription regulation , signal transduction , synaptic transmission , synaptic proteins , and abnormal neuron morphology and physiology . Three of the six clusters were particularly important for neuronal function . Cluster 1 is especially enriched in members of the TGFbeta/SMAD signaling pathways and the FOX protein family . Cluster 2 contains many actors of the IGF-I axis , which is important for neuronal development including neurogenesis , myelination , synaptogenesis , dendritic branching and neuroprotection after neuronal damage . Finally , cluster 3 is almost exclusively formed of synaptic proteins including many postsynaptic scaffolding proteins , members of the neuroligin/neurexin families and glutamatergic receptors or voltage-gated channels . Taken together , these data demonstrate that nELAVL associates with transcripts encoding proteins involved in key aspects of neuronal physiology . To investigate if nELAVL-mediated RNA regulation is conserved , we compared our dataset with previously published nELAVL targets in mice ( Ince-Dunn et al . , 2012; Bolognani et al . , 2010 ) . At the transcript level , we found that more than 90% of mouse nELAVL targets were among human nELAVL targets ( Figure 2A ) . However , only 20% of human targets were bound by nELAVL in mouse brain , which is at least partly due to the 10-fold increased depth of the human dataset ( more than 10 million human CLIP tags compare to less than a million mouse CLIP tags ) . Yet these differences could also reflect an increased functional complexity of nELAVL regulation in the human brain , and/or the fact that mouse targets were identified at different developmental stages . 10 . 7554/eLife . 10421 . 010Figure 2 . nELAVL mediated regulation is conserved in mouse and human . ( A ) Overlap of nELAVL targets in human and mouse . Human nELAVL targets ( n = 8681 ) were intersected with mouse targets identified by RIP ( Bolognani et al . , 2010 ) or HITS-CLIP ( Ince-Dunn et al . , 2012 ) . 538 genes were identified as nELAVL targets by RIP and were expressed in human brain . 1978 expressed genes had HITS-CLIP nELAVL clusters that were present in at least 3 samples ( biological complexity ( BC ) ≥ 3 ) . Both overlaps ( n = 500 and n = 1835 ) were highly significant ( p = 6 . 5e-74 and p = 2 . 3e-287; hypergeometric test ) , compared to expressed transcripts ( n = 14 , 737 ) . ( B ) Only few nELAVL binding sites are conserved between mice and human , which are predominantly present within 3’UTRs . The genomic distribution of all human nELAVL binding sites ( total ) and nELAVL binding sites conserved in mouse is shown . The number of nELAVL binding sites ( n ) within each category is indicated . ( C ) UCSC Genome Browser images illustrating the 3’UTRs of RAB6B , HCN3 , and KCNMB2 and their normalized nELAVL binding profile in human brain . The maximum PeakHeight is indicated by numbers in the right corner . ( D ) The mRNA levels of transcripts with nELAVL 3’UTR binding decrease in Elavl3/4 knockout ( KO ) mice . Shown are the mRNA expression fold changes ( knockout/wildtype ) of RAB6B , HCN3 , and KCNMB2 . *p< 0 . 01 ( two-tailed t test; Ince-Dunn et al . , 2012 ) . ( E ) UCSC Genome Browser images showing pink cassette exons in the DST , NRXN1 , and CELF2 genes and their normalized nELAVL binding profiles in human brain . The maximum PeakHeight is indicated by numbers in the right corner . ( F ) nELAVL binding adjacent to a cassette exon in the DST gene prevents exon inclusion . Downstream nELAVL binding promotes the inclusion of cassette exons in the NRXN1 and CELF2 genes . The change in alternative exon inclusion ( delta inclusion ( ΔI ) : wildtype - Elavl3/4 KO ) is shown . * significantly changing ( analyzed by Aspire2; Ince-Dunn et al . , 2012 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01010 . 7554/eLife . 10421 . 011Figure 2—figure supplement 1 . Comparison of nELAVL binding ( CLIP tags within binding sites per transcript ) between mice and human . The correlation coefficient is indicated . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 011 We additionally investigated the overlap of nELAVL binding at individual binding sites . Surprisingly , only a small percentage of binding sites overlapped between mouse and human; 3% of human binding sites showed nELAVL binding in mouse , and 17% of mouse binding sites were bound by nELAVL in human brain ( Figure 2B ) . The vast majority of these overlapping binding sites were in 3’UTRs ( 88% ) . These results indicate that many nELAVL targets are shared between mouse and human and that nELAVL binding at the transcript level is conserved , whereas individual binding sites have diverged drastically , especially within introns . These results reflect analogous observations of evolutionary conservation of transcriptional regulation at the gene rather than the positional level ( Stergachis et al . , 2014 ) . We further observed that nELAVL binding on entire transcripts correlated between mouse and human ( Figure 2—figure supplement 1 ) , which prompted us to overlay our human CLIP dataset with previously published transcriptome profiling of Elavl3/4 double KO mice ( Ince-Dunn et al . , 2012 ) . 119 transcripts showed significant changes in their steady-state level in Elavl3/4 KO mice , 91 of which were expressed in human brain . 37 of these 91 transcripts were nELAVL 3’UTR targets in human brain ( Supplementary file 2A ) , and the majority of them decreased in the absence of ELAVL3/4 ( n = 26 ) , including transcripts important for neuronal transport and excitation such as RAB6B , HCN3 , and KCNMB2 ( Figure 2C/D ) . This indicates that nELAVL 3’UTR binding is likely to be important for increasing the abundance of these transcripts in human brain , and likely has conserved functions across species . Elavl3/4 KO mice also reported splicing defects and 59 alternative exons showed a significant change in their inclusion rate ( delta inclusion rate , ΔI ) between wildtype and Elavl3/4 KO mice ( Ince-Dunn et al . , 2012 ) . 54 of the misregulated exons were conserved in the human genome , and 25 of them were adjacent to intronic nELAVL binding sites in human brain ( Supplementary file 2B ) . We observed both increased and decreased inclusion of alternative exons – independently of the position of the peak relative to the exon , which has previously been observed for nELAVL mediated splicing regulation ( Ince-Dunn et al . , 2012 ) . Three alternative exons are shown in Figure 2E , F , and whereas nELAVL seems to prevent splicing of DST by binding upstream and downstream of an alternative exon , nELAVL might promote the inclusion of alternative exons of NRXN1 and CELF2 by binding to downstream sequences . Given that nELAVL regulates the splicing of these 25 exons in mice and that we observe intronic nELAVL binding sites in human brain adjacent to these exons , we propose that nELAVL regulates the inclusion of these exons in human brain . Collectively , these analyses show that many confirmed functional nELAVL interactions in mouse brain show evidence for nELAVL binding in human brain . To further validate potential nELAVL targets , we analyzed the effect of nELAVL depletion on mRNA abundance and splicing in human neuroblastoma IMR-32 cells . We subjected IMR-32 cells to mock or ELAVL2/3/4 triple RNAi , achieving 70% knockdown of all three neuronal ELAVL proteins ( Figure 3—figure supplement 1 ) . These cells were then analyzed by RNAseq ( Figure 3A , Supplementary file 1A/2C ) . The steady-state level of 784 transcripts was significantly changed in nELAVL RNAi treated cells ( Figure 3A ) , with ~45% showing a decrease in mRNA abundance . Among those genes were all of the neuronal ELAVL paralogs ( ELAVL2/3/4 ) , while the ubiquitously expressed paralog ELAVL1 was not affected by nELAVL RNAi depletion . 10 . 7554/eLife . 10421 . 012Figure 3 . nELAVL proteins regulate mRNA abundance of human brain targets . ( A ) nELAVL depletion causes mRNA level changes in IMR-32 neuroblastoma cells . The mRNA abundance change was plotted against average mRNA abundance . Significantly changing transcripts ( FDR < 0 . 05; n = 784 ) are colored in blue . Shown are only expressed genes ( n = 12 , 743 ) , and ELAVL1/2/3/4 transcripts are indicated . ( B ) nELAVL with exclusively 3’UTR binding decrease upon nELAVL RNAi depletion . Box plots represent the distribution of mRNA level differences between mock and nELAVL RNAi . We compared genes with exclusively 3’UTR ( n = 2346 ) or intronic ( n = 1693 ) binding that were expressed in IMR-32 cells . nELAVL binding was defined as CLIP tags within binding sites per transcript . Transcripts with exclusively 3’UTR binding were less abundant upon nELAVL RNAi compared to remaining transcripts ( p = 3 . 8e-15; two-tailed t-test ) . In contrast , mRNA levels of transcripts with exclusively intron binding were even slightly increased compared to remaining transcripts ( p = 1 . 7e-4; two-tailed t-test ) . ( C ) Transcripts with nELAVL 3’UTR binding decrease upon nELAVL RNAi . Cumulative fraction curves for genes with no 3’UTR nELAVL binding in human brain , 3’UTR binding , and top 3’UTR targets . Top targets were identified as 1000 genes with highest normalized nELAVL 3’UTR binding ( binding sites were normalized for mRNA abundance before summarized per gene ) . 952 of the top 1000 targets were expressed in IMR-32 cells . A curve displacement to the left indicates a downregulation of mRNA abundance upon nELAVL RNAi . p values were calculated with a one-sided KS test , comparing ( top ) targets to non-targets . ( D ) Many transcripts that are decreasing upon nELAVL depletion are top nELAVL 3’UTR targets . The mRNA abundance change ( nELAVL/mock RNAi ) of transcripts expressed in IMR-32 cells and in human brain ( n = 12 , 242 ) was plotted against average mRNA abundance . Significantly changing transcripts ( FDR<0 . 05; n = 743 ) are colored in blue and additionally boxed if they are top nELAVL 3’UTR targets . Transcripts shown in E/F are indicated . ( E ) UCSC Genome Browser images illustrating the 3’UTRs of APPBP2 , ATXN3 , and SHANK2 and their normalized nELAVL binding profile in human brain . The maximum PeakHeight is indicated by numbers in the right corner . ( F ) The mRNA abundance of top nELAVL 3’UTR targets decreases upon nELAVL RNAi . Shown are the mRNA level changes ( nELAVL/mock RNAi ) of APPBP2 , ATXN3 , and SHANK2 . * FDR<0 . 05 ( derived from edgeR ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01210 . 7554/eLife . 10421 . 013Figure 3—figure supplement 1 . Western blot and its quantification showing protein levels of nELAVL and the housekeeping genes HSP90 and Histone H3 in mock and nELAVL RNAi-treated IMR-32 cells . Protein expression was normalized to the house keeping gene Histone H3 and to mock RNAi treated cell . Error bars represent SEM . * p<0 . 01 ( two-tailed t-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 013 We then compared this RNAseq dataset with the nELAVL CLIP analysis in human brain . 96% of the IMR-32 expressed transcripts were expressed in human brain ( 12 , 242 out of 12 , 743 ) , and were used for subsequent analyses . Since nELAVL binding to 3’UTRs can mediate mRNA stabilization , we investigated the change in mRNA abundance of 3’UTR targets upon nELAVL RNAi , specifically examining 3’UTR targets that did not display any intronic binding . These transcripts were less abundant in nELAVL RNAi conditions ( Figure 3B , left panel ) . In contrast , the mRNA abundance of intron targets ( intron binding but no 3’UTR binding ) slightly increased upon nELAVL RNAi ( Figure 3B , right panel ) . This suggests that specifically nELAVL binding to 3’UTRs increases mRNA abundance . We further observed that nELAVL depletion affected top nELAVL 3’UTR targets as well as nELAVL 3’UTR targets in general ( Figure 3C ) . Out of 784 genes that changed significantly upon nELAVL RNAi , 743 genes were expressed in human brain , 327 of which decreased while 416 increased . We investigated which of these transcripts were direct targets of nELAVL based on nELAVL 3’UTR binding ( Supplementary file 2D ) . Significantly changing transcripts that are top nELAVL 3’UTR targets are boxed in blue in Figure 3D . We observed that 68% of downregulated transcripts were 3’UTR targets ( n = 226; p = 9 . 6e-13; hypergeometric test ) , and that 16% of downregulated transcripts were even among top 3’UTR targets ( n = 51; p = 1 . 3e-6; hypergeometric test ) . In contrast , only 7% of upregulated transcripts were among top 3’UTR targets ( p = 0 . 76; hypergeometric test ) , further supporting a role of nELAVL 3’UTR binding in positively regulating mRNA abundance . Several 3’UTR targets showed an mRNA abundance change in both mouse and IMR-32 datasets ( n = 8 ) , and in all but one case this change correlated positively between the datasets , providing support for the accuracy of target validation applied here . Because the abundance of multiple disease associated genes , including APPBP2 , ATXN3 , and SHANK2 ( Figure 3E , F ) , is regulated by nELAVL , we propose that nELAVL mediated regulation of mRNA abundance plays an important role in the human brain . To validate a role of nELAVL in the splicing regulation of human brain targets , we analyzed the inclusion rate of cassette exons in mock and nELAVL RNAi treated IMR-32 cells . We compared the change in exon inclusion of 7903 expressed cassette exons ( Supplementary file 2E ) and observed that 473 cassette exons were differentially spliced upon nELAVL depletion ( FDR<0 . 05 and ΔI > 0 . 1; Supplementary file 2F ) . Many differentially spliced exons were adjacent ( +/- 2 . 5 kb ) to at least one intronic nELAVL binding site in human brain ( n = 155; p = 1 . 3e-7; hypergeometric test; Figure 4A , Supplementary file 2F ) , indicating that these exons might be directly regulated by nELAVL . For example , downstream binding in BIN1 and PICALM was associated with lower exon inclusion upon nELAVL depletion , and binding in APP was associated with higher inclusion of both upstream and downstream exons upon nELAVL depletion ( Figure 4B/C ) . Overall , three exons that were differentially spliced upon nELAVL RNAi depletion also changed in Elavl3/4 KO mice , and the splicing changes in both datasets changed in the same direction . We generated a map from intronic nELAVL binding sites that flanked the 155 nELAVL regulated exons as previously described ( Licatalosi et al . , 2008 ) , revealing that upstream nELAVL binding can promote both exon inclusion and skipping ( Figure 4D ) . In conclusion , these data indicate that intronic nELAVL binding regulates alternative splicing of numerous transcripts in human brain , including transcripts associated with central nervous system disorders . 10 . 7554/eLife . 10421 . 014Figure 4 . nELAVL regulates splicing of human brain targets . ( A ) Analysis of splicing changes upon nELAVL RNAi . Shown is the exon inclusion fraction of cassette exons that are expressed in IMR-32 cells and in human brain ( n = 7903 ) . Significantly changing exons ( FDR<0 . 05 and ΔI>0 . 1 ) are colored in light blue ( n = 473 ) , and additionally boxed in dark blue if adjacent ( +/- 2 . 5 kb ) to intronic nELAVL binding sites ( n = 155 ) . Significantly changing exons shown in ( B/C ) are boxed in pink . The two alternative events within PICALM correspond to the same alternative exon with two different 3’ splice sites . ( B ) UCSC Genome Browser images depicting cassette exons in pink in the BIN1 , PICALM , and APP genes and their normalized nELAVL binding profiles in human brain . The maximum PeakHeight is indicated by numbers in the right corner . ( C ) nELAVL binding downstream of cassette exons in BIN1 and PICALM promotes exon inclusion , whereas intronic nELAVL binding of APP prevents exon inclusion downstream and upstream . The change in alternative exon inclusion ( ΔI: mock – nELAVL RNAi ) is shown . *FDR< 0 . 0005; **FDR< 1e-4; ***FDR<1e-16 ( GLM likelihood ratio test ) . ( D ) Normalized nELAVL binding map of nELAVL regulated exons . Only exons that changed significantly upon nELAVL RNAi ( FDR<0 . 05 and ΔI>0 . 1 ) and that are adjacent ( +/- 2 . 5 kb ) to intronic nELAVL binding sites ( n = 155 ) were included . Red and blue peaks represent binding associated with nELAVL-dependent exon inclusion and exclusion , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 014 nELAVL has previously been linked to neurological diseases and we observed that nELAVL regulated the mRNA abundance and splicing of multiple disease-associated genes . We examined nELAVL binding in a set of genes with disease associated 3’UTR single nucleotide polymorphisms ( SNPs ) ( Bruno et al . , 2012 ) . We found that these genes were enriched among nELAVL 3’UTR targets ( n = 200; p = 0 . 001; hypergeometric test ) , and that nELAVL binding sites directly overlapped with 45 disease associated SNPs , including SNPs associated with autism , schizophrenia , depression , AD , and PD ( Figure 5—figure supplement 1 , Supplementary file 3A ) . nELAVL proteins have been implicated in AD ( Amadio et al . , 2009; Kang et al . , 2014 ) , and among the validated nELAVL regulated RNAs were also several AD-related transcripts , which led us to investigate additional AD-linked genes ( hereafter termed AD genes; n = 96; Supplementary file 3B ) . Indeed , we found that the top nELAVL targets were enriched among AD genes ( n = 11; p = 0 . 03; hypergeometric test; contained in Supplementary file 3B ) as well as among AD risk loci identified in a genome-wide association study ( GWAS ) in AD ( Naj et al . , 2011 ) ( n = 77; p = 1 . 7e-14; hypergeometric test; Supplementary file 3C ) . To investigate if nELAVL mediated regulation of AD related and other transcripts might be affected in AD , we performed nELAVL CLIP and RNAseq on AD subject brains , age-matched to control subjects ( Figure 5—figure supplement 2 , Supplementary file 1A/B and 3D ) . Importantly , ELAVL3/4 mRNA levels were similar between control and AD samples and ELAVL2 showed only a slight decrease in transcript abundance in AD brains ( Supplementary file 1B ) , which allowed us to compare nELAVL binding profiles between control and AD brains . We did not detect many significant changes in nELAVL binding nor mRNA abundance ( Figure 5A/B , Supplementary file 1B and 3D ) , probably due to the variation between human samples , the small sample size , and the potential heterogeneity of AD . We did however observe that 150 transcripts were differentially spliced in the 9 AD subjects ( FDR<0 . 05 and ΔI>0 . 1; Figure 5C , Supplementary file 3E ) . Two of these transcripts , BIN1 and PTPRD , have previously been linked to AD ( Tan et al . , 2013; Ghani et al . , 2012 ) , suggesting that the differential splicing of these two transcripts as well as other RNAs might be linked to AD . 10 . 7554/eLife . 10421 . 015Figure 5 . RNA regulation changes in AD . ( A ) nELAVL binding changes in AD . The nELAVL peak binding change ( AD/Control ) was plotted against average nELAVL peak binding . Significantly changing peaks ( FDR<0 . 05; n = 52 ) are colored in blue , and peaks within AD genes are colored in pink ( 1811 peaks within 69 genes ) . Shown are only peaks that are bound in control or AD brain ( n = 115 , 393 ) . ( B ) mRNA abundance changes in AD . The mRNA abundance change ( AD/Control ) was plotted against average mRNA abundance . Significantly changing transcripts ( FDR<0 . 05; n = 3 ) are colored in blue , and AD transcripts are colored in pink ( n = 89 ) . Shown are only transcripts that are expressed in control or AD brain ( n = 14 , 875 ) . ( C ) Analysis of splicing changes in AD . Shown is the inclusion fraction of expressed cassette exons in control and AD subjects ( n = 8163 ) . Exons within AD genes are colored in pink ( n = 79 ) . Significantly changing exons ( FDR<0 . 05 and ΔI>0 . 1 ) are colored in light blue ( n = 170 ) , and additionally boxed in pink if within AD genes ( n = 2 ) . ( D ) BIN1 is alternatively spliced in AD . UCSC Genome Browser image illustrating a cassette exon in the BIN1 gene and normalized nELAVL binding profiles in control and AD brain . The maximum PeakHeight is indicated by numbers in the right corner . Bar graphs depict the difference in alternative exon inclusion ( ΔI: Control – AD ) and nELAVL peak binding ( AD/Control ) in control and AD brain . Corresponding FDR values derived from edgeR are shown . The inclusion of the exon is promoted by nELAVL ( see Figure 4 ) , and exon inclusion as well as nELAVL peak binding are reduced in AD subjects . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01510 . 7554/eLife . 10421 . 016Figure 5—figure supplement 1 . Examples of disease associated SNPs with corresponding nELAVL binding sites . UCSC Genome Browser images depicting the last exon of GABRB1 , KCNJ10 , KCNK2 , LIPA , and USP24 and the normalized nELAVL binding profile in human brain . Pink bars illustrate SNPs overlapping with nELAVL binding sites , and the maximum PeakHeight is indicated by numbers in the right corner . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01610 . 7554/eLife . 10421 . 017Figure 5—figure supplement 2 . Correlations between control and AD samples . ( A ) Cross-correlation plot comparing nELAVL peak binding between eight controls and nine AD subjects ( n = 247 , 547 ) . Shown are R values . ( B ) Cross-correlation plot comparing the mRNA abundance of all transcripts between eight controls and nine AD subjects ( n = 19 , 185 ) . R values are depicted . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 017 As shown above ( Figure 4 ) , nELAVL depletion in IMR-32 cells was associated with the reduced inclusion of an alternative exon of BIN1 , suggesting that nELAVL binding promotes the inclusion of this exon . Precisely this exon was differentially spliced in AD subjects , with AD subjects showing a reduced exon inclusion rate compared to control subjects ( Figure 5D ) . Along with the differential exon inclusion , we observed that nELAVL peak binding was fourfold decreased in AD subjects ( log2 fold change = -2 . 35; p = 0 . 16; Figure 5D ) . These results are consistent with nELAVL-mediated dysregulation of this exon in AD subjects , with decreased binding leading to decreased exon inclusion . In conclusion , while we did not detect global nELAVL binding and mRNA abundance changes in AD subjects , we observed that splicing of 150 transcripts was affected , which in some cases might be linked to nELAVL dysregulation . The largest fold changes in nELAVL binding in AD ( relative to the age-matched control population ) occurred on a specific class of non-coding RNAs , Y RNAs ( Wolin et al . , 2013 ) . Y RNAs are 100 nt long structured RNAs usually found in complex with RO60 ( also known as TROVE2; Figure 6A; modified from Chen and Wolin , 2004 ) . RO60 is believed to act as a sensor of RNA quality , targeting defective RNAs for degradation ( Sim and Wolin , 2011 ) . RO60 was initially identified as an autoantigen targeted in systemic lupus ( Lerner et al . , 1981 ) and some subjects with the paraneoplastic encephalopathy syndrome harbor both anti-RO and anti-nELAVL ( Hu ) autoantibodies ( Manley et al . , 1994 ) . Four canonical Y RNAs , Y1/3/4/5 , have been characterized in humans , but numerous slightly divergent copies of these Y RNAs , especially Y1 and Y3 , are distributed throughout the human genome ( Perreault et al . , 2005 ) . 10 . 7554/eLife . 10421 . 018Figure 6 . Non-coding Y RNAs are bound by nELAVL in AD . ( A ) Secondary structures of Y1 and Y3 . Binding sites of nELAVL and Ro are indicated . Modified from ( Chen and Wolin , 2004 ) . ( B ) The nELAVL binding motif ( UUUUUU , allowing a G at any position ) is enriched in nELAVL-bound Y RNAs compared to non-bound Y RNAs ( p = 1 . 1e-7; Fisher’s exact test ) . Y RNAs were scanned for ( T ) 6 , allowing a G at any position . nELAVL-bound Y RNAs: nELAVL CLIP tags in at least two samples; n = 320 . ( C ) nELAVL binding of Y RNAs increases in AD compared to control samples ( p = 4 . 47e-51; paired one-sided Wilcoxon rank sum test ) . The axes depict nELAVL Y RNA binding ( nELAVL CLIP tags per Y RNA ) in control and AD subjects . Y RNAs with nELAVL binding motif are colored in green . ( D ) Y RNA levels do not change in AD . Y RNA abundance ( RNAseq tags per Y RNA ) in AD subjects was plotted against Y RNA abundance in control subjects . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01810 . 7554/eLife . 10421 . 019Figure 6—figure supplement 1 . Y RNAs with a motif that are not bound are not expressed . Box plots represent the distribution of Y RNA abundance in human brain . Non-bound Y RNAs with an nELAVL binding motif show a lower expression than nELAVL-bound Y RNAs with motif ( p = 0 . 002; two-tailed t test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 01910 . 7554/eLife . 10421 . 020Figure 6—figure supplement 2 . nELAVL:Y RNA binding increases in AD . ( A ) A subset of AD subjects shows increased nELAVL binding to Y RNAs . Box plots represent the distribution of nELAVL Y RNA binding ( tags per Y RNA ) for each individual . AD subjects were grouped into AD_Y and AD_nY subjects based on nELAVL binding . Only nELAVL bound Y RNAs are included: nELAVL CLIP tags in at least two samples; n = 320 . ( B , C ) nELAVL Y RNA binding but not Y RNA expression changes in AD_Y subjects . Comparison of Y RNA abundance and nELAVL Y RNA binding changes ( AD/Control ) in AD_Y ( B ) and AD_nY ( C ) subjects . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 020 Surprisingly , we observed nELAVL binding to a total of 320 Y RNAs , although Y RNA copies other than the canonical four Y1/3/4/5 genes had previously been considered to be non-functional and were labeled 'pseudogenes' ( Supplementary file 3F ) . We found that 237 of the 320 nELAVL bound Y RNAs were Y3-like RNAs ( Supplementary file 3F ) , and that nELAVL bound Y RNAs showed an enrichment of the nELAVL binding motif ( 202 Y RNAs contained UUUUUU , allowing a G at any one position ) , which is also present in the canonical hY3 RNA ( Figure 6A/B ) . We examined the 118 nELAVL bound Y RNAs that did not fit this consensus in more detail . 91 of these Y RNAs ( 77% ) contained either a 5mer version of the motif or the motif with an A or C instead of a G , and we found U/G rich stretches in the remaining 27 Y RNAs ( Supplementary file 3F ) . In addition , some Y RNAs with a strong binding motif did not show any evidence of nELAVL binding . In general , these Y RNAs showed a lower expression compared to nELAVL bound Y RNA , which may explain the absence of detectable nELAVL binding ( Figure 6—figure supplement 1 ) . We next explored nELAVL/Y RNA binding in AD brain . We observed a drastic increase in nELAVL/Y RNA association in AD subjects ( Figure 6C ) , while Y RNA levels remained largely unchanged ( Figure 6D ) . This suggests that Y RNPs undergo nELAVL-dependent remodeling in AD . Interestingly , we did observe a high variability in nELAVL/Y RNA association between AD samples ( Figure 6—figure supplement 2 ) , with three of them showing a very strong nELAVL/Y RNA association . Efforts to relate this difference to the expression of stress-related genes , post-mortem interval , age , extent of disease and cause of death were not conclusive , and the cause for the variation in nELAVL binding to Y RNAs among AD subjects remains elusive . The observation of increased nELAVL/Y RNA association in AD raised the possibility that Y RNP remodeling is associated with neuronal stress . Y RNP remodeling has previously been linked to UV-induced stress ( Sim et al . , 2009 ) , and both bacterial ( Chen et al . , 2000; Wurtmann and Wolin , 2010 ) and mouse cells ( Chen et al . , 2003 ) show an increased sensitivity to UV stress in the absence of RO60 . ELAVL binding can be modulated in response to stress in cultured cells ( Bhattacharyya et al . , 2006 ) , and ELAVL proteins , which shuttle between nucleus and cytoplasm in response to environmental cues , preferentially accumulate in cytoplasmic stress granules upon stress ( Gallouzi et al . , 2000; Fan and Steitz , 1998b ) . We therefore examined the effect of acute UV stress on Y RNP remodeling in IMR-32 cells . IMR-32 cells were exposed to a low dose of UV stress ( not sufficient to induce RNA:protein crosslinking ) and allowed to recover for 24 h before being analyzed by nELAVL CLIP . We found that nELAVL bound 132 Y RNAs in neuroblastoma cells ( Supplementary file 3F ) , that Y RNAs showed an enrichment of the nELAVL binding motif ( Figure 7A ) or at least contained a degenerate version of it ( Supplementary file 3F ) , and that non-bound Y RNAs with a motif show a very low expression ( Figure 7—figure supplement 1 ) . Moreover , nELAVL binding on Y RNAs was dynamic and increased in UV stressed cells compared to non-stressed cells ( Figure 7B and Figure 7—figure supplement 2 ) , while their abundance did not change upon UV irradiation ( Figure 7C ) . To assess whether Y RNA levels were affected by nELAVL , we depleted nELAVL by RNAi three days prior to the UV exposure , and analyzed Y RNA levels by RNAseq . Y RNA abundance was not affected by nELAVL depletion in UV stressed IMR-32 cells ( Figures 7D ) . These results indicate that increased nELAVL binding to Y RNAs is not a function of Y RNA levels , and that nELAVL binding during stress is not required for Y RNA stability . 10 . 7554/eLife . 10421 . 021Figure 7 . Y RNPs are remodeled during UV stress . ( A ) The nELAVL binding motif ( UUUUUU , allowing a G at any position ) is enriched in nELAVL-bound Y RNAs compared to non-bound Y RNAs ( p = 6 . 2e-6; Fisher’s exact test ) . Y RNAs were scanned for ( T ) 6 , allowing a G at any position . nELAVL-bound Y RNAs: nELAVL CLIP tags in at least two samples; n = 132 . ( B ) nELAVL binding of Y RNAs increases during UV stress compared to non-stressed cells ( p = 8 . 23e-29; paired one-sided Wilcoxon rank sum test ) . The axes depict nELAVL Y RNA binding ( nELAVL CLIP tags per Y RNA ) in control and UV stressed cells . Y RNAs with nELAVL binding motif are colored in green . ( C ) Y RNA levels do not change upon UV stress . Y RNA abundance ( RNAseq tags per Y RNA ) in UV stressed cells was plotted against Y RNA abundance in non-stressed control cells . ( D ) nELAVL is binding is not required for Y RNA stability . Comparison of Y RNA abundance between mock and nELAVL RNAi treated UV stressed cells . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02110 . 7554/eLife . 10421 . 022Figure 7—figure supplement 1 . Y RNAs with a motif that are not bound are not expressed . Box plots represent the distribution of Y RNA abundance in IMR-32 cells . Non-bound Y RNAs with an nELAVL binding motif show a lower expression than nELAVL-bound Y RNAs with motif ( p = 0 . 08; two-tailed t-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02210 . 7554/eLife . 10421 . 023Figure 7—figure supplement 2 . UV-stressed cells show increased nELAVL binding to Y RNAs . Box plots represent the distribution of nELAVL binding ( tags per Y RNA ) for each individual . Only nELAVL bound Y RNAs are included: nELAVL CLIP tags in at least two samples; n = 132 . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02310 . 7554/eLife . 10421 . 024Figure 7—figure supplement 3 . UV does not induce changes in the nucleocytoplasmic localization of Y RNP components . ( A ) Validation of UV stress induction . Bar graphs depict the fold change in RNA expression in UV stressed cells compared to non-stressed control cells . CDKN1A ( the most upregulated transcripts in the RNAseq dataset ) but not control mRNAs ( ACTB , GAPDH , ELAVL4 ) nor Y RNAs increase upon UV stress . RNA expression was normalized to non-UV treated cell . Error bars represent SEM . p values were calculated with a two-tailed t test ( ns: not significant; * p = 6 . 9e-5; two-tailed t-test ) . ( B ) UV stress does not induce changes in Y RNA distribution . Bar graphs depict the percentage of cytoplasmic RNA levels ( cytoplasmic RNA levels divided by the sum of cytoplasmic and nuclear RNA levels ) of mRNA controls ( ACTB , GAPDH , ELAVL4 ) and Y RNAs . Error bars represent SEM . Changes in cytoplasmic RNA levels were not significant ( ns; p<0 . 05; two-tailed t-test ) . ( C ) UV stress does not induce changes in protein distribution . Western Blot and its quantification showing cytoplasmic and nuclear protein levels of nELAVL , RO60 , as well as cytoplasmic ( HSP90 and GAPDH ) and nuclear ( RNA PolII and Histone H3 ) markers . Bar graphs depict the percentage of cytoplasmic protein levels ( cytoplasmic protein levels divided by the sum of cytoplasmic and nuclear protein levels ) . Error bars represent SEM . Changes in cytoplasmic proteins levels were not significant ( ns; p<0 . 05; two-tailed t test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02410 . 7554/eLife . 10421 . 025Figure 7—figure supplement 4 . Up to 5% of nELAVL CLIP map to Y RNAs in AD_Y subjects ( AD subjects with increased nELAVL/Y RNA association ) and UV stressed cells when mapped with Bowtie 2 ( allowing multiple alignments and reporting one ) . Columns represent the percentage of nELAVL tags that mapped to Y RNAs with Bowtie 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 025 To determine if UV stress induced localization changes of Y RNP components , we investigated the distribution of nELAVL , RO60 as well as Y RNAs upon UV exposure using cell fractionation followed by western blot and qPCR analysis . The induction of a UV stress response was confirmed by measuring CDKN1A mRNA levels ( Figure 7—figure supplement 3A ) . We did not observe a change in nucleocytoplasmic localization of the investigated RNAs or proteins ( Figure 7—figure supplement 3B/C ) , suggesting that the increased nELAVL/Y RNA association upon UV exposure does not result from a difference in the nucleocytoplasmic distribution of nELAVL or Y RNAs . These results are consistent with previous observations that neuronal ELAVL proteins show a higher cytoplasmic localization than the ubiquitous paralog ELAVL1 ( Kasashima et al . , 1999 ) , and that stress-induced nuclear-cytoplasmic shuttling might be limited to ELAVL1 ( Burry and Smith , 2006 ) . Nonetheless , these results do not rule out the possibility that there may be changes of nELAVL proteins within the nuclear or cytoplasmic compartments themselves with respect to Y RNA binding and localization . We next sought to measure the proportion of nELAVL bound to Y RNAs in stressed and non-stressed conditions . Because Y RNAs are relatively short and have a high degree of similarity , our mapping strategy ( reporting only unambiguous mapping events ) discarded numerous reads that were assigned to multiple Y RNAs . We therefore re-mapped CLIP tags , allowing multiple alignments , but reporting only the best match , permitting a more accurate estimate of overall Y RNA binding . The fraction of the short CLIP reads mapping to Y RNAs was considerably higher using this strategy , revealing that up to 6% of nELAVL CLIP tags map to Y RNAs in AD and UV stressed cells , compared to less than 0 . 5% in control brain and ~1% in non-stressed cells ( Figure 7—figure supplement 4 ) . The significant increase in nELAVL/Y RNA association and our observation that up to 6% of nELAVL was bound to Y RNAs might in fact lead to a sequestration of nELAVL from its targets . To investigate if the increased nELAVL/Y RNA association was linked to decreased intronic and 3’UTR nELAVL binding , we grouped AD subjects based on their Y RNA association and compared the two different AD groups to control subjects . We found that the majority of changing nELAVL binding sites decreased in AD subjects with high nELAVL/Y RNA , while nELAVL binding in AD subjects with low nELAVL/Y RNA association was mostly increasing ( Figure 8A ) . Because nELAVL binding in UV-stressed cells also predominantly decreased ( Figure 8A ) and most of the decreased binding sites were in introns ( 85% , assessed by annotation of peak locations ) , we speculate that nELAVL/Y RNA association leads to a sequestration of nELAVL specifically from its intron targets , which might induce similar splicing changes as nELAVL depletion by RNAi . Of note is our observation that nELAVL binding decreased at only a subset of intronic binding sites . 10 . 7554/eLife . 10421 . 026Figure 8 . nELAVL/Y RNA correlates with loss of nELAVL-mediated splicing . ( A ) Samples with high nELAVL/Y RNA association show decreased nELAVL binding on mRNA targets . Columns represent significantly changing nELAVL binding sites . Shown are changes in AD subjects with and without Y RNA association ( AD_Y and AD_nY ) and changes upon UV treatment . The number of nELAVL binding sites ( n ) within each category is indicated . ( B ) Identification of nELAVL-dependent UV-induced splicing changes . Comparison of the differential inclusion rate of expressed cassette exons upon UV stress between mock and nELAVL RNAi treated IMR-32 cells ( n = 9397 ) . Significant UV-induced splicing changes that do not change upon UV stress in nELAVL RNA treated cells are boxed in dark blue ( FDR<0 . 05 and ΔI>0 . 1; n = 260 ) . ( C ) Many exons that are alternatively spliced upon nELAVL RNAi treatment also change during UV stress in an nELAVL-dependent manner . Shown is the inclusion rate of expressed cassette exons in IMR-32 cells that were subjected to mock or nELAVL RNAi ( n = 9397 ) . nELAVL RNAi induced splicing changes are colored in light blue ( n = 553 ) , and are additionally boxed in dark blue if they are UV-induced in an nELAVL-dependent manner ( n = 68 ) . The plot is related to Figure 4A but contains additional cassette exons expressed in UV stressed cells . ( D ) nELAVL binding adjacent to exons that are alternatively spliced upon nELAVL RNAi and UV treatment decreases only in AD subjects with an increased Y RNA association . Displayed is the change in nELAVL peak binding . nELAVL peak binding changes were not significant except for CBFA2T2 ( boxed in pink ) . * FDR<0 . 05 ( derived from edgeR ) . ( E ) UCSC Genome Browser images depicting an overview and an enlarged view of a cassette exon within the CBFA2T2 gene that is alternatively spliced in nELAVL RNAi and UV-treated IMR-32 cells . The nELAVL binding track in human brain and RNAseq tracks in mock and nELAVL RNAi treated non-stressed and UV-stressed IMR-32 cells are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02610 . 7554/eLife . 10421 . 027Figure 8—figure supplement 1 . UV does not affect nELAVL RNA or protein levels . ( A ) nELAVL protein levels decrease upon nELAVL RNAi treatment in non-stressed and UV-stressed IMR-32 cells but are not affected by UV stress . Western Blot and its quantification showing protein levels of nELAVL and the house keeping genes HSP90 and Histone H3 in mock and nELAVL RNAi treated non-stressed and UV-stressed IMR-32 cells . Protein expression was normalized to the house keeping gene Histone H3 and to mock RNAi treated cell . Error bars represent SEM . p-values were calculated with a two-tailed t-test ( ns: not significant; *p<0 . 01 ) . ( B ) nELAVL mRNA levels decreased upon nELAVL RNAi treatment in non-stressed and UV-stressed IMR-32 cells but were not affected by UV stress . Shown is the mRNA abundance assessed by RNAseq in response to nELAVL RNAi and UV stress . FDR values were derived from edgeR ( ns: not significant; * FDR<1e-2; ** FDR<1e-4; *** FDR<1e-20 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02710 . 7554/eLife . 10421 . 028Figure 8—figure supplement 2 . Analysis of splicing changes in nELAVL RNAi and UV treated IMR-32 cells and AD subjects with and without Y RNA association ( AD_Y and AD_nY ) . Shown is the differential inclusion rate of cassette exons that change similarly upon nELAVL RNAi and UV treatment and that were adjacent ( +/- 2 . 5 kb ) to intronic nELAVL binding sites in human brain . Splicing changes in AD subjects were not significant except for UTX ( boxed in pink ) . * FDR<0 . 05 ( GLM likelihood ratio test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 028 We further examined the possibility of Y RNA mediated nELAVL sequestration upon UV stress by subjecting mock and nELAVL RNAi treated IMR-32 cells to UV stress and analyzing these cells by RNAseq . nELAVL mRNA and proteins levels decreased upon nELAVL RNAi in non-stressed and UV stressed cells but were not affected by UV stress ( Figure 8—figure supplement 1 and Supplementary file 2C ) . We analyzed exon inclusion rates as above ( Figure 4 ) and found that 9397 cassette exons were expressed between the four conditions ( Supplementary file 2E ) . Comparing UV-induced splicing changes between mock and nELAVL RNAi treated cells , we identified 260 cassette exons that showed a differential inclusion rate upon UV stress only in the presence of nELAVL ( Figure 8B ) . We intersected these splicing changes with nELAVL RNAi induced splicing changes ( n = 553 ) , and found a significant overlap between the two lists ( n = 68; p = 9 . 7e-28; hypergeometric test; Figure 8C and Supplementary file 2E ) . Importantly , splicing of the vast majority of exons ( 66 out of 68 ) changed in the same direction , indicating that changes in nELAVL binding due to UV stress partially recapitulate nELAVL RNAi depletion . This finding is consistent with a model of UV-induced nELAVL sequestration from a subset of targets . Due to the wide difference between AD subjects and UV stressed IMR-32 cells , the targets affected by nELAVL sequestration in the two systems are likely to be markedly divergent . We nevertheless explored if any of the 66 affected exons in IMR-32 cells were adjacent ( +/- 2 . 5 kb ) to intronic nELAVL binding sites in human brain , and observed that 7 alternatively spliced exons were indeed next to intronic nELAVL binding sites ( Figure 8D/ Figure 8—figure supplement 2 ) . Remarkably , nELAVL binding at all of these sites decreased in AD subjects with Y RNA association but not in AD subjects without Y RNA association ( Figure 8D ) , although nELAVL peak binding changed significantly at only one binding site ( boxed in Figure 8E , FDR = 0 . 003 ) . We also investigated if decreased nELAVL peak binding was associated with corresponding splicing changes in AD subjects ( Figure 8—figure supplement 2 ) . The inclusion rate of only one of the 7 exons changed significantly in AD patients with high nELAVL/Y RNA association ( FDR = 0 . 04 ) , and the direction of the splicing change was indeed the same as the splicing changes observed upon UV or nELAVL RNAi treatment . This is consistent with our observation that only few splicing changes are shared between AD and UV treatment , which likely reflects the much more complex situation in human brain . To directly test the model of Y RNA mediated nELAVL sequestration , we overexpressed canonical Y3wt ( wild type ) and Y3mut ( with a mutated nELAVL binding site ) using lentiviral infections of IMR-32 cells . We initially confirmed the overexpression of the infected Y RNAs using qPCR , which was more pronounced in the Y3mut infected cells ( Figure 9A ) . The distribution of neither nELAVL nor RO60 was affected upon infection ( Figure 9—figure supplement 1 ) . To evaluate the extent of Y3wt and Y3mut overexpression compared to endogenous Y3 RNA expression we additionally analyzed infected cells by RNAseq ( Figure 9B , Figure 9—figure supplement 2 , Supplementary file 3F ) . While we consistently observed an increase in the Y3-like RNA expression upon infection , the magnitude of overexpression was modest relative to the endogenous expression of Y RNA copies . Nonetheless , we observed a small increase in total Y3-like RNAs in Y3wt but not Y3mut infected cells ( Figure 9B ) . 10 . 7554/eLife . 10421 . 029Figure 9 . Y RNA overexpression is linked to nELAVL sequestration from mRNA targets . ( A ) Validation of Y RNA overexpression . Shown are RNA expression fold changes of Y3wt or Y3mut infected IMR-32 cells compared to non-infected IMR-32 cells assessed by qPCR . Y RNAs expression increased while control mRNAs ( ACTB , GAPDH , ELAVL4 ) were not affected . Error bars represent SEM . p values were calculated with a two-tailed t-test ( ns: not significant; * p<0 . 05 ) . ( B ) The expression of endogenous Y3-like Y RNAs increases upon Y3wt but not Y3mut infection . Box plots represent the distribution of endogenous Y3-like and non-Y3-like Y RNA expression fold changes upon Y3wt or Y3mut infection . Y3-like Y RNAs show a slight increase in abundance upon Y3wt compared to non-Y3-like Y RNAs ( p = 0 . 057; one-tailed t-test ) . In contrast , the mRNA abundance of Y3-like Y RNAs does not change upon Y3mut infection , when compared to non-Y3 like Y RNAs ( p = 0 . 602; one-tailed t-test ) . ( C ) Identification of Y3 dependent splicing changes . Shown is the exon inclusion fraction of cassette exons that are expressed in IMR-32 cells subjected to Y3wt or Y3mut infection ( n = 10 , 189 ) . Exons changing significantly between Y3wt and Y3mut infection ( FDR<0 . 05 and ΔI>0 . 1 ) are colored in light blue ( n = 191 ) . ( D ) Exons that are alternatively spliced upon Y3wt infection are enriched for nELAVL bound exons . Bar graph representing total expressed exons ( n = 10 , 189 ) , exons that change in either Y3wt ( n = 240; blue points in the left panel of Figure 9—figure supplement 4 ) or Y3mut ( n = 151; blue points in the right panel of Figure 9—figure supplement 4 ) infected cells compared to non-infected cells , and exons that change in Y3wt compared to Y3mut infected cells ( n = 191; blue points in Figure 9C ) . Exons that are alternatively spliced upon Y3wt infection compared to either non-infected ( p = 0 . 037; hypergeometric test ) or Y3mut infected cells ( p = 0 . 069; hypergeometric test ) are enriched for nELAVL bound exons . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 02910 . 7554/eLife . 10421 . 030Figure 9—figure supplement 1 . Y3 overexpression does not induce changes in protein distribution . Western Blot and its quantification showing cytoplasmic and nuclear protein levels of nELAVL , RO60 , as well as cytoplasmic ( HSP90 and GAPDH ) and nuclear ( RNA PolII and Histone H3 ) markers . Bar graphs depict the percentage of cytoplasmic protein levels ( cytoplasmic protein levels divided by the sum of cytoplasmic and nuclear protein levels ) . Error bars represent SEM . Changes in cytoplasmic proteins levels were not significant ( ns; p<0 . 05; two-tailed t-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 03010 . 7554/eLife . 10421 . 031Figure 9—figure supplement 2 . Validation of Y RNA overexpression . Bar graphs represent log2 number of reads of Y3wt and Y3mut in non-infected , Y3wt and Y3mut infected IMR-32 cells . Read numbers were assessed by searching raw fastq files for Y3wt and Y3mut sequences , respectively . Searched sequences were either 40 ( left panel ) or 68 ( right panel ) nucleotides in length . Both sequence lengths encompassed the sequence mutated in Y3mut . While the 40nt Y3wt sequence is present in numerous Y3 RNA copies , the 68nt Y3wt sequence should only be present in the infected Y3wt RNA and the endogenous canonical hY3 RNA . Error bars represent SEM . p values were calculated with a two-tailed t-test ( ns: not significant; * p<0 . 01 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 03110 . 7554/eLife . 10421 . 032Figure 9—figure supplement 3 . Y3 overexpression does not lead to nELAVL 3'UTR target sequestration . ( A ) Correlation of mRNA abundance changes upon Y3wt and Y3mut infection compared to non-infected cell . The mRNA abundance fold change ( Y3 infected/non-infected ) of Y3mut infected cells was plotted against the mRNA abundance fold change of Y3wt infected cells . Transcripts that change significantly ( FDR<0 . 05 ) upon either Y3wt infection ( n = 502 ) or Y3mut infection ( n = 1920 ) are colored in light blue . Transcripts that change in both infections and are therefore likely to be virus dependent are colored in dark blue ( n = 349 ) . Shown are only transcripts that are expressed ( n = 12 , 659 ) and the R value is indicated . ( B ) The mRNA abundance change ( Y3mut/Y3wt infection ) of transcripts was plotted against average mRNA abundance . Significantly changing transcripts ( FDR<0 . 05; n = 435 ) are colored in blue , and are not enriched for nELAVL 3’UTR targets . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 03210 . 7554/eLife . 10421 . 033Figure 9—figure supplement 4 . Identification of Y3wt and Y3mut dependent splicing changes . Shown is the exon inclusion fraction of cassette exons that are expressed in IMR-32 cells subjected to Y3wt or Y3mut infection ( n = 10 , 189 ) . Exons changing significantly ( FDR<0 . 05 and ΔI > 0 . 1 ) upon Y3wt ( left panel; n = 240 ) or Y3mut ( right panel; n = 151 ) infection are colored in light blue . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 03310 . 7554/eLife . 10421 . 034Figure 9—figure supplement 5 . Exons that are alternatively spliced upon Y3wt infection are enriched for nELAVL RNAi dependent exons . Bar graph representing total expressed exons ( n = 10 , 189 ) , exons that change in either Y3wt ( n = 240; blue points in the left panel of Figure 9—figure supplement 4 ) or Y3mut ( n = 151; blue points in the right panel of Figure 9—figure supplement 4 ) infected cells compared to non-infected cells , and exons that change in Y3wt compared to Y3mut infected cells ( n = 191; blue points in Figure 9C ) . Exons that are alternatively spliced upon Y3wt infection compared to either non-infected ( p = 0 . 011; hypergeometric test ) or Y3mut infected cells ( p = 0 . 099; hypergeometric test ) are enriched for nELAVL RNAi dependent exons . DOI: http://dx . doi . org/10 . 7554/eLife . 10421 . 034 We next investigated mRNA abundance and splicing changes upon Y RNA overexpression ( Supplementary file 2C/E ) . The mRNA abundance changes upon Y3wt and Y3mut infection compared to non-infected controls were very similar , indicating that most mRNA abundance changes are due to lentiviral infection ( Figure 9—figure supplement 3A; 70% of Y3wt changes overlapped with Y3mut changes; p = 1 . 3e-175; hypergeometric test ) . To investigate virus-independent changes , we focused on the changes between Y3wt and Y3mut infection . nELAVL 3’UTR targets were not enriched among mRNAs that changed between Y3mut compared to Y3wt infected cells ( Figure 9—figure supplement 3B ) , which is in agreement with our hypothesis that nELAVL sequestration predominantly affects intronic nELAVL binding sites and thus nELAVL mediated splicing . In contrast to the mRNA abundance changes , only few splicing changes overlapped between Y3wt and Y3mut infection when compared to non-infected cells ( 17% of Y3wt induced changes overlapped with Y3mut induced changes ) . Most of the observed splicing changes are therefore likely to be specific to Y RNA overexpression . Importantly , we observed an enrichment of nELAVL bound exons and of nELAVL RNAi dependent exons among the exons that changed upon Y3wt but not Y3mut overexpression ( Figure 9C/D and Figure 9—figure supplement 4 , 5 ) . The relatively small enrichment is consistent with the modest increase in total Y3-like Y RNAs . These results suggest that Y RNA overexpression results in nELAVL sequestration from some of its intronic targets and consequent splicing changes , and partially recapitulates the stress induced nELAVL sequestration due to increased nELAVL/Y RNA association seen in AD patients and UV treated IMR-32 cells . nELAVL proteins are abundant neuron-specific RNA binding proteins which have been suggested to regulate various neurological processes and have been linked to neurodegenerative disorders including AD and PD . Yet the RNA targets of nELAVL in human brain were completely unknown . Here , we generated a comprehensive genome-wide RNA binding map of nELAVL in human brain , identifying 75 , 592 significant binding events within 8681 transcripts . We observed a significant overlap between these binding sites and disease-associated 3’UTR SNPs , and the potential disruption of nELAVL-mediated RNA regulation at these sites might contribute to disease manifestation . Most deleterious variants to date have been identified by exome sequencing while as many as 50% of disease-causing mutations are thought to affect splicing ( Ward and Cooper , 2009 ) . With whole genome sequencing being increasingly available , non-coding variants are also increasingly detected , some of which may be linked to disease . As the majority of nELAVL binding occurs in introns and 3’UTRs , we expect that many binding sites will overlap with prospective disease-associated non-coding variants . The overlap between deleterious variants and nELAVL binding sites , and the observation that nELAVL binding at individual sites diverged between mice and human , underscores the importance of this study and illustrates the caveat of relying solely on mouse models when studying human disease . Considering the widespread nature of nELAVL binding in human brain and that RNA dysregulation has been linked to numerous neurological disorders , we believe that this binding map will be a valuable resource for the scientific community . To analyze the functional consequences of nELAVL binding , we used two different loss-of-function models: Elavl3/4 KO mice and nELAVL RNAi depletion in neuroblastoma cells . Due to the incomplete RNAi depletion of nELAVL in neuroblastoma cells , and potential differences in mRNA abundance and therefore nELAVL binding between the different samples , it is likely that we validated only a fraction of nELAVL-regulated transcripts . Despite these technical limitations we demonstrated that nELAVL impacts mRNA abundance and/or splicing of hundreds of targets . Among the nELAVL regulated transcripts were many transcripts implicated in human disease , including AD , which led us to investigate RNA regulation in AD subjects . Due to the relatively small sample size and the heterogeneity between these samples , likely due to both differences between individuals and sample preservation during postmortem collection , we did not detect many reproducible changes in mRNA abundance or nELAVL binding between AD and non-AD subjects . However , we found that 150 transcripts were differentially spliced in AD subjects , which in some cases coincided with differential nELAVL binding . Unexpectedly , the most significant binding change in AD was a dramatic increase in nELAVL binding to a class of non-coding RNAs , termed Y RNAs . This change was evident on a specific subset of Y RNAs harboring the nELAVL binding site . nELAVL/Y RNA binding also increased during UV stress in human neuroblastoma cells , while the abundance of Y RNAs remained constant in AD subjects and upon UV exposure . The increased nELAVL/Y RNA association correlated with decreased nELAVL binding at a subset of intronic binding sites , and was associated with similar splicing changes as induced by nELAVL depletion , suggesting that nELAVL/Y RNP remodeling during acute and chronic stress sequesters nELAVL from its mRNA targets . We provided further evidence for a Y RNA dependent nELAVL sequestration by overexpressing Y3 RNAs harboring either a wild type or mutated nELAVL binding site . Exons that were differentially spliced upon Y RNA overexpression were enriched for nELAVL bound exons , indicating nELAVL sequestration , which was dependent on an intact nELAVL binding site in the Y RNA . nELAVL 3’UTR binding has been implicated in increasing mRNA abundance in vivo ( Ince-Dunn et al . , 2012 ) . We described numerous nELAVL 3’UTR targets in brain , and were able to validate many of these targets , including disease-associated transcripts , indicating that nELAVL 3’UTR binding is important for the regulation of mRNA abundance in human brain . While ELAVL binding is frequently reported to result in an increase in mRNA abundance , we found several cases where nELAVL binding seemed to have an opposing effect . ELAVL proteins can compete or collaborate with miRNAs as well as RBPs like AUF1 , CUGBP1 and TIA1 to regulate its targets ( Bhattacharyya et al . , 2006; Kawai et al . , 2006; Lal et al . , 2004; Young et al . , 2009; Yu et al . , 2013; Kim et al . , 2009 ) . The ultimate outcome of nELAVL 3’UTR binding might therefore vary between individual transcripts . nELAVL has also been shown to regulate splicing in mouse brain by binding to intronic sequences ( Ince-Dunn et al . , 2012 ) . We observed many instances of intronic nELAVL binding events adjacent to alternative exons in brain , and confirmed that nELAVL regulates many of these exons in mice and neuroblastoma cells . In contrast to the position-dependent splicing observed for other RBPs ( Licatalosi and Darnell , 2010 ) , we observed that upstream nELAVL binding was associated with both exon skipping and inclusion . While nELAVL binding was observed within 25-50 nucleotides upstream of skipped exons , coinciding with the branch point sequence , nELAVL binding peaked within the proximal 25 nucleotides upstream of included exons , overlapping the polypyrimidine tract . Binding of auxiliary splicing factors , including nELAVL , to the branch point sequence usually interferes with spliceosome assembly and thus leads to exon skipping ( Licatalosi and Darnell , 2010 ) . Polypyrimidine tract binding however can lead to both exon inclusion and skipping ( Licatalosi et al . , 2012; Wei et al . , 2012 ) , presumably depending on the recruitment of splicing enhancers or silencers . Our data indicates that upstream nELAVL binding can both interfere with the assembly of the spliceosome as well as promote splicing , most likely by recruiting splicing enhancers . Splicing defects have been associated with many neurological diseases ( Licatalosi and Darnell , 2006 ) , and among the nELAVL-regulated transcripts we describe here are numerous transcripts related to disease , including AD . For example , intronic nELAVL binding of the gene encoding the amyloid precursor protein , APP , was associated with skipping of exons 7 and 8 . Both exons have previously been shown to be alternatively spliced and encode for the Kunitz protease inhibitory ( KPI ) motif , a domain that has been linked to APP processing ( Ben Khalifa et al . , 2012 ) . Remarkably , KPI domain containing isoforms of APP have been shown to be increased in AD ( Zhang et al . , 2012 ) , indicating that APP splicing might contribute to AD pathogenesis , and that nELAVL binding in human brain might be important to regulate the inclusion of the KPI domain . nELAVL regulates the splicing of two more AD-related transcripts , PICALM and BIN1 , by promoting the inclusion of alternative exons 13 and 6a , respectively . Both proteins have been implicated in APP trafficking and both exons lie within domains mediating protein-protein interactions ( Tan et al . , 2013; Treusch et al . , 2011 ) . Moreover , inclusion of the alternative exon 13 in PICALM has been linked to an AD-associated SNP ( Parikh et al . , 2014 ) , and we observed in this study that exon 6a of BIN1 shows a higher inclusion rate in controls compared to AD subjects . Since nELAVL binding promotes the inclusion of this exon , and control subjects show higher nELAVL binding , we propose that the altered splicing of BIN1 in AD subjects might be due to differential nELAVL binding . In fact , several nELAVL-regulated exons have been shown to be differentially spliced in AD subjects , further strengthening the link between nELAVL dysregulation and AD . While Y RNAs have not been linked to AD before , they have been implicated in various types of stress responses . The RNA binding protein RO60 usually associates with Y RNAs and is required for their stabilization ( Chen et al . , 2000; 2003; Labbé et al . , 1999; Wolin et al . , 2013; Xue et al . , 2003 ) . Besides RO60 , Y RNPs contain several other RBPs such as ZBP1 , MOV10 , and Y-box proteins , and have been found to be remodeled upon stress ( Sim et al . , 2012 ) . Our data suggests that nELAVL becomes increasingly associated with specific Y RNAs during both UV-induced stress and AD . ELAVL proteins can shuttle between nucleus and cytoplasm in response to environmental cues and preferentially accumulate in cytoplasmic stress granules upon cellular stress ( Fan and Steitz , 1998a; Gallouzi et al . , 2000 ) , and ELAVL binding to the CAT-1 transcript is modulated in response to stress in cultured cells ( Bhattacharyya et al . , 2006 ) . Interestingly , while we found that nELAVL specifically associates with Y RNAs during AD and acute UV stress , the nucleocytoplasmic distribution of nELAVL , RO60 , and Y RNAs was not affected by UV stress . Because Y RNA levels remained constant , we propose that Y RNP complexes are specifically remodeled during AD and acute stress , which is not likely due to a change in nucleocytoplasmic protein/RNA distribution . These results are consistent with previous observations that stress induced shuttling might be limited to ELAVL1 ( Burry and Smith , 2006 ) . Our observation of Y RNP remodeling in two very different systems of neuronal stress suggests that differential nELAVL/Y RNA association may be a widespread phenomenon and a focus of future studies . In addition to the four canonical human Y RNAs , hY1/3/4/5 , hundreds of additional Y RNA genes are distributed throughout the human genome ( Perreault et al . , 2005 ) . The apparent lack of promoters upstream led to a premature designation of these Y RNAs as pseudogenes . Surprisingly , we found that hundreds of these Y RNA copies are expressed in human brain and neuroblastoma cells , although it remains unclear if these Y RNAs can still associate with RO60 , because the RO60 binding site in many Y RNA copies is mutated ( Perreault et al . , 2005 ) . We observed that numerous Y RNA copies were more strongly associated with nELAVL in AD brain and acutely stressed cells , yet nELAVL binding did not affect their levels , indicating a function for this interaction other than Y RNA stabilization . While the outcome of nELAVL/Y RNA remains to be elucidated , our work revealed an aspect of nELAVL/Y RNA association related to stoichiometry . Hundreds of Y RNAs are bound by nELAVL in AD and UV-stress , which corresponds to up to 5% of all nELAVL CLIP tags . This shift of nELAVL binding may distort the normal stoichiometry of nELAVL interactions with its mRNA targets . Indeed , non-coding RNAs have previously been shown to affect RBP-RNA stoichiometry and therefore the biological function of other RNAs or RBPs ( Borah et al . , 2011; Cazalla et al . , 2010; Hansen et al . , 2013 ) . Our data indicate that the binding of nELAVL to Y RNAs during stress may lead to a redistribution of nELAVL binding and/or competition of nELAVL from other RNAs . Consistently , we found that high nELAVL/Y RNA association was associated with a general decrease in nELAVL binding at a subset of binding sites , especially within introns , and consequential splicing changes were reminiscent of splicing changes provoked by nELAVL depletion . Consistently , splicing changes induced by Y RNA overexpression showed an enrichment of nELAVL binding that was dependent on the presence of the ELAVL binding motif in Y RNAs . Hence we propose that the increased association of nELAVL and Y RNAs during stress causes sequestration of nELAVL from its mRNA targets . Taken together , our data indicate that nELAVL becomes strongly associated with Y RNAs in some AD subjects as well as in cells subjected to UV stress , and this is linked to a sequestration of nELAVL from some of its intronic targets , partially recapitulating splicing changes induced by nELAVL depletion . Our results are consistent with a hypothesis that a relatively subtle and perhaps long-term effect of Y RNA binding on normal nELAVL stoichiometry may underlie subtle and long-term changes in nELAVL biology . Perhaps analogously , the sequestration of the RBP , TDP-43 , has previously been linked to neurodegenerative disorders ( Lee et al . , 2012 ) . While the underlying mechanisms of TDP-43 and nELAVL sequestration are distinct , relatively subtle and long-term rearrangement of RNA:protein stoichiometry and interactions might be a recurrent theme of neurodegeneration . In conclusion , we have determined a robust and reproducible map of nELAVL binding sites on both mRNA and Y RNA targets in human brain . This database has both common and human-specific features that confirm and enhance previous work in mice and underscore its value as a resource for scientific inquiry . We have linked the data to genome-wide measurements of both mRNA levels and splicing suggesting specific functions for these important interactions . Moreover , we have uncovered a stress-modulated interaction of nELAVL proteins with non-coding Y RNAs . Y RNPs are remodeled during both UV-induced and chronic AD-related stress , which may be causally related to the pathophysiology of stress by causing a redistribution of nELAVL RNA target binding . Data implicating nELAVL proteins in several neurologic diseases , as well as the overlap of nELAVL binding sites we describe here with SNPs linked to the same and additional human diseases underscore the importance of this resource for the ongoing study of the molecular basis of human neurologic disease .
Frozen brain tissue was obtained from the Mount Sinai Brain Bank . Subjects were classified according to CERAD criteria . Summaries of cognitive performance ( assessed by clinical dementia rating , CDR ) , neurofibrillary tangles ( assessed by Braak staging , BB ) and plaque pathology ( assessed by plaque counts ) are shown in Supplementary file 1A . Brains were dissected along Brodmann areas and the two hemispheres were either stored as crushed powder at -80°C for biochemical analyses , or processed for stereological analyses , respectively . Eight control ( no neurofibrillary tangles or plaque pathology ) , and nine advanced stage AD ( CDR between 4 and 5 ) subjects matched for age and gender with short post mortem intervals ( PMI ) were selected . Crushed brain powder derived from the dorsolateral prefrontal cortex was either subjected to Trizol extraction or UV irradiation ( 3x 400 mJ/cm; see below ) followed by CLIP analysis . Human HEK293T and neuroblastoma IMR-32 cells were obtained from ATCC ( Manassas , VA ) and maintained in DMEM ( Fisher Scientific , Pittsburgh , PA ) , containing 10% ( v/v ) fetal bovine serum ( Thermo Scientific , Waltham , MA ) , and 100 unit/ml penicillin/streptomycin ( Life Technologies , Carlsbad , CA ) . 0 . 25% trypsin and 1% EDTA ( Invitrogen ) were used to passage cells every three days . Prior to UV treatment , cells were transferred to poly-D-lysine-coated 10-cm culture dishes ( BD Biosciences , San Jose , CA ) and allowed to adhere for at least 24 hr . Cells were washed in PBS ( Invitrogen ) , layered with 5 ml PBS , and exposed to UVC ( 254 nm; 0 . 2 and 0 . 5 mJ/cm2 ) with 15 cm distance from the UV source using a Stratalinker . Cells were allowed to recover for 24 hr in fresh media before being harvested . For RNAseq , cells were washed in 5 ml PBS , and frozen in 1 ml Trizol ( Invitrogen ) at -80°C . For CLIP , cells were washed in 5 ml PBS and irradiated at 400 mJ/cm2 ( see above ) . Cell pellets were collected by centrifugation at 2500 rpm for 3 min at 4°C and frozen at -80°C . For nELAVL RNAi experiments , cells were to transferred to poly-D-lysine-coated 6-well plates ( BD Biosciences , San Jose , CA ) and allowed to adhere for at least 24 hr . Accell Non-targeting pool ( Cat#D-001910-10-20 ) , and a mixture of Accell siRNA pools against Elavl2/3 and 4 ( Cat#E019801-00-0010 , #E011264-00-0010 , #E016006-00-0010 ) from Dharmacon ( LaFayette , CO ) were added to Accell siRNA Delivery Media ( Cat#B-001910-10 ) , supplemented with 2% FBS for a final concentration of 1 µM per pool . Growth medium was removed and 1 ml Accell siRNA and media mixture was added . The Accell siRNA and media mixture was replaced with growth medium after 48h , cells were UV treated at 72 hr ( see above , cells were layered with 1 ml PBS during UVC exposure ) , and cells were harvested at 96 hr for RNAseq analysis . Cells were washed in 1 ml PBS and frozen in 1 ml Trizol ( 75% ) or lysed ( 25% ) in 50 µl CLIP Wash Buffer 1 ( see below ) . Canonical hY3 and hY3mut ( sequences are shown below and mutated nucleotides are indicated ) were cloned with AgeI and EcoRI into the transfer plasmid plKO . 1 ( Moffat et al . , 2006; Addgene [Cambridge , MA] #10878 ) . The puromycin selection cassette was replaced with GFP to monitor infection efficiency . HEK293T cells were transfected with transfer and packaging plasmids ( of a three-plasmid lentivirus packaging system ) at 80% confluency , using X-tremeGENE9 DNA transfection agent ( Roche , Indianapolis , IN ) . Lentivirus containing supernatant was harvested after 24 hr , concentrated , aliquoted and stored at -80°C . IMR-32 cells were transduced with virus in the presence of polybrene ( Sigma , St . Louis , MO ) with supernatant of one 10 cm plate per 6 well ( transductions were performed in triplicates ) . The infection efficiency was assessed after 48 hr ( 80% of cells were GFP-positive ) , cells were expanded and harvested after 72 hr for RNAseq analysis ( one 6-well per 1 ml Trizol ) and 96 hr for cell fractionation experiments . GCTGGTCCGAGTGCAGTGGTGTTTACAACTAATTGATCACAACCAGTTACAGATTTCTTTGTTCCTTCTCCACTCCCACTGCTTCACTTGACTAGCCTTTT GCTGGTCCGAGTGCAGTGGTGTCTACAACTAATTGATCACAACCAGTTACAGATCTCTCCGTTCCTTCTCCACTCCCACTGCTTCACTTGACTAGCCTTTT Cells grown in 10-cm culture dishes were scraped in 1 ml PBS after media removal and spun at 500 g for 5 min at 4°C . Cells were lysed in 500 µl buffer A ( 150 ml NaCl; 50 mM Tris , pH 7 . 4; 0 . 01% Saponin; 1x Protease Inhibitor Cocktail [Thermo Fisher Scientific , Waltham , MA] ) , and incubated on ice for 10 min . Cytoplasm and nuclei were separated with a 10 min spin at 3000 g at 4°C , and both fractions were washed in 500 µl buffer A before an additional 10 min spin at 3000 g at 4°C . Nuclei were resuspended in 500 µl buffer A and sonicated 3x for 5 s . RNA was Trizol ( Life Technologies ) extracted from 100 µl lysate and reverse transcribed using iScript ( Bio-Rad , Hercules , CA ) . qPCR was performed with FastStart SYBR Green Master ( Roche , Indianapolis , IN ) , requiring at least one primer in each mRNA primer pair to be specific for an exon junction . qPCR results were normalized as indicated . Western blot analysis was performed using 15 µl cell lysate per lane , and the following antibodies: α-GAPDH ( 1:25:000; Abcam [Cambridge , MA] ab8245 ) , Hu subject antiserum ( 1:1000 dilution ) , α-HSP90 ( 1:1000; Cell Signaling Technology [Danvers , MA] 4877S ) , α-H3 ( 1: 2000; Abcam ab1791 ) , α-RNA PolII ( 1:1000; Millipore [Billerica , MA] 05–623 ) , and α-RO60 ( 1:50; gift from S . Wolin ) . Immunoreactive bands were analyzed with the Odyssey Infrared Imaging System ( LI-COR , Lincoln , NE ) and normalized as indicated . nELAVL HITS CLIP was performed as previously described ( Ule et al . , 2005 ) with the following modifications . nELAVL-RNP complexes were immunoprecipitated using paraneoplastic Hu-antiserum , which recognizes all three neuronal ELAVL isoforms . 80 mg human brain powder or one 10-cm 70% confluent plate of IMR-32 cells were used per immunoprecipitation . For controls ( no UV irradiation , control serum , and overdigested control ) , 40 mg of human brain powder or half a mouse brain were used , respectively . Prior to phosphatase treatment , beads were washed for 3 min each in a series of wash buffers ( see below ) . nELAVL bound RNA fragments from two IMR-32 samples were ligated to an indexed degenerate 5’ RNA linker ( see Supplementary file 1A ) ; RNA fragments from the remaining samples were ligated to a degenerate 5’ RNA linker . The two-step PCR amplification was performed with Accuprime Pfx ( Invitrogen ) . cDNA from IMR-32 and subject samples was initially amplified with DP3/5 . Brain cDNA from subjects 1–3 , and 9–11 was then amplified with DSFP3/5 , samples 4–8 , and 12–17 were amplified with MSFP3 and indexed MSFP5 . Brain samples were sequenced on an Illumina ( San Diego , CA ) GAIIX at the Rockefeller University Genomics Resource Center with the standard Illumina primer ( samples 4–8 , and 12–17 ) or the custom primer SSP1 ( samples 1–3 , and 9–11 ) . IMR-32 cDNA was either amplified with SP3/5-PE ( samples with indexed 5’linker ) , and sequenced on the MiSeq system with the custom primer SSP1 , or with MSFP3 and indexed MSFP5 and sequenced on the MiSeq system with the standard Illumina primer . See Supplementary file 1A for used indexes . Analyses were carried out using the Galaxy suite of bioinformatics tools ( http://main . g2 . bx . psu . edu/ ) , in addition to publicly available in-house tools . Data was visualized with UCSC genome browser ( http://genome . ucsc . edu/ ) . To reduce mis-alignments due to sequencing errors , reads were initially filtered based on quality score ( ≥20 in the degenerate linker region; average of ≥20 in the remaining read ) . Exact sequences were collapsed to remove PCR duplicates . The degenerate barcode ( and index if applicable ) were removed and the 3’ linker was trimmed . Using FASTA files as input , reads were subsequently mapped to the hg18 build of the human genome by novoalign ( www . novocraft . com ) , requiring unambiguous mapping and a maximum of two mismatches . To identify unique CLIP tags , we applied stringent filtering , and collapsed reads with the same starting genomic positions ( Darnell et al . , 2011 ) , and only unique tags ( Supplementary file 1A ) were used for subsequent analyses . Peaks with significant nELAVL binding compared to background ( p-value<0 . 01 ) were identified utilizing a similar approach from previous studies ( Xue et al . , 2009 ) . Specifically , we used a scan statistics ( Glaz et al . , 2013 ) to compute p-values in which each observed PeakHeight ( PH , CLIP tags within a peak ) was compared to the PH one would expect by random chance , assuming a background of uniformly distributed CLIP tags in each gene . Bonferroni correction was applied to the resulting p-values to correct for multiple hypothesis testing . All scripts used in the analysis including the peak finding algorithm and more information can be publically obtained at http://zhanglab . c2b2 . columbia . edu/index . php/Resources . RNA from human brain and IMR-32 neuroblastoma cell lines was Trizol ( Invitrogen ) -extracted , Ribo-Zero-selected ( Epicentre , Madison , WI ) , DNase-treated ( Roche ) , and prepared for sequencing , following the Illumina High-throughput TruSeq RNA Sample Preparation Guidelines . The libraries from subjects 6–8 and 15–17 , as well Y RNA overexpression samples , were sequenced on an Illumina HiSeq 2500 at the New York Genome Center , generating 125-bp paired end reads . The remaining libraries were sequenced on an Illumina HiSeq 2000 at the Rockefeller University Genomics Resource Center , yielding 100-bp paired end reads . Reads were aligned to the hg18 build of the human genome using TopHat , allowing a maximum of two mismatches . Only unambiguously mapped reads were kept for analysis . Additional exon junctions not observed due to the gap between paired end mates were inferred using a Bayesian method , and a set of non-redundant constitutive exons with relative high inclusion rate ( according to ESTs ) was used to estimate gene expression ( Charizanis et al . , 2012 ) . Exon and exon junction reads were inferred as previously described ( Charizanis et al . , 2012 ) . 8163 cassette exons in brain , 9629 cassette exons in the RNAi/UV IMR-32 samples , and 10 , 432 cassette exons in the Y RNA infected IMR-32 samples remained after filtering on exon junction coverage to reduce multiple testing ( coverage was normalized for library size; normalized coverage of each isoform and each condition ≥5 ) . CLIP and RNAseq data were deposited in the GEO database with accession number GSE53699 . Analyses of unique CLIP tags and RNAseq data were carried out in R ( www . r-project . org; [Ihaka and Gentleman , 1996] ) using Bioconductor ( Gentleman et al . , 2004 ) and the packages Biostrings , edgeR , GenomicRanges , ggplot2 , Hmisc , plyr , qvalue , reshape , scales , and VennDiagram ( Robinson and Smyth , 2007; 2008; Robinson et al . , 2010; McCarthy et al . , 2012; Harrell et al . , 2014; Wickham , 2007; 2009; 2011; 2014; Aboyoun et al . , 2014; Pages et al . , 2014; Dabney , et al . , 2014; Chen and Boutros , 2011 ) . mRNA abundance and nELAVL peak binding ( PeakHeight , PH ) changes were assessed by differential analysis of raw sequencing counts in edgeR using the TMM methodology ( Robinson and Oshlack , 2010 ) for normalization and a negative binomial generalized linear model . Only expressed genes and robustly bound peaks were analyzed to reduce multiple testing . Expressed genes were defined as genes that had more than one count per million ( cpm ) in at least 5 control brain samples ( out of 8 ) , in 6 AD brain samples ( out of 9 ) , or in 4 IMR-32 samples ( out of 12 RNAi/UV samples or 9 Y RNA infection samples , respectively ) . Robustly bound peaks were defined as peaks that had more than one cpm in at least 5 control or AD brain samples ( out of 8 and 9 , respectively ) , or in 2 control or 4 UV IMR-32 samples ( out of 3 and 6 , respectively ) . We controlled for batch effects of brain samples ( see Supplementary file 1A ) and estimated the false discovery rate ( FDR ) with an optimized FDR approach ( q-value methodology [Storey and Tibshirani , 2003] ) to correct for multiple hypothesis testing . PeakHeight ( PH , CLIP tags within peaks ) was normalized for library size , and , to account for differences in gene expression level , normalized PH was determined by dividing PH by rpkm of the corresponding gene . nELAVL binding was defined as the summary of PH per gene , whereas normalized nELAVL binding was defined as the summary of normalized PH per gene . We similarly defined 3’UTR and intronic binding by summarizing only 3’UTR or intronic peaks , respectively . Top targets were defined as the top 1000 genes according to normalized nELAVL binding . mRNA abundance was defined as cpm RNAseq tags per gene . We did not normalize nELAVL binding nor mRNA abundance for transcript length as we performed differential analysis of the datasets . A pseudocount of 1 was added before log2 transformation . Cross-correlation plots show log2 raw counts of PH or RNAseq reads per gene . Splicing changes were determined using the observed inclusion and exclusion junction read counts and by fitting a generalized linear model with a logit link function . For each exon , GLM likelihood ratio test was conducted to test if there was a significant difference in the fraction of exon inclusion ( delta Inclusion , ΔI ) between conditions , and controlling for batch variables of brain samples . ΔI ( equivalent to delta PSI ) was calculated by subtracting fraction exon inclusion of Sample 2 from fraction exon inclusion of Sample 1 . FDR was calculated as described before using the q-value method . High confidence alternative splicing events were identified by requiring a stringent criteria of FDR<0 . 05 and ΔI≥0 . 1 . P-values to assess the statistical significance of the overlap between gene lists were calculated with a hypergeometric test ( Figure 2A/9D , Figure 9—figure supplement 5 ) . A paired Wilcoxon rank sum test was used to evaluate differences in Y RNA binding ( Figure 6C/7B ) , and a t-test was used to evaluate changes in mRNA/Y RNA/protein abundance ( Figure 3B/9A , B , Figure 3—figure supplement 1 , Figure 6—figure supplement 1 , Figure 7—figure supplement 1 , 3 , Figure 8—figure supplement 1A , Figure 9—figure supplement 1 , 2 ) . Differences in cumulative density curves were evaluated with a one-sided KS ( Kolmogorov-Smirnov ) test ( Figure 3B ) . P-values to assess motif enrichment were calculated with a Fisher’s exact test ( Figure 6B/7A ) . Y RNAs were derived from GENCODE ( v19 release ) . nELAVL binding on Y RNAs was defined as CLIP tags on Y RNAs . To discriminate between individual Y RNA copies we used CLIP reads , aligned with novoalign . While this mapping strategy allowed us to discrimate between individual Y RNA copies with high confidence , CLIP reads that mapped to multiple Y RNAs were discarded . Exclusively for Figure 7—figure supplement 4 , CLIP tags were therefore additionally aligned with Bowtie2 ( Langmead et al . , 2009 ) , allowing multiple alignments and reporting one . This allowed us to estimate the overall amount of CLIP tags on all Y RNAs ( shown in Figure 7—figure supplement 4 ) . Y RNA abundance in brain , and UV- and RNAi-treated IMR-32 cells was defined as RNAseq tags on Y RNAs , using unambiguously mapped tags aligned with TopHat . Similar to above , reads that mapped to multiple Y RNAs were discarded using this strategy . To estimate overall Y RNA overexpression upon lentiviral infection , we mapped RNAseq reads of infected samples with Bowtie2 , allowing multiple alignments and reporting the best matched alignment ( Figure 9B ) . Y RNA overexpression was determined with two additional strategies . We performed qPCR with primers that recognized infected Y RNAs ( Y3wt and Y3mut ) but also an unknown number of endogenous Y RNAs ( Figure 9A ) . Additionally , we directly searched for 40- and 68-nt sequences of Y3wt and Y3mut sequences in the Illumina sequenced library reads ( both 40- and 68-nt sequences spanned the mutated residues ) . The 68-nt sequence was only present in the infected Y RNAs and the canonical hY3 RNA , and searching for this sequencing length showed that both Y3wt as well as Y3mut were overexpressed . To more accurately estimate the extent of overexpression , we additionally searched for a 40-nt Y RNA sequence . The Y3wt sequence is present in a large number of endogenous Y RNAs , whereas the Y3mut sequence only detects infected Y3mut . By comparing Y3wt and Y3mut reads in Y3mut infected samples , we observed that infected Y RNA reads ( Y3mut reads ) correspond to 10% of total Y3 RNA reads ( Y3wt reads ) . The MEME-ChIP Suite ( Machanick and Bailey , 2011 ) and HOMER ( Heinz et al . , 2010 ) were used for motif discovery . Coordinates of the top 500 peaks in healthy brain +/- 25 nt were used as input . The given strand was searched for 6–10-nt-long motifs for up to 10 motifs , allowing any number of repetitions with MEME-ChIP . We additionally searched the given strand with HOMER v4 . 7 using default parameters and 50 nt flanking sequences of the 500 peaks as background sequences . The respectively top enriched motif is shown . The 1000 nELAVL top target gene products were seeded in a large protein-protein interaction ( PPI ) network containing 14 , 191 genes and more than 197 , 000 interactions ( Neale et al . , 2012 ) compiled from various online databases: BioGrid ( Stark et al . , 2011 ) , MINT ( Ceol et al . , 2010 ) , KEGG ( Aoki and Kanehisa , 2005 ) , PPID ( Hermjakob et al . , 2004 ) , HPRD ( Prasad et al . , 2009 ) , DIP ( Xenarios et al . , 2002 ) , BIND ( Isserlin et al . , 2011 ) , IntAct ( Aranda et al . , 2010 ) , InnateDB ( Lynn et al . , 2008 ) , and SNAVI ( Ma'ayan et al . , 2009 ) . To reduce the false positive interactions in the background network , we excluded interactions reported using high-throughput approaches . Direct interactions with other seed genes were kept for each seed gene , and a connected subnetwork was created using these seed genes as nodes . Organic clustering of the obtained subnetwork was performed using the network visualization software yEd ( http://www . yworks . com/en/products_yed_about . html ) . In a second network , six AD genes ( APP , BACE1 , MAPT , PICALM , PSEN1 and PSEN2 ) were concatenated with the input seed gene lists to investigate their relationship with nELAVL target genes . The composition and the clustering of the PPI networks with and without those six genes were identical . Only the network including those additional seeds is shown . The clusters of nELAVL target genes in the PPI subnetwork represent protein clusters based on direct physical interactions . Gene products in clusters with at least 10 nodes were examined for an enrichment of functional annotations with Enrichr ( Chen et al . , 2013 ) using the Fisher’s exact test and computing an adjusted p-value for multiple hypothesis testing using the Benjamini-Hochberg correction . The six additional AD associated genes used for the subnetwork clustering were not taken into account for the enrichment analysis . | When a gene is active , its DNA is copied into a molecule of RNA . This molecule then undergoes a process called splicing which removes certain segments , and the resulting ‘messenger RNA’ molecule is then translated into protein . Many messenger RNAs go through alternative splicing , whereby different segments can be included or excluded from the final molecule . This allows more than one type of protein to be produced from a single gene . Specialized RNA binding proteins associate with messenger RNAs and regulate not only their splicing , but also their abundance and location within the cell . These activities are crucially important in the brain where forming memories and learning new skills requires thousands of proteins to be made rapidly . Many members of a family of RNA binding proteins called ELAV-like proteins are unique to neurons . These proteins have also been associated with conditions such as Alzheimer’s disease , but it was not known which messenger RNAs were the targets of these proteins in the human brain . Scheckel , Drapeau et al . have now addressed this question and used a method termed 'CLIP' to identify thousands of messenger RNAs that directly bind to neuronal ELAV-like proteins in the human brain . Many of these messenger RNAs coded for proteins that are important for the health of neurons , and neuronal ELAV-like proteins were shown to regulate both the alternative splicing and the abundance of these messenger RNAs . The regulation of RNA molecules in post-mortem brain samples of people with or without Alzheimer’s disease was then compared . Scheckel , Drapeau et al . unexpectedly observed that , in the Alzheimer’s disease patients , the neuronal ELAV-like proteins were very often associated with a class of RNA molecules known as Y RNAs . These RNA molecules do not code for proteins , and are therefore classified as non-coding RNA . Moreover , massive shifts in the binding of ELAV-like proteins onto Y RNAs were observed in neurons grown in the laboratory that had been briefly stressed by exposure to ultraviolet radiation . Scheckel , Drapeau et al . suggest that the strong tendency of neuronal ELAV-like proteins to bind to Y RNAs in conditions of short- or long-term stress , including Alzheimer’s disease , might prevent these proteins from associating with their normal messenger RNA targets . This was supported by finding that some messenger RNAs targeted by neuronal ELAV-like proteins showed altered regulation after stress . Such changes to the normal regulation of these messenger RNAs could have a large impact on the proteins that are produced from them . Together , these findings link Y RNAs to both neuronal stress and Alzheimer’s disease , and suggest a new way that a cell can alter which messenger RNAs are expressed in response to changes in its environment . The next step is to explore what causes the shift in neuronal ELAV-like protein binding from messenger RNAs to Y RNAs and how it might contribute to disease . | [
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] | 2016 | Regulatory consequences of neuronal ELAV-like protein binding to coding and non-coding RNAs in human brain |
Many aspects of the brain’s design can be understood as the result of evolutionary drive toward metabolic efficiency . In addition to the energetic costs of neural computation and transmission , experimental evidence indicates that synaptic plasticity is metabolically demanding as well . As synaptic plasticity is crucial for learning , we examine how these metabolic costs enter in learning . We find that when synaptic plasticity rules are naively implemented , training neural networks requires extremely large amounts of energy when storing many patterns . We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms . This algorithm , termed synaptic caching , boosts energy efficiency manifold and can be used with any plasticity rule , including back-propagation . Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally , including synaptic tagging and capture phenomena . Furthermore , our results are relevant for energy efficient neuromorphic designs .
The human brain only weighs 2% of the total body mass but is responsible for 20% of resting metabolism ( Attwell and Laughlin , 2001; Harris et al . , 2012 ) . The brain’s energy need is believed to have shaped many aspects of its design , such as its sparse coding strategy ( Levy and Baxter , 1996; Lennie , 2003 ) , the biophysics of the mammalian action potential ( Alle et al . , 2009; Fohlmeister , 2009 ) , and synaptic failure ( Levy and Baxter , 2002; Harris et al . , 2012 ) . As the connections in the brain are adaptive , one can design synaptic plasticity rules that further reduce the energy required for information transmission , for instance by sparsifying connectivity ( Sacramento et al . , 2015 ) . But in addition to the costs associated to neural information processing , experimental evidence suggests that memory formation , presumably corresponding to synaptic plasticity , is itself an energetically expensive process as well ( Mery and Kawecki , 2005; Plaçais and Preat , 2013; Jaumann et al . , 2013; Plaçais et al . , 2017 ) . To estimate the amount of energy required for plasticity , Mery and Kawecki ( 2005 ) subjected fruit flies to associative conditioning spaced out in time , resulting in long-term memory formation . After training , the fly’s food supply was cut off . Flies exposed to the conditioning died some 20% quicker than control flies , presumably due to the metabolic cost of plasticity . Likewise , fruit flies doubled their sucrose consumption during the formation of aversive long-term memory ( Plaçais et al . , 2017 ) , while forcing starving fruit flies to form such memories reduced lifespan by 30% ( Plaçais and Preat , 2013 ) . A massed learning protocol , where pairings are presented rapidly after one another , leads to less permanent forms of learning that don’t require protein synthesis . Notably this form of learning is energetically less costly ( Mery and Kawecki , 2005; Plaçais and Preat , 2013 ) . In rats ( Gold , 1986 ) and humans ( Hall et al . , 1989 , but see Azari , 1991 ) beneficial effects of glucose on memory have been reported , although the intricate regulation of energy complicates interpretation of such experiments ( Craft et al . , 1994 ) . Motivated by the experimental results , we analyze the metabolic energy required to form associative memories in neuronal networks . We demonstrate that traditional learning algorithms are metabolically highly inefficient . Therefore , we introduce a synaptic caching algorithm that is consistent with synaptic consolidation experiments , and distributes learning over transient and persistent synaptic changes . This algorithm increases efficiency manifold . Synaptic caching yields a novel interpretation to various aspects of synaptic physiology , and suggests more energy efficient neuromorphic designs .
To examine the metabolic energy cost associated to synaptic plasticity , we first study the perceptron . A perceptron is a single artificial neuron that attempts to binary classify input patterns . It forms the core of many artificial networks and has been used to model plasticity in cerebellar Purkinje cells . We consider the common case where the input patterns are random patterns each associated to a randomly chosen binary output . Upon presentation of a pattern , the perceptron output is calculated and compared to the desired output . The synaptic weights are modified according to the perceptron learning rule , Figure 1A . This is repeated until all patterns are classified correctly ( Rosenblatt , 1962 , see Materials and methods ) . Typically , the learning takes multiple iterations over the whole dataset ( ’epochs’ ) . As it is not well known how much metabolic energy is required to modify a biological synapse , and how this depends on the amount of change and the sign of the change , we propose a parsimonious model . We assume that the metabolic energy for every modification of a synaptic weight is proportional to the amount of change , no matter if this is positive or negative . The total metabolic cost M ( in arbitrary units ) to train a perceptron is the sum over the weight changes of synapses ( 1 ) Mperc=∑i=1N∑t=1T|wi ( t ) -wi ( t-1 ) |α , where N is the number of synapses , wi denotes the synaptic weight at synapse i , and T is the total number of time-steps required to learn the classification . The exponent α is set to one , but our results below are similar whenever 0≤α≲2 , Figure 1—figure supplement 1 . As there is evidence that synaptic depression involves different pathways than synaptic potentiation ( e . g . Hafner et al . , 2019 ) , we also tried a variant of the cost function where only potentiation costs energy and depression does not . This does not change our results , Figure 1—figure supplement 1 . Learning can be understood as a search in the space of synaptic weights for a weight vector that leads to correct classification of all patterns , Figure 1B . The synaptic weights approximately follow a random walk ( Materials and methods ) , and the metabolic cost is proportional to the length of this walk under the L1 norm , Equation 1 . The perceptron learning rule is energy inefficient , because repeatedly , weight modifications made to correctly classify one pattern are partly undone when learning another pattern . However , as both processes require energy , this is inefficient . The energy required by the perceptron learning rule depends on the number of patterns P to be classified . The set of correct weights spans a cone in N-dimensional space ( grey region in Figure 1B ) . As the number of patterns to be classified increases , the cone containing correct weights shrinks and the random walk becomes longer ( Gardner , 1987 ) . Near the critical capacity of the perceptron ( P=2N ) , the number of epochs required diverges as ( 2-P/N ) -2 , Opper ( 1988 ) . The energy required , which is proportional to the number of updates that the weights undergo , follows a similar behavior , Figure 1C . It is useful to consider the theoretical minimal energy required to classify all patterns . The most energy efficient algorithm would somehow directly set the synaptic weights to their desired final values . Geometrically , the random walk trajectory of the synaptic weights to the target is replaced by a path straight to the correct weights ( green arrow in Figure 1B ) . Given the initial weights wi ( 0 ) and the final weights wi ( T ) , the energy required in this idealized case is ( 2 ) Mmin=∑i|wi ( T ) −wi ( 0 ) | . While the minimal energy also grows with memory load ( Materials and methods ) , it increases less steeply , Figure 1C . We express the metabolic efficiency of a learning algorithm as the ratio between the energy the algorithm requires and the minimal energy ( the gap between the two log-scale curves in Figure 1C ) . As the number of patterns increases , the inefficiency of the perceptron rule rapidly grows as ( see Materials and methods ) ( 3 ) MpercMmin=πP2-P/N , which fits the simulations very well , Figure 1D , black curve and dashed blue curve . There is evidence that both cerebellar and cortical neurons are operating close to their maximal memory capacity ( Brunel et al . , 2004; Brunel , 2016 ) . Indeed , it would appear wasteful if this were not the case . However , the above result demonstrates that for instance classifying 1900 patterns by a neuron with 1000 synapses with the traditional perceptron learning requires about ∼900 times more energy than minimally required . As the fruit-fly experiments indicate that even storing a single association in long-term memory is already metabolically expensive , storing many memories would thus require very large amounts of energy if the biology would naively implement these learning rules . How can the conflicting demands of energy efficiency and high storage capacity be met ? The minimal energy argument presented above suggests a way to increase energy efficiency . There are forms of plasticity – anesthesia-resistant memory in flies and early-LTP/LTD in mammals – that decay and do not require protein synthesis . Such transient synaptic changes can be induced using a massed , instead of a spaced , stimulus presentation protocol . Fruit-fly experiments show that this form of plasticity is much less energy-demanding than long-term memory ( Mery and Kawecki , 2005; Plaçais and Preat , 2013; Plaçais et al . , 2017 ) . In mammals , there is evidence that synaptic consolidation , but not transient plasticity , is suppressed under low-energy conditions ( Potter et al . , 2010 ) . Inspired by these findings , we propose that the transient form of plasticity constitutes a synaptic variable that accumulates the synaptic changes across multiple updates in a less expensive transient form of memory; only occasionally the changes are consolidated . We call this synaptic caching . Specifically , we assume that each synapse is comprised of a transient component si and a persistent component li . The total synaptic weight is their sum , wi=si+li . We implement synaptic caching as follows , Figure 2A: For every presented pattern , changes in the synaptic strength are calculated according to the perceptron rule and are accumulated in the transient component that decays exponentially to zero . If , however , the absolute value of the transient component of a synapse exceeds a certain consolidation threshold , all synapses of that neuron are consolidated ( vertical dashed line in Figure 2A ) ; the value of the transient component is added to the persistent weight; and the transient weight is reset to zero . The efficiency gain of synaptic caching depends on the limitations of transient plasticity . If the transient synaptic component could store information indefinitely at no metabolic cost , consolidation could be postponed until the end of learning and the energy would equal the minimal energy Equation 2 . Hence the efficiency gain would be maximal . However , we assume that the efficiency gain of synaptic caching is limited because of two effects: ( 1 ) The transient component decays exponentially ( with a time-constant τ ) . ( 2 ) There might be a maintenance cost associated to maintaining the transient component . Biophysically , transient plasticity might correspond to an increased/decreased vesicle release rate ( Padamsey and Emptage , 2014; Costa et al . , 2015 ) so that it diverges from its optimal value ( Levy and Baxter , 2002 ) . To estimate the energy saved by synaptic caching , we assume that the maintenance cost is proportional to the transient weight itself and incurred every time-step Δt ( shaded area in the top traces of Figure 2A ) Mtrans=c∑i∑t|si ( t ) | . While experiments indicate that transient plasticity is metabolically far less demanding than the persistent form , the precise value of the maintenance cost is not known . We encode it in the constant c; the theory also includes the case that c is zero . It is straightforward to include a cost term for changing the transient weight ( Materials and methods ) ; such a cost would reduce the efficiency gain attainable by synaptic caching . Next , we need to include the energetic cost of consolidation . Currently it is unknown how different components of synaptic consolidation , such as signaling , protein synthesis , transport to the synapses and changing the synapse , contribute to this cost . We assume the metabolic cost to consolidate the synaptic weights is Mcons=∑i∑t|li ( t ) -li ( t-1 ) | . This is identical to Equation 1 , but in contrast to standard perceptron learning where synapses are consolidated every time a weight is updated , now changes in the persistent component li only occur when consolidation occurs . One could add a maintenance cost term to the persistent weight as well , in that case postponing consolidation would save even more energy . To maximize the efficiency gain achieved by synaptic caching one needs to tune the consolidation threshold , Figure 2B . When the threshold is low , consolidation occurs often and the energy approaches the one without synaptic caching . When on the other hand the consolidation threshold is high , the expensive consolidation process occurs rarely , but the maintenance cost of transient plasticity is high; moreover , the decay will lead to forgetting of unconsolidated memories , slowing down learning and increasing the energy cost . Thus , the consolidation energy decreases for larger thresholds , whereas the maintenance energy increases , Figure 2B ( see Materials and methods ) . As a result of this trade-off , there is an optimal threshold – which depends on the decay and the maintenance cost – that balances persistent and transient forms of plasticity . To analyze the efficiency gain below , we numerically optimize the consolidation threshold . First , we consider the case when the transient component does not decay . Figure 2C shows the energy required to train the perceptron . When the maintenance cost is absent ( c=0 ) , consolidation is best postponed until the end of the learning and the energy is as low as the theoretical minimal bound . As c increases , it becomes beneficial to consolidate more often , that is the optimal threshold decreases , Figure 2C bottom panel . The required energy increases until the maintenance cost becomes so high that it is better to consolidate after every update , the transient weights are not used , and no energy is saved with synaptic caching . The efficiency is well estimated by analysis presented in the Materials and methods , Figure 2C ( theory ) . Next , we consider what happens when the transient plasticity decays . We examine the energy and learning time as a function of the decay rate for various levels of maintenance cost , Figure 3 . As stated above , if there is no decay , efficiency gain can be very high; the consolidation threshold has no impact on the learning time , Figure 3 bottom . In the other limit , when the decay is rapid ( right-most region ) , it is best to consolidate frequently as otherwise information is lost . As expected , the metabolic cost is high in this case . The regime of intermediate decay is quite interesting . When maintenance cost is high , it is of primary importance to keep learning time short , and in fact the learning time can be lower than in a perceptron without decay , Figure 3 , bottom , light curves . When on the other hand maintenance cost is low , the optimal solution is to set the consolidation threshold high so as to minimize the number of consolidation events , even if this means a longer learning time , Figure 3 , bottom , dark curves . For intermediate decay rates , the consolidation threshold trades off between learning time and energy efficiency , Figure 3—figure supplement 1A . That is , by setting the consolidation threshold the perceptron can learn either rapidly or efficiently . Such a trade-off could be of biological relevance . We found a similar trade-off in multi-layer perceptrons ( see below ) , Figure 3—figure supplement 1B . ( although we found no evidence that learning can be sped up there ) . In summary , when the transient component decays the learning dynamics is altered , and synaptic caching can not only reduce metabolic cost but can also reduce learning time . Next , to show that synaptic caching is a general principle , we implement synaptic caching in a spiking neural network with a biologically plausible perceptron-like learning rule proposed by D'Souza et al . ( 2010 ) . The optimal scenario , where the transient weights do not decay and have no maintenance cost , is assumed . The network is able to save 80% of the energy with synaptic caching , Figure 2—figure supplement 1 . Hence , efficiency gains from synaptic caching do not rely on exact implementation . In the above implementation of synaptic caching , consolidation of all synapses was triggered when transient plasticity at a single synapse exceeded a certain threshold . This resembles the synaptic tagging and capture phenomenon where plasticity induction leads to transient changes and sets a tag; only strong enough stimulation results in proteins being synthesized and being delivered to all tagged synapses , consolidating the changes ( Frey and Morris , 1997; Barrett et al . , 2009 ) . There is a number of ways synapses could interact , Figure 4A . First , consolidation might be set to occur whenever transient plasticity at a synapse crosses the threshold and only that synapse is consolidated . Second , a hypothetical signal might send to the soma and consolidation of all synapses occurs once transient plasticity at any synapse crosses the threshold ( used in Figures 2 and 5 ) . Third , a hypothetical signal might be accumulated in or near the soma and consolidation of all synapses occurs once this total transient plasticity across synapses crosses the threshold . Only cases 2 and 3 are consistent with synaptic tagging and capture experiments , where consolidation of one synapse also leads to consolidation of another synapse that would otherwise decay back to baseline ( Frey and Morris , 1997; Sajikumar et al . , 2005 ) . However , all variants lead to comparable efficiency gains , Figure 4B . In summary , we see that synaptic caching can in principle achieve large efficiency gains , bringing efficiency close to the theoretical minimum . Since the perceptron is a rather restrictive framework , we wondered whether the efficiency gain of synaptic caching can be transferred to multilayer networks . Therefore , we implement a multi-layer network trained with back-propagation . Back-propagation networks learn the associations of patterns by approaching the minimum of the error function through stochastic gradient descent . We use a network with one hidden layer with by default 100 units to classify hand-written digits from the MNIST dataset . As we train the network , we intermittently interrupt the learning to measure the energy consumed for plasticity thus far and measure the performance on a held-out test-set . This yields a curve relating energy to accuracy . Similar to a perceptron , learning without synaptic caching is metabolically expensive in a back-propagation network . Until reaching maximal accuracy , energy rises approximately exponentially with accuracy , after which additional energy do not lead to further improvement . When the learning rate is sufficiently small , the metabolic cost of plasticity is independent of the learning rate . At larger learning rates , learning no longer converges and energy goes up steeply without an increase in accuracy , Figure 5A . With the exception of these very large rates , these results show that lowering the learning rate does not save energy . Similar to the perceptron , we evaluate how much energy would be required to directly set the synaptic weights to their final values . Traditional learning without synaptic caching is once again energetically inefficient , expending at least ∼20 times more energy compared to this theoretical minimum whatever the desired accuracy level is , Figure 5B . However , by splitting the weights into persistent synaptic weights and transient synaptic caching weights , the network can save substantial amounts of energy . As for the perceptron , depending on the decay and the maintenance cost the energy ranges from as little as the minimum to as much as the energy required without caching . Thus , the efficiency gain of synaptic caching found for the perceptron carries over to multilayer networks . It might seem that smaller networks would be metabolically less costly , because small networks simply contain fewer synapses to modify . On the other hand , we saw above that for the perceptron metabolic costs rise rapidly when cramming many patterns into it . We wondered therefore how energy cost depends on network size in the multilayer network . Since the number of input units is fixed to the image size and the number of output units equals the ten output categories , we adjust the number of hidden units . The network fails to reach the desired accuracy if the number of hidden units is too small , Figure 5C . When the network size is barely above the minimum requirement , the network has to compensate the lack of hidden units with longer training time and hence a larger energy expenditure . However , very large networks also require more energy . These results show that from an energy perspective there exists an optimal number of neurons to participate in memory formation . The optimal number depends on the accuracy requirement; as expected , higher accuracies require more hidden units and energy .
Experiments on formation of a long-term memory of a single association suggest that synaptic plasticity is an energetically expensive process . We have shown that energy requirements rise steeply as memory load or designated accuracy level increase . This indicates trade-offs between energy consumption , and network capacity and performance . To improve efficiency , we have proposed an algorithm named synaptic caching that temporarily stores changes in the synaptic strength in transient forms of plasticity , and only occasionally consolidates into the persistent forms . Depending on the characteristics ( decay and maintenance cost ) of transient plasticity , this can lead to large energy savings in the energy required for synaptic plasticity . We stress that from an algorithmic point of view , synaptic caching can be applied to any synaptic learning algorithm ( unsupervised , reinforcement , supervised ) and does not have specific requirements . Further savings might be possible by adjusting the consolidation threshold as learning progresses and by being pathway-specific ( Leibold and Monsalve-Mercado , 2016 ) . The implementation of a consolidation threshold is similar to what has been observed in physiology , in particular in the synaptic tagging and capture literature ( Redondo and Morris , 2011 ) . Our results thus give a novel interpretation of those findings . Synaptic consolidation is known to be affected by reward , novelty and punishment ( Redondo and Morris , 2011 ) , which is compatible with a metabolic perspective as energy is expended only when the stimulus is worth remembering . In addition , our results for instance explain why consolidation is competitive , but transient plasticity is less so ( Sajikumar et al . , 2014 ) , namely the formation of long-term memory is precious . Consistent with this , there is evidence that encouraging consolidation increases energy consumption ( Plaçais et al . , 2017 ) . We also predict that the transient weight changes act as an accumulative threshold for consolidation . That is , sufficient transient plasticity should trigger consolidation , even in the absence of other consolidation triggers . Future characterization of the energy budget of synaptic plasticity should allow more precise predictions of our theory . Combining persistent and transient storage mechanisms is a strategy well known in traditional computer systems to provide a faster and often energetically cheaper access to memory . In computer systems , permanent storage of memories typically requires transmission of all information across multiple transient cache systems until reaching a long-term storage device . The transfer of information is often a bottleneck in computer architectures and consumes considerable power in modern computers ( Kestor et al . , 2013 ) . However , in the nervous system transient and persistent synapses appear to exist next to each other . Local consolidation in a synapse does not require moving information . Using this setup , biology appears to have found a more efficient way to store information . Memory stability has long fascinated researchers ( Richards and Frankland , 2017 ) , and in some cases forgetting can be beneficial ( Brea et al . , 2014 ) . Splitting plasticity into transient and persistent forms might prevent catastrophic forgetting in networks ( Leimer et al . , 2019 ) . Here , we argue that the main benefit of more transient forms of plasticity is to permit the network to explore the weight space to find a desirable weight configuration using less energy . While this work focuses solely on the metabolic cost of synaptic plasticity , the brain also expends significant amounts of energy on spiking , synaptic transmission , and maintaining resting potential . Learning rules can be designed to reduce costs associated to computation once learning has finished ( Sacramento et al . , 2015 ) . It would be of interest to next understand the precise interaction of computation and plasticity cost during and after learning .
For perceptron , we can calculate the energy efficiency of both the classical perceptron and the gain achieved by synaptic caching . We first consider the case that transient plasticity does not decay , as this allows important theoretical simplifications . In the perceptron learning to classify binary patterns Equation 8 , the weight updates are either +η or -η , where η is the learning rate , so that the energy spent ( Equation 1 , α=1 ) per update per synapse equals η . Hence the total energy spent to classify all patterns Mperc=NKη , where K is the total number of updates . Opper ( 1988 ) showed that learning time diverges as K∼ ( 2-P/N ) -2 . We found the numerator numerically to yield K=2P/ ( 2-P/N ) 2 . To calculate the efficiency , we compare this to the minimal energy necessary to reach the final weight vector in the perceptron . We approximate the weight trajectory followed by the perceptron algorithm by a random walk . After K updates of step-size η the weights approximate a Gaussian distribution with zero mean and variance Kη2 . By short-cutting the random walk , the minimal energy required to reach the weight vector is Mmin=N⟨|wi|⟩=2πηNK . Hence , we find for the inefficiency ( see Figure 1D ) MpercMmin=πP2-P/N . Simulations show that the variance in the weights is actually about 20% smaller than a random walk , likely reflecting correlations in the learning process not captured in the random walk approximation . This explains most of the slight deviation in the ineffeciency between theory and simulation , Figure 1D . To calculate the efficiency gained with synaptic caching , we need to calculate both the consolidation energy and the maintenance energy . The consolidation energy equals the number of consolidation events times the size of the updates . The size of the weight updates is equal to the consolidation threshold θ , while the number of consolidation events follows from a random walk argument as NK/⌈θ/η⌉2 . The ceiling function expresses the fact that when the threshold is smaller than learning rate , consolidation will always occur; we temporarily ignore this scenario . In addition , at the end of learning all remaining transient plasticity is consolidated , which requires an energy N⟨|si ( T ) |⟩ . Assuming that the probability distribution of transient weights , Ps ( s ) , has reached steady state at the end of learning , it has a triangular shape ( see below ) and mean absolute value ⟨|si ( T ) |⟩=13θ , so that the total consolidation energyMcons=η2NKθ+13Nθ . The energy associated to the transient plasticity is ( again assuming that Ps ( s ) has reached steady state ) ( 4 ) Mtrans=cNTθ/3 , where T is the number of time-steps required for learning . We find numerically that T=P3/2 ( 2-P/N ) 2 . Hence the total energy when using synaptic caching is Mcache=Mcons+Mtrans=N[η2K/θ+13θ ( 1+cT ) ] . The optimal threshold θ^ is given by ddθ[Mcons+Mtrans]=0 , orθ^2=η23K1+cTat which the energy is Mcache=2ηNK1+cT/3 . And so the efficiency of synaptic caching is McacheMmin=2π31+cT . However , as consolidation can maximally occur only once per time-step , Mcons cannot exceed Mperc so that the inefficiency isMcacheMmin=min ( 2π3 ( 1+cT ) , π2K ) . This equation reasonably matches the simulations , Figure 2C ( labeled ’theory’ ) . One can include a cost for changing the transient weight , so that Mtrans=c∑i∑t|si ( t ) |+b∑i∑t|si ( t+1 ) -si ( t ) | , where b codes the cost of making a change . Assuming that consolidating immediately after a weight change does not incur this cost , this yields an extra term in Equation4 of bNK ( 1-1/⌈θ/η⌉2 ) . Such costs will reduce the efficiency gain achievable by synaptic caching . When b≥1 , it is always cheaper to consolidate . When transient plasticity decays , the situation is more complicated as the learning time depends on the strength of the decay and to our knowledge no analytical expression exists for it . However , it is still possible to estimate the power , that is the energy per time unit , for both the transient component , denoted mtrans , and the consolidation component , mcons . Under the random walk approximation every time the perceptron output does not match the desired output , the transient weight si is updated with an amount Δsi drawn from a distribution Q , with zero mean and variance σ2 . Given the update probability p , that is the fraction of patterns not yet classified correctly , one has Qs ( η ) =Qs ( -η ) =p/2 and Qs ( 0 ) =1-p , so that σs2=pη2 . We assume that the synaptic update rate decreases very slowly as learning progresses , hence p is quasi-stationary . Every time-step Δt=1 the transient weights decay with a time-constant τ . The synapse is consolidated and si is reset to zero whenever the absolute value of the caching weight |si| exceeds θ . Given p and τ , we would like to know: 1 ) how often consolidation events occur which gives consolidation power and 2 ) the maintenance power mtrans=cN⟨|si|⟩ . This problem is similar to the random walk to threshold model used for integrate-and-fire neurons , but here there are two thresholds: θ and -θ . Under the assumptions of small updates and a smooth resulting distribution , the evolution of the probability distribution Ps ( si ) is described by the Fokker-Planck equation , which in the steady state gives0=-1τ∂∂si[siPs ( si ) ]+12σs2∂2∂si2Ps ( si ) +rδ ( si ) . The last term is a source term that describes the re-insertion of weights by the reset process . The boundary conditions are Ps ( si=±θ ) =0 . While Ps ( si ) is continuous in si , the source introduces a cusp in Ps ( si ) at the reset value . Conservation of probability ensures that r equals the outgoing flux at the boundaries . One findsPs ( si ) =1Zexp[-si2σ2][erfi ( |si|σ ) -erfi ( θσ ) ] , where erfi ( x ) =-ierf ( ix ) , σ2=τΔtσs2 and with normalization factorZ=2θ2πσ2F2 ( 1 , 1;32 , 2;− ( θσ ) 2 ) −πσerf ( θσ ) erfi ( θσ ) , where F22 is the generalized hypergeometric function . ( In the limit of no decay this becomes a triangular distribution Ps ( si ) =[θ-|si|]/θ2 . ) We obtain maintenance power ( 5 ) mtrans=cN⟨|si|⟩ ( 6 ) =cNZ[2θσπ−σ2erfi ( θσ ) ] . For small θ/σ , that is small decay , this is linear in θ , mtrans≈cNθ3 . It saturates for large θ because then the decay dominates and the threshold is hardly ever reached . The consolidation rate follows from Fick’s lawr=12σ2Ps′ ( −θ ) −12σ2Ps′ ( θ ) =−2σZπ . The consolidation power is ( 7 ) mcons=Nθr . In the limit of no decay one has r=σ2/θ2 , so that mcons=pNη2/θ . Strictly speaking this approximates learning with a random walk process and assumes local consolidation , Figure 4A . However , Equations 6 and 7 give a good prediction of the simulation when provided with the time-varying update probability from the simulation , Figure 6 . | The brain expends a lot of energy . While the organ accounts for only about 2% of a person’s bodyweight , it is responsible for about 20% of our energy use at rest . Neurons use some of this energy to communicate with each other and to process information , but much of the energy is likely used to support learning . A study in fruit flies showed that insects that learned to associate two stimuli and then had their food supply cut off , died 20% earlier than untrained flies . This is thought to be because learning used up the insects’ energy reserves . If learning a single association requires so much energy , how does the brain manage to store vast amounts of data ? Li and van Rossum offer an explanation based on a computer model of neural networks . The advantage of using such a model is that it is possible to control and measure conditions more precisely than in the living brain . Analysing the model confirmed that learning many new associations requires large amounts of energy . This is particularly true if the memories must be stored with a high degree of accuracy , and if the neural network contains many stored memories already . The reason that learning consumes so much energy is that forming long-term memories requires neurons to produce new proteins . Using the computer model , Li and van Rossum show that neural networks can overcome this limitation by storing memories initially in a transient form that does not require protein synthesis . Doing so reduces energy requirements by as much as 10-fold . Studies in living brains have shown that transient memories of this type do in fact exist . The current results hence offer a hypothesis as to how the brain can learn in a more energy efficient way . Energy consumption is thought to have placed constraints on brain evolution . It is also often a bottleneck in computers . By revealing how the brain encodes memories energy efficiently , the current findings could thus also inspire new engineering solutions . | [
"Abstract",
"Introduction",
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"neuroscience"
] | 2020 | Energy efficient synaptic plasticity |
Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing . Here , we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2-infected individuals using data from known infector–infectee pairs . We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches . The mechanistic method provides an improved fit to data from SARS-CoV-2 infector–infectee pairs compared to commonly used approaches . Our best-fitting model indicates a high proportion of presymptomatic transmissions , with many transmissions occurring shortly before the infector develops symptoms . High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely , even if contacts from a short time window before symptom onset alone are traced . Engineering and Physical Sciences Research Council ( EPSRC ) .
The precise proportion of SARS-CoV-2 transmissions arising from non-symptomatic ( either presymptomatic or asymptomatic ) infectors , as well as from unreported infected hosts with only mild symptoms , remains uncertain ( Buitrago-Garcia et al . , 2020; Casey et al . , 2020 ) . Statistical models can be used to assess the relative contributions of presymptomatic and symptomatic transmission using data from infector–infectee transmission pairs ( Ferretti et al . , 2020a; Ferretti et al . , 2020b; Zhang , 2020; Liu et al . , 2020; Tindale et al . , 2020 ) . The distributions of three important epidemiological time periods – the generation time ( the difference between the infection times of the infector and infectee ) ( Ferretti et al . , 2020a; Ferretti et al . , 2020b; Deng et al . , 2020; Ganyani et al . , 2020 ) , the time from onset of symptoms to transmission ( TOST ) ( Ferretti et al . , 2020b; He et al . , 2020; Ashcroft et al . , 2020 ) , and the serial interval ( the difference between the symptom onset times of the infector and infectee ) ( Ferretti et al . , 2020b; Du et al . , 2020 ) – can also be inferred ( Figure 1A ) . The generation time and TOST distributions indicate the average infectiousness of a host at each time since infection and time since symptom onset , respectively ( He et al . , 2020; Fraser , 2007 ) . These distributions are important for assessing the effectiveness of public health measures such as isolation ( Ashcroft et al . , 2021; Wells et al . , 2021 ) and contact tracing ( Ferretti et al . , 2020a; Fraser et al . , 2004; Davis et al . , 2020 ) . Estimates of the SARS-CoV-2 generation time have typically involved an assumption that a host’s infectiousness is independent of their symptom status ( Ferretti et al . , 2020a; Deng et al . , 2020; Ganyani et al . , 2020; Knight and Mishra , 2020; Lehtinen et al . , 2021; Figure 1B , left ) . However , such an assumption is unjustified ( Lehtinen et al . , 2021; Bacallado et al . , 2020 ) and can lead to a poor fit to data ( Ferretti et al . , 2020b ) . Here , we develop a mechanistic approach for inferring key epidemiological time periods using data from infector–infectee pairs ( Figure 1B , right ) . This approach was motivated by compartmental epidemic models with Gamma distributed stage durations ( Lloyd , 2009; Wearing et al . , 2005 ) and changes in infectiousness during infection ( Hethcote et al . , 1991; Christofferson et al . , 2014; Hart et al . , 2019; Hart et al . , 2020; Gatto et al . , 2020; Aleta et al . , 2020 ) . Our method provides an improved fit to data from SARS-CoV-2 transmission pairs compared to previous approaches , namely , ( 1 ) a model assuming that transmission and symptoms are independent ( Ferretti et al . , 2020a; Deng et al . , 2020; Ganyani et al . , 2020; Knight and Mishra , 2020 ) and ( 2 ) a previous statistical method in which this assumption is relaxed ( Ferretti et al . , 2020b ) . Under our best-fitting model , the proportion of presymptomatic transmissions is high , with many transmissions occurring in a short time window prior to symptom onset . We consider the implications of these results for contact tracing and isolation strategies .
We considered four different models of infectiousness ( see Materials and methods ) : We fitted each model to data from 191 SARS-CoV-2 transmission pairs ( Ferretti et al . , 2020b; Figure 2—source data 1 ) obtained by combining data from five studies ( Ferretti et al . , 2020a; He et al . , 2020; Xia et al . , 2020; Cheng et al . , 2020; Zhang et al . , 2020 ) . To account for uncertainty in the precise times of symptom appearance within the day of onset for the infector and infectee ( Thompson , 2020 ) , we used data augmentation Markov chain Monte Carlo ( MCMC ) . Point estimates and credible intervals for model parameters are given in Supplementary file 1 . The Ferretti model and independent transmission and symptoms model were also fitted to the same data in Ferretti et al . , 2020b ( the parameter estimates obtained in Ferretti et al . , 2020b lie within the credible intervals shown in Supplementary file 1 ) , but estimates of epidemiological quantities obtained using those models were not compared directly in that study . For each model , we calculated the generation time ( Figure 2A ) , TOST ( Figure 2B ) , and serial interval ( Figure 2C ) distributions using point estimates for the fitted parameters ( Supplementary file 1 ) . The empirical serial interval distribution is also plotted in Figure 2C , to give an approximate visual indication of the goodness of fit of the different models . However , since the data contained intervals of possible exposure times in addition to symptom onset dates , this only gives a partial picture of the goodness of fit . Therefore , we also calculated the Akaike information criterion ( AIC ) for each model . When calculating AIC values , we considered maximum likelihood parameter estimates with symptom onsets occurring in the middle of the onset dates , to avoid comparing models based on likelihoods calculated using augmented data . The best fit to the data was obtained using the variable infectiousness model ( ΔAIC = 0 ) . The constant infectiousness model gave the next best fit ( ΔAIC = 1 . 3 ) , followed by the Ferretti model ( ΔAIC = 5 . 1 ) . Finally , the model with the standard assumption of independent transmission and symptoms fitted least well ( ΔAIC = 38 . 9 ) . The predicted variability in the generation time between individuals was lower for the independent transmission and symptoms model compared to the other three models ( Figure 2A ) . On the other hand , the TOST distribution was most concentrated around the time of symptom onset for the best-fitting variable infectiousness model , and least concentrated for the independent transmission and symptoms model ( Figure 2B ) . In the best-fitting model , a decrease in infectiousness was inferred following symptom onset , likely due to behavioural factors that reduce the transmission risk following symptom appearance ( Manfredi and D’Onofrio , 2013 ) . Using the full posterior distributions of model parameters obtained when fitting the models to data , we calculated posterior estimates of the proportion of transmissions occurring before symptom onset ( for hosts who developed symptoms ) for each model ( Figure 3A ) . The median ( 95% credible interval ) proportion of presymptomatic transmissions was 0 . 65 ( 0 . 53–0 . 77 ) , 0 . 56 ( 0 . 50–0 . 62 ) , 0 . 55 ( 0 . 48–0 . 62 ) , and 0 . 49 ( 0 . 43–0 . 56 ) under the variable infectiousness model , constant infectiousness model , Ferretti model , and independent transmission and symptoms model , respectively . The central estimate of 65% of transmissions occurring prior to symptom onset using the best-fitting model is higher than estimated in most previous studies in which the generation time and/or TOST were estimated ( Ferretti et al . , 2020a; Ferretti et al . , 2020b; He et al . , 2020; Ashcroft et al . , 2020 ) . In the wider literature , we note significant variation in estimates of the contribution of presymptomatic transmission ( obtained under a range of different modelling assumptions ) , including estimates exceeding 65% ( Casey et al . , 2020; Tindale et al . , 2020; Ganyani et al . , 2020 ) . We also combined the estimates in Figure 3A with the results of a previous study ( Buitrago-Garcia et al . , 2020 ) in which the extent of asymptomatic transmission ( i . e . , transmissions from individuals who never display symptoms ) was characterised ( Figure 3—figure supplement 1 ) , to obtain estimates for the total proportion of non-symptomatic ( either presymptomatic or asymptomatic ) transmissions for the different models ( Figure 3B ) . The non-symptomatic proportion was highest for the variable infectiousness model and lowest for the independent transmission and symptoms model . Finally , we explored the implications of these results for isolation and contact tracing ( Figure 4 ) , under the simplifying assumptions of perfect isolation ( i . e . , isolation prevents transmission completely ) and perfect contact tracing ( i . e . , all contacts are traced successfully during periods of contact tracing ) . Imperfect isolation and contact tracing are considered in Figure 4—figure supplement 1 . Considering a scenario in which a case ( referred to here as the ‘index case’ ) is detected following symptom onset , we first calculated how many transmissions from the index case are expected to be prevented for different time delays between the index case developing symptoms and being isolated ( Figure 4A ) , compared to a scenario in which the index case is never isolated . We then considered tracing the contacts of that index case , inferring the proportion of presymptomatic contacts identified for different contact elicitation windows ( Figure 4B ) . As an example , a contact elicitation window of 2 days means that all contacts of the index case that occurred in the 2 days prior to the index case developing symptoms are traced ( in addition to contacts that occurred after the index case developed symptoms ) . Finally , we considered isolation of infected contacts of the index case . We calculated the expected proportion of transmissions generated by those contacts prevented for different time periods between the index case transmitting the virus to the contact and the contact being isolated ( Figure 4C ) . Under the best-fitting variable infectiousness model , 23% ( 17–31% ) of all transmissions that would be generated by a symptomatic host are prevented if the host is isolated one day after symptom onset ( Figure 4A , blue ) . This compares to a higher estimate of 38% ( 32–44% ) with the standard independent transmission and symptoms assumption ( Figure 4A , purple dashed ) and intermediate estimates for the constant infectiousness ( Figure 4A , red ) and Ferretti ( Figure 4A , orange dashed ) models . The limited impact of isolation of symptomatic hosts alone under the variable infectiousness model , which is due to the high predicted proportion of presymptomatic transmissions ( Figure 3A ) , highlights the need to also conduct contact tracing . The variable infectiousness model indicates that 69% ( 57–81% ) of presymptomatic infectious contacts are identified if a contact elicitation window of ( up to ) 2 days before the index host develops symptoms is used ( as in the UK [UK Government , 2021] and USA [Centres for Disease Control and Prevention , 2021] ) , compared to only 49% ( 44–53% ) for the independent transmission and symptoms model ( Figure 4B ) . If the contact elicitation window is extended to 4 days , then 93% ( 88–97% ) of presymptomatic infectious contacts are identified under the variable infectiousness model . However , while choosing a longer contact elicitation window ensures more infected contacts are identified , it also requires more contacts to be traced , many of whom are likely to be uninfected . This effect is enhanced by the fact that index cases are expected to be less infectious at longer time periods prior to symptom onset ( Figure 2B ) . For practical assessments of contact tracing and isolation effectiveness , it may be necessary to consider the combined effects of different delays at each stage of the contact tracing and isolation process . For example , if there is a delay of 2 days between an index case infecting a contact and the index case showing symptoms , and a further delay of 2 days between the index case showing symptoms and the contact being traced and isolated , then this corresponds to a total delay of 4 days between the contact being infected and isolated ( assuming that the contact elicitation window is at least 2 days , so that the contact is traced ) . Under the variable infectiousness model , 71% of onward transmissions from the contact would then be expected to be prevented after this delay ( Figure 4C ) . In contrast , for an infectious contact that occurred 4 days before the index host developed symptoms ( so that the total delay between the contact being infected and isolated is 6 days , assuming that the contact elicitation window is at least 4 days so the contact is traced ) , only 41% of the contact’s onward infections would be expected to be prevented ( Figure 4C ) .
Here , we have considered a range of approaches for estimating epidemiological time periods using data from SARS-CoV-2 infector–infectee transmission pairs . Our mechanistic framework provides an improved fit to data compared to a model predicated on the assumption that infectiousness is independent of symptoms . Despite neglecting potential relationships between viral shedding and symptoms , as well as behavioural changes in response to symptoms ( Manfredi and D’Onofrio , 2013 ) , that assumption underlies most previous studies in which the SARS-COV-2 generation time distribution has been estimated ( Ferretti et al . , 2020a; Deng et al . , 2020; Ganyani et al . , 2020; Knight and Mishra , 2020 ) . Some previous studies in which the generation time ( Ferretti et al . , 2020b; Davis et al . , 2020 ) and/or TOST distributions ( Ferretti et al . , 2020b; He et al . , 2020; Ashcroft et al . , 2020 ) were estimated have considered an alternative assumption that infectiousness depends only on the time since symptom onset , independent of the time of infection . If the serial interval is always positive , which is not the case for COVID-19 ( Du et al . , 2020 ) , this is equivalent to assuming that the serial interval and generation time distributions are identical ( Lehtinen et al . , 2021; Cori et al . , 2013; Britton and Scalia Tomba , 2019 ) . In one article ( Ferretti et al . , 2020b ) , a non-mechanistic model ( the Ferretti model ) was developed in which a host’s infectiousness could depend on both the time since infection and the time since symptom onset . However , as we have demonstrated , our mechanistic approach provides an improved fit to data compared to that model . In addition , our method is useful for parameterising population-scale compartmental epidemic forecasting models , since the time periods derived using our approach correspond naturally to compartments ( Hart et al . , 2020 ) . It should be noted that an assumption underlying the ‘E/P/I’ structure of the best-fitting variable infectiousness model ( Figure 1B , right , solid line ) is that infectiousness may change when individuals develop symptoms . The relative infectiousness of presymptomatic and symptomatic infectious individuals is then estimated from the data . Here , we attributed the inferred reduction in transmission following symptom onset found in Figure 2B ( blue line ) to behavioural factors . However , in practice behavioural changes may not occur immediately after symptoms appear , particularly if initial symptoms are mild or non-specific . A delay between symptom onset and a change in infectiousness could in principle be incorporated into our mechanistic framework by adding an additional stage of infection . This would generate a continuous TOST profile . However , we did not take this approach here since such increased model complexity would require additional parameters to be estimated , likely requiring further data . One caveat of this study is that our estimates were obtained using data collected early in the COVID-19 pandemic ( January–March 2020 ) . Since local case numbers were then increasing in locations where some ( although not all ) of the data were collected ( Ferretti et al . , 2020b ) , shorter serial intervals may have been over-represented in the dataset ( Britton and Scalia Tomba , 2019 ) . On the other hand , studies from China have indicated a shortening of the generation time ( Sun et al . , 2021 ) and serial interval ( Ali et al . , 2020 ) over time due to non-pharmaceutical interventions , perhaps suggesting longer serial intervals at the beginning of the pandemic . Differences in isolation policies are also likely to affect predictions of the contribution of presymptomatic transmission ( Casey et al . , 2020; Sun et al . , 2021 ) . We did not explicitly account for isolation policies already in place when the transmission pair data were collected , potentially lowering the estimated effectiveness of isolating symptomatic hosts . More recently , the emergence of novel variants may also have affected the generation time , although their impact is not yet fully clear ( Davies et al . , 2021 ) . Therefore , while our main aim was to compare estimates of key epidemiological quantities under different modelling assumptions , it would be of interest to update our analyses when more recent data from infector–infectee pairs become available . In summary , using a novel mechanistic approach in combination with data from SARS-CoV-2 infector–infectee pairs to infer key epidemiological quantities indicates that a higher proportion of transmissions occur prior to symptoms than predicted by existing methods . A significant proportion of these transmissions arise immediately before symptom onset . This shows that , while the impact of isolation of symptomatic hosts alone may be limited , combining this with contact tracing and isolation of presymptomatic infected contacts is valuable even if the contact elicitation window is short . The use and refinement of contact tracing programmes in countries worldwide is therefore of clear public health importance .
Here , we outline the notation used in this section when describing the different models that we considered . For a given transmission pair , we label the infector as 1 and the infectee as 2 , and define:tik= ( time of infection of host k ) , k=1 , 2 , tsk= ( time of symptom onset of host k ) , k=1 , 2 , τinc , k= ( incubation period of host k ) , k=1 , 2 , τgen= ( generation time ) , xtost= ( time from symptom onset of 1 to transmission to 2 ( TOST ) ) , xser= ( serial interval ) . In the above , t is used to denote calendar times , τ for time intervals relative to the time of infection , and x for time intervals relative to the time of symptom onset . We denote the probability density functions of the incubation period , generation time , TOST , and serial interval as finc , fgen , ftost , and fser , respectively , and use a capital F for the corresponding cumulative distribution functions . In addition , we denote the expected infectiousness of a host at time since infection τ as βτ , and the expected infectiousness at time since symptom onset x as bx . These infectiousness profiles are related to the generation time and TOST distributions , respectively , byβ ( τ ) =β0fgen ( τ ) , bx=β0ftostx . Here , β0 corresponds to the expected number of transmissions generated by each host who develops symptoms at some stage during infection , that is , the ( instantaneous ) reproduction number of such hosts ( at least if corrections to the reproduction number within a finite contact network [Keeling and Grenfell , 2000; Enright and Kao , 2018] can be neglected ) . However , the exact value of β0 has no effect on our analyses , since it simply adds a constant factor to the likelihood function given below . We also let βτ∣τinc and bx∣τinc be the expected infectiousness at time τ since infection and at time x since symptom onset , respectively , conditional on an incubation period of τinc ( these are related by βτ∣τinc=bτ-τincτinc and bx∣τinc=βx+τincτinc ) . We considered several different models for infectiousness ( details of individual models are given below ) . In each model , the conditional infectiousness , βτ∣τinc , or equivalently , bx∣τinc , is specified . The distributions of the generation time and TOST can be recovered from this conditional infectiousness by averaging over the incubation period distribution ( which is assumed to be known ) :βτ=β0fgenτ=∫0∞βτ∣τincfinc ( τinc ) dτinc , bx=β0ftostx=∫0∞bx∣τincfinc ( τinc ) dτinc . Alternative ( equivalent ) expressions for the generation time and TOST distributions are available for some of the models considered ( these are detailed in the “Models of infectiousness” subsection below ) . To obtain an expression for the serial interval distribution , we note thatxser=xtost+τinc , 2 . We assume throughout that xtost and τinc , 2 are independent , so that the serial interval distribution is given by the convolutionfserxser=∫0∞ftostxser-τincfincτincdτinc . The proportion of presymptomatic transmissions ( out of all transmissions generated by individuals who develop symptoms ) can be calculated asqP=∫-∞0ftostxtostdxtost , although simpler equivalent expressions for individual models are also detailed later . Following Ferretti et al . , 2020b , we considered SARS-COV-2 transmission pair data from five different studies ( Ferretti et al . , 2020a; He et al . , 2020; Xia et al . , 2020; Cheng et al . , 2020; Zhang et al . , 2020 ) , totalling 191 infector–infectee pairs ( Figure 2—source data 1 ) . In all 191 transmission pairs , both the infector and the infectee developed symptoms , and the symptom onset date of each host was recorded . In four of the five studies ( Ferretti et al . , 2020a; He et al . , 2020; Xia et al . , 2020; Cheng et al . , 2020 ) , intervals of exposure were available for either the infector or infectee ( or both ) , whereas in the other ( Zhang et al . , 2020 ) , only symptom onset dates were recorded . In our main analyses , the incubation period was assumed to follow a Gamma distribution with shape parameter 5 . 807 and scale parameter 0 . 948 ( Lauer et al . , 2020 ) . This corresponds to a mean incubation period of 5 . 5 days and a standard deviation of 2 . 3 days . However , to demonstrate that our main conclusions are robust to the exact incubation period distribution used , we also repeated our analyses using an alternative , more dispersed , Gamma distributed incubation period with a mean of 5 . 3 days and a standard deviation of 3 . 2 days ( Linton et al . , 2020; Figure 2—figure supplement 2 , Figure 3—figure supplement 2 , Figure 4—figure supplement 2 ) . For a single transmission pair ( labelled n ) , suppose that the times of infection for the infector and infectee are known to lie in the intervals [ti1 , L , ti1 , R] and [ti2 , L , ti2 , R] , respectively ( where these intervals may be infinitely wide ) , and that their symptom onset times , ts1 and ts2 , are known exactly . In this case ( when only that transmission pair is observed ) , the likelihood of the parameters , θ , of the model of infectiousness under consideration is given byL ( n ) ( θ ) =1β0∫ti2 , Lti2 , R∫ti1 , Lti1 , Rb ( ti2−ts1∣ts1−ti1 , θ ) finc ( ts1−ti1 ) finc ( ts2−ti2 ) dti1dti2 , where the dependence of the conditional expected infectiousness , b ( x∣τinc , θ ) , on the model parameters , θ , is indicated explicitly . A derivation of this expression is given in Appendix . Assuming that each transmission pair in our dataset is independent , the overall likelihood is therefore given by the product of the contributions , L ( n ) ( θ ) , from each individual transmission pair , that is , L ( θ ) =∏n=1NL ( n ) ( θ ) , where N is the total number of transmission pairs . To account for uncertainty in the exact symptom onset times within the day of onset ( and so avoid imparting bias by fitting continuous-time models to discrete-time symptom onset data ) , we fitted the models to the data using data augmentation MCMC ( Thompson , 2020 , Ferguson et al . , 2005 , Cauchemez et al . , 2004 ) . In alternating steps of the chain , we updated either the vector of model parameters , N , or the exact symptom onset times of each infector and infectee . The chain was run for 2 . 5 million steps , of which the first 500 , 000 were discarded as burn-in . Posterior distributions of model parameters were obtained by recording only every 100 iterations of the chain ( assuming independent uniform prior distributions for each entry of θ ) . Point estimates of model parameters ( Supplementary file 1 ) were obtained by calculating the posterior mean of θ . Full details of the MCMC procedure are given in Appendix . In order to provide a straightforward comparison of the goodness of fit between models , we also determined the parameters , θ^ , that maximised the likelihood , L ( θ ) , for each model under the assumption that each host developed symptoms exactly in the middle of the known onset date . The AIC for each model could then be calculated asAIC=2× ( numberofestimatedparameters ) −2log ( L ( θ^ ) ) , where three parameters were estimated for the variable infectiousness and Ferretti models , and two parameters for the constant infectiousness and independent transmission and symptoms models . Since the maximum likelihood estimators , θ^ , did not account for uncertainty in exact symptom onset times , they were not used elsewhere in our analyses ( however , these all lay within the credible intervals obtained in the MCMC procedure , which are given in Supplementary file 1 ) . Expressions for the proportion of transmissions , qP , generated prior to symptom onset , are given for the individual models above . Once asymptomatic cases are accounted for , the overall non-symptomatic proportion of transmissions can be written aspAxA+ ( 1−pA ) qPpAxA+ ( 1−pA ) , where pA is the proportion of infected individuals who remain asymptomatic and xA is the ratio between the average number of secondary cases generated by an asymptomatic host and the number generated by a host who develops symptoms at some stage during infection . A derivation of this expression is given in Appendix . For each model , we used the posterior parameter distributions that were obtained when we fitted the model to data to obtain a sample from the posterior distribution of qP . In order to estimate the total proportion of non-symptomatic transmissions , we assumed the distributionspA∼Beta ( 85 , 186 ) , [mean 0 . 31 , standard deviation 0 . 03] , xA∼Lognormal ( −1 . 04 , 0 . 652 ) , [mean 0 . 44 , standard deviation 0 . 32] , which are consistent with estimates in Buitrago-Garcia et al . , 2020 . These distributions are shown in Figure 3—figure supplement 1 . We then combined samples from the assumed distributions of pA and xA with the sample that we generated from the posterior distribution of qP to obtain a distribution for the total proportion of non-symptomatic transmissions . First , we considered the proportion of transmissions that can be prevented if a symptomatic host is isolated d1 days after symptom onset . Assuming that a proportion ε1 of infectious contacts that would otherwise occur are prevented during the isolation period ( and neglecting any transmissions that occur after the end of the isolation period ) , the overall proportion of transmissions prevented through isolation isε1 ( 1−Ftost ( d1 ) ) . We then predicted the proportion of the presymptomatic infectious contacts of a symptomatic index case that will be found , if contacts are traced up to d2 days before the time of symptom onset of the index case . In this scenario , assuming that it is possible to trace a fraction ε2 of the host’s presymptomatic contacts ( at times when tracing takes place ) , then the proportion of presymptomatic infectious contacts found is equal toε2 ( qP−Ftost ( −d2 ) ) qP . Finally , we considered the proportion of onward transmissions that can be prevented if an infected individual , who is identified through contact tracing , is isolated d3 days after exposure . Assuming that a proportion d3 of infectious contacts that would otherwise occur are prevented during the isolation period , the overall proportion of onward transmissions prevented through isolation isε3 ( 1−Fgen ( d3 ) ) . In the main text ( Figure 4 ) , we assumed that ε1=ε2=ε3=1 ( i . e . , isolation of symptomatic hosts , contact identification , and isolation of infected contacts are all 100% effective ) . Values of ε1 , ε2 , and ε3 below 1 are considered in Figure 4—figure supplement 1 . | The risk of a person with COVID-19 spreading the SARS-CoV-2 virus that causes it to others varies over the course of their infection . Transmission depends both on how much virus is in the infected person’s airway and their behaviors , such as whether they wear a mask and how many people they have contact with . Learning more about when people are most infectious would help public health officials stop the spread of the virus . For example , officials can then introduce policies that ensure that people are isolated when they are most infectious . The majority of studies assessing when people with COVID-19 are most infectious so far have assumed that transmission is not linked to when symptoms appear . But that may not be true . After people develop symptoms , they may be more likely to stay home , avoid others , or take other measures that prevent transmission . Using computer modeling and data from previous studies of individuals who infected others with SARS-CoV-2 , Hart et al . show that about 65% of virus transmission occurs before symptoms develop . In fact , the computational experiments show the risk of transmission is highest immediately before symptoms develop . This highlights the importance of identifying people exposed to someone infected with the virus and isolating potential recipients before they develop symptoms . This information may help public health officials develop more effective strategies to prevent the spread of SARS-CoV-2 . It may also help scientists develop more accurate models to predict the spread of the virus . However , the computational experiments used data on infections early in the pandemic that may not reflect the current situation . Changes in public health policy , the behavior of individuals and the appearance of new strains of SARS-CoV-2 , all affect the timing of transmission . As more recent data become available , Hart et al . plan to explore how characteristics of transmission have changed as the pandemic has progressed . | [
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] | 2021 | High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing |
Hypoxia is a common challenge faced by bacteria during associations with hosts due in part to the formation of densely packed communities ( biofilms ) . cbb3-type cytochrome c oxidases , which catalyze the terminal step in respiration and have a high affinity for oxygen , have been linked to bacterial pathogenesis . The pseudomonads are unusual in that they often contain multiple full and partial ( i . e . ‘orphan’ ) operons for cbb3-type oxidases and oxidase subunits . Here , we describe a unique role for the orphan catalytic subunit CcoN4 in colony biofilm development and respiration in the opportunistic pathogen Pseudomonas aeruginosa PA14 . We also show that CcoN4 contributes to the reduction of phenazines , antibiotics that support redox balancing for cells in biofilms , and to virulence in a Caenorhabditis elegans model of infection . These results highlight the relevance of the colony biofilm model to pathogenicity and underscore the potential of cbb3-type oxidases as therapeutic targets .
Among the oxidants available for biological reduction , molecular oxygen ( O2 ) provides the highest free energy yield . Since the accumulation of O2 in the atmosphere between ~2 . 4 and 0 . 54 billion years ago ( Kirschvink and Kopp , 2008; Dietrich et al . , 2006b ) , organisms that can use it for growth and survival , and tolerate its harmful byproducts , have evolved to exploit this energy and increased in complexity ( Knoll and Sperling , 2014; Falkowski , 2006 ) . At small scales and in crowded environments , rapid consumption of O2 leads to competition for this resource and has promoted diversification of bacterial and archaeal mechanisms for O2 reduction that has not occurred in eukaryotes ( Brochier-Armanet et al . , 2009 ) . The various enzymes that allow bacteria to respire O2 exhibit a range of affinities and proton-pumping efficiencies and likely contribute to competitive success in hypoxic niches ( Morris and Schmidt , 2013 ) . Such environments include the tissues of animal and plant hosts that are colonized by bacteria of high agricultural ( Preisig et al . , 1996 ) and clinical ( Way et al . , 1999; Weingarten et al . , 2008 ) significance . The opportunistic pathogen Pseudomonas aeruginosa , a colonizer of both plant and animal hosts ( Rahme et al . , 1995 ) , has a branched respiratory chain with the potential to reduce O2 to water using five canonical terminal oxidase complexes: two quinol oxidases ( bo3 ( Cyo ) and a bd-type cyanide-insensitive oxidase ( CIO ) ) and three cytochrome c oxidases ( aa3 ( Cox ) , cbb3-1 ( Cco1 ) , and cbb3-2 ( Cco2 ) ) ( Figure 1A ) . Several key publications have described P . aeruginosa’s complement of terminal oxidases and oxidase subunits , revealing features specific to this organism ( Williams et al . , 2007; Comolli and Donohue , 2004; Alvarez-Ortega and Harwood , 2007; Arai et al . , 2014; Kawakami et al . , 2010; Jo et al . , 2014 ) . P . aeruginosa is unusual in that it encodes two oxidases belonging to the cbb3-type family . These enzymes are notable for their relatively high catalytic activity at low O2 concentrations and restriction to the bacterial domain ( Brochier-Armanet et al . , 2009; Pitcher and Watmough , 2004 ) . ( The P . aeruginosa cbb3-type oxidases are often referred to as cbb3-1 and cbb3-2; however , we will use ‘Cco1’ and ‘Cco2’ for these enzymes , consistent with the annotations of their encoding genes . ) Most bacterial genomes that encode cbb3-type oxidases contain only one operon for such a complex , which is induced specifically under conditions of O2 limitation ( Cosseau and Batut , 2004 ) . In P . aeruginosa , the cco2 operon is induced during growth at low O2 concentrations , but the cco1 operon is expressed constitutively at high levels ( Comolli and Donohue , 2004; Kawakami et al . , 2010 ) . An additional quirk of the P . aeruginosa terminal oxidase complement lies in the presence of genes for ‘orphan’ cbb3-type subunits at chromosomal locations distinct from the cco1 and cco2 operons . While the cco1 and cco2 operons , which are chromosomally adjacent , each contain four genes encoding a functional Cco complex ( consisting of subunits N , O , P , and Q ) , the two additional partial operons ccoN3Q3 and ccoN4Q4 each contain homologs coding for only the Q and catalytic N subunits ( Figure 1B ) . Expression of the ccoN3Q3 operon is induced under anaerobic denitrification conditions ( Alvarez-Ortega and Harwood , 2007 ) , and by nitrite exposure during growth under 2% O2 ( Hirai et al . , 2016 ) . During aerobic growth in liquid cultures , ccoN4Q4 is induced by cyanide , which is produced in stationary phase ( Hirai et al . , 2016 ) . However , additional expression studies indicate that ccoN4Q4 transcription is influenced by redox conditions , as this operon is induced by O2 limitation ( Alvarez-Ortega and Harwood , 2007 ) and slightly downregulated in response to pyocyanin , a redox-active antibiotic produced by P . aeruginosa ( Dietrich et al . , 2006a ) . In a recent study , Hirai et al . characterized the biochemical properties and physiological roles of P . aeruginosa cbb3 isoforms containing combinations of canonical and orphan subunits ( Hirai et al . , 2016 ) . In a strain lacking all of the aerobic terminal oxidases , expression of any isoform conferred the ability to grow using O2 , confirming that isoforms containing the orphan N subunits are functional . When preparations from wild-type , stationary-phase P . aeruginosa cells were separated on 2D gels and probed with anti-CcoN4 antibody , this subunit was detected at the same position as the assembled CcoNOP complex , showing that CcoN4-containing heterocomplexes form in vivo . Furthermore , the authors found that the products of ccoN3Q3 and ccoN4Q4 contributed resistance to nitrite and cyanide , respectively , during growth in liquid cultures under low-O2 conditions . While these results provide insight into contributions of the cbb3 heterocomplexes to growth in liquid cultures , potential roles for N3- and N4-containing isoforms in biofilm growth and pathogenicity have yet to be explored . The biofilm lifestyle—in which cells grow in a dense community encased in a self-produced matrix—has been linked to the establishment and persistence of infections in diverse systems ( Edwards and Kjellerup , 2012; Rybtke et al . , 2015 ) . Biofilm development promotes the formation of O2 gradients such that cells at a distance from the biofilm surface are subjected to hypoxic or anoxic conditions ( Werner et al . , 2004 ) . Using a colony morphology assay to study redox metabolism and its relationship to community behavior , we have shown that O2 limitation for cells in biofilms leads to an imbalance in the intracellular redox state . This can be relieved by a change in community morphology , which increases the surface area-to-volume ratio of the biofilm and therefore access to O2 for resident cells ( Kempes et al . , 2014 ) . For P . aeruginosa cells in biofilms , the intracellular accumulation of reducing power can also be prevented by production and reduction of endogenous antibiotics called phenazines , which mediate extracellular electron transfer to oxidants available at a distance ( Dietrich et al . , 2013 ) . We have found that biofilm-specific phenazine production contributes to pathogenicity in a murine model of acute pulmonary infection ( Recinos et al . , 2012 ) , further illustrating the importance of phenazine-mediated redox balancing for P . aeruginosa cells in communities . Because of the formation of an O2 gradient inherent to the biofilm lifestyle , we hypothesized that the differential regulation of the P . aeruginosa cco operons affects their contributions to metabolic electron flow in biofilm subzones . We evaluated the roles of various cbb3-type oxidase isoforms in multicellular behavior and virulence . Our results indicate that isoforms containing the orphan subunit CcoN4 can support survival in biofilms via O2 and phenazine reduction and contribute to P . aeruginosa pathogenicity in a Caenorhabditis elegans ‘slow killing’ model of infection .
Biochemical , genetic , and genomic analyses suggest that the CcoN and CcoO subunits , typically encoded by an operon , form the minimal functional unit of cbb3-type oxidases ( Ducluzeau et al . , 2008; de Gier et al . , 1996; Zufferey et al . , 1996 ) . CcoN is the membrane-integrated catalytic subunit and contains two b-type hemes and a copper ion . CcoO is membrane-anchored and contains one c-type heme . Additional redox subunits and/or subunits implicated in complex assembly , such as CcoQ and CcoP , can be encoded by adjacent genes ( Figure 1B ) . ccoNO-containing clusters are widely distributed across phyla of the bacterial domain ( Ducluzeau et al . , 2008 ) . We used the EggNOG database , which contains representative genomes for more than 3000 bacterial species ( Huerta-Cepas et al . , 2016 ) to obtain an overview of the presence and frequency of cco genes . Out of 3318 queried bacterial genomes , we found 467 with full cco operons ( encoding potentially functional cbb3-type oxidases with O and N subunits ) . Among these , 78 contain more than one full operon . We also used EggNOG to look for orphan ccoN genes by examining the relative numbers of ccoO and ccoN homologs in individual genomes . We found 14 genomes , among which Pseudomonas species are overrepresented , that contain orphan ccoN genes ( Figure 1C ) , and our analysis yielded three species that contain more than one orphan ccoN gene: Pseudomonas mendocina , Pseudomonas aeruginosa , and Achromobacter xylosoxidans . P . mendocina is a soil bacterium and occasional nosocomial pathogen that is closely related to P . aeruginosa , based on 16S rRNA gene sequence comparison ( Anzai et al . , 2000 ) . A . xylosoxidans , in contrast , is a member of a different proteobacterial class but nevertheless is often mistaken for P . aeruginosa ( Saiman et al . , 2001 ) . Like P . aeruginosa , it is an opportunistic pathogen that can cause pulmonary infections in immunocompromised individuals and patients with cystic fibrosis ( De Baets et al . , 2007; Firmida et al . , 2016 ) . Hirai et al . previously reported a ClustalW-based analysis of CcoN homologs specifically from pseudomonads , which indicated the presence of orphan genes in additional species not represented in the EggNOG database . These include P . denitrificans , which contains two orphan genes ( Hirai et al . , 2016 ) . During growth in a biofilm , subpopulations of cells are subjected to regimes of electron donor and O2 availability that may create unique metabolic demands and require modulation of the respiratory chain for survival ( Alvarez-Ortega and Harwood , 2007; Borriello et al . , 2004; Werner et al . , 2004 ) . We therefore investigated the contributions of individual cco genes and gene clusters to P . aeruginosa PA14 biofilm development using a colony morphology assay , which has demonstrated sensitivity to electron acceptor availability and utilization ( Dietrich et al . , 2013 ) . Because the Cco1 and Cco2 complexes are the most important cytochrome oxidases for growth of P . aeruginosa in fully aerated and O2-limited liquid cultures ( Alvarez-Ortega and Harwood , 2007; Arai et al . , 2014 ) , we predicted that mutations disabling the functions of Cco1 and Cco2 would affect colony growth . Indeed , a mutant lacking both the cco1 and cco2 operons ( ‘∆cco1cco2’ ) produced thin biofilms with a smaller diameter than the wild type . After 5 days of development , this mutant displayed a dramatic phenotype consisting of a tall central ring feature surrounded by short ridges that emanate radially ( Figure 2A , Figure 2—figure supplement 1A ) . ∆cco1cco2 colonies were also darker in color , indicating increased uptake of the dye Congo red , which binds to the extracellular matrix produced by biofilms ( Friedman and Kolter , 2004 ) . Surprisingly , a strain specifically lacking the catalytic subunits of Cco1 and Cco2 ( ‘∆N1∆N2’ ) , while showing a growth defect similar to that of ∆cco1cco2 when grown in liquid culture ( Figure 2C ) , showed biofilm development that was similar to that of the wild type ( Figure 2A , Figure 2—figure supplement 1A ) . As it is known that CcoN3 and CcoN4 can form functional complexes with subunits of the Cco1 and Cco2 oxidases in P . aeruginosa PAO1 ( Hirai et al . , 2016 ) , this led us to hypothesize that Cco isoforms containing the orphan subunits CcoN3 and/or CcoN4 could substitute for Cco1 and Cco2 in the biofilm context . Deleting ccoN3 ( ‘∆N3’ or ‘∆N1∆N2∆N3’ ) did not have an observable effect on biofilm development when mutants were compared to respective parent strains ( Figure 2—figure supplement 1A ) . However , the phenotype of a ‘∆N1∆N2∆N4’ mutant was consistent with our model , as it mimicked that of the ∆cco1cco2 mutant in both liquid-culture and biofilm growth ( Figure 2A and C , Figure 2—figure supplement 1A ) . Furthermore , we found that a mutant lacking only ccoN4 ( ‘∆N4’ ) displayed an altered phenotype in that it began to form wrinkle structures earlier than the wild type ( Figure 2—figure supplement 1A ) , which developed into a disordered region of wrinkles inside a central ring , surrounded by long , radially emanating ridges ( Figure 2A ) . Reintroduction of the ccoN4 gene into either of these strains restored the phenotypes of the respective parent strains ( Figure 2—figure supplement 1A ) . Deletion of either ccoN2 or ccoN3 in the ∆N4 background did not exacerbate the colony phenotype seen in ∆N4 alone . However , the ‘∆N1∆N4’ double mutant showed an intermediate phenotype relative to ∆N4 and ∆N1∆N2∆N4 ( Figure 2—figure supplement 1B ) , suggesting some functional redundancy for CcoN1 and CcoN4 . The developmental pattern of the ∆N4 colony is reminiscent of those displayed by mutants defective in phenazine production and sensing ( Figure 2—figure supplement 1A ) ( Dietrich et al . , 2008; 2013; Sakhtah et al . , 2016; Okegbe et al . , 2017 ) . Although ∆N4 itself showed a unique phenotype in the colony morphology assay , its growth in shaken liquid cultures was indistinguishable from that of the wild type ( Figure 2C ) . Finally , deleting the three non-cbb3-type terminal oxidases ( ‘∆cox∆cyo∆cio’ ) , did not affect biofilm morphology ( Figure 2—figure supplement 2C ) . These results suggest that CcoN4-containing Cco isoforms play physiological roles that are specific to the growth conditions encountered in biofilms . Next , we asked whether CcoN4 contributes to respiration in biofilms . We tested a suite of cco mutants for reduction of triphenyl tetrazolium chloride ( TTC ) , an activity that is associated with cytochrome c oxidase-dependent respiration ( Rich et al . , 2001 ) . The ∆cco1cco2 mutant showed a severe defect in TTC reduction , which was recapitulated by the ∆N1∆N2∆N4 mutant . As in the colony morphology assay , this extreme phenotype was not recapitulated in a mutant lacking only CcoN1 and CcoN2 , indicating that CcoN4 contributes to respiratory activity in PA14 biofilms . Although we did not detect a defect in TTC reduction for the ∆N4 mutant , we saw an intermediate level of TTC reduction for ∆N1∆N4 compared to ∆N1∆N2 and ∆N1∆N2∆N4 , further implicating the CcoN4 subunit in this activity ( Figure 2B ) . A recent study demonstrated a role for CcoN4 in resistance to cyanide , a respiratory toxin that is produced by P . aeruginosa ( Hirai et al . , 2016 ) . The altered biofilm phenotypes of ∆N4 mutants could therefore be attributed to an increased sensitivity to cyanide produced during biofilm growth . We deleted the hcn operon , coding for cyanide biosynthetic enzymes , in wild-type , phenazine-null ( ∆phz ) , and various cco mutant backgrounds . The biofilm morphologies and liquid-culture growth of these strains were unaffected by the ∆hcnABC mutation , indicating that the biofilm-specific role of CcoN4 explored in this work is independent of its role in mediating cyanide resistance ( Figure 2—figure supplement 2 ) . Additionally , we examined genomes available in the Pseudomonas Genome Database for the presence of homologs encoding CcoN subunits ( ccoN genes ) and enzymes for cyanide synthesis ( hcnABC ) ( Winsor et al . , 2016 ) and did not find a clear correlation between the presence of hcnABC and ccoN4 homologs ( Figure 2—figure supplement 3 ) . Together , the effects of cco gene mutations that we observed in assays for colony morphogenesis and TTC reduction suggest that one or more CcoN4-containing Cco isoform ( s ) support respiration and redox balancing , and is/are utilized preferentially in comparison to CcoN1- and CcoN2-containing Cco complexes , in biofilms . We performed a sequence alignment of the CcoN subunits encoded by the PA14 genome and identified residues that are unique to CcoN4 or shared uniquely between CcoN4 and CcoN1 , which showed the strongest functional redundancy with CcoN4 in our assays ( Figure 2—figure supplement 4A ) . We also threaded the CcoN4 sequence using the available structure of the CcoN subunit from P . stutzeri ( Buschmann et al . , 2010 ) and highlighted these residues ( Figure 2—figure supplement 4B ) . It is noteworthy that most of the highlighted residues are surface-exposed , specifically on one half of the predicted CcoN4 structure , where they may engage in binding an unknown protein partner or specific lipids . In contrast , sites that have been described as points of interaction with CcoO and CcoP are mostly conserved , further supporting the notion that CcoN4 can interact with these subunits in Cco complexes . To further test CcoN4’s contribution to growth in biofilms , we performed competition assays in which ∆N4 and other mutants were grown as mixed-strain biofilms with the wild type . In each of these assays , one strain was labeled with constitutively expressed YFP so that the strains could be distinguished during enumeration of colony-forming units ( CFUs ) . Experiments were performed with the label on each strain to confirm that YFP expression did not affect fitness ( Figure 3—figure supplement 1A , B ) . When competitive fitness was assessed after 3 days of colony growth ( Figure 3A ) , ∆N4 cells showed a disadvantage , with the wild type outcompeting ∆N4 by a factor of two . This was similar to the disadvantage observed for the ∆N1∆N2 mutant , further suggesting that the orphan subunit CcoN4 plays a significant role in biofilm metabolism . Remarkably , deletion of ccoN4 in mutants already lacking ccoN1 and ccoN2 led to a drastic decrease in fitness , with the wild type outcompeting ∆N1∆N2∆N4 by a factor of 16 . This disadvantage was comparable to that observed for the mutant lacking the full cco operons ( ∆cco1cco2 ) , underscoring the importance of CcoN4-containing isoforms during biofilm growth . To further explore the temporal dynamics of N subunit utilization , we repeated the competition assay , but sampled each day over the course of 3 days ( Figure 3B ) . The fitness disadvantage that we had found for strains lacking CcoN1 and CcoN2 was evident after only 1 day of growth and did not significantly change after that . In contrast , the ∆N4-specific decline in fitness did not occur before the second day . These data suggest that the contributions of the various N subunits to biofilm metabolism differ depending on developmental stage . DIC imaging of thin sections from wild-type colonies reveals morphological variation over depth that may result from decreasing O2 availability ( Figure 3—figure supplement 1C ) . We have previously reported that 3-day-old PA14 colony biofilms are hypoxic at depth ( Dietrich et al . , 2013 ) and that O2 availability is generally higher in thinner biofilms , such as those formed by the phenazine-null mutant ∆phz . We have proposed that the utilization of phenazines as electron acceptors in wild-type biofilms enables cellular survival in the hypoxic zone and promotes colony growth ( Okegbe et al . , 2014 ) . The relatively late-onset phenotype of the ∆N4 mutant in the competition assay suggested to us that CcoN4 may play a role in survival during formation of the hypoxic colony subzone and that this zone could arise at a point between 1 and 2 days of colony growth . We measured O2 concentrations in wild-type and ∆phz biofilms at specific time points over development , and found that O2 declined similarly with depth in both strains ( Figure 3D ) . The rate of increase in height of ∆phz tapered off when a hypoxic zone began to form , consistent with the notion that the base does not increase in thickness when electron acceptors ( O2 or phenazines ) are not available . Although we cannot pinpoint the exact depth at which the O2 microsensor leaves the colony base and enters the underlying agar , we can estimate these values based on colony thickness measurements ( Figure 3C ) . When we measured the thickness of wild-type and ∆phz biofilms over 3 days of incubation , we found that the values began to diverge between 30 and 48 hr of growth , after the colonies reached ~70 µm in height , which coincides with the depth at which O2 becomes undetectable . ∆phz colonies reached a maximum thickness of ~80 µm , while wild-type colonies continued to grow to ~150 µm ( Figure 3C ) . In this context , it is interesting to note that the point of divergence for the increase in wild-type and ∆phz colony thickness—between 30 and 48 hr—corresponds to the point at which CcoN4 becomes important for cell viability in our mixed-strain colony growth experiments ( Figure 3B ) . We hypothesize that this threshold thickness leads to a level of O2 limitation that is physiologically relevant for the roles of phenazines and CcoN4 in biofilm metabolism . P . aeruginosa’s five canonical terminal oxidases are optimized to function under and in response to distinct environmental conditions , including various levels of O2 availability ( Arai et al . , 2014; Kawakami et al . , 2010; Alvarez-Ortega and Harwood , 2007; Comolli and Donohue , 2004 ) . Furthermore , recent studies , along with our results , suggest that even within the Cco terminal oxidase complexes , the various N subunits may perform different functions ( Hirai et al . , 2016 ) . We sought to determine whether differential regulation of cco genes could lead to uneven expression across biofilm subzones . To test this , we engineered reporter strains in which GFP expression is regulated by the cco1 , cco2 , or ccoN4Q4 promoters . Biofilms of these strains were grown for 3 days , thin-sectioned , and imaged by fluorescence microscopy . Representative results are shown in the left panel of Figure 4 . The right panel of Figure 4 contains plotted GFP signal intensity and O2 concentration measurements over depth for PA14 wild-type colonies . cco1 and ccoN4 expression patterns indicate that the Cco1 oxidase and the CcoN4 subunit are produced throughout the biofilm ( Figure 4 ) . cco2 expression , on the other hand , is relatively low in the top portion of the biofilm and shows a sharp induction starting at a depth of ~45 µm . This observation is consistent with previous studies showing that cco2 expression is regulated by Anr , a global transcription factor that controls gene expression in response to a shift from oxic to anoxic conditions ( Comolli and Donohue , 2004; Kawakami et al . , 2010; Ray and Williams , 1997 ) . Although previous studies have evaluated expression as a function of growth phase in shaken liquid cultures for cco1 and cco2 , this property has not been examined for ccoN4Q4 . We monitored the fluorescence of our engineered cco gene reporter strains during growth under this condition in a nutrient-rich medium . As expected based on the known constitutive expression of cco1 and Anr-dependence of cco2 induction , we saw cco1-associated fluorescence increase before that associated with cco2 . Induction of ccoN4Q4 occurred after that of cco1 and cco2 ( Figure 4—figure supplement 1 ) , consistent with microarray data showing that this locus is strongly induced by O2 limitation ( Alvarez-Ortega and Harwood , 2007 ) . However , our observation that ccoN4Q4 is expressed in the aerobic zone , where cco2 is not expressed , in biofilms ( Figure 4 ) suggests that an Anr-independent mechanism functions to induce this operon during multicellular growth . Our results indicate that different Cco isoforms may function in specific biofilm subzones , but that CcoN4-containing isoforms could potentially form throughout the biofilm . These data , together with our observation that ∆N4 biofilms exhibit a fitness disadvantage from day 2 ( Figure 3B ) , led us to more closely examine the development and chemical characteristics of the biofilm over depth . The results shown in Figure 2B implicate CcoN4-containing isoforms in the reduction of TTC , a small molecule that interacts with the respiratory chain ( Rich et al . , 2001 ) . Similar activities have been demonstrated for phenazines , including the synthetic compound phenazine methosulfate ( PMS ) ( Nachlas et al . , 1960 ) and those produced naturally by P . aeruginosa ( Armstrong and Stewart-Tull , 1971 ) . Given that CcoN4 and phenazines function to influence morphogenesis at similar stages of biofilm growth ( Figures 2A and 3 , Figure 2—figure supplement 1 , Figure 3—figure supplement 1A , B ) , we wondered whether the role of CcoN4 in biofilm development was linked to phenazine metabolism . We used a Unisense platinum microelectrode with a 20–30 µm tip to measure the extracellular redox potential in biofilms as a function of depth . This electrode measures the inclination of the sample to donate or accept electrons relative to a Ag/AgCl reference electrode . We found that wild-type colonies showed a decrease in redox potential over depth , indicating an increased ratio of reduced to oxidized phenazines , while the redox potential of ∆phz colonies remained unchanged ( Figure 5A ) . To confirm that phenazines are the primary determinant of the measured redox potential in the wild type , we grew ∆phz colonies on medium containing PMS ( which resembles the natural phenazines that regulate P . aeruginosa colony morphogenesis [Sakhtah et al . , 2016] ) and found that these colonies yielded redox profiles similar to those of the wild type ( Figure 5—figure supplement 1A ) . Therefore , although the microelectrode we employed is capable of interacting with many redox-active substrates , we found that its signal was primarily determined by phenazines in our system . In addition , while wild-type colonies showed rapid decreases in O2 availability starting at the surface , the strongest decrease in redox potential was detected after ~50 µm ( Figure 5A ) . These results suggest that the bacteria residing in the biofilm differentially utilize O2 and phenazines depending on their position and that O2 is the preferred electron acceptor . We hypothesized that one or more of the CcoN subunits encoded by the PA14 genome is required for phenazine reduction and tested this by measuring the redox potential over depth for a series of cco mutants ( Figure 5B , top ) . We saw very little reduction of phenazines in the ∆cco1cco2 colony , suggesting that cbb3-type oxidases are required for this activity . In contrast , the mutant lacking the catalytic subunits of Cco1 and Cco2 , ∆N1∆N2 , showed a redox profile similar to the wild type , indicating that isoforms containing one or both of the orphan CcoN subunits could support phenazine reduction activity . Indeed , although redox profiles obtained for the ∆N1∆N2 and ∆N4 mutants were similar to those obtained for the wild type , the redox profile of the ∆N1∆N2∆N4 mutant recapitulated that of ∆cco1cco2 . These results indicate redundancy in the roles of some of the CcoN subunits . Consistent with this , ∆N1∆N4 showed an intermediate defect in phenazine reduction . We note that the triple mutant ∆cox∆cyo∆cio showed a wild-type-like redox profile , indicating that the cbb3-type terminal oxidases are sufficient for normal phenazine reduction ( Figure 5—figure supplement 1B ) . Extraction and measurement of phenazines released from wild-type and cco mutant biofilms showed that variations in redox profiles could not be attributed to differences in phenazine production ( Figure 5—figure supplement 1C ) . Our group has previously shown that a ∆phz mutant compensates for its lack of phenazines by forming thinner colonies , thus limiting the development of the hypoxic subzone seen in the wild type ( Dietrich et al . , 2013 ) . We therefore hypothesized that mutants unable to reduce phenazines would likewise result in thinner colonies . Indeed , we observed that the cco mutants that lacked phenazine reduction profiles in the top panel of Figure 5B produced biofilms that were significantly thinner than wild-type and comparable to that of the ∆phz mutant ( Figure 5B , bottom ) . Our group has also reported that reduction of nitrate , an alternate electron acceptor for P . aeruginosa ( Williams et al . , 2007 ) , can serve as an additional redox-balancing strategy for cells in biofilms ( Dietrich et al . , 2013 ) . Colony wrinkling is stimulated by a reduced cellular redox state; thus , provision of nitrate in the growth medium inhibits colony feature formation . We hypothesized that nitrate reduction could compensate for defects in O2 and phenazine reduction and inhibit colony wrinkling in the cco mutants that are the focus of this study . To test this , we grew strains on medium containing 10 or 40 mM potassium nitrate . We found that 10 mM nitrate was sufficient to inhibit wrinkling for up to 4 days of incubation in the wild type , ∆N4 , and ∆N1∆N4 , but that ∆phz and ∆N1∆N2∆N4 had initiated wrinkling at this point ( Figure 5—figure supplement 1D ) . When we grew these strains on medium containing 40 mM nitrate , we saw increased inhibition of wrinkling such that the wild type , ∆phz , ∆N4 , and ∆N1∆N4 remained completely smooth at 4 days of incubation . Although ∆N1∆N2∆N4 had shown some feature formation after 4 days on this medium , it was diminished relative to the same point on 10 mM nitrate . These results suggest that O2 reduction , phenazine reduction , and nitrate reduction can operate in synchrony to oxidize the redox states of cells in biofilms and that provision of nitrate can compensate for defects in O2 and phenazine reduction to enable maintenance of redox homeostasis . We have recently demonstrated that extracellular matrix production , a hallmark of biofilm formation , is regulated by redox state in PA14 colony biofilms . Increased matrix production correlates with the accumulation of reducing power ( as indicated by higher cellular NADH/NAD+ ratios ) due to electron acceptor limitation and is visible in the hypoxic region of ∆phz colonies ( Dietrich et al . , 2013; Okegbe et al . , 2017 ) . The morphologies of our cco mutants ( Figure 2A ) suggest that matrix production can also be induced by respiratory chain dysfunction , which may be linked to defects in phenazine utilization ( Figure 5B ) . To further examine the relationships between Cco isoforms and redox imbalance in biofilms , we prepared thin sections from 2-day-old colonies and stained with fluorescein-labeled lectin , which binds preferentially to the Pel polysaccharide component of the matrix ( Jennings et al . , 2015 ) . Consistent with their similar gross morphologies , the wild-type and ∆N1∆N2 biofilms showed similar patterns of staining , with a faint band of higher intensity at a depth of ~40 µm ( Figure 5C ) . ∆N4 also showed a similar pattern , with a slightly higher intensity of staining in this band . ∆N1∆N2∆N4 and ∆cco1cco2 showed more staining throughout each sample , with wider bands of greater intensity at the ~40 µm point . These data suggest that deletion of the Cco complexes leads to a more reduced cellular redox state , which induces production of more matrix , and that CcoN4 contributes significantly to maintaining redox homeostasis when O2 is limiting . We have previously shown that a mutant defective in biofilm-specific phenazine production , which also shows altered colony morphology ( Dietrich et al . , 2008; 2013 ) , exhibits decreased virulence ( Recinos et al . , 2012 ) . We and others have suggested that one way in which phenazines could contribute to virulence is by acting as electron acceptors to balance the intracellular redox state in the hypoxic conditions that are encountered during infection ( Price-Whelan et al . , 2006; Newman , 2008; Dietrich et al . , 2013 ) . Because CcoN4 is required for wild-type biofilm architecture and respiration ( Figures 2A , C and 5C ) , we hypothesized that it could also contribute to virulence . To test this , we conducted virulence assays using the nematode Caenorhabditis elegans as a host . It has been shown that P . aeruginosa is pathogenic to C . elegans and that the slow killing assay mimics an infection-like killing of C . elegans by the bacterium ( Tan et al . , 1999 ) . While ∆N1∆N2 killed with wild-type-like kinetics , ∆N1∆N2∆N4 and ∆cco1cco2 showed comparably-impaired killing relative to wild-type PA14 ( Figure 6 ) .
Biofilm formation contributes to P . aeruginosa pathogenicity and persistence during different types of infections , including the chronic lung colonizations seen in individuals with cystic fibrosis ( Tolker-Nielsen , 2014; Rybtke et al . , 2015 ) . The conditions found within biofilm microenvironments are distinct from those in well-mixed liquid cultures with respect to availability of electron donors and acceptors . We have previously described the roles of phenazines , electron-shuttling antibiotics produced by P . aeruginosa , in biofilm-specific metabolism . In this study , we focused on P . aeruginosa’s large complement of genes encoding cbb3-type cytochrome oxidase subunits and set out to test their contributions to metabolic electron flow in biofilms . The P . aeruginosa genome contains four different homologs of ccoN , encoding the catalytic subunit of cbb3-type oxidase . Only two of these ( ccoN1 and ccoN2 ) are co-transcribed with a ccoO homolog , encoding the other critical component of an active cbb3-type oxidase ( Figure 1B ) . However , genetic studies have demonstrated that all four versions of CcoN can form functional complexes when expressed with either of the two CcoO homologs ( Hirai et al . , 2016 ) . In well-mixed liquid cultures , mutants lacking the ‘orphan’ subunits did not show growth defects ( Figure 2C ) ( Hirai et al . , 2016 ) . We were therefore surprised to find that the ∆N4 mutant showed a unique morphotype in a colony biofilm assay ( Figure 2A , Figure 2—figure supplement 1A ) . We have applied this assay extensively in our studies of the mechanisms underlying cellular redox balancing and sensing and noted that the phenotype of ∆N4 was similar to that of mutants with defects in electron shuttling and redox signaling ( Dietrich et al . , 2013; Okegbe et al . , 2017 ) . We characterized the effects of a ∆N4 mutation on biofilm physiology through a series of assays . In well-mixed liquid cultures , ∆cco1cco2 showed a growth phenotype similar to that of ∆N1∆N2 . While Hirai et al . have shown that wild-type P . aeruginosa cultures grown planktonically do form Cco heterocomplexes containing CcoN4 , our observations suggest that such complexes do not contribute significantly to growth under these conditions . Consistent with this , deleting ccoN4 in the ∆N1∆N2 background had no effect on planktonic growth ( Figure 2C ) . However , in biofilm-based experiments , we found that deleting N4 alone was sufficient to cause an altered morphology phenotype ( Figure 2A and Figure 2—figure supplement 1A ) , and that deleting N4 in either a ∆N1 or a ∆N1∆N2 background profoundly affected biofilm physiology . These experiments included quantification of respiratory activity in colonies , in which deletion of CcoN4 led to a significant decrease ( Figure 2B ) ; biofilm co-culturing , in which CcoN4 was required for competitive fitness ( Figure 3A and B , Figure 3—figure supplement 1 ) ; redox profiling , which showed that CcoN4 can contribute to phenazine reduction ( Figure 5B , top ) ; colony thickness measurements , which showed that CcoN4 is required for the formation of the hypoxic and anoxic zones ( Figure 5B , bottom ) ; and matrix profiling , which showed that CcoN4 contributes to the repression of Pel polysaccharide production ( Figure 5C ) . The overlap in zones of expression between cco1 , cco2 , and ccoN4Q4 seen in colony thin sections ( Figure 4 ) implies that CcoN4 can form heterocomplexes with Cco1 and Cco2 subunits that span the depth of the colony and function to influence the physiology of P . aeruginosa biofilms in these ways . The mutant phenotypes and gene expression profiles reported in this study suggest roles for CcoN4 in O2 and phenazine reduction specifically in the biofilm context , and allow us to draw conclusions about the roles of other CcoN subunits . The expression of ccoN4Q4 throughout the biofilm depth suggests that CcoN4-containing isoforms could contribute to cytochrome c oxidation in both oxic and hypoxic zones ( Figure 4 ) . This constitutes a deviation from the previously published observation that these genes are specifically induced in hypoxic liquid cultures when compared to well-aerated ones ( Alvarez-Ortega and Harwood , 2007 ) . Therefore , the ccoN4Q4 expression we observed in the relatively oxic , upper portion of the colony may be specific to biofilms . ∆N4 displayed a colony morphology indicative of redox stress and had a fitness disadvantage compared to the wild type ( Figures 2A and 3A , B , Figure 5B , bottom , Figure 3—figure supplement 1 ) . However , because it did not show a defect in phenazine reduction ( Figure 5B , top ) , we attribute its colony morphology and impaired fitness phenotypes to its proposed role in O2 reduction ( Hirai et al . , 2016 ) . Similarly , ∆N1∆N2 showed reduced fitness compared to the wild type ( Figure 3A and B , Figure 3—figure supplement 1 ) while showing phenazine reduction comparable to that of the wild type ( Figure 5B ) , implying that one or both of these subunits contribute to O2 reduction in biofilms . When CcoN4 was deleted in conjunction with CcoN1 and CcoN2 , however , the resulting strain showed a severe phenazine reduction defect , a phenotype recapitulated by deleting both cco operons ( Figure 5B ) . Thus , our observations suggest a role for the cbb3-type oxidases in phenazine reduction in addition to their established roles in O2 reduction , thereby expanding our understanding of their overall contributions P . aeruginosa’s physiology and viability . The results described here can inform our model of how cells survive under distinct conditions in the microenvironments within biofilms . Previous work has shown that pyruvate fermentation can support survival of P . aeruginosa under anoxic conditions ( Eschbach et al . , 2004 ) and that phenazines facilitate this process ( Glasser et al . , 2014 ) . Additional research suggests that phenazine reduction is catalyzed adventitiously by P . aeruginosa flavoproteins and dehydrogenases ( Glasser et al . , 2017 ) . Our observation that cbb3-type cytochrome oxidases , particularly those containing the CcoN1 or CcoN4 subunits , were required for phenazine reduction in hypoxic biofilm subzones ( Figure 5B ) further implicates the electron transport chain in utilization of these compounds . It is also interesting in light of the historical roles of phenazines acting as mediators in biochemical studies of the cytochrome bc1 complex and cytochrome oxidases ( King , 1963; Armstrong and Stewart-Tull , 1971; Davidson et al . , 1992 ) . Based on this earlier work , we can speculate that different CcoN subunits may indirectly influence phenazine reduction , which could occur at the cytochrome c binding site of the CcoO subunit or elsewhere in the electron transport chain , through effects these CcoN subunits have on the overall function or stability of respiratory complexes . Ultimately , various mechanisms of phenazine reduction and phenazine-related metabolisms may be relevant at different biofilm depths or depending on electron donor availability . Our results suggest that , in the colony biofilm system , enzyme complexes traditionally considered to be specific to O2 reduction may contribute to anaerobic survival . Because biofilm formation is often associated with colonization of and persistence in hosts , we tested whether CcoN4 contributes to P . aeruginosa pathogenicity in C . elegans . Similar to our observations in biofilm assays , we found that the ∆cco1cco2 mutant displayed a more severe phenotype than the ∆N1∆N2 mutant , suggesting that an orphan subunit can substitute for those encoded by the cco1 and cco2 operons . We also found that deleting ccoN4 in ∆N1∆N2 led to a ∆cco1cco2-like phenotype , suggesting that CcoN4 is the subunit that can play this role ( Figure 6 ) . In host microenvironments where O2 is available , CcoN4-containing isoforms could contribute to its reduction . Additionally , in hypoxic zones , CcoN4-containing isoforms could facilitate the reduction of phenazines , enabling cellular redox balancing . Both these functions would contribute to persistence of the bacterium within the host . The contributions of the cbb3-type oxidases to P . aeruginosa pathogenicity raise the possibility that compounds interfering with Cco enzyme function could be effective therapies for these infections . Such drugs would be attractive candidates due to their specificity for bacterial respiratory chains and , as such , would not affect the host’s endogenous respiratory enzymes . Our discovery that an orphan cbb3-type oxidase subunit contributes to growth in biofilms further expands the scope of P . aeruginosa’s remarkable respiratory flexibility . Beyond modularity at the level of the terminal enzyme complex ( e . g . utilization of an aa3- vs . a cbb3-type oxidase ) , the activity of P . aeruginosa’s respiratory chain is further influenced by substitution of orphan cbb3-type catalytic subunits for native ones . Utilization of CcoN4-containing isoforms promotes phenazine reduction activity and may influence aerobic respiration in P . aeruginosa biofilms . For the exceptional species that contain orphan cbb3-type catalytic subunits , this fine level of control could be particularly advantageous during growth and survival in environments covering a wide range of electron acceptor availability ( Cowley et al . , 2015 ) .
P . aeruginosa strain UCBPP-PA14 ( Rahme et al . , 1995 ) was routinely grown in lysogeny broth ( LB; 1% tryptone , 1% NaCl , 0 . 5% yeast extract ) ( Bertani , 2004 ) at 37˚C with shaking at 250 rpm unless otherwise indicated . Overnight cultures were grown for 12–16 hr . For genetic manipulation , strains were typically grown on LB solidified with 1 . 5% agar . Strains used in this study are listed in Table 1 . In general , liquid precultures served as inocula for experiments . Overnight precultures for biological replicates were started from separate clonal source colonies on streaked agar plates . For technical replicates , a single preculture served as the source inoculum for subcultures . For making markerless deletion mutants in P . aeruginosa PA14 ( Table 1 ) 1 kb of flanking sequence from each side of the target gene were amplified using the primers listed in Table 2 and inserted into pMQ30 through gap repair cloning in Saccharomyces cerevisiae InvSc1 ( Shanks et al . , 2006 ) . Each plasmid listed in Table 3 was transformed into Escherichia coli strain UQ950 , verified by restriction digests , and moved into PA14 using biparental conjugation . PA14 single recombinants were selected on LB agar plates containing 100 µg/ml gentamicin . Double recombinants ( markerless deletions ) were selected on LB without NaCl and modified to contain 10% sucrose . Genotypes of deletion mutants were confirmed by PCR . Combinatorial mutants were constructed by using single mutants as hosts for biparental conjugation , with the exception of ∆cco1cco2 , which was constructed by deleting the cco1 and cco2 operons simultaneously as one fragment . ccoN4 complementation strains were made in the same manner , using primers LD438 and LD441 listed in Table 2 to amplify the coding sequence of ccoN4 , which was verified by sequencing and complemented back into the site of the deletion . Overnight precultures were diluted 1:100 in LB ( ∆N1∆N2 , ∆N1∆N2∆N3 , ∆N1∆N2∆N4 , ∆N1∆N2∆N4∆N3 , ∆N1∆N2∆N4::N4 , ∆cco1cco2 , ∆N1∆N2∆hcn , ∆N1∆N2∆N4∆hcn , ∆cco1cco2∆hcn , and ∆cox∆cyo∆cio were diluted 1:50 ) and grown to mid-exponential phase ( OD at 500 nm ≈ 0 . 5 ) . Ten microliters of subcultures were spotted onto 60 ml of colony morphology medium ( 1% tryptone , 1% agar [Teknova ( Hollister , CA ) A7777] containing 40 µg/ml Congo red dye [VWR ( Radnor , PA ) AAAB24310-14] and 20 µg/ml Coomassie blue dye [VWR EM-3300] ) in a 10 cm x 10 cm x 1 . 5 cm square Petri dish ( LDP [Wayne , NJ] D210-16 ) . For preparation of biofilms grown on on phenazine methosulfate ( PMS ) , colony morphology medium was supplemented with 200 µM PMS ( Amresco [Solon , OH] 0361 ) after autoclaving . For nitrate experiments , colony morphology medium was supplemented with 0 , 10 , or 40 mM potassium nitrate . Plates were incubated for up to 5 days at 25˚C with >90% humidity ( Percival [Perry , IA] CU-22L ) and imaged daily using a VHX-1000 digital microscope ( Keyence , Japan ) . Images shown are representative of at least 10 biological replicates . 3D images of biofilms were taken on day 5 of development using a Keyence VR-3100 wide-area 3D measurement system . ∆cox∆cyo∆cio , hcn deletion mutants , and strains grown for the nitrate experiment were imaged using a flatbed scanner ( Epson [Japan] E11000XL-GA ) and are representative of at least three biological replicates One microliter of overnight cultures ( five biological replicates ) , grown as described above , was spotted onto a 1% tryptone , 1 . 5% agar plate containing 0 . 001% ( w/v ) TTC ( 2 , 3 , 5-triphenyl-tetrazolium chloride [Sigma-Aldrich ( St . Louis , MO ) T8877] ) and incubated in the dark at 25˚C for 24 hr . Spots were imaged using a scanner ( Epson E11000XL-GA ) and TTC reduction , normalized to colony area , was quantified using Adobe Photoshop CS5 ( San Jose , CA ) . Colorless TTC undergoes an irreversible color change to red when reduced . Pixels in the red color range were quantified and normalized to colony area using Photoshop CS5 . ( i ) Overnight precultures were diluted 1:100 ( ∆N1∆N2 , ∆N1∆N2∆N4 , and ∆cco1cco2 were diluted 1:50 ) in 1% tryptone in a clear- flat-bottom polystyrene 96-well plate ( VWR 82050–716 ) and grown for two hours ( OD500nm ≈ 0 . 2 ) . These cultures were then diluted 100-fold in 1% tryptone in a new 96-well plate and incubated at 37°C with continuous shaking on the medium setting in a Synergy 4 plate reader ( BioTek , Winooski , VT ) . Growth was assessed by taking OD readings at 500 nm every 30 min for at least 24 hr . ( ii ) hcn mutants: Overnight precultures were diluted 1:100 ( ∆N1∆N2∆hcn , ∆N1∆N2∆N4∆hcn , and ∆cco1cco2∆hcn were diluted 1:50 ) in MOPS minimal medium ( 50 mM 4-morpholinepropanesulfonic acid ( pH 7 . 2 ) , 43 mM NaCl , 93 mM NH4Cl , 2 . 2 mM KH2PO4 , 1 mM MgSO4•7H2O , 1 µg/ml FeSO4•7H2O , 20 mM sodium succinate hexahydrate ) and grown for 2 . 5 hr until OD at 500 nm ≈ 0 . 1 . These cultures were then diluted 100-fold in MOPS minimal medium in a clear , flat-bottom polystyrene 96-well plate and incubated at 37°C with continuous shaking on the medium setting in a plate reader . Growth was assessed by taking OD readings at 500 nm every 30 min for at least 24 hr . ( iii ) Terminal oxidase reporters: Overnight precultures were grown in biological triplicate; each biological triplicate was grown in technical duplicate . Overnight precultures were diluted 1:100 in 1% tryptone and grown for 2 . 5 hr until OD at 500 nm ≈ 0 . 1 . These cultures were then diluted 100-fold in 1% tryptone in a clear , flat-bottom , polystyrene black 96-well plate ( VWR 82050–756 ) and incubated at 37°C with continuous shaking on the medium setting in a plate reader . Expression of GFP was assessed by taking fluorescence readings at excitation and emission wavelengths of 480 nm and 510 nm , respectively , every hour for 24 hr . Growth was assessed by taking OD readings at 500 nm every 30 min for 24 hr . Growth and RFU values for technical duplicates were averaged to obtain the respective values for each biological replicate . RFU values for a strain without a promoter inserted upstream of the gfp gene ( MCS-gfp ) were considered background and subtracted from the fluorescence values of each reporter . Overnight precultures of fluorescent ( YFP-expressing ) and non-fluorescent strains were diluted 1:100 in LB ( ∆N1∆N2 , ∆N1∆N2∆N4 and ∆cco1cco2 were diluted 1:50 ) and grown to mid-exponential phase ( OD at 500 nm ≈ 0 . 5 ) . Exact OD at 500 nm values were read in a Spectronic 20D+ spectrophotometer ( Thermo Fisher Scientific [Waltham , MA] ) and cultures were adjusted to the same OD . Adjusted cultures were then mixed in a 1:1 ratio of fluorescent:non-fluorescent cells and 10 µl of this mixture were spotted onto colony morphology plates and grown for 3 days as described above . At specified time points , biofilms were collected , suspended in 1 ml of 1% tryptone , and homogenized on the ‘high’ setting in a bead mill homogenizer ( Omni [Kennesaw , GA] Bead Ruptor 12 ) ; day 1 colonies were homogenized for 35 s while days 2 and 3 colonies were homogenized for 99 s . Homogenized cells were serially diluted and 10−6 , 10−7 , and 10−8 dilutions were plated onto 1% tryptone plates and grown overnight at 37°C . Fluorescent colony counts were determined by imaging plates with a Typhoon FLA7000 fluorescent scanner ( GE Healthcare Life Sciences [United Kingdom] ) and percentages of fluorescent vs . non-fluorescent colonies were determined . Translational reporter constructs for the Cco1 , Cco2 , and CcoN4Q4 operons were constructed using primers listed in Table 1 . Respective primers were used to amplify promoter regions ( 500 bp upstream of the operon of interest ) , adding an SpeI digest site to the 5’ end of the promoter and an XhoI digest site to the 3’ end of the promoter . Purified PCR products were digested and ligated into the multiple cloning site ( MCS ) of the pLD2722 vector , upstream of the gfp sequence . Plasmids were transformed into E . coli strain UQ950 , verified by sequencing , and moved into PA14 using biparental conjugation with E . coli strain S17-1 . PA14 single recombinants were selected on M9 minimal medium agar plates ( 47 . 8 mM Na2HPO4•7H2O , 22 mM KH2PO4 , 8 . 6 mM NaCl , 18 . 6 mM NH4Cl , 1 mM MgSO4 , 0 . 1 mM CaCl2 , 20 mM sodium citrate dihydrate , 1 . 5% agar ) containing 100 µg/ml gentamicin . The plasmid backbone was resolved out of PA14 using Flp-FRT recombination by introduction of the pFLP2 plasmid ( Hoang et al . , 1998 ) and selected on M9 minimal medium agar plates containing 300 µg/ml carbenicillin and further on LB agar plates without NaCl and modified to contain 10% sucrose . The presence of gfp in the final clones was confirmed by PCR . Two layers of 1% tryptone with 1% agar were poured to depths of 4 . 5 mm ( bottom ) and 1 . 5 mm ( top ) . Overnight precultures were diluted 1:100 ( ∆N1∆N2 , ∆N1∆N4 , ∆N1∆N2∆N4 , ∆cco1cco2 were diluted 1:50 ) in LB and grown for 2 hr , until early-mid exponential phase . Five to 10 µl of subculture were then spotted onto the top agar layer and colonies were incubated in the dark at 25˚C with >90% humidity ( Percival CU-22L ) and grown for up to 3 days . At specified time points to be prepared for thin sectioning , colonies were covered by a 1 . 5-mm-thick 1% agar layer . Colonies sandwiched between two 1 . 5-mm agar layers were lifted from the bottom layer and soaked for 4 hr in 50 mM L-lysine in phosphate buffered saline ( PBS ) ( pH 7 . 4 ) at 4˚C , then fixed in 4% paraformaldehyde , 50 mM L-lysine , PBS ( pH 7 . 4 ) for 4 hr at 4˚C , then overnight at 37°C . Fixed colonies were washed twice in PBS and dehydrated through a series of ethanol washes ( 25% , 50% , 70% , 95% , 3 × 100% ethanol ) for 60 min each . Colonies were cleared via three 60-min incubations in Histoclear-II ( National Diagnostics [Atlanta , GA] HS-202 ) and infiltrated with wax via two separate washes of 100% Paraplast Xtra paraffin wax ( Thermo Fisher Scientific 50-276-89 ) for 2 hr each at 55˚C , then colonies were allowed to polymerize overnight at 4˚C . Tissue processing was performed using an STP120 Tissue Processor ( Thermo Fisher Scientific 813150 ) . Trimmed blocks were sectioned in 10-µm-thick sections perpendicular to the plane of the colony using an automatic microtome ( Thermo Fisher Scientific 905200ER ) , floated onto water at 45˚C , and collected onto slides . Slides were air-dried overnight , heat-fixed on a hotplate for 1 hr at 45˚C , and rehydrated in the reverse order of processing . Rehydrated colonies were immediately mounted in TRIS-Buffered DAPI:Fluorogel ( Thermo Fisher Scientific 50-246-93 ) and overlaid with a coverslip . Differential interference contrast ( DIC ) and fluorescent confocal images were captured using an LSM700 confocal microscope ( Zeiss , Germany ) . Each strain was prepared in this manner in at least biological triplicates . Colonies were prepared for thin sectioning as described above , but growth medium was supplemented with 40 µg/ml Congo Red dye and 20 µg/ml Coomassie Blue dye . Colony height measurements were obtained from confocal DIC images using Fiji image processing software ( Schindelin et al . , 2012 ) . Two-day-old colonies were prepared for thin sectioning as described above . Rehydrated colonies were post-stained in 100 µg/ml fluorescein-labeled Wisteria floribunda lectin ( Vector Laboratories ( Burlingame , CA ) FL-1351 ) in PBS before being washed twice in PBS , mounted in TRIS-buffered DAPI and overlaid with a coverslip . Fluorescent confocal images were captured using an LSM700 confocal microscope ( Zeiss ) . A 25-µm-tip redox microelectrode and external reference ( Unisense [Denmark] RD-25 and REF-RM ) were used to measure the extracellular redox state of day 2 ( ~48 hr ) biofilms ( grown as for the colony biofilm morphology assays ) . The redox microelectrode measures the tendency of a sample to take up or release electrons relative to the reference electrode , which is immersed in the same medium as the one on which the sample is grown . The redox microelectrode was calibrated according to manufacturer’s instructions using a two-point calibration to 1% quinhydrone in pH 4 buffer and 1% quinhydrone in pH 7 buffer . Redox measurements were taken every 5 µm throughout the depth of the biofilm using a micromanipulator ( Unisense MM33 ) with a measurement time of 3 s and a wait time between measurements of 5 s . Profiles were recorded using a multimeter ( Unisense ) and the SensorTrace Profiling software ( Unisense ) . A 25-µm-tip oxygen microsensor ( Unisense OX-25 ) was used to measure oxygen concentrations within biofilms during the first 2 days of development , grown as described above . For oxygen profiling on 3-day-old colonies ( Figure 4 ) , biofilms were grown as for the thin sectioning analyses . To calibrate the oxygen microsensor , a two-point calibration was used . The oxygen microsensor was calibrated first to atmospheric oxygen using a calibration chamber ( Unisense CAL300 ) containing water continuously bubbled with air . The microsensor was then calibrated to a ‘zero’ point using an anoxic solution of water thoroughly bubbled with N2; to ensure complete removal of all oxygen , N2 was bubbled into the calibration chamber for a minimum of 30 min before calibrating the microsensor to the zero calibration point . Oxygen measurements were then taken throughout the depth of the biofilm using a measurement time of 3 s and a wait time between measurements of 5 s . For 6-hr-old colonies , a step size of 1 µm was used to profile through the entire colony; for 12 hr and 24 hr colonies , 2 µm; for 48 hr colonies , 5 µm . A micromanipulator ( Unisense MM33 ) was used to move the microsensor within the biofilm and profiles were recorded using a multimeter ( Unisense ) and the SensorTrace Profiling software ( Unisense ) . Overnight precultures were diluted 1:10 in LB and spotted onto a 25 mm filter disk ( pore size: 0 . 2 µm; GE Healthcare 110606 ) placed into the center of one 35 × 10 mm round Petri dish ( VWR 25373-041 ) . Colonies were grown for 2 days in the dark at 25˚C with >90% humidity . After 2 days of growth , each colony ( with filter disk ) was lifted off its respective plate and weighed . Excreted phenazines were then extracted from the agar medium overnight in 5 ml of 100% methanol ( in the dark , nutating at room temperature ) . Three hundred µl of this overnight phenazine/methanol extraction were then filtered through a 0 . 22 µm cellulose Spin-X column ( Thermo Fisher Scientific 07-200-386 ) and 200 µl of the flow-through were loaded into an HPLC vial . Phenazines were quantified using high-performance liquid chromatography ( Agilent [Santa Clara , CA] 1100 HPLC System ) as described previously ( Dietrich et al . , 2006a; Sakhtah et al . , 2016 ) . Slow killing assays were performed as described previously ( Tan et al . , 1999; Powell and Ausubel , 2008 ) . Briefly , 10 µl of overnight PA14 cultures ( grown as described above ) were spotted onto slow killing agar plates ( 0 . 3% NaCl , 0 . 35% Bacto-Peptone , 1 mM CaCl2 , 1 mM MgSO4 , 5 µg/ml cholesterol , 25 mM KPO4 , 50 µg/ml FUDR , 1 . 7% agar ) and plates were incubated for 24 hr at 37°C followed by 48 hr at room temperature ( ~23°C ) . Larval stage 4 ( L4 ) nematodes were picked onto the PA14-seeded plates and live/dead worms were counted for up to four days . Each plate was considered a biological replicate and had a starting sample size of 30–35 worms . Data analysis was performed using GraphPad Prism version 7 ( GraphPad Software , La Jolla , CA ) . Values are expressed as mean ±SD . Statistical significance of the data presented was assessed with the two-tailed unpaired Student’s t-test . Values of p≤0 . 05 were considered significant ( *p≤0 . 05; **p≤0 . 01; ***p≤0 . 001; ****p≤0 . 0001 ) . Full statistical reporting for relevant figures can be found in Table 4 . | Bacteria often form communities called biofilms to make them stronger and more ‘invincible’ . However , when these communities become too crowded , oxygen levels can drop , which makes it harder for them to survive . Some types of bacteria , such as Pseudomonas aeruginosa , have found different ways to cope with lower levels of oxygen . For example , they produce enzymes that use oxygen more efficiently or are better at scavenging low concentrations of oxygen . When organisms – including bacteria – produce energy , they break down nutrients into small molecules to extract electrons . These electrons are then transported along their membrane until they reach their final destination – an oxygen molecule . Studies of P . aeruginosa grown in the laboratory have shown that it uses several types of enzymes called terminal oxidases to complete this last electron transfer . The bacterium can also make chemicals that help to shuttle electrons to remote oxygen sources . For example , they can produce compounds called phenazines that can transport electrons and also compensate for low oxygen levels . However , the conditions in biofilms can be very different to those in a laboratory environment , and until now it was not known what role the different oxidases play in biofilm communities , or how phenazines can compensate for low oxygen levels . To investigate this further , Jo et al . studied P . aeruginosa in an artificial biofilm environment and in a nematode worm host . The results showed that a specific part of the terminal oxidases – a protein called CcoN4 – was necessary for P . aeruginosa to grow optimally in both instances . Mutant bacteria that lacked CcoN4 struggled to survive . Moreover , bacteria containing CcoN4 were able to deliver the electrons to phenazines . This suggests that CcoN4 is also needed for phenazines to work properly . This study shows that blocking terminal oxidases that contain CcoN4 can weaken P . aeruginosa and consequently its ability to cause infections . Furthermore , these types of terminal oxidases are only found in bacteria , which makes them attractive targets for potential drugs that would have minimal side effects on the host’s metabolism . P . aeruginosa infections are a leading cause of death for people suffering from cystic fibrosis , a genetic condition that affects the lungs and the digestive system . A better understanding of what makes P . aeruginosa so infectious will help to find new treatments for these patients . | [
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"microbiology",
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] | 2017 | An orphan cbb3-type cytochrome oxidase subunit supports Pseudomonas aeruginosa biofilm growth and virulence |
Many pathogens possess the capacity for sex through outcrossing , despite being able to reproduce also asexually and/or via selfing . Given that sex is assumed to come at a cost , these mixed reproductive strategies typical of pathogens have remained puzzling . While the ecological and evolutionary benefits of outcrossing are theoretically well-supported , support for such benefits in pathogen populations are still scarce . Here , we analyze the epidemiology and genetic structure of natural populations of an obligate fungal pathogen , Podosphaera plantaginis . We find that the opportunities for outcrossing vary spatially . Populations supporting high levels of coinfection –a prerequisite of sex – result in hotspots of novel genetic diversity . Pathogen populations supporting coinfection also have a higher probability of surviving winter . Jointly our results show that outcrossing has direct epidemiological consequences as well as a major impact on pathogen population genetic diversity , thereby providing evidence of ecological and evolutionary benefits of outcrossing in pathogens .
Many pathogens possess the capacity for sex – here defined in its broadest sense as the coming together of genes from different individuals ( Lehtonen and Kokko , 2014 ) – despite being able to reproduce also asexually and/or via selfing . Individuals that undergo sexual reproduction transmit only half their genome per offspring produced in contrast to asexual and selfing individuals ( Lehtonen et al . , 2012 ) and hence , understanding the maintenance of sex is one of the fundamental challenges in evolutionary biology . To counteract this two-fold cost of sex , sexual outcrossing is assumed to provide both ecological and evolutionary advantages ( Otto , 2009 ) . The Red Queen Hypothesis predicts sexual reproduction to be advantageous in the presence of coevolving parasites , as offspring that are genetically different from their parents should have higher fitness than non-sexual offspring ( Bell , 1982; Hamilton , 1980; Lively , 2010 ) . In support of this prediction , empirical studies have demonstrated parasite mediated selection to explain the observed distribution of outcrossing in hosts ( King et al . , 2011; Wilson and Sherman , 2013; Lively , 1987 ) . Just as sexual reproduction is expected to be selected for in hosts to evade parasitism , parasites should equally be under selection to generate novel genetic variation to infect their ever-changing host populations . Indeed , theoretically it has been possible to identify a parameter space where coevolution with the host favors sexual reproduction in the parasite ( Howard and Lively , 2002; Galvani et al . , 2003; Salathé et al . , 2008 ) . However , the empirical evidence for such advantages of sex in parasite populations are still few and conflicting ( Gouyon and de Vienne , 2015 ) . Sexual reproduction may also confer ecological advantages by bridging unfavorable seasons or habitats . In free-living facultatively sexual organisms that alternate between asexual and sexual reproduction , the sexual offspring are often the dormant or dispersing life stages ( Stelzer and Lehtonen , 2016; Simon et al . , 2002 ) . Similarly , in some of the most devastating fungal pathogens of crops , the spores that are produced via outcrossing are also those that are suitable for long-distance dispersal ( Rieux et al . , 2014 ) , or provide means of surviving unfavorable environmental conditions ( Billiard et al . , 2012; Burt , 2000; Saleh et al . , 2012 ) . While sexual offspring do not contribute to current local population growth , the ability to outcross may be a key determinant of both the numeric and genetic composition of the next season’s epidemic ( Penczykowski et al . , 2015; Tack and Laine , 2014 ) . For such strategies where outscrossing is timed with low potential asexual growth , for example due to seasonality , the cost of sex is expected to be reduced ( Gerber et al . , 2018 ) . Homothallic species , where a haploid individual may mate with other haploid individuals of its species , as well as with itself , may be considered a special case of facultative outcrossing . Here , the maintenance of outcrossing is particularly puzzling given that the offspring produced via haploid selfing are expected to yield the same ecological functions as those produced by outcrossing . Nonetheless , there is evidence of high rates of outcrossing in homothallic species ( Billiard et al . , 2012 ) . To date the relevance of these short-term ecological processes in favoring selfing vs . outcrossing in pathogens populations has remained largely unknown . Even when outcrossing is expected to provide short- or long-term advantages , maintenance of selfing may be favored when mate availability is spatially and/or temporally variable ( Jarne and Charlesworth , 1993 ) . For many pathogens , coinfection is the ecological prerequisite of sexual recombination , as outcrossing and hybridization take place during active infection of the same host individual by different strains ( Froissart et al . , 2005 ) . With molecular tools becoming increasingly available for pathogens , we are beginning to unravel the spatio-temporal distribution of coinfection as well as the ecological outcomes , which may range from facilitation to competition ( Tollenaere et al . , 2016 ) . Although coinfections have been widely reported for different pathogens , remarkably little is understood of the determinants of coinfection ( Tollenaere et al . , 2016 ) . Identifying factors that increase the probability of coinfection also shed light on where we expect to see outcrossing in pathogens with mixed mating strategies . Mixed reproductive strategies have been described for a wide range of pathogen species ( Billiard et al . , 2012; Billiard et al . , 2011 ) . However , the sexual stage is methodologically notoriously difficult to study in many pathogens given their microscopic size and the fact that mating types cannot be identified morphologically . Moreover , sexual reproduction may take place inside the host , and may be triggered by specific environmental cues that are difficult to mimic under controlled experimental conditions ( Billiard et al . , 2012; Tack and Laine , 2014 ) . Hence , remarkably little is understood of this critical life-history stage . To understand why outcrossing is broadly maintained in pathogens despite the costs , here we investigate the ecological and genetic consequences of putative outcrossing in a large natural pathogen metapopulation . Our analysis is based on data collected from Podosphaera plantaginis , a specialist powdery mildew fungus naturally infecting Plantago lanceolata . The visually conspicuous symptoms caused by P . plantaginis enable accurate tracking of infection in the wild . Long-term epidemiological data across approximately 4000 local plant populations in the Åland Islands , southwest of Finland , have demonstrated this pathogen to persist as a highly dynamic metapopulation with frequent extinctions and ( re ) colonizations of local populations ( Jousimo et al . , 2014 ) . Overwinter survival of local pathogen populations has proven to be the vulnerable life-history stage of P . plantaginis in Åland with a high fraction of the local pathogen populations going extinct ( Jousimo et al . , 2014 ) . The pathogen survives the winter in resting structures , chasmothecia ( Tack and Laine , 2014 ) . These resting structures are produced through sexual reproduction as the hyphal cells of one ( selfing ) or two strains ( outcrossing ) fuse when infecting the same host plant . The resulting diploid zygote undergoes meiotic division to yield haploid ascospores that develop inside the chasmothecium . At the onset of the growing season these chasmothecia rupture , releasing the ascopores that initiate new infections ( Tollenaere and Laine , 2013 ) . Here , we ( i ) determine how coinfection - the pre-requisite for sexual outcrossing - varies in natural pathogen populations . We then measure whether ( ii ) putative outcrossing ( i . e . coinfection ) is associated with the generation of novel pathogen multilocus genotypes , and ( iii ) increased pathogen population overwintering success ( Jousimo et al . , 2014 ) . We’ve surveyed and sampled all found pathogen populations in the Åland Islands for four consecutive years , and we use Spatial Bayesian models ( Integrated Nested Laplace Approximation; INLA; Lindgren and Rue , 2015 ) to analyse data on disease dynamics and genotypic diversity from the natural metapopulation .
We first quantify how the opportunities for sexual outcrossing – thta is coinfection - vary across hundreds of wild pathogen populations in four consecutive seasons . We sampled 619 , 703 , 693 and 833 populations in 2012–15 for subsequent genotyping ( Table S1 ) . We used a SNP genotyping protocol to estimate the number of multilocus genotypes ( MLGs ) and prevalence of coinfection within pathogen populations ( Tollenaere et al . , 2012 ) . Coinfection proved to be common yet spatially variable across the P . plantaginis metapopulation ( Figure 1A ) ( Susi et al . , 2015 ) . In all years approximately half of the pathogen populations supported at least one coinfected sample , ( 45–58%; Supplementary file 1 ) . We found that coinfection was more likely to be found in larger and more diverse pathogen populations ( Significant positive effect of number of MLGs and infection abundance; Table 1 ) . Connectivity of pathogen populations , which is considered a proxy for gene flow among populations as it is estimated from distances separating local pathogen populations ( Jousimo et al . , 2014 ) , had a positive , albeit not significant , effect on the probability of coinfection ( Table 1 ) . The INLA model we use here , controls for spatio-temporal autocorrelation characteristic of spatial ecological data due to unmeasured variables , thereby providing a conservative estimate of the model parameters as evidenced by model validation checks ( Figure 1—figure supplements 1–5 ) ( Lindgren and Rue , 2015 ) . We then used an Approximate Bayesian Computation ( ABC ) approach to determine whether we detect more coinfection within populations than would be expected based on the number of parasite genotypes and host availability . Our results show that an already infected plant is more likely to be infected by another strain ( Figure 1—figure supplement 6 ) . In other words , coinfections were more common than expected by chance under the assumption that infections by different MLGs are statistically independent . The result was consistent in both years 2012 and 2013 ( posterior probability 0 . 98 and 0 . 988 , respectively; Figure 1—figure supplement 6 ) , with the parameter controlling the prevalence of coinfections , γ , being reliably estimated under different modeling assumptions ( Figure 1—figure supplement 7–8 ) . The detection of novel MLGs in the pathogen metapopulation from one year to another suggests that sexual outcrossing is common for this pathogen . When all located pathogen populations were genotyped , we identified 182 , 189 , and 235 novel MLGs in 2013–2015 , respectively , when compared to MLGs detected the previous year ( Supplementary file 1; Figure 1B ) . The number of new MLGs at the population level increased with the prevalence of coinfection at the end of the previous epidemic season which is the time when outcrossing takes place ( Figure 1C; Table 1 ) . The prevalence of coinfection was a strong predictor of novel MLGs the following year even after controlling for the effects of local pathogen population size , diversity ( number of MLGs ) , pathogen population connectivity ( proxy for gene flow ) as well as spatial and temporal autocorrelation ( Table 1 ) . Using data from the natural pathogen metapopulation , we also found that in those pathogen populations where the prevalence of coinfection is high – and hence where sexual reproduction can take place – the pathogen has a higher survival probability ( Table 1; Figure 2A and B ) . The effect of coinfection on successful overwintering is positive even after controlling for the effects of pathogen population size and diversity , which both increase survival probability . The INLA model also controls for spatial and temporal autocorrelation in these data that may be generated by abiotic variation known to be important for overwintering ecology of this pathogen ( Penczykowski et al . , 2015 ) . Hence , we view this as a conservative estimate of the effect of coinfection on overwintering . Resting spores , which were visually scored in the field ( Tack and Laine , 2014 ) , were produced in nearly all pathogen populations regardless of whether they supported coinfection or not ( 96% vs . 93% , respectively ) .
Here , we report compelling evidence of outcrossing that generates novel genetic diversity in the pathogen metapopulation . Our results demonstrate variation in how opportunities for outcrossing are distributed across space . Finding more coinfection than would be expected by chance is in line with previous fine-scale field sampling of infections and experimental work , which show that hosts already infected with one strain of the pathogen are more likely to become infected by another strain of the same pathogen than uninfected hosts ( Laine , 2011; Susi and Laine , 2017 ) . This may be due to already infected individuals becoming more susceptible to subsequent infection , or due to strains aggregating on those hosts that are the most susceptible genotypes ( Susi and Laine , 2017 ) . Moreover , variation in host density and ( micro ) climatic conditions may be an important driver of infection patters in the wild ( Penczykowski et al . , 2018 ) . Our results do not support the priming hypothesis ( Hilker et al . , 2016 ) , whereby prior attack provides increased protection against later attack . We find that spatial variation in coinfection results in spatially delineated hotspots of novel genetic diversity . The high number of new MLGs detected every year is indicative of outcrossing taking place in this pathogen metapopulation . Although our sampling is likely to miss some rare strains , and novel MLGs may be generated through mutations , these are unlikely to explain the high turnover of MLGs between years we report here . Mainland populations , which are separated by at least 38 km of open water , are also expected to play a negligible role as sources of gene flow given that experimental and field data have confirmed this pathogen to typically disperse short distances ( Jousimo et al . , 2014; Tack et al . , 2014 ) . Our results suggest that sexual outcrossing takes place where there is the opportunity for it , that is in populations where levels of coinfection are high . To date , this phenomenon has only received limited experimentally derived support in pathogens ( Schelkle et al . , 2012 ) . Spatio-temporal variation in outcrossing is expected to have both evolutionary and epidemiological consequences for the pathogen . In the short term , generation of novel genetic diversity may increase transmission across host populations that support considerable resistance diversity both within and among populations ( Jousimo et al . , 2014; Laine et al . , 2011 ) . Novel genetic diversity may also increase the evolutionary potential of pathogens that need to adapt to both biotic and abiotic variation in their environment ( Greischar and Koskella , 2007; Wolinska and King , 2009 ) . Our multi-year census data further revealed the putative outcrossing to yield a benefit that is realized in an ecologically important function – higher overwintering success . Overwintering determines both the genetic and numeric structure of the next epidemic ( Tack and Laine , 2014; Penczykowski et al . , 2015 ) , and hence may be a sufficiently important trait to promote the maintenance of outcrossing in a pathogen that is able to complete its life-cycle also through haploid selfing . Our results suggest that successful overwintering is not due to higher production of resting spores . Hence , there may be a difference in the quality of progeny produced via selfing vs . outcrossing . Prior experimental work has demonstrated significant variation in spore viability in the resting structures of P . plantaginis . There is evidence of higher viability of progeny from coinfections than from single infections , but the strength and direction of this trend is affected by the genotypes of the interacting strains , as well as by temperature ( Vaumourin and Laine , 2018 ) . Despite the higher overwintering success of outcrossed progeny , haploid selfing may be preserved due to the low probability of encountering a suitable mating partner infecting the same host . Moreover , there may be a cost to outcrossing as it breaks up locally adapted pathogen populations by producing novel – and potentially maladapted – genetic variation . Overall , the selection pressures and opportunities to mate vary considerably across space and time , and hence , it is not surprising that many pathogens have evolved highly complex mating strategies ( Billiard et al . , 2011 ) . A loss of sexual reproduction in pathogens has been linked to homogenous habitat ( Saleh et al . , 2012 ) or stable environmental conditions ( Barrett et al . , 2008 ) . Maintaining a mixed mating system may provide a bet-hedging strategy for this pathogen to survive in a fragmented landscape , with a high probability of population extinction during the off-season . It is noteworthy that here we succeeded in identifying predictors of how coinfection is spatially distributed - and hence where hotspots of outcrossing are formed - despite the considerable environmental ‘noise’ this natural system supports . The correlations in field collected data we have observed here are a promising start to uncovering the variable selective pressures and advantages of outcrossing in pathogens . Establishing direct links between variation in reproductive strategies and epidemiological dynamics offers an exciting venue of research , and is needed to truly predict where risks of infection and disease emergence are the highest .
Plantago lanceolata is a perennial rosette-forming herb that is naturally infected by Podosphaera plantaginis ( Castagne; U . Braun and S . Takamatsu ) , a powdery mildew fungus in the order Erysiphales within the Ascomycota . This pathogen is a host-specific obligate biotroph that completes its entire life cycle on the surface of the host plant where it is visible as localized ( nonsystemic ) white powdery lesions . The interaction between P . lanceolata and P . plantaginis functions in a two-step manner typical of many plant–pathogen associations . First , as the pathogen attempts to infect a new host , the interaction is strain specific as a given host genotype expresses resistance against some strains ( recognition ) of the pathogen while being susceptible to others ( nonrecognition ) ( Jones and Dangl , 2006 ) . Once a P . plantaginis strain has successfully established there is still considerable variation in its development that is affected by both pathogen and host genotype ( Laine , 2007 ) . The pathogen is a significant stress factor for its host and may cause host mortality ( Penczykowski et al . , 2015 ) . The locations of P . lanceolata populations have been systematically mapped in the Åland Islands , southwest of Finland , since the 1990s . There are currently c . 4000 known host populations that range in size from a few square meters to several hectares , with a median size of 300 m2 ( Jousimo et al . , 2014 ) . Within host populations , initial pathogen foci are established from resting spores ( chasmothecia ) , or from a spore immigrating into the local population from another population . The first visible signs of infection appear in late June as white-greyish lesions consisting of mycelium supporting spores ( conidia ) are formed . The spores are dispersed by wind to the same or new host individuals . Some six to eight clonally produced generations ( estimated from spore germination-production times observed in the laboratory ) follow one another in quick succession , often leading to a substantial proportion of the host individuals within a population being infected by late summer ( Ovaskainen and Laine , 2006 ) . Resting spores ( chasmothecia ) appear towards the end of the growing season in August–September . Each chasmothecia contains eight ascospores that can each cause a new infection in the spring upon their release . Infected leaves may support hundreds of chasmothecia . In P . plantaginis , chasmothecia production is achieved via both haploid selfing as well as outcrossing between two strains simultaneously infecting the same host plant . Pure strains of P . plantaginis have been shown to carry both MAT1-1-1 and MAT1-2-1 that determine compatibility in several other powdery mildew species ( Tollenaere and Laine , 2013 ) . In early September every year since 2001 , all known P . lanceolata populations have been surveyed for the presence/absence of the powdery mildew ( for details on the survey , please see Jousimo et al . , 2014 ) . These data can be used to identify pathogen populations , which have persisted from one year to the next , newly colonized populations , and populations that have gone extinct . These data have demonstrated that P . plantaginis persists as a highly dynamic metapopulation through extinction and ( re ) colonization of local host populations ( Jousimo et al . , 2014 ) . In 2012–15 nearly all located pathogen populations were sampled for genotyping ( N = 619 , 703 , 693 , and 833 populations , respectively , which represented 96–97% of all located pathogen populations each year; Supplementary file 1 ) . A sample consists of one infected leaf collected from an infected plant , and infected plants were sampled at a minimum distance of five meters between infected plants . The aim was to collect ten samples from each population but in smaller pathogen populations sampling effort needed to be scaled to how much infection was available for sampling ( Please see ‘Pathogen population size’ below in Model variables -section below for a description , and for numbers of samples in each pathogen population size category , please see Figure 1—figure supplement 9 ) . The infected leaves were placed in separate falcon tubes and brought back to the laboratory where fungal material for each sample was collected by scraping off the surface of the infected leaf . This material and a 1 cm2 piece of the same infected leaf were placed in an individual well of a 96-well plate . Samples were stored at −20°C until DNA extraction . DNA extraction was performed using E . Z . N . A . Plant DNA kit ( Omega Bio Tek Inc , Norcross , GA , USA ) at The Institute of Biotechnology ( BI , Helsinki , Finland ) . Samples were genotyped with 27 SNP markers using Sequenom MassARRAY iPLEX platform as described in Tollenaere et al . ( 2012 ) at the Finnish Institute for Molecular Medicine ( FIMM , Helsinki , Finland ) . Automatic calling of the genotypes was performed using MassARRAY Typer four software ( Sequenom , San Diego , CA ) . Because of the presence of null alleles in the studied populations , eight SNP were discarded from the analysis . Allele frequencies are shown in Figure 1—figure supplement 10 . The genotyping results were used to identify the multilocus genotypes ( MLGs ) of each sample and to detect coinfection in the collected samples . Podosphaera plantaginis is haploid , and therefore the detection of a heterozygote genotype for one or more SNP markers is a clear highly repeatable method for calling coinfections ( Tollenaere et al . , 2012; Susi et al . , 2015 ) . The survey data from the natural populations and the genotyping data from years 2012–15 were used to generate the following variables used in the analyses ( See Statistical modeling below ) : We modeled the following events of interest: the effect of the number of coinfections on persistence of infection from one year to the next , and the number of new , previously unidentified , MLGs in a pathogen population that survived from one year to the next . In addition , we assessed the drivers of the presence/absence of resting spores and coinfections among the infected populations . Our models were fitted to data from years 2012-2015 . To control for the possible effect that population size , connectivity and diversity could have on the results , our models included the following predictors ( described above in detail ) : Pathogen population size , host population size , number of distinct pathogen strains , and pathogen connectivity Sip . Predictors with continuous support and the number of observed coinfections , were scaled and centered around zero , and factors transformed into binary 0/1-variables . We take an Approximate Bayesian Computation approach to determine whether there is more co-infection in the pathogen metapopulation than expected by chance . The model does not take into account the spatial structure of the patches , but considers them independent conditional on the model parameters . All data and scripts used to perform the analyses presented in this paper are available in the git repository at https://github . com/ComputerBlue/FungalSex ( Laine , 2019; copy archived at https://github . com/elifesciences-publications/FungalSex ) . | The existence of sex – broadly defined as the coming together of genes from different individuals – is one of the big evolutionary puzzles . Reproduction allows an organism to pass on its genes to future generations . However , while asexual and self-fertilizing individuals transmit all of their genes to their offspring , individuals that reproduce through sex transmit only half of their genome . This is considered the cost of sex . Many pathogens reproduce through sex , despite often also being able to reproduce asexually or by self-fertilization . Typically a pre-requisite of sex in pathogens is for at least two different strains to infect the same host . Aside from this limitation , little is known about when , where and why pathogens have sex . It has been tricky to study due to the microscopic size of pathogens and the difficulties of identifying different sexes . Moreover , sexual reproduction may be triggered by environmental cues that are difficult to mimic under controlled experimental conditions . Are there any benefits associated with pathogen sex ? To find out , Laine et al . analyzed data collected over the course of four years from thousands of populations of a powdery mildew fungus that infected plants across the Åland islands . This revealed that the opportunities for pathogen sex vary in different locations . Areas where multiple strains of the fungus commonly infect the same plants result in hotspots of new genetic diversity . These mixed populations are also more likely to survive winter . This demonstrates the potential for pathogen sexual reproduction to provide an ecological benefit . Identifying areas and populations where pathogens have sex can help to identify when and where new strains are most likely to emerge . In the future , studies that use similar methods to Laine et al . could help to predict where infections and diseases are highly likely to arise . | [
"Abstract",
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"ecology",
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] | 2019 | Variable opportunities for outcrossing result in hotspots of novel genetic variation in a pathogen metapopulation |
We report quantitative label-free imaging with phase and polarization ( QLIPP ) for simultaneous measurement of density , anisotropy , and orientation of structures in unlabeled live cells and tissue slices . We combine QLIPP with deep neural networks to predict fluorescence images of diverse cell and tissue structures . QLIPP images reveal anatomical regions and axon tract orientation in prenatal human brain tissue sections that are not visible using brightfield imaging . We report a variant of U-Net architecture , multi-channel 2 . 5D U-Net , for computationally efficient prediction of fluorescence images in three dimensions and over large fields of view . Further , we develop data normalization methods for accurate prediction of myelin distribution over large brain regions . We show that experimental defects in labeling the human tissue can be rescued with quantitative label-free imaging and neural network model . We anticipate that the proposed method will enable new studies of architectural order at spatial scales ranging from organelles to tissue .
The function of living systems emerges from the interaction of its components over spatial and temporal scales that range many orders of magnitude . Light microscopy is uniquely useful to record dynamic arrangement of molecules within the context of organelles , of organelles within the context of cells , and of cells within the context of tissues . Combination of fluorescence imaging and automated analysis of image content with deep learning ( Moen et al . , 2019; Belthangady and Royer , 2019; Van Valen et al . , 2016 ) has opened new avenues for understanding complex biological processes . However , characterizing the architecture and dynamics with fluorescence remains challenging in many important biological systems . The choice of label can introduce observation bias in the experiment and may perturb the biological process being studied . For example , labeling cytoskeletal polymers often perturbs their native assembly kinetics ( Belin et al . , 2014 ) . Genetic labeling of human tissue and non-model organisms is not straightforward and the labeling efficiency is often low . Labeling with antibodies or dyes can lead to artifacts and requires careful optimization of the labeling protocols . The difficulty of labeling impedes biological discoveries using these systems . By contrast , label-free imaging requires minimal sample preparation as it measures the sample’s intrinsic properties . Lable-free imaging is capable of visualizing many biological structures simultaneously with minimal photo-toxicity and no photo-bleaching , making it particularly suitable for live-cell imaging . Measurements made without label are often more robust since experimental errors associated with the labeling are avoided . Multiplexed imaging with fluorescence and label-free contrasts enables characterization of the dynamics of labeled molecules in the context of organelles or cells . Thus , label-free imaging provides measurements complementary to fluorescence imaging for a broad range of biological studies , from analyzing architecture of archival human tissue to characterizing organelle dynamics in live cells . Classical label-free microscopy techniques such as phase contrast ( Zernike , 1955 ) , differential interference contrast ( DIC ) ( Nomarski , 1955 ) , and polarized light microscopy ( Schmidt , 1926; Inoue , 1953 ) are qualitative . They turn specimen-induced changes in phase ( shape of the wavefront ) and polarization ( the plane of oscillation of the electric field ) of light into intensity modulations that are detectable by a camera . These intensity modulations are related to specimens’ properties via complex non-linear transformation , which makes it difficult to interpret . Computational imaging turns the qualitative intensity modulations into quantitative measurements of specimens’ properties with inverse algorithms based on models of image formation . Quantitative phase imaging ( Popescu et al . , 2006; Waller et al . , 2010; Tian and Waller , 2015 ) measures optical path length , that is , specimen phase , which reports density of the dry mass ( Barer , 1952 ) . Quantitative polarization microscopy in transmission mode reports angular anisotropy of the optical path length , that is , retardance , ( Inoue , 1953; Oldenbourg and Mei , 1995; Mehta et al . , 2013 ) and axis of anisotropy , that is , orientation , without label . Quantitative label-free imaging measures intrinsic properties of the specimen and provides insights into biological processes that may not be obtained with fluorescence imaging . For example , Quantitative phase microscopy ( Park et al . , 2018 ) has been used to analyze membrane mechanics , density of organelles ( Imai et al . , 2017 ) , cell migration , and recently fast propagation of action potential ( Ling et al . , 2019 ) . Similarly , quantitative polarization microscopy has enabled discovery of the dynamic microtubule spindle ( Inoue , 1953; Keefe et al . , 2003 ) , analysis of retrograde flow of F-actin network ( Oldenbourg et al . , 2000 ) , imaging of white matter in adult human brain tissue slices ( Axer et al . , 2011a; Axer et al . , 2011b; Menzel et al . , 2017; Mollink et al . , 2017; Zeineh et al . , 2017; Henssen et al . , 2019 ) , and imaging of activity-dependent structural changes in brain tissue ( Koike-Tani et al . , 2019 ) . Given the complementary information provided by specimen density and anisotropy , a joint imaging of phase and retardance has also been attempted ( Shribak et al . , 2008; Ferrand et al . , 2018; Baroni et al . , 2020 ) . However , current methods for joint imaging of density and anisotropy are limited in throughput due to complexity of acquisition or can only be used for 2D imaging due to the lack of accurate 3D image formation models . We sought to develop a computational imaging method for joint measurements of phase and retardance of live 3D specimens with simpler light path and higher throughput . In comparison to fluorescence measurements that provide molecular specificity , label-free measurements provide physical specificity . Obtaining biological insights from label-free images often requires identifying specific molecular structures . Recently , deep learning has enabled translation of qualitative and quantitative phase images into fluorescence images ( Ounkomol et al . , 2018; Christiansen et al . , 2018; Rivenson et al . , 2018a; Rivenson et al . , 2019; Lee et al . , 2019; Petersen et al . , 2017 ) . Among different neural network architecture , U-Net has been widely applied to image segmentation and translation tasks ( Ronneberger et al . , 2015; Milletari et al . , 2016; Ounkomol et al . , 2018; Lee et al . , 2019 ) . U-Net’s success arises primarily from its ability to exploit image features at multiple spatial scales , and its use of skip connections between the encoding and decoding blocks . The skip connections give decoding blocks access to low-complexity , high-resolution features in the encoding blocks . In image translation , images from different modalities ( label-free vs . fluorescence in our case ) of the same specimen are presented to the neural network model . The neural network model learns the complex transformation from label-free to fluorescence images through the training process . The trained neural network model can predict fluorescence images from label-free images to enable analysis of distribution of a specific molecule . The accuracy with which the molecular structure can be predicted depends not just on the model , but also on the dynamic range and the consistency of the contrast with which the structure is seen in the label-free data . Some of the anisotropic structures are not visible in phase imaging data and therefore cannot be learned from phase imaging data . Reported methods of image translation have not utilized optical anisotropy , which reports important structures such as cell membrane and axon bundles . Furthermore , previous work has mostly demonstrated prediction of single 2D fields of view . Volumetric prediction using 3D U-Net has been reported , but it is computationally expensive , such that downsampling the data at the expense of spatial resolution is required ( Ounkomol et al . , 2018 ) . We sought to improve the accuracy of prediction of fluorescence images by using information contained in complementary measurements of density and anisotropy . In this work , we report a combination of quantitative label-free imaging and deep learning models to identify biological structures from their density and anisotropy . First , we introduce quantitative label-free imaging with phase and polarization ( QLIPP ) that visualizes diverse structures by their phase , retardance , and orientation . QLIPP combines quantitative polarization microscopy ( Oldenbourg and Mei , 1995; Shribak and Oldenbourg , 2003; Mehta et al . , 2013 ) with the concept of phase from defocus ( Streibl , 1984; Waller et al . , 2010; Streibl , 1985; Noda et al . , 1990; Claus et al . , 2015; Jenkins and Gaylord , 2015a; Jenkins and Gaylord , 2015b; Soto et al . , 2017 ) , to establish a novel method for volumetric measurement of phase , retardance , and orientation ( Figure 1A ) . Data generated with QLIPP can distinguish biological structures at multiple spatial and temporal scales , making it valuable for revealing the architecture of the postmortem archival tissue and organelle dynamics in live cells . QLIPP’s optical path is simpler relative to earlier methods ( Shribak et al . , 2008 ) , reconstruction algorithms are more accurate , and reconstruction software is open-source . QLIPP can be implemented on existing microscopes as a module and can be easily multiplexed with fluorescence . To translate 3D distribution of phase , retardance , and orientation to fluorescence intensities , we implement a computationally efficient multi-channel 2 . 5D U-Net architecture ( Figure 1B ) based on a previously reported single-channel 2 . 5D U-Net ( Han , 2017 ) . We use QLIPP for imaging axon tracts and myelination in archival brain tissue sections at two developmental stages . Label-free measurement of anisotropy allowed us to visualize axon orientations across whole sections . We demonstrate that QLIPP data increases accuracy of prediction of myelination in developing human brain as compared to brighfield data . Finally , we demonstrate robustness of the label-free measurements to experimental variations in labeling , which leads to more consistent prediction of myelination than possible with the experimental staining . Collectively , we propose a novel approach for imaging architectural order across multiple biological systems and analyzing it with a judicious combination of physics-driven and data-driven modeling approaches .
The light path of QLIPP is shown in Figure 1A . It is a transmission polarization microscope based on computer controlled liquid crystal universal polarizer ( Oldenbourg and Mei , 1995; Shribak and Oldenbourg , 2003; Mehta et al . , 2013 ) . QLIPP provides an accurate image formation model and corresponding inverse algorithm for simultaneous reconstruction of specimen phase , retardance , and slow axis orientation . In QLIPP , specimens are illuminated with five elliptical polarization states for sensitive detection of specimens’ retardance ( Shribak and Oldenbourg , 2003; Mehta et al . , 2013 ) . For each illumination , we collect a Z-stack of intensity to capture specimens’ phase information . Variations in the density of the specimen , for example lower density of nuclei relative to the cytoplasm , cause changes in refractive index and distort the wavefront of the incident light . The wavefront distortions lead to detectable intensity modulations through interference in 3D space as the light propagates along the optical axis . Intensity modulations caused by isopropic density variations ( specimen phase ) can be captured by acquiring a stack of intensities along the optical ( Z ) axis ( Streibl , 1984; Waller et al . , 2010 ) . Anisotropic variations in the specimens’ density result from alignment of molecules along a preferential axis , for example lipid membrane has higher anisotropy relative to the cytoplasm due to the alignment of lipid molecules . This anisotropic density variation ( specimen retardance ) induces polarization-dependent phase difference . Specimen retardance is often characterized by the axis along which anisotropic material is the densest ( slow-axis ) or by the axis perpendicular to it ( fast-axis ) ( de Campos Vidal et al . , 1980; Salamon and Tollin , 2001 ) , and the difference in specimen phase between these two axes . In addition , multiple scattering by the specimen can reduce degree of polarization of light . The specimen retardance , slow-axis orientation , and degree of polarization can be measured by probing the specimen with light in different polarization states . We develop a forward model of transformation using the formalism of partial polarization and phase transfer function to describe the relation between specimen physical properties and detected intensities . We then leverage above forward model to design an inverse algorithm that reconstructs quantitative specimen physical properties in 3D from the detected intensity modulations as illustrated in Figure 1A . First , we utilize Stokes vector representation of partially polarized light ( Born and Wolf , 2013; Bass et al . , 2009; Azzam , 2016 ) to model the transformation from specimens’ optical properties to acquired intensities ( Equation 7 ) . By inverting this transformation , we reconstruct 3D volumes of retardance , slow-axis orientation , brightfield , and degree of polarization . Proper background correction is crucial for detection of low retardance of the biological structures in the presence of high , non-uniform background resulting from the optics or imaging chamber . We use a two-step background correction method ( Materials and methods ) to correct the non-uniform background polarization ( Figure 2—figure supplement 2 ) . In addition to retardance and slow-axis orientation , our use of Stokes formalism enables reconstruction of brightfield and degree of polarization , in contrast to previous work that reconstructs just retardance and slow-axis orientation ( Shribak and Oldenbourg , 2003; Mehta et al . , 2013 ) . The degree of polarization measures the fitness of our model with the experiment as explained later and the brightfield images enables reconstruction of specimen phase . Second , we utilize phase transfer function formalism ( Streibl , 1985; Noda et al . , 1990; Claus et al . , 2015; Jenkins and Gaylord , 2015a; Jenkins and Gaylord , 2015b; Soto et al . , 2017 ) to model how 3D phase information is transformed into brightfield contrast ( Equation 17 ) . Specimen phase information is encoded in the brightfield images but in a complex fashion . In brightfield images , optically dense structures appear in brighter contrast than the background on one side of the focus , almost no contrast at the focus , and darker contrast than the background on the other side of the focus . This is illustrated by 3D brightfield images of nucleoli , the dense sub-nuclear domains inside nuclei ( Figure 2—video 1 ) . We invert our forward model to estimate specimen phase from 3D brightfield stack ( Equation 19 ) . Phase reconstruction from the brightfield volume shows nucleoli in positive contrast relative to background consistently as the nucleoli move through the focus ( Figure 2—video 1 ) . We note that the two-step background correction is essential for background-free retardance and orientation images , but not for phase image ( Figure 2—figure supplement 2 ) . We illustrate wide applicability of QLIPP with images of human bone osteosarcoma epithelial ( U2OS ) cells , tissue section from adult mouse braintissue section from In the dividing U2OS cell ( Figure 2—video 2 , Figure 2—video 3 ) , the phase image shows three-dimensional dynamics of dense cellular organelles , such as lipid vesicles , nucleoli , and chromosomes . The retardance and slow-axis orientation in U2OS cells ( Figure 2—video 2 , Figure 2—video 3 ) show dynamics of membrane boundaries , spindle , and lipid droplets . We note that the two-step background correction is essential to remove biases in the retardance and orientation images , but not for phase image ( Figure 2—figure supplement 2 ) . Figure 2—video 3 shows that specific organelles can be discerned simply by color-coding the measured phase and retardance , illustrating that quantitative label-free imaging provides specificity to physical properties . At larger spatial scale , the phase image identifies cell bodies and axon tracts in mouse and developing human brain tissue sections because of variations in their density . These density variations are more visible and interpretable in phase image as compared to the brightfield image ( Figure 2—figure supplement 3 ) . Axon tracts appear with noticeably high contrast in retardance and orientation images of mouse and human brain slices ( Figure 2 ) . The high retardance of the axons arises primarily from myelin sheath that has higher density perpendicular to the axon axis ( de Campos Vidal et al . , 1980; Menzel et al . , 2015 ) . Therefore , the slow axis of the axon tracts is perpendicular to the orientation of the tracts . . Figure 2—figure supplement 4 and Figure 5 show stitched retardance and orientation images of a whole mouse brain slice , in which not only the white matter tracts , but also orientation of axons in cortical regions is visible . Note that the fine wavy structure in the right hemisphere of the slice is caused by sample preparation artifacts ( Figure 2—figure supplement 3 ) . We show degree of polarization measurements in ( Figure 2—figure supplement 1 ) . The difference between retardance and degree of polarization is that retardance measures single scattering events within the specimen that alter the polarization of the light , but do not reduce the degree of polarization . On the other hand , low degree of polarization indicates multiple scattering events that reduce the polarization of light and thus mismatch of the specimen optical properties from the model assumptions . In the future , we plan to pursue models that account for diffraction and scattering effects in polarized light microscopy that would enable more precise retrieval of specimen properties . Data reported above illustrate simultaneous and quantitative measurements of density , structural anisotropy , and orientation in 3D biological specimens , for the first time to our knowledge . The Python software for QLIPP reconstruction is available at https://github . com/mehta-lab/reconstruct-order . In the next sections , we discuss how these complementary label-free measurements enable prediction of fluorescence images and analysis of architecture . In contrast to fluorescence imaging , label-free measurement of density and anisotropy visualize several structures simultaneously but individual structures can be difficult to identify . Label-free measurements are affected by the expression of specific molecules , but do not report the expression directly . To obtain images of specific molecular structures from QLIPP data , we optimized convolutional neural network models to translate 3D label-free stacks into 3D fluorescence stacks . Proper prediction of of fluorescent structures with deep learning requires joint optimization of image content , architecture of the neural network , and the training process . The optimization led us to a residual 2 . 5D U-Net that translates a small stack ( 5–7 slices ) of label-free channels to the central slice of fluorescent channel throughout 3D volume . We use images of the mouse kidney tissue section as a test dataset for optimizing the model architecture and training strategies . We chose the mouse kidney tissue section because it has both anisotropic and isotropic structures ( F-actin and nuclei ) . Additionally , both structures are robustly labeled with no noticeable artifacts . Later we demonstrate predicting the fluorescent labels in specimen where labeling is not robust ( Figure 6 ) . Our work builds upon earlier work ( Ounkomol et al . , 2018 ) on predicting fluorescence stacks from brightfield stacks using 3D U-Net . Ounkomol et al . , 2018 showed fluorescence predicted by 3D U-Net is superior than 2D U-Net . However , applying 3D U-Net to microscopy images poses a few limitations . Typical microscopy stacks are bigger in their extent in the focal plane ( ∼2000 × 2000 pixels ) and smaller in extent along the optical axis ( usually <40 Z slices ) . Since the input is isotropically downsampled in the encoding path of the 3D U-Net , it requires sufficiently large number of Z slices to propagate the data through encoding and decoding blocks . As an example , for a minimum of 3 layers in U-Net and 16 pixels at the end of the encoder path , one will need at least 64 Z slices ( Figure 3—figure supplement 1 ) . Therefore , the use of 3D translation models often requires upsampling of the data in Z , which increases data size and makes training 3D translation model computationally expensive . To reduce the computational cost without losing accuracy of prediction , we evaluated the prediction accuracy as a function of model dimensions for a highly ordered , anisotropic structure ( F-actin ) and for less ordered , isotropic structure ( nuclei ) in mouse kidney tissue . In mouse kidney tissue , the retardance image highlights capillaries within glomeruli , and brush borders in convoluted tubules , among other components of the tissue . The nuclei appear in darker contrast in the retardance image , because of the isotropic architecture of chromatin . We evaluated three model architectures to predict fluorescence volumes: slice→slice ( 2D in short ) models that predict 2D fluorescence slices from corresponding 2D label-free slices , stack→slice ( 2 . 5D in short ) models that predict the central 2D fluorescence slice from a stack of adjacent label-free slices , and stack→stack ( 3D in short ) models that predict 3D fluorescent stacks from label-free stacks . For 2 . 5D models , 3D translation is achieved by predicting one 2D fluorescence plane per stack ( z = 3 , 5 , 7 ) of label-free inputs . We added a residual connection between the input and output of each block to speed up model training ( Milletari et al . , 2016; Drozdzal et al . , 2016 ) . In order to fit 3D models on the GPU , we needed to predict overlapping sub-stacks , which were stitched together to get the whole 3D stack ( see Materials and methods and Figure 3—figure supplement 1 for the description of the network architecture and training process ) . We used Pearson correlation coefficient and structural similarity index ( SSIM ) ( Wang and Bovik , 2009 ) between predicted fluorescent stacks and target fluorescent stacks to evaluate the performance of the models ( Materials and methods ) . We report these metrics on the test set ( Table 1 , Table 2 , Table 3 ) , which was not used during the training . The predictions with 2D models show discontinuity artifacts along the depth ( Figure 3 , Figure 3—video 2 ) , as also observed in prior work ( Ounkomol et al . , 2018 ) . The 3D model predicts smoother structures along the Z dimension with improved prediction in the XY plane . 2 . 5D model shows prediction accuracy comparable to 3D model , with higher prediction accuracy as the number of z-slices in the 2 . 5D model input increases . ( Figure 3C and D; Table 1; Figure 3—video 2 ) . While 2 . 5D model shows similar performance to 3D model , we note that we could train the 2 . 5D model with ∼3× more parameters than 3D model ( Materials and methods ) in shorter time . In our experiments , training a 3D model with 1 . 5M parameters required 3 . 2 days , training a 2D model with 2M parameters required 6 hr , and training a 2 . 5D model with 4 . 8M parameters and five input z-slices required 2 days , using ∼100 training volumes . This is because the large memory usage of 3D model significantly limits its training batch size and thus the training speed . The Python code for training our variants of image translation models is available at https://github . com/czbiohub/microDL . Considering the trade-off between computation speed and model performance , we adopt 2 . 5D models with five input Z-slices to explore how combinations of label-free inputs affect the accuracy of prediction of fluorescent structures . We found that when multiple label-free measurements are jointly used as inputs , both F-actin and nuclei are predicted with higher fidelity compared to when only a single label-free measurement is used as the input ( Table 2 and Table 3 ) . Figure 4C–D shows representative structural differences in the predictions of the same glomerulus as Figure 3 . The continuity of prediction along Z-axis improves as more label-free contrasts are used for prediction ( Figure 4—video 1 ) . These results indicate that our model leverages information in complementary physical properties to predict target structures . We note that using complementary label-free contrasts boosts the performance of 2 . 5D models to exceed the performance of 3D single-channel models without significantly increasing the computation cost ( compare Table 1 and Table 2 ) . Noticeably , fine F-actin bundles have been shown challenging to predict from single label-free input . We found fine F-actin bundles can be predicted from multiple label-free inputs when the model is trained to minimize the difference between the fluorescence target and prediction over only the foreground pixels in the image ( Figure 4—figure supplement 2 ) . Interestingly , when only a single contrast is provided as the input , a model trained on phase images has higher prediction accuracy than the model trained on brightfield images . This is possibly because the phase image has consistent , quantitative contrast along z-axis , while the depth-dependent contrast in brightfield images makes the translation task more challenging . This improvement of using phase over brightfield images , however , is not observed when the retardance and orientation are also included as inputs . This is possibly because quantitative retardance and orientation complement the qualitative brightfield input and simplify the translation task . In conclusion , above results show that 2 . 5D multi-contrast models predict 3D structures with superior accuracy than single channel 3D U-Net models , but have multiple practical advantages that facilitate scaling of the approach . In addition , the results show that structures of varying density and order can be learned with higher accuracy when complementary physical properties are combined as inputs . Among electron microscopy , light microscopy , and magnetic resonance based imaging of brain architecture , the resolution and throughput of light-microscopy provides the ability to image whole brain slices at single axon resolution in a reasonable time ( Kleinfeld et al . , 2011; Axer et al . , 2011a; Axer et al . , 2011b; Menzel et al . , 2017; Mollink et al . , 2017; Zeineh et al . , 2017; Henssen et al . , 2019 ) . Light-microscopy is also suitable for imaging biological processes while brain tissue is kept alive ( Ohki et al . , 2005; Koike-Tani et al . , 2019 ) . With quantitative imaging of brain architecture and activity at light resolution , one can envision the possibility of building probabilistic models that relate connectivity and function . QLIPP’s high-resolution , quantitative nature , sensitivity to low anisotropy of gray matter ( Figure 2 ) , and throughput make it attractive for imaging the architecture and activity in brain slices . Here , we explore how QLIPP can be used to visualize the architecture of the sections of adult mouse brain and archival sections of prenatal human brain . We first imaged an adult mouse brain tissue section located at bregma −1 . 355 mm ( level 68 in Allen brain reference atlas [Lein et al . , 2007] ) with QLIPP and rendered retardance and slow-axis orientation in two ways as shown in Figure 5 . The left panel renders the measured retardance in brightness and slow-axis orientation in color , highlighting anatomical features of all sizes . The right panel renders the fast-axis orientation of the mouse brain section ( orthogonal to the slow-axis orientation ) as colored lines . It has been shown ( de Campos Vidal et al . , 1980; Menzel et al . , 2015 ) that when axons are myelinated , the slow axis is perpendicular to the axon axis , while the fast axis is parallel to it . The visualization in the right panel highlights meso-scale axon orientation in the mouse brain tissue with spatial resolution of ∼ 100 μm , that is , each line represents net orientation of the tissue over the area of ∼ 100 μm × 100 μm . The full section rendered with both approaches is shown in Figure 5—figure supplement 1 . By comparing the size and optical measurements in our label-free images against Allen brain reference atlas , we are able to recognize many anatomical landmarks . For example , the corpus callosum ( cc ) traversing the left and right hemispheres of the brain is a highly anisotropic bundle of axons . The cortex ( CTX ) is the outermost region of the brain , with axons projecting down towards the corpus callosum and other sub-cortical structures . Within the inner periphery of the corpus callosum , we can identify several more structures such as hippocampus ( HPF ) , lateral ventricle ( VL ) , and caudoputamen ( CP ) . With these evident anatomical landmarks , we are able to reference to Allen brain reference atlas ( Lein et al . , 2007 ) and label more anatomical areas of the brain such as the sensory ( SSp , SSs ) and motor ( MOp , MOs ) cortical areas . We also found that six cortical layers are distinguishable in terms of strength of the retardance signal and the orientational pattern . These data are consistent with reports that layer I contains axon bundles parallel to the cortical layer ( Zilles et al . , 2016 ) . Layer VI contains axon bundles that feed to and from the corpus callosum , so the orientation of the axon is not as orthogonal to the cortical layers as the axons in the other layers . The retardance signal arises from the collective anisotropy of myelin sheath wrapping around axons . Layers IV and V contain higher density of cell bodies and correspondingly lower density of the axons , leading to lower signal in retardance . We next imaged brain sections from developing human samples of two different ages , gestational week 24 ( GW 24 ) ( Figure 6A–C , Figure 6—figure supplement 1A ) and GW20 ( Figure 6D–F , Figure 6—figure supplement 1A ) which correspond to the earliest stages of oligodendrocyte maturation and early myelination in the cerebral cortex ( Jakovcevski et al . , 2009; Miller et al . , 2012; Snaidero and Simons , 2014 ) . Similar to the observations in the mouse brain section ( Figure 5 , Figure 2—figure supplement 4 ) , the stitched retardance and orientation images show both morphology and orientation of the axon tracts that are not accessible with brightfield or phase imaging , with fast axis orientation parallel to the axon axis . The retardance in subplate is higher than cortical plates at both time points , which is consistent with the reduced myelin density in the cortical plate relative to the white matter . Importantly , with our calibration and background correction procedures ( Materials and methods ) , our imaging approach has the sensitivity to detect axon orientation in the developing cortical plate , despite the lower retardance in developing brain compared to adult brain due to the low myelination in early brain development ( Miller et al . , 2012; Snaidero and Simons , 2014 ) . Different cortical layers are visible in the retardance and orientation images at both time points . With this approach , we could identify different anatomical structures in the developing human brain without additional stains by referencing to the developing human brain atlas ( Bayer and Altman , 2003 , Figure 6 ) . The individual axon tracts are also visible in phase image while with lower contrast as the phase image measures the density variation but not the axon orientation . To analyze the variations in the density of the human brain tissue , we reconstructed 2D phase , unlike 3D phase reconstruction for U2OS cells ( Figure 2 ) and kidney tissue ( Figure 4 ) . The archival tissue was thinner ( 12 μm thick ) than the depth of field ( ∼16 μm ) of the low magnification objective ( 10X ) we used for imaging large areas . Figure 6B , C , E and F show the retardance , slow-axis orientation , axon orientation , brightfield , and phase images . Major regions such as the subplate and cortical plate can be identified in both samples . While density information represented by brightfield and phase images can identify some of the anatomical structures , axon-specific structures can be better identified with measurements of anisotropy . To our knowledge , the above data are the first report of label-free imaging of architecture and axon tract orientation in prenatal brain tissue . The ability to resolve axon orientation in the cortical plate of the developing brain , which exhibits very low retardance , demonstrates the sensitivity and resolution of our approach . Next , we explore how information in the phase and retardance measurements can be used to predict myelination in prenatal human brain . The human brain undergoes rapid myelination during late development as measured with magnetic resonance imaging ( MRI ) ( Heath et al . , 2018 ) . Interpretation of the myelination from MRI contrast requires establishing its correlation with histological measurements of myelin levels ( Khodanovich et al . , 2019 ) . Robust measurements of myelination in postmortem human brains can provide new insights in myelination of human brain during development and during degeneration . QLIPP data in Figure 6 indicate that label-free measurements are predictive of the level of myelination but relationship among them is complex ( Figure 7C and F ) . We employed our multi-channel 2D and 2 . 5D U-Net models to learn the complex transformation from label-free contrasts to myelination . Importantly , we developed a data normalization and training strategy that enables prediction of myelination across large slices and multiple developmental time points . We also found that a properly trained model can rescue inconsistencies in fluorescent labeling of myelin , which is often used as histological groundtruth . In order to train the model , we measured the level of myelination with FluoroMyelin , a lipophilic dye that can stain myelin without permeabilization ( Monsma and Brown , 2012 ) . We found the detergents used in most permeabilization protocols remove myelin from the tissue and affect our label-free measurements . We trained multi-contrast 2D and 2 . 5D models with different combinations of label-free input contrasts and FluoroMyelin as the target to predict . To avoid overfitting and build a model that generalizes to different developmental ages and different types of sections of the brain , we pooled imaging datasets from GW20 and GW24 with two different brain sections for each age . The pooled dataset was then split into training , validation , and test set . Similar to the observations in the mouse kidney tissue , the prediction accuracy improves as more label-free contrasts are included in training but with higher accuracy gain compared to the mouse kidney tissue . This is most likely because the additional information provided by adding more label-free channels is more informative for the model to predict the more complex and variable of human brain structures . On the other hand , 2 . 5D model with all four input channels shows similar performance as 2D model for this dataset due to the relatively large depth of field ( ∼16 μm ) compared to the sample thickness ( 12 μm thick ) , so additional Z-slices only provide phase information but no extra structural information along the z dimension . ( Table 4 ) . To test the accuracy of prediction over large human brain slices that span multiple fields of view , we predicted FluoroMyelin using label-free images of whole sections from GW24 and GW20 brains that were not used for model training or validation . We ran model inference on each field of view and then stitched the predicted images together to obtain a stitched prediction with 20 , 000 × 20 , 000 pixels ( Figure 7A and D ) . To the best of our knowledge , these are the largest predicted fluorescence image of tissue sections that have been generated . We were able to predict myelination level in sections from both time points with a single model , with increasing accuracy as we included more label-free channels as the input , similar to our observations from the test dataset of the mouse kidney slice ( Table 4 and Figure 7B and E ) . The scatter plots of pixel intensities show that model-predicted FluoroMyelin intensities correlate with the target FluoroMyelin stain significantly better than the label-free contrasts alone ( Figure 7C and Figure 7F ) . This illustrates the value of predicting fluorescence from label-free contrasts: while the label-free contrasts are predictive of FluoroMyelin stain , the complex relations between them makes estimation of myelin level from label-free contrasts challenging . The neural network can learn the complex transformation from label-free contrasts to FluoroMyelin stain and enables reliable estimation of myelin levels . In addition to architecture , it is essential to devise proper image normalization for correctly predicting the intensity across different fields of view in large stitched images . We found that per-image normalization commonly applied to image segmentation tasks did not preserve the intensity variation across images and led to artifacts in prediction . The two main issues that need to be accounted for in image translation tasks are: ( 1 ) numbers of background pixels vary across images and can bias the normalization parameters if not excluded from normalization ( Yang et al . , 2019 ) , and ( 2 ) there are batch variations in the staining and imaging process when pooling multiple datasets together for training . While batch variation is less pronounced in quantitative label-free imaging , it remains quite significant in fluorescence images of stained samples and therefore needs to be corrected . We found that normalizing per-dataset with the median of inter-quartile range of foreground pixel intensities gives the most accurate intensity prediction ( Figure 7—figure supplement 1 ) . Notably , the 2D model with phase , retardance , and orientation as the input has correlation and similarity scores close to the best 2 . 5D model but the training takes just 3 . 7 hr to converge , while the best 2 . 5D model takes 64 . 7 hr to converge ( Table 4 ) . This is likely because the 2D phase reconstruction captures the density variation encoded in the brightfield Z-stack that is informative for the model to predict axon tracts accurately . Robust fluorescent labeling usually requires optimization of labeling protocols and precise control of labeling conditions . Sub-optimal staining protocols often lead to staining artifacts and make the samples unusable . Quantitative label-free imaging , on the other hand , provides more robust measurements as it generates contrast in physical units and does not require labeling . Therefore , fluorescence images predicted from quantitative label-free inputs are more robust to experimental variations . For example , we found FluoroMyelin stain intensity faded unevenly over time and formed dark patches in the images ( indicated by cyan arrow heads in Figure 7A and D ) , possibly due to quenching of FluoroMyelin by the antifade chemical in the mounting media . However , this quenching of dye does not affect the physical properties measured by the label-free channels . Therefore , the model trained on images without artifacts predicted the expected staining pattern even with the failure of experimental stain . This robustness is particularly valuable for precious tissue specimens such as archival prenatal human brain tissue .
In summary , we report reconstruction of specimen density and anisotropy using quantitative label-free imaging with phase and polarization ( QLIPP ) and prediction of fluorescence distribution from label-free images using deep convolutional neural networks . Our reconstruction algorithms ( https://github . com/mehta-lab/reconstruct-order ) and computationally efficient U-Net variants ( https://github . com/czbiohub/microDL ) facilitate measurement and interpretation of physical properties of the specimens . We reported joint measurement of phase , retardance , and orientation with diffraction-limited spatial resolution in 3D dividing cells and in 2D brain tissue slices . We demonstrated visualization of diverse biological structures: axon tracts and myelination in mouse and human brain slices , and multiple organelles in cells . We demonstrated accurate prediction of fluorescent images from density and anisotropy with multi-contrast 2 . 5D U-Net model . We demonstrated strategies for accurate prediction myelination in centimeter-scale prenatal human brain tissue slices . We showed that inconsistent labeling of human tissue can be rescued with qualitative label-free imaging and trained models . We anticipate that our approach will enable quantitative label-free analysis of architectural order at multiple spatial and temporal scales , particularly in live cells and clinically-relevant tissues .
We describe dependence of the polarization resolved images on the specimen properties using Stokes vector representation of partially polarized light ( Bass et al . , 2009 , Ch . 15 ) . This representation allows us to accurately measure the polarization sensitive contrast in the imaging volume . First , we retrieve the coefficients of the specimen’s Mueller matrix that report linear retardance , slow-axis orientation , transmission ( brightfield ) , and degree of polarization . For brevity , we call them ‘Mueller coefficients’ of the specimen in this paper . Mueller coefficients are recovered from the polarization-resolved intensities using the inverse of an instrument matrix that captures how Mueller coefficients are related to acquired intensities . Assuming that the specimen is mostly transparent , more specifically satisfies the first Born approximation ( Born and Wolf , 2013 ) , we reconstruct specimen phase , retardance , slow axis , and degree of polarization stacks from stacks of Mueller coefficients . The assumption of transparency is generally valid for the structures we are interested in , but does not necessarily hold when the specimen exhibits significant absorption or diattenuation . To ensure that the inverse computation is robust , we need to make judicious decisions about the light path , calibration procedure , and background estimation . A key advantage of Stokes instrument matrix approach is that it easily generalizes to other polarization diverse imaging methods - A polarized light microscope is represented directly by a calibrated instrument matrix . For sensitive detection of retardance , it is advantageous to suppress isotropic background by illuminating the specimen with elliptically polarized light of opposite handedness to the analyzer in the detector side ( Shribak and Oldenbourg , 2003 ) . For experiments reported in this paper , we acquired data by illuminating the specimen sequentially with right-handed circular and elliptical polarized light and analyzed the transmitted left-handed circular polarized light in detection . We assume a weakly scattering specimen modeled by properties of linear retardance ρ , orientation of the slow axis ω , transmission t , and depolarization p . The Mueller matrix of the specimen can be expressed as a product of two Mueller matrices , 𝐌t , accounting for the effect of transmission and depolarization from the specimen , and 𝐌r , accounting for the effect of retardance and orientation of the specimen . The expression of 𝐌r is a standard Mueller matrix of a linear retarder that can be found in Bass et al . , 2009 , Ch . 14 , and 𝐌t is expressed as ( 1 ) 𝐌t=[t0000tp0000tp0000tp] . With 𝐌t and 𝐌r , the Mueller matrix of the specimen is then given by ( 2 ) 𝐌sm=𝐌t⋅𝐌r=[m00000**m10**m20-m1-m2m3] , where * signs denote irrelevant entries that cannot be retrieved under our experiment scheme . The relevant entries that are retrievable can be expressed as a vector of Mueller coefficients , which is ( 3 ) 𝐦=[m0m1m2m3]=[ttpsin2ωsinρ-tpcos2ωsinρtpcosρ] This vector is coincidentally the Stokes vector when right-handed circularly polarized light passing through the specimen . The aim of the measurement we describe in the following paragraphs is to accurately measure these Mueller coefficients at each point in the image plane of the microscope by illuminating the specimen and detecting the scattered light with mutually independent polarization states . Once a map of these Mueller coefficients has been acquired with high accuracy , the specimen properties can be retrieved from the above set of equations . To acquire the above Mueller coefficients , we illuminate the specimen with a series of right-handed circularly and elliptically polarized light ( Shribak and Oldenbourg , 2003 ) . The Stokes vectors of our sequential illumination states are given by , ( 4 ) Si=[1001]i=RCP , [1sinχ0cosχ]i=0 , [1−sinχ0cosχ]i=45 , [10sinχcosχ]i=90 , [10−sinχcosχ]i=135where χ is the compensatory retardance controlled by the LC that determines the ellipticity of the four elliptical polarization states . After our controlled polarized illumination has passed through the specimen , we detect the left-handed circular polarized light by having a left-handed circular analyzer in front of our sensor . We express the Stokes vector before the sensor as ( 5 ) 𝐒sensor , i=𝐌LCA𝐌sm𝐒i , where i={RCP , 0 , 45 , 90 , 135} depending on the illumination states , and 𝐌LCA is the Muller matrix of a left-handed circular analyzer ( Bass et al . , 2009 , Ch . 14 ) . The detected intensity images are the first component of Stokes vector at the sensor under different illuminations ( Ii=[𝐒sensor , i]0 ) . Stacking the measured intensity images to form a vector ( 6 ) 𝐈=[IRCPI0I45I90I135] , we can link the relationship between the measured intensity and the specimen vector through an ‘instrument matrix’ 𝐀 as ( 7 ) 𝐈=𝐀𝐦 , where ( 8 ) 𝐀=[100-11sinχ0-cosχ10sinχ-cosχ1-sinχ0-cosχ10-sinχ-cosχ] . Each row of the instrument matrix is determined by the interaction between various illumination polarization states and the specimen’s properties . Any polarization-resolved measurement scheme can be characterized by an instrument matrix that transforms specimen’s polarization property to the measured intensities . Calibration of the polarization imaging system is then done through calibrating this instrument matrix . Once the instrument matrix has been experimentally calibrated , the vector of Mueller coefficients can be obtained from recorded intensities using its inverse ( compare Equation 7 ) , ( 9 ) 𝐦=𝐀-1𝐈 , We retrieved the vector of Mueller coefficients , 𝐦 , by solving Equation 9 . Slight strain or misalignment in the optical components or the specimen chamber can lead to background that masks out contrast from the specimen . The background typically varies slowly across the field of view and can introduce spurious correlations in the measurement . It is crucial to correct the vector of Mueller coefficients for non-uniform background retardance that was not accounted for by the calibration process . To correct the non-uniform background retardance , we acquired background polarization images at the empty region of the specimen . We then transformed specimen ( i=sm ) and background ( i=bg ) vectors of Mueller coefficients as follows , ( 10 ) m1¯i=m1i/m3i , m2¯i=m2i/m3i , DOPi= ( m1i ) 2+ ( m2i ) 2+ ( m3i ) 2m0i , We then reconstructed the background corrected properties of the specimen: brightfield ( BF ) , retardance ( ρ ) , slow axis ( ω ) , and degree of polarization ( DOP ) from the transformed specimen and background vectors of Mueller coefficients 𝐦¯sm and 𝐦¯bg using the following equations: ( 11 ) m1¯=m1¯sm−m1¯bg ( 12 ) m2¯=m2¯sm−m2¯bg ( 13 ) BF=m0sm/m0bg ( 14 ) ρ=arctan2 ( m1¯2+m2¯2 ) ( 15 ) ω=12arctan2 ( m1¯−m2¯ ) ( 16 ) DOP=DOPsm/DOPbg When the background cannot be completely removed using the above background correction strategy with a single background measurement , ( i . e . the specimen has spatially varying background retardance ) , we applied a second round of background correction on the measurements . In this second round , we estimated the residual transformed background Mueller coefficients by fitting a low-order 2D polynomial surface to the transformed specimen Mueller coefficients . Specifically , we downsampled each 2048 × 2048 image to 64 × 64 image with 32 × 32 binning . We took the median of each 32 × 32 bin to be each pixel value in the downsampled image . We then fitted a second-order 2D polynomial surface to the downsampled image of each transformed specimen Mueller coefficient to estimate the residual background . With this newly estimated background , we performed another background correction . The effects of two rounds of the background corrections are shown in Figure 2—figure supplement 2 . As seen from Equation 3 , the first component in the vector of Mueller coefficients , m0 , is equal to the total transmitted intensity of electric field in the focal plane . Assuming a specimen with weak absorption , the intensity variations in a Z-stack encode the phase information via the transport of intensity ( TIE ) equation ( Streibl , 1984 ) . In the following , we leverage weak object transfer function ( WOTF ) formalism ( Streibl , 1985; Noda et al . , 1990; Claus et al . , 2015; Jenkins and Gaylord , 2015a; Jenkins and Gaylord , 2015b; Soto et al . , 2017 ) to retrieve 2D and 3D phase from this TIE phase contrast and describe the corresponding inverse algorithm . The linear relationship between the 3D phase and the through focus brightfield intensity was established in Streibl , 1985 with Born approximation and weak object approximation . In our context , we reformulated as ( Streibl , 1985; Noda et al . , 1990; Soto et al . , 2017 ) ( 17 ) m0 ( 𝐫 ) =m0 , dc+ϕ ( 𝐫 ) ⊗𝐫hϕ ( 𝐫 ) +μ ( 𝐫 ) ⊗𝐫hμ ( 𝐫 ) , where 𝐫= ( 𝐫⟂ , z ) = ( x , y , z ) is the 3D spatial coordinate vector , m0 , dc is the constant background of m0 component , ⊗𝐫 denotes convolution operation over 𝐫 coordinate , Φ refers to phase , μ refers to absorption , hϕ ( 𝐫 ) is the phase point spread function ( PSF ) , and hμ ( 𝐫 ) is the absorption PSF . Strictly , Φ and μ are the real and imaginary part of the scattering potential scaled by Δz/2k , where Δz is the axial pixel size of the experiment and k is the wavenumber of the incident light . When the refractive index of the specimen and that of the environment are close , the real and imaginary scaled scattering potential reduce to two real quantity , phase and absorption . When specimen’s thickness is larger than the depth of field of the microscope ( usually in experiments with high NA objective ) , the brightfield intensity stack contains 3D information of specimen’s phase and absorption . Without making more assumptions or taking more data , solving 3D phase and absorption from 3D brightfield is ill-posed because we are solving two unknowns from one measurement . Assuming the absorption of the specimen is negligible ( Noda et al . , 1990; Jenkins and Gaylord , 2015b; Soto et al . , 2017 ) , which generally applies to transparent biological specimens , we turn this problem into a linear deconvolution problem , where 3D phase is retrieved . When specimen’s thickness is smaller than the depth of field of the microscope ( usually in experiments with low NA objective ) , the whole 3D intensity stack is coming from merely one effective 2D absorption and phase layer of specimen . We rewrite Equation 17 as ( Claus et al . , 2015; Jenkins and Gaylord , 2015a ) ( 18 ) m0 ( r ) =m0 , dc+ϕ ( r⊥ ) ⊗r⊥hϕ ( r⊥ , z ) +μ ( r⊥ ) ⊗r⊥hμ ( r⊥ , z ) . In this situation , we have multiple 2D defocused measurements to solve for one layer of 2D absorption and phase of the specimen . With the linear relationship between the first component of the Mueller coefficients vector and the phase , we then formulated the inverse problem to retrieve 2D and 3D phase of the specimen . When we recognize the specimen as a 3D specimen , we then use Equation 17 and drop the absorption term to estimate specimen’s 3D phase through the following optimization algorithm: ( 19 ) minϕ ( 𝐫 ) ∑𝐫|m0′ ( 𝐫 ) -ϕ ( 𝐫 ) ⊗𝐫hϕ ( 𝐫 ) |2+τϕReg ( ϕ ( 𝐫 ) ) , where m0′ ( 𝐫 ) =m0 ( 𝐫 ) -m0 , dc , τϕ is the regularization parameter for applying different degree of denoising effect , and the regularization term depending on the choice of either Tikhonov or anisotropic total variation ( TV ) denoiser is expressedReg ( ϕ ( r ) ) ={∑r|ϕ ( r ) |2 , Tikhonov∑r∑i=x , y , z|∂iϕ ( r ) | , TV When using Tikhonov regularization , this optimization problem has an analytic solution that has previously described by Noda et al . , 1990; Jenkins and Gaylord , 2015b; Soto et al . , 2017 . As for TV regularization , we adopted alternating minimization algorithm that is proposed and applied to phase imaging in Wang et al . , 2008 and Chen et al . , 2018 , respectively , to solve the problem . If we consider the specimen as a 2D specimen , we then turn Equation 18 into the following optimization problem: ( 20 ) minϕ , μ ( r⊥ ) ∑r|m0′ ( r ) −ϕ ( r⊥ ) ⊗r⊥hϕ ( r⊥ , z ) −μ ( r⊥ ) ⊗r⊥hμ ( r⊥ , z ) |2+τϕReg ( ϕ ( r⊥ ) ) +τμReg ( μ ( r⊥ ) ) , where we have an extra regularization parameter τμ here for the absorption . When Tikhonov regularization is selected , the analytic solution similar to the one described in Chen et al . , 2016 is adopted . When the signal to noise ratio of the brightfield stack is high , Tikhonov regularization gives satisfactory reconstruction in a single step with computation time proportional to the size of the image stack . However , when the noise is high , Tikhonov regularization can lead to high- to medium-frequency artifacts . Using iterative TV denoising algorithm , we can trade-off reconstruction speed with robustness to noise . Mouse kidney tissue slices were purchased ( Thermo-Fisher Scientific ) . In the mouse kidney tissue slice , F-actin was labeled with Alexa Fluor 568 phalloidin and nuclei was labeled with DAPI . U2OS cells were seeded and cultured in a chamber made of two strain-free coverslips that allowed for gas exchange . The mice were anesthetized by inhalation of isoflurane in a chemical fume hood and then perfused with 25 ml phosphate-buffered saline ( PBS ) into the left cardiac ventricle and subsequently with 25 ml of 4% paraformaldehyde ( PFA ) in the PBS solution . Thereafter , the brains were post-fixed with 4% PFA for 12–16 hr and then transferred to 30% sucrose solution at the temperature of 4°C for 2–3 days until the tissue sank to the bottom of the container . Then , the brains were embedded in a tissue freezing medium ( Tissue-Tek O . C . T compound 4583 , Sakura ) and kept at the temperature of −80°C . Cryostat-microtome ( Leica CM 1850 , Huston TX ) was used for preparing the tissue sections ( 12 and 50 µm ) at the temperature of −20°C and the slides were stored at the temperature of −20°C until use . In order to analyze myelination with QLIPP , the OCT on the slides were melted by keeping the slides at 37°C for 15–30 min . Then , the slides were washed in PBST ( PBS+Tween-20 [0 . 1%] ) for five minutes and then washed in PBS for five minutes and coversliped by mounting media ( F4680 , FluromountTM aqueousm sigma ) . De-identified brain tissue samples were received with patient consent in accordance with a protocol number approved by the Human Gamete , Embryo , and Stem Cell Research Committee ( institutional review board ) at the University of California , San Francisco . Human prenatal brain samples were fixed with 4% paraformaldehye in phosphate-buffered solution ( PBS ) overnight , then rinsed with PBS , dehydrated in 30% sucrose/OCT compound ( Agar Scientific ) at 4°C overnight , then frozen in OCT at −80°C . Frozen samples were sectioned at 12 μm and mounted on microscope slides . Sections were stained directly with red FluoroMyelin ( Thermo-Fisher Scientific , 1:300 in PBS ) for 20 min at room temperature , rinsed three times with PBS for 10 min each , then mounted with ProLong Gold antifade ( Invitrogen ) with a coverslip . We implemented LC-PolScope on a Leica DMi8 inverted microscope with Andor Dragonfly confocal for multiplexed acquisition of polarization-resolved images and fluorescence images . We automated the acquisition using Micro-Manager v1 . 4 . 22 and OpenPolScope plugin for Micro-Manager that controls liquid crystal universal polarizer ( custom device from Meadowlark Optics , specifications available upon request ) . We multiplexed the acquisition of label-free and fluorescence volumes . The volumes were registered using transformation matrices computed from similarly acquired multiplexed volumes of 3D matrix of rings from the ARGO-SIM test target ( Argolight ) . In transmitted light microscope , the resolution increases and image contrast decreases with increased numerical aperture of illumination . We used 63 × 1 . 47 NA oil immersion objective ( Leica ) and 0 . 9 NA condenser to achieve a good balance between image contrast and resolution . The mouse kidney tissue slice was imaged using 100 ms exposure for five polarization channels , 200 ms exposure for 405 nm channel ( nuclei ) at 1 . 6 mW in the confocal mode , 100 ms exposure for 561 nm channel ( F-actin ) at 2 . 8 mW in the confocal mode . The mouse brain slice were imaged using 30 ms exposure for five polarization channels . U2OS cells were imaged using 50 ms exposure for five polarization channels . For training the neural network , we acquired 160 non-overlapping 2048 × 2048 × 45 z-stacks of the mouse kidney tissue slice with Nyquist sampled voxel size 103 nm × 103 nm ×250 nm . Human brain sections were imaged with a 10 × 0 . 3 NA objective and 0 . 2 NA condenser with a 200 ms exposure for polarization channels , 250 ms exposure for 568 channel ( FluoroMyelin ) in the epifluorescence mode . The full brain sections were imaged , approximately 200 images depending on the size of the section , with 5 Z-slices at each location . The registered images mouse kidney tissue slice are available in the BioImage Archive ( https://www . ebi . ac . uk/biostudies/BioImages/studies/S-BIAD25 ) . The images were flat-field corrected . For training 3D models , the image volumes were upsampled along Z to match the pixel size in XY using linear interpolation . The images were tiled into 256 × 256 patches with a 50% overlap between patches for 2D and 2 . 5D models . The volumes were tiled into 128 × 128 × 96 patches for 3D models with a 25% overlap along XYZ . Tiles that had sufficient fluorescence foreground ( 2D and 2 . 5D: 20% , 3D: 50% ) were used for training . Foreground masks were computed by summing the binary images of nuclei and F-actin obtained from Otsu thresholding in the case of mouse kidney tissue sections , and binary images of FluoroMyelin for the human brain sections . Images of human brain sections were visually inspected and curated to exclude images containing quenching artifacts as shown in Figure 7 before training . Proper data normalization is essential for predicting the intensity correctly across different fields-of-views . We found the common normalization scheme where each image is normalized by its mean and standard deviation does not produce correct intensity prediction ( Figure 7—figure supplement 1 ) . We normalized the images on a per dataset basis to correct the batch variation in the staining and imaging process across different datasets . To balance contributions from different channels during training of multi-contrast models , each channel needs to be scaled to similar range . Specifically , for each channel , we subtracted its median and divided by its inter-quartile range ( range defined by 25% and 75% quantiles ) of the foreground pixel intensities . We used inter-quartile range to normalize the channel because standard deviation underestimates the spread of the distribution of highly correlated data such as pixels in images . We experimented with 2D , 2 . 5D and 3D versions of U-Net models Figure 3—figure supplement 1 . Across the three U-Net variants , each convolution block in the encoding path consists of two repeats of three layers: a convolution layer , ReLU non-linearity activation , and a batch normalization layer . We added a residual connection from the input of the block to the output of the block to facilitate faster convergence of the model ( Milletari et al . , 2016; Drozdzal et al . , 2016 ) . 2 × 2 downsampling is applied with 2 × 2 convolution with stride two at the end the each encoding block . On the decoding path , the feature maps were passed through similar convolution blocks , followed by up-sampling using bilinear interpolation . Feature maps output by every level of encoding path were concatenated to feature maps in the decoding path at corresponding levels . The final output block had a convolution layer only . The encoding path of our 2D and 2 . 5D U-Net consists of five layers with 16 , 32 , 64 , 128 and 256 filters respectively , while the 3D U-Net consists of four layers with 16 , 32 , 64 and 128 filters each due to its higher memory requirement . The 2D and 3D versions use convolution filters of size of 3 × 3 and 3 × 3 × 3 with a stride of 1 for feature extraction and with a stride of 2 for downsampling between convolution blocks . The 2 . 5D U-Net has the similar architecture as the 2D U-Net with following differences: The 2D , 2 . 5D , 3D network with single channel input consisted of 2 . 0 M , 4 . 8M , 1 . 5M learnable parameters , respectively . We randomly split the images in groups of 70% , 15% , and 15% for training , validation and test . The split are kept consistent across all model training to make the results comparable . All models are trained with Adam optimizer , L1 loss function , and a cyclic learning rate scheduler with a min and max learning rate of 5 × 10−5 and 6 × 10−3 respectively . The 2D , 2 . 5D , 3D network were trained on mini-batches of size 64 , 16 , and four to accommodate the memory requirements of each model . Models were trained until there was no decrease in validation loss for 20 epochs . The model with minimal validation loss was saved . Single channel 2D models converged in 6 hr , 2 . 5D model converged in 47 hr and the 3D model converged in 76 hr on NVIDIA Tesla V100 GPU with 32 GB RAM . As the models are fully convolutional , model predictions were obtained using full XY images as input for the 2D and 2 . 5D versions . Due to memory requirements of the 3D model , the test volumes were tiled along x and y while retaining the entire z extent ( patch size: 512 × 512 × 96 ) with an overlap of 32 pixels along X and Y . The predictions were stitched together by linear blending of the model predictions across the overlapping regions . Inference time for a single channel U-Net model was 105 , 3 and 18 seconds/frame for 2D , 2 . 5D , and 3D models respectively , with 2048 × 2048 pixels to a frame . Pearson correlation and structural similarity index ( SSIM ) along the XY , XZ and XYZ dimensions of the test volumes were used for evaluating model performance . The Pearson correlation coefficient between a target image T and a prediction image P is defined as ( 21 ) r ( T , P ) =σTPσTσPwhere σTP is the covariance of T and P , and σT and σP are the standard deviations of T and P respectively . SSIM compares two images using a sliding window approach , with window size N×N ( N×N×N for XYZ ) . Assuming a target window t and a prediction window p , ( 22 ) SSIM ( t , p ) = ( 2μtμp+c1 ) ( 2σtp+c2 ) ( μt2+μp2+c1 ) ( σt2+σp2+c2 ) where c1= ( 0 . 01L ) 2 and c2= ( 0 . 03L ) 2 , and L is the dynamic range of pixel values . Mean and variance are represented by μ and σ2 respectively , and the covariance between t and p is denoted σtp . We use N=7 . The total SSIM score is the mean score calculated across all windows , SSIM ( T , P ) =1M∑SSIM ( t , p ) for a total of M windows . For XY and XZ dimensions , we compute one test metric per plane and for XYZ dimension , we compute one test metric per volume . Importantly , it is essential to scale the the model prediction back to the original range before normalization for correct calculation of target-prediction SSIM . This is because unlike Pearson correlation coefficient , SSIM is not a scale-independent metrics . | Microscopy is central to biological research and has enabled scientist to study the structure and dynamics of cells and their components within . Often , fluorescent dyes or trackers are used that can be detected under the microscope . However , this procedure can sometimes interfere with the biological processes being studied . Now , Guo , Yeh , Folkesson et al . have developed a new approach to examine structures within tissues and cells without the need for a fluorescent label . The technique , called QLIPP , uses the phase and polarization of the light passing through the sample to get information about its makeup . A computational model was used to decode the characteristics of the light and to provide information about the density and orientation of molecules in live cells and brain tissue samples of mice and human . This way , Guo et al . were able to reveal details that conventional microscopy would have missed . Then , a type of machine learning , known as ‘deep learning’ , was used to translate the density and orientation images into fluorescence images , which enabled the researchers to predict specific structures in human brain tissue sections . QLIPP can be added as a module to a microscope and its software is available open source . Guo et al . hope that this approach can be used across many fields of biology , for example , to map the connectivity of nerve cells in the human brain or to identify how cells respond to infection . However , further work in automating other aspects , such as sample preparation and analysis , will be needed to realize the full benefits . | [
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Cancer-associated fibroblasts ( CAFs ) are a major cellular component of tumor microenvironment in most solid cancers . Altered cellular metabolism is a hallmark of cancer , and much of the published literature has focused on neoplastic cell-autonomous processes for these adaptations . We demonstrate that exosomes secreted by patient-derived CAFs can strikingly reprogram the metabolic machinery following their uptake by cancer cells . We find that CAF-derived exosomes ( CDEs ) inhibit mitochondrial oxidative phosphorylation , thereby increasing glycolysis and glutamine-dependent reductive carboxylation in cancer cells . Through 13C-labeled isotope labeling experiments we elucidate that exosomes supply amino acids to nutrient-deprived cancer cells in a mechanism similar to macropinocytosis , albeit without the previously described dependence on oncogenic-Kras signaling . Using intra-exosomal metabolomics , we provide compelling evidence that CDEs contain intact metabolites , including amino acids , lipids , and TCA-cycle intermediates that are avidly utilized by cancer cells for central carbon metabolism and promoting tumor growth under nutrient deprivation or nutrient stressed conditions .
The understanding of interaction mechanisms between cancer cells and the tumor microenvironment ( TME ) is crucial for developing therapies that can arrest tumor progression and metastasis . Recent studies have identified the TME as a key player in regulating cancer cell growth ( Whiteside , 2008 ) . Although the TME is comprised of a variety of cell types including cancer-associated fibroblasts cells ( CAFs ) , immune cells , and angiogenic elements , CAFs are the major constituent of the TME in many cancers ( Whiteside , 2008; Allinen et al . , 2004; Feig et al . , 2012 ) . Accumulating evidence suggests that paracrine signals from cancer cells can both recruit and activate CAFs within the TME , and contribute to their activation ( Whiteside , 2008; Liao et al . , 2009; Orimo et al . , 2005; Chung et al . , 2006 ) . Although CAFs have been associated with tumor growth , progression , and metastasis through intercellular communications with cancer cells; little is known about their role in inducing metabolic reprogramming in cancer cells . Studies have shown that extracellular vesicles known as exosomes can facilitate crosstalk between cancer and stromal cells in the TME . Exosomes have emerged as a vital communication mechanism between different cell types in the TME . Exosomes carry information from one cell to another and reprogram the recipient cells ( Gangoda et al . , 2015 ) , and recent findings report that exosomes harbor the potential to regulate proliferation , survival and immune effector status in recipient cells . Exosomes range between 30–100 nm in diameter , have a bilayered membrane ( Johnstone et al . , 1987 ) and express surface marker such as CD63 ( Christianson et al . , 2013 ) . Recent studies indicate that they contain proteins , nucleic acids and miRNAs ( Ekström et al . , 2012; Costa-Silva et al . , 2015; Simons and Raposo , 2009 ) . Most of the current studies are focused on cancer cell secreted exosomes; and little is known about CAF-derived exosomes ( CDEs ) and their metabolic influence on cancer cells . Although it has been shown that CAFs can induce metabolic reprogramming in cancer cells ( Brauer et al . , 2013 ) , the contribution of CDEs in this phenomenon , if any , has not been elucidated . Here , we report a novel regulation of cancer cell metabolism in prostate and pancreatic cancers mediated by CDEs . Our results demonstrate that patient-derived CDEs reprogram cancer cell metabolism through disabling mitochondrial oxidative metabolism and providing de novo 'off the shelf' metabolites through exosomal cargo . Specifically , we find that inhibition of mitochondrial oxidative phosphorylation by CDEs is associated with a compensatory increase in glycolysis . Interestingly , the inhibition of electron transport chain by CDEs significantly increased glutamine’s reductive carboxylation for biosynthesis in cancer cells . Further , we demonstrate through isotope tracing and intra-exosomal metabolomic experiments that exosomes act as a source of metabolite cargo carrying lactate , acetate , amino acids , TCA cycle intermediates , and lipids; and these metabolites are utilized by recipient cancer cells for proliferation , precursor metabolites and replenishing levels of TCA cycle metabolites . Notably , we demonstrate in wild-type and activated Kras-expressing pancreatic cancer cells that the metabolite cargo delivery mechanism by exosomes is similar to macropinocytosis , albeit without the previously described dependence on oncogenic Kras signaling ( Commisso et al . , 2013 ) . Our results reveal a novel metabolism-centric regulatory role of TME-secreted exosomes in cancers and we uncover the underlying mode of action of this regulation . These findings can lead to novel therapeutics targeting communication between cancer cells and their microenvironment .
To illustrate that CAFs secrete exosomes , and that cancer cells internalize these exosomes , we first isolated exosomes from conditioned media obtained from patient-derived prostate CAFs . The particle size analysis of isolated exosomes showed particles with size distribution from 30 to 100 nm ( Figure 1A ) , which is consistent with previous observations ( Xiao et al . , 2014 ) . Since exosomes are below the size range to allow direct detection by flow cytometry , we confirmed exosomes’ expression of CD63 , a surface antigen marker , through flow analysis of Dynabeads conjugated with anti-CD63 antibody ( Figure 1B ) . To examine if CDEs are taken up by prostate cancer cells ( PC3 ) , we pre-labeled CDEs with PKH green dye and added them to PC3 cells for 3h and analyzed their internalization by cancer cells . As indicated by shift in the peaks , CDEs are indeed taken up by cancer cells ( Figure 1C ) . Examination by fluorescence microscopy also confirmed the uptake of PKH red labeled exosomes by PC3 cells , evidenced through colocalization of red fluorescence and DAPI ( Figure 1D ) . Furthermore , we estimated the saturable concentration of CDEs taken up by cancer cells ( Figure 1E ) . Hence , in subsequent experiments we used 200 μg/ml of CDEs as the working concentration ( Zhu et al . , 2012 ) . 10 . 7554/eLife . 10250 . 003Figure 1 . Exosomes secreted by CAF-derived from prostate cancer patients are internalized by prostate cancer cells . ( A ) Size analysis of stromal exosomes . Three samples of exosomes derived from prostate cancer patient CAFs were analyzed with the Zetaview instrument . The profiles indicate that the size distribution of exosomes is within the range of 30-100 nm . For exosomes isolation , conditioned medium was obtained from CAFs cultured with exosomes-depleted FBS . ( B ) Flow analysis of CAF exosomes bound to Dynabeads conjugated with anti-CD63 antibody ( anti-CD63 ) or an irrelevant control antibody ( anti-Rabbit IgG antibody , Rb IgG ) . The graph and table show that these microvesicles express CD63 , an exosome surface antigen biomarker . ( C ) Flow cytometry analysis shows uptake of CAF exosomes by prostate cancer cells . Prostate cancer cells were incubated with PKH67-labeled stromal exosomes for 3 hr . Freshly prepared exosomes were used in this and subsequent experiments . Exosome-depleted serum was used for cell culture . ( D ) Representative fluorescence image shows CAFs exosomes were uptaken by prostate cancer cells . Prostate cancer cells were incubated with PKH26-labeled CAFs exosomes for 3 hr . Blue , cell nuclei; Red , PKH-Exo . ( E ) Flow cytometry analysis shows saturable uptake curve of CAFs secreted exosomes in prostate cancer cells . ( n=4 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 003 Since CAFs have been shown to regulate cancer cell growth ( Liao et al . , 2009 ) , we first examined influence of CDEs on cancer cell proliferation . We isolated exosomes from the conditioned media of CAFs derived from a prostate cancer patient and cultured prostate cancer cells in the presence of freshly isolated exosomes . CDEs enhanced proliferation of PC3 cells with increasing exosomes concentration ( Figure 2A ) . To determine whether CDEs induce metabolic rewiring in cancer cells , we cultured PC3 cells in CDEs for 24 hr and measured the oxygen consumption rate ( OCR ) with increasing amounts of exosomes . Surprisingly , we observed that basal oxidative phosphorylation ( OXPHOS , indicated by OCR ) was significantly inhibited with increasing concentration of CDEs added to PC3 cells ( Figure 2B ) . To ascertain whether the inhibition of mitochondrial respiration of cancer cells is specific to CDEs and to prove similar behavior is not exhibited with exosomes derived from other cells , we isolated exosomes from prostate cancer cell line ( PC3 ) , human fibroblasts ( IMR-90 ) , and also used blank media for isolation method control ( Figure 2—figure supplement 1 ) . As seen in the figure , exosomes from control conditions were ineffective in modulating cancer cells’ OCR . To expand our observations , we next isolated exosomes from three independent prostate cancer patient CAFs and cultured four prostate cancer cell lines ( PC3 , DU145 , 22RV1 and E006AA ) in presence and absence of the exosomes ( Figure 2C ) . Remarkably , exogenous addition of CDEs reduced OCR in all prostate cancer cell lines . To confirm if this reduction of OCR in cancer cells was indeed because of uptake of exosomes , we added the endocytosis inhibitor Cytochalasin D ( CytoD ) in PC3 culture media along with CDEs . CytoD has been shown to inhibit exosome uptake in various cell systems ( Casella et al . , 1981; Feng et al . , 2010 ) . Notably , CytoD could partially rescue this reduction of OCR in PC3 cells , thus confirming the CAF exosomes mediated reduction of OCR in cancer cells ( Figure 2D ) . 10 . 7554/eLife . 10250 . 004Figure 2 . CDEs increase proliferation of prostate cancer cells but significantly downregulate their mitochondrial function . ( A ) Effect of CAFs-derived exosomes on viability of prostate cancer cells , 48h culture period ( PC3 ) ( n≥9 ) . ( B ) Prostate cancer cells show reduced basal mitochondrial oxygen consumption rate ( OCR ) when cultured with range of concentrations of CDEs for 24 hr . Basal OCR is a measure of OXPHOS activity . The OCR was normalized with protein content inside cells . PC3 cells were cultured with patient-1 derived CAFs’ exosomes ( n≥9 ) . ( C ) Basal OCR was measured for PC3 , DU145 , 22RV1 , E006AA prostate cancer cell lines cultured with patient derived CDEs and control conditions . Six patient-derived CAFs were used for exosomes isolation . ( n≥9 ) . ( D ) OCR of prostate cancer cells were measured after 24 hr culture with and without CDEs . Cytochalasin D ( CytoD ) , an inhibitor of exosomes uptake through actin depolymerization , rescues reduced OCR in prostate cancer cells when cultured with CAFs exosomes . CytoD disturbs actin filament inside cells , thus inhibit phagocytosis . CytoD concentration of 1 . 5 μg/ml was used . ( n≥5 ) . ( E ) Maximal and reserve mitochondrial capacities were measured using FCCP and antimycin . Maximal OCR is maximal capacity of mitochondrial OCR . ( n≥9 ) . ( F ) Role of CAFs secreted exosomes in regulating mitochondrial membrane potential ( MMP ) of prostate cancer cells . MMP is an important indicator of mitochondrial functions . ( n≥5 ) . ( G ) Reduced OXPHOS genes expression in cancer cells cultured with exogenous CDES . ( H ) qPCR results show that mitochondrial OXPHOS genes of prostate cancer cells were downregulated when cultured with CDEs . ( n=3 ) . ( I ) Most abundant miRNAs targeting OXPHOS genes were abundant in CAFs exosomes . ( n=4 ) . ( J ) miRNAs in CAFs exosomes targeting specific OXPHOS genes . Nanostring was used to measure miRNA expression levels in stromal exosomes . ( n=4 ) . ( K ) OCR of PC3 were measured after transfection of targeted miRNAs together into cells . ( n=5 ) . miRNAs were transfected into cells according to the manufacturer’s protocol ( Lipofectamine 2000 Transfection Reagent , Thermofisher ) . Cells were seeded in 6-well plate for 24 hr . Transfection was performed followed by incubation for 48 hr . Cells were then reseeded onto Seahorse plates for OCR measurements after the cells were attached . Data information: data in ( A ) , ( B ) , ( C ) , ( F ) , ( H ) are expressed as mean ± SD , data in ( D ) , ( E ) , ( K ) are expressed as mean ± SEM;*p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Figure 2—figure supplement 1–2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 00410 . 7554/eLife . 10250 . 005Figure 2—figure supplement 1 . Specificity of CDEs in regulating mitochondrial respiration of cancer cells is demonstrated . ( A ) PC3 secreted exosomes ( 100 μg/ml ) were isolated according to the same procedure as used in Figure 2 , and added to PC3 cells . OCR of the PC3 cells cultured with PC3 cells-derived exosomes was measured after 24 hr . ( B ) IMR90 secreted exosomes ( 100 μg/ml ) were isolated and added to PC3 cells . OCR of the PC3 cells cultured with IMR-90-derived exosomes was measured after 24 hr . ( C ) Fresh CAF culture medium was incubated for 48 hr , and collected to perform the same steps as for isolating exosomes . Next , RPMI medium was used to dissolve any trace amount of chemicals which may be left from exosomes isolation to culture PC3 cells . OCR of the PC3 cells cultured with blank media-derived isolates was measured after 24 hr . No difference in OCR was observed between experimental and control conditions , while OCR of PC3 showed more than 50% decrease when cultured with CDEs . Data information: data in ( A–C ) are expressed as mean ± SD , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . ( n=3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 00510 . 7554/eLife . 10250 . 006Figure 2—figure supplement 2 . Effects of synthetic liposomes in regulating PC3 metabolism and viability . ( A , B ) Synthetic liposomes were uptaken by PC3 cells . ( C , D ) Synthetic liposomes did not enhance PC3 cells viability in complete medium or in deprivation condition . ( E-G ) Synthetic liposomes did not downregulate OCR of BxPC3 , MiaPaCa-2 , PC3 cells . ( H-J ) Synthetic liposomes did not upregulate ECAR of BxPC3 , MiaPaCa-2 , PC3 cells . Data information: data in ( C–J ) are expressed as mean ± SD , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . ( n≥3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 006 To conclusively associate CDEs induced metabolic reprogramming with metabolic content of exosomes , we verified if synthetic liposomes ( DOPC/CHOL liposomes labeled with DiO , size 85-110 nm; DOPC: 1 , 2-dioleoyl-sn-glycero-3-phosphocholine , CHOL: cholesterol ) with a size distribution similar to exosomes could similarly modulate cancer cells . Our data suggests that liposomes did not alter cell proliferation , OCR and ECAR in both prostate ( PC3 ) and pancreatic cancer cells ( MiaPaCa-2 and BxPC3 ) ( Figure 2—figure supplement 2 ) . These results implicate metabolic content of exosomes towards observed changes in CDEs-induced increase in cancer cell proliferation , mitochondrial dysfunction and increased glycolysis . The mitochondrial respiratory capacity inhibition in prostate cancer cells by prostate CAF-exosomes was further confirmed by measuring maximal and reserve mitochondrial capacity using oligomycin , the protonophoric uncoupler FCCP , and the electron transport inhibitor rotenone . Both maximal and reserve mitochondrial capacity of cancer cells were significantly reduced in presence of CDEs ( Figure 2E ) . These results suggest that CAFs downregulate mitochondrial OXPHOS in cancer cells and CDEs play a key role in this reprogramming . To further examine the effect of these exosomes on mitochondrial activity , we measured the mitochondrial membrane potential of PC3 cells with and without exosomes . As seen in Figure 2F , exogenous addition of CDEs significantly reduced mitochondrial membrane potential within cancer cells . To unravel the mechanism behind OCR reduction in cancer cells by CDEs; we performed microarray and q-PCR analysis to estimate the changes in mitochondrial gene expression levels of PC3 cells with and without exogenous CDEs ( Figures 2G , H ) . Microarray data revealed that transcript levels for OXPHOS related ATP synthase complex genes were downregulated in cells cultured with CDEs . Gene Set Enrichment Analysis ( GSEA ) on microarray data corroborates these observations by estimating a negative enrichment score ( p-value <0 . 05 ) for the entire set of 109 OXPHOS-related genes in cancer cells cultured in presence of CDEs relative to control ( Figure 2G ) . Furthermore , we found that both cytochrome B ( CYTB ) and cytochrome C oxidase I ( COXI ) , which are components of Complexes III and IV of the electron transport chain , respectively , have lower transcript expression levels when PC3 cells were cultured with exogenous CDEs ( Figure 2H ) . It is well established that exosomes contain noncoding RNAs ( e . g . miRNAs ) which can serve as a communication mechanism between stromal and cancer cells . We measured miRNA levels in the CDEs to determine whether miRNA underlay the molecular mechanism by which the exosomes exert the metabolic changes we observed . We extracted miRNAs from purified CDEs from three patients and measured the levels of a panel of 800 human miRNAs using NanoString technology . We grouped the miRNA by whether or not they target genes involved in oxidative phosphorylation ( miRNA-mRNA interactions taken from starBase v2 . 0 , starbase . sysu . edu . cn ) . The relative abundance of miRNAs that target oxidative phosphorylation genes is higher in exosomes than most other miRNA as seen in the density distribution plot ( Figure 2I ) . The top 30 most abundant miRNAs across the exosomes sampled , target one or more OXPHOS genes , which is validated by measuring the decrease in mRNA levels of these genes after treatment with exosomes ( Figure 2J ) . The miRNAs and their targets that we have identified are based on experimental miRNA abundance data from Nanostring assays followed by miRNA target prediction integrated with AGO-CLIP-SEQ data ( Li et al . , 2014 ) . In order to confirm the inhibition of OXPHOS through miRNA we co-transfected mir-22 , let7a and mir-125b present in the group of miRNA targeting OXPHOS , in PC3 cells , and measured OCR ( Figure 2K ) . In line with our hypothesis , we see a decrease in OCR in PC3 cells co-transfected with miRNAs . Although , the reduction of OCR is moderate , this is due to the technical limitation of co-transfection experiments which can only allow using a small subset of the miRNAs that target OXPHOS in PC3 . In summary , these results suggest that CDEs reduced mitochondrial oxidative phosphorylation and induced metabolic alterations in cancer cells mimicking hypoxia-induced alterations . The above experiments showed that CDEs downregulate mitochondrial activity . We further investigated whether this reduced mitochondrial activity leads to increased glycolysis in cancer cells in presence of CDEs . We first measured levels of basal glycolysis ( indicated by extracellular acidification rate , ECAR ) in four prostate cancer cell lines in presence of exosomes from three independent prostate cancer patient CAFs ( Figure 3A ) . As seen in the figure , these exosomes significantly increased glycolysis in cancer cells when compared to cancer cells cultured without exosomes . Notably , CytoD partially inhibited this increase of ECAR , thus confirming the role of exosomes in increase of glycolysis in cancer cells ( Figure 3B ) . To expand our findings on the exosomes mediated increase of glycolysis in cancer cells; we measured both glucose uptake and lactate secretion in cancer cells cultured with and without exosomes for 24 h ( Figures 3C , D ) . Consistent with above results , CDEs increased glucose uptake and lactate secretion when compared to cancer cells cultured without exogenously added exosomes . 10 . 7554/eLife . 10250 . 007Figure 3 . CDEs upregulate glycolysis in cancer cells . ( A ) Extracellular acidification rates ( ECAR ) of prostate cancer cells were measured after 24 hr culture with and without CAFs exosomes . ECAR is a measure of glycolytic capacity of cells . The ECAR was normalized with protein content inside cells . Four prostate cancer cell lines: PC3 , DU145 , 22RV1 , E006AA were used . Six patients derived CAFs were used for exosomes isolation ( n≥9 ) . ( B ) ECAR of prostate cancer cells was measured . CytoD increased ECAR in prostate cancer cells when cultured with CAFs exosomes . CytoD concentration of 1 . 5 μg/ml was used . ( n≥6 ) . ( C , D ) Effect of CAFs-secreted exosomes on glucose uptake ( C ) and lactate secretion fluxes ( D ) in prostate cancer cells . ( n=9 ) . ( E ) Schematic of carbon atom transitions using 1:1 mixture of 13C6 glucose and 1-13C1-labeled glucose . ( F ) Relative lactate abundances were measured using GC-MS in PC3 cells cultured with and without CAFs-secreted exosomes for 24 hr . ( n=4 ) . ( G–M ) Contribution of glucose towards TCA cycle metabolites and glycolysis is measured using the labeled glucose . Comparison of mass isotopologue distributions ( MID ) of lactate , pyruvate , citrate , α-ketoglutarate , malate , fumarate , and glutamate in PC3 cancer cells cultured with and without CAFs-secreted exosomes . ( n=4 ) . ( N ) Percentage of glucose contribution to α-ketoglutarate in PC3 cells with and without CAFs-secreted exosomes . ( n=4 ) . Data information: data in ( A , C and D ) are expressed as mean ± SD , data in ( B , F–N ) are expressed as mean ± SEM; *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Figure 3—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 00710 . 7554/eLife . 10250 . 008Figure 3—figure supplement 1 . Total ion currents of metabolites in PC3 with or without coculture of CDEs . Data information: data are expressed as mean mean ± SEM; *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . ( n=4 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 008 To understand the underlying changes in metabolite abundances induced by CAF exosomes in cancer cells , we performed 13C GC-MS based isotope tracer analysis using a 1:1 mixture of U-13C6 glucose and 1-13C1 glucose ( Figure 3E–N ) . GC-MS results are reported as mass isotopologue distributions ( MIDs ) , which represent the relative abundance of different mass isotopologues of each metabolite; where M0 refers to the isotopologue with all 12C atoms and M1 and higher refer to heavier isotopologues with one or more 13C atoms derived from the tracer . It is well established that isotope tracer analysis can reveal the alterations in contributions of a substrate within a particular metabolic pathway ( Figure 3E ) . We found that the CDEs increased the lactate levels in the cancer cells ( Figure 3F , Figure 3—figure supplement 1 ) . Further , the percentage of M3 pyruvate and M3 lactate was increased due to CDEs; however , there is a corresponding decrease in the percentage of M2 citrate and M3 citrate ( Figure 3G-I ) . The increase of M3 pyruvate and M3 lactate indicates higher contribution of glucose to pyruvate and lactate in prostate cancer cells conditioned with CDEs . Moreover , there was a decrease of M0 pyruvate and M0 lactate with a corresponding increase of M1 pyruvate and M1 lactate , thus suggesting that the exosomes enhance glycolysis . The latter conclusion was based on the principle that M1 pyruvate is only produced by glucose-6-phosphate metabolized by phospho glucoisomerase . Consistent with the decreased OXPHOS observed in cancer cells due to CDEs , the percentage of M2 citrate , M2 α-ketoglutarate , M2 fumarate , M2 malate , and M2 glutamate was also significantly reduced in cancer cells in presence of CDEs ( Figure 3I–M ) . In line with our observations , the percentage of 13C α-ketoglutarate from 13C labeled glucose was significantly reduced ( Figure 3N ) . This further confirms that CDEs decreased the percentage contribution of glucose to a-ketoglutarate in cancer cells and instead diverted it towards lactate . The above results conclusively show that CDEs induce a Warburg type phenotype in cancer cells , by disabling normal oxidative mitochondrial function with a compensatory increase in glycolysis . Glutamine serves as an anaplerosis substrate to fuel the TCA cycle for energy generation and also provides nitrogen for protein synthesis ( Dang , 2010; Wise and Thompson , 2010; DeBerardinis and Cheng , 2010; Daye and Wellen , 2012; Gaglio et al . , 2009; Johnson et al . , 2003; Rajagopalan and DeBerardinis , 2011; Shanware et al . , 2011; Weinberg et al . , 2010 ) . To further unravel the mechanistic links between disabled normal oxidative mitochondrial function in cancer cells by CDEs and its influence on cancer cells’ mitochondrial metabolism , we analyzed glutamine’s contribution to the TCA cycle metabolite pools in cancer cells using labeled U-13C5 glutamine ( Figure 4A ) . Proliferating cells under both normoxia and hypoxia can utilize glutamine by oxidative metabolism and produce pyruvate through malic enzyme and further combine oxaloacetate with acetyl-CoA to form M4 citrate ( obtained by condensing of labeled oxaloacetate obtained from glutamine and unlabeled acetyl CoA ) . Alternatively , proliferating cells under hypoxia have been reported to predominantly reductively carboxylate glutamine generated α-ketoglutarate through IDH 1/2 to generate M5 citrate ( Figure 4A ) ( Metallo , 2012 ) . M5 citrate is further catalyzed to M3 fumarate and M3 malate in this reductive glutamine metabolism . Our MID data reveals that addition of CDEs increased M5 glutamate and M5 α-ketoglutarate in cancer cells thereby indicating that exosomes enhance glutamine’s entry into TCA cycle ( Figure 4B , C ) . Notably , there was significant increase in M5 citrate , M3 fumarate and M3 malate in cancer cells in the presence of exogenously added exosomes thus suggesting that cancer cells rely critically on reductive glutamine metabolism when normal mitochondrial function is disrupted by stromal microenvironment ( Figure 4D–F ) . 10 . 7554/eLife . 10250 . 009Figure 4 . CDEs increase glutamine driven reductive carboxylation and lipogenesis in prostate cancer cells . ( A ) Schematic of carbon atom transitions using 13C5 glutamine . Black color represents labeled carbon of glutamine before entering into TCA cycle . Blue color represents glutamine's direct effect on canonical TCA cycle and red color represents glutamine's effect on TCA cycle through reductive carboxylation . ( B–F ) Mass isotopologue distribution ( MID ) of glutamate , α-ketoglutarate , citrate , malate , and fumarate in PC3 cancer cells cultured with and without CDEs in U-13C5 glutamine ( n=4 ) . ( G ) Ratio of α-ketoglutarate and citrate pools in PC3 cancer cells cultured with and without CDEs measured using GC-MS . Higher ratio correlates with higher glutamine driven reductive carboxylation ( n=4 ) . ( H–I ) Ratio of glutamine contribution to citrate via oxidative and reductive pathways . Lower ratio indicates higher reductive carboxylation . CDEs increased reductive glutamine metabolism in PC3 cells ( I ) . Oxidative contribution to citrate is determined by calculating M4 citrate percentage; reductive contribution to citrate is determined by M5 citrate percentage ( n=4 ) . ( J ) Glucose contribution to palmitate synthesis in PC3 cells cultured with or without CAFs exosomes for 72 hr was measured using GC-MS ( n=6 ) . ( K ) Glutamine contribution to palmitate synthesis in prostate cancer cells with or without CAFs exosomes measured using GC-MS ( n=6 ) . ( L ) Isotopologue spectral analysis ( ISA ) of both glucose and glutamine contribution to lipid synthesis in PC3 cells under control or CAFs exosomes culture conditions . CAFs exosomes enhance reductive carboxylation to lipid synthesis . However , total percentage of glucose and glutamine contribution to palmitate is about 60% . ( M ) Acetate concentration in cancer cell culture medium . ( N ) Acetate contribution to palmitate synthesis in PC3 cells with or without CAFs exosomes . Acetate spiked concentration was 500 μM ( n=4 ) . ( O ) Pyruvate contribution to palmitate synthesis in PC3 cells with or without CAFs exosomes ( n=4 ) . ( P ) ISA analysis of both pyruvate and acetate contribution to lipid synthesis in PC3 cancer cells under control or CAFs exosomes culture conditions . CAFs exosomes enhance acetate contribution to palmitate synthesis . Data information: data in ( B–P ) are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 00910 . 7554/eLife . 10250 . 010Figure 4—figure supplement 1 . Schematic of isotopologue spectral analysis ( ISA ) method . Isotopologue spectral analysis ( ISA ) is used to determine the contribution of 13-carbon labeled sources ( e . g . U-13C6-Glucose and U-13C5-glutamine ) to lipogenic Acetyl-CoA , which produces fatty acids ( Kharroubi et al . , 1992 ) . Cells are cultured with 13-carbon tracers to produce labeled fatty acids of different mass isotopologues . The mass isotopologue distribution ( MID ) of these fatty acids depends on the rate of de novo synthesis [represented by the parameter g ( t ) ] and fraction of Acetyl-CoA pool ( represented by parameter D ) enriched by tracers . The ISA method is designed to estimate the parameters D and g ( t ) , by minimizing error between MID of fatty acids measured with GC-MS and MID generated from binomial functions based on the aforementioned parameters . The MID of Acetyl-CoA derived from tracers is denoted by vector T and the naturally labeled Acetyl-CoA pool by vector N . The MID of mixed pool X isX=D . T+ ( 1−D ) . NThe vectors T and N are of size ( 3x1 ) with N0 , T0 and N1 , T1 and N2 , T2 denoting fractions of ( M+0 ) , ( M+1 ) and ( M+2 ) labeled acetyl-CoA , respectively . The probability of production of individual mass isotopologues of palmitate are derived from binomial functionsPM+x=g ( t ) fM+x ( X ) + ( 1−g ( t ) ) fM+x ( N ) wherefM+x is probability function of generating ( M+x ) labeled palmitate from combinations of labeled acetyl-CoA in pool X or naturally labeled pool N . For e . g . the ( M+3 ) mass isotopologue of palmitate can be derived from either 5 ( M+0 ) molecules and 3 ( M+1 ) molecules of acetyl-CoA; or 6 ( M+0 ) molecules , 1 ( M+1 ) molecule and 1 ( M+2 ) molecule of acetyl-CoA . Therefore the probability function for ( M+3 ) palmitate will befM+3= ( 85 ) ( X0 ) 5 . ( 83 ) ( X1 ) 3+ ( 86 ) ( X0 ) 6 . ( 81 ) ( X1 ) 1 . ( 81 ) ( X2 ) 1DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 010 To obtain mechanistic understanding of CDEs induced increased reductive carboxylation in cancer cells; we measured the ratio of α-ketoglutarate to citrate abundance in cancer cells and found that exosomes increased this ratio significantly ( Figure 4G ) . The increased ratio of α-ketoglutarate to citrate was recently shown to promote reductive glutamine metabolism and it correlated with reductive glutamine’s contribution to citrate ( Fendt et al . , 2013 ) . The inhibition of respiratory chain components , or hypoxic conditions , was found to increase this ratio . Consistent with previous reports , the ratio of M4/M5 citrate , which represents the ratio of glutamine to citrate through oxidative metabolism over reductive metabolism , confirmed our above results that there is a significant increase in glutamine’s reductive metabolism in presence of exosomes ( Figure 4H–I ) . This is further substantiated through significant increase in the percentage contribution of glutamine through reductive pathway in the TCA cycle in cancer cells in presence of exosomes when compared to control condition . Also , there was concomitant decrease in the percentage contribution of glutamine through oxidative pathway in the TCA cycle . One of the key metabolic requirements of rapidly dividing cancer cells pertains to availability of adequate pool of fatty acids for enabling membrane synthesis . Therefore , to further investigate the effect of exogenous CDEs on nutritional substrates’ incorporation into lipogenesis; we used U-13C6 glucose or U-13C5 glutamine to estimate their conversion to cytosolic acetyl-CoA , which is the precursor for palmitate ( fatty acid ) synthesis . Similar to our earlier observation that CDEs reduced glucose contribution to TCA cycle metabolites in cancer cells , we found that exosomes also decreased glucose contribution to palmitate . This is evident from the shift in high mass isotopologues of palmitate to lower mass isotopologues derived from U-13C6 glucose , when cells are cultured with exosomes ( Figure 4J ) . Conversely , there is a shift in the reverse direction , i . e . from low to high mass palmitate isotopologues derived from U-13C5 glutamine , in presence of CDEs ( Figure 4K ) . To quantify the percentage contribution of these substrates to the lipogenic acetyl-CoA pool , we performed isotopologue spectral analysis ( ISA ) ( Metallo , 2012; Kamphorst et al . , 2014 ) . In line with above results , ISA ( Figure 4—figure supplement 1 ) indicated a significant decrease in the fraction of glucose contribution to lipogenic acetyl-CoA in cancer cells cultured with exosomes ( Figure 4L ) . More importantly , there is a two-fold increase in glutamine’s contribution to lipogenic acetyl-CoA via the reductive carboxylation pathway . Additionally , these intriguing results suggest that there are likely to be other sources apart from glucose and glutamine that contribute to fatty acid synthesis . Recently it was shown that acetate could be an important source for lipogenic acetyl-CoA in cancer cells , especially under hypoxic conditions ( Kamphorst et al . , 2014 ) . To ascertain if acetate contributed to fatty acid synthesis , we measured acetate content in media ( Figure 4M ) . Indeed , acetate concentrations were detected in our cancer cell media at significant levels . Since pyruvate was available in significant amounts in our cancer cell culture media , we included it in our estimates for palmitate synthesis . In order to quantify contribution from these alternative sources , we performed tracer experiments with U-13C3 pyruvate and U-13C2 acetate in cancer cells cultured with and without exosomes . We found that their contribution to lipogenic acetyl-CoA is significantly lower when compared to that of glucose and glutamine . This is evident from the low mass isotopologues of palmitate generated when cells are cultured with U-13C3 pyruvate or U-13C2 acetate ( Figure 4N–O ) in both control and exosomes-treated conditions . Interestingly , we noticed from the shift in palmitate mass isotopologues that CDEs increased acetate contribution to lipids ( Figure 4N ) but decreased the pyruvate contribution ( Figure 4O ) . ISA results confirm these observations , where cancer cells cultured with CDEs showed an increase in acetate’s contribution with a concomitant decrease in pyruvate’s contribution to palmitate production ( Figure 4P ) . These experiments collectively substantiate that exosomes have a significant effect on fatty acid synthesis in cancer cells by switching the carbon source from the oxidative glucose pathway to glutamine via the reductive carboxylation pathway in the TCA cycle . Exosomes are known to carry a complex cargo that includes proteins , lipids , and miRNAs ( Costa-Silva et al . , 2015; Simons and Raposo , 2009 ) . Results from the previous sections indicate that exosomes may act as a source of metabolites and proliferating cancer cells use these metabolites for lipogenesis . To ascertain whether exosomes contain significant amount of de novo metabolites , we first measured lactate and acetate contained inside the prostate and pancreatic CDEs . We included intra-exosomal metabolic measurements of pancreatic CAFs in order to generalize our conclusions . Notably , we found high amounts of lactate and acetate in both prostate and pancreatic CDEs ( Figure 5A , B ) . This suggests that exosomes can not only replenish TCA cycle metabolites but also act as source of lipids . Further , to prove these hypotheses we performed GC-MS analysis for intra-exosomal metabolites and found high concentrations of citrate and pyruvate along with significant presence of α-ketoglutarate , fumarate and malate ( Figure 5C ) . To further expand our findings , we performed ultra-high-performance liquid chromatography ( UPLC ) , and found markedly high levels of glutamine , arginine , glutamate , proline , alanine , threonine , serine , asparagine , valine , and leucine in prostate CAF exosomes ( Figure 5D ) . Additionally , in pancreatic CDEs , we found high levels of glutamine , threonine , phenylalanine , valine , isoleucine , glycine , arginine , and serine ( Figure 5E ) . Remarkably , through GC-MS analysis of intra-exosomal lipids , we found intact stearate ( Figure 5F ) and palmitate ( Figure 5G ) at high levels in both prostate and pancreatic CDEs . Our results offer definitive proof for the first time that exosomes harbor an 'off-the-shelf' pool of metabolite cargo , TCA cycle metabolites , amino acids , and lipids , which can fuel the metabolic activity of the recipient cells . 10 . 7554/eLife . 10250 . 011Figure 5 . Prostate and pancreatic CAFs secreted exosomes carry metabolite cargo . Intra-exosomal lactate ( A ) and acetate ( B ) concentrations were measured in exosomes isolated from three prostate and two pancreatic CAFs using enzymatic assays . Intra-exosomal metabolites were extracted by methanol/chloroform method and protein concentration was used for normalization . ( n=3 ) . ( C ) TCA cycle metabolites , including pyruvate , citrate , α-ketoglutarate , fumarate and malate were measured using GC-MS in exosomes isolated from pancreatic CAF35 . ( n=3 ) . ( D , E ) Amino acids were measured using ultra-high performance liquid chromatography ( UPLC ) inside CDEs ( prostate CAFs: [D]; pancreatic CAFs: [E] ) . Significant levels of amino acids were detected inside CDEs . ( n=3 ) . ( F-G ) Stearate and palmitate were detected at high levels using GC-MS inside pancreatic and prostate CDEs . ( n≥3 ) . Data information: data in ( A–C ) , ( F–G ) are expressed as mean ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 011 Recent studies have shown that macropinocytosis of circulating proteins ( especially albumin ) could supply amino acids to nutrient-deprived cancer cells ( Commisso et al . , 2013 ) . Having established that CDEs could act as a source of metabolites , we further postulated that CDEs could act as source of TCA cycle metabolites for cancer cells . To establish whether metabolites contained in exosomes could fuel TCA cycle , we cultured patient-derived fibroblasts with 13C-labeled glucose , glutamine , pyruvate , leucine , lysine , and phenylalanine for 72 hr . We selected leucine , lysine and phenylalanine for labeling , because these were the most abundant amino acids in human serum albumin ( Saifer and Palo , 1969 ) . The extended timescale of 72 hr was adopted to allow detectable incorporation of labeling in proteins , amino acids , and lipids , and their compartmentalization within exosomal cargo . We then isolated labeled exosomes from the CAF culture spent medium . We postulated that nutrient deprived conditions will enhance cancer cells’ dependence on the nutrient cargo in exosomes and therefore tested our hypothesis under both nutrient replete and nutrient deprived conditions . The isolated CDEs were then spiked into complete or nutrient-deprived ( without lysine , leucine , phenylalanine , glutamine , and pyruvate ) cultures of prostate cancer cells for 48h . The intracellular metabolites were isolated from cancer cells and their 13C enrichment was determined . Notably , our results substantiate that exosomes can supply metabolites to cancer cells under both complete and nutrient deprivation conditions ( Figure 6A ) . However , and in concordance with hypothesis , we found that there is a significant increase in the contribution of CDEs to cancer cells’ metabolites pools in nutrient deprived conditions , as compared to complete medium cultures . To definitively prove the direct export of metabolites by exosomes , we measured MIDs of metabolites in cancer cells when cultured with 13C labeled exosomes . We detected substantial labeling of intracellular amino acids in cancer cells , which included M5 glutamine , M6 lysine , and M6 leucine . We also detected M5 glutamate derived from mitochondrial glutaminolysis and labeled TCA cycle metabolites from labeled 13C-glutamine supplied by CDEs ( Figure 6B ) . Further , it is important to note that substrates within CDEs will not be fully 13C labeled since 72 hr are not sufficient for CAFs to undergo at least one replication , and hence labeled metabolites in CDEs may be diluted with pre-existent unlabeled ( M0 ) isoforms . This results in small , but significant levels of labeled metabolites within the cancer cells . Importantly , it provides a compelling proof-of-concept that CDEs can supply TCA cycle metabolites to cancer cells . 10 . 7554/eLife . 10250 . 012Figure 6 . CDEs supply metabolites to cancer cells . To label metabolites , proteins and lipids in CAFs-secreted exosomes , CAFs were cultured in RPMI with labeled 13C3 pyruvate ( pyr ) , 13C5 glutamine ( gln ) , 13C6 leucine ( leu ) , 13C6 lysine ( lys ) , 13C9-phenylalanine ( phe ) and U-13C6 glucose . After 72h of CAFs cultures , sufficient labeling was observed in metabolites , proteins and lipids contained in exosomes . Supply of metabolites to prostate cancer cells from labeled CDEs were measured under complete or deprivation medium cultures in culture media without labeling . ( A ) Percentage labeling ( mean enrichment ) observed in metabolites inside PC3 cells cultured with labeled CDEs . ( n=4 ) . Mean enrichment is calculated as ME= ( ∑i=1Ni×Mi ) /N . where N is number of carbons in the metabolite and Mi is abundance of ( M+i ) isotopologue ( B ) Mass fraction of heaviest labeled isotopologues of TCA cycle metabolites enriched by labeled CDEs , in prostate cancer cells cultured under complete or nutrient-deprived ( without lys , phe , gln , pyr , leu ) unlabeled medium ( n=4 ) . ( C ) Effect of CDEs on PC3 cell viability under deprivation ( without lys , phe , gln , pyr , leu ) conditions and exosome uptake inhibitors . CDEs rescue loss of PC3 cell proliferation under deprivation medium . CytoD , ( 1 . 5 μg/ml ) , heparin ( 50 μg/ml ) , and CQ ( chloroquine , 20 μM ) inhibited this rescue of viability under deprivation conditions n=10 . ( D ) EIPA ( 25 μM ) inhibited rescue of PC3 viability by CDEs under deprivation conditions , ( n≥7 ) . Data information: data are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 01210 . 7554/eLife . 10250 . 013Figure 6—figure supplement 1 . Contribution of metabolites from labeled exosomes to PC3 cells . ( A ) Mean enrichment after natural abundance correction is calculated for amino acids not synthesized by PC3 cells . Contribution of these metabolites from exosomes to PC3 cells are estimated by normalizing mean enrichment in PC3 with enrichment in labeled exosomes n=4 . ( B ) Mass isotopologue distributions of amino acids and central carbon metabolites were measured by GC-MS in labeled exosomes ( Exo ) and PC3 cells cultured with labeled exosomes ( PC3+Exo ) in deprivation condition ( n=3 ) . Data information: data are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 013 To estimate the contribution of CDEs in labeling cancer cells’ metabolites , we determined MIDs of metabolites derived from isolated labeled CDEs and also from cancer cells cultured with labeled exosomes under nutrient deprivation conditions ( Figure 6—figure supplement 1A ) . The deprivation and labeling conditions used were similar to Figure 6A and B . In order to quantify the contribution of metabolites from exosomes to cancer cells , we calculated the mean enrichment of 13C labeled amino acids and normalized them with their corresponding enrichment in exosomes . We observed that exosomes account for approximately 16% of phenylalanine , 14% of glutamine , 12% of lysine and 5% of leucine pools in PC3 cells ( Figure 6—figure supplement 1A ) . Since MIDs are measured 48 hr after introduction of exosomes to cancer cells , several of the supplied metabolites would have been catabolized into other intermediates or incorporated into biomass precursors . Therefore , it is important to note that the contributions of essential amino acids estimated would be lower than their total contribution from exosomes over the course of 48 hr . In order to achieve higher detection of labeled isoforms , CAFs will have to be cultured with labeled substrates for multiple passages to completely replace unlabeled metabolite pools with their labeled isoforms . Nevertheless , these results substantiate that CDEs are key player in enriching metabolite pools in cancer cells under nutrient deprived conditions seen in the TME . To evaluate the requirement of metabolites derived from exosomes for promoting tumor growth under nutrient deprivation conditions ( without leucine , lysine , glutamine , pyruvate , and phenylalanine ) , we cultured cancer cells with exosomes under nutrient deprivation and complete media conditions with and without CytoD and heparin . Similar to CytoD , heparin has been recently shown to inhibit uptake of exosomes by cells ( Christianson et al . , 2013 ) . CDEs were able to rescue reduction of proliferation under nutrient deprivation conditions ( Figure 6C ) . However , this rescue effect is reduced to varying extents by adding CytoD , heparin and lysosomal degradation inhibitor choloroquine ( Figure 6C ) . Similarly , addition of macropinocytosis inhibitor EIPA also counters the rescue of CDEs under deprivation ( Figure 6D ) . These data suggest that uptake of exosomes and release of their cargo is necessary to rescue cell proliferation under nutrient deprived conditions . Taken together , these results provide evidence that CDEs can reprogram cancer cells’ metabolism by acting as source of amino acids under nutrient depleted conditions in the TME . We have shown that prostate CDEs can supply metabolites to prostate cancer cells . Our results were similar to the process of macropinocytosis , which was revealed as a mechanism to supply amino acids through extracellular proteins in oncogenic Ras-expressing pancreatic cancer cells . To expand the scope of our findings and understand whether Ras can similarly promote the supply of metabolites by CDEs in pancreatic cancer cells , we isolated exosomes from pancreatic CAFs cell line ( CAF-19 ) and used them to study their metabolic influence in two pancreatic cancer cell lines: BxPC3 ( wild type Kras ) and MiaPaCa-2 ( homozygous Kras ) . Since it was observed that CDEs supply metabolites to prostate cancer cells , we cultured both BxPC3 and MiaPaCa-2 cell lines , with and without CDEs under complete media and nutrient deprivation ( without glutamine , leucine , lysine , phenylalanine , and pyruvate ) conditions ( Figure 7A ) . Notably , CDEs could rescue loss of proliferation in both cancer cell lines , thereby suggesting that internalization or uptake and supply of exosomes derived metabolites in cancer cells is Kras independent . Furthermore , this rescue of proliferation by CDEs in pancreatic cancer cell lines was inhibited by receptor mediated endocytosis inhibitor heparin ( Figure 7B , C ) . 10 . 7554/eLife . 10250 . 014Figure 7 . Pancreatic CDEs' metabolic reprogramming of pancreatic cancer cells is Kras independent . ( A ) Effect of pancreatic CDEs on pancreatic cancer cell viability under nutrient deprivation ( without lys , phe , gln , pyr , leu ) conditions . CDEs rescue loss of both wild-type and activated Kras expressing pancreatic cancer cells proliferation under deprivation conditions . Viability of cancer cells with and without exosomes in deprivation condition was measured after 48 hr ( n=10 ) . ( B , C ) Heparin inhibit exosomes uptake and thus inhibit the rescue of proliferation by exosomes under nutrient deprived conditions . Heparin ( 50μg/ml ) disrupts receptor-mediated endocytosis . Before adding exosomes , heparin was added to wells for incubation for at least 0 . 5 hr ( n=5 ) . ( D ) Basal OCR were measured for BxPC3 and MiaPaCa-2 , pancreatic cancer cell lines cultured with pancreatic CAFs ( CAF19 ) exosomes . OCR of both BxPC3 and MiaPaCa-2 were downregulated by CAF19 exosomes . ( n=10 ) . Maximal OCR and reserve OCR of BxPC3 and MiaPaCa-2 were downregulated by CAF19 exosomes ( n=10 ) . ( E ) ECAR of both BxPC3 and MiaPaCa-2 were upregulated by CAF19 exosomes ( n=10 ) . ( F ) Relative lactate abundances were measured using GC-MS in BxPC3 and MiaPaCa-2 cells cultured with and without CAF19-secreted exosomes for 24 hr ( n=4 ) . ( G ) Percentage of glucose contribution to α-ketoglutarate in BxPC3 and MiaPaCa-2 cells with and without CAF19-secreted exosomes ( n=4 ) . ( H ) Pancreatic CDEs increased reductive glutamine metabolism in wild-type and activated Kras expressing pancreatic cancer cells . Oxidative contribution to citrate is determined by calculating M4 citrate percentage; reductive contribution to citrate is determined by M5 citrate percentage ( n=4 ) . ( I ) Ratio of oxidative to reductive glutamine contribution to citrate in wild-type and activated Kras expressing pancreatic cancer cells with CAF19-secreted exosomes ( n=4 ) . ( J ) Mass isotopologue distributions ( MID ) of glutamate , α-ketoglutarate , citrate , malate , and fumarate in BxPC3 and MiaPaCa-2 cancer cells cultured with and without CAF19-secreted exosomes in U-13C5 glutamine ( n=4 ) . Higher reductive glutamine metabolism is detected through higher M5 citrate , M3 fumarate , M3 malate , M3 aspartate in pancreatic cancer cells cultured in presence of exosomes . Reductive glutamine metabolism ( n=4 ) . Data information: data in ( A ) are expressed as mean ± SD , data in ( B–J ) are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Figure 7—figure supplements 1–4 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 01410 . 7554/eLife . 10250 . 015Figure 7—figure supplement 1 . Effect of drugs inhibiting CDEs uptake and utilization on BxPC3 or MiaPaCa-2 cell proliferation under deprivation ( without lys , phe , gln , pyr , leu ) conditions . EIPA , CytoD , and CQ ( chloroquine ) inhibited the rescue of proliferation by CDEs under deprivation conditions to different extents . Working concentrations used: EIPA ( 25 μM ) , CytoD , ( 1 . 5 μg/ml ) , CQ ( chloroquine , 20 μΜ ) ( n≥7 ) . Data information: data are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 01510 . 7554/eLife . 10250 . 016Figure 7—figure supplement 2 . Effect of pancreatic CDEs on pancreatic cancer cell proliferation under nutrient deprivation ( without lys , phe , gln , pyr , leu ) conditions . CDEs rescue loss of both Kras+ and Kras- expressing pancreatic cancer cell viability under nutrient deprivation conditions . Viability of cancer cells with and without exosomes in deprivation condition was measured after 48 hr ( n=3 ) . Data information: data are expressed as mean ± SD , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 01610 . 7554/eLife . 10250 . 017Figure 7—figure supplement 3 . Effect of pancreatic CDEs on glycolysis in pancreatic cancer cells using labeled glucose . Comparison of mass isotopologue distributions ( MIDs ) of intracellular lactate , pyruvate , citrate , α-ketoglutarate , fumarate , malate , glutamate in pancreatic cancer cells with or without pancreatic CAF19-derived exosomes . Two pancreatic cancer cell lines ( BxPC3 and MiaPaCa-2 ) and one CAF cell line ( CAF19 ) were used for isotope tracing experiments . CAF19 cells were cultured for 48h to obtain supernatants for exosomes isolation . Pancreatic cancer cells were cultured for 24 hr to obtain intracellular metabolites for GC-MS measurement . Data information: data are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 01710 . 7554/eLife . 10250 . 018Figure 7—figure supplement 4 . Effect of pancreatic CDEs on glutamine metabolism in pancreatic cancer cells using labeled glutamine . Comparison of mass isotopologue distributions ( MIDs ) of intracellular citrate , α-ketoglutarate , fumarate , malate , glutamate and aspartate in pancreatic cancer cells with or without pancreatic CAF19-derived exosomes . Two pancreatic cancer cell lines ( BxPC3 and MiaPaCa-2 ) and one CAF cell line ( CAF19 ) were used for isotope tracing experiments . CAF19 cells were cultured for 48 hr to obtain supernatants for exosomes isolation . Pancreatic cancer cells were cultured for 24 hr to obtain intracellular metabolites for GC-MS measurement . Data information: data are expressed as mean ± SEM , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 018 Further , since both EIPA and CytoD could also inhibit rescue effect of proliferation in BxPC3 and MiaPaCa-2 cells ( Figure 7—figure supplement 1 ) , suggesting that endocytosis pathway dependent on macropinocytosis and caveolae mediated endocytosis are also associated with uptake of CDEs in pancreatic cancer cells . Additionally , CQ reduced rescue of proliferation by CDEs , thereby suggesting that release of exosomal content through lysosomes may play a role in some cancer cells . Nevertheless , these data suggest that CDEs internalization may happen to various modes of internalization . To further substantiate the role of KRAS , we used doxycycline inducible Kras-G12D cell line ( iKras-1 , [Ying et al . , 2012] ) to test if enhancement of proliferation by CDEs indeed was KRAS independent . As shown in Figure 7—figure supplement 2 , iKras-1 cells with or without doxycycline showed similar proliferation increases with CDEs , thereby suggesting that internalization or uptake and supply of exosome-derived metabolites in cancer cells is Kras independent . Clearly , these results suggest that exosomes derived from TME could drive proliferation of PDAC cells by supplying metabolites independent of activated Kras expression . Previous studies have suggested that Kras can upregulate glycolysis and glutaminolysis . To further evaluate if exosomes mediated metabolic reprogramming is Kras mediated , we measured mitochondrial respiration in PDAC cells with and without pancreatic CDEs . In line with results obtained in prostate cancer cells , we found that OCR of both BxPC3 and MiaPaCa-2 cells were decreased in presence of pancreatic CDEs ( Figure 7D ) . Both maximal and reserve mitochondrial capacity of pancreatic cancer cells were significantly reduced in presence of pancreatic CDEs further confirming mitochondrial respiratory capacity inhibition in cancer cells by CAF exosomes ( Figure 7D ) . These results suggest that CDEs’ action of disabling normal oxidative mitochondrial function is exhibited also in pancreatic cancer and that this regulation is similar in both wild-type ( BxPC3 ) and activated Kras ( MiaPaCa-2 ) expressing cells . Furthermore , there was a corresponding increase in ECAR in both pancreatic cancer cell lines in presence of CDEs ( Figure 7E ) . This was corroborated by increased lactate levels in the cancer cells in presence of exosomes using U-13C6 glucose labeling based isotope tracer analysis of pancreatic cancer cells in presence of CAF exosomes ( Figure 7F ) . Concomitantly , CDEs decreased percentage contribution of glucose to α-ketoglutarate in both pancreatic cancer cell lines ( Figure 7G , Figure 7—figure supplement 3 ) . To further elucidate the metabolic reprogramming induced by pancreatic CDEs in PDAC cells , we performed GC-MS based isotope labeling experiments using 13C5 labeled glutamine . In line with the results obtained in prostate cancers , we found that exosomes from pancreatic CAFs significantly increased the reductive glutamine metabolism ( Figure 7H–J , Figure 7—figure supplement 4 ) . Remarkably , this CAF exosomes-mediated increase of reductive glutamine metabolism was detected in both wild-type and activated Kras expressing pancreatic cancer cells , thus suggesting that metabolic reprogramming induced by stromal exosomes in cancer cells is not only Kras independent but is broadly observed in many cancers .
Altered cell metabolism is one of the hallmarks of cancer . While much of the mechanistic underpinnings in this regard have focused on cell autonomous means of metabolic reprogramming , the objective of this study was to examine the critical role of TME , and in particular , CAFs , might be playing in this adaptive phenomenon . CAFs comprise the majority of cell types within the TME , and their reciprocal interactions with cancer cells have been well documented ( Whiteside , 2008; Hazlehurst et al . , 2003; Karnoub et al . , 2007 ) . Recently , Hu et al . have discovered that CDEs enhance drug resistance in colorectal cancer stem cells by regulating the Wnt pathway and this effect can be reversed by inhibiting exosome secretion ( Hu et al . , 2015 ) . These studies suggest that CDEs may not only enhance cancer growth but may also induce chemoresistance . However , there continues to be sparse data vis-à-vis the role , if any , of the effects CAFs might have on altered cancer cell metabolism , and the channels of communication that enable such paracrine effects . The objective of our study was to determine whether exosomes released from CAFs might play a role in modulating cancer cell metabolism , allowing neoplastic cells to survive in the nutrient-deprived conditions , a characteristic of many tumors . Our results convincingly demonstrate that not only do exosomes enhance the phenomenon of 'Warburg effect' in tumors , but remarkably , contain de novo 'off-the-shelf' metabolites within their cargo that can contribute to the entire compendia of central carbon metabolism within cancer cells . The Warburg effect , commonly observed characteristic of many cancer types , is identified as the reliance of cancer cells on aerobic glycolysis even under normoxia . This leads to diversion of glucose to lactate thereby creating low pH conditions which modulates TME ( Warburg et al . , 1927; Gatenby et al . , 2006; Salimian Rizi et al . , 2015 ) . Although recent studies ( Ishikawa et al . , 2008; Santidrian et al . , 2013 ) , including our own ( Yang et al . , 2014 ) , implicate mitochondrial activity in cancer metastasis , literature is abound with previous studies based on the hypothesis that aerobic glycolysis may be an outcome of impaired mitochondrial functions . The role of mitochondrial metabolism in tumorigenesis and cancer progression is likely to be organ-specific . Mitochondrial dysfunction could impair oxidative phosphorylation , the TCA cycle , fatty acid oxidation , the urea cycle , gluconeogenesis , and apoptotic pathways ( King et al . , 2006; Modica-Napolitano and Singh , 2004 ) . TCA cycle dysfunction leads to oncogenesis by regulating signaling pathway and stabilizing HIF1-α ( Selak et al . , 2005 ) . While many studies in cancer cell metabolism have highlighted higher compensatory glycolysis because of mitochondrial dysfunction; recent studies show that some tumors predominantly use glutamine under hypoxia or conditions mimicking hypoxia such as electron transport chain inhibition through reductive carboxylation for biosynthetic needs ( Yang et al . , 2014; Wise et al . , 2008 ) . In reductive carboxylation , glutamine is converted to α-ketoglutarate , followed by the conversion of α-ketoglutarate to isocitrate through isocitrate dehydrogenase ( IDH ) , and isocitrate is converted to acetyl-CoA used for lipid synthesis . The upregulation of the reductive carboxylation pathway enhances cancer cells proliferation ( Metallo , 2012; Mullen , 2012; Wise et al . , 2011 ) . Although less studied , pyruvate or acetate can act as an alternative source to glucose and glutamine for lipid biosynthesis . Pyruvate is converted to acetyl-CoA through mitochondrial pyruvate dehydrogenase ( PDH ) , whereas acetate is transported into cells and converted to acetyl-CoA through acetyl-CoA synthase ( De Schrijver et al . , 2003; Feron , 2009; Koukourakis et al . , 2005; Pizer et al . , 1996; Yoshimoto et al . , 2001; Zaidi et al . , 2012 ) . Acetyl-CoA is the first step in lipid biosynthesis , which serves biosynthetic needs of proliferating cells . Recent findings implicate TME in the induced metabolic rewiring in cancer cells ( Cairns et al . , 2011; Fiaschi and Chiarugi , 2012; Rattigan et al . , 2012 ) . However , role of exosomes in the metabolic crosstalk between cancer cells and TME is still unknown . We first asked whether CDEs could reprogram cancer cell metabolism . To determine this metabolic regulation , we first cultured CDEs with prostate cancer cells and performed 13C based isotope tracing of metabolic fluxes and bioenergetics analysis . We have used 200 μg/ml for the different types of experiments conducted herein ( proliferation assays , metabolic assays and tracer experiments ) . The working concentration was chosen to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1 and 10 ( Brauer et al . , 2013; Hu et al . , 2015; Delinassios , 1987 ) . A concentration of 200 μg/ml corresponded to ratio between 1 and 5 CAFs per cancer cell and hence , is within the range reported in the literature . However , for different CAFs and cancer cells system this ratio should be individually estimated based on the functional effect of stromal cells on cancer cells . It is to be noted that due to protracted purification steps during exosome isolation , degradation of metabolites can occur and hence , replenishment of exosomes for functional studies was followed and recommended . Additionally , to minimize degradation of metabolites in exosomes , we used fresh exosomes for all the experiments and avoided any freeze-thaw cycle in exosomes that were introduced to cancer cells cultures . Intriguingly , cancer cells cultured with exosomes had significantly reduced OXPHOS with a concomitant increase in glycolysis . Our results were further corroborated by observations regarding higher glucose uptake and lactate secretion by cancer cells in the presence of exosomes . We show for the first time that stromal exosomes shift cellular metabolism towards glycolysis in cancer cells ( Figure 8 ) . We further extended these observations in prostate cancer to pancreatic cancer and found similar inhibitory effect of pancreatic CDEs on mitochondrial respiration . Interestingly , this regulation was Kras independent in pancreatic cancers . In Figure 2 , we observe a reduction of OCR in PC3 cells co-transfected with miR-22 , let7a and miR-25b , in line with our hypothesis of the inhibitory effect of miRNAs from CDEs . However , the caveat associated with this is that due to limitations in genome engineering technologies , it is extremely difficult to insert multiple miRNAs in exosomes or transfect multiple miRNAs together without causing significant toxicity . Nonetheless , our results show that abundant miRNA in exosomes that have been previously shown to target OXPHOS , indeed cause inhibition of oxygen consumption in our system . In light of these results we believe further studies are needed to uncover the mechanism of ETC inhibition by CDEs-derived miRNAs that are out of scope for this study . 10 . 7554/eLife . 10250 . 019Figure 8 . Pleiotropic regulation of cancer cell metabolism by CDEs . Schematic shows the metabolic regulation of CDEs in cancer cells through inhibition of oxidative phosphorylation and contribution of metabolite cargo . This regulation leads to significant increase of reductive glutamine metabolism in cancer cells in presence of exosomes . CDEs are also cargo of amino acids , TCA cycle metabolites , and lipids . In nutrient starved TME metabolites derived from exosomes enrich cancer cells with biosynthesis building blocks and thereby promote tumor growth . DOI: http://dx . doi . org/10 . 7554/eLife . 10250 . 019 Previous studies , reported that under disabled mitochondrial metabolic conditions such as hypoxia , or inhibition of electron transport complexes , cancer cells increasingly rely on reductive glutamine metabolism as compared to oxidative glutamine metabolism . To unravel the contribution of major nutrients to cancer cells , we performed 13C based metabolite tracing and isotopologue spectral analysis ( ISA ) . Indeed , CDEs upregulated reductive carboxylation of glutamine in cancer cells . Previous reports showed that reductive carboxylation is a critical pathway to support the growth of tumor cells under hypoxia ( Metallo , 2012; Mullen , 2012 ) . This suggests that CDEs create hypoxia mimicking environment in cancer cells leading to an increase in reductive carboxylation of glutamine in cancer cells . Our ISA results of glucose and glutamine contribution to acetyl-CoA , a precursor for fatty acid synthesis , confirmed the increased reliance of cancer cells on reductive glutamine metabolism in presence of stromal exosomes . However , we were not able to balance palmitate synthesis through glucose and glutamine in cancer cells cultured with CDEs , which led us to measure contributions of acetate and pyruvate . Consistent with recent reports that acetate in media could serve as source of lipid synthesis in cancer cells , we found that acetate could contribute between 10–15% towards lipogenesis . However , pyruvate contribution was much lower and was between 3–8% in cancer cells . These results suggested that exosomes themselves might be acting as source of metabolites in a manner similar to macropinocytosis observed recently in Kras expressing pancreatic cancer cells ( Commisso et al . , 2013 ) . CDE-mediated metabolic changes in cancer cells , metabolic flux analysis is warranted Since exosomes contained carbon sources such as proteins and lipids , we inquired if exosomes could act as source of building blocks for biosynthesis and proliferation . To our remarkable surprise , we found that exosomes from pancreatic and prostate CAFs contained intact components of the intracellular metabolite pool , including amino acids , acetate , stearate , palmitate , and lactate . We provide previously unidentified evidence that these nutrients can enrich cancer cells under nutrient deprived or nutrient stress conditions . To label the metabolites , proteins and lipids contained in exosomes , we cultured CAFs in media supplemented with13C-labeled amino acids dominantly found in the serum ( lysine , leucine , phenylalanine , and glutamine ) along with nutrients such as glucose and pyruvate . We then cultured cancer cells under nutrient deprived conditions with these labeled exosomes and found that these labeled exosomes could indeed contribute to TCA cycle metabolites in cancer cells . These results conclusively showed that TME can supply metabolites directly to cancer cells through exosomes and these metabolites indeed can fuel TCA cycle in cancer cells . Recently published article by Lyden et al ( Hoshino et al . , 2015 ) , showed that exosomes precondition specific organs for metastatic invasion . Hence , future studies may be directed towards determining organ-specific metabolic reprogramming of CDEs in cancer cells . Having established that exosomes can fuel TCA cycle in a manner similar to macropinocytosis in prostate cancer , we further showed that this exosomes derived metabolite enrichment is independent of activated Kras expression . Previous studies have shown that tumor cells uptake extracellular nutrients through a mechanism regulated by Kras . From observations made in our pancreatic tumor cell lines , BxPC3 ( wild type Kras ) and MiaPaCa-2 ( activated Kras ) we showed that exosomes uptake pathways were independent of Kras expression levels . In our results , BxPC3 and MiaPaCa-2 showed similar extents of metabolic profile regulation as well as enhanced growth rate because of stromal exosomes . These results along with our exosomes uptake inhibition experiments suggest that exosomes uptake occurs in cancer cells through multiple pathways that are independent of activated Kras expression levels . Through endocytosis inhibitors CytoD and receptor mediated endocytosis inhibitor heparin , we inhibited exosomes uptake , and thereby repressed exosomes' influence on increasing growth rates of pancreatic cancer cells BxPC3 and MiaPaCa-2 . In summary , our results reveal insights into intercellular communication between tumor microenvironment and cancer cells . For the first time , we provide evidence that CAFs derived exosomes can reprogram cancer cell metabolism through a metabolite cargo based nutrient enrichment mechanism . Our results will invigorate development of targeted methods for disrupting the exosomes-mediated communication between cancer and stromal cells for in vivo studies and therapeutics based on the targeted inhibition of this crosstalk .
PC3 , DU145 , 22RV1 , BxPC3 and MiaPaCa-2 were received from ATCC and authenticated by STR profiling with online ATCC profile . E006AA was kindly povided by Dr . Denis Wirtz ( Johns Hopkins University ) . Patient derived fibroblast cells were kindly provided by Drs . Donna Peehl and Anirban Maitra of Stanford University and MD Anderson , respectively and internal STR profiling is maintained and checked annually . All cell lines were mycoplasma free based on PCR based assays run every three months in the lab . PKH26 and PKH 67 fluorescent cell linker kits were from Sigma ( St . Louis , MO ) . Exosome–Dynabeads Human CD63 Detection kit ( 10606D ) , sheep anti-rabbit IgG Dynabeads ( 11203D ) were from Life technologies ( Carlsbad , CA ) . 13C carbon-labeled isotopes were from Cambridge Isotope Laboratories ( Tewksbury , MA ) . MiRCURYM RNA Isolation Kit was from Exiqon ( Vedbaek , Denmark ) . Cytochalasin D was from Sigma . Cell counting kit-8 was from Dojindo ( Rockville , MD ) . 3000 W spin columns were from Life technologies . Heparin and EIPA were from Sigma . Chloroquine was from VWR ( Radnor , PA ) . Synthetic liposomes ( F60103F-DO ) were from FormuMax Scientific ( Sunnyvale , CA ) . Prostate cancer cells and BxPC3 cells were cultured in RPMI containing 1 mM pyruvate , supplemented with 10% fetal bovine serum ( Invitrogen , Carlsbad , CA ) , 100 U/ml penicillin and 100 U/ml streptomycin . Exosome-depleted FBS ( Systems Biosciences , Palo Alto , CA ) was used for cell culture when metabolic analysis or proliferation rate measurements were performed . CAF19 , CAF35 and MiaPaCa-2 cells were cultured in DMEM . Prostate cancer patient derived fibroblast cells were cultured in MCDB105 ( Sigma ) supplemented with 5% fetal bovine serum ( Invitrogen ) , 5 ng/ml fibroblast growth factor ( FGF ) ( PeproTech , Rocky Hill , NJ ) , 5 ng/ml insulin ( Sigma ) , 100 μg/ml gentamicin . All cells were incubated in 5% CO2 , and 37°C incubator . CAFs were seeded in T75 flasks , and when the CAFs were 70% confluent , PBS was used to wash cell twice , then the fresh MCDB medium with exosomes depleted FBS ( Systems Biosciences ) was added to the flask . After 48 hr , exosomes were isolated from the spent medium , and added into the medium incubating prostate cancer cells . For 13C labeled RPMI medium , we used RPMI without amino acids and supplemented it with appropriate levels of labeled 13C3-pyruvate , 13C6-glucose , 13C5-glutamine , 13C6-leucine , 13C6-lysine , 13C9-phenylalanine; ultracentrifugation was used to remove possible exosomes in FBS of this medium; CAFs were cultured in this medium for 72 hr and labeled exosomes were isolated . To isolate exosomes , cells were cultured with exosome-depleted serum . We collected the conditioned medium to isolate exosomes according to the instructions of the protocol ( Life technologies ) . The collected medium was centrifuged in 2000 xg for 30 min to remove cells and debris . We then transferred the supernatant containing the cell-free culture media to a new tube without disturbing the pellet . Next , we transferred the required volume of cell-free culture media to a new tube and added 0 . 5 volumes of the Total exosomes isolation ( for cell culture media ) reagent and mixed the culture media/reagent mixture well by vortexing until there was a homogenous solution . Incubate samples at 2°C to 8°C overnight . After incubation , the samples were centrifuged at 10 , 000 × g for 1 hr at 2°C to 8°C . The supernatant was aspirated and discarded . Exosomes were contained in the pellet at the bottom of the tube . Re-suspended the pellet in a convenient volume of working medium with exosomes depleted FBS ( Systems Biosciences ) . The concentration of CDEs was measured by BCA kit , which represents the protein concentration of CDEs . The exosome concentration of 200 μg/ml was obtained by diluting an average yield of 270 μg exosome protein ( which is equivalent to 5 . 5x1010 particles ) which was produced from 120 ml of supernatant . This corresponds to 28000 particles per CAF over a period of 48 hr . Equivalent particle of exosomes was obtained from a measurement of 4 . 9 μg for 109 particles . The utilization of exosomes in cancer cell cultures were based on application of 100–400 μg/ml of exosomes concentration that has been reported in literature ( Christianson et al . , 2013; Zhu et al . , 2012 ) . A working concentration of 200 μg/ml was chosen for most of the experiments in this study to maintain a physiological ratio of CAFs to cancer cells which is reported to be between 1–10 ( Brauer et al . , 2013; Hu et al . , 2015; Delinassios , 1987 ) . Hence , the number of CAFs required to secrete the amount of exosomes that the cancer cells are exposed to should reflect the ratio of CAFs to cancer cells in tumor . For the different types of experiments conducted herein ( proliferation assays , metabolic assays and tracer experiments ) , this ratio was maintained between 1 and 5 CAFs per cancer cell , and hence is physiologically relevant . Exosomes size and particles density were measured by Zetaview ( Particle Metrix , Diessen , Germany ) . Exosomes resuspended in PBS were diluted 1000 fold for measurement and size distribution . Briefly , 5 μl of exosomes in medium or PBS were added to the measurement system . According to particles’ Brownian motion , the diffusion constant is calculated and transferred into a size histogram via the Einstein Stokes relation between diffusion constant and particle size . Enriched exosomes were captured using the CD63+ Dynabead exosomes isolation kit ( Invitrogen , Life Technologies #10606D ) . The Flow Analysis of stromal exosomes bound to Dynabeads conjugated with antibody was done according to the manufacture’s protocol . Briefly , 10 µl of exosomes ( 200 µg/mL ) were incubated with 90 µl of CD63+ Dynabeads overnight at 4°C . Dynabead magnet was then used to positively select for bound exosomes which were then stained with PE Mouse Anti-Human CD63 ( BD Bioscience , San Jose , CA ) . Isotype control was stained by Simultest IgG2a/IgG1 ( BD Bioscience , 340394 ) . Flow cytometry was performed on a Accuri C6 System ( BD Bioscience ) and analyzed on Flow Jo software . To analyze exosomes uptaken by prostate cancer cells , exosomes were pre-labeled by PKH67 dye ( Sigma ) , and 3000 spin columns were used to remove extra dye . The dyed exosomes were added to RPMI medium to culture cancer cells for 3 hr and then flow cytometry was performed to measure fluorescence intensity of cells . Exosomes were pre-labeled according to PKH26 cell linker kit ( Sigma ) . 3000 spin columns ( Sigma ) were used to remove extra dye . PC3 Cells were grown to 50% confluence in 8-well chamber slides and incubated with PKH26 labeled exosomes ( 200 µg/mL ) for 3 hr . Cells were then washed two times with PBS solution and fixed with 4% PFA for 10 min . Nuclei were stained with 4’ , 6-diamidino-2-phenylindole ( DAPI ) and slides were viewed under a Axio Observer Z1 Inverted fluorescence microscope ( Zeiss ) and analyzed on Zen software . Cells viability was measured by Cell counting kit-8 ( Dojindo Molecular Technologies , Inc . , Rockville , MD ) . Cells were cultured on 96-well plate in the indicated conditions . Viability assay solution was added to the plate for incubation of 3 hr and absorbance was measured at 450 nm . Total RNA was extracted from cells using the Quick-RNA MiniPrep ( Zymo Research , Irvine , CA ) , following the manufacturer’s instructions . RNA amplification was performed using Illumina TotalPrepTM RNA amplification kit ( AMIL1791 , Life Technologies ) , according to the manufacturer’s instructions . Briefly , 500 ng total RNA was used to synthesize the first strand cDNA using a MyCycler thermal cycler ( Bio-Rad , Hercules , CA ) . Subsequently , the second strand cDNA was synthesized and cDNA was purified with 20 µl of 55°C nuclease-free water . In vitro transcription for cRNA synthesis was carried out using 14 hr incubation at 37°C . cRNA was then eluted with 200 µl of 55°C nuclease-free water . Hybridization and imaging were done using the HumanHT-12 v4 Expression BeadChip Kit ( Illumina , San Diego , CA ) according to manufacturer’s protocol . Total RNA was isolated using a Zymo mini kit ( Qiagen , Valencia , CA ) . High Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) was used to synthesize cDNA from 1 μg of total RNA . The levels of COX-1 and CYTB were examined by real-time PCR using 50 ng of the synthesized cDNA . Real-time PCR was performed with the SYBR Green PCR MasterMix ( Applied Biosystems , Warrington , UK ) . All reactions with COX-1 and CYTB were normalized against glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) . Specific primer sets were as follows ( listed 5’–3’; forward and reverse , respectively ) : COX-1 , TCGCATCTGCTATAGTGGAG and ATTATTCCGAAGCCTGGTAGG; CYTB , TGAAACTTCGGCTCACTCCT and AATGTATGGGATGGCGGATA . Reactions were performed in a volume of 20 μl . Isolation of miRNA from exosomes was done with the MiRCURY RNA Isolation Kit . In brief , steps of lysis , precipitation , repeated washing , and elution were performed to isolate purified small RNAs and then miRNA expression levels were measured by NanoString miRNA assays . miRNA levels were measured using the nCounter Human V2 miRNA expression analysis kit ( Nanostring ) , according to the manufacturer’s instructions . The data were corrected for loading using the relative geometric means of endogenous miRNA levels as a correction factor . The miRNAs were ranked by their average count across all exosomes samples . Glucose assay were done according to the instructions of assay kit ( Wako Glucose kit , Wako Diagnostics , Mountain View , CA ) . In brief , a 250 μl of reconstituted Wako glucose reagent was added to a 96-well assay plate followed with 2 μl sample addition in each well . The plate was incubated at 37°C for 5 min . The change in absorbance , which indicates the amount of glucose present , was measured at 505 nm and 600 nm by using a spectrophotometer ( SpectraMax M5; Molecular Devices , Sunnyvale , CA ) . Lactate secretion was determined using the Trinity Lactate Kit ( Trinity Biotech Plc . , Co Wicklow , Ireland ) . Media samples were diluted 1:10 in PBS , and lactate reagent was reconstructed and added to the diluted samples in an assay plate . The plate was incubated for 1 hr at 37°C , protecting from light . Afterwards the change in absorbance was read on a spectrophotometer at 540 nm . Protein assays are used to do normalization in our experiment and is done according to Bicinchoninic Acid Protein Assay ( Thermo Fisher Scientific , Waltham , MA ) protocol . In brief , protein reagent was added to a 96-well assay plate and mix with samples or standard , and then incubated at 37°C for 30 min . The absorbance was read on a spectrophotometer at 562 nm . Acetate concentration was measured according to manufacturer’s instructions for acetate colorimetric assay kit ( BioVision #K658 , Milpitas , CA ) . Briefly , samples or acetate standards were mixed with reaction mixtures , incubated at room temperature for 40 min , and measured at OD450 nm . Mitochondrial permeability transition was determined by staining the cells with TMRM ( Molecular Probes , Eugene , OR ) . The mitochondrial membrane potential was quantified by SpectraMax M5 ( SpectraMax M5; Molecular Devices , Sunnyvale , CA ) . Mitochondrial oxygen consumption was monitored with an XF24 Extracellular Flux Analyzer ( Seahorse Bioscience , North Billerica , MA ) . The cells were seeded in Seahorse 24-well microplates at a cell density of 70% confluent cells per well in 100 μL of culture media with indicated conditions . After overnight incubation at 37°C with 5% CO2 , the media was replaced with 700 μL of assay medium . Then incubate the plate at 37°C without CO2 for at least 1 hr . The oxygen consumption rate ( OCR ) was then measured . The endogenous coupling degree of the OXPHOS system was assessed using oligomycin ( 2 μg/ml ) , an inhibitor of the F1FO-ATPsynthase . The uncoupled OCR was also measured in presence of 2 . 5 μM of FCCP . Finally , the cells were treated with a mitochondrial complex I inhibitor , Rotenone ( 2 μM ) in order to assess the mitochondrial contribution to OCR . Extracellular acidification rate ( ECAR ) can be measured in a similar way to OCR . All OCR or ECAR value was normalized with protein content of cells . The results presented are expressed in mean value of N experiments ± S . D or SEM , with N≥2 , n≥ 3 . Comparison of the data sets obtained from the different experiment conditions was performed with the Student t test . In the bar graphs , single asterisk ( * ) represents p<0 . 05 , double asterisks ( ** ) represent p<0 . 01 and triple asterisks ( *** ) represent p<0 . 001 . | Cancer cells behave differently from healthy cells in many ways . Healthy cells rely on structures called mitochondria to provide them with energy via a process that requires oxygen . However cancer cells don’t rely on this process , and instead release energy by breaking down sugars outside of the mitochondria . This may explain why cancer cells are able to thrive even when little oxygen is available . Cancer cells also interact with neighboring cells called fibroblasts , which are a major part of a tumor’s microenvironment , and recruit them into the tumors . The fibroblasts communicate with cancer cells , in part , by releasing chemical messengers packaged into tiny bubble-like structures called exosomes . Recent studies have suggested that these exosomes may help cancer cells to thrive , but there are many questions remaining about how they might do this . Now , Zhao et al . show that the fibroblasts smuggle essential nutrients to cancer cells via the exosomes and disable oxygen-based energy production in cancer cells . First , exosomes released by cancer-associated fibroblasts from people with prostate cancer were collected and marked with a green dye . Next , the green-labeled exosomes were mixed with prostate cancer cells , and shown to be absorbed by the cells . Oxygen-based energy release was dramatically reduced in the exosome-absorbing cells , and sugar-based energy release increased . Next , Zhao et al examined the contents of the exosomes , and found that they contain the building blocks of proteins , fats , and other important molecules . Next , the experiments revealed that both prostate cancer and pancreatic cancer cells deprived of nutrients can use these smuggled resources to continue to grow . Importantly , this process did not involve the protein Kras , which previous studies had show helps cancer cells absorb nutrients . These findings suggest that preventing exosomes from smuggling resources to starving cancer cells might be an effective strategy to treat cancers . | [
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] | 2016 | Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism |
It has been recognized for nearly a century that diet modulates aging . Despite early experiments suggesting that reduced caloric intake augmented lifespan , accumulating evidence indicates that other characteristics of the diet may be equally or more influential in modulating aging . We demonstrate that behavior , metabolism , and lifespan in Drosophila are affected by whether flies are provided a choice of different nutrients or a single , complete medium , largely independent of the amount of nutrients that are consumed . Meal choice elicits a rapid metabolic reprogramming that indicates a potentiation of TCA cycle and amino acid metabolism , which requires serotonin 2A receptor . Knockdown of glutamate dehydrogenase , a key TCA pathway component , abrogates the effect of dietary choice on lifespan . Our results reveal a mechanism of aging that applies in natural conditions , including our own , in which organisms continuously perceive and evaluate nutrient availability to promote fitness and well-being .
For nearly all metazoans , many aspects of biology are continuously influenced by information about nutrient availability . Interest in the nutritional determinants of aging date back to the early 1900s , as experiments in fruit flies ( Chippindale et al . , 1993; Chapman and Partridge , 1996 ) and rodents ( McCay et al . , 1935; Weindruch et al . , 1986; Weindruch and Walford , 1982 ) revealed that limiting total food intake or reducing diet quality , without malnutrition , increased lifespan . Dietary restriction ( DR , also called caloric restriction ) is now known to effectively increase the lifespan of nearly every species to which it has been applied . In mammals , increased lifespan in response to DR is accompanied by a broad-spectrum improvement in health during aging ( Weindruch and Walford , 1988; Longo and Finch , 2003; Barger et al . , 2008; Contestabile , 2009 ) . Research over the past century on the effects of DR has revealed how food characteristics influence healthy aging ( Fontana and Partridge , 2015; Tatar et al . , 2014 ) . For many years , it was believed that the caloric content of the food and the amount of energy consumed by the animal were the driving forces behind the effects of restriction on lifespan ( Masoro , 2005 ) . More recently , however , conceptual advances such as the geometric framework for nutrition ( Lee et al . , 2008; Solon-Biet et al . , 2014; Skorupa et al . , 2008 ) , together with comprehensive experiments that manipulate diet in model systems ( Skorupa et al . , 2008; Bruce et al . , 2013; Solon-Biet et al . , 2014; Jensen et al . , 2015 ) , have made it clear that dietary composition and availability of specific nutrients are often more relevant than calories in shaping life histories . For example , in homogenous nutritional environments where all nutrients are mixed together in a single food , animals fed a high carbohydrate–low protein diet generally live longer than siblings fed a calorically equivalent low carbohydrate–high protein diet ( Lee et al . , 2008; Bruce et al . , 2013 ) . Short-term experiments in heterogeneous nutritional environments , where animals are given a choice of two foods with different macronutrient ratios , reveal a behavioral preference for a diet that is high in carbohydrate and low in protein , suggesting that animals will adjust their dietary choices to optimize evolutionary fitness ( Lee et al . , 2008; Jensen et al . , 2015; Bunning et al . , 2016 ) . The long-term effects of a complex nutritional environment and the accompanying dietary choices on physiology and healthy aging are unknown . Macronutrient intake will almost surely not be the only factor here because sensory perception of specific nutrients ( Ostojic et al . , 2014; Waterson et al . , 2014 ) , and even the presence of choice itself ( Avondo et al . , 2013; Steenfeldt et al . , 2019 ) , may be influential . We have recently reported that how a meal is presented , or perhaps the way in which it is eaten , modulates aging in Drosophila ( Ro et al . , 2016 ) . We found that flies were longer-lived when aged in an environment in which all dietary components were mixed together ( termed a fixed diet ) compared to one in which the two macronutrients , yeast and sugar , were available separately ( termed a choice diet ) . When presented with a choice diet , flies consume an equivalent amount of protein and a higher carbohydrate:protein ratio than they do when fed a nutritionally equivalent fixed diet . Contrary to the results of most geometric nutritional studies ( Bruce et al . , 2013; Lee et al . , 2008 ) , however , their lifespan is shorter than those fed on high carbohydrate:protein fixed diets . This led us to speculate that the effects of dietary choice were derived , at least in part , from unknown effects of the complex nutritional environment . Here we present a series of experimental results that indicate that dietary choice elicits significant changes in the physiology and lifespan of male Drosophila melanogaster through mechanisms that are distinct , at least in part , from nutrient consumption . Coincident changes in behavioral and health metrics suggest that flies exhibit distinct neural and metabolic states when presented with a choice diet than they do when presented with one that is nutritionally homogeneous . We report that neuronal expression of serotonin receptor 2A ( 5-HT2A ) during the adult stage is required for many of the effects caused by dietary choice , including a 5-HT2A-dependent metabolic reprogramming that is consistent with a significant upregulation of the tricarboxylic acid ( TCA ) cycle and amino acid metabolism when nutrients were presented separately . Transgenic knockdown of a key enzyme , glutamate dehydrogenase ( GDH ) , which is predicted to diminish the metabolic changes induced by a choice diet , abrogated its effects on lifespan . Future research may elucidate the downstream targets through which neuronal 5-HT2A signals acts as a potent modulator of aging and physiology in an environment much like our own , where animals must constantly choose between different nutrients .
As with most model organisms , D . melanogaster reared in a conventional laboratory setting is normally provided with a homogenous , complete media that meets all of its nutritional needs . For flies , this medium is comprised predominantly of a source of protein and micronutrients ( e . g . , brewer’s yeast ) and a carbohydrate source ( e . g . , sucrose ) . In natural conditions , however , a complete food source is unlikely to be found , and animals must seek out and identify nutritionally complementary foods ( Csata et al . , 2020; Mayntz et al . , 2005 ) . Indeed , fruit flies are known to recognize and choose among different macronutrients based on palatability ( Weiss et al . , 2011; Harris et al . , 2015 ) and physiological demand ( Ribeiro and Dickson , 2010; Vargas et al . , 2010; Ro et al . , 2016; Steck et al . , 2018 ) . While studying the mechanisms underlying protein feeding , we observed significant modulation of survival depending on whether the animals were given a complete diet or were provided with a dietary choice by presenting the two macronutrient sources separately ( Figure 1A , also see Ro et al . , 2016 ) . These effects were observed in both sexes ( Figure 1A ) and in different laboratory strains ( Supplementary file 1 ) . The magnitude of the choice effect was consistently greater in males ( 29–37% change in mean lifespan ) than in females ( 5–15% change in mean lifespan , see Supplementary file 1 ) . Similar effects were observed using two D . melanogaster lines that were recently collected from the wild: the lifespans of male strains were consistently and significantly reduced in response to dietary choice and there was variability in the female response ( Figure 1B ) . The effects on lifespan are not due to some sort of acute toxicity because flies maintained on a choice diet for a short period ( e . g . , 2 weeks ) exhibited general health metrics , such as intestinal integrity and climbing ability ( Figure 1C , D ) , that were indistinguishable from siblings that were fed a fixed diet . Together these results indicate that the effects of dietary choice on lifespan are sex specific and are not an artifact of long-term laboratory culture . Four lines of evidence indicate that the effect of a choice diet on lifespan is driven , at least in part , by mechanisms that are independent of increased sucrose consumption . First , increased sugar consumption alone is generally not associated with a large reduction in fly lifespan; even extremely sugar-rich diets ( e . g . , greater than 8× sugar intake ) have only modest effects ( Dobson et al . , 2017 ) , much smaller than we observed following dietary choice . New data that we obtained when we measured lifespan on fixed diets designed to approximate the nutritional composition that flies voluntarily constructed from their choice diet were consistent with this interpretation . We compared the lifespans of flies aged on a choice diet with those obtained from flies aged on one of two high-sugar , fixed diets ( S30Y3 and S24Y3; i . e . , 30% [w/v] sugar or 24% [w/v] sucrose mixed with 3% [w/v] yeast , respectively ) , among which flies consumed statistically indistinguishable amounts of both sugar and yeast ( Figure 1F , represented as mass of nutrient consumed ) . The effect of dietary choice , relative to the standard SY10 diet , was much greater than that from homogeneous high-sugar diets ( Figure 1G ) . Second , the effects of the choice diet on gross measures of fly physiology were distinctly different from those caused by increased sugar intake in the fixed diet . Flies exposed to a choice diet exhibited a significant reduction in triglyceride/protein ratio ( Figure 1H ) and triglyceride abundance ( Figure 1—figure supplement 1; total weight was not affected; ) , while increased sugar in the fixed diets had no such effect ( Figure 1H , Figure 1—figure supplement 1 ) . Third , we observed that simply changing the nature of the choice , by manipulating the concentration of the yeast component of the choice diet , was sufficient to modulate lifespan even though both cohorts of flies consumed the same amount of sugar and protein ( Figure 1I , J , represented as mass of nutrient consumed ) . Moreover , the effect was opposite to that normally seen when similar manipulations were applied to a homogeneous diet ( Mair et al . , 2005 ) ; an increased concentration of the yeast component of choice extended lifespan ( Figure 1I ) . Fourth , the transcription factor dFoxo is required both for transcriptional changes and for nutritional programming of lifespan caused by excess dietary sugar ( Dobson et al . , 2017 ) , but it is dispensable for the lifespan effect of dietary choice ( Figure 1—figure supplement 2 ) . Together , these observations suggest that animals respond differently to macronutrients depending on how they are presented and that the effects of dietary choice on lifespan and physiology may be mediated by processes that are distinct from macronutrient intake . Although protein intake was not different in flies fed different diets , it seemed possible that dietary choice improved protein absorption , which might be important considering the influential role of this macronutrient in reducing lifespan ( Fontana and Partridge , 2015 ) . Evidence suggests that this is not the case . Individual weight was not different among flies fed different diets ( Figure 1—figure supplement 1 ) , and total protein was lower in flies given a dietary choice than it was in their siblings fed a fixed diet , which is the opposite of what would be expected from the effect of dietary choice on lifespan . Furthermore , molecular and physiological markers that were strongly affected by protein availability , including target of rapamycin ( TOR ) activity ( Fontana and Partridge , 2015 ) and female fecundity ( Jensen et al . , 2015 ) , were not altered by dietary choice ( Figure 1—figure supplement 3 ) . Previously , we hypothesized that neuronal mechanisms involved in nutrient evaluation might influence lifespan when macronutrients are presented separately ( Ro et al . , 2016 ) . Our candidate screen of neurotransmitters and their receptors revealed that the presence of a P-element insertion in the promoter region of serotonin receptor 2A ( 5-HT2APL00052 ) , which creates a putative loss of function mutation in this gene ( Nichols , 2007 ) , reduced protein preference in starved female flies and significantly extended lifespan under dietary choice ( Ro et al . , 2016 ) . Loss of function in other serotonin receptors had no influence on protein preference ( Ro et al . , 2016 ) , yet it remains to be tested whether they influence the aging processes in a choice environment . Here we focused on elucidating the mechanisms underlying the effects of the 5-HT2A receptor and expanded our analysis of its effects on dietary choice and male lifespan . Indeed , we found that the P-element insertion in 5-HT2A fully abrogated the male lifespan differences between fixed and choice dietary environments , largely by increasing lifespan when nutrients were presented individually ( see Figure 2A ) . Mutant flies were modestly short-lived on the standard , fixed diet , which is likely due to the pleotropic effects of this receptor ( Gasque et al . , 2013; Chakraborty et al . , 2019; Howard et al . , 2019 ) . We also investigated the influence of RNAi-based knockdown of 5-HT2A expression using multiple RNAi lines . Because 5-HT2A is highly expressed in the central nervous system ( Supplementary file 2 ) , we used the pan-neuronal driver elav-GAL4 to achieve 5-HT2A knockdown , and we observed that this manipulation abrogated the lifespan effect of dietary choice using one of the RNAi lines ( UAS-5-HT2A-RNAiTRiP ) and strongly reduced the effect in the second ( UAS-5-HT2A-RNAiKK; Figure 2B ) . We also observed that the survivorship of adult flies fed with 200 µM pirenperone , an anxiolytic known to antagonize 5-HT2A receptor signaling in mammals , was unaffected by nutrient presentation , further supporting a role for this receptor and ruling out a necessity for loss of function during development ( Figure 2C , Figure 2—figure supplement 1 for the second replicate ) . Together , these results implicate neuronal expression of receptor 5-HT2A in the adult male fly in mediating the effect of dietary choice on lifespan . Although we concluded that nutrient intake was unlikely to be responsible for the difference in lifespan in wild-type flies , it remained formally possible that feeding differences between control and 5-HT2A mutant flies were responsible for the dissimilar response to dietary choice . Our previous work demonstrated that 5-HT2A influenced protein preference after starvation ( Ro et al . , 2016 ) , and we asked whether it influenced nutrient consumption in fed flies . Surprisingly , we found that , in the choice environment , 5-HT2A mutant flies exhibited feeding characteristics similar to control animals: each consumed significantly more sucrose than yeast when given a dietary choice ( Figure 2D ) , despite spending the majority of their time physically located on the yeast food ( Figure 2E ) . Unlike control animals , however , the behavioral differences in 5-HT2A mutants were not coupled to changes in lifespan . DR , and the extended lifespan that accompanies it , are associated with increased locomotion behavior in several species ( Weindruch et al . , 1986; Weed et al . , 1997; Chen , 2005 ) , presumably reflecting a motivational state of hunger . By video tracking control and mutant flies in each of the dietary environments ( using our DTrack system , see Materials and methods ) , we found that control flies were generally more active when given a dietary choice: they traveled significantly farther in a 6 hr window when compared to siblings housed on a fixed diet . 5-HT2A mutant flies responded similarly ( Figure 2F , two-way ANOVA on the square root transformed values , pDiet*Genotype>0 . 05 ) . Behavioral classification of the movements during this time ( Corrales-Carvajal et al . , 2016 ) revealed that diet-dependent differences were mirrored in both the time spent walking ( speed > 2 mm/s ) , in executing micromovements ( e . g . , grooming or when 0 . 2 mm/s < movement speed ≤ 2 mm/s ) , and in the time spent sleeping or resting ( speed ≤ 0 . 2 mm/s ) ( Figure 2G ) . As with total distance moved , 5-HT2A mutants exhibited similar responses as control animals in all of these measures ( Figure 2G , two-way ANOVA on the square root transformed values , pDiet * Genotype>0 . 05 ) . From these data we concluded that diet-dependent changes in locomotion are not causal to changes in lifespan . Increased locomotor behavior has been linked with stress in mice ( Sestakova et al . , 2013 ) and perhaps in flies ( Mohammad et al . , 2016 ) , leading us to examine whether other stress-related phenotypes were changed in flies fed a choice diet ( Strekalova et al . , 2005; Linford et al . , 2015; Yang et al . , 2015 ) . First , we measured alcohol consumption because ethanol intake is known to be potentiated by conditions of stress or reward depletion ( Devineni and Heberlein , 2009; Shohat-Ophir et al . , 2012 ) . We found that when flies were provided a dietary choice for 10 days prior to being exposed to sucrose food containing 15% ( v/v ) ethanol , they interacted with the ethanol food significantly more than did flies previously fed a homogenous fixed diet ( Figure 2H , see Materials and methods for more details ) . 5-HT2A mutant flies did not exhibit a difference in ethanol food interactions based on previous diet ( Figure 2H ) . Second , we measured triglyceride ( TAG ) abundance ( Figures 1H and 2J ) , which has been postulated to be a physiological indicator of stress ( Starzec et al . , 1981; Starzec and Berger , 1986 ) . We found that flies given a dietary choice had reduced TAG and lived shorter following starvation on 2% agar than did flies fed a homogenous food ( Figure 2I ) . Both phenotypes were 5-HT2A dependent ( Figure 2I , J ) . Our results indicated that 5-HT2A is not involved in determining how animals interact with the choice environment , yet the requirement of this neuronal signaling in lifespan ( Figure 2B ) and physiological traits ( Figure 2I , J ) suggested that it may play a role in mediating internal nutrient homeostasis when flies are presented with a dietary choice . We therefore sought to identify changes in metabolite abundance using targeted metabolomics . To delineate an appropriate window of time to study , we completed a demographic analysis of the effect of dietary choice on age-specific mortality . Mortality rates of male flies whose nutrients were presented separately following eclosion exhibited measurable and high mortality rates by day 10 , while similar levels of mortality in flies exposed to a homogenous food did not appear until day 40 ( Figure 3A , compare black and orange solid lines ) . To determine how quickly the effects of nutrient presentation manifest , we performed a switch experiment in which flies fed a homogenous medium since eclosion were given a dietary choice beginning on day 20 . We observed that the mortality rate of the switched cohort rose within 48 hr after choice began , and after roughly 5 days , it reached the level of the cohort whose nutrients had been separate since eclosion . We also performed the opposite experiment in which nutrients were combined into a fixed diet at day 20 . Flies in this cohort exhibited a rapid decrease in mortality , again within 48 hr . Mortality rates in this cohort reached the level observed for flies that had been fed a homogenous food since eclosion , but this process took roughly 10 days ( Figure 3B ) , which was longer than the reverse ( Figure 3A ) . Cohorts that comprised 5-HT2A mutant animals exhibited similar patterns of mortality regardless of nutrient presentation , and these patterns were also unaffected by dietary switch ( Figure 3C , D ) . We reasoned that metabolic changes that are causally related to aging would be identified by focusing on those that were abrogated by loss of 5-HT2A . Others , perhaps those triggered by differences in feeding behaviors or food intake ( and independence of the lifespan effect ) , would persist in the two strains . We also chose to sample flies 48 hr after a dietary switch because this period was sufficient to significantly affect mortality rates ( see Figure 3A and B ) and because we felt that a comparison of switched to unswitched cohorts at this time would help further isolate changes that were causal to lifespan ( Figure 3E ) . Finally , although neural expression of 5-HT2A was required for the lifespan effects of dietary choice ( Figure 2B above ) , it is also putatively expressed in salivary glands , in digestive tissues such as the crop , and in reproductive organs ( Supplementary file 2 ) . We therefore collected heads and bodies separately to determine whether the metabolomic effects of the two nutritional environments were enriched in the brain or whether they also manifested in peripheral tissues . Using principal component analysis ( PCA ) , we identified three principal components ( PCs ) in each of the tissue samples by which metabolome profiles effectively separated genotypic effects from those caused by nutrient presentation ( additional PCA are presented in Figure 3—figure supplement 1 and Figure 3—figure supplement 2 ) . Genotype effects on the metabolome were demonstrated by PC2 and PC3 in the head and body data , respectively ( Figure 3F , G ) . A second PC in each data set identified diet history ( PC3 and PC2 in heads and bodies , respectively; Figure 3F , G ) . These combinations of metabolites provided separation based on how nutrients were presented to the flies for the 20 days prior to the dietary switch , and they were largely unaffected by the switch itself . Separation was also observed in 5-HT2A mutant flies , suggesting that the metabolites that load strongly on these axes exhibit slow , persistent changes in response to differences in nutrient presentation that are not associated with changes in mortality . Finally , a single PC in both heads and bodies ( represented by PC6 in both data sets ) distinguished acute responses to dietary switch in both tissues that were abrogated in 5-HT2A mutant animals ( Figure 3H , I ) . These PCs identified specific metabolites in either heads or bodies that were correlated with changes in mortality and that shared a common 5-HT2A dependence . The PCA results suggested that nutrient presentation influenced two processes: ( A ) a relatively stable 5-HT2A-independent metabolic network that responded slowly to dietary environment and that accounted for a significant amount of metabolome variation ( 10 . 2–11 . 2% ) and ( B ) a dynamic 5-HT2A-dependent metabolic network that accounted for a smaller component of variation ( 2 . 0–2 . 2% ) that responded quickly to dietary choice and that was associated with mortality . Using generalized linear models , we identified individual metabolites that exhibited statistically significant changes consistent with membership in each of these two groups ( a summary of the analysis of a third group that distinguished genotype effects is presented in Figure 3—figure supplement 3; statistics for each metabolite are presented in Supplementary file 3 ) . Criteria for inclusion in Group A consisted of a statistically significant effect of diet ( pDiet<0 . 05 ) and a non-significant diet × genotype interaction ( pDiet*Genotype>0 . 05 ) . Of the 103 and 125 metabolites that were detected in each tissue sample , respectively , we found that 45 ( heads ) and 46 ( bodies ) satisfied these criteria ( Figure 4A ) . Among those metabolites , 13 ( heads ) and 21 ( bodies ) exhibited an increase in abundance when nutrients were presented independently , while abundances of the remaining metabolites were reduced . Although our sample of measured metabolites was small and precluded a comprehensive pathway analysis ( see Materials and methods ) , MetaboAnalystR ( Pang et al . , 2020 ) applied to the full list of significant metabolites revealed several candidate pathways ( i . e . , those with more than three significantly affected metabolites in each ) , nearly all involved in amino acid metabolism , which may be specifically affected by dietary choice ( Figure 4C , see Materials and methods ) . Over 40% of the metabolites ( N = 19 of 45 or 46 metabolites in the head or body , respectively ) were modulated in both samples ( starred in Figure 4A ) . While 75% of these ( N = 14 ) were modulated in the same direction , there remained nearly a quarter of them that exhibited opposite changes in abundance . Changes in Group B , which represented the 5-HT2A-dependent metabolome , were fewer in number but more consistent across tissue samples . We identified 18 of 103 total metabolites in the head and 19 of 125 total metabolites in the body that were present in this group ( Figure 4B , ANOVA , pDiet*Genotype≤0 . 05 ) . In this case , six of seven of the metabolites that were present in both groups ( starred in Figure 4B ) were modulated in the same direction . Furthermore , MetaboAnalystR identified related pathways , involving the synthesis of aminoacyl-tRNAs or specific amino acids , which were found either in both tissues or only in the body ( Figure 4D ) . Among them , ‘alanine , aspartate and glutamate metabolism’ was the only category of interest in both tissues , specifically in a 5-HT2A-dependent manner . A closer look into its metabolic map ( KEGG ID: map00250 ) revealed that candidate metabolites are either members of the TCA cycle or the direct precursors of TCA metabolites , which indicated the modulation of this metabolic process in response to dietary choice . A detailed examination of 5-HT2A-dependent metabolites , and the relationships among them , indicated significant upregulation of the TCA cycle and amino acid metabolism when nutrients were presented separately ( Figure 5A ) . The TCA cycle is a series of biochemical reactions that generates energy ( ATP ) through the oxidation of acetyl-CoA derived from different nutrients , while the same reactions also provide precursors for amino acids ( Figure 5B ) . Specifically , we found that oxaloacetate abundance was increased in both tissue samples in a 5-HT2A-dependent manner , while α-ketoglutarate and fumarate were upregulated only in heads and bodies , respectively . Further analysis revealed consistency among the abundances of the amino acid precursors of these intermediates , which were also increased in their corresponding tissues ( Figure 5A , B ) . Asparagine , the immediate precursor for oxaloacetate , was increased in both heads and bodies; glutamine , the major precursor for α-ketoglutarate , was increased only in heads; and aspartate , the precursor of fumarate , was increased only in bodies . Although we were unable to measure abundance of acetyl-CoA directly , we observed that the abundance of lysine , its precursor , was increased in both tissue samples . Inspired by concurrent changes in TCA intermediates and their precursors , we investigated the direct roles of biochemical reactions that produce the TCA metabolites in the modulation of aging in response to dietary choice . We targeted the enzyme GDH , which converts glutamate to α-ketoglutarate , for RNAi-mediated knockdown . Our metabolomic data revealed significantly increased abundance of α-ketoglutarate in the heads of animals given a dietary choice , and α-ketoglutarate has been shown to modulate longevity in Caenorhabditis elegans ( Chin et al . , 2014 ) . Knocking-down GDH abundance in adult flies using the RU486-dependent GeneSwitch system ( Roman et al . , 2001 ) ( tub-GS-GAL4 ) coupled with UAS-GDH-RNAi blocked the lifespan differences between two dietary environments ( Figure 5C ) . This was not caused by the transcriptional inducer , RU486 , because control animals ( tub-GS-GAL4 coupled with UAS-GFP ) responded as expected to dietary choice when RU486 was presented ( Figure 5—figure supplement 1 ) . GDH knockdown had no effect on nutrient consumption ( Figure 5D ) or total protein abundance in the fly ( Figure 5—figure supplement 2 ) . Which tissues/cells are critical for the diet-specific lifespan effects induced by GDH knockdown , and whether such influences are due to changes in α-ketoglutarate per se or to alterations in the metabolic network more broadly ( Hoffman et al . , 2017 ) remain to be determined .
Here we studied the effects of meal presentation on aging and physiology in Drosophila . By separating the two macronutrients of the standard Drosophila laboratory diet ( i . e . , brewer’s yeast and sucrose ) and allowing the animals to behaviorally structure their own diet , we found that lifespan was significantly shortened in both male and female flies . In addition , changes in activity , stress resistance , lipid metabolism , and critical metabolic processes were also elicited by dietary choice . Flies carrying a loss-of-function mutation in serotonin receptor 5A ( 5-HT2A ) , with RNAi-mediated neuronal knockdown of 5-HT2A expression , or fed the putative receptor antagonist pirenperone , exhibited no differences in lifespan between choice and fixed diets , indicating that serotonin signaling through this receptor plays an essential role in the effect of nutrient presentation on aging . Flies voluntarily consumed more sugar than yeast when presented with a choice diet . Although it is difficult to definitively rule out differences in consumption as a cause for the phenotypes we observe , several lines of evidence indicate that is not the major factor influencing lifespan: ( 1 ) the effect of dietary choice on lifespan is significantly larger than that caused by even the most extreme carbohydrate-rich fixed diets ( as shown in both Figure 1G and previous studies from Skorupa et al . , 2008; Dobson et al . , 2017 ) ; ( 2 ) the choice diet induced physiological effects ( e . g . , reduced triglyceride abundance ) opposite of those associated with a high-sugar fixed diet ( e . g . , increased or unchanged triglyceride abundance depending on the genetic background , see Skorupa et al . , 2008 ) ; ( 3 ) the insulin-responsive transcription factor , dFoxo , which is an essential effector of dietary sugar on aging ( Dobson et al . , 2017 ) , is not required for the effect of dietary choice on lifespan; ( 4 ) manipulating the nature of the choice is sufficient to influence lifespan without altering the amount of nutrients eaten in a choice paradigm; and ( 5 ) flies with loss of 5-HT2A exhibited the same diet-dependent changes in feeding as did control animals , yet they experienced no diet-induced changes in lifespan ( Figure 2A , D ) . We found that dietary choice influenced lifespan in both male and female laboratory flies and that 5-HT2A is required for this effect in both sexes ( see female data in Ro et al . , 2016 ) , suggesting the mechanisms are at least partially shared between them . However , sex-specific phenomena were also observed: lifespan was significantly shortened in male flies for all strains tested ( from 11 to 37% in laboratory and wild strains ) , whereas the effects on lifespan were variable in recently wild-caught females ( an average of 3% reduction ) relative to a fixed diet . This is , perhaps , not surprising given the effects of different macronutrients among different strains ( Jin et al . , 2020 ) and sexes ( Hudry et al . , 2019; Camus et al . , 2019; Jensen et al . , 2015 ) . Future studies that focus on dissecting the role of individual macronutrients on choice effects or on investigating the involvement of sex-specific nutrient sensing proteins ( see Hudry et al . , 2019 ) , perhaps with a focus on population genetics of natural populations , will be useful for understanding the nature of sexual dimorphism on lifespan and physiology . We found that dietary choice led to a rapid and reversible change in age-specific mortality only during a defined period of life . Dietary choice significantly increased mortality within 48 hr of exposure , and this effect was observed only up until middle age ( roughly 40 days in our experiments ) after which time mortality rates in cohorts provided a fixed or choice diet were indistinguishable . This was true regardless of whether the flies had been presented with a dietary choice starting at eclosion or starting at 20 days of age , and these effects required serotonin receptor 5-HT2A . The effects of dietary choice on demographic aging , therefore , do not appear to accumulate , and they are simply absent in older flies , perhaps because of an age-dependent reduction in serotonin signaling , a lack of consequences of such signaling in older animals , or the manifestation of demographic heterogeneity ( Vaupel and Yashin , 1985 ) . These mortality patterns are distinct from those observed following other types of dietary or environmental perturbations . DR in flies is typically carried out by diluting both the yeast and carbohydrate components of a complete , fixed diet ( Chapman and Partridge , 1996 ) . DR rapidly influences mortality dynamics , and it is reversible at early ages . Unlike dietary choice , however , DR influences demographic aging across the whole of adulthood ( Mair et al . , 2003 ) . The effect of different temperatures on mortality is cumulative and permanent ( Mair et al . , 2003; Carvalho et al . , 2017 ) . The neuronal response to dietary choice reshapes metabolic networks that are associated with energy homeostasis in peripheral tissues ( see our model in Figure 6 ) . Some of these changes are mediated by 5-HT2A , which is widely expressed in the central nervous system ( Gnerer et al . , 2015 ) and in the thoracicoabdominal ganglion ( Supplementary file 2 ) . Our metabolomic data revealed that TCA cycle components , as well as key components of amino acid metabolism , are strongly affected by food choice and that such changes require 5-HT2A . There is evidence that at least one such metabolite , α-ketoglutarate ( αKG ) , is directly involved in the effect of dietary choice on lifespan . αKG was more abundant when flies were presented with a choice diet , and inhibition of GDH , which is involved in αKG synthesis , abrogated the difference in lifespan between choice and fixed diets ( see also Talbert et al . , 2015 ) . In C . elegans , it has been reported that αKG modulates longevity by inhibiting TOR signaling ( Chin et al . , 2014 ) and that oxaloacetate increases lifespan through an AMPK/FOXO-dependent pathway ( Williams et al . , 2009 ) . The effect of αKG on lifespan has recently been independently documented in flies ( Su et al . , 2019 ) , and , more recently , in mice ( Asadi Shahmirzadi et al . , 2020 ) , suggesting a conserved role for αKG in modulation of aging . Published data in Drosophila suggest that supplementation of αKG to a standard laboratory diet increased transcript abundance of AMPK and FOXO and reduces that of TOR ( Su et al . , 2019 ) . However , our data indicate that neither FOXO ( Figure 1—figure supplement 2 ) nor TOR ( Figure 1—figure supplement 3 ) signaling is involved in the effects of dietary choice on lifespan , suggesting that αKG may recruit different mechanisms in this environment . Perhaps instead of energy sensing pathways , metabolic enzymes that control the branch point of crucial reactions ( such as GDH ) are more relevant in the context of a choice environment ( Smith et al . , 2019 ) . We propose the idea that the effects of dietary choice may result from metabolic changes stimulated by an alteration in metabolic state induced by the need to continuously evaluate macronutrient availability against physiological demand , which would not exist in a fixed-diet environment . Flies housed in a choice environment , for example , are allowed to interact with each macronutrient individually , and this behavior would be expected to result in the sequential activation of sugar and protein sensing pathways . Flies exposed to a complete , fixed diet would only experience nutrients simultaneously . The perceived flavor of each macronutrient may be different when yeast and sugar are consumed separately compared to when they are paired together on a fixed diet ( Ahn et al . , 2011 ) and that may also influence lifespan and metabolism ( Ostojic et al . , 2014 ) . Notably , when given a choice , flies consumed more sugar than yeast but spent the majority of time physically on the yeast food . Loss of 5-HT2A did not alter these behaviors , establishing that control flies are capable of recognizing the components of a choice diet and that animals lacking 5-HT2A retain this ability . Perhaps the act of decision-making per se is costly ? In nature , decisions are made continuously following evaluation of environmental resources to allow animals to adjust their behavior in a way that would maximize their fitness ( Galef , 1996; Lima , 1998 ) . In humans , an abundance of choices can be stressful , which is thought to reflect the perceived risk of making a bad choice and therefore losing out on other , more valuable resources ( Schwartz , 2004; Inbar et al . , 2011 ) . Although acute stress can be beneficial ( Dhabhar et al . , 2012 ) , chronic stress is often detrimental to lifespan and health ( Epel et al . , 2004; Glaser and Kiecolt-Glaser , 2005; Lupien et al . , 2009 ) . Drosophila exhibit stress-related behaviors that are mediated by neuronal signaling pathways that are conserved in mice and humans , including serotonin signaling ( Yang et al . , 2013; Sachs et al . , 2015; Mohammad et al . , 2016; Ries et al . , 2017 ) . It seems reasonable , therefore , to consider the possibility that 5-HT2A influences metabolic decisions in response to the perception or consumption of individual nutrients in ways that are important for aging . Biogenic amines , including serotonin and dopamine , have been shown to encode protein hunger and reward ( Ribeiro and Dickson , 2010; Vargas et al . , 2010; Ro et al . , 2016; Liu et al . , 2017 ) , and serotonin receptor 5-HT2A has been implicated in mammalian stress ( Weisstaub et al . , 2006; Sachs et al . , 2015 ) and in a putatively stressful situation in flies in which prolonged sight of dead conspecifics increases mortality and shortens lifespan ( Chakraborty et al . , 2019 ) . Notably , a recent study in mice reported that unknown characteristics of each meal modulate aging independent of dietary composition or calories ( Mitchell et al . , 2019 ) , suggesting that how animals recognize , interact with , or interpret their food may be a mechanism of aging that is conserved across taxa .
The standard laboratory stocks w1118 , Canton‐S , and the mutant 5-HT2APL00052 were originally obtained from the Bloomington Drosophila Stock Center . As described in our previous publication ( Ro et al . , 2016 ) , 5-HT2APL00052 were backcrossed 10 generations to w1118 prior to all the experiments . The wDahomey and dFoxoΔ94 lines were provided by L . Partridge ( University College London , UK ) . dFoxoΔ94 were backcrossed into wDahomey prior to the lifespan experiments . Two isofemale lines ( 18In08-17 and 18Ln10-4 , progeny from single wild females that were collected from two different orchards in Media , PA , in 2018 ) were gifts from P . Schmidt ( University of Pennsylvania , PA ) . Tub-GS-GAL4 was obtained from R . Davis ( The Scripps Research Institute , Jupiter , FL ) . elav-GAL4 ( BDSC#458 ) and UAS transgenic lines , including UAS-5-HT2A-RNAiTRiP ( BDSC#31882 ) , UAS-5-HT2A-RNAiKK ( VDRC#102105 ) , UAS-GDH-RNAi ( BDSC#51473 ) , and UAS-eGFP ( BDSC#79025 ) , were purchased from either the Bloomington Drosophila Stock Center or the Vienna Drosophila Resource Center . All fly stocks were maintained on a standard cornmeal-based larval growth medium ( produced by LabScientific Inc and purchased from Fisher Scientific ) and in a controlled environment ( 25 °C , 60% humidity ) with a 12:12 hr light:dark cycle . We controlled the developmental larval density by manually aliquoting 32 µl of collected eggs into individual bottles containing 25 ml of food . Following eclosion , mixed-sex flies were kept on SY10 ( 10% [w/v] sucrose and 10% [w/v] yeast ) medium for 2–3 days until they were used for experiments . Pioneer table sugar ( purchased from Gordon Food Service , MI ) and MP Biomedicals Brewer’s Yeast ( purchased from Fisher Scientific ) were used in our study . To study the lifespan , behavior , and physiology of flies that have a dietary choice , we created food wells that were divided in the middle ( Figure 1A ) . This allowed us to expose the flies to two separate sources of food simultaneously . For these experiments , we either loaded different foods on each side ( choice diet ) or the same food on both sides ( fixed diet ) . Unless otherwise mentioned , the choice diets contained 10% ( w/v ) sucrose or 10% ( w/v ) yeast on either side , and the fixed diets contained a mix of 10% ( w/v ) sucrose and 10% ( w/v ) yeast on each side . We purchased all drugs from Sigma-Aldrich . In the pirenperone experiment , drug was initially dissolved in 100% dimethyl sulfoxide ( DMSO ) at 10 mM concentration , aliquoted and stored at −20°C . Prior to the experiment , 200 µM pirenperone and the same dilution of DMSO ( control vehicle ) were made . Then , 50 µl of the diluted drug or control vehicle was added to each food well . After the liquid evaporated ( ~2 hr ) , the food plates were ready for use as described in Survival assays . In the GeneSwitch experiment , RU486 ( mifepristone ) was first dissolved in 80% ( v/v ) ethanol at 10 mM concentration , marked by blue dye ( 5% [w/v] FD and C Blue No . 1; Spectrum Chemical ) , and stored at −20°C . For experimental food , 200 µM RU486 or the same dilution of control vehicle ( 80% [v/v] ethanol ) was made from the stock and added to the food . To ensure flies consumed the same amount of RU486 between the choice diet and the fixed diet , drug was only administrated in the yeast portion of the choice diet , since the yeast intake on a choice diet is indistinguishable from the sugar–yeast intake on the fixed diet ( Figure 1E ) . Lifespan assay was carried out as described below . Lifespans were measured using established protocols ( Linford et al . , 2013 ) . Normally , six replicate vials ( ~150 experimental flies ) were established for each treatment . Flies were transferred to fresh media every 2‐3 days , at which time dead flies were removed and recorded using the DLife system developed in the Pletcher Laboratory ( Linford et al . , 2013 ) . Flies were kept in constant temperature ( 25°C ) and humidity ( 60% ) conditions with a 12:12 hr light:dark cycle . We used the protocol described previously ( Shell et al . , 2018 ) . Experimental flies were co-housed in vials ( 10 flies per vial , 8–10 replicates for each group ) for 2–3 days following eclosion and then sorted into individual sex cohorts . After exposure to the choice or fixed diet for 12 days ( food was changed every 2–3 days ) , flies were transferred to the choice or fixed diet with 1% ( w/v ) FD and C Blue No . 1 in either ( 1 ) only the S10 ( 10% [w/v] sucrose ) food in the choice diet; ( 2 ) only the Y10 ( 10% [w/v] yeast ) food in the choice diet; or ( 3 ) food in both wells in the fixed diet SY10 ( 10% [w/v] sucrose and 10% [w/v] yeast ) . Vials were discarded if one or more dead flies were observed after the 24 hr feeding period . Excreted dye ( ExVial ) was collected by addition of 3 ml of Milli-Q water of vials followed by vortexing . Concentration of the ExVial dye in water extracts was determined by absorbance at 630 nm , which was used to infer macronutrient consumption . Flies were tested on the Fly Liquid-Food Interaction Counter ( FLIC ) system as previously described to monitor ethanol feeding ( Ro et al . , 2014 ) after exposure to a choice or fixed diet for 10 days . We filled the liquid-food reservoir with 15% ( v/v ) ethanol + 5% ( w/v ) sucrose in 4 mg/l MgCl2 . We synchronized the feeding state prior to loading flies onto the FLIC system . Flies were removed from their food environment to kimwipes with 2 ml of water 2 hr before their natural breakfast time . At the peak of their normal breakfast meal ( lights on ) , we flipped flies back onto their respective choice or fixed diets and let them feed for 2 hr . Following breakfast feeding , flies were briefly fasted for 3 hr on a wet kimwipe with 2 ml of water . We began the FLIC recording software before aspirating flies into the system and analyzed food or ethanol interactions for 6 hr . Flies were anesthetized briefly on ice and manually aspirated into the Drosophila feeding monitors ( DFMs ) . Each DFM was loaded with flies from at least two treatment groups to reduce technical bias from the DFM signals . FLIC data were analyzed using custom R code , which is available at wikiflic . com . Default thresholds were used for analysis except for the following: minimum feeding threshold = 20 , minimum events = 1 , and tasting threshold = ( 10 , 20 ) . Flies that had zero feeding events over the testing interval were removed from the analysis . Experimental males were reared on SY10 food for 2–3 days shortly after eclosion . We aspirated individual flies into the two-choice food arenas ( diameter = 31 mm , either a choice diet or a fixed diet , N = 12 for each group ) and let them adapt to the environment for 1 day . Then , 6 hr video recording ( 10:00–16:00 , at two frames per second with a resolution at 528 × 320 ) was taken and analyzed with DTrack , a video analysis package developed in the Pletcher Laboratory . Using the same speed threshold published before ( Corrales-Carvajal et al . , 2016 ) , we classified the movements of flies into resting or sleeping ( speed ≤ 0 . 2 mm/s ) , micromovement ( 0 . 2 mm/s < speed ≤2 mm/s ) , and walking ( speed > 2 mm/s ) . For positional preference assay , we aspirated individual male flies into the two-choice food arenas ( N = 24 for each genotype ) after the designated exposure to the choice diet for 7 days . Three hour video recordings were taken ( at two frames per second with a resolution at 528 × 320 ) and analyzed with DTrack . The fraction of time flies spent on each macronutrient was computed and used for statistical analyses . After the designated exposure to the choice or fixed diet for 10 days ( food was changed every 2–3 days ) , male flies were transferred to the negative geotaxis apparatus using brief CO2 anesthesia , after which they were allowed to recover for 30 min . We used an automated process in which flies were dropped from 24’ . After freefall , which effectively knocked the flies to the bottom of the chamber , a video camera was triggered and individual fly movements were tracked for 10 s using the DDrop software developed in our laboratory . From the tracking data , we were able to calculate , for each male , the total distance traveled up in 10 s . After the designated exposure to the choice or fixed diet for 2 or 3 weeks ( food was changed every 2–3 days ) , experimental flies were quickly frozen , collected into groups of five , weighed , and then homogenized in 200 μl of cold phosphate-buffered saline containing 0 . 1% Triton X-100 ( IBI Scientific ) for 30 s at 30 Hz using a QIAGEN TissueLyser . For TAG quantification , the homogenate ( 20 μl ) was added into 200 μl of Infinity Triglyceride Reagent ( Thermo Electron Corp . ) and incubated at 37°C for 10 min with constant agitation . TAG concentrations were determined by the absorbance at 520 nm and estimated by a known triglyceride standard . For protein measurement , 5 μl of fly homogenate was incubated with 200 μl of ( 1:50 ) 4% ( w/v ) cupric sulfate/bicinchoninic acid solution ( Novagen ) at room temperature for 30 min . Protein concentrations were estimated by the absorbance at 562 nm through the comparison with bovine serum albumin standards . Average weight , TAG , and protein values were based on at least six independent biological replicates ( of five flies each ) from multiple vials . After the designated exposure to the choice or fixed diet for 20 days ( food was changed every 2–3 days ) , flies were transferred to fresh vials containing 1% ( w/v ) agar . The number of dead flies was recorded approximately every 2–5 hr using DLife system . We investigated intestinal integrity using the Smurf assay ( Regan et al . , 2016; Rera et al . , 2012 ) . Experimental flies were grouped in vials ( 25 flies per vial and 12 replicates per each diet ) 2–3 days following eclosion . Following exposure to a choice or fixed diet for 12 days ( food was changed every 2–3 days and survivorship of w1118 males on choice diet was roughly 70% ) , flies were transferred to S30 ( 30% [w/v] sucrose ) food with 2 . 5% ( w/v ) blue dye ( FD and C Blue No . 1; Spectrum Chemical ) for 24 hr . A fly was counted as a Smurf when blue was observed outside of the digestive tract ( Regan et al . , 2016; Rera et al . , 2012 ) . The proportion of Smurf flies in each replicate was calculated and used as a single observation for statistical analysis . Flies were raised according to our standard husbandry protocols ( see above ) , and following eclosion , males and females were allowed to mate on SY10 food for 48–72 hr . Eight females and eight males were then sorted into individual vials , which contained either a choice or a fixed diet . Egg counts were taken on days 1 , 2 , 5 , and 8 after sorting . Flies were transferred to fresh SY food 24 hr prior to the counting . After exposure to the choice or fixed diet for 20 days , experimental flies were flash frozen in liquid nitrogen . Heads and bodies were separated using a metal sieve on dry ice , and 10 heads were pooled for each biological replicate . Heads were first pulverized to a fine powder using a plastic pestle on dry ice . Protein extraction was carried out on ice using RIPA buffer ( Sigma–Aldrich ) supplemented with protease inhibitor cocktail ( Sigma ) , phosphatase inhibitor cocktail ( Sigma ) , sodium orthovanadate ( NEB , 1 mM ) , and sodium fluoride ( NEB , 1 mM ) . Ice cold buffer ( 105µl ) was added to heads followed by immediate homogenization with a motorized pestle for 10 s on ice . Lysates were incubated on ice for 10 min followed by 15 s of sonication and centrifugation at 16 , 000 × g at 4°C for 10 min . Supernatants were recovered , and protein concentration was determined using the Pierce BCA Protein Assay Kit ( Fisher Scientific ) . Thirty micrograms of protein lysate was added to 2× protein sample buffer ( 1 mM Tris–HCL pH 6 . 8 , 10% sodium dodecyl sulphate [SDS] , 1% bromophenol blue , and 1M dithiothreitol ) and denatured at 95°C for 10 min . Protein was separated by SDS–polyacrylamide gel electrophoresis on a 4–12% gel ( Bio-Rad ) at 200 V for 30 min , followed by electrophoretic transfer to PVDF membrane at 70 V for 1 hr . Blots were incubated in 5% milk in 1% TBS-T at room temperature for 1 hr , followed by overnight incubation with primary antibodies overnight at 4°C ( anti-dS6K gift from Thomas Neufeld; dilution at 1:3500 , anti-pS6KThr398 Cell Signaling Technologies , #9209; dilution at 1:1000 , anti-GAPDH; dilution 1:6000 ) . Membranes were washed with 1% TBS-T and incubated with HRP-conjugated secondary antibodies ( Abcam ) at room temperature for 4 hr . Membranes were washed again with 1% TBS-T and then incubated briefly in ECL substrate ( SuperSignal West Femto , ThermoFisher ) before imaging . Male flies were exposed to the choice or fixed diet for 20 days ( food was changed every 2–3 days ) and then switched to the other diet ( experimental groups ) or the same diet ( control groups ) . Heads were removed via vortexing and manually separated from the bodies . Heads or bodies were then homogenized for 20 s in 200 μl of a 1:4 ( v:v ) water:MeOH solvent mixture using the Fast Prep 24 ( MP Biomedicals ) . Following the addition of 800 μl of methanol , the samples were incubated for 30 min on dry ice , then homogenized again . The mixture was spun at 13 , 000 RPM for 5 min at 4°C , and the soluble extract was collected into vials . This extract was then dried in a speedvac at 30°C for approximately 3 hr . Using a liquid chromatography triple quadrupole tandem mass spectrometry ( LC–QQQ–MS ) machine in the multiple reaction monitoring ( MRM ) mode , we targeted 205 metabolites in 25 important metabolic pathways , in both positive MS and negative MS modes . After removing any metabolites missing from more than 8 of 76 head samples ( 10 . 5% ) and 8 of 78 body samples ( 10 . 3% ) , we were left with 103 head metabolites and 125 body metabolites . Metabolite abundance for remaining missing values in this data set were log-transformed and imputed using the k-nearest neighbor algorithm with the impute package of R Bioconductor ( http://www . bioconductor . org ) . We then normalized the data to the standard normal distribution ( μ = 0 , σ2 = 1 ) . PCA was performed using the made4 package of R Bioconductor . Our PCA results suggest the choice diet can elicit two processes: ( 1 ) a 5-HT2A-dependent , fast-responding metabolic network that is associated with aging despite only accounting for a small number of variation in the metabolomes ( 2 . 0–2 . 2% ) and ( 2 ) a 5-HT2A-independent , stable metabolic component that accounts for a significant amount of metabolome variation ( 10 . 2–11 . 2% ) . To distinguish these two groups of metabolites , we used a linear model to analyze the variance attributed to diet , genotype , and interaction of the two for each metabolite: Metabolite abundance ( Y ) = Diet + Genotype + Diet * Genotype . We found 18 of 103 total metabolites in the head and 19 of 125 total metabolites in the body are modulated by choice diet in a 5-HT2A-dependent manner ( Figure 4A , B , ANOVA , pDiet*Genotype≤0 . 05 ) . To identify 5-HT2A independent change , we searched for metabolites that have a diet effect ( pDiet≤ 0 . 05 ) but did not show a diet–genotype interaction ( pDiet*Genotype>0 . 05 ) . In doing so , we found 45 and 46 metabolites in the head and body , respectively . We plotted a heatmap to illustrate the expression changes of the metabolite upon nutrient presentation using the R package gplots . The metabolites were mapped to biochemical pathways using MetaboAnalystR 3 . 0 ( Pang et al . , 2020 ) . Because the small number of detectable compounds in our targeted metabolomics panel ( N = 103 in heads or 125 in bodies ) limits the power of the enrichment analysis , candidate pathways were therefore identified as those with the number of hits greater than 3 . Unless otherwise indicated , pairwise comparisons between different treatment survivorship curves ( both lifespan and starvation resistance ) were carried out using the statistical package R within DLife ( Linford et al . , 2013 ) . p‐Values were obtained using log‐rank test . For testing the interaction between genotypes and diets , we used Cox-regression analysis to report p-value for the interaction term . Mortality rates were calculated using standard methods ( Partridge et al . , 2005 ) . To test the effects of diet and genotype involved in behavior classification and metabolites , we performed two-way ANOVA ( on the square root- or log-transformed values ) followed by post hoc significance test . To test for a diet effect on gut integrity , climbing ability , weight , TAG/protein levels , nutrient assumption , and positional preference , we used Mann–Whitney U test . Two-tailed p-values were reported unless otherwise mentioned . We designed the layout of individual samples for lifespan , behavior , physiology , and metabolomic analyses , so non-biological factors ( e . g . the location of samples ) do not contribute to the differences between groups . Source data for all quantifications shown in Data Figures 1–5 , figures supplements , and the supplementary files are provided with the paper . Metabolomic raw data , analyses , and statistics can be obtained from Supplementary files 3–4 and our GitHub repository ( github . com/ylyu-fly/Metabolomics-FlyChoiceDiet; Lyu , 2021; copy archived at swh:1:rev:86daef110d9a1eb27262bc5310b54cc2d315f7c1 ) . | The foods we eat can affect our lifespan , but it is also possible that thinking about food may have effects on our health . Choosing what to eat is one of the main ways we think about food , and most animals , including the fruit fly Drosophila melanogaster , choose their foods . The effects of these choices can affect health via a chemical in the brain called serotonin . This chemical interacts with proteins called serotonin 2A receptors in the brain , which then likely primes the body to process nutrients . To understand how serotonin affected the lifespan and health of fruit flies , Lyu et al . compared flies that were offered a single food to those that could choose between several foods . The flies that had a choice of foods lived shorter lives and produced more serotonin , but these effects were reversed when Lyu et al . limited the amount of a protein called glutamate dehydrogenase , which helps cells process nutrients . These results suggest that choosing what we eat can impact lifespan , ageing and health . Human and fly brains share many similarities , but human brain chemistry is more complex , as is our experience of food . This work demonstrates that food choices can affect lifespan . More research into this phenomenon may shed further light onto how our thoughts and decision-making impact our health . | [
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] | 2021 | Drosophila serotonin 2A receptor signaling coordinates central metabolic processes to modulate aging in response to nutrient choice |
We wish to identify determinants of endothelial lineage . Murine embryonic stem cells ( mESC ) were fused with human endothelial cells in stable , non-dividing , heterokaryons . Using RNA-seq , it is possible to discriminate between human and mouse transcripts in these chimeric heterokaryons . We observed a temporal pattern of gene expression in the ESCs of the heterokaryons that recapitulated ontogeny , with early mesodermal factors being expressed before mature endothelial genes . A set of transcriptional factors not known to be involved in endothelial development was upregulated , one of which was POU class 3 homeobox 2 ( Pou3f2 ) . We confirmed its importance in differentiation to endothelial lineage via loss- and gain-of-function ( LOF and GOF ) . Its role in vascular development was validated in zebrafish embryos using morpholino oligonucleotides . These studies provide a systematic and mechanistic approach for identifying key regulators in directed differentiation of pluripotent stem cells to somatic cell lineages .
Our understanding of the genetic and epigenetic processes governing endothelial development and differentiation is limited ( Yan et al . , 2010; De Val and Black , 2009 ) . Accordingly , our methodologies for obtaining endothelial cells from pluripotent stem cells are empirically driven and suboptimal ( Choi et al . , 2009; James et al . , 2010; Huang et al . , 2010a , 2010b; Wong et al . , 2012 ) . There is unexplained inconsistency in the yield of iPSC-ECs; in the stability of their phenotype; and in the fidelity of differentiation ( in terms of replicating the epigenetic and genetic profile of a mature endothelial cell ) . Furthermore , our ability to efficiently generate specific endothelial subtypes ( e . g . arterial , venous , lymphatic ) is poor . Thus , a systematic approach is needed to more completely define the genetic and epigenetic programs required for differentiating pluripotent stem cells to the endothelial phenotype . Here , we propose an unbiased systematic approach to discover determinants of differentiation . We use interspecies heterokaryons , RNA sequencing and third-generation bioinformatics to discover novel candidate genes critical for proper endothelial differentiation and specification .
To discover new genes involved in endothelial specification , we made heterokaryons consisting of human endothelial cells ( hEC ) and murine embryonic stem cells ( mESC ) ( Figure 1a–c ) , which expressed cell surface markers and characteristics of both cell types . We hypothesized that the factors that are actively maintaining endothelial phenotype ( transcription factors , epigenetic modifiers and non-coding RNA etc ) would act on the pluripotent stem cell nuclei to induce expression of key determinants of endothelial lineage . We reasoned that we could use RNA seq to monitor global changes in the transcriptome of the pluripotent nucleus as it is reprogrammed in the heterokaryon toward an endothelial fate . In 95% of cases , the species-specific nucleotide differences between the mouse and human transcripts would permit us to differentiate between reads of murine versus human transcripts when the sequences were aligned to their respective genomes . 10 . 7554/eLife . 23588 . 003Figure 1 . Heterokaryon recapitulates gene expression of endothelial ontogeny . ( a ) Scheme for heterokaryon generation . GFP-labeled murine ESCs ( mESCs ) were fused with Cell Tracker Red labeled human ECs ( hECs ) by HVJ-enveloped fusagen to induce multinucleated heterokaryons . ( b ) Representative image of non-dividing multinucleated heterokaryons labeled with CD31 ( Red ) and GFP ( Green ) , Hoechst ( Blue ) dye were used to label nuclei . ( c ) Representative FACS plots for heterokaryons . ( d–g ) Up-regulation of murine EC genes including Kdr , Tie2 , Cdh5 and Vwf in heterokaryons consisting of mESC and hEC compared to co-culture control . ( h–k ) Up-regulation of human EC genes including Kdr , Tie2 , Cdh5 and Vwf in heterokaryons consisting of human iPSC ( hiPSC ) and murine EC ( mEC ) compared to Co-culture control . ( l–n ) Increased expression of transcription factors involved in endothelial development such as Etv2 , Ets1 and Tal1 during cell fusion of mESC with hEC . ( p–r ) Increased expression of transcription factors involved in endothelial development such as Etv2 , Ets1 and Tal1 during cell fusion of hiPSC with mEC . ( o and s ) Down-regulation of genes encoding pluripotent factors ( Oct4 , Sox2 and Nanog ) in heterokaryons compared to Co-culture control . All data represented as mean ± S . E . M . ( n = 3 ) . p<0 . 05 vs Co-culture control . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 003 Reprogramming of the cell population is synchronized upon the addition of the fusagen . Since there is no nuclear fusion , chromosome rearrangement , or chromosome loss in the heterokaryons ( Bhutani et al . , 2010 ) , we reasoned that this synchronization would permit us to study the temporal sequence of reprogramming to endothelial lineage using RNA seq . We optimized the cell fusion strategy using the fusagen HVJ ( Sendai virus ) envelope protein . By skewing the ratio of the input cells so that endothelial cells outnumbered pluripotent stem cells in the multinucleate heterokaryon , we forced reprogramming of the pluripotent stem cell nuclei toward an endothelial phenotype . To confirm that the system was working as anticipated , we assessed the expression of the mESC mRNA transcripts using murine-specific primers . In mESCs which were fused with human endothelial cells , we observed upregulation of murine endothelial genes including Kdr , Tie2 , Cdh5 and Vwf ( Figure 1d–g ) , and transcription factors involved in endothelial development such as Etv2 , Ets1 and Tal1 ( Figure 1l–n ) . Intriguingly , the expression of these genes seemed to mirror ontogeny , in that genes involved early in mesoderm specification ( e . g . Kdr ) were expressed earlier in the heterokaryon , whereas genes that are more specific to hemato-endothelial lineage ( e . g . Von Willebrand factor ) were expressed later . In parallel to the upregulation of genes involved in endothelial development , we observed down-regulation of genes encoding pluripotent factors ( Oct4 , Sox2 and Nanog ) ( Figure 1o ) . As a complementary approach , we generated heterokaryons consisting of murine endothelial cells ( mEC ) and human-induced pluripotent stem cells ( hiPSC ) . We fused hiPSCs with mECs , and the expression of human mRNA transcripts was assessed using human specific primers . We observed in the pluripotent cells an upregulation of human genes including Kdr , Tie2 , Cdh5 and Vwf ( Figure 1h–k ) , and transcription factors involved in endothelial development such as Etv2 , Ets1 and Tal1 ( Figure 1p–r ) . Again , we observed a temporal sequence that mirrored ontogeny , with a parallel downregulation of genes encoding pluripotent factors ( Oct4 , Sox2 and Nanog ) ( Figure 1s ) . These results demonstrate rapid induction of endothelial genes in the heterokaryon which appears to recapitulate endothelial development . Having observed that the heterokaryon system seemed to mirror endothelial ontogeny , we applied RNA-seq to identify novel determinants of endothelial lineage ( Figure 2 ) . Heterokaryons were double-positive cells ( GFP+ for mESC and CellTracker Red+ for hECs ) and were harvested at 6–24 hr post-fusion . These time points were chosen based on our qPCR data , which showed rapid sequential activation of endothelial genes as early as 6 hr post-fusion . Total RNA ( ~2 ug ) was isolated from heterokaryons or co-culture controls , and prepared for RNA-seq studies . We also did RNA-Seq for parental mESC; and for mESC that were exposed to an endothelial differentiation protocol for 4 or 8 days . We defined genes that are up- or down-regulated in each sample relative to the parental mESC . We were most interested in novel genes that encode transcriptional factors that are not known to be involved in mesodermal lineages or endothelial specification . We postulated that the heterokaryon system would be particularly useful to detect transcription factors that are transiently expressed during endothelial differentiation ( Figure 2a ) . The RNAseq analysis of the heterokaryon samples ( mESC/hEC ) revealed a set of 1297 genes in the mESC that were upregulated as well as a set of 795 genes that were downregulated after fusion with hEC ( Figure 2b ) . The upregulated genes included known endothelial related genes , for example , Kdr and Vwf , as well as hemangioblast markers , that is , angiotensin-converting enzyme ( ACE , CD143 ) . Conversely , murine pluripotency markers were down-regulated . We identified some novel transcriptional factors ( such as Tbx1 , Cebpd , Nrarp , Pou3f2 , Maf , Tbx20 , Tigd5 , Pdn and Batf2 ) , and targeted one of these ( Pou3f2 ) for further study . 10 . 7554/eLife . 23588 . 004Figure 2 . Heterokaryon bi-species RNA-seq reveals transcriptome reprogramming during differentiation of pluripotent stem cells into endothelial lineages . ( a ) Heat map to show expression level of genes that are up- or down-regulated either uniquely in the Heterokaryon ( Het ) at 6 hr after fusion or commonly in all het samples relative to the mESC samples . mESCs were exposed to a standard endothelial differentiation protocol for 4 or 8 days . Het_6 hr , Het_12 hr , and Het_24 hr indicate mouse stem cells ( mESC ) fused with human endothelial cells ( hEC ) for 6 , 12 or 24 hr . Co-Culture_6 hr and Co-Culture_24 hr indicate mESC co-cultured but not fused with hEC . ( b ) Venn diagram to show overlap of upregulated ( left ) or downregulated ( right ) mouse genes in heterokaryon at 6 , 12 and 24 hr after fusion relative to mESC . ( c ) Unbiased hierarchical clustering of all samples based on all genes that are differentially expressed in mESC samples relative to at least one of the other samples . ( d ) Heat map displaying upregulated and downregulated genes in heterokaryons and in differentiating mESC at different time points . Genes differentially expressed were clustered into groups for functional analysis and presented as a heat map based on their enrichment Q value . ( e–f ) Bar plot showing enrichment Q values of 17 functional terms in genes upregulated ( left ) or downregulated ( right ) in mESCs within heterokaryons relative to the mESCs . Upregulated or downregulated genes were defined based on EdgeR FDR cutoff 1e-5 . Overlap p value in pie chart was calculated based on Fisher’s Exact test . N = 3 for Het , n = 2 for mESCs , n = 1 for co-culture . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 004 Unbiased hierarchical clustering analysis indicated that the transcriptome of the mESC in the heterokaryon is closer to that of mESCs differentiated toward EC lineage , than to the parental mESCs ( Figure 2c ) . By contrast , the transcriptome of mESC co-cultured ( but not fused ) with hECs bears a closer relationship to the transcriptome of the parental mESCs . Global gene function enrichment analysis indicates that differentially up- or down-regulated genes in the mESC in heterokaryons are similar to those in mESC exposed to the endothelial differentiation protocol ( Figure 2d ) . In particular , the upregulated genes in the heterokaryons tend to be involved in transcription regulation , RNA alternative splicing , DNA binding , embryonic organ development , differentiation and regulation of cell proliferation ( Figure 2e ) , whereas downregulated genes are implicated in ATP and ribonucleotide binding ( Figure 2f ) . These results suggest that the heterokaryon system may serve as an effective model for cell differentiation . To validate our approach in discovery of novel determinants of cell lineage , we focused on Pou3f2 , which was identified as a candidate EC transcription factor in the RNA seq studies . Accordingly , we examined the temporal sequence of gene expression of Pou3f2 in the heterokaryon system ( Figure 3a ) . The expression of Pou3f2 was up-regulated at 6-hr post-fusion and its expression then declined over the ensuing 48 hr . In addition , we examined Pou3f2 expression during the differentiation of murine ESC to endothelial cells using our standard differentiation protocol ( Figure 3b ) . Intriguingly , the pattern of expression was similar , albeit with a slower time course . Specifically , the expression of Pou3f2 increased from Day 3 to Day 7 and then declined in the later phase ( 11 days ) of the endothelial differentiation protocol . Notably , the expression of other mesodermal and EC genes followed a similar pattern , in that their regulation was accelerated when ESCs were placed into heterokaryons versus the standard endothelial differentiation protocol . This observation is consistent with the notion that current differentiation protocols could be considerably improved . 10 . 7554/eLife . 23588 . 005Figure 3 . Role of Pou3f2 as a novel transcription factor in endothelial differentiation from murine embryonic stem cells . ( a ) Gene expression pattern of Pou3f2 in heterokaryons consisting of mESC and hEC compared to Co-culture control . ( b ) Validation of expression of Pou3f2 during differentiation of mESC into endothelial lineage . ( c ) Lentiviral mediated shRNA KD of Pou3f2 reduced the gene expressions of endothelial markers including Kdr , Tie2 , Nos3 , Cdh5 and Vwf at Day 8 following endothelial differentiation from mESC . ( d ) KD of Pou3f2 reduced the gene expressions of transcription factors involved in endothelial differentiation such as Ets1 , Erg , Etv2 and Fli1 in mESC differentiated to endothelial lineage at Day 8 . ( e ) No differences were found in the expressions of mesodermal ( Bmp4 , T ) , endodermal ( Cxcr4 and Gata4 ) and ectodermal ( Pax6 and Nestin ) in Pou3f2 shRNA treated mESC following endothelial differentiation at Day 8 . ( f and g ) Representative FACS plots and summarized diagram showing that Pou3f2 KD reduces the yield of mESC-derived CD31+ and CD144+ cells at Day 10 of endothelial differentiation protocol . ( h ) Representative images showing that Pou3f2 KD mESC-derived ECs manifest an impaired ability to form endothelial networks on matrigel . All data represented as mean ± S . E . M . ( n = 3 ) . p<0 . 05 vs control shRNA . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 005 To further characterize the role of Pou3f2 in the differentiation of mESC into EC , we performed lentiviral shRNA knockdown ( KD ) of Pou3f2 in mESCs , and subjected the KD mESCs to the endothelial cell differentiation protocol . We found that KD of Pou3f2 reduced the expression of endothelial genes including Kdr , Tie2 , Nos3 , Cdh5 and Vwf at Day 8 of the differentiation protocol ( Figure 3c ) . Similarly , the expression of endothelial transcription factors such as Ets1 , Erg , Etv2 and Fli1 were also reduced in the Pou3f2 KD mESC ( Figure 3d ) . The KD of Pou3f2 in mESC did not affect the expression of mesoderm- ( Bmp4 , T ) , endoderm- ( Cxcr4 , Gata4 ) or ectoderm- ( Pax6 and Nestin ) related genes ( Figure 3e ) . Notably , the generation of mESC-derived CD31+ and CD144+ cells were reduced by over 50% in Pou3f2 KD group ( Figure 3g ) . Furthermore , Pou3f2 KD mESC-derived endothelial cells manifested poor network formation on matrigel ( Figure 3h ) . To summarize , Pou3f2 seems to be necessary for the full expression of genes known to be involved in endothelial development , and for the efficient generation of fully functioning endothelial cells . Amongst the factors released from endothelial cells in the heterokaryons that could control Pou3f2 expression there is Wnt and β-catenin . The Wnt/B-catenin signalling pathway is highly conserved and regulates vascular cell fate and development through Dll4/Notch signalling ( Corada et al . , 2010 ) . The promoter for the Pou3f2 gene is a direct target for β-catenin/Lef1 ( Goodall et al . , 2004a ) . Endothelial cells in the heterokaryon might also contribute phosphatidylinositol 3-kinase to activate Pou3f2 . The PI3K pathway mediates angiogenesis and the expression of growth factors in endothelial cells ( Jiang et al . , 2000 ) and also regulates Pou3f2 in melanoma cells ( Bonvin et al . , 2012 ) . Pou3f2 promotes neurogenesis ( Jaegle et al . , 2003; Castro et al . , 2006; Dominguez et al . , 2013; Sugitani et al . , 2002 ) . Specifically , it activates the Notch ligand Delta1 , synergistically with Mash1 , to maintain a subset of neural progenitors in an undifferentiated state ( Castro et al . , 2006 ) , whereas it suppresses the Notch effector Hes5 ( Dominguez et al . , 2013 ) that negatively regulate transcription of neurogenesis-promoting genes Neuregulins ( Imayoshi et al . , 2008 ) . In human melanoma spheres and tumor xenograft , Pou3f2 is proposed to induce the Notch pathway ( Thurber et al . , 2011 ) . In our studies , the importance of Pou3f2 in endothelial development in vivo was assessed using the zebrafish model . The availability of transgenics expressing endothelial-specific fluorescent reporters , for example Tg ( fli1:EGFP ) y1 , combined with the transparency of the embryo , facilitate visualization of vascular development and blood flow in real time ( Baldessari and Mione , 2008; Ellertsdóttir et al . , 2010; Holden et al . , 2011; Kamei et al . , 2010 ) . In situ hybridization for Pou3f2 showed that this transcription factor is expressed in most embryo structures , although the staining is particularly evident in the brain region ( Figure 4—figure supplement 1 ) . Because endothelial and hematopoietic cells share a common progenitor , we assessed the expression of Pou3f2 in both endothelium and hematopoietic cells . To do so , we purified GFP+ cells from tg ( fli1:EGFP ) y1 or tg ( cmyb:GFP ) embryos . We found expression of Pou3f2 in both type of cells , although more pronounced in the GFP+ cells from tg ( fli1:EGFP ) y1 embryos ( Figure 4—figure supplement 2 ) . Injection of morpholino targeting Pou3f2 ( the relevant zebrafish analogue ) resulted in 100% embryo death , as embryo could not reach 50% epiboly stage , at 5 . 3 hr post-fertilization . Both Pou3f2-targeted morpholinos caused the same effects . Therefore , we used light-activated cyclic caged morpholinos against Pou3f2 to study the role of this gene in vascular development ( Figure 4a–d ) . When the Pou3f2-targeted morpholino was activated by UV light at six hpf the embryo survival was around 80% . We observed a general phenotype characterized by curved body and curly tail , and specific dysmorphogenesis of the vascular system in most of the tg ( fli1:EGFP ) y1 embryos ( Figure 4a ) . A reduced number of intersegmental vessels ( ISVs ) was observed ( Figure 4b–c ) , which could be rescued with Pou3f2 mRNA . In situ hybridization showed that Fli1 and Kdr were downregulated following Pou3f2 KD , particularly evident in the intersegmental vessels , confirming the role for Pou3f2 in endothelial development ( Figure 4d ) . These results were confirmed by real-time PCR ( Figure 4—figure supplement 3 ) , where Fli1 and Kdr were significantly reduced in Pou3f2 knockdown embryos compared to control . Effective knockdown by the morpholino in zebrafish embryos was documented by Western blotting using a Pou3f2-specific antibody ( Figure 4e ) . In addition , there was a global reduction of endothelial cells in Pou3f2 KD animals . Specifically , the percentage of GFP+ cells isolated from the tg ( fli1:EGFP ) y1 zebrafish embryos at 24 hr post-fertilization was 6 . 7% of total cells in control compared to 3 . 7% following Pou3f2 KD ( Figure 4f–g ) . Injection of Pou3f2-targeted caged morpholino in tg ( cmyb:GFP ) did not significantly reduce the percentage of GFP+ cells , suggesting that Pou3f2 is more critical for endothelial rather than hematopoietic development . Notably , caged MO activation at 24 hpf or later did not produce a significant phenotype , indicating that Pou3f2 is necessary early in endothelial development , but is not required for maintenance of the endothelial phenotype . This observation is consistent with the lack of expression of Pou3f2 in mature endothelial cells . Our studies suggest that Pou3f2 could act upstream of Notch during endothelial , as well as neuronal development . In this regard , Pou3f2 could have a role in the human vascular diseases caused by mutations in Notch family members , such as Alagille syndrome and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy ( CADASIL ) ( Loomes et al . , 1999; Joutel et al . , 1997 ) . As Notch signaling is also required for arterio-venous differentiation during vascular development ( Lawson et al . , 2001 ) , this suggests that Pou3f2 could be also upstream of the eph-ephrin system and play a role in arterio-venous specification . Pou3f2 gene expression seems to be controlled by MAPK ( Goodall et al . , 2004b ) and Wnt/β-catenin ( Goodall et al . , 2004a ) signaling , two key pathways linked to cell proliferation . Pou3f2 function can be also modulated by posttranslational modifications including sumoylation , ubiquitinylation , glycosylation and in particular phosphorylation ( Kasibhatla et al . , 1999; Augustijn et al . , 2002; Diamond et al . , 1999; Nieto et al . , 2007 ) . 10 . 7554/eLife . 23588 . 006Figure 4 . Pou3f2 knockdown in the tg ( fli1:EGFP ) y1 zebrafish embryo . ( a ) Bright-field images of embryos injected with caged morpholino against Pou3f2 translation start site in the absence of photoactivation ( control ) , or with photoactivation with UV light at 6 or 24 hpf . ( b ) Fluorescence images of embryos at 48 hpf . Experimental groups were injected with caged morpholino against Pou3f2 in the absence of photoactivation ( control ) , or with photoactivation with UV light at 6 or 24 hpf , or with photoactivation at 6 hr in the presence of rescue mRNA encoding Pou3f2 . ( c ) Quantitation of the number of intersegmental vessels in 20 somites in embryos at 48 hpf . ( d ) In situ hybridization with antisense RNA probes specific for Kdr and Fli1 in whole zebrafish embryos 28 hpf . ( e ) Western blotting showing the reduction level of Pou3f2 following morpholino injection and rescue by mRNA encoding Pou3f2 . β-Tubulin was used as loading control . ISV – Intersegmental Vessels; hpf – hour post fertilization . ( f and g ) . Representative FACS plot and scatter plot showing a significant reduction of GFP+ cells in Pou3f2 KD embryos . GFP+ cells were sorted following isolation by enzymatic digestion from tg ( fli1:EGFP ) y1 zebrafish embryos at 24 hpf . All data represented as mean ± S . E . M . N = 3 . Student t-test , *p=0 . 01; ***p=0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 00610 . 7554/eLife . 23588 . 007Figure 4—source data 1 . Intersegmental vessel analysis in zebrafish embryos following Pou3f2 knockdown . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 00710 . 7554/eLife . 23588 . 008Figure 4—figure supplement 1 . In-situ hybridization for Pou3f2 in zebrafish embryos . Sense and Pou3f2-specific antisense RNA probe shows high expression of Pou3f2 in the head region , including the eye , hindbrain , midbrain and forebrain . Sense RNA probe was used as negative control . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 00810 . 7554/eLife . 23588 . 009Figure 4—figure supplement 2 . Pou3f2 gene expression in endothelial and hematopoietic lineages . Endothelial ( A ) and hematopoietic ( B ) cells were FACS purified from Tg ( Fli1:EGFP cells ) and Tg ( C-myb:EGFP ) larvae at 48 and 96 hpf , respectively . Total RNA was extracted and real-time PCR performed for Pou3f2 ( β-actin was used as housekeeping gene ) . All data represented as mean ± S . E . M . N = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 00910 . 7554/eLife . 23588 . 010Figure 4—figure supplement 2—source data 1 . Real time PCR analysis of Pou3f2 in zebrafish embryo Fli1+ and Cmyb+ cells . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 01010 . 7554/eLife . 23588 . 011Figure 4—figure supplement 3 . Gene expression of endothelial markers following Pou3f2 knockdown . Total RNA was isolated from whole embryos injected with Pou3f2-targeted morpholino ( Pou3f2-Mo ) or a mismatch ( Ctrl-Mo ) . Real-time PCR showed that Pou3f2 KD impaired significantly the expression of Fli1 and Kdr . All data represented as mean ± S . E . M . N = 3 . Student t-test , *p=0 . 01; **p=0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 011 To determine the role of Pou3f2 during endothelial differentiation from human iPSC , we examined the expression of Pou3f2 in the heterokaryon system consisting of hiPSCs and murine endothelial cells ( Figure 5a ) . In addition , we examined the expression of Pou3f2 in the hiPSC during our endothelial differentiation protocol ( Figure 5b ) . The expression patterns for Pou3f2 were similar to that which we observed in mESC ( Figure 3a and b ) . Notably , we saw the same accelerated regulation of Pou3f2 in the hiPSCs in the heterokaryon by comparison to hiPSCs exposed to the endothelial differentiation protocol . 10 . 7554/eLife . 23588 . 012Figure 5 . Role of Pou3f2 as a novel transcription factor in endothelial differentiation from human-induced pluripotent stem cells . ( a ) Gene expression pattern of Pou3f2 in heterokaryons consisting of hiPSC and mEC compared to co-culture control . ( b ) Validation of expression of Pou3f2 during differentiation of hiPSC into endothelial lineage . ( c ) Expression of Pou3f2 in lentiviral mediated shRNA KD of Pou3f2 in hiPSC following differentiation into endothelial phenotype compared to Control shRNA group . ( d ) Representative images of Western blots showing the KD effects of Pou3f2 in hiPSC during endothelial differentiation , the same results were obtained at least three times . ( e–j ) Pou3f2 KD reduced the gene expression of endothelial markers including Kdr , Tie2 , Cdh5 , Pecam1 , Nos3 and Vwf following endothelial differentiation of hiPSC . All data represented as mean ± S . E . M . ( n = 3 ) . p<0 . 05 vs co-culture control or control shRNA group . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 012 Next , we assessed the effects of shRNA-mediated KD of Pou3f2 on differentiation of endothelial cells from human iPSC . Pou3f2 shRNA significantly reduced the expression of Pou3f2 during the endothelial differentiation protocol ( Figure 5c , d ) . Furthermore , Pou3f2 KD significantly inhibited the expression of endothelial-related genes during the course of the differentiation protocol , including Kdr , Tie2 , CDdh5 Pecam1 , Nos3 and Vwf ( Figure 5e–j ) . Notably , Pou3f2 KD also reduced iPSC-EC generation by ~50% ( Figure 6a–c ) . In addition , protein expression of CD31 , CD144 and Vwf ( Figure 6d ) and formation of tubular networks on matrigel ( Figure 6e ) were reduced in ECs derived from Pou3f2 KD iPSCs . Furthermore , Pou3f2 KD iPSC-ECs exhibited impairment of other endothelial functions including nitric oxide generation ( Figure 6f ) , and uptake of acetylated LDL ( Figure 6g ) . These functional impairments were associated with reduced expression of EC markers including Kdr , Tie2 , Nos3 , CD31 , Cdh5 and Vwf ( Figure 6h ) . We have previously shown that endothelial cells derived from iPSCs ( hiPSC-ECs ) expressed various markers associated with arterial , venous and lymphatic ECs and thereby represent a heterogeneous population of ECs ( Rufaihah et al . , 2011 ) . We found that ECs generated from Pou3f2 KD iPSCs resemble those generated from wild-type iPSCs with respect to venous ( Ephb4 and Coup-TFII ) and lymphatic markers ( Pdpn and Lyve1 ) but have a significant reduction of arterial markers ( Notch4 , Efnb2 and Hey2 ) compared to scrambled control ( Figure 6i ) . 10 . 7554/eLife . 23588 . 013Figure 6 . Functional assays further reveal the importance of Pou3f2 during differentiation of human iPSC into endothelial phenotype . ( a–c ) Representative FACS plots and summarized diagram showing Pou3f2 KD reduced iPSC-EC generation compared to scrambled control . ( d ) Representative immunofluorescence images revealed lower expression of CD31 , CD144 and Vwf in Pou3f2 KD iPSC-ECs . ( e ) The iPSC-ECs generated from Pou3f2 KD cells manifested poor formation of networks of tubular structures on matrigel . ( f ) The ability of Pou3f2 KD iPSC-ECs to produce nitric oxide in response to calcium ionophore A23187 was significantly reduced compared to scrambled control iPSC-ECs . ( g ) Reduced capacity in taking up AcLDL in Pou3f2 KD iPSC-ECs compared to scrambled control iPSC-ECs . ( h ) Reduced gene expression of endothelial markers including Kdr , Tie2 , Nos3 , CD31 , Cdh5 and Vwf in Pou3f2 KD human iPSC-ECs . ( i ) Arterial markers ( Notch4 , Efnb2 , Hey2 ) but not venous ( Ephb4 and Coup-TFII ) nor lymphatic markers ( Pdpn and Lyve1 ) were affected in Pou3f2 KD iPSC-ECs compared to scrambled control iPSC-ECs . All data represented as mean ± S . E . M . ( n = 3 ) . p<0 . 05 vs Control shRNA group . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 013 To determine if Pou3f2 binds to the promoters of endothelial-related transcription factors , we performed ChIP-PCR . We observed that during differentiation of iPSCs to ECs , the binding of Pou3f2 to the promoters of endothelial related transcription factors including Ets1 , Lmo2 , Hey1 and Hey2 ( Figure 7a–d ) was inhibited in Pou3f2 KD cells , in association with reduced gene expression of these factors ( Figure 7f–i ) . Finally , we observed that the generation of CD31+CD144+ cells was reduced in the Pou3f2 KD hiPSCs and could be rescued with modified mRNA encoding Pou3f2 ( Figure 7j ) . 10 . 7554/eLife . 23588 . 014Figure 7 . Chromatin immunoprecipitation ( ChIP ) and qPCR analysis reveal Pou3f2 binds to the promoters of endothelial-related transcription factors . ( a–d ) Binding of Pou3f2 to the promoters of endothelial-related transcription factors including Ets1 , Lmo2 , Hey1 and Hey2 was significantly inhibited in Pou3f2 KD cells compared to scrambled control at Day 8 of endothelial differentiation protocol , without affecting the control promoter RPL30 Exon 3 ( e ) . ( f–i ) Downregulation of gene expression of Ets1 , Lmo2 , Hey1 and Hey2 in Pou3f2 KD cells during differentiation into endothelial lineage . ( j ) Rescue experiments with modified mRNA encoding Pou3f2 improved CD31+CD144+ cell generation from Pou3f2 KD cells . All data represented as mean ± S . E . M . ( n = 3 ) . p<0 . 05 vs co-culture control or control shRNA group . DOI: http://dx . doi . org/10 . 7554/eLife . 23588 . 014
Our current understanding of the genetic and epigenetic processes governing endothelial development and differentiation is limited . We lack comprehensive knowledge regarding all endothelial lineage factors and have sparse information regarding the magnitude and temporal sequence of their expression . In this paper , we find that the bi-species heterokaryons combined with RNAseq can provide new insights into determinants of endothelial lineage . Our work suggests that transcription factors and epigenetic machinery which actively maintain endothelial phenotype can also act on the pluripotent cell nucleus to recapitulate ontogeny . This system is likely to generate useful insights to improve the yield and fidelity of reprogramming to endothelial phenotype . A tangible and immediate outcome of this line of inquiry will be a more complete knowledge of the hierarchy of genes regulating differentiation to the EC lineage . Insights into these processes will be of general interest to investigators of vascular differentiation and development and may lead to new therapeutic targets for endothelial regeneration and the treatment of vascular diseases . Finally , and perhaps most importantly , this model system should be amenable to discovery of novel determinants of other cell lineages . We believe that our studies provide proof-of-concept for using bi-species heterokaryon technology as a tool to elucidate novel genes regulating differentiation to any somatic cell . Our work opens a new vista of exploration for the broader community of scientists working in tissue regeneration , development , differentiation and the therapeutic applications of these insights .
Human-induced progenitor stem cells ( Takara Bio USA , Mountain View , CA ) line authentication was achieved by genetic profiling using polymorphic short tandem repeat ( STR profiling ) loci . Our cell cultures were tested weekly for mycoplasma by real-time PCR approach and were mycoplasma-free . HiPSC lines were generated using retroviral factors encoding Oct4 , Sox2 , Cmyc and Klf4 in adult dermal fibroblasts . The hiPSCs were characterized for their pluripotency using PCR and IHC for known pluripotency markers , and were maintained in mTeSR1 ( Stem Cell Technology , Vancouver , Canada ) . Murine ESCs ( D3 , ATCC , Manassus , VA ) of the SV129 strain were cultured on gelatin-coated dish and maintained in ESGRO media plus GSK3β inhibitors . Human microvascular endothelial cells ( HMVECs ) ( obtained from Lonza , Walkersville , MD ) and murine endothelial cells ( obtained from Applied Stemcell , Menlo Park , CA ) were cultured in EC growth medium EGM-2 MV ( cc-3162 ) . Cells were used for all experiments at passage 6–8 . One day before cell fusion , 4 × 105 endothelial cells were seeded on one well of a 6-well dish . The endothelial cells were confluent on the day of cell fusion . On the same day , 1 hr prior to cell fusion , the endothelial cell medium was replaced with fresh EGM-2 MV medium supplemented with 1 μM Cell Tracker Red , then cells were incubated at 37°C in darkness for 30 min . Human iPSCs or murine ESCs labeled by transduction with retroviruses encoding GFP were rinsed with PBS followed by accutase treatment at 37°C for 5 min to dissociate the pluripotent stem cells into single-cell suspension . The cells were then collected in conical tubes after neutralization by MEF media containing 10% FBS . The cells were then counted with hemocytometer and 2 × 105 pluripotent stem cells were taken . The cells were then centrifuged at 200 x g for 5 min at 4°C , the supernatant was removed , and the cell pellet was resuspended in 25 μL ice-cold cell fusion buffer with 2 . 5 μL ice-cold HVJ-envelope fusagen . The reaction mixture was placed on ice for 5 min with regular agitation in 2 . 5 min apart . After 5 min , the cells were centrifuged again and the supernatant was discarded and 2 ml cell fusion buffer was added . The pluripotent cells were then plated onto the endothelial cells . The six-well dish was then centrifuged at 200 g for 5 min at 4°C . After centrifugation the dish was placed into a 37°C incubator to induce cell fusion . Twenty minutes later , the medium was removed and EGM-2 MV medium was added . For the Co-culture Control , the described procedure was the same except HVJ-enveloped fusagen was not added . The heterokaryons ( double-positive cells ) can be efficiently sorted by FACS . Heterokaryons ( GFP+ and CellTracker Red+ ) were harvested by FACS at 6 , 12 , 24 , 48 and 72 hr post-fusion . The species-specific nucleotide differences between the mouse and human transcripts enable us to differentiate between reads of transcripts from the murine ESC versus those from the human EC when the sequences are aligned to their respective genomes . Heterokaryons ( GFP+ and CellTracker Red+ ) were harvested by FACS at 6 , 12 and 24 hr post-fusion and prepared for analysis by RNA-seq . Total RNA from heterokaryons were isolated . Human and mouse mRNA transcripts were isolated from the total RNA samples using polyA-based enrichment using oligo-dT magnetic beads . The majority of contaminating ribosomal RNA was eliminated by this approach . The resulting mRNA was fragmented , reverse transcribed to cDNA , ligated to adapters , and subject to brief PCR amplification in preparation of the Illumina library . The integrity and quality of RNA and complementary DNA were monitored using an Agilent Bioanalyzer 2100 . The samples were sequenced using pair-end 100 base-pair reads . For the estimation of gene expression and data analysis , any remaining ribosomal reads were discarded , and the resulting murine and human transcripts were mapped to their respective genomes . Reads that map to both transcriptomes would be discarded and the RPKMs adjusted accordingly ( discarded reads represent only 5% of the total reads; furthermore , virtually all genes have at least one unique read that is different between species , so that no gene is completely discarded ) . RNA-Seq reads were aligned to the mouse genome version mm9 using TopHat version 2 . 1 . 0 . We use the full set of knownGene downloaded from the UCSC Genome browser ( http://genome . ucsc . edu/cgi-bin/hgTables ) as reference genes . RNA-Seq read counts for each gene in each sample was calculated using Cuffdiff function in Cufflinks version 2 . 2 . 1 . The Cuffdiff also calculates fragment per kilobase per million reads ( FPKM ) for each gene . We further subject the reads counts to EdgeR version 3 . 12 . 0 for differential expression analysis , and define differential genes based on false discovery rate ( FDR ) cutoff 1e-5 . We subject interesting gene groups to the DAVID website ( https://david . ncifcrf . gov ) for functional enrichment analysis . Enriched functional terms were defined based on Benjamini adjusted p value cut-off 0 . 05 . Hierarchical clustering of gene expression heatmap was conducted using MEV based on Pearson correlation distance metric and the average linkage method . Using RNeasy Mini Kit ( Qiagen , Chatsworth , CA ) , total RNA was extracted . The Quantitect reverse transcription kit ( Qiagen ) was used to generate cDNA and SYBR Green PCR kit ( Invitrogen , Carlsbad , CA ) was used for real-time qPCR with the QuantStudio 12 k Flex system ( Applied Biosystems , Foster City , CA ) following the manufacturer’s instructions . Genes were analyzed with the data normalized to Gapdh and expressed as relative fold changes using the ΔCt method of analysis . Murine ESC-EC Differentiation: Endothelial differentiation of ESCs was carried out using the suspension culture approach with modifications . To initiate differentiation , ESCs were cultured in ultralow nonadhesive dishes to form embryoid body aggregates in a differentiation medium that consisted of α-Minimum Eagle’s Medium , 10% FBS , 1% penicillin/streptomycin , and 0 . 05 mmol/L β-mercaptoethanol ( Sigma , St Louis , MO ) . After 4 days of suspension culture , the embryoid bodies were reattached onto 0 . 2% gelatin-coated dishes and cultured in differentiation medium . After 3 weeks of differentiation , the cells were purified by fluorescence-activated cell sorting ( FACS ) using anti-mouse vascular endothelial cadherin ( VE-cadherin ) antibody ( Ab ) ( BD Biosciences , Bedford , CA ) . Human iPSC-EC differentiation: Confluent cultures of hiPSCs were incubated with 1 mg/ml type IV collagenase for 10 min and transferred to ultra low attachment dishes containing differentiation media for 4 days to form embryoid bodies ( EBs ) . The differentiation media used consisted of α-Minimum Eagle’s Medium , 20% fetal bovine serum , L-glutamine , β-mercaptoethanol ( 0 . 05 mmol/L ) and 1% non-essential amino acids supplemented with bone morphogenetic protein-4 ( BMP-4 , 50 ng/ml , Peprotech ) and vascular endothelial growth factor ( VEGF-A , 50 ng/ml , Peprotech ) . The four-day EBs were reattached to gelatin-coated dishes in the presence of VEGF-A for another 10 days before purification . ECs derived from pluripotent stem cells were purified using FACS . Cells were dissociated into single cells with Accutase ( Invitrogen ) for 5 min at 37°C , washed with 1x PBS containing 5% BSA and passed through a 70-μm cell strainer . Cells were then incubated with either Alexa Fluor 488-conjugated CD31 antibody ( BD Bioscience , San jose , CA ) or PE-conjugated CD144 antibody ( BD Bioscience ) for 30 min . Isotype-matched antibody served as negative control . The purified ESC- or iPSC-ECs were expanded in EGM-2 media . Human iPSC-ECs were fixed with 4% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 , blocked with 1% normal goat serum and stained for anti-human CD31 ( R and D Systems ) , anti-human CD144 ( R &D Systems , Minneapolis , MN ) , anti-human von Willebrand factor ( vWF , Abcam , Cambridge , UK ) overnight at 4°C . After washes with PBS , the cells were treated with Alexa Fluor-488 or -594 secondary antibodies . Cell nuclei were stained with Hoechst 33342 ( Sigma ) . Images were acquired on a confocal microscope ( FV1000-IX81 , Olympus , Tokyo , Japan ) . Cells were homogenized with ice-cold RIPA lysis buffer containing 1 µg/mL leupeptin , 5 µg/mL aprotonin , 100 µg/mL PMSF , 1 mmol/L sodium orthovanadate , 1 mmol/L EDTA , 1 mmol/L EGTA , 1 mmol/L sodium fluoride and 2 µg/mL β-glycerolphosphate . The protein concentration was determined by Bradford method and aliquots of 20 µg of the total proteins were separated on 10% SDS-poly-acrylamide gel . Proteins were then transferred to immobilon-P polyvinylidene difluoride ( PVDF ) membrane ( Millipore , Billerica , MA ) . Membranes were blocked with 5% non-fat milk in TBS-T and subsequently exposed to Pou3f2 primary antibody ( Genetex , Irvine , CA ) followed by HRP-conjugated secondary antibody and developed by chemiluminescence . Uptake of Ac-LDL: was evaluated by incubating cells with ac-LDL-594 at 1:200 dilution for 5 hr before washing the cells with PBS and then measuring the mean fluorescence of the cells . Endothelial network formation , the ability of cells to form tube-like structures , was assessed in vitro by seeding 1 . 2 × 105 cells in wells coated with matrigel in the presence of EGM-2 media containing 50 ng/ml VEGF and incubated for 24 hr . The ability of the cells to produce NO was assessed by measuring the concentration of NO in the culture medium using the NO detection kit ( Molecular Probe , Carlsbad , CA ) according to the manufacturer’s instructions . The amount of nitrate was determined by converting it to nitrite , followed by the colorimetric determination of the total concentration of nitrite as a colored azo dye product of the Griess reaction that absorbed visible light at 540 nm using a microplate reader . hiPSCs were differentiated towards EC lineage and collected at Day 8 . Samples were prepared by SimpleChIP enzymatic chromatin IP kit ( Cell Signaling Technology ) . Chromatin immunoprecipitation was performed using human Pou3f2 antibody ( Genetex ) , rabbit IgG ( CST ) , histone H3 antibody ( CST ) . DNA was purified using Nucleospin PCR clean-up kit ( Macherey-Nagel , Bethlehem , PA ) and used for quantitative PCR with primers against regions predicted within the promoter of Ets1 , Lmo2 , Hey1 and Hey2 . Recovery of genomic DNA as the percentage input was calculated as the ratio of copy numbers in the immunoprecipitate to the input control . Adult zebrafish ( wild-type Wik and tg ( fli1:EGFP ) y1 strains ) were acquired from the Zebrafish International Resource Center and raised according to standard procedures and kept at 28°C under a 14/10 hr light/dark cycle and fed with dry meal ( Gemma Micro , Westbrook , ME ) twice per day . Embryos used in these studies were obtained by natural matings and cultured in E3 embryo medium at 28 . 5°C . Animals were housed and all experiments were carried out in accordance with the recommendations of the Institutional Animal Care and Use Committee . All surgery procedures were performed under anesthesia with Tricaine 0 . 02 mg/ml . Pou3f2 KD in zebrafish was achieved using two different antisense morpholinos ( Gene Tools , Oregon ) targeting the Pou3f2 mRNA AUG translational start site with sequence: ( Mo1 ) 5’-ATGATTGGATGCTGTAGTCGCCATG-3’ , and ( Mo2 ) 5’-CGGACTGATCGCTCCTATTAAAGGA-3’ . As one control we used a 5-base pair mismatch MO: sequence 5’-ATcATTcGATcCTGTAcTCcCCATG-3’ . To decipher the roles of Pou3f2 transcription factor in specific stages of endothelial development , we used Pou3f2-targeted caged morpholino ( cMOs ) ( Shestopalov et al . , 2007 ) . This chemically modified morpholino allowed temporal gene silencing by using targeted UV illumination . An optimized dose of 0 . 5 ng/eggs ( 0 . 5 nL bolus ) of Pou3f2 targeted morpholino was injected in each embryo at 1–2 cell stage , just below the cell mass . To photoactivate the cMOs , injected zebrafish embryos were arrayed in an agarose microinjection template ( 560 μm x 960 μm wells ) , with the animal pole facing the light source . Then , the mercury lamp light was focused onto individual embryos for 10 s , using a Leica DM4500B epifluorescence microscope equipped with an A4 filtercube ( Ex: 360 nm , 40 nm bandpass ) and a 20 x/0 . 5 NA water-immersion objective . Individual embryos were irradiated at 6 or 24 hr post-fertilization . As control , we also performed rescue experiments by co-injecting Pou3f2-targeted MO together with Pou3f2 modified mRNA into one-cell-stage embryos . The Pou3f2 modified mRNA version used , produced from the RNA Core available in our Institute , was modified at the 5′ untranslated region so that it was not recognized by the morpholino . An optimized dose of 300 pg was co-injected with the morpholino in rescue experiments . The embryos were manually dechorionated at 24 or 48 hpf . Brightfield images were acquired using a Leica M205FA fluorescence stereoscope equipped with a Leica DFC500 digital camera . For immunofluorescence imaging , bright-field images of embryos were obtained with a Leica DM4500B compound microscope equipped with a 20 x/0 . 12 NA water-immersion objectives and a QImaging Retiga-SRV digital camera . Fluorescence images were obtained with the DM4500B/Retiga-SRV system equipped with a mercury lamp and GFP ( Ex: 470 nm , 40 nm bandpass; Em: 525 nm , 50 nm bandpass ) filter sets . The embryos were de-yolked in TM1 buffer ( 100 mM NaCl , 5 mM KCl , 5 mM HEPES pH 7 . 0 , 1% ( w/v ) PEG-200 , 000 ) . Twenty de-yolked embryos from each experimental condition were homogenized in SDS-PAGE loading buffer ( 50 μH 7 . 0 , 1% ( w/mM 2-mercaptoethanol , 4% ( w/v ) glycerol , 100 mM DTT , 100 mM Tris-HCl , pH 6 . 8 ) , vortexed , and heated to 95°C for 5 min . The resulting lysates were used for gel electrophoresis followed by blotting with Pou3f2 antibody ( rabbit polyclonal , Abcam 137469 ) . β-Tubulin ( rabbit polyclonal , Abcam 6046 ) was used as loading control . The preparation of sense ( used as control ) and antisense RNA probes for Kdr and Fli1 and in situ hybridization procedure were performed according to Thisse and Thisse ( Thisse and Thisse , 2008 ) . Cells were isolated according to Shestopalov et al . ( Shestopalov et al . , 2012 ) with modifications . Briefly , Tg ( fli1:EGFP ) y1 embryos at the appropriate developmental stage were dechorionated , transferred in an eppendorf tube with calcium-free Ringer’s solution ( 200 μl for 25–30 embryos; 116 mM NaCl , 2 . 6 mM KCl , 5 mM HEPES , pH 7 . 0 ) and dissociated with a 200 μl pipette tip . Then 1 ml solution of 1X PBS containing trypsin ( 0 . 25% , Gibco ) , 50 μg collagenase P ( Roche , Indianapolis , IN ) and 1 mM EDTA was added and samples were incubated for 30 min at 28 . 5°C with further pipetting every 5 min . Enzymatic processing was quenched with stop solution ( 200 μl; 1X PBS containing 30% ( v/v ) calf serum and 10 mM CaCl2 ) , and cells were collected by centrifugation ( 400 g , 5 min , 4°C ) . After aspirating the supernatant , cells were resuspended in a chilled solution of DMEM containing 1% ( v/v ) calf serum , 0 . 8 mM CaCl2 , 50 V ml−1 penicillin/streptomycin , centrifuged and resuspended in the same medium . The cell suspension was filtered through a 40 μm cell strainer ( BD Biosciences ) into FACS sample tubes . Cell suspensions were analyzed using a BD FACSAria . Wild-type zebrafish ( Wik ) was used as GFP negative control . Viable fluorescent single cells were identified as DAPI-negative . Cells viability was confirmed under fluorescence stereomicroscope ( Leica M205 ) by using a Neubauer chamber . All RNA-Seq data have been deposited to the GEO database by the accession number GSE84558 . Statistical analysis was performed with SPSS software ( SPSS Inc . , Chicago , IL , USA ) . Results were expressed as mean ± SEM . The Shapiro-Wilk test was used to confirm the null hypothesis that the data follow a normal distribution . Statistical comparisons were performed via Student t-test for two groups and via one-way ANOVA test for multiple groups . Bonferroni corrections test was applied for multiple comparisons . p<0 . 05 was considered significant . | Endothelial cells form the inner surface of blood vessels , acting like a non-stick coating . In addition to making substances that keep blood from sticking to the vessel wall , endothelial cells generate compounds that relax the vessel , and prevent it from thickening . Endothelial cells also form capillaries , the smallest vessels that provide oxygen and nutrients for all tissues . A regenerating organ , or a bioengineered tissue , requires a system of capillaries and other microvessels . Thus , regenerative medicine could benefit from a knowledge of how to generate endothelial cells from pluripotent stem cells – cells that can “differentiate” to form almost any type of cell in the body . Wong , Matrone et al . have now used a cell fusion model ( named heterokaryon ) to track the changes in gene expression that occur as a pluripotent stem cell differentiates to ultimately become an endothelial cell . In this model , mouse embryonic stem cells ( ESCs ) are fused to human endothelial cells . Over time the human endothelial cells drive gene expression in the ESCs toward that of endothelial cells . Wong , Matrone et al . discovered changes in gene expression in many genes that have not previously been described as involved in the differentiation of endothelial cells . When one of these genes – named Pou3f2 – was inactivated in ESCs , they could not be differentiated into endothelial cells . The absence of Pou3f2 also drastically impaired how blood vessels developed in zebrafish embryos . Thus the heterokaryon model can generate important information regarding the dynamic changes in gene expression that occur as a pluripotent cell differentiates to become an endothelial cell . This model may also be useful for discovering other genes that control the differentiation of other cell types . | [
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] | 2017 | Discovery of novel determinants of endothelial lineage using chimeric heterokaryons |
The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis . Despite their importance , a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking . To address this limitation , we globally define the effects of mutations across the capsid of a human picornavirus . Using this resource , we identify structural and sequence determinants that accurately predict mutational fitness effects , refine evolutionary analyses , and define the sequence specificity of key capsid-encoded motifs . Furthermore , capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites , we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases . Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology , evolution , and host interactions .
The capsids of non-enveloped viruses are among the most complex of any viral protein . These highly multimeric structures must correctly assemble around the genome from numerous subunits , at times numbering in the hundreds , while avoiding aggregation ( Harrison , 2013; Hunter , 2013; Perlmutter and Hagan , 2015 ) . Moreover , the assembled structure must be both sufficiently stable to protect the viral genome during its transition between cells yet readily disassemble upon entry to initiate subsequent infections . For these functions to be achieved , viral capsids must encode the information for interacting with numerous cellular factors that are required to correctly fold and assemble around the genome ( Callaway et al . , 2001; Fields et al . , 2013; Geller et al . , 2007; Jiang et al . , 2014; Macejak and Sarnow , 1992 ) . Viral capsids also play key roles in pathogenesis , dictating host and cell tropism by encoding the determinants for binding cellular receptors ( Helenius , 2013; Rossmann et al . , 2002 ) and mediating escape from humoral immune responses ( Cifuente and Moratorio , 2019; Heise and Virgin , 2013 ) . As a result , viral capsids show the highest evolutionary rates among viral proteins . The picornaviruses constitute a large group of single-stranded , positive-sense RNA viruses and include several pathogens of significant medical and economic impact ( Racaniello , 2013 ) . Their relative simplicity and ease of culture have made picornaviruses important models for understanding virus biology . Among the many breakthroughs achieved with these viruses was the determination of the first high-resolution structure of the capsid of an animal virus , making the picornavirus capsid the prototypical non-enveloped , icosahedral viral capsid ( Racaniello , 2013 ) . Picornavirus capsid genesis initiates with the co-translational release of the P1 capsid precursor protein from the viral polyprotein via the proteolytic activity of the viral encoded 2A protease ( Jiang et al . , 2014; Racaniello , 2013 ) . Subsequently , the viral encoded 3CD protease ( 3CDpro ) cleaves the P1 capsid precursor to liberate three capsid proteins ( VP0 , VP3 , and VP1 ) , generating the capsid protomer . Five protomers then assemble to form the pentamer , twelve of which assemble around the viral genome to yield the virion . Finally , in some picornaviruses , VP0 is further cleaved into two subunits , VP4 and VP2 , following genomic encapsidation to generate the infectious , 240 subunit particles ( Jiang et al . , 2014; Racaniello , 2013 ) . Work over the years has identified numerous host factors that help support capsid formation ( Corbic Ramljak et al . , 2018; Geller et al . , 2007; Macejak and Sarnow , 1992; Qing et al . , 2014; Thibaut et al . , 2014 ) , defined antibody neutralization sites ( Cifuente and Moratorio , 2019 ) , and identified numerous host receptors for many members of this viral family ( Rossmann et al . , 2002 ) . Despite significant progress in understanding the structure and function of picornavirus capsids , a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes is lacking . To address this , we perform a comprehensive analysis of mutational fitness effects ( MFE ) across the complete capsid region of the human picornavirus coxsackievirus B3 ( CVB3 ) , analyzing >90% of all possible single amino acid mutations . Furthermore , using these data , we develop models to predict the effect of mutations with high accuracy from available sequence and structural information , improve evolutionary analyses of CVB3 , and define the sequence preferences of several viral encoded motifs . Finally , we use the information obtained in our dataset for the sequence requirements of capsid-encoded 3CD protease cleavage sites to identify host targets of this viral protease . Overall , our data comprise the most comprehensive survey of MFE effects in a picornavirus capsid to date and provide important insights into virus biology , evolution , and interaction with the host .
To generate CVB3 libraries encoding a large amount of diversity in the capsid region , we used a codon-level PCR mutagenesis method ( Bloom , 2014 ) . The mutagenesis protocol was performed on the capsid precursor region P1 in triplicate to generate three independent mutagenized libraries ( Mut Library 1–3; Figure 1A ) . From these , three independent viral populations ( Mut Virus 1–3 ) were derived by electroporation of in vitro transcribed viral RNA into HeLa-H1 cells ( Figure 1A ) . High-fidelity next-generation sequencing ( Schmitt et al . , 2012 ) was then used to analyze the mutagenized libraries and resulting viruses , unmutagenized virus populations ( WT virus 1–2 ) , as well as controls for errors occurring during PCR ( PCR ) and reverse transcription ( RT-PCR ) . High coverage was obtained for all samples ( >106 per codon across all experimental conditions and >6 . 5×105 for the controls; Supplementary file 2 ) . Due to the high rate of single mutations within codons observed in the RT-PCR control compared to the mutagenized virus populations ( Supplementary file 2 ) , all single mutants were omitted from our analysis to increase the signal-to-noise ratio . While this resulted in an inability to analyze 83 . 4% of synonymous codons in the capsid region ( 1746/2094 ) , only 2 . 8% of non-synonymous mutations were lost to analysis ( 458/16 , 169 ) . Upon removing single mutations within codons , we obtained a large signal-to-noise ratio in the average mutation rate of 510× ( range 449–572 ) and 245× ( range 174–285 ) for the mutagenized libraries and viruses , respectively , compared to their error controls ( Figure 1B and Supplementary file 2 ) . On average , 0 . 9 ( range 0 . 8–1 . 02 ) codon mutations were observed per genome , which was in agreement with Sanger sequencing of 59 clones ( range 18–23 per library; Figure 1—figure supplement 1 and Supplementary file 3 ) . As expected , the rate of stop codons , which should be invariably lethal in the CVB3 capsid , decreased significantly following growth in cells to <0 . 5% of that observed in the corresponding mutagenized libraries ( p<0 . 005 by paired t-test on log-transformed data; Supplementary file 2 ) . No major bias was observed in the position within a codon where mutations were observed ( Figure 1—figure supplement 2 ) or in the type of mutation ( Figure 1—figure supplement 2 ) , except for the WT virus , which had a high rate of A to G transitions in the two independent replicates analyzed . Of all 16 , 169 possible amino acid mutations in the capsid region ( 851 AA × 19 AA mutation = 16 , 169 ) , a total of 14 , 839 amino acid mutations were commonly observed in all three mutagenized libraries , representing a 91 . 8% of all possible amino acid mutations in the capsid region , allowing us to globally assess the effects of the vast majority of amino acid mutations on the capsid ( Figure 1C ) . We next derived the MFE of each observed mutation by examining how its frequency changed relative to that of the WT sequence following growth in cells . The preferences for the different amino acids at each position ( amino acid preferences [Bloom , 2015] ) showed a high correlation between biological replicates ( Spearman’s ρ > 0 . 83; Figure 2—figure supplement 1 and Supplementary file 4 MFE ) . Overall , most mutations in the capsid were deleterious to growth in cell culture , with only 1 . 2% of mutations increasing fitness relative to the WT amino acid ( Figure 2A and Supplementary file 4; Interactive heatmap available at https://rgellerlab . github . io/CVB3_capsid_DMS_Interactive_Heatmap/ ) . Hotspots where mutations were tolerated were observed at several regions across the capsid ( Figure 2A ) . These hotspots largely overlapped with highly variable regions in natural sequences , as measured by Shannon entropy in the enterovirus B family , indicating that lab measured MFE reflect natural evolutionary processes ( Figure 2A , top ) . Indeed , a strong correlation was observed between the average MFE observed at each site and sequence variability for the enterovirus B genus ( Spearman’s ρ = 0 . 59 , p<10−16; Figure 2B ) . Similarly , antibody neutralization sites overlapped with hotspots for mutations ( Figure 2A , top ) , with individual mutations in antibody neutralization sites showing lower MFE ( p<10−16 by Mann–Whitney test; Figure 2C ) . As expected , mutations were also less deleterious in loops compared to β-strands ( p<10−16 by Mann–Whitney test; Figure 2D ) , at surface residues compared to core residues ( p<10−16 by Mann–Whitney test; Figure 2E ) , and for mutations predicted to be destabilizing or aggregation-prone ( p<10−16 by Mann–Whitney test for both; Figure 2F ) . Importantly , independent validation of the MFE of 10 different mutants using a sensitive qPCR-based competition assay ( Moratorio et al . , 2017 ) showed a strong correlation with the deep mutational scanning ( DMS ) results ( Spearman’s ρ = 0 . 9 , p<0 . 001; Figure 2G and Supplementary file 5 ) . It is important to note that laboratory-measured MFE may not always reflect those in nature due to differences in the environments . As MFE correlated with natural sequence variation and different structural features of the capsid ( Figure 2 ) , we next investigated if MFE could be predicted from available structural and sequence information . For this , we obtained a dataset of 52 parameters , including structural information derived from the crystal structure of the CVB3 capsid ( PDB:4GB3 ) , amino acid properties , and natural variation in available enterovirus sequences ( Shannon entropy ) , and predicted the effects of mutation on stability and aggregation propensity using FoldX ( Schymkowitz et al . , 2005 ) and TANGO ( Fernandez-Escamilla et al . , 2004 ) , respectively ( Supplementary file 6 ) . We then employed a random forest algorithm to identify the parameters that can best predict MFE , limiting our analysis to sites that present in the crystal structure and where mutations were observed in at least two replicates to improve accuracy ( total of 9685 mutations ) . Overall , a model trained on 70% of the dataset was able to predict the remaining 30% of the data ( 2905 mutations ) with high accuracy ( Spearman’s ρ > 0 . 75 , Pearson’s r = 0 . 76; p<10−16; Figure 3—figure supplement 1 ) . Surprisingly , a random forest model trained on the top five predictors alone showed similar accuracy ( Spearman’s ρ = 0 . 73 , Pearson’s r = 0 . 73; p<10−16; Figure 3B ) . Excluding natural sequence variation , amino acid identity , or structural attributes reduced model predictability significantly ( >20%; data not shown ) , suggesting a combination of evolutionary , sequence , and structural information best explains MFE . Using an alternative approach , we were able to predict the data with slightly lower accuracy using a linear model with the same five predictors ( p<10−16 , Spearman’s ρ = 0 . 67 , Pearson’s r = 0 . 67; Figure 3—figure supplement 1 ) . Together , these results suggest that the prediction of MFE in the CVB3 capsid can be achieved at relatively high accuracy based on available structural and sequence information . Due to the high conservation of capsid structure in picornaviruses , as well as the availability of numerous capsid sequences and structures , these findings are likely generalizable to related picornaviruses . We next examined if our experimentally measured MFE could improve phylogenetic models of CVB3 evolution by incorporating site-specific amino acid preferences using PhyDMS ( Hilton et al . , 2017 ) . Indeed , significant improvement in model fit was observed ( Table 1 PHY; p<10−16 using a log-likelihood test compared to non-site-specific codon models ) , supporting the relevance of our results to understanding evolutionary processes in nature . Nevertheless , selection in nature was significantly more stringent than in the lab ( β = 2 . 18 ) , indicating the presence of additional selection pressures . As laboratory conditions lack selection from antibodies , we used the sum of the absolute differential selection observed at each site ( Bloom , 2017 ) to examine whether known antibody neutralization sites show differential selection between the two environments ( Supplementary file 7 ) . Indeed , antibody neutralization sites showed significantly higher differential selection values compared to other residues ( p<10−6 by Mann–Whitney test; Figure 4A ) . Moreover , the three sites showing the strongest overall differential selection were found in known antibody neutralization sites: positions 226 and 242 in the EF loop ( residues 157 and 173 of VP2 ) and position 650 in the BC loop ( residue 80 of VP1; Figure 4B–D and Supplementary file 7 ) . In summary , incorporation of experimentally derived amino acid preferences into phylogenetic analyses significantly improved model fit and identified residues in antibody neutralization sites that show differential selection , suggesting these may play important roles in immune evasion in vivo . Picornavirus capsids undergo a complex assembly path to generate the infectious particle . These include myristoylation , cleavage by the viral proteases 2A and 3CDpro , as well as interaction with cellular chaperones and glutathione ( Corbic Ramljak et al . , 2018; Geller et al . , 2007; Jiang et al . , 2014; Qing et al . , 2014; Thibaut et al . , 2014; Figure 5A ) . Having obtained a comprehensive dataset for MFE across the capsid , we next examined the sequence requirements for several of these capsid-encoded motifs . Specifically , myristoylation of the N-terminal glycine is essential for virion assembly ( Corbic Ramljak et al . , 2018 ) . In agreement with this , the N-terminal glycine in the CVB3 capsid showed the strongest average fitness cost upon mutation in the capsid ( Figure 4—figure supplement 1 and Supplementary file 4 ) . The remaining sites in the myristoylation motif agreed with the canonical myristoylation motif in cellular proteins ( Prosite pattern PDOC00008 ) ( Bologna et al . , 2004 ) , albeit with increased selectivity at three of the six positions ( Figure 4—figure supplement 1 ) . On the other hand , a conserved WCPRP motif in the C-terminal region of VP1 that was shown to be important for 3CDpro cleavage of the related foot and mouth disease virus capsid ( FDMV; YCPRP motif ) ( Kristensen and Belsham , 2019 ) was found to be intolerant to mutations compared to other capsid residues ( p<0 . 05 versus all other positions by Mann–Whitney test; sites 815–819 in CVB3 ) . Moreover , within this motif , the sites showing the highest average fitness cost in our DMS dataset were identical to analogous positions in FMDV that resulted in a loss of viability upon mutation to alanine ( Figure 4—figure supplement 1; Kristensen and Belsham , 2019 ) , highlighting the conservation of this motif across different picornaviruses . The viral 3C protease ( 3Cpro ) cleaves the picornavirus capsid at two conserved glutamine–glycine ( QG ) pairs to liberate the viral capsid proteins VP0 , VP3 , and VP1 ( Figure 5A ) . Previous work has defined the sequence specificity of several picornavirus 3Cpro enzymes by examining both natural sequence variation and in vitro cleavage assays using synthetic peptides ( Laitinen et al . , 2016 ) . However , unlike other 3Cpro-mediated cleavage events in the viral polyprotein , the capsid is only efficiently cleaved by the precursor protein 3CDpro ( Ypma-Wong et al . , 1988 ) . To gain insights into the sequence specificity of 3CDpro , we examined the amino acid preferences for a 10 amino acid region surrounding the protease cleavage site ( P5–P5’ ) . As expected based on the known specificity of the 3C protease ( Laitinen et al . , 2016 ) , a strong preference for the presence of QG was observed at both 3CDpro cleavage sites in our dataset ( positions P1 and P1’ in the cleavage site; Figure 5B , C ) . Interestingly , significant correlation in amino acid preferences between the two cleavage sites was observed only at P1–P1’ ( Pearson’s ρ > 0 . 99 , p<10−16 ) and P4 ( Pearson’s ρ > 0 . 49 , p<0 . 05 ) , as was the case in the enterovirus B alignments ( Pearson’s ρ > 0 . 84 and p<10−6 for positions P4 , P1 , and P’1; data not shown ) . Hence , the low agreement in amino acid preferences observed for most positions across the two 3CDpro cleavage sites suggests cleavage is strongly dictated by positions P4 , P1 , and P1’ . In addition to cleaving the viral polyprotein , the picornavirus proteases cleave cellular factors to facilitate viral replication , including both antiviral factors and cellular factors that favor viral IRES-driven translation mechanism over cellular cap-dependent translation ( e . g . DDX58 , eIF4G , and PABP ) ( Laitinen et al . , 2016; Sun et al . , 2016 ) . As the canonical 3C/3CDpro QG cleavage site occurs on average 1 . 6 times per protein in the human proteome ( ~33 , 000 , 000 times ) , we sought to examine whether the rich dataset we obtained for the amino acid preferences of the capsid 3CDpro cleavage sites can be used to identify novel cellular factors that are targeted by the viral protease . Specifically , a position-specific score matrix ( PSSM ) was generated for the 10 amino acid regions spanning the two protease cleavage sites in the CVB3 capsid ( P5–P5’ ) based on the amino acid preferences identified in our study ( Figure 5D ) . This PSSM was then used to query the human proteome for potential cleavage sites , yielding a total of 746 cytoplasmic proteins ( Figure 5D; Supplementary file 8 ) . Eleven cellular factors that are known to be cleaved during enterovirus infection were identified using this approach , including the viral sensor Probable ATP-dependent RNA helicase DDX58 ( RIG1 ) , the immune transcription factors p65 ( RELA ) and interferon regulatory factor 7 ( IRF7 ) , and polyadenylate-binding protein 1 ( PABPC1 ) , an important factor in translation initiation and mRNA stability ( Supplementary file 8; Jagdeo et al . , 2018; Laitinen et al . , 2016 ) . To evaluate whether our approach can identify novel cellular targets for the viral protease , we examined the ability of 3CDpro to cleave eight different proteins found in the data set , focusing on those with cellular functions of potential relevance to CVB3 biology and which could be readily detected in our cell culture assay ( e . g . availability of antibodies or tagged-variants , cleavage fragments of observable size , and high expression level ) . These included four interferon-inducible proteins ( Pleckstrin homology domain containing A4 , PLEKHA4; phospholipid scramblase 1 , PLSCR1; NOD-like receptor family CARD domain containing 5 , NLRC5; zinc finger CCCH-type containing , antiviral 1 , ZC3HAV1 ) and four proteins involved in various cellular functions , namely apoptosis ( MAGE family member D1 , MAGED1 ) , RNA processing ( WD repeat domain 33 , WDR33 ) , and vesicle transport ( cyclin G-associated kinase , GAK; tumor susceptibility 101 , TSG101 ) . Of these , three proteins were cleaved upon expression of the viral protease to generate fragments of the expected size ( PLSCR1 , PLEKHA4 , and WDR33; Figure 5E and Supplementary file 8 ) . Of note , while WDR33 was predicted to harbor two potential cleavage sites , only a single cleavage event was observed . Treatment with a specific 3CDpro inhibitor , rupintrivir ( Dragovich et al . , 1999 ) , blocked the cleavage of these proteins , indicating the effect was due to the viral protease ( Figure 5D ) . In contrast , five of the proteins were found to not be cleaved upon 3CDpro expression , suggesting additional determinants are involved in the cleavage of host factors ( Figure 5—figure supplement 1 ) . Hence , our approach correctly identified 30% of the predicted cleavage sites ( three of the nine different cleavage sites ) , indicating a strong enrichment of cellular targets of the 3CDpro in the dataset .
The picornavirus capsid is a highly complex structure that plays key roles in viral biology and pathogenesis . In the current study , we employ a comprehensive approach to define the effects of single amino acid mutations in the CVB3 capsid , measuring the effects of >90% of all possible mutations . We find that most mutations in the capsid are deleterious to growth in cell culture , with very few mutations showing higher fitness than the WT sequence ( 1 . 2% of all mutations ) . Similar results have been reported in other non-enveloped capsid proteins ( Acevedo et al . , 2014; Hartman et al . , 2018; Ogden et al . , 2019 ) as well as non-capsid viral proteins ( Ashenberg et al . , 2017; Bloom , 2014; Doud and Bloom , 2016; Du et al . , 2016; Haddox et al . , 2016; Hom et al . , 2019; Thyagarajan and Bloom , 2014; Wu et al . , 2015 ) . In light of these results , it is likely that the large population sizes of RNA viruses help maintain viral fitness in the face of high mutation rates and strong mutational fitness costs . It is important to note that the effect of a particular mutation on fitness observed under laboratory conditions may not always reflect its effect in nature due to inherent differences between these two environments . Investigation of the factors that influence MFE in the capsid revealed a strong correlation with various structural and functional attributes . These included computationally predicted effects on stability and aggregation propensity , secondary structure , and surface exposure ( Figure 2 ) . Surprisingly , we find that MFE can be predicted with relatively high accuracy using only five parameters: natural sequence variation , the identity of the original and mutant amino acids , the predicted effect on protein stability , and relative solvent accessibility ( Figure 3 ) . A recent study examined the ability of 46 different variant effect prediction tools to predict MFE from 31 different DMS datasets of both viral and non-viral proteins ( Livesey and Marsh , 2020 ) . Overall , viral proteins showed the lowest predictability ( Spearman’s correlation of <0 . 5 ) . In contrast , we were able to predict MFE using a random forest model using these above-mentioned five parameters with an accuracy similar to the best prediction obtained in this analysis for any viral or non-viral protein ( Pearson’s r = 0 . 73; Spearman’s ρ = 0 . 73; Figure 3B ) . Interestingly , SNAP2 ( Hecht et al . , 2015 ) , a neural network-based classifier of mutational effects that was shown to correlate well with MFE in other studies ( Gray et al . , 2018; Livesey and Marsh , 2020; Reeb et al . , 2020 ) , correlated poorly with our data ( R2 = −0 . 26 ) . Overall , considering the relative conservation of capsid structure in picornaviruses as well as the availability of both capsid sequences and high-resolution structures for numerous members of this family , it is likely that these findings can be extrapolated to additional picornaviruses . Incorporating site-specific amino acid preferences obtained from our DMS results into phylogenetic models was found to significantly improve model accuracy . This has been observed in DMS studies with other RNA viruses ( Bloom , 2017; Doud and Bloom , 2016; Haddox et al . , 2018 ) and indicated that our laboratory-measured MFE capture additional information that cannot be obtained from sequence analysis alone . In addition , this approach allowed us to assess which sites show differential selection patterns as a result of the distinct environments encountered in nature and the laboratory . As expected , pressure from the adaptive immune system was found to be the major difference between these environments , with residues in antibody neutralization sites showing higher differential selection compared to other sites in the capsid ( Figure 4A ) . Moreover , the sites showing the highest degree of differential selection were found in known antibody neutralization sites ( Figure 4B–D ) . However , why these particular residues within antibody neutralization sites show differential selection , while others do not , remain to be elucidated . It has been shown that one , or a few , sites within antibody binding regions can have strong effects on escape from antibody neutralization ( Lee et al . , 2019 ) , potentially explaining these findings . Interestingly , while the top three sites showing differential selection were in antibody neutralization sites , the mutation showing the fourth-highest differential selection was found in the HI loop of VP1 . While not classically considered an antibody epitope , this loop has been shown to interact with an antibody fragment in the picornavirus coxsackievirus A6 ( Xu et al . , 2017 ) , is known to mediate receptor binding in different picornaviruses ( Belnap et al . , 2000; Xing et al . , 2000 ) , and to interact with host cyclophilin A to facilitate uncoating ( Qing et al . , 2014 ) . Whether these factors or others are responsible for the observed differential selection remains to be elucidated . The CVB3 capsid encodes the information for directing myristoylation , protease cleavage , and interaction with host factors . We took advantage of our data to examine the sequence specificity and mutational tolerance of several known capsid-encoded motifs . First , we examined the amino acid preferences of the CVB3 capsid myristoylation motif . We observe a strong correlation with the canonical myristoylation pattern ( Prosite pattern PDOC00008 ) , although with greater intolerance to mutations in three of the six residues in the capsid ( Figure 4—figure supplement 1 ) . This is likely to stem from additional constraints imposed by capsid structure . On the other hand , we examined the amino acid preference of a conserved motif in VP1 that is required for 3CDpro-mediated cleavage of picornavirus capsids ( Kristensen and Belsham , 2019 ) . Our data showed a higher cost to mutation in this motif relative to other capsid positions ( Figure 4—figure supplement 1 ) , highlighting its importance for capsid function . Finally , we examined the sequence preferences surrounding the two 3CDpro cleavage sites . We find a strong dependence on the cleavage site residues ( positions P1 and P1’; Figure 5 ) and to a lesser degree position P4 , with large variation in the sequence preferences across the remaining positions between the two cleavage sites . Overall , our experimentally measured MFE are congruent with existing information regarding the sequence preferences of the examined capsid motifs , yet provide in-depth insights into sequence specificity that cannot be obtained from examining natural sequence variation . Finally , we used the amino acid preferences observed in 3CDpro cleavage sites within the capsid to query the human genome for potential cellular targets of this protease ( Figure 5D ) . Using this approach , we identify 746 cytoplasmic proteins that harbor a potential 3CDpro target sequence , including 11 proteins previously shown to be cleaved by different picornavirus 3C proteases . We then validated our approach using eight proteins , comprising nine predicted cleavage sites . Six of the predicted cleavage sites were not affected by 3CDpro expression ( Figure 5—figure supplement 1 ) . On the other hand , three proteins were observed to be specifically cleaved by the viral protease ( Figure 5E ) : WD repeat domain 33 ( WDR33 ) , an important factor for polyadenylation of cellular pre-mRNAs ( Chan et al . , 2014 ) that has been shown to act as a restriction factor during influenza infection ( Brass et al . , 2009 ) ; the interferon-induced protein phospholipid scramblase 1 ( PLSCR1 ) , which is involved in the replication of numerous viruses , likely due to its ability to enhance the expression of certain interferon-stimulated genes ( Kodigepalli et al . , 2015 ) ; and the interferon-induced Pleckstrin homology domain containing A4 ( PLEKHA4 ) , a plasma membrane-localized signaling modulator ( Shami Shah et al . , 2019 ) that is currently not known to play a role in viral infection . Overall , our approach correctly predicts 30% of the identified cleavage sites . It is likely that incorporating additional selection criteria , such as accessibility of the cleavage peptide in the folded structure , can be used to further reduce false positives . Nevertheless , extrapolating our validation results to the larger dataset suggests >200 new host targets of the protease are identified , some of which could play key roles in viral biology and pathogenesis .
HeLa-H1 ( CRL-1958; RRID:CVCL_3334 ) and HEK293 ( CRL-1573; RRID:CVCL_0045 ) cells were obtained from ATCC and were periodically validated to be free of mycoplasma . All work with CVB3 was based on the Nancy infectious clone ( kind gift of Dr . Marco Vignuzzi , Institute Pasteur ) . Cells were cultured in culture media ( Dulbecco’s modified Eagle’s medium [DMEM] with 10% heat-inactivated fetal bovine serum ( FBS ) , Pen-Strep , and l-glutamine ) with FBS concentrations of 2% during infection . For plaque assays , serial dilutions of the virus were used to infect confluent HeLa-H1 cells in six-well plates for 45 min , followed by overlaying the cells with a 1:1 mixture of 56°C 1 . 6% agar ( Arcos Organics 443570010 ) and 37°C 2× DMEM with 4% FBS . Two days later , plates were fixed with formaldehyde ( 2% final concentration ) after which the agar was removed and the cells stained with crystal violet to visualize plaques . The infectious clone was modified by site-directed mutagenesis to remove an XhoI site present in the capsid region ( P1 ) and introduce an XhoI site at position 692 as well as a Kpn2I site at position 3314 , generating a pCVB3-XhoI-P1-Kpn2I clone ( Bou et al . , 2019 ) . In addition , a pCVB3-XhoI-ΔP1-Kpn2I plasmid was generated by replacing the region between the XhoI and Kpn2I sites in pCVB3-XhoI-P1-Kpn2I with a short linker . To generate the template for DMS , the capsid region was amplified by PCR from pCVB3-XhoI-P1-Kpn2I with Phusion polymerase ( Thermo Scientific ) and primers HiFi-F ( CTTTGTTGGGTTTATACCACTTAGCTCGAGAGAGG ) and HiFi-R ( CCTGTAGTTCCCCACATACACTGCTCCG ) and gel purified ( Zymoclean Gel DNA Recovery Kit ) . Primers spanning the full coding region of the capsid region were designed using the CodonTilingPrimers software from the Bloom lab ( https://github . com/jbloomlab/CodonTilingPrimers; Dingens et al . , 2017 ) with the default parameters and synthesized by IDT ( Supplementary file 1 ) . These primers were used to perform the mutagenesis PCR on the capsid template together with the HiFi-F or HiFi-R primers in triplicate following published protocols ( Dingens et al . , 2017 ) with the exception that 10 rounds of mutagenesis were performed for libraries 1 and 2 , while a second round of seven mutagenesis cycles was performed for library three to increase the number of mutation per clone . The products were gel purified and ligated to an XhoI and Kpn2I digested and gel purified pCVB3-XhoI-ΔP1-Kpn2I using NEBuilder HiFi DNA Assembly reaction ( NEB ) for 25 min . Mutagenesis efficiency was evaluated by the transformation of the assembled plasmids into NZY5α competent cells ( NZY Tech ) , Sanger sequencing of 18–23 clones per library , and mutation analysis using the Sanger Mutant Library Analysis script ( https://github . com/jbloomlab/SangerMutantLibraryAnalysis; Bloom , 2014 ) . Subsequently , the assembled plasmid reactions were purified using a Zymo DNA Clean and Concentrator-5 kit ( Zymo Research ) and used to electroporate MegaX DH10B T1R Electrocomp cells ( ThermoFisher ) using a Gene Pulser XCell electroporator ( Bio-Rad ) according to the manufacturer’s protocol . Cells were then grown overnight in a 50 mL liquid culture at 33°C and DNA purified using the PureLink HiPure plasmid midiprep kit ( Invitrogen ) . Transformation efficiency was estimated by plating serial dilutions of the transformation on agar plates . In total , 4 . 44 × 105 , 1 . 46 × 105 , and 2 . 19 × 105 transformants were obtained for lines 1 , 2 , and 3 , respectively . Viral genomic RNA was then transcribed from SalI linearized , gel-purified full-length plasmids using the TranscriptAid T7 kit ( Thermo Scientific ) , and four electroporations were performed using 4 × 106 HeLa-H1 cells in a 4 mm cuvette in 400 μL of calcium- and magnesium-free phosphate-buffered saline ( PBS ) using with 8 μg of RNA in a Gene Pulser XCell ( Bio-Rad ) set to 240 V and 950 µF . Electroporated cells were then pooled , and one-fourth was cultured for 9 hr to produce the passage 0 virus ( P0 ) . Following three freeze–thaw cycles , 2 × 106 plaque-forming units ( PFU ) were used to infect a 90% confluent 15 cm plate in 2 . 5 mL of infection media for 1 hr . Cells were then washed with PBS and incubated in 12 mL of infection media for 9 hr . Finally , cells were subjected to three freeze–thaw cycles , debris removed by centrifugation at 500 × g , and the supernatants collected to generate P1 virus stocks . All infections produced >2 . 38 × 106 PFU in P0 and >1 . 2 × 107 PFU in P1 as judged by plaque assay . Libraries were prepared following published protocols ( Kennedy et al . , 2014 ) , and each library was run on a Novaseq6000 2 × 150 at a maximum of 30G per lane to reduce potential index hopping . Reads trimming was performed using fastp ( Chen et al . , 2018 ) ( command: -max_len1 150 --max_len2 150 --length_required 150 -x -Q -A ) , unsorted bam files were generated from fastq files using Picard tools FastqToSam ( version 2 . 2 . 4 ) and merged into a single bam using the cat command of Samtools ( version 1 . 5 ) . The duplex pipeline was then implemented ( https://github . com/KennedyLabUW/Duplex-Sequencing/UnifiedConsensusMaker . py; Kennedy et al . , 2014 ) . using the UnifiedConsensusMaker . py script and a minimum family size of 3 , a cutoff of 0 . 9 for consensus calling , and an N cutoff of 0 . 3 . The single-stranded consensus files ( SSCS ) were then aligned using BWA mem ( version 0 . 7 . 16 ) , sorted using Samtools , size selected to be 133 bp long using VariantBam ( Wala et al . , 2016 ) , unaligned reads were discarded ( Samtools view command with -F 4 ) , and the resulting bam file indexed with Samtools . Subsequently , fgbio ( http://fulcrumgenomics . github . io/fgbio/; version 1 . 1 . 0 ) was used to hard-clip 10 bp from each end and upgrade all clipping to hard-clip ( -c Hard --upgrade-clipping true --read-one-five-prime 10 --read-one-three-prime 10 --read-two-five-prime 10 --read-two-three-prime 10 ) . Variant bam was then used to keep all reads that were between 50 and 150 bp , well-mapped , and had either no indels and less than five mutations ( command –r {‘‘:{‘rules’:[{‘ins’:[0 , 0] , ‘del’:[0 , 0] , ‘nm’:[0 , 4] , ‘mate_mapped’:true , ‘fr’:true , ‘length’:[50 , 150]}]}}” ) . Finally , the codons in each read were identified using the VirVarSeq ( Verbist et al . , 2015 ) Codon_table . pl script using a minimum read quality of 20 . A custom R script was then used to generate a codon counts table for each codon position by eliminating all codons containing ambiguous nucleotides and codons with a strong strand bias ( StrandOddsRatio > 4 ) , as well as all codons that are reached via a single mutation ( available at https://github . com/RGellerLab/CVB3_Capsid_DMS ) ; Mattenberger , 2021; copy archived at swh:1:rev:29dd205182f0886dc5bad3e6b4ddd6e786c58a75 ) . Amino acid preferences and MFE were determined using DMStools2 ( Bloom , 2015 ) , with the Bayesian option and the default settings . The crystal structure PDB:4GB3 ( Yoder et al . , 2012 ) was used for all structural analyses . The effects of mutations on aggregation were determined using TANGO version 2 . 3 . 1 ( Fernandez-Escamilla et al . , 2004 ) using the default settings , and the effect on stability on the monomer and pentamer was determined using FoldX 4 ( Schymkowitz et al . , 2005 ) using the default settings . For the latter , the pentamer subunits were renamed to unique letters , all mutations between the reference sequence and the structure sequence were introduced using the BuildModel command , the structure was optimized using the RepairPDB command 5 or 10 times for the pentamer or monomer , respectively , and then the effects of the mutations were predicted using the BuildModel command ( modified PDB files can be found at https://github . com/RGellerLab/CVB3_Capsid_DMS ) . Secondary structure and RSA were obtained from DSSP ( http://swift . cmbi . ru . nl/gv/dssp/ ) using the dms_tools2 . dssp function of dms_tools2 , while interface , surface , and core residues as well as residue contact number , and presence in the twofold , threefold , and fivefold axes were obtained from ViprDB ( http://viperdb . scripps . edu/ ) ( Carrillo-Tripp et al . , 2009 ) . Distance from the center was calculated with Pymol using the Distancetoatom . py script on the monomer or pentamer . Finally , the location of antibody neutralization sites in CVB3 was obtained from an analysis of the CVB3 capsid structure in a previous publication ( Muckelbauer et al . , 1995 ) . With the exception of mutant N395H ( kind gift of Rafael Sanjuan ) ( Bou et al . , 2019 ) , all other mutants were generated by site-directed mutagenesis . For this , the PCR of the capsid region used as a template for DMS was phosphorylated and cloned into a SmaI digested pUC19 vector for use in the mutagenesis reactions ( pUC19-HiFi-P1 ) . For each mutant , non-overlapping primers containing the mutation in the middle of the forward primer were used to introduce the mutation with Phusion polymerase , followed by DpnI ( Thermo Scientific ) treatment , phosphorylation , ligation , and transformation of chemically competent bacteria . Successful mutagenesis was verified by Sanger sequencing . Subsequently , the capsid region was subcloned into pCVB3-XhoI-∆P1-Kpn2I using XhoI and Kpn2I sites . Plasmids were then linearized with MluI , and 2 μg of plasmid was transfected into 5 × 105 HEK293 cells , together with a plasmid encoding the T7 polymerase ( Yun et al . , 2015 ) ( Addgene 65974 ) using calcium phosphate . Briefly , an equal volume of 2× HBS ( 274 mM NaCl , 10 mM KCl , 1 . 4 mM Na2HPO4 ) was added dropwise to DNA containing 0 . 25M CaCl2 while mixing , incubated 15 min at RT , and then added dropwise to cells . Following 48 hr , passage 0 ( P0 ) virus was collected and titered by plaque assay . From this , 105 PFU were used to infect 90% confluent six-well HeLa-H1 cells ( multiplicity of infection ( MOI ) 0 . 1 ) for 1 hr at 37°C , after which the cells were washed twice with PBS and 2 mL of infection media added . Cells were then incubated until cytopathic effect ( CPE ) was observed . Emerging viral populations were titered by plaque assay and the capsid region sequenced to ensure no compensatory mutations or reversions arose during replication . The fitness of these mutants was then tested by direct competition with a marked reference virus using a Taqman RT-PCR method ( Moratorio et al . , 2017 ) . Briefly , using four biological replicates , confluent HeLa-H1 cells in a 24-well plate were infected with 200 µL of a 1:1 mixture of 4 × 103 PFU ( MOI 0 . 01 ) of the test and marked reference viruses for 45 min . Subsequently , the inoculum was removed , the cells were washed twice with PBS , 200 µL of infection media was added , and the cells were incubated for 24 hr at 37°C . Finally , cells were subjected to three freeze–thaw cycles , debris removed by centrifugation at 500 × g , the supernatants collected and treated with 2 µL of RNase-Free DNaseI ( ThermoFisher ) for 15 min at 37°C , and viral RNA extracted using the Quick-RNA Viral Kit ( Zymo Research ) , eluting in 20 μL . Quantification of the replication of each mutant versus the reference was performed using Luna Universal Probe One-Step RT-qPCR kit ( New England BioLabs ) containing 3 µL of total RNA , 0 . 4 µM of each qPCR primers , and 0 . 2 µM of each probe . The standard curve was performed using 10-fold dilutions of RNA extracted from 107 PFU of wild-type and reference viruses . All samples were performed with three technical replicates . The relative fitness ( W ) of each mutant versus the common marked reference virus was calculated using the following formula: W = [R ( t ) /R ( 0 ) ]1/t , where R ( 0 ) and R ( t ) represent the ratio of the mutant to the reference virus genomes in the initial mixture used for the infection and after 1 day ( t = 1 ) , respectively ( Carrasco et al . , 2007; Moratorio et al . , 2017 ) . Amino acid variability was assessed using Shannon entropy . Briefly , all available , non-identical , full-genome CVB3 , CVB , or enterovirus B sequences were downloaded from Virus Pathogen Resource ( Pickett et al . , 2012 ) ( http://www . viprbrc . org ) and codon-aligned using the DECIPHER package in R ( available at https://github . com/RGellerLab/CVB3_Capsid_DMS ) . All alignment positions not present in our reference strain were removed , and a custom R script was used to calculate Shannon entropy . For phylogenetic and differential selection analyses , PhyDMS was run using the default settings on an alignment of CVB3 genomes that was processed with the phydms_prealignment module and using the average preferences from the three DMS replicates . The amino acid preferences ( the relative enrichment of each amino acid at each position standardized to 1 ) was used to generate in silico 1000 peptides spanning the 10 amino acid regions surrounding each cleavage site using a custom R script ( available at https://github . com/RGellerLab/CVB3_Capsid_DMS ) . Specifically , for each peptide position , 100 peptides were generated that encoded each amino acid at a frequency corresponding to its preference observed in the DMS results , with the remaining positions unchanged . The resulting 1000 peptides from each cleavage site were uploaded to PSSMSearch ( Krystkowiak et al . , 2018 ) ( http://slim . icr . ac . uk/pssmsearch/ ) using the default setting ( psi_blast IC ) . Results were filtered to remove proteins indicated to be secreted , lumenal , or extracellular in the Warnings column . To test whether proteins were cleaved by the viral 3 CD protease , the corresponding region was PCR amplified from the Nancy infectious clone ( primers 3C-For: TATTCTCGAGACCATGGGCCCTGCCTTTGAGTTCG and 3D-Rev: TATTGCGGCCGCCTAGAAGGAGTCCAACCATTTCCT ) and cloned into the pIRES plasmid ( Clonetech ) using the restriction sites XhoI and NotI ( pIRES-3CDpro ) . For analysis of fusion proteins , HEK293 cells were transfected with GFP-PLEKHA4 ( kind gift of Dr . Jeremy Baskin , Cornell University ) , GFP-PLSCR1 ( kind gift of Dr . Serge Benichou , Institut Cochin ) , pAcGFP-WDR33 ( Kind gift of Dr . Matthias Altmeyer , University of Zurich ) , FLAG-NLCR5 ( Addgene #37521 ) , HA-ZC3HAV1 ( Addgene #45907 ) , or the control plasmid FLuc-eGFP ( Addgene #90170 ) , together with the pIRES-3CDpro plasmid using Lipofectamine 2000 . Following 24 hr , proteins were collected by lysing in lysis buffer ( 50 mM Tris–HCl , 150 mM NaCl , 1% NP40 , and protease inhibitor cocktail [Complete Mini EDTA-free , Roche] ) and subjected to western blotting with the corresponding antibody ( anti-GFP , Santa Cruz sc-9996; anti–FLAG , Santa Cruz sc-166335; anti-HA , Santa Cruz , sc-7392 ) . For analysis of endogenous proteins , 3CDpro was expressed for 48 hr before cell lysis , and western blotting using antibodies against WDR33 ( Santa Cruz sc-374466 ) , TSG101 ( Santa Cruz sc-136111 ) , GAK ( Santa Cruz sc-137053 ) , and MAGED1 ( Santa Cruz sc-393291 ) . When indicated , the 3Cpro inhibitor rupintrivir ( Tocris Biosciences ) was added at a concentration of 2 µM for the last 24 hr before collection . The predicted molecular weight of cleaved fragments was calculated using the mw function of the Peptides R package ( version 2 . 4 . 2 ) . All experiments were performed with at least three biological replicates with the exception of the analysis of protein cleavage by western blotting , which was performed in duplicate . All statistical analyses were performed in R and were two tailed . For random forest prediction , the R RandomForest package ( version 4 . 6–14 ) was employed using the default setting with an mtry of 10 , and for the linear model , the formula lm ( MFE ~ enterovirus B entropy + WT amino acid * mutant amino acid + predicted effect of mutations on stability in the pentamer + relative surface exposure ) was used ( available at https://github . com/RGellerLab/CVB3_Capsid_DMS ) . Sequence logoplots were producing using Logolas ( Dey et al . , 2018 ) . Unaligned bam files have been uploaded to SRA ( Bioproject PRJNA643896 , SRA SRP269871 , Accession SRX8663374-SRX8663384 ) . The scripts and data required to obtain the codon count tables for all samples , to perform the random forest and linear model predictions , to generate the peptides for use with PSSMsearch , as well as the sequence alignments and modified structure files for FoldX analysis , can be found on GitHub ( https://github . com/RGellerLab/CVB3_Capsid_DMS ) . Finally , the interactive heatmap of MFE across the capsid was generated by modifying a script from a prior publication ( Starr et al . , 2020 ) ( available at https://github . com/jbloomlab/SARS-CoV-2-RBD_DMS/blob/master/interactive_heatmap . ipynb ) and can be found on this projects’ GitHub page ( https://github . com/RGellerLab/CVB3_Capsid_DMS ) . | A virus is made up of genetic material that is encased with a protective protein coat called the capsid . The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material . Inside the cell , the virus hitchhikes the infected cell’s machinery to grow or replicate its own genetic material . Viral capsids are the main target of the host’s defence system , and therefore , continuously change in an attempt to escape the immune system by introducing alterations ( known as mutations ) into the genes encoding viral capsid proteins . Mutations occur randomly , and so while some changes to the viral capsid might confer an advantage , others may have no effect at all , or even weaken the virus . To better understand the effect of capsid mutations on the virus’ ability to infect host cells , Mattenberger et al . studied the Coxsackievirus B3 , which is linked to heart problems and acute heart failure in humans . The researchers analysed around 90% of possible amino acid mutations ( over 14 , 800 mutations ) and correlated each mutation to how it influenced the virus’ ability to replicate in human cells grown in the laboratory . Based on these results , Mattenberger et al . developed a computer model to predict how a particular mutation might affect the virus . The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks , such as building the capsid . It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein , which cuts the capsid proteins into the right size from a larger precursor . By looking for similar sequences in human genes , the researchers identified several ones that the virus may attack and inactivate to support its own replication . These findings may help identify potential drug targets to develop new antiviral therapies . For example , proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment . They also highlight new proteins in human cells that could potentially block the virus in cells . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"evolutionary",
"biology",
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] | 2021 | Globally defining the effects of mutations in a picornavirus capsid |
Stress often affects eating behaviors , increasing caloric intake in some individuals and decreasing it in others . The determinants of feeding responses to stress are unknown , in part because this issue is rarely studied in rodents . We focused our efforts on the novelty-suppressed feeding ( NSF ) assay , which uses latency to eat as readout of anxiety-like behavior , but rarely assesses feeding per se . We explored how key variables in experimental paradigms – estrous and diurnal cyclicity , age and duration of social isolation , prandial state , diet palatability , and elevated body weight – influence stress-induced anxiety-like behavior and food intake in male and female C57BL/6J mice . Latency to eat in the novel environment is increased in both sexes across most of the conditions tested , while effects on caloric intake are variable . In the common NSF assay ( i . e . , lean mice in the light cycle ) , sex-specific effects of the length of social isolation , and not estrous cyclicity , are the main source of variability . Under conditions that are more physiologically relevant for humans ( i . e . , overweight mice in the active phase ) , the novel stress now elicits robust hyperphagia in both sexes . This novel model of stress eating can be used to identify underlying neuroendocrine and neuronal substrates . Moreover , these studies can serve as a framework to integrate cross-disciplinary studies of anxiety and feeding related behaviors in rodents .
Most studies in humans focus on stress-induced overeating ( also called emotional eating or stress eating ) , but stress can also lead to decreased caloric intake in some people ( Greeno and Wing , 1994; Oliver and Wardle , 1999; Wallis and Hetherington , 2009 ) . Although stress-related eating behaviors are heterogeneous at the population level , individual behaviors are highly predictable ( Oliver and Wardle , 1999; Stone and Brownell , 1994 ) . Clinical and epidemiological studies support the idea that properties of the stressor influence the direction of the response . The effect of a mild stress is variable , but as the intensity of stress increases , people are more likely to eat less ( Stone and Brownell , 1994; Kandiah et al . , 2008 ) . The type of stress also matters , with physical stressors more likely to suppress intake than psychosocial stressors ( O’Connor et al . , 2008 ) . While stress affects eating in both men and women ( Rutters et al . , 2009 ) , there are sex differences in responsiveness . Females are typically more sensitive to interpersonal and emotional stress , while males are more sensitive to ego-threatening situations ( Tanofsky-Kraff et al . , 2000; Laitinen et al . , 2002; Clauss and Byrd-Craven , 2019 ) . Moreover , the threshold at which stress preferentially suppresses food intake is lower for males than females ( Stone and Brownell , 1994 ) . While properties of the stressor shape eating behaviors , these influences cannot account for all of the heterogeneity . Even when a common stress is shared by many , such as the quarantine during the COVID-19 pandemic , both overeating and restricting are increased ( Phillipou et al . , 2020; Coulthard et al . , 2021 ) . These observations are consistent with the idea that physiological and psychological traits determine the direction of eating responses to stress at the individual level ( Stone and Brownell , 1994 ) . Elevated BMI is the variable most consistently associated with eating in the absence of hunger in both children ( Miller et al . , 2019 ) and adults ( Laitinen et al . , 2002; Coulthard et al . , 2021; Lemmens et al . , 2011 ) . However , the underlying mechanism is unknown . Basic principles governing stress-related eating behaviors are conserved in rodent models , providing construct validity . The likelihood of hypophagic responses increases with the stress intensity ( Levine and Morley , 1981; Michajlovskij et al . , 1988; Martí et al . , 1994; Harris et al . , 1998; Vallès et al . , 2000; Michel et al . , 2005; Barfield et al . , 2013 ) . Moreover , diet-induced obesity exaggerates hyperphagic behavior in models of social defeat stress in male mice and rats ( Bartolomucci et al . , 2009; Razzoli et al . , 2015 ) . Early experiments in rats reported stress-induced increases in food intake in males and females ( Antelman et al . , 1976 ) . Over time , the field gradually shifted toward measurements of stress-induced suppression of eating behaviors as a readout of anxiety- or depressive-like states , without explicitly assessing caloric intake ( Cryan and Sweeney , 2011; Kokras and Dalla , 2014 ) . Current studies of stress-induced overeating are largely limited to models of binge-eating disorders , which involve restricted access to a palatable diet , often in combination with a chronic physical stress ( Boggiano and Chandler , 2006 ) . Rodent models recapitulate the preferential susceptibility to binge-eating disorder and subclinical bingeing behavior in women ( Klump et al . , 2011; Jacobi et al . , 2004; Hudson et al . , 2007; Croll et al . , 2002 ) . However , there are no established models to study effects of short-term exposure to psychological stress on eating ( reviewed in François et al . , 2021 ) . There are several obstacles to studying the effects of acute stress on feeding behaviors in female rodents . They often exhibit reduced anxiety-like behaviors , especially when tasks involve locomotor activity or arousal ( Fernandes et al . , 1999; File , 2001; Doremus et al . , 2006 ) . Moreover , behavioral endpoints often vary across the ovarian cycle ( Becker et al . , 2005; Beery and Zucker , 2011 ) . Together , these observations have been used as a rationale for excluding females in basic research . The paucity of rodent studies in females is particularly unfortunate , since the prevalence of anxiety , depressive symptoms , eating disorders , and subclinical disordered eating behaviors is higher in women ( Jacobi et al . , 2004; Hudson et al . , 2007; Altemus et al . , 2014 ) . Moreover , single housing can exert opposite effects in males and females ( Oliver et al . , 2020 ) , which is rarely considered when using this manipulation to measure feeding or other behaviors . We set out to uncover aspects of experimental paradigms that promote hyperphagic vs . hypophagic responses to stress in female mice . We used the novelty-suppressed feeding ( NSF ) paradigm as the foundation for these studies . It has strong predictive validity with respect to antidepressant and anxiolytic therapeutics but is rarely exploited to examine the effect of stress on food intake ( Dulawa and Hen , 2005 ) . We performed 15 variations of the NSF assay to parse influences of sex , estrous and diurnal cyclicity , age and duration of social isolation , prandial state , diet palatability , and chronic high-fat diet ( HFD ) exposure on anxiety-like and feeding behaviors in mice .
We tested males and females in the manual NSF assay . We acclimated the mice to single housing for 2 weeks , subjected them to an overnight fast , and recorded responses to a chow diet in the home vs . novel cage environment ( Figure 1A ) . Novel environment stress increased latency in both sexes ( Figure 1B , males , Paired t-test: t=4 . 094; df=9; p=0 . 027 and females , Wilcoxon test: W=141; p=0 . 0002 ) . Food intake was significantly decreased in the novel cage in males ( Figure 1E , Wilcoxon test: W=-36; p=0 . 0078 ) but not in females ( Figure 1E , Wilcoxon test: W=-67; p=0 . 0562 ) . We examined the contribution of estrous cyclicity to the variability in the effects of novel environment stress . Females in diestrus ( D ) exhibited significantly higher latency ( Figure 1C , One-way ANOVA: F ( 3 , 23 ) =22 . 52; p<0 . 0001 ) and lower food intake ( Figure 1F , Kruskal-Wallis test: H ( 3 ) =15 . 86; p=0 . 0012 ) in the home cage compared to males . In contrast , estrous cyclicity did not affect these behaviors in the novel cage ( Figure 1D , G , Kruskal-Wallis test: H ( 3 ) =16 . 80; p=0 . 0008 and One-way ANOVA: F ( 3 , 23 ) =2 , 370; p=0 . 0968 , respectively ) . Even though mice were acclimated to the experimental paradigm for 5 days , performing the NSF on the benchtop introduces stresses associated with an open cage and human contact . We examined the impact of minimizing these environmental stresses in females by performing the NSF assay in a system that allows automated control of food access and measurement of food intake . We evaluated adult females that were socially isolated for 2 weeks and provided with access to a chow diet in the morning after an overnight fast ( Figure 2A ) . These minimally stressed conditions eliminated the effect of the estrous cycle ( Figure 2B , C , E , F , Kruskal-Wallis test: H ( 2 ) =1 . 269; p=0 . 5479 , One-way ANOVA: F ( 2 , 18 ) =1 . 385; p=0 . 2757 , One-way ANOVA: F ( 2 , 18 ) =0 . 0701; p=0 . 9326 , and One-way ANOVA: F ( 2 , 18 ) =0 . 2336; p=0 . 7941 , respectively ) . Latencies were lower in both the home cage ( X2 ( 1 , n=38 ) = 11 . 8 , p = 0 . 001; Supplementary file 1B-I ) and novel cage ( X2 ( 1 , n=38 ) = 22 . 2 , p < 0 . 001; Supplementary file 1B-I ) , but the responsiveness to the novel environment stress was maintained ( Figure 2D , Wilcoxon test: W=151; p=0 . 0071 ) . Eliminating the effect of estrous cyclicity did not improve the consistency of the hypophagic response to novel environment stress ( Figure 2G , Paired t-test: t=1 . 810; df=20; p=0 . 0854 ) , arguing against the idea that estrous cycle is the primary driver of variability . We next examined another aspect of the NSF paradigm that could affect food intake in females – the 2-week period of social isolation stress needed to acclimate mice to the testing conditions . Timing of social isolation modulates anxiety-related behavior in a sex-specific manner ( Donner and Lowry , 2013; Walker et al . , 2019 ) . We assessed the impacts of starting social isolation in adolescence ( 5 weeks ) on behaviors in the manual NSF assay in adulthood in males and females ( Figure 3A ) . All mice exhibited higher latencies in the novel cage , regardless of sex ( Figure 3B , males , Wilcoxon test: W=66; p=0 . 001 and females , Wilcoxon test: W=36; p=0 . 0078 ) . Social isolation from adolescence was associated with decreased food intake in the home cage in males ( X2 ( 1 , n=22 ) = 7 . 2 , p = 0 . 007; Supplementary file 1C-vi ) , which dampens the overall hypophagic effect of the novel cage ( Figure 3C , males , Paired t-test: t=2 . 193; df=10; p=0 . 0531 ) . In contrast , in females , social isolation from adolescence increased the magnitude of the hypophagic response to the novel cage ( X2 ( 1 , n=79 ) = 6 . 1 , p = 0 . 014; Supplementary file 1C-iv ) and eliminated variability in the assay ( Figure 3C , females , Paired t-test: t=6 . 347; df=7; p=0 . 0004 ) . Body weight was not impacted by the timing of social isolation ( data not shown ) . Notably , sex interacts with the adolescent vs . adult social isolation stress paradigm to modulate both latency and food intake in the home cage ( latency: X2 ( 1 , n=47 ) = 9 . 7 , p = 0 . 002 , Figure 1—figure supplement 1C-ix; food intake: X2 ( 1 , n=47 ) = 6 . 2 , p = 0 . 013 , Supplementary file 1C-x ) and novel cage ( latency: X2 ( 1 , n=47 ) = 19 . 4 , p < 0 . 001 , Supplementary file 1C-ix; food intake: X2 ( 1 , n=47 ) = 4 . 2 , p = 0 . 041 , Supplementary file 1C-x ) . We parsed potential contributions of adolescent onset and the length of social isolation . We performed the NSF assay in females that were exposed to prolonged ( ~6 weeks ) social isolation stress as adults ( Figure 3—figure supplement 1A , red ) . Latencies were increased in the novel cage ( Figure 3—figure supplement 1B , red , Wilcoxon test: W=43; p=0 . 00 ) , while hypophagic responses did not reach significance ( Figure 3—figure supplement 1C , red , Paired t-test: t=2 . 211; df=8; p=0 . 058 ) . We also examined whether adolescent social isolation for 2 weeks is sufficient to increase reliability of feeding behavior in females . To this end , we performed the NSF assay in 7-week-old females that were singly housed from 5 weeks ( Figure 3—figure supplement 1A , green ) . Whereas latencies were increased in the novel cage ( Figure 3—figure supplement 1B , green , Wilcoxon test: W=85; p=0 . 0012 ) , feeding responses were not consistent ( Figure 3—figure supplement 1C , green , Wilcoxon test: W=18; p=0 . 2656 ) . In females , the length of social isolation impacted the feeding response , with the longer period promoting hypophagic responses ( X2 ( 1 , n=79 ) = 6 . 1 , p = 0 . 014; Supplementary file 1C-iv ) , but not latencies ( X2 ( 1 , n=79 ) = 0 . 1 , p = 0 . 712; Supplementary file 1C-iii ) . In contrast , the timing of social isolation decreased anxiety-like responses ( X2 ( 1 , n=79 ) = 6 . 3 , p = 0 . 012; Supplementary file 1D-iii ) , but did not impact food intake ( X2 ( 1 , n=79 ) = 0 . 7 , p = 0 . 421; Supplementary file 1D-iv ) . In summary , the combination of adolescent onset and prolonged exposure to social isolation is required to obtain consistent and significant hypophagic responses in females , but it has the opposite effect in males ( i . e . , caloric intake no longer significantly decreased in the novel cage ) . We defined sex-specific conditions that produce consistent effects of novel stress on feeding behavior in adults – short single housing in males and prolonged single housing from adolescence in females . With the ability to study behaviors in both sexes , we next sought to address a major gap between studies of stress-related eating behaviors in humans and rodents ( François et al . , 2021 ) . In the standard NSF assay , an overnight fast is used to motivate mice to eat in the morning , a period when mice typically consume very little food ( Figure 4B ) . In contrast , studies in humans deliberately focus on emotional eating in the absence of hunger . We next studied two conditions that can promote food consumption without the physiological stress of fasting – the dark phase of the diurnal cycle and diet palatability . Diurnal misalignment has sex-specific effects on systems regulating energy homeostasis in humans that increase susceptibility to obesity ( Qian et al . , 2019 ) . To perform the NSF assay in the period of activity and feeding in mice , we examined behaviors at the onset of the dark phase of the cycle . We used conditions for each sex that produce consistent results in the light cycle – 2-week social isolation in adult males and ~6-week social isolation from adolescence in females ( Figure 4A , blue and green ) . As we observed in the light cycle , latencies were increased in the novel cage in both groups ( Figure 4C , blue and green , Wilcoxon test: W=92; p=0 . 0067 and Wilcoxon test: W=120; p=0 . 0008 , respectively ) . In contrast to the uniform hypophagic response to the novel test in the light phase in males , the same conditions produce mixed results in the dark phase ( Figure 4D , blue , Wilcoxon test: W=-4 . 0; p=0 . 9229 ) . In females , longer social isolation produced hypophagic responses ( Figure 4D , green , Paired t-test: t=2 . 166; df=15; p=0 . 0468 ) , while shorter social isolation produced hyperphagic responses ( Figure 4D , red , Wilcoxon test: W=106; p=0 . 0038 ) , as we had observed in the light phase assay . Overall , the time of day had no effect on latency endpoints ( Supplementary file 1E–I , iii ) . Although mice ate less in the dark cycle assay in both home cage ( X2 ( 1 , n=88 ) = 32 . 0 , p < 0 . 001; Supplementary file 1E-ii ) and novel cage ( X2 ( 1 , n=88 ) = 10 . 9 , p = 0 . 001; Supplementary file 1E-ii ) , responses to stress were shifted toward hyperphagia ( X2 ( 1 , n=88 ) = 10 . 1 , p = 0 . 002; Supplementary file 1E-iv ) . We asked whether estrous cyclicity influences eating behaviors when the test is performed in the dark phase ( undefinedundefined ) . Since social isolation length does not interact with the estrous cyclicity to influence latency and food intake ( Supplementary file 1C-xiii , xiv ) , we pooled both groups to increase statistical power . Latencies to eat were not affected by estrous stage in the home cage ( Figure 4—figure supplement 1A , Kruskal-Wallis test: H ( 2 ) = 0 . 5602; p=0 . 7557 ) , but were significantly reduced in proestrus in the novel cage ( Figure 4—figure supplement 1B , One-way ANOVA: F ( 2 , 25 ) =1 . 197; p=0 . 0053 ) . In contrast , food intake was reduced in estrus compared to diestrus in the home cage ( Figure 4—figure supplement 1C , Kruskal-Wallis test: H ( 2 ) =7 . 649; p=0 . 0218 ) , but there was no effect of cyclicity in the novel cage ( Figure 4—figure supplement 1D , Kruskal-Wallis test: H ( 2 ) =0 . 4612; p=0 . 7941 ) . Because mice consume ~90% of their caloric intake in the dark phase , the test performed in the morning after an overnight fast imposes a state of negative energy balance , while daily intake is not reduced in mice with restricted access to food in the dark phase ( Figure 4B ) . To parse the effects of diurnal influences vs . prandial state , we matched the 90% caloric restriction achieved by an overnight fast by allowing mice to consume an equivalent number of calories ( ~2 kcal ) at the start of the dark cycle on the previous day ( Figure 4—figure supplement 2A , B ) . Latency outcomes were not impacted by the fast ( Figure 4C , Paired t-test: t=2 . 932 , df=7; p=0 . 02 , and Supplementary file 1E–I , iii ) . While food intake was consistently and significantly decreased in the novel cage during the light phase test ( Figure 3C , green , Paired t-test: t=6 . 347; df=7; p=0 . 0004 ) , this effect was lost in the dark cycle test ( Figure 4—figure supplement 2D , Paired t-test: t=2 . 104 , df=7; p=0 . 0734 ) . Therefore , diurnal influences , and not prandial state , are the primary determinants of whether stress increases or decreases intake . Next , we investigated whether elevated body weight also increases the likelihood of hyperphagic responses , as has been observed in humans ( Laitinen et al . , 2002; Coulthard et al . , 2021; Lemmens et al . , 2011 Figure 5A ) . Mice exposed to HFD for 10–12 weeks increased body weight ( Figure 5B , males , Paired t-test: t=11 . 25; df=12; p<0 . 0001 and females , Paired t-test: t=13 . 53; df=13; p<0 . 0001 ) but were not frankly diabetic ( Figure 5C ) . Males increased latency in the novel cage ( Figure 5D , Paired t-test: t=4 . 751; df=12; p=0 . 0005 ) , but females did not ( Figure 5D , Wilcoxon test: W=5; p=0 . 9032 ) . Under these conditions , both males and females increased their caloric intake in the novel cage ( Figure 5E , males , Wilcoxon test: W=89; p=0 . 0005 and females , Wilcoxon test: W=105; p=0 . 0001 ) . Body weight gain was not correlated with the change in food intake between the home and novel cage ( Figure 5F , Linear regression: R2=0 . 01017; F ( 1 , 25 ) =0 . 2568; p=0 . 6167 ) . In summary , stress-induced hyperphagia in the dark phase assay was exacerbated by chronic exposure to HFD in both sexes ( X2 ( 1 , n=58 ) = 11 . 8 , p = 0 . 001; Supplementary file 1F-iv ) . We next parsed the effects of chronic exposure to HFD vs . access to a highly palatable diet during the test ( Figure 5—figure supplement 1A ) . Mice were acclimated to HFD for 30 min per night for 5 days , which did not affect body weight ( Figure 5—figure supplement 1B , males , Paired t-test: t=1 . 266; df=10; p=0 . 2343 and females , Paired t-test: t=1 . 389; df=15; p=0 . 1865 ) . Males increased latency in the novel cage ( Figure 5—figure supplement 1C , Wilcoxon test: W=46; p=0 . 04 ) , but females did not ( Figure 5—figure supplement 1C , Paired t-test: t=0 . 6366; df=14; p=0 . 5347 ) , as observed in chronic HFD exposure . As anticipated , lean mice had tenfold lower latencies ( X2 ( 1 , n=56 ) = 28 . 5 , p < 0 . 001; Supplementary file 1G-iii ) and consumed >10 times more calories of the palatable HFD than the chow in the home cage ( X2 ( 1 , n=56 ) = 398 . 2 , p < 0 . 001; Supplementary file 1G-ii ) . They also ate more of the HFD than the overweight group ( X2 ( 1 , n=51 ) = 29 . 8 , p < 0 . 001; Supplementary file 1G-x ) . Males decreased food intake in response to the novel cage stress ( Figure 5—figure supplement 1D , Paired t-test: t=3 . 956; df=10; p=0 . 0027 ) , while females did not ( Figure 5—figure supplement 1D , Paired t-test: t=0 . 5385; df=14; p=0 . 5987 ) . In summary , chronic exposure to HFD , and not the palatability of the test diet , is the primary driver of the hyperphagic response to stress observed in both males and females .
By focusing exclusively on latency as a readout of anxiety-like behavior in the NSF , investigators miss the opportunity to evaluate the effect of stress on feeding behavior . We incorporated several features into the basic NSF paradigm to permit reliable and meaningful assessments of caloric intake . We trained mice to eat under the test conditions for several days until baseline levels of intake stabilized and excluded those that failed to train ( Schalla et al . , 2020 ) . We calculated intake from the first bite and not the start of the test . This is critical , as the high novel cage latencies ( ≥15 min ) exhibited by most of the female groups and the overweight males would confound interpretation of feeding measurements that started at the onset of the test . We evaluated intake for 30 min , as recommended ( Schalla et al . , 2020; Ellacott et al . , 2010 ) . Finally , analyzing both baseline and stress-induced conditions makes it possible to parse the effects of acute stress from those imposed by stressors built into the paradigm . This is important because we observed the most variability in chow-based studies females in the home cage . Some factors , such as adolescent social isolation , have opposite effects on behavior in the home and novel cages . Uncovering experimental conditions that influence behavior across the estrous cycle in the home cage helped to define conditions that produce consistent behavioral outcomes in females . The novel cage consistently induced higher latencies , except for females acutely or chronically exposed to HFD . In contrast , feeding responses to stress were variable between groups . Moreover , distinct sets of factors influenced latency and feeding outcomes . The manual test and the timing of the onset of social isolation affected only latency outcomes , while the length of social isolation , time of day , and chronic HFD exposure only impacted feeding outcomes . The only feature of the paradigm that influenced both latency and food intake was acute exposure to the palatable diet . Together , these observations support the idea that the circuits regulating stress-induced anxiety-like and feeding behaviors are likely distinct . Sex differences in feeding behavior are well documented ( Asarian and Geary , 2013 ) . Daily food intake fluctuates across the estrous cycle , with reductions during proestrus , when estrogen is at its peak , in rodents and humans ( Tarttelin and Gorski , 1971; Petersen , 1976; Buffenstein et al . , 1995 ) . In contrast , fasting-induced food intake was significantly higher in proestrus than in diestrus , consistent with studies involving estrogen replacement in ovariectomy ( Shakya and Briski , 2017 ) . Low home cage intake after an overnight fast in diestrus likely reflects increased stress , because it is restored by automated measurements . When we eliminated the fast by performing the test in the dark cycle , the anorexigenic effects of estrogen were retrieved , as reported by others ( Asarian and Geary , 2013 ) . Performing automated measurements eliminated effects of the estrous cycle on baseline intake but did not reduce the heterogeneity of feeding responses to stress . Conversely , we observed consistent hyperphagic responses in the dark cycle in the face of variability in home cage intake across the estrous cycle . Therefore , our studies debunk the assumption that estrous cyclicity drives variability in stress responses in females that precludes their use in neurobehavioral studies . Social isolation in adulthood produces opposite effects in males and female feeding behavior in the NSF , with males more likely to exhibit hypophagic behavior ( Oliver et al . , 2020 ) . Moreover , exposure to social isolation in the postweaning period has sex-specific effects on neurobehavioral outcomes , with increased sensitivity in males ( Weiss et al . , 2004; Fone and Porkess , 2008 ) . Here , we examined the impact of social isolation in adolescence , because it is a sensitive period for programming stress responses ( Wright et al . , 1991 ) and the peak onset of eating disorders ( Jacobi et al . , 2004; Bulik , 2002 ) . Longer exposure to social isolation influenced feeding behavior in a sex-dependent manner , promoting hypophagia in females . Exploring whether the impacts of adolescent social isolation on stress-induced feeding behavior are permanently programmed or can be reversed is an important area for future research . Assessing behaviors in the dark cycle is more physiologically relevant for feeding and minimizes stresses associated with the external environment and fasting , which impose sex-specific effects in mice and humans ( described above ) . However , reported effects of diurnal factors on anxiety-like behavior are inconsistent , and are sex and assay dependent ( Richetto et al . , 2019; Nakano et al . , 2016 ) . Here , we found that performing the NSF assay at the start of the dark cycle promoted hyperphagic responses in both sexes . This could stem from a lower appetitive drive in the home test compared to an overnight fast . Diurnal influences , and not prandial state , are also more important influences on food intake in rats ( Schalla et al . , 2020 ) . The high degree of variability in food intake responses in the dark cycle in lean females and males is similar to what is seen in humans ( Wardle et al . , 2011 ) . In men and women , emotional eating is most commonly observed in the context of ‘comfort’ foods ( Wallis and Hetherington , 2009; Rutters et al . , 2009; Kandiah et al . , 2006; Oliver et al . , 2000; Zellner et al . , 2006 ) and is associated with elevated BMI ( Laitinen et al . , 2002; Lemmens et al . , 2011; Cohen et al . , 2002 ) . Consistent with these observations , we found that chronic exposure to HFD promoted hyperphagic responses to novel environment stress in both males and females . Stress-induced anxiety-like and feeding behaviors in females exposed to chronic HFD were discordant , consistent with observations in humans ( Wardle et al . , 2011 ) . These effects are likely the consequence of the chronic exposure to HFD in contrast to the diet palatability , as performing the assay with HFD in lean mice produced opposite effects in both sexes . Although we did not observe correlations between body weight gain and feeding responses , identifying neuroendocrine or neuronal biomarkers that predict stress-induced hypophagia vs . hyperphagia is an important topic for future research . We identified factors that have predictable effects on stress-induced feeding responses in both sexes ( Figure 6 ) . Performing the assay under conditions that are more physiologically relevant for humans , during the active phase and in conjunction with chronic HFD exposure , promotes hyperphagic responses . On the other hand , conducting the test in the inactive phase in lean mice , conditions used in most behavioral assays , promotes hypophagic responses . The duration of social isolation exerted sex-dependent effects . The use of other stress paradigms and other diets will be critical to draw more generalized conclusions . These studies can serve as a framework to develop sex-specific variations of paradigms to model subclinical and disordered eating behaviors in humans .
C57BL/6J wild-type male and female mice ( WT , Jax strain #000664 , RRID:IMSR JAX:000664 ) were maintained on a 12 hr/12 hr light/dark cycle , with ad libitum access to food and water , unless stated otherwise . Tests were performed in adult ( ~12–30 weeks ) or adolescent ( 7 weeks ) mice , fed either a standard chow diet ( PicoLab Rodent Diet ) or a HFD ( D12492 , 60% fat , 20% sugar , Research Diets ) . All procedures were performed within the guidelines of the Institutional Animal Care and Use Committee ( IACUC ) at the Columbia University Health Science Division . The impact of novel stress was evaluated by comparing behaviors in the familiar environment of the home cage ( home test , H ) vs . a new cage without bedding , under bright lighting , and lined with white paper ( novel test , N ) . The outcome measures were latency to eat and the amount consumed within 30 min from the first bite . Mice did not have access to water during the tests . Body weight and daily food intake were monitored throughout the study . The bedding was not changed throughout the length of the experiment . Mice were acclimated to the test conditions for 30 min every morning for five consecutive days . Their home cages were moved to a bench , where they were trained to eat chow from the floor in the open cage in the presence of the experimentalist . Mice that did not train to eat from the floor were excluded from further analyses . Both tests were performed following an overnight fast ( ~16 h ) . Mice were allowed to recover for 3–4 days with additional training sessions between the two tests . To minimize stressors associated with manual measurements , such as cage transport , lid opening , and human interaction , we adapted the assay to an automated food intake monitoring system ( BioDAQ , Research Diets ) ( Schalla et al . , 2020 ) . Mice were acclimated to the BioDAQ cages and food hoppers for 5–7 days . Mice that did not train to eat or drink from the BioDAQ hoppers were excluded from further analyses . Daily food intake , latency to eat and 30 min caloric intake ( after the first bite ) were assessed using the BioDAQ Dataviewer software . Home and novel tests were counterbalanced and performed 1–3 days apart . To study behaviors at the start of the dark phase , precise timing of access to food was provided by programming automated gates to open at the start of the dark cycle ( 7 pm ) . After acclimating to the BioDAQ system for 3–4 days , mice were trained to a schedule of restricted access to food from 7 pm to 7 am for 5–7 days . In one cohort , we controlled for the severity of the fast associated with the light cycle test , by performing the assay in the dark phase in adult females that were exposed to a 90% caloric restriction by limiting access to the food from 7 pm to 8 pm on the previous day . Mice were given access to HFD at 7 pm for 30 min during 5 days prior to the tests , as well as during the home and novel tests . Mice were given access to both chow and HFD for 8 weeks in males and 10 weeks in females before the 2-week training and acclimation period to the BioDAQ . Body weight was monitored weekly . Once in the BioDAQ , mice had access to HFD only . Mice that did not eat the HFD during the first hour of the dark cycle during acclimation were excluded from further analysis . Mice were euthanized by an overdose of isoflurane followed by decapitation , at the end of either home or novel tests ( 30–45 min after onset of eating ) . Trunk blood was collected in Microvette gel tubes ( Nalgene , 5000-1012 ) and centrifuged at 10 , 000 × g for 15 min . Serum was stored at −80°C until further analysis . Serum levels of luteinizing hormone ( LH ) , follicle-stimulating hormone ( FSH ) , and β-estradiol were measured by the Ligand Assay and Analysis Core at the University of Virginia Center for Research in Reproduction , using the Millipore Pituitary Panel Multiplex kit for LH/FSH , and the Calbiotech Mouse/Rat Estradiol ELISA kits . Vaginal swabs were collected at the end of each test ( in both home and novel conditions ) , and estrous cycle phases were classified by cytology ( McLean et al . , 2012 ) . Criteria for classification were: Diestrus: a mix of cornified epithelial cells and leukocytes ( early , or metestrus ) , or leukocytes only ( late ) ; Proestrus: a mix of leukocytes and nucleated epithelial cells ( early ) , or a majority of nucleated epithelial cells ( late ) ; Estrus: a mix of nucleated epithelial cells and cornified epithelial cells ( early ) or a majority of cornified epithelial cells ( late ) ( Figure 1—figure supplement 1A ) . High LH levels were detected in 4 of 25 proestrus females ( Figure 1—figure supplement 1B ) . FSH levels were significantly higher in proestrus females ( Figure 1—figure supplement 1C ) . Estradiol levels were similar across all phases of the estrous cycle ( Figure 1—figure supplement 1D ) . Intragroup statistical tests were performed with GraphPad Prism software . Grubb’s test was used to detect outliers in each experimental group . Outliers were excluded from all analyses . The distribution of values for latency , food intake , body weight , and glucose were assessed with the Shapiro–Wilk normality test . Differences in behaviors between home and novel environments were subsequently analyzed with either a paired Student’s t-test or Wilcoxon matched pairs test , as appropriate for the distribution . Differences between phases of the estrous cycle were analyzed with one-way analysis of variance and Tukey’s multiple comparison post-test , or Kruskal–Wallis and Dunn’s multiple comparison post-tests , according to the distribution . Pearson’s correlation was used to measure the relationship between change in food intake from the home to the novel cage and gain of body weight during chronic exposure to HFD . A 95% confidence interval was used to determine significance , which was reported on graphs using *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 , and ****p < 0 . 0001 . We built regression models to analyze variables across groups using SPSS 28 . 0 ( RRID:SCR_002865 ) . In each of the two cage types ( i . e . , home cage and novel cage ) , we used a generalized line model ( GLM ) with identity link function to examine the association between potential risk factors ( such as sex , social isolation , high fact diet exposure , and estrus cycle ) and the two primary outcomes ( i . e . , latency and food intake ) . In addition to constant term and the risk factors , the analysis model also included body weight , age , and testing order to adjust for potential confounding . We conducted the analysis in the full sample as well as several specific subgroups of interest using the same analytic approach . We then compared the strength of association between the risk factors and outcomes by cage type using GLM . Generalized estimating equation methodology with exchangeable working correlation matrix and robust variance estimator was employed to account for the within-mouse correlation due to repeated measure of outcomes from the same mouse . For such analysis , the statistical model included the constant term , risk factor , cage type ( novel vs . home ) , the cage type-by-risk factor interaction , and the potential confounding factors ( i . e . , body weight , age , and testing order ) . Findings with p values <0 . 05 were declared as statistically significant . | In times of heightened anxiety – say , during a global pandemic – many of us will reach for donuts or a particularly appetizing pizza for comfort . Others , however , will tend to shun food . What underlies these differences , and , in fact , the neural and hormonal pathways at play during stress eating ( when people eat without being hungry due to emotional reasons ) , remain unclear . This is partly because scientists lack good animal models in which to study these behaviors . In particular , female rodents are usually excluded from studies under the assumption that their hormonal cycles will disrupt the results . Yet , women are overrepresented in studies on feeding habits . Modeling human behaviors using rodents is harder than it may appear . These animals are most active at night – yet most experiments are performed during the day . The same stressors also have different outcomes in males and females . François et al . therefore explored better ways to induce anxiety and evaluate feeding behavior in mice , hoping to reliably elicit stress eating . The starting point was a common type of experiments known as novelty-suppressed feeding . First , mice are kept alone in a cage for up to two weeks on a normal diet so that they are used to experimental conditions . Then they are deprived of food overnight , before being given free access to food in the morning in a new environment . This stressful experience normally causes mice to take longer to eat than in their home cage . In rodents , the delay is thought to reflect stress as it is reliably reversed by anti-anxiety compounds approved for human use . In the novelty-suppressed feeding assay , both male and female animals exhibit signs of anxiety , but how much females eat is variable . François et al . showed that this variability is not due to hormonal changes , but instead to how long female mice had been kept alone . Crucially , the test could be adapted so that mice would consistently exhibit behavior similar to human stress eating , whereby they eat more during the test without having fasted the night before . The changes included running the experiment at night , when the animals are normally most active , and using overweight mice ( which captures the fact that , in humans , being overweight is associated with being prone to stress eating ) . Stress eating is an important clinical issue , hindering weigh loss in people with obesity . The new model developed by François et al . could be adopted by other laboratories , enabling better research into this behavior . | [
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Iron storage proteins are essential for cellular iron homeostasis and redox balance . Ferritin proteins are the major storage units for bioavailable forms of iron . Some organisms lack ferritins , and it is not known how they store iron . Encapsulins , a class of protein-based organelles , have recently been implicated in microbial iron and redox metabolism . Here , we report the structural and mechanistic characterization of a 42 nm two-component encapsulin-based iron storage compartment from Quasibacillus thermotolerans . Using cryo-electron microscopy and x-ray crystallography , we reveal the assembly principles of a thermostable T = 4 shell topology and its catalytic ferroxidase cargo and show interactions underlying cargo-shell co-assembly . This compartment has an exceptionally large iron storage capacity storing over 23 , 000 iron atoms . Our results reveal a new approach for survival in diverse habitats with limited or fluctuating iron availability via an iron storage system able to store 10 to 20 times more iron than ferritin .
Iron is essential to virtually all organisms on earth . It is needed for a wide variety of catalytic and redox processes ranging from cellular energy production via oxidative phosphorylation to oxygen transport by hemoglobin ( Sánchez et al . , 2017 ) . However , the same properties that make iron useful for cellular metabolism can result in toxicity under aerobic conditions ( Sánchez et al . , 2017 ) . Ferrous iron ( Fe2+ ) is easily oxidized to insoluble ferric iron ( Fe3+ ) resulting in the formation of harmful precipitates and reactive oxygen species ( ROS ) via Fenton chemistry ( Dixon and Stockwell , 2014 ) . Cells have evolved to cope with these problems by strictly controlling the intracellular concentration and reactivity of free iron ( Crichton , 2002 ) . Ferritin proteins are used as the main iron storage system by animals , plants and most microbes ( Arosio et al . , 2017 ) . The main ferritin-like proteins involved in iron storage are ferritin ( Ftn ) , bacterioferritin ( Bfr ) and DNA-binding proteins from starved cells ( Dps ) all able to oxidize Fe2+ to Fe3+ via a ferroxidase activity ( Andrews , 2010 ) . While Ftn and Bfr are primarily used as a dynamic iron storage ( Honarmand Ebrahimi et al . , 2015 ) , the main function of Dps proteins is to counteract oxidative stress ( Chiancone et al . , 2004 ) . Ferritins ( Ftn and Bfr ) assemble into 24 subunit protein compartments up to 12 nm in diameter able to store 2000 to 4 , 000 Fe atoms in their interior ( Andrews , 1998; Harrison and Arosio , 1996 ) . However , some organisms do not encode ferritin genes in their genomes and their iron storage systems have remained elusive . A newly discovered class of protein organelles called encapsulin nanocompartments have been shown to be involved in microbial iron storage and redox metabolism ( Giessen and Silver , 2017; He et al . , 2016; McHugh et al . , 2014; Sutter et al . , 2008 ) . Previously reported encapsulins share an HK97 phage-like fold and self-assemble from a single capsid protein into icosahedral compartments between 24 and 32 nm in diameter with triangulation numbers of T = 1 ( 60 subunits ) and T = 3 ( 180 subunits ) , respectively ( Akita et al . , 2007; McHugh et al . , 2014; Sutter et al . , 2008 ) . Their key feature is the ability to specifically encapsulate cargo proteins ( Figure 1a ) . Encapsulation is mediated by short C-terminal sequences referred to as targeting peptides ( TPs ) ( Sutter et al . , 2008; Tamura et al . , 2015 ) . Genes encoding encapsulin shell proteins and dedicated cargo proteins are organized in co-regulated operons ( Giessen and Silver , 2017; Sutter et al . , 2008 ) . So far , operons involved in hydrogen peroxide and nitric oxide detoxification as well as iron mineralization have been reported ( Nichols et al . , 2017 ) . The main cargo protein-types described to date are DyP-type peroxidases , hemerythrins and different classes of ferritin-like proteins ( Contreras et al . , 2014; Giessen and Silver , 2017; McHugh et al . , 2014; Rahmanpour and Bugg , 2013 ) . We have identified a novel type of encapsulin operon involved in iron metabolism in a range of Firmicutes we term the Iron-Mineralizing Encapsulin-Associated Firmicute ( IMEF ) -system ( Giessen and Silver , 2017 ) . Here , we report the structural and mechanistic characterization of the IMEF-system found in Quasibacillus thermotolerans ( Qs ) , an organism that does not encode any ferritins in its genome . We show that this encapsulin-based system self-assembles into a thermostable 42 nm 9 . 6 MDa protein compartment with a novel T = 4 topology able to mineralize and store an exceptionally large quantity of iron .
IMEF-systems are found in Firmicute genomes and their operon organization indicates a function in dynamic iron storage . To investigate the distribution of IMEF-systems in microbes , we carried out BLASTp searches using IMEF cargo proteins as queries and identified 71 operons in a range of Firmicutes including Qs ( Figure 1—figure supplement 1a ) . The core operon consists of the encapsulin capsid protein and the IMEF cargo protein with 70% of operons also encoding a 2Fe-2S ferredoxin homologous to bacterioferritin-associated ferredoxins ( Bfd ) . Bfd proteins are involved in the mobilization of iron under iron-limited conditions ( Yao et al . , 2012 ) . In addition , 31% of operons are associated with proteins similar to ferrochelatases involved in catalyzing the insertion of ferrous iron into protoporphyrins ( Dailey et al . , 2000 ) . The majority of IMEF-encoding genomes do not contain any ferritin or bacterioferritin genes ( Supplementary file 1 ) . Most IMEF genomes do however contain Dps-encoding genes . Overall , the operon organization of IMEF-systems and the lack of other known primary iron storage proteins indicate a function for IMEF-systems in dynamic iron storage similar to that of Ftn and Bfr . Using a recombinant system for the expression of the two-gene IMEF operon containing the IMEF cargo protein gene and the encapsulin capsid protein gene , we produced homogeneous IMEF cargo-loaded encapsulins ( Figure 1—figure supplement 1b ) . Through single-particle cryo-EM analysis , we determined the structure of the Qs IMEF encapsulin shell at an overall resolution of 3 . 85 Å ( Figure 1—figure supplement 2a and Supplementary file 2 ) . The IMEF encapsulin self-assembles into a 240-subunit icosahedral compartment with a diameter of 42 nm ( Figure 1b and Figure 1—figure supplement 2a–d ) . The IMEF compartment is substantially larger than previously reported encapsulins and possesses a triangulation number of T = 4 instead of T = 1 ( 60 subunits , 24 nm ) or T = 3 ( 180 subunits , 32 nm ) and represents the largest encapsulin compartment reported to date ( Figure 1—figure supplement 2e ) . The shell is composed of 12 pentameric and 30 hexameric capsomers occupying icosahedral vertices and faces , respectively . In contrast , T = 1 encapsulins consist of only 12 pentameric capsomers while the T = 3 encapsulin shell is made up of 12 pentameric and 20 hexameric capsomers . The T = 4 IMEF-system consequently possesses an internal volume 530% and 220% larger than that of T = 1 and T = 3 encapsulins , respectively . The 5-fold symmetry axes are located at the pentameric vertices while 3-fold symmetry axes are present at all interfaces where three hexameric capsomers meet . The center of each hexameric capsomer corresponds to an icosahedral edge possessing 2-fold symmetry . The icosahedral asymmetric unit consists of one pentameric and three hexameric monomers ( Figure 1b and Figure 1—figure supplement 2c ) . Symmetrically arranged lower resolution density ( ca . 10 Å ) representing the IMEF cargo is visible in the compartment interior ( Figure 1b and Figure 1—figure supplement 2d ) . 42 distinct densities , one for each capsomer of the T = 4 structure , can be observed . No connection of cargo and shell density is visible , likely due to averaging or the flexibility of a 37 amino acid linker preceding the IMEF targeting peptide that directs and anchors the IMEF cargo to the shell interior . Averaging and linker flexibility likely also contribute to the lower resolution observed for the interior IMEF densities . The distance between the shell and cargo densities is 4 . 5 nm which can be bridged by the 37 amino acid linker . To further investigate and better resolve the cargo densities , we applied an approach combining symmetry expansion and focused classification with residual signal subtraction ( Figure 1—figure supplement 3 ) . This approach was able to separate cargo densities bound at slightly different locations indicating that the symmetry observed for the cargo densities ( Figure 1b ) is a result of averaging . The observed non-symmetrical densities are still weak compared to the shell density . At low threshold values possible connections between cargo densities and the shell are visible , potentially representing the linker connecting the cargo with the bound TP ( Figure 1—figure supplement 3 ) . The four capsid proteins of the asymmetric unit adopt different conformations with significant differences found in the E-loop and A-domain ( Figure 1c ) . E-loops are located at capsomer interfaces and their relative orientation plays a key role in determining the overall topology and triangulation number of encapsulin compartments as evidenced by comparison of the IMEF T = 4 monomer with T = 1 ( Thermotoga maritima ) , T = 3 ( Pyrococcus furiosus ) and T = 7 ( HK97 phage ) capsid proteins ( Figure 1c ) . A-domain loops form compartment pores and are likely adapted to optimize the particular function of a given encapsulin , for example ROS detoxification or iron mineralization . In addition , local resolution maps indicate that E-loops and A-domain loops represent the most flexible parts of the shell which suggests a certain structural flexibility of the pores formed by A-domain loops ( Figure 1—figure supplement 4 ) . The IMEF encapsulin shell contains negatively charged pores at the 3- and 5-fold symmetry axes . The surface view of the intact shell ( Figure 2—figure supplement 1a ) shows a tight packing with pores at the 3- and 5-fold symmetry axes and at the interface between two hexameric and one pentameric capsomer ( pseudo 3-fold ) representing the only conduits to the interior . Similarly , pores at the symmetry axes were also reported for T = 1 and T = 3 encapsulin systems . All pores in the IMEF-system are negatively charged on both the exterior and interior surface due to the presence of conserved aspartate , glutamate and asparagine residues ( Figure 2a , b , Figure 2—figure supplement 1b and Figure 2—figure supplement 2 ) . This is similar to the negatively charged pores in ferritin systems that guide positively charged iron to the ferritin interior ( Arosio et al . , 2017 ) . In no other encapsulin system are all pores negatively charged indicating that pores in the IMEF-system are optimized for attracting and channeling positively charged ions . The 2-fold pores observed at the interface of two capsomers in T = 1 and T = 3 encapsulins are not present in the IMEF-system ( Nichols et al . , 2017 ) . The 3-fold pore forms the largest channel to the IMEF compartment interior and is 7 . 2 Å wide at its narrowest point , substantially larger than previously reported encapsulin pores . Extra cryo-EM density is observed at the center of both the 3-fold and 5-fold pores . This could be a result of averaging accentuating noise on symmetry axes or potentially represent bound ions ( e . g . Fe2+/3+ ) or even water molecules . The 2-fold symmetry axes at the center of hexameric capsomers also represent potential channels , as observed in T = 3 systems ( Nichols et al . , 2017 ) , but the conformation of two asparagine side chains prevents the formation of a 2-fold opening in the T = 4 shell leading to a closed pore ( Figure 2c ) . This observation combined with the flexibility observed for loops around the 2- and 5-fold symmetry axes in local resolution maps ( Figure 1—figure supplement 4 ) could indicate the presence of gated pores in encapsulins that may regulate ion flux to the compartment interior , similar to some ferritins ( Theil et al . , 2008 ) . The IMEF compartment possesses a non-covalent chainmail topology and is highly thermostable . E-loops and P-domains of neighboring capsid monomers arrange head to tail to form interlocking concatenated rings resulting in a non-covalent chainmail topology ( Figure 2d ) ( Zhang et al . , 2013 ) . This architecture has only been observed in a number of viral capsids including the HK97 bacteriophage but not in a bacterial system . In contrast to HK97 where an isopeptide bond covalently links E-loops and P-domains ( Duda , 1998 ) , the IMEF encapsulin uses non-covalent interactions . At each 3-fold pore , E-loops connect with two neighboring P-domains including the G-loop conserved in T = 4 encapsulins and their interfaces contain complementary electrostatic as well as aromatic and potential anion-π interactions ( Figure 2e and Figure 2—figure supplement 1c , d ) ( Philip et al . , 2011 ) . The IMEF cargo protein shows a linear unfolding curve starting at ca . 40°C and extending to ca . 75°C followed by a hyperbolic increase leading to a midpoint of the thermal unfolding curve of 80 . 6°C . The shell protein is highly thermostable with a melting temperature of 86 . 6°C , respectively ( Figure 2f ) . A stabilizing effect is observed for the cargo-loaded compartment ( 88 . 9°C ) . Compartments isolated from high iron conditions show even greater thermal stability ( 91 . 8°C ) likely due to the internal cavity being stabilized by mineralized material . Sequence and x-ray structure analysis show that the IMEF cargo represents a distinct class of ferritin-like protein ( Flp ) with an unusual ferroxidase center . Phylogenetic analysis revealed that the IMEF cargo protein is a member of the Flp superfamily and is most closely related to Dps proteins ( Figure 3a and Supplementary file 3 ) but no known ferroxidase motifs could be detected based on the primary sequence alone ( Andrews , 2010 ) . IMEF proteins form a separate clade distinct from other Flp proteins associated with encapsulin systems . All IMEF proteins share a conserved C-terminal TP ( Figure 3b ) . We determined the x-ray crystal structure of the IMEF cargo to a final resolution of 1 . 72 Å ( Figure 3c and Supplementary file 4 ) . The cargo adopts a four-helix bundle fold characteristic of other members of the Flp superfamily and forms a dimer with two Fe atoms bound at the subunit interface creating a ferroxidase site based on an alternative ferroxidase sequence motif ( Figure 3d , Figure 3—figure supplement 1a , b ) . This leads to a combined molecular weight of the fully cargo-loaded IMEF compartment of 9 . 6 MDa ( 42 × cargo dimer [22 . 6 kDa]+240 × capsid protein , [32 . 2 kDa] ) . Through structure and sequence analysis , we identified a set of conserved residues involved in the formation of the dinuclear ferroxidase center . This IMEF ferroxidase motif differs from known examples and represents an alternative way of forming an inter-subunit ferroxidase center ( Figure 3d ) . Due to flexibility , the C-terminal linker and TP are not resolved in the cargo x-ray structure in accordance with observations from our cryo-EM analysis . Removal of the 13 C-terminal residues results in empty encapsulin shells confirming that the IMEF TP is necessary for cargo encapsulation ( Figure 3e ) . Additional cryo-EM density around the 2- and 5-fold symmetry axes reveals TP-binding sites and illuminates cargo-shell co-assembly . Through analysis of the T = 4 cryo-EM map , additional densities were identified that could not be explained by the encapsulin capsid protein ( Figure 3f ) . These densities represent bound TPs anchoring IMEF cargo to the interior surface of the compartment . Even though only 42 cargo densities are observed , TP densities can be found at all 240 capsid monomers indicating averaging during cryo-EM reconstruction . Strong TP density is observed for all 180 monomers that are part of 2-fold symmetrical hexameric capsomers ( Figure 3f ) while substantially weaker density is found for TPs bound to the 60 pentameric monomers ( Figure 3—figure supplement 1c ) thus revealing higher occupancy and preferential TP binding around 2-fold symmetry axes which can be explained by different binding site conformations ( Figure 3—figure supplement 1c–e ) and higher local shell mobility ( Figure 1—figure supplement 4 ) . The main TP binding sites surrounding the 2-fold symmetry axes are formed by conserved residues of the P-domain and N-terminal helix ( Figure 2—figure supplement 2 ) similar to the T . maritima T = 1 encapsulin system ( Sutter et al . , 2008 ) . No TP binding site has been identified for T = 3 encapsulins yet . The presence of the N-terminal helix and the resulting binding site seems to generally underpin encapsulins’ ability to interact with TPs and encapsulate cargo proteins . The TP residues TVGSLIQ were tentatively built and refined into the additional density present at hexameric capsomers producing a model with good geometry ( Figure 3—figure supplement 1e ) . The TP binds to a surface groove based on shape complementarity and two key ionic interactions with highly conserved positively charged residues locking the TP in place . Heterologous expression of the IMEF core operon in E . coli leads to in vivo formation of large Fe- and P-rich electron-dense particles . Thin section negative stain transmission electron microscopy ( TEM ) of E . coli cells grown in Fe-rich ( 4 mM ) medium and expressing the Qs IMEF core operon results in the formation of clusters of large intracellular electron-dense particles ( Figure 4a and Figure 4—figure supplement 1a ) . Scanning TEM-energy-dispersive x-ray spectroscopy ( EDS ) revealed that these particles primarily contain uniformly distributed Fe , P and O with an estimated Fe:P ratio near 1 ( Figure 4b ) . Selected area electron diffraction ( SAED ) further indicates that this mineralized material is amorphous ( Figure 4—figure supplement 1b , c ) , similar to bacterioferritin systems ( Andrews et al . , 1993 ) . The high P content and amorphous cores described for the IMEF encapsulin are similar to bacterioferritin systems ( Aitken-Rogers et al . , 2004; Mann et al . , 1986 ) . It has been hypothesized that amorphous material can be more readily mobilized under iron-limited condition than crystallized iron mineral ( Watt et al . , 1992; Watt et al . , 2010 ) . The IMEF encapsulin mineralizes up to 30 nm Fe-rich cores in its interior with up to 23 , 000 Fe atoms stored per particle . IMEF encapsulins purified from E . coli grown under high Fe conditions contain electron dense cores visible in unstained samples with an average diameter of 23 nm ( Figure 4c , d and Figure 4—figure supplement 2a ) . The largest observed particles are up to 30 nm in diameter . The theoretical size limit imposed by the T = 4 encapsulin protein shell is 36 nm and particles close to this size are observed in thin-sections of Geobacillus natively encoding the IMEF-system ( Figure 4—figure supplement 2b–d ) . EDS analysis of particles isolated from E . coli and comparison with standards indicate a very similar elemental composition and elemental distribution as observed for thin section samples with a Fe:P ratio of 1:1 . 1 ( Figure 4—figure supplement 3a ) . To determine the number of iron atoms stored per particle , we carried out electron energy loss spectroscopy ( EELS ) on purified Fe-loaded compartments ( Figure 4e , f and Figure 4—figure supplement 3b , c ) . The highest observed number of stored Fe per particle was 23 , 293 ( 23 . 6 nm ) ( Supplementary file 5 ) . Extrapolating to the maximum theoretical particle diameter of 36 nm and the highest density observed ( 3 . 40 Fe atoms/nm3 ) leads to a maximum number of Fe atoms that can be stored by the IMEF-system of around 83 , 000 ( Supplementary file 5 ) . Thus , IMEF-systems are able to store substantially more iron than any known ferritin system ( 2 , 000–4 , 000 Fe atoms ) ( Andrews , 1998; Harrison and Arosio , 1996 ) . To learn more about the mechanism of iron mineralization , we assayed peroxidase and ferroxidase activity . Due to the IMEF cargo being most closely related to Dps proteins we initially performed peroxidase assays using hydrogen peroxide as the oxidant . However , no peroxidase activity could be observed . Next , we assayed ferroxidase activity using O2 as the oxidant . For the IMEF cargo alone , a sigmoidal ferroxidase iron oxidation curve was observed indicative of autocatalytic Fe oxidation taking place at newly formed mineral surfaces ( Bou-Abdallah et al . , 2005; Sun and Chasteen , 1992 ) . However , assaying the IMEF cargo-loaded encapsulin results in a typical hyperbolic enzyme catalysis curve . These observations imply that the encapsulin shell controls the flux of iron to the inside of the compartment leading to a controlled and low concentration of soluble iron in the encapsulin interior . Therefore , the IMEF cargo protein is able to enzymatically oxidize the majority of ferrous iron before uncontrolled autocatalytic mineralization can lead to bulk precipitation of iron which would likely destroy the iron storage function of the IMEF-system ( Figure 4—figure supplement 4 ) . Our structural model and functional analysis of the IMEF encapsulin system reveal an alternative way to store large amounts of Fe independent of ferritins . The IMEF-system can in principle store more than 20 times more Fe than Ftn or Bfr systems . In contrast to ferritin systems , IMEF encapsulins are two-component systems with the catalytic activity separated from the protein shell . The IMEF cargo protein is flexibly tethered and primarily localizes 4 . 5 nm away from the capsid interior . This suggests that once iron enters the encapsulin interior via pores , it diffuses to the ferroxidase active site of the IMEF cargo , making it necessary to strictly control interior iron concentration to prevent runaway mineralization . This is different compared with ferritin systems where the ferroxidase activity is part of the shell and negatively charged surface patches guide iron from the pores to ferroxidase sites . It is striking that IMEF-systems are confined to spore-forming Firmicutes . They inhabit a broad range of habitats with many of them initially isolated from hot springs or soil , environments with often limited or fluctuating iron availability ( Colombo et al . , 2014; Hou et al . , 2013; Huang et al . , 2013 ) . The ability to store a much larger amount of iron than other microbes might benefit IMEF-encoding organisms in these environments and thereby contribute to their wide geographical distribution ( Zeigler , 2014 ) . In sum , we have elucidated the structure and mechanism of the largest iron storage complex to date indicating that alternative systems exist across nature to address the critical problem of safe and dynamic iron storage .
Initial identification of IMEF-systems was achieved by utilizing the Enzyme Similarity Tool ( ESI ) in combination with the Genome Neighborhood Network Tool ( GNT ) of the Enzyme Function Initiative ( EFI ) ( Gerlt et al . , 2015 ) . The previously identified IMEF cargo protein from Q . thermotolerans ( WP_039238471 ) was used as a query to initiate an ESI Sequence BLAST search of the UniProt database . UniProt BLAST Query E-value was chosen to be 5 . After the initial dataset was created , we used an alignment score ( based on the alignment score vs percent identity plot ) that would correspond to a percent identity of 20 for initial outputting and interpretation of protein sequences and sequence similarity networks ( SSNs ) . The resulting xgmml network file was then submitted to GNT . The resulting Genome Neighborhood Diagrams of all identified IMEF operons where analyzed using the GNT diagram explorer and operon diagrams were downloaded as svg files . Genomes of IMEF-system-encoding organisms were searched for Ftn , Bfr and Dps proteins using NCBI’s blastp suite . As queries , Firmicute homologs of ferritin , bacterioferritin and Dps were used ( Ftn: OTY20392 , Bfr: EEK74551 , Dps: WP_039234032 ) . Phylogenetic analysis was based on Clustal Omega ( ClustalO ) alignments carried out using the default settings of the Multiple Sequence Alignment online tool of the European Molecular Biology Laboratory’s European Bioinformatics Institute ( EMBL-EBI ) . A nearest-neighbor phylogenetic trees based on the ClustalO alignment were generated using the Simple Phylogeny Tool at EMBL-EBI . Alignments and trees were then annotated and analyzed using Geneious 9 . 1 . 4 . Cryo-EM data and structural models were analyzed using UCSF Chimera 1 . 13 . 1rc and Open Source PyMOL 1 . 8 . x . Structural alignments of capsid protein monomers were carried out in PyMOL using the align command . The IMEF model used for molecular replacement was generated using the I-TASSER webserver ( Roy et al . , 2010; Yang and Zhang , 2015 ) . All constructs used in this study were ordered as gBlock Gene Fragments from Integrated DNA Technologies ( IDT ) . Codon usage was optimized for E . coli expression using the IDT Codon Optimization Tool with the amino acid sequences of the respective proteins of interest as input . For the IMEF operon containing multiple genes , intergenic regions were not changed . The IMEF cargo protein construct was ordered with a C-terminal His6 tag . For the operon construct containing the TP-less IMEF cargo , the 13 C-terminal residues ( HKKKGFTVGSLIQ ) were omitted from the IMEF cargo protein , thus removing the TP . Gibson Assembly Master Mix was obtained from New England BioLabs ( NEB ) . DNA sequencing was carried out by GENEWIZ . MegaX DH10B T1R electrocompetent E . coli cells ( ThermoFisher ) were used for all cloning procedures while One Shot BL21 Star ( DE3 ) chemically competent E . coli cells ( Invitrogen ) were used for protein production and all other experiments . pETDuet1 was used as the expression vector for all constructs . For the construction of expression vectors , Gibson Assembly was employed . gBlock Gene Fragments containing 20 bp overlaps for direct assembly were combined with NdeI and PacI digested pETDuet1 resulting in assembled expression vectors ( fragments were inserted in MCS2 ) . Electrocompetent E . coli DH10B cells were transformed and the resulting plasmids confirmed via sequencing . All non-high iron expression experiments were carried out in lysogeny broth ( LB ) supplemented with ampicillin ( 100 μg/mL ) . Size exclusion chromatography/gel filtration for capsid purification was performed with an ÄKTA Explorer 10 ( GE Healthcare Life Sciences ) equipped with a HiPrep 16/60 Sephacryl S-500 HR column ( GE Healthcare Life Sciences ) . For analytical size exclusion , a Superdex 200 10/300 GL column ( GE Healthcare Life Sciences ) was used . Protein samples were concentrated using Amicon Ultra Filters ( Millipore ) . For SDS-PAGE analysis , 14% Novex Tris-Glycine Gels ( ThermoFisher Scientific ) were used . DNA concentrations were measured using a Nanodrop ND-1000 instrument ( PEQLab ) . Sequence-confirmed plasmids were used to transform E . coli BL21 ( DE3 ) Star cells ( 0 . 5 ng total plasmid DNA ) . Resulting colonies were used to inoculate pre-expression cultures . For large scale protein expressions , 500 mL of LB in 2 L baffled flasks were inoculated ( 1:50 ) using an over-night culture , grown at 37°C and 200 rpm to an OD600 of 0 . 5 . The temperature was then shifted to 30°C and the cultures induced with IPTG ( final concentration: 0 . 05 mM ) . Cultures were grown at 30°C for 18 hr , harvested through centrifugation ( 4000 rpm , 15 min , 4°C ) and pellets either immediately used or frozen in liquid nitrogen and stored at −20°C for later use . For encapsulin and His-tagged protein purifications , pellets were thawed , resuspended in 5 mL Tris buffer ( 50 mM Tris , 150 mM NaCl , pH 8 ) , then lysozyme ( 1 mg/mL ) and DNaseI ( 1 μg/mL ) were added and the cells incubated on ice for 20 min . Cell suspensions were subjected to sonication using a 550 Sonic Dismembrator ( FisherScientific ) . Power level 3 . 25 was used with a pulse time of 8 s and an interval of 10 s . Total pulse time was 4 min . Cell debris was subsequently removed through centrifugation ( 8000 rpm , 15 min , 4°C ) . The cleared supernatant was then used either for protein affinity or encapsulin compartment purification . His-tagged IMEF cargo was purified using Ni-NTA agarose resin ( Qiagen ) via the batch Ni-NTA affinity procedure following the supplier’s instructions . Buffer A ( 50 mM Tris , 150 mM NaCl , 20 mM imidazole , pH 8 ) was used to wash the resin after protein binding and buffer B ( 50 mM Tris , 150 mM NaCl , 250 mM imidazole , pH 8 ) was used to elute bound protein . Samples were concentrated and dialyzed using Amicon filters ( 10 kDa molecular weight cutoff ) and Tris ( pH 7 . 4 ) buffer and evaluated using SDS-PAGE . Further analyses were carried out directly or the next day with protein being stored on ice . For encapsulin purification , 0 . 1 g NaCl and 0 . 5 g of PEG-8000 were added ( 10% w/v final concentration ) to 5 mL cleared lysate , followed by incubation on ice for 20 min . The precipitate was collected through centrifugation ( 8000 rpm , 15 min , 4°C ) , suspended in 3 mL Tris ( pH 8 ) buffer and filtered using a 0 . 2 μm syringe filter . The samples were then subjected to size exclusion chromatography using Tris ( pH 8 ) buffer and a flow rate of 1 mL/min . Fractions were evaluated using SDS-PAGE analysis and encapsulin-containing fractions were combined , concentrated and dialyzed using Amicon filters ( 100 kDa molecular weight cutoff ) and Tris buffer without NaCl ( 20 mM Tris , pH 8 ) . The low salt sample was then loaded on a HiPrep DEAE FF 16/10 Ion Exchange column ( GE Healthcare Life Sciences ) . The gradient used for ion-exchange chromatography was as follows: 100% A for 0–100 mL , 100% A to 50% A + 50% B for 100–200 mL , 100% B for 200–300 mL , 100% A for 300–400 mL ( A: 20 mM Tris , pH 8 , B: 20 mM Tris , 1 M NaCl , pH 8 , flow rate: 3 mL/min ) . Again , SDS-PAGE was used to identify product fractions followed by Amicon filter concentration and buffer exchange to Tris buffer ( 50 mM Tris , 150 mM NaCl , pH 8 ) . Final samples were either directly subjected to additional experiments or stored on ice overnight . 200 Mesh Gold Grids ( FCF-200-Au , EMS ) were used for all negative stain TEM experiments . TEM experiments of negatively stained protein samples were carried out at the HMS Electron Microscopy Facility using a Tecnai G2 Spirit BioTWIN instrument . For negative-staining TEM , encapsulin samples were diluted to 1–10 μM using Tris buffer ( 50 mM Tris , 150 mM NaCl , pH 8 ) and subsequently adsorbed onto formvar/carbon coated gold grids . Prior to applying 5 μL of diluted sample , grids were glow-discharged using a 100x glow discharge unit ( EMS ) to increase their hydrophilicity ( 10 s , 25 mA ) . After 1 min adsorption time , excess liquid was blotted off using Whatman #1 filter paper , washed one time with distilled H2O and floated on a 10 μL drop of staining solution ( 0 . 75% uranyl formate in H2O ) for 35 s . After removal of excess staining solution , samples were used for TEM analysis at 80 kV . For TEM analysis of fixed cells , 0 . 5 mL of early stationary phase bacterial culture was fixed by adding fixative ( 1:1 v/v , 1 . 25% formaldehyde , 2 . 5% glutaraldehyde , 0 . 03% picric acid in 0 . 1 M sodium cacodylate buffer , pH 7 . 4 ) . The sample was then incubated at 25°C for 1 hr and centrifuged for 3 min at 3000 rpm . The sample was then further incubated for 6–18 hr at 4°C . Cells were subsequently washed three times in cacodylate buffer , 4 times with maleate buffer pH 5 . 15 followed by staining with 1% uranyl acetate for 30 min . The sample was dehydrated ( 15 min 70% ethanol , 15 min 90% ethanol , 2 × 15 min 100% ethanol ) and exposed to propyleneoxide for 1 hr . For infiltration , a mixture of Epon resin and proylenoxide ( 1:1 ) was incubated for 2 hr at 25°C before moving it to an embedding mold filled with freshly mixed Epon . The sample was allowed to sink and subsequently moved to a polymerization oven ( 24 hr , 60°C ) . Ultrathin sections ( 60–90 nm ) were then cut at −120°C using a cryo-diamond knife ( Reichert cryo-ultramicrotome ) and transferred to formvar/carbon coated grids . To prepare grids for cryo-EM imaging , 2 . 5 μL of purified cargo-loaded IMEF encapsulin at a concentration of 1 . 5 mg/mL was applied to glow-discharged Quantifoil holey carbon grids ( 1 . 2/1 . 3 , 400 mesh ) , and blotted for 3 s with ~90% humidity before plunge-freezing in liquid ethane using a Cryoplunge 3 System ( CP3 , Gatan ) . Cryo-EM images were collected at Harvard Medical School on a Tecnai F20 electron microscope ( FEI ) operating at 200 kV and equipped with a K2 Summit direct electron detector ( Gatan ) . Movies were collected at a nominal magnification of 29 , 000 with a calibrated pixel size of 0 . 64 Å . All movies were collected in super-resolution counting mode using UCSFImage4 , with a total exposure time of 7 . 2 s and a frame time of 200 milliseconds . The details of EM data collection parameters are listed in Supplementary file 2 . Dose-fractionated super-resolution movies collected on the K2 detector were binned over 2 × 2 pixels , and subjected to motion correction using the program MotionCor2 ( Zheng et al . , 2017 ) . Dose-weighted sums from all frames were used for all subsequent image-processing steps except for defocus determination . The CTFFIND4 program ( Rohou and Grigorieff , 2015 ) was used to determine the defocus values of the summed images from all movie frames without dose weighting . Semi-automated particle picking from 6x binned images was performed with SAMUEL and SamViewer ( Ru et al . , 2015 ) . Selected particles were extracted from unbinned images with an initial box size of 512 pixels , and subsequently binned to a box size of 128 pixels with a pixel size of 5 . 12 Å for two rounds of 2D classification using RELION 3 . 0 ( Scheres , 2012 ) . An initial 3D model was generated via SPIDER ( Frank et al . , 1996 ) 3D projection matching refinement ( samrefine . py ) using 2D class averages , starting from a sphere density similar in size and shape of the IMEF encapsulin . The selected particles after 2D classification were binned to a box size of 480 pixels ( corresponding to a pixel size of 1 . 365 Å ) and used for 3D refinement in RELION 3 . 0 with icosahedral symmetry ( ‘I’ ) imposed . A final round of 3D refinement was performed in RELION 3 . 0 after fitting individual particle defocus parameters and beam-tilt with ‘relion_ctf_refine’ . Post-processing was performed with ‘relion_postprocess’ to apply a negative b-factor and correct the amplitude information in the final map . The overall resolutions were estimated based on the gold-standard criterion of Fourier shell correlation ( FSC ) = 0 . 143 . Local resolution variations were estimated from two half data maps using ResMap ( Swint-Kruse and Brown , 2005 ) . An initial model of an IMEF encapsulin monomer was generated by homology modeling with the I-TASSER webserver ( Zhang , 2008 ) using the x-ray crystal structure of the T = 3 Pyrococcus furiosus encapsulin ( PDB ID: 2E0Z ) as a template . The monomer model was then fit into the 3D map in UCSF Chimera ( Pettersen et al . , 2004 ) , and subsequently adjusted manually in COOT ( Emsley et al . , 2010 ) prior to refinement in PHENIX ( Adams et al . , 2010 ) with phenix . real_space_refine . The refined monomer coordinates were copied and manually positioned to occupy the four monomer positions of the asymmetric unit ( ASU ) , followed by manual adjustment of each monomer in COOT . Several rounds of real-space refinement and manual adjustment of the coordinates for four monomers in the ASU were performed in phenix . real_space_refine and COOT . During refinement of coordinates in the ASU no non-crystallographic symmetry restraints were utilized in order to avoid distortion of the E-loop in each monomer . The refined coordinates for the ASU were subsequently expanded using the symmetry matrices utilized by RELION 3 . 0 during 3D reconstruction to generate a model of the entire encapsulin cage containing 60 ASUs and 240 total IMEF encapsulin capsid protein polypeptide chains . Coordinates for the entire IMEF encapsulin cage were refined in phenix . real_space_refine with proper NCS restraints between corresponding chains in individual ASUs in order to resolve any inter-protomer clashes . In an attempt to better resolve cargo density within the encapsulin shell we used an approach combining symmetry expansion and focused classification with residual signal subtraction . Prior to symmetry expansion and focused classification , particles were binned to a box size of 192 with a corresponding pixel size of 3 . 41 Å . Following refinement of binned particles with icosahedral symmetry , a 60 Å low-pass filtered mask of a hexameric encapsulin shell unit with associated cargo density was generated ( Figure 1—figure supplement 3a ) . Symmetry expansion was performed with relion_particle_symmetry_expand specifying ‘I’ symmetry to generate a new particle stack with 60x increased particle number . Residual signal subtraction was performed as described previously ( Bai et al . , 2015 ) to subtract encapsulin shell and cargo densities outside of the 60 Å low-pass filtered mask from the symmetry expanded particle dataset ( Figure 1—figure supplement 3b ) . Focused classification without alignment and without applied symmetry was then performed in Relion3 . 0 to resolve cargo density bound in different configurations to the encapsulin shell and potential connections between the cargo and targeting peptide ( Figure 1—figure supplement 3c ) . DSF measurements were performed using a NanoTemper Tycho NT . 6 instrument according to the manufacturer’s instructions . Samples in Tris buffer ( 50 mM Tris , 150 mM NaCl , pH 8 ) at a concentration of 0 . 5 mg/mL were measured in triplicate and subjected to a temperature gradient from 35°C to 95°C at 0 . 5°C per second . Data were analyzed using NT Melting Control software . Melting temperatures ( Tm ) were determined by automatic fitting of experimental data using a polynomial function , where the maximum slope ( Tm ) is indicated by the peak of its first derivative . Initial crystallization conditions were determined using the Midas screen ( Grimm et al . , 2010 ) . Large single crystals were grown in sitting drop plates by the vapor diffusion method . Reservoir solutions contained 10% v/v Pentaerythritol ethoxylate ( 3/4 EO/OH ) and 10% butanol . Crystals were cryo-protected in reservoir solution supplemented with 15% ethylene glycol and 20 mM glycolic acid pH 7 . 5 . Diffraction data were collected at the European Synchrotron Radiation Facility ( ESRF ) Grenoble outstation at the ID-30b beamline at 100 K with a Pilatus3 6M pixel detector ( DECTRIS , Switzerland ) . Data were indexed , processed , and scaled with the XDS package ( Kabsch , 2010 ) . The structure was solved by molecular replacement using an I-TASSER homology model and the program ACRIMBOLDO_LITE ( Sammito et al . , 2015 ) incorporating PHASER ( McCoy et al . , 2007 ) and SHELX ( Thorn and Sheldrick , 2013 ) from the CCP4 suite ( Winn et al . , 2011 ) . Model building and refinement was carried using COOT ( Emsley and Cowtan , 2004 ) and REFMAC5 ( Murshudov et al . , 1997 ) , respectively . To determine the size distribution of electron-dense cores resulting from IMEF mineralization under high iron conditions , TEM micrographs were analyzed using the open source image processing package Fiji based on ImageJ 1 . 52 hr ( Schindelin et al . , 2012 ) . Micrographs were converted to 8-bit binary images , thresholded and processed using the particle analyzer plugin . The diameters reported are based on Fiji Feret diameter output values . Overnight cultures were used to inoculate 500 mL LB medium ( 1:50 ) supplemented with ampicillin and grown at 37°C to an OD600 of 0 . 5 . Expression was induced with 0 . 05 mM IPTG . Cultures were incubated at 30°C for 2 hr . LB medium was removed and replaced with fresh modified LB ( LB +50 mM Hepes , 4 mM Trisodium citrate , pH 7 ) supplemented with freshly prepared ammonium iron ( II ) sulfate ( Fe ( NH4 ) 2 ( SO4 ) 2 , final concentration: 4 mM; stock solution: 400 mM in 0 . 1 M HCl ) . The cultures were then incubated at 30°C for 18 hr and used for either the purification of iron-loaded encapsulin compartments or thin section TEM analysis . TEM and high angle angular dark field ( HAADF ) STEM imaging and analysis were performed on a JEOL ARM 200F operated at 80 kV . EDS spectra were collected using an EDAX Octane W 100mm2 detector , and spectra analyzed post-collection both via TEAM software and offline using the k-ratio method ( thin film approximation ) . EELS mapping data of the Fe L edge were acquired using a Gatan Enfinium spectrometer with dispersion 0 . 25 eV/ch using DualEELS mode with simultaneous zero loss spectrum collection . EELS data were processed using the Gatan EELS analysis plug-in . The processing steps involved a Gaussian fitting of the zero loss peak , integrating under the FeL edge up to 780 eV after applying a power law or first order log-polynomial ( whichever fit the background better , as this depended on local carbon contamination levels ) and correcting for the Fe cross section of 2664 . 9 barns , from which the average number of Fe per nm2 was calculated per pixel of data . These pixels were summed over the area of each particle to estimate the total number of Fe atoms . Errors in this measurement were calculated from a statistical analysis of the data fitting combined with the expected error from Fe cross sectional extrapolation . Particle diameters were estimated using a histogram method to determine the edge onset of each particle , with the mean of multiple measurements from each particle used ( and error determined by the standard deviation of these measurements ) . For normal growth of G . stearothermophilus , Meat Media ( 3 g meat extract , 5 g peptone , 1 L H2O ) was utilized . G . stearothermophilus was maintained on Meat Media agar plates ( 15 g agar/L ) . All growth was carried out a 55°C . For high iron growth experiments Meat Media was supplemented with 50 mM Hepes , 4 mM Trisodium citrate and 4 mM Fe ( NH4 ) 2 ( SO4 ) 2 and the pH adjusted to seven using HCl . Growth curves were recorded in high iron Meat Media in 96-well plates ( volume: 500 μL ) using a Synergy H1 plate reader ( BioTek ) and inoculated ( 1:50 ) from a pre-culture grown for 24 hr in standard Meat Media . Peroxidase activity of free IMEF cargo and cargo-loaded IMEF encapsulin was assayed by measuring the oxidation of ortho-phenylenediamine ( OP ) by hydrogen peroxide ( Pesek et al . , 2011 ) . OP dilutions from 10 to 80 mM were prepared from a stock solution ( 92 . 5 mM in 50 mM Tris , pH 8 ) using Tris buffer ( pH 8 ) . 96-well plates were used to carry out the assays in triplicate . Each well contained 100 μL of OP dilution and 0 . 5 μM of IMEF cargo protein ( protein concentrations were determined via Bradford assay ( Pierce Coomassie , ThermoFisher ) following the manufacturer’s instructions ) . To start the assays , 2 μL of 30% hydrogen peroxide solution was added . After 15 min of incubation in the dark , assays were stopped by the addition of 100 μL of 0 . 5 M H2SO4 . Then , absorbance at 490 nm was determined using a Synergy H1 plate reader . Protein solutions in Tris buffer ( 50 mM Tris , 150 mM NaCl , pH 8 ) and Fe ( NH4 ) 2 ( SO4 ) 2 stock solutions in 0 . 1 M HCl were made anaerobic by incubation in a Vinyl Anaerobic Chamber ( Coy Lab Products ) for 24 hr . All solutions were exposed to the anaerobic atmosphere inside the chamber and protein solutions were kept on ice . IMEF cargo protein was used at a final concentration of 50 μM while cargo-loaded encapsulin concentrations were used that would correspond to 5 μM IMEF cargo ( higher concentrations led to rapid protein precipitation upon iron addition ) . Final iron ( II ) concentrations ranged from 10 to 100 μM . Ferroxidase activity was initiated by combining appropriate dilutions of protein and iron solution to a final volume of 250 μL in a quartz cuvette in the air , directly after removing solutions from the anaerobic chamber . Ferroxidase activity was immediately measured by monitoring Fe3+ formation at a wavelength of 315 nm in a Nanodrop 2000c for 25 min . A cryo-EM density map of the cargo-loaded IMEF encapsulin has been deposited in the Electron Microscopy Data Bank under the accession number 9383 . The corresponding atomic coordinates for the atomic model have been deposited in the Protein Data Bank ( accession number: 6NJ8 ) . Atomic coordinates for the IMEF cargo protein have been deposited in the Protein Data Bank under accession number 6N63 . Correspondence and requests for materials should be addressed to the corresponding authors . | People often think of the cell as the basic unit of life . Despite this , individual cells are also subdivided into many compartments , called ‘organelles’ because they act like the internal organs of the cell . For example , organelles can break down nutrients , store information in the form of DNA , or help remove waste . Even bacterial cells , despite being smaller and simpler than most other cell types , contain organelle-like structures . These are tiny compartments , termed protein organelles , which are enclosed by ‘shells’ made from self-assembling proteins within the cell . Cells need iron to carry out the chemical reactions necessary for life . Iron is therefore an essential nutrient , but it can also be toxic if not stored properly inside the cell . Cells often solve this problem by locking iron away inside small , specialised protein cages called ferritins until it can be used . Most organisms , from humans to bacteria , have ferritins , but some do not , and the way these organisms store iron remains largely unknown . The bacterium Quasibacillus thermotolerans is an example of an organism that lacks ferritins . However , it does contain a recently discovered type of protein organelle , called an encapsulin . Giessen et al . wanted to find out more about the structure of this protein organelle , and to determine if it helped these bacteria store iron . Q . thermotolerans’ encapsulin turned out to be the largest of its kind discovered to date . Detailed imaging experiments , using a combination of electron microscopy and X-ray- based techniques , revealed that the protein shell of the encapsulin had an overall structure resembling chain mail and contained multiple pores . These pores were negatively charged , meaning that they could efficiently attract iron ( which has a positive charge ) and funnel it into the interior of the compartment . The compartment itself was able to store at least 20 times more iron than ferritins , making this encapsulin one of the most efficient methods of iron storage in any cell . These findings will help us better understand how bacteria that lack ferritins cope with the problem of iron storage . In the future , encapsulins could also be used as a target for new therapies to fight bacterial infections , or even as the building blocks for microscopic chemical reactors or ‘storage facilities’ in industrial applications . | [
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] | 2019 | Large protein organelles form a new iron sequestration system with high storage capacity |
Shorter childhood telomere length ( TL ) and more rapid TL attrition are widely regarded as manifestations of stress . However , the potential effects of health interventions on child TL are unknown . We hypothesized that a water , sanitation , handwashing ( WSH ) , and nutritional intervention would slow TL attrition during the first two years of life . In a trial in rural Bangladesh , we randomized geographical clusters of pregnant women into individual water treatment , sanitation , handwashing , nutrition , combined WSH , combined nutrition plus WSH ( N + WSH ) , or control arms . We conducted a substudy enrolling children from the control arm and the N + WSH intervention arm . Participants and outcome assessors were not masked; analyses were masked . Relative TL was measured at 1 and 2 years after intervention , and the change in relative TL was reported . Analysis was intention-to-treat . Between May 2012 and July 2013 , in the overall trial , we randomized 720 geographical clusters of 5551 pregnant women to a control or an intervention arm . In this substudy , after 1 year of intervention , we assessed a total of 662 children ( 341 intervention and 321 control ) and 713 children after 2 years of intervention ( 383 intervention and 330 control ) . Children in the intervention arm had significantly shorter relative TL compared with controls after 1 year of intervention ( difference −163 base pairs ( bp ) , p=0 . 001 ) . Between years 1 and 2 , TL increased in the intervention arm ( +76 bp ) and decreased in the controls ( −23 bp ) ( p=0 . 050 ) . After 2 years , there was no difference between the arms ( p=0 . 305 ) . Our unexpected finding of increased telomere attrition during the first year of life in the intervention group suggests that rapid telomere attrition during this critical period could reflect the improved growth in the intervention group , rather than accumulated stress . Funded by The Bill and Melinda Gates Foundation . NCT01590095 .
Children in low-income countries often experience infectious diseases and nutritional deficiencies leading to impaired growth , poor development , and early mortality ( Black et al . , 2017; GBD 2015 Mortality and Causes of Death Collaborators , 2016; Victora et al . , 2008 ) . During early life , children exhibit heightened developmental plasticity and are more sensitive to environmental conditions than later in life ( Barker , 2007 ) . The theory of developmental origins of health and disease postulates that multiple , cumulative early life exposures to adverse environmental factors may increase allostatic load ( the cumulative biological damage from chronic stress ) and susceptibility to adult diseases ( Barker , 2007; Juster et al . , 2010; Price et al . , 2013 ) . Accumulating evidence implicates telomere length ( TL ) attrition as a potentially important underlying mechanism that links early life insults with adverse health outcomes later in life ( Price et al . , 2013 ) . Telomeres , the repetitive DNA sequences and protein complexes protecting the ends of linear chromosomes , gradually shorten during normal cell division . Progressive telomere shortening leads to chromosome instability and cell senescence ( Blackburn , 2001 ) . Shorter TL has been linked to several age-related conditions including diabetes , heart disease , and early mortality ( Cawthon et al . , 2003; Fitzpatrick et al . , 2007; Salpea et al . , 2010 ) . It remains an open debate whether TL serves as a ‘molecular clock’ that gauges cumulative stress exposures over a lifespan or plays a role in the etiology of various diseases ( Blackburn et al . , 2015; Hamad et al . , 2016; Zhan et al . , 2015 ) . Acute or chronic infections may contribute to childhood TL attrition , and inflammation and oxidative stress may be potential mediators ( Houben et al . , 2008 ) . Infections may induce T-cell proliferation and accelerated telomere attrition ( Aviv , 2004 ) . Although no studies have directly investigated the relationship between infection and childhood TL , related studies in adults and animals support the plausibility of an association . Animal models have demonstrated that repeated exposures to Salmonella enterica cause telomere attrition ( Ilmonen et al . , 2008 ) , and prenatal chronic malaria infections shorten offspring TL ( Asghar et al . , 2015 ) . In adult humans , Helicobacter pylori infection , hepatitis C virus , HIV , and experimentally induced respiratory infection have been associated with shorter TL ( Cohen et al . , 2013; Gianesin et al . , 2016; Hou et al . , 2009; Zanet et al . , 2014 ) . Furthermore , caregiver-reported diarrhea in the first two years of life predicted shorter adult TL ( Eisenberg et al . , 2017 ) . Early life water , sanitation , and handwashing ( WSH ) interventions could potentially prevent or reduce infections and slow telomere attrition . A systematic review and meta-analysis of WSH interventions reported a reduction in diarrheal illness ( pooled estimate of relative risk 0 . 67 , 95% CI 0 . 59–0 . 76 ) ( Fewtrell et al . , 2005 ) , and a randomized controlled trial in Pakistan showed that promotion of handwashing decreased acute respiratory infections in children ( Luby et al . , 2005 ) . To our knowledge , no studies have examined the impact of WSH interventions on TL . Early life nutrition may affect childhood TL . Breast milk could potentially reduce telomere attrition by protecting against inflammation and oxidative stress ( Cacho and Lawrence , 2017; Matos et al . , 2015 ) – exposures associated with telomere attrition ( Houben et al . , 2008 ) . Studies have found an association between exclusive breastfeeding and preschool TL ( Wojcicki et al . , 2016a ) , but no association with adult TL ( Eisenberg et al . , 2017 ) . Improved intake of micronutrients ( vitamins and minerals ) may promote telomere maintenance ( Bull and Fenech , 2008 ) . Studies in adult populations have found mixed evidence for associations between TL , multivitamin usage , and various micronutrients ( Cassidy et al . , 2010; Liu et al . , 2013; Paul et al . , 2015; Richards et al . , 2007; Xu et al . , 2009 ) . To our knowledge , no randomized controlled trials have assessed the effect of nutritional interventions on child TL . Telomeres shorten fourfold faster in infants compared to adults ( Zeichner et al . , 1999 ) ; however , only a few studies have assessed the potential associations between environmental factors and TL in early childhood , a sensitive window of growth and development ( Entringer et al . , 2013; Marchetto et al . , 2016; Theall et al . , 2013a; Theall et al . , 2013b; Wojcicki et al . , 2016a ) . The trajectories of infant TL in low-income countries and the potential impact of early life health-improvement interventions on TL are unknown . We conducted a substudy within a randomized trial in rural Bangladesh to evaluate if an intensive , early life nutrition , water , sanitation , and handwashing intervention would slow telomere attrition among children in their first two years of life ( Arnold et al . , 2013 ) .
The objective of the WASH Benefits trial was to compare the effects of individual and combined interventions on child health in the first two years of life – the critical window to prevent growth faltering ( Arnold et al . , 2013 ) . Between 31 May 2012 and 7 July 2013 , in the overall trial , 5551 compounds ( collections of related households ) with pregnant women in their first or second trimester were randomly allocated to one of the six intervention groups or a double sized control group as follows: ( 1 ) chlorinated drinking water and safe storage vessel , ( 2 ) upgraded sanitation ( child potties , sani-scoop hoes to remove feces , and a double pit latrine with a hygienic water seal ) , ( 3 ) handwashing promotion ( handwashing stations with detergent soap ) , ( 4 ) combined water + sanitation + handwashing ( 5 ) nutrition ( lipid-based nutrient supplements and age-appropriate recommendations on maternal nutrition and infant feeding practices ) , ( 6 ) combined nutrition + water + sanitation + handwashing ( N + WSH ) , ( 7 ) control group , which did not receive any interventions ( Figure 1 ) . Community health promoters visited study compounds in the intervention arms to promote behaviors . We conducted a substudy within the trial to evaluate if the N + WSH intervention would slow telomere attrition among children in their first two years of life ( Arnold et al . , 2013 ) . The substudy team visited 996 index children after 1 year of intervention ( Y1 ) and 1021 children after 2 years of intervention ( Y2 ) in the control and N + WSH arms only . TL outcomes were measured in 66 . 5% of the children ( N = 662 ) at Y1% and 69 . 8% of the children ( N = 713 ) at Y2 , but not at birth ( Figure 1 ) . We expect TL at birth to be similar for the intervention and control arms because household enrollment characteristics were balanced between both arms ( Table 1 ) . In the substudy , a quarter of the fathers were engaged in agriculture . 61% of households reported having electricity available , and 86% had an earthen floor . At enrollment , 72% of households were food secure , 56% of households owned a latrine , and 9% of households had a handwashing station with soap near the latrine . The primary water source for the majority of households ( 72% ) was a shallow tubewell . Respondents reported the occurrence of daily open defecation in 80% of children less than 3 years of age . The substudy household enrollment characteristics were similar to the overall trial ( Table 2 ) . We measured whole blood relative telomere length by quantitative polymerase chain reaction ( qPCR ) , expressed as the ratio of telomere to single-copy gene abundance ( T/S ratio ) ( Cawthon , 2002; Lin et al . , 2010 ) . At the time of TL assessment , the mean ( ±SD ) age was 14 . 1 ( ±2 . 1 ) months at Y1 and 28 . 2 ( ±1 . 9 ) months at Y2 . TL was normally distributed ( Figure 2 ) . The mean ( ±SD ) TL was 1 . 43 ( ±0 . 23 ) T/S ratio at Y1 ( 6729 ± 549 base pairs ( bp ) ) and 1 . 45 ( ±0 . 24 ) T/S ratio at Y2 ( 6763 ± 586 bp ) . Although these averages are short for young children compared to some previous findings ( Factor-Litvak et al . , 2016; Frenck et al . , 1998; Wojcicki et al . , 2016a ) , they are similar to a recent report of newborns whose mothers experienced a high level of stress during pregnancy ( Marchetto et al . , 2016 ) ; the stress associated with low socioeconomic status may have contributed to the short average TL in this study . We conducted an intention-to-treat analysis using generalized linear models with robust standard errors . Measures of intervention adherence ( including observed hardware availability ) were high ( over 80% ) and sustained throughout Y1 and Y2 ( S . P . Luby et al . in review ) . After 1 year of intervention , compared with controls ( mean = 1 . 47 T/S or 6813 bp ) , children in the combined intervention arm ( mean = 1 . 40 T/S or 6650 bp ) had significantly shorter relative TL ( difference −0 . 07 T/S or −163 bp , p=0 . 001; Table 3 ) . The entire distribution of the T/S ratio was shifted lower for the intervention arm compared to the control at Y1 ( Figure 2 ) . Unadjusted , adjusted , and inverse probability of censoring weighting ( IPCW; to adjust for potential bias from loss to follow-up ) analyses yielded similar estimates ( Table 3 ) . At the Y2 measurement , there was no significant difference ( p=0 . 305 ) between the intervention arm and the control ( Table 3 , Figure 2 ) . Next , we compared the change in TL between Y1 and Y2 . Household enrollment characteristics were balanced between individuals who had TL outcomes at Y1 versus those who were lost to follow-up at Y2 ( Table 2 ) . Of index children with a TL measurement at both Y1 and Y2 ( N = 557; 54 . 6% of the total children visited ) , relative TL increased by +0 . 03 T/S ( +76 bp ) in the intervention group and decreased by −0 . 01 T/S ( −23 bp ) in the control group ( Table 3 ) . Overall , the difference between the control and intervention arms in the change in relative TL from Y1 to Y2 was 0 . 04 T/S and was borderline significant ( p=0 . 050; Table 3 ) . Unadjusted , adjusted , and inverse probability of censoring weighting analyses generated similar estimates but only the unadjusted model was significant ( Table 3 ) . Finally , we conducted a subgroup analysis by sex because biological differences , differential care practices by sex , or other sex-specific behaviors could modify the effect of the intervention on TL when stratifying by sex ( Wojcicki et al . , 2016b ) . Sex was not a significant effect modifier ( sex by treatment interaction p=0 . 435 at Y1 and p=0 . 105 at Y2; Table 4 ) . Boys had shorter TL than girls at Y1 ( −0 . 07 T/S , p=0 . 007 ) and Y2 ( −0 . 09 T/S , p=0 . 001 ) , consistent with previous studies ( Gardner et al . , 2014 ) . Although a previous study reported seasonal variation in TL ( Rehkopf et al . , 2014 ) , we did not observe seasonal changes within this study .
Here , we demonstrate an effect of an intervention on TL in infants and report the trajectories of infant TL in a low-income country . In rural Bangladesh , an intensive , combined water , sanitation , handwashing , and nutrition intervention delivered to compounds of newborn children increased TL attrition during the critical first year of life . This result is contrary to many studies that reported increased TL attrition associated with prenatal psychosocial stress ( Entringer et al . , 2013; Marchetto et al . , 2016 ) , childhood institutional care ( Drury et al . , 2012 ) , disease ( Fitzpatrick et al . , 2007; Salpea et al . , 2010 ) , and mortality ( Cawthon et al . , 2003 ) ; however , these prior studies did not examine participants in the same age range ( 1–2 years ) as this study . The difference between the intervention and control arms in the change in relative TL from Y1 to Y2 was borderline significant , and at Y2 , we found no difference in relative TL between the arms . In high- and low-resource settings , there is a dearth of evidence on the effect of environmental exposures on TL trajectories in this age range and its implications for adult health outcomes . The accelerated TL attrition observed during the first year of life in the intervention arm may reflect improved immune system development . The WASH Benefits trial found that children in the intervention arm had reductions in caregiver-reported diarrhea , soil-transmitted helminth infections , and Giardia duodenalis infections compared with the control arm ( S . P . Luby et al . , A . Ercumen et al . , and A . Lin et al . in review ) . It is plausible that the interventions improved immune system development resulting in subsequent protection against infections or that the decreased exposure to pathogens may have improved immunity . The concept of accelerated TL attrition during the first year of life as a reflection of better immune system development is consistent with studies that have reported rapid telomere attrition and accelerated differentiation of hematopoietic stem cells ( HSCs ) , stem cells that give rise to all lineages of immune cells , during the first year of life . A healthy individual experiences ~ 17 HSC divisions in the first year of life , followed by ~2 . 5 divisions/year between ages 3–13 years , and ~0 . 6 divisions/year in adults ( Elwood , 2004; Kimura et al . , 2010; Rufer et al . , 1999; Sidorov et al . , 2009 ) . Since TL shortens in proportion to the number of cell replications , HSC progenitor divisions correspond with TL attrition rates ( Elwood , 2004; Kimura et al . , 2010; Rufer et al . , 1999; Sidorov et al . , 2009 ) . In the first year of life , children in the control group with poor immune system development would potentially experience less HSC divisions and slower rates of TL attrition compared to the intervention group . The accelerated TL attrition during the first year of life in the intervention arm may also reflect better linear growth . The intervention improved child linear growth at both Y1 and Y2 measurements ( S . P . Luby et al . in review ) . An evolutionary adaptation of organisms during infections is to restrict growth , and instead redirect nutrients and energy to ensure survival ( O'Connor et al . , 2008 ) . We postulate that the decreased pathogen exposure and the subsequent reduction in acquired infections are potential mechanisms by which better growth was attained in the intervention arm . Some studies have reported high synchrony between peripheral blood and TL attrition rates within other somatic tissues in the same individual ( Daniali et al . , 2013; Takubo et al . , 2002 ) , while others have found differences ( Dlouha et al . , 2014; Thomas et al . , 2008 ) . Although the results of these prior investigations are equivocal , the accelerated peripheral blood TL attrition in the intervention group during the first year of life could potentially indicate rapid muscle or bone cell division involved in growth . We hypothesize that early life TL may be a proxy measure for growth and development of the immune system , the brain , and other vital tissues . Forthcoming WASH Benefits studies focused on child inflammation and enteric pathogen burden will provide evidence on mechanistic pathways leading to different TL , growth , and development outcomes . After the initial period of rapid linear growth during Y1 , our borderline significant finding that telomere length increased in the intervention arm and decreased in the control arm from Y1 to Y2 seems to represent a preview of the TL trajectories that are set: we hypothesize that the intervention children will experience slower lifetime TL attrition and more physiological resistance to stress-related diseases ( Juster et al . , 2010 ) . The change in relative TL from Y1 to Y2 was small ( 0 . 04 T/S 95% confidence interval 0 . 00 , 0 . 08 ) and the p-value borderline ( p=0 . 050 ) . Nevertheless , our diarrhea , soil-transmitted helminth , Giardia duodenalis , and growth results suggest that these interventions are potentially interrupting key infection and malnutrition pathways to reduce exposure to biological adversity and allostatic load ( S . P . Luby et al . , A . Ercumen et al . , and A . Lin et al . in review ) . In a Filipino cohort , decreased infant diarrheal prevalence was associated with longer adult TL ( Eisenberg et al . , 2017 ) . In observational studies , various vitamins were associated with longer adult TL ( Xu et al . , 2009 ) , and early exclusive breastfeeding was associated with longer child TL at ages 4–5 years ( Wojcicki et al . , 2016a ) . Our nutrition intervention included promotion of exclusive breastfeeding and micronutrient fortified lipid-based nutrient supplements . The Y1 to Y2 changes in TL that we observed are consistent with these studies . The smaller than expected treatment effect and the borderline p-value indicate that the trial was slightly underpowered to detect differences in the change in relative TL from Y1 to Y2 among the intervention and control groups . Alternatively , our finding of a modest , borderline impact from Y1 to Y2 may suggest that the effect of the interventions is diminished in children over 1 year of age . This potential waning of intervention effects as a child ages could be due to less consumption of breast milk and thus , reductions in its protective effects or the increased mobility of children in environments contaminated with animal feces . This interpretation would also be consistent with no differences in relative TL observed at Y2 between the two groups , underscoring the importance of targeting interventions early in life during the sensitive period when they are likely to have the largest impact on childhood TL . This study had several limitations . These results from a rural , low-resource area in Bangladesh may not generalize to other populations . The lack of geographic matching in the substudy could have resulted in imbalances between study arms in factors associated with geography . In addition , differential loss to follow-up in the control and intervention arms may have biased results due to differences in unmeasured characteristics . However , our IPCW analysis showed that bias from loss to follow-up was unlikely based on a large set of measurable characteristics . The lithium heparin additive in the whole blood may have inhibited the qPCR reaction and the long duration of time from sample collection to TL measurement may have affected TL , but any systematic measurement errors would likely affect both study groups equally . These factors may provide potential explanations for the shorter average TL observed in this study compared to other studies ( Factor-Litvak et al . , 2016; Frenck et al . , 1998; Wojcicki et al . , 2016a ) . We only measured TL in whole blood , which might yield different results compared to TL measured in less proliferative tissue types ( e . g . , muscle or fat ) ( Daniali et al . , 2013 ) . Finally , we did not measure TL at birth ( Wojcicki et al . , 2016b ) , but we would expect it to be similar for both groups because household characteristics were balanced by randomization . Our findings are surprising , and they represent important contributions to the nascent and rapidly expanding field of infant telomere biology . Most studies to date have highlighted shorter TL as a measure of intrauterine or childhood stress ( Drury et al . , 2012; Entringer et al . , 2013; Marchetto et al . , 2016; Price et al . , 2013 ) , disease ( Fitzpatrick et al . , 2007; Salpea et al . , 2010 ) , and mortality ( Cawthon et al . , 2003 ) . However , our results motivate an intriguing hypothesis: here , we suggest that during the first year of life , accelerated TL attrition could reflect better child growth , neurodevelopment , and immune function . Although TL was a sensitive outcome that responded to an intervention , this trial underscores our limited understanding of environmental exposures contributing to TL dynamics during early life and the critical physiological pathways linking TL and lifelong health trajectories . Since potential confounding could plague observational studies , evaluating the relationship between modifiable exposures and TL within the context of randomized controlled trials could provide valuable contributions to the field of telomere biology .
We conducted a cluster-randomized trial in the rural subdistricts in Gazipur , Mymensingh , Tangail and Kishoreganj districts of Bangladesh . The main trial enrolled geographically matched clusters of compounds that were randomly allocated to a double-sized control or one of the six intervention arms ( Arnold et al . , 2013 ) . Each compound in rural Bangladesh consists of a collection of households of extended families . Due to logistical constraints for specimen collection , the substudy enrolled a subsample of randomized clusters that was balanced across arms ( allocation ratio 1:1 ) , but not geographically matched . We enrolled pregnant mothers in their first or second trimester . Exclusion criteria included households with plans to move in the following year , households that did not own their home , and households that drew water from a source with high iron content . The children born to the enrolled pregnant mothers were considered index children and are the focus of this analysis . In this substudy , if any two of the following criteria for moderate to severe dehydration were met , the child was excluded from the venipuncture: ( 1 ) restless , irritable , ( 2 ) sunken eyes , ( 3 ) drinks eagerly , thirsty , ( 4 ) pinched skin returns to normal position slowly . Children were also excluded from the venipuncture if they were listless or unable to perform their normal activities . Two children met the exclusion criteria: one child in the control arm was excluded at Year 1 , and one child in the intervention arm was excluded at Year 2 . Primary caregivers of all children provided written informed consent . The study protocols were approved by human subjects committees at icddr , b ( PR-11063 and PR-14108 ) , the University of California , Berkeley ( 2011-09-3652 and 2014-07-6561 ) and Stanford University ( 25863 and 35583 ) . A data safety monitoring committee convened by icddr , b oversaw the study . We formed clusters of 8 neighboring households with eligible pregnant women and created a 1 km buffer around each cluster to prevent spillover between clusters . A block was the equivalent of eight geographically-adjacent clusters . An investigator at UC Berkeley ( B . F . A . ) used a random number generator to block randomize clusters to one of the six interventions or to a double sized control arm as follows: ( 1 ) drinking water treatment and safe storage , ( 2 ) sanitation , ( 3 ) handwashing , ( 4 ) combined water + sanitation + handwashing ( WSH ) ( 5 ) nutrition , ( 6 ) combined nutrition + water + sanitation + handwashing ( N + WSH ) and ( 7 ) non-intervention control group . This substudy only included children in the control and the combined N + WSH arms . Participants and the data collection team were not masked because each intervention delivered had visible hardware . One laboratory investigator ( J . L . ) , who was masked to group assignments , conducted all of the TL measurements . Two investigators ( A . L . , A . N . M . ) conducted independent masked statistical analyses to generate final estimates following the pre-registered analysis protocol . After all masked analyses were replicated , the results were unmasked . The combined N + WSH interventions were previously described ( Arnold et al . , 2013 ) . Briefly , the components of the combined intervention were as follows: water treatment ( Aquatabs; NaDCC ) and safe storage vessel , sanitation ( child potties , sani-scoop hoes to remove feces , and a double pit latrine with a hygienic water seal ) , handwashing ( handwashing stations near the latrine and kitchen , including soapy water bottles and detergent soap ) , and nutrition ( lipid-based nutrient supplements [Nutriset , Malauny , France] that included ≥100% of the recommended daily allowance of 12 vitamins and 9 minerals with 9 . 6 g of fat and 2 . 6 g of protein daily for children 6 to 24 months of age and age-appropriate recommendations on maternal nutrition and infant feeding practices ) . Community health promoters visited study compounds in the intervention arms at least once per week during the first 6 months and at least once biweekly to promote behaviors . The control group received no intervention . TL was measured at 1 year and 2 years after intervention delivery when the children were approximately ages 14 and 28 months respectively . For each child , trained icddr , b staff collected a 5 ml venipuncture sample from the antecubital area of the arm into a Sarstedt S-monovette lithium heparin collection tube . All specimens were labeled with identification numbers only . Specimens were transported on ice to the laboratory , immediately centrifuged , and whole blood was stored at −80°C . Specimens were shipped on dry ice ( −79°C ) to Dr . Elizabeth Blackburn’s laboratory at the University of California , San Francisco . The duration of time from sample collection to TL measurement ranged from 8 to 32 months . Genomic DNA was extracted from heparin-anti-coagulated whole blood stored at −80°C using QIAamp DNA Mini Kit ( QIAGEN , Hilden , Germany ) . DNA was quantified by measuring OD260 with a NanoDrop 200 c Spectrophotometer ( Nanodrop Products , Wilmington , DE ) . Samples that passed the quality control of OD260/OD280 between 1 . 7–2 . 0 were used for TL measurement . Of the 1384 samples , eight did not pass quality control . Of the remaining 1376 samples assayed , one sample was not amplified , resulting in 1375 samples with valid TL data . TL was measured in whole blood by quantitative PCR ( qPCR ) using a protocol adapted from the published original method by Cawthon ( Cawthon , 2002; Lin et al . , 2010 ) . This method determines relative TL ( i . e . , T/S ratio ) by measuring the factor by which each DNA sample differs from a reference DNA sample in its ratio of telomere repeat copy number ( T ) to single-copy gene copy number ( S ) ( Cawthon , 2002 ) . The primers for the telomere PCR were tel1b [5'-CGGTTT ( GTTTGG ) 5GTT-3'] , used at a final concentration of 100 nM , and tel2b [5'-GGCTTG ( CCTTAC ) 5CCT-3'] , used at a final concentration of 900 nM . The primers for the single-copy gene ( human beta-globin ) PCR were hbg1 [5' GCTTCTGACACAACTGTGTTCACTAGC-3'] , used at a final concentration of 300 nM , and hbg2 [5'-CACCAACTTCATCCACGTTCACC-3'] , used at a final concentration of 700 nM . The final reaction mix contained 20 mM Tris-HCl , pH 8 . 4; 50 mM KCl; 200 μM each dNTP; 1% DMSO; 0 . 4x Syber Green I; 22 ng E . coli DNA; 0 . 4 Units of Platinum Taq DNA polymerase ( Invitrogen Inc . , Carlsbad , CA ) , and approximately 6 . 6 ng of genomic DNA per 11 microliter reaction . The telomere ( T ) thermal cycling qPCR profile consisted of denaturing at 96°C for 1 min followed by 30 cycles of denaturing at 96°C for 1 s and annealing or extension at 54°C for 60 s with fluorescence data collection . The single-copy gene ( S ) thermal cycling qPCR profile consisted of denaturing at 96°C for 1 min followed by 8 cycles of denaturing at 95°C for 15 s , annealing at 58°C for 1 s , and extension at 72°C for 20 s , followed by 35 cycles of denaturing at 96°C for 1 s , annealing at 58°C for 1 s , extension at 72°C for 20 s , and holding at 83°C for 5 s with data collection . The T/S ratio for each sample was measured twice ( technical replicates ) . When the duplicate T/S value and the initial value varied by more than 7% , the sample was run a third time and the two closest values were reported . The average coefficient of variation for TL measurement in this study was 2 . 1% . Tubes containing 26 , 8 . 75 , 2 . 9 , 0 . 97 , 0 . 324 and 0 . 108 ng of a reference DNA ( pooled genomic DNA from 100 females ) were included in each PCR run so that the quantity of targeted templates in each research sample could be determined relative to the reference DNA sample by the standard curve method . The same reference DNA was used for all PCR runs . To control for inter-assay variability , eight control DNA samples were included in each run . In each batch , the T/S ratio of each control DNA was divided by the average T/S for the same DNA from 10 runs to calculate a normalizing factor . This is done for all eight samples and the average normalizing factor for all eight samples was used to correct the participant DNA samples to calculate the final T/S ratio . The assay was performed as plates of 96 samples . Control and intervention samples were randomly interspersed to minimize potential plate effects . DNA extraction and TL assay were performed in two batches ( 3 . 5 months apart ) . The first batch contained 663 samples and the second batch contained 721 samples . All assays were performed using the same lots of reagents . To adjust for assay batch variations , 48 samples from the first batch were re-assayed together with the second batch of samples and data from the second batch of samples were adjusted based on the systematic difference between the first batch values versus the second batch values for these 48 samples . This adjustment factor was 1 . 05 . To determine the conversion factor for the calculation of approximate base pair telomere length from T/S ratio , the above method was used to determine the T/S ratios , relative to the same reference DNA , for a set of genomic DNA samples from the ATCC authenticated human fibroblast primary cell line IMR-90 ( ATCC: CCL-186 , cell line authentication method: STR profiling; ATCC determined the cell line was free of mycoplasma contamination ) at different population doublings , as well as with the telomerase protein subunit gene ( hTERT ) transfected into a lentiviral construct . The mean TRF length from these DNA samples was determined using Southern blot analysis . Comparison of T/S ratios versus base pairs derived from the Southern blot analysis generated the following equation for conversion from T/S ratio to base pairs: base pairs = 3274 + 2413 * ( T/S ) ( Farzaneh-Far et al . , 2010 ) . The pre-specified outcome measures were TL ( T/S ratios ) measured at 1 year and 2 years after intervention delivery ( Y1 and Y2 respectively ) , and the change in TL from Y1 to Y2 . The complete pre-registered analysis protocol is available ( https://osf . io/cjjwa/ ) . We provide a summary of our analyses below . Analyses were conducted using R statistical software version 3 . 2 . 3 ( www . r-project . org ) . The trial was registered at ClinicalTrials . gov ( NCT01590095 ) . The funder approved the study design , but was not involved in data collection , analysis , interpretation or any decisions related to publication . The corresponding author had full access to all study data and final responsibility for the decision to submit for publication . The WASH Benefits data and code that support the findings of this study are available in Open Science Framework ( https://osf . io/evc98/ ) . | Stress negatively affects health by causing changes in cells . As a result , excess stress may predispose people to fall ill more often or age faster . It is difficult to measure stress . Some studies suggest that measuring the ends of chromosomes , known as telomeres , may be one way to measure stress . Like the plastic tips on shoelaces , telomeres protect chromosomes from fraying . All peoples’ telomeres shorten over their lifetime with each cell division . Many studies show that telomeres shorten faster in people who experience more stress . When telomeres become too short , cells die faster without being replaced , and the body ages . Most studies on telomere length have looked at adults . Few studies have looked at children early in life or asked whether there are ways to intervene to stop or reverse stress-related telomere shortening . The first two years of life are a crucial period for the developing brain and immune system , which could set children on a lifelong course toward health or disease . Young children living in low-resource settings often encounter many sources of stress , like poor nutrition , infectious diseases or violence . Studies are needed to determine if interventions in early childhood aimed at reducing some sources of stress improve telomere length or long-term health . Now , Lin et al . show that interventions to provide safe water , sanitation , handwashing facilities , and better nutrition to children in rural Bangladesh unexpectedly shortened telomeres . As part of a larger study , pregnant women in rural Bangladesh were divided , at random , into groups . One group received a suite of interventions , which included more sanitary toilets , handwashing facilities , and nutritional supplements for their infants . Another group served as a control and did not receive this extra help . Lin et al . looked at telomere length , growth , and infections in a subset of 713 children whose mothers participated in the study . Children who got the extra help grew faster and were less likely to get diarrhea or parasitic infections than the children in the control group . Unexpectedly , children in the intervention group had shorter telomeres at 14 months of age than the children in the control group . Lin et al . suggest that the telomere shortening in the intervention group might be a consequence of rapid growth and immune system development in the first year of life rather than resulting from biological stress . More studies are needed to ask whether telomere shortening is indeed linked to faster growth and development early in life . The strong and unexpected findings highlight how little is known about how the length of telomeres can be used to predict future health or disease . Interpreting the length of telomeres over a person’s lifetime could prove more nuanced than originally thought . | [
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] | 2017 | Effects of water, sanitation, handwashing, and nutritional interventions on telomere length among children in a cluster-randomized controlled trial in rural Bangladesh |
Positive-sense RNA viruses hijack intracellular membranes that provide niches for viral RNA synthesis and a platform for interactions with host proteins . However , little is known about host factors at the interface between replicase complexes and the host cytoplasm . We engineered a biotin ligase into a coronaviral replication/transcription complex ( RTC ) and identified >500 host proteins constituting the RTC microenvironment . siRNA-silencing of each RTC-proximal host factor demonstrated importance of vesicular trafficking pathways , ubiquitin-dependent and autophagy-related processes , and translation initiation factors . Notably , detection of translation initiation factors at the RTC was instrumental to visualize and demonstrate active translation proximal to replication complexes of several coronaviruses . Collectively , we establish a spatial link between viral RNA synthesis and diverse host factors of unprecedented breadth . Our data may serve as a paradigm for other positive-strand RNA viruses and provide a starting point for a comprehensive analysis of critical virus-host interactions that represent targets for therapeutic intervention .
Positive-strand RNA viruses replicate at membranous structures that accommodate the viral replication complex and facilitate RNA synthesis in the cytosol of infected host cells ( Romero-Brey and Bartenschlager , 2016; Romero-Brey et al . , 2012; Cortese et al . , 2017; Knoops et al . , 2008; Miorin et al . , 2013 ) . Rewiring host endomembranes is hypothesized to provide a privileged microenvironment physically separated from the cytosol , thereby ensuring adequate concentrations of macromolecules for viral RNA synthesis , preventing recognition of replication intermediates such as double-stranded RNA ( dsRNA ) by cytosolic innate immune receptors ( Overby et al . , 2010; Neufeldt et al . , 2016 ) , and providing a platform that facilitates molecular interactions with host cell proteins . Ultrastructural studies have reported the origin , nature , and extent of membrane modifications induced by coronaviruses ( order Nidovirales , family Coronaviridae ) , which materialize as an ER-derived network of interconnected double-membrane vesicles ( DMVs ) and convoluted membranes ( CM ) in perinuclear regions of infected cells to which the viral replication/transcription complex ( RTC ) is anchored ( Knoops et al . , 2008; Ulasli et al . , 2010; Oudshoorn et al . , 2017 ) . The RTC is generated by translation of the genomic RNA into two large polyproteins that are extensively auto-proteolytically processed by viral proteases to give rise to 16 processing end-products , termed non-structural proteins ( nsps ) 1–16 . Nsp1 is rapidly cleaved from the polyproteins and not considered an integral component of the coronaviral RTC , but interferes with host cell translation by inducing degradation of cellular mRNAs ( Huang et al . , 2011; Züst et al . , 2007; Lokugamage et al . , 2015 ) . Although it has not yet been formally demonstrated , the remaining nsps ( nsp2-16 ) are thought to comprise the RTC and harbor multiple enzymes and functions , such as de-ubiquitination , proteases , helicase , polymerase , exo- and endonuclease , and N7- and 2’O-methyltransferases ( Thiel et al . , 2003; Snijder et al . , 2003; Decroly et al . , 2008; Barretto et al . , 2005; Lindner et al . , 2005; Athmer et al . , 2017 ) . Many of these functions have been studied using reverse genetic approaches , which revealed their importance in virus-host interactions ( Kindler et al . , 2017; Züst et al . , 2011; Eckerle et al . , 2007; Deng et al . , 2017; Zhang et al . , 2015 ) . In most cases , phenotypes were described via loss-of-function mutagenesis . However , in the context of virus infection , the specific interactions of RTC components with host cell factors remain largely unknown . A number of individual host cell proteins have been shown to impact coronavirus replication by using various screening methods , such as genome-wide siRNA , kinome , and yeast-two-hybrid screens ( Verheije et al . , 2008; Reggiori et al . , 2010; de Wilde et al . , 2015; Wong et al . , 2015; Pfefferle et al . , 2011 ) . Likewise , genome-wide CRISPR-based screens have been applied to other positive-stranded RNA viruses , such as flaviviruses , and identified critical host proteins required for replication ( Marceau et al . , 2016; Zhang et al . , 2016 ) . Some of these proteins were described in the context of distinct ER processes , such as N-linked glycosylation , ER-associated protein degradation ( ERAD ) , and signal peptide insertion and processing . Although individual proteins identified by these screens may interact with viral replication complexes , they likely constitute only a small fraction of the global replicase microenvironment . To capture the full breadth of host cell proteins and cellular pathways that are spatially associated with viral RTCs , we employed a proximity-based labeling approach involving a promiscuous E . coli-derived biotin ligase ( BirAR118G ) . BirAR118G biotinylates proximal ( <10 nm ) proteins in live cells without disrupting intracellular membranes or protein complexes , and hence , does not rely on high-affinity protein-protein interactions but is also able to permanently tag transient interactions ( Roux et al . , 2012 ) . Covalent protein biotinylation allows stringent lysis and washing conditions during affinity purification and subsequent mass spectrometric identification of captured factors . By engineering a recombinant MHV harboring BirAR118G as an integral component of the RTC , we identified >500 host proteins reflecting the molecular microenvironment of MHV replication structures . siRNA-mediated silencing of each of these factors highlighted , amongst others , the functional importance of vesicular ER-Golgi apparatus trafficking pathways , ubiquitin-dependent and autophagy-related catabolic processes , and translation initiation factors . Importantly , the detection of active translation in close proximity to the viral RTC highlighted the critical involvement of translation initiation factors during coronavirus replication . Collectively , the determination of the coronavirus RTC-associated microenvironment provides a functional and spatial link between conserved host cell processes and viral RNA synthesis , and highlights potential targets for the development of novel antiviral agents .
To insert the promiscuous biotin ligase BirAR118G as an integral subunit of the MHV RTC , we used a vaccinia virus-based reverse genetic system ( Coley et al . , 2005; Eriksson et al . , 2008 ) to generate a recombinant MHV harboring an in-frame fusion of myc-tagged BirAR118G to nsp2 . MHV-BirAR118G-nsp2 retained the cleavage site between nsp1 and BirAR118G , while a deleted cleavage site between BirAR118G and nsp2 ensured the expression of a BirAR118G-nsp2 fusion protein ( Figure 1a ) . This strategy was chosen because it was recently employed by Freeman et al . for a fusion of green fluorescent protein ( GFP ) with nsp2 and represents the only known site tolerating large insertions within the MHV replicase polyprotein ( Freeman et al . , 2014 ) . MHV-BirAR118G-nsp2 replicated to comparable peak titers and replication kinetics as the parental wild-type MHV-A59 ( Figure 1b ) . MHV-GFP-nsp2 , which was constructed in parallel and contained the coding sequence of EGFP ( Freeman et al . , 2014 ) instead of BirAR118G , was used as a control and also reached wild-type virus peak titers , with slightly reduced viral titers at 9 hr post- infection ( h . p . i . ) compared to MHV-A59 and MHV-BirAR118G-nsp2 ( Figure 1b ) . Western blot analysis confirmed that the BirAR118G-nsp2 fusion protein is specifically detected in MHV-BirAR118G-nsp2-infected cells and that the BirAR118G biotin ligase remains fused to nsp2 during MHV-BirAR118G-nsp2 infection ( Figure 1—figure supplement 1 ) . To further confirm the accommodation of BirAR118G within the viral RTC , MHV-A59- , MHV-BirAR118G-nsp2- , and mock-infected L929 fibroblasts were visualized using indirect immunofluorescence microscopy . BirAR118G-nsp2 remained strongly associated with the MHV RTC throughout the entire replication cycle , as indicated by the co-localization of BirAR118G-nsp2 with established markers of the MHV replicase , such as nsp2/3 and nsp8 ( Figure 1c , Figure 1—figure supplement 2 , Figure 1—figure supplement 3 ) . This observation corroborates previous studies demonstrating that nsp2 , although not required for viral RNA synthesis , co-localizes with other nsps of the coronaviral RTC ( Schiller et al . , 1998; Hagemeijer et al . , 2010; Graham et al . , 2005 ) . Importantly , by supplementing the culture medium with biotin , we could readily detect biotinylated proteins with fluorophore-coupled streptavidin that appeared close to the MHV RTC throughout the entire replication cycle in MHV-BirAR118G-nsp2-infected cells , demonstrating efficient proximity-dependent biotinylation of RTC-proximal host factors ( Figure 1c , Figure 1—figure supplement 2 , Figure 1—figure supplement 3 ) . Furthermore , to define the localization of the nsp2 fusion protein at the ultrastructural level , we replaced the BirAR118G biotin ligase with the APEX2 ascorbate peroxidase to generate recombinant MHV-APEX2-nsp2 . APEX2 mediates the catalysis of 3 , 3’-diaminobenzidine ( DAB ) into an insoluble polymer that can be readily observed by electron microscopy ( Martell et al . , 2017 ) . As shown in Figure 1d , APEX2-catalized DAB polymer deposition was readily detectable at characteristic coronavirus replication compartments , such as DMVs and CM , categorically demonstrating that the nsp2 fusion proteins localize to known sites of coronavirus replication ( Knoops et al . , 2008; Ulasli et al . , 2010 ) . Collectively , these results establish that the recombinant MHV-BirAR118G-nsp2 replicates with comparable kinetics to wild-type MHV-A59 , expresses a functional BirAR118G biotin ligase that is tightly associated with the MHV RTC , and that biotinylated , RTC-proximal proteins can be readily detected in MHV-BirAR118G-nsp2 infected cells . To further demonstrate the efficiency and specificity of BirAR118G-mediated biotinylation we assessed , by western blot analysis , fractions of biotinylated proteins derived from MHV-A59- , MHV-BirAR118G-nsp2- , or non-infected cells that were grown with or without the addition of biotin ( Figure 2a , Figure 2b ) . A characteristic pattern of endogenously biotinylated proteins was observed under all conditions where no exogenous biotin was added to the culture medium ( Figure 2b ) . The same pattern was detectable in non-infected and wild-type MHV-A59-infected cells when the culture medium was supplemented with biotin , suggesting that the addition of biotin in the absence of the BirAR118G biotin ligase does not recognizably change the fraction of endogenously biotinylated proteins . In contrast , we observed a greatly increased fraction of biotinylated proteins in lysates derived from MHV-BirAR118G-nsp2-infected cells treated with biotin . This result demonstrates that virus-mediated expression of the BirAR118G biotin ligase results in efficient biotinylation when biotin is added to the culture medium . Moreover , we could readily affinity purify , enrich , and recover the fraction of biotinylated proteins under stringent denaturing lysis and washing conditions by using streptavidin-coupled magnetic beads ( Figure 2b ) . Affinity purified proteins derived from biotin-treated MHV-A59- and MHV-BirAR118G-nsp2-infected cells were subjected to mass spectrometric analysis ( n = 3 ) . Liquid chromatography tandem-mass spectrometry ( LC-MS/MS ) was performed from in-gel digested samples and log-transformed label-free quantification ( LFQ ) levels were used to compare protein enrichment between samples ( Figure 2c ) . Overall , 1381 host proteins were identified , of which 513 were statistically significantly enriched in MHV-BirAR118G-nsp2-infected samples over MHV-A59-infected samples . These host proteins represent a comprehensive repertoire of RTC-proximal factors throughout MHV infection ( Figure 2c , Supplementary file 1 ) . Thirty-four factors , that are mainly involved in fatty acid β-oxidation biological processes in the mitochondrion , displayed significant enrichment in MHV-WT compared to MHV-BirAR118G-nsp2 infections ( Figure 2c ) . Since the biotinylation of these factors is not caused by the BirAR118G biotin ligase , these factors were not considered for further investigation in the context of our RTC-proximal biotinylation proteomic screen . Importantly , besides the 513 host proteins that were enriched in MHV-BirAR118G-nsp2-infected cells , we noted that viral replicase gene products nsp2-10 and nsp12-16 , and the nucleocapsid protein were also significantly enriched in fractions derived from MHV-BirAR118G-nsp2-infected cells , suggesting that they are all in close proximity to the BirAR118G-nsp2 fusion protein ( Figure 2c , Figure 2d ) . This is in agreement with studies demonstrating co-localization and interactions amongst individual nsps , and with studies showing association of the nucleocapsid protein with the coronavirus RTC ( Ulasli et al . , 2010; Denison et al . , 1999; Sims et al . , 2000; van der Meer et al . , 1999; Bost et al . , 2001 ) . It also highlights the specificity and effectiveness of the labeling approach in live cells and is the first experimental evidence showing that collectively these viral nsps and the nucleocapsid ( N ) protein are subunits of the coronavirus RTC . Furthermore , these results corroborate previous reports that nsp1 is likely not an integral component of the coronavirus RTC ( Huang et al . , 2011; Züst et al . , 2007; Lokugamage et al . , 2015; Denison et al . , 1992 ) . Amongst the ‘not detected’ or ‘not enriched’ viral proteins are ( i ) nsp11 , which is a short peptide of only 14 amino acids at the carboxyterminus of polyprotein 1a with a yet unassigned role or function in coronavirus replication , ( ii ) the structural proteins spike ( S ) protein , envelope ( E ) protein , and membrane ( M ) protein , which mainly localize to sites of viral assembly before being incorporated into newly-formed viral particles , and ( iii ) all accessory proteins ( NS2a , HE , ORF4 , ORF5a ) . Altogether , these results validate the proximity-dependent biotinylation approach and demonstrate the specific and exclusive labeling of MHV-RTC-associated proteins ( Figure 2d ) . The BirAR118G biotin ligase biotinylates proteins in its close proximity that must not necessarily have tight , prolonged , or direct interaction ( Roux et al . , 2012 ) . Therefore , the identified RTC-proximal host proteins , recorded over the entire duration of the MHV replication cycle , likely include proteins that display a prolonged co-localization with the MHV RTC , proteins that may locate only transiently in close proximity to the RTC , and proteins of which only a minor fraction of the cellular pool may associate with the RTC . To this end , we assessed the localization of a limited number of host proteins from our candidate list in MHV-infected cells . Accordingly , we identified RTC-proximal host proteins displaying a pronounced co-localization with the MHV RTC , such as the ER protein reticulon 4 ( RTN4; Figure 2e ) , and host proteins where co-localization by indirect immunofluorescence microscopy was not readily detectable , such as the eukaryotic translation initiation factor 3E ( eIF3E; Figure 2e ) . However , in the latter case , a more sensitive detection technique , such as a proximity ligation assay that relies on proximity-dependent antibody-coupled DNA probe amplification ( Söderberg et al . , 2006 ) , demonstrated proximity of eIF3E and dsRNA in MHV-infected cells ( Figure 2f ) . Collectively , our results show that the approach of integrating a promiscuous biotin ligase as an integral subunit into a coronavirus RTC revealed a comprehensive list of host cell proteins that comprises the RTC microenvironment . The efficacy and specificity of our approach is best illustrated by the fact that we were able to identify all expected viral components of the MHV RTC , while other viral proteins , such as nsp1 , structural proteins S , E , and M , and accessory proteins , were not amongst the significantly enriched proteins . Since the biotin-based proximity labeling was performed during the entire viral life cycle , our data likely also contains proteins that are only transiently present in the RTC microenvironment or only comprise a sub-fraction of the cellular pool in close proximity to the MHV RTC . To categorize functionally related proteins from the list of RTC-proximal host proteins and identify enriched biological themes in the dataset , we performed a functional classification of RTC-proximal factors using Gene Ontology ( GO ) enrichment analysis . 86 GO biological process ( BP ) terms were significantly enriched in the dataset ( p-value < 0 . 05 ) , of which 32 terms were highly significant ( p-value < 0 . 005 ) ( Figure 3a , Supplementary file 2 ) . Additional analysis using AmiGO revealed that 25 of these 32 highly significant GO BP terms fell into five broad functional categories , namely cell adhesion , transport , cell organization , translation , and catabolic processes . To examine these categories further , identify important cellular pathways within them , and extract known functional associations among RTC-proximal host proteins , we performed STRING network analysis on the RTC-proximal proteins in each category ( Figure 3b , Figure 3c , Figure 3—figure supplement 1 ) . Despite ‘cell-cell adhesion’ scoring high , it likely represents a typical limitation of gene annotation databases , where many genes play multiple roles in numerous pathways and processes . Accordingly , most genes assigned to the GO BP term 'cell-cell adhesion' are also found in the other categories described below . The category ‘transport’ included protein trafficking and vesicular-mediated transport pathways and comprised the majority of RTC-proximal factors ( Figure 3a , Figure 3b ) . Protein interaction network analysis , using STRING , revealed at least four distinct clusters of interacting factors within this category ( Figure 3b ) . Cluster I , protein transport , comprised nuclear transport receptors at nuclear pore complexes , such as importins and transportins . Interestingly , this cluster also contained Sec63 , which is part of the Sec61 translocon ( Rapoport , 2007 ) and has been implicated in protein translocation across ER membranes . The list of RTC-proximal factors also included signal recognition particles SRP54a and SRP68 proteins ( Supplementary file 2 ) that promote the transfer of newly synthetized integral membrane proteins or secreted proteins across translocon complexes . Furthermore , the list contained Naca and BTF3 , which prevent the translocation of non-secretory proteins toward the ER lumen ( Wiedmann et al . , 1994; Gamerdinger et al . , 2015 ) . Cluster II included vesicle components , tethers and SNARE ( Soluble N-ethylmaleimide-sensitive-factor Attachment protein Receptor ) proteins characteristic of the COPII-mediated ER-to-Golgi apparatus anterograde vesicular transport pathway whereas , cluster III contained components of the COPI-related retrograde Golgi-to-ER transport machinery . Moreover , Cluster IV was comprised of proteins that mediate clathrin-coated vesicle ( endosomal ) transport between the plasma membrane and the trans-Golgi network ( TGN ) , which is also closely associated with the actin cytoskeleton . Together with sorting nexins , cluster IV components can be regarded as regulating late-Golgi trafficking events and interacting with the endosomal system . Many of the cellular processes and host proteins assigned to ‘transport’ ( specifically in clusters II-IV ) are also listed in the category ‘cell organization’ ( Figure 3a , Figure 3—figure supplement 1a ) . However , this category actually extends the importance of vesicular transport as it also contains factors involved in the architecture , organization , and homeostasis of the ER and Golgi apparatus , and the cytoskeleton-supporting these organelles . Notably , a number of MHV RTC-proximal factors were part of the host translation machinery and assigned to category ‘translation’ ( Figure 3a , Figure 3c ) . We found enrichment of factors involved in translation initiation , particularly multiple subunits of eIF3 and eIF4 complexes , as well as eIF2 , eIF5 , the Ddx3y helicase , and the Elongation factor-like GTPase 1 , which are required for the formation of 43S pre-initiation complexes , 48S initiation complexes , and the assembly of elongation-competent 80S ribosomes ( Jackson et al . , 2010 ) . The high degree of interaction between these subunits is suggestive of the presence of the entire translation initiation apparatus in close proximity to the viral RTC . The 60S ribosomal protein L13a ( Rpl13a ) , ribosome biogenesis protein RLP24 ( Rsl24d1 ) , ribosome-binding protein 1 ( Rbp1 ) , release factor Gspt1 , and regulatory elements , such as Igf2bp1 , Gcn1l1 , Larp , Fam129a and Nck1 , are further indicative of the host cell translation machinery near sites of viral RNA synthesis . Lastly , the category ‘catabolic processes’ ( Figure 3a , Figure 3—figure supplement 1b ) includes a subset of autophagy-related factors and numerous ubiquitin-dependent ERAD components , including the E3 ubiquitin-protein ligase complex and 26S proteasome regulatory subunits ( Psmc2 , Psmd4 ) . Collectively , the coronavirus RTC-proximal proteins identified by proximity labeling greatly expand the repertoire of candidate proteins implicated in the coronavirus replication cycle . Importantly , since this screening approach was tailored to detect host factors associated with the coronavirus RTC , it provides a spatial link of these factors to the site of viral RNA synthesis . In order to assess the potential functional relevance of RTC-proximal factors identified in our MHV- BirAR118G-nsp2-mediated proximity-dependent screen , we designed a custom siRNA library individually targeting the expression of each of the 513 identified RTC-proximal host proteins . siRNA-treated L929 cells were infected ( MOI = 0 . 05 , n = 4 ) with a recombinant MHV expressing a Gaussia luciferase reporter protein ( MHV-Gluc ) ( Lundin et al . , 2014 ) and replication was assessed by virus-mediated Gaussia luciferase expression ( Figure 4a ) . Cell viability after siRNA knockdown was also assessed and genes resulting in cytotoxicity following silencing were discarded from further analysis . Importantly , we included internal controls of known relevance for MHV entry ( MHV receptor Ceacam1a ) and replication ( Gbf1 , Arf1 ) on each plate and found in each case that siRNA silencing of these factors significantly reduced MHV replication , which underscores the robustness and effectiveness of our approach ( Figure 4—figure supplement 1a ) ( Verheije et al . , 2008 ) . We found that siRNA-mediated silencing of 53 RTC-proximal host factors significantly reduced MHV replication compared to non-targeting siRNA controls . These factors can therefore be considered proviral and required for efficient replication ( Figure 4b; Supplementary file 3 ) . In contrast , we did not find antiviral factors that resulted in significant enhancement of viral replication upon siRNA knockdown . While this work was performed in a murine fibroblast cell line , the identification of antiviral proteins may be anticipated in a similar siRNA-mediated knockdown screen using primary target cells such as macrophages , that are better equipped in eliciting antiviral responses upon virus infection . Notably , siRNA targets that had the strongest impact on MHV replication were in majority contained within the functional categories highlighted in Figure 3a ( Figure 4b ) . Indeed , in line with the hypothesis that MHV subverts key components mediating both anterograde and retrograde vesicular transport between the ER , Golgi apparatus and endosomal compartments for the establishment of replication organelles , several factors contained within these pathways impaired MHV replication as exemplified by the siRNA-mediated silencing of Kif11 , Snx9 , Dnm11 , Scfd1 , Ykt6 , Stx5a , Clint1 , Aak11 , or Vapa ( Figure 4b ) . Consistently , ER-associated protein sorting complexes associated with the ribosome and newly synthetized proteins ( Naca , BTF3 , SRP54a , SRP68 ) that were revealed in the GO enrichment analysis ( Figure 3a , Supplementary file 2 ) , also appear to be required for efficient MHV replication ( Figure 4b ) . Furthermore , we also observed significantly reduced MHV replication upon silencing of core elements of the 26S and 20S proteasome complex ( Psmd1 and Psmc2 , and Psmb3 , respectively ) , suggesting a crucial role of the ubiquitin-proteasome pathway for efficient CoV replication ( Wong et al . , 2015; Raaben et al . , 2010a ) . Indeed , this finding may provide a link to the described coronavirus RTC-encoded de-ubiquitination activity residing in nsp3 that has been implicated in innate immune evasion ( Barretto et al . , 2005; Lindner et al . , 2005; Bailey-Elkin et al . , 2014 ) . Most interestingly , this custom siRNA screen identified a crucial role of the host protein synthesis apparatus that was associated with the MHV RTC as indicated by the proximity-dependent proteomic screen ( Figure 3a , Figure 3c ) . Silencing of ribosomal proteins Rpl13a and Rls24d1 and several subunits of the eIF3 complex resulted in greatly reduced MHV replication and scored with highest significance in the siRNA screen , suggesting that proximity of the host cell translation machinery to the viral RTC likely has functional importance for coronavirus replication ( Figure 4b ) . Due to the striking dependence of MHV replication on a subset of RTC-proximal translation initiation factors , we extended these results in independent assays . For this , we selected all host factors assigned to the category ‘translation’ ( Figure 3a ) and assessed virus replication following siRNA-mediated silencing of each factor . Measurement of luciferase activity after MHV-Gluc infection confirmed initial findings obtained by screening the entire siRNA library of MHV RTC-proximal factors ( Figure 4c ) . Specifically for Rpl13a , and eIFs 3i , 3 f , and 3e viral replication was reduced to levels comparable to our controls Ceacam1a ( MHV receptor ) and Gbf1 ( Verheije et al . , 2008 ) . Consistently , cell-associated viral mRNA levels ( Figure 4d ) and viral titers ( Figure 4e ) were reduced upon siRNA silencing of these factors . Although the silencing of a subset of host translation factors severely restricted MHV replication , effective knockdown of these factors ( Figure 4—figure supplement 1c ) did not affect cell viability ( Figure 4—figure supplement 1b , Figure 4—figure supplement 1d ) and only moderately affected host cell translation levels ( Figure 4f , Figure 4—figure supplement 1e ) . This data demonstrates that the reduced viral replication observed after siRNA knockdown is not due to a general impairment of host translation . To confirm the knockdown of host translation factors on the protein level we employed antibodies that were available for eIF3e , eIF3f , and eIF3i , and as shown in Figure 5 , murine L929 fibroblasts that were treated individually with four target-specific siRNAs displayed significantly reduced expression of eIF3e , eIF3f , and eIF3i proteins ( Figure 5a , Figure 5b ) . Importantly , under conditions of eIF3e , eIF3f , and eIF3i knockdown , viral replication was also significantly restricted , confirming the importance of these translation initiation factors for MHV replication ( Figure 5c ) . Subsequently , we aimed to visualize the localization of active translation during virus infection by puromycin incorporation into nascent polypeptides on immobilized ribosomes ( ribopuromycylation ) followed by fluorescence imaging using antibodies directed against puromycin ( David et al . , 2012 ) . In non-infected L929 cells , ribopuromycylation resulted in an expected diffuse , mainly cytosolic , staining pattern interspersed with punctate structures indicative of translation localized to dedicated subcellular cytosolic locations ( Figure 6 ) . In striking contrast , MHV-infected L929 cells displayed a pronounced enrichment of actively translating ribosomes near the viral RTC as indicated by the strong overlap between the viral replicase and the ribopuromycylation stain . Interestingly , active translation in vicinity of the RTC was strongest during the early phase of infection at 6 h . p . i . , and was observed until 8 h . p . i . , before gradually decreasing as the infection advanced along with the appearance of typical syncytia formation indicative of cytopathic effect ( CPE ) . Remarkably , we observed a similar phenotype in Huh7 cells infected with human coronaviruses , such as HCoV-229E or the highly pathogenic MERS-CoV ( Figure 7 ) . The HCoV-229E RTC , which was detected with an antiserum directed against nsp8 , appeared as small and dispersed perinuclear puncta during early infection and eventually converged into larger perinuclear structures later in infection . Consistent with findings obtained for MHV , we observed a striking co-localization of the HCoV-229E RTC with sites of active translation during the early phase of the infection ( Figure 7 , Figure 7—figure supplement 1 ) . The co-localization gradually decreased as the infection reached the late phase with upcoming signs of CPE . Finally , we further demonstrated that active translation is localized to the site of MERS-CoV RNA synthesis as dsRNA puncta highly overlapped with the ribopuromycylation stain in MERS-CoV-infected Huh7 cells ( Figure 7 ) . Collectively , these results not only confirm the spatial link between individual components of the host cell translation machinery and coronavirus replication compartments as identified by proximity-dependent biotinylation using MHV-BirAR118G-nsp2 , but they also demonstrate that active translation is taking place in close proximity to the viral RTC .
In this study , we made use of a recently developed system based on proximity-dependent biotinylation of host factors in living cells ( Roux et al . , 2012 ) . By engineering a promiscuous biotin ligase ( BirAR118G ) as an integral component of the coronavirus replication complex , we provide a novel approach to define the molecular mircoenvironment of viral replication complexes that is applicable to many other RNA and DNA viruses . We show that nsp2 fusion proteins encoded by recombinant MHV-APEX2-nsp2 and MHV-BirAR118G-nsp2 , are indeed part of the RTC and localize to characteristic coronavirus replicative structures . On the ultrastrurctural level , APEX2-catalized DAB polymer depositions were detected at DMVs and CMs , and we observed co-localization of BirAR118G with established coronavirus RTC markers , such as nsp2/3 and nsp8 , by indirect immunofluorescence microscopy . Notably , in MHV-BirAR118G-nsp2-infected cells the detection of biotinylated coronavirus replicase gene products nsp2-10 , nsp12-16 , and the nucleocapsid protein by mass spectrometry demonstrates that these proteins are in close proximity during infection . This extends previous immunofluorescence and electron microscopic studies that were limited by the availability of nsp-specific antibodies and could only show localization of individual nsps to coronavirus replicative structures ( Knoops et al . , 2008; Ulasli et al . , 2010; Schiller et al . , 1998; Hagemeijer et al . , 2010; Graham et al . , 2005 ) . Moreover , the close proximity of BirAR118G-nsp2 to MHV replicative enzymes , such as the RNA-dependent RNA polymerase ( nsp12 ) , the NTPase/helicase ( nsp13 ) , the 5’-cap methyltransferases ( nsp14 , nsp16 ) , the proof-reading exonuclease ( nsp14 ) , in MHV-BirAR118G-nsp2-infected cells further suggests close proximity of nsp2 to the site of viral RNA synthesis . We thus propose that nsp2-16 and the nucleocapsid protein collectively constitute a functional coronavirus replication and transcription complex in infected cells . The analysis of the host proteome enriched at MHV replication sites revealed a comprehensive list of host proteins that constitute the coronavirus RTC microenvironment . This included several individual factors and host cell pathways , especially transport mechanisms involving vesicle-mediated trafficking , which have been shown to assist coronavirus replication . Indeed , previous findings have reported the importance of the early secretory pathway , as well as key proteins for these processes such as Gbf1 and Arf1 , for efficient coronavirus replication ( Verheije et al . , 2008; de Wilde et al . , 2015; Oostra et al . , 2007; Knoops et al . , 2010; Vogels et al . , 2011; Hsu et al . , 2010 ) . Other markers such as Sec61α have also been detected in proximity of viral RTCs in SARS-CoV-infected cells ( Knoops et al . , 2010 ) . Correspondingly , the implications of proteins involved in catabolic processes such as autophagy have also been linked to coronavirus replication and other positive-strand RNA viruses ( Reggiori et al . , 2010; Sharma et al . , 2014; Monastyrska et al . , 2013 ) . Of note , the ubiquitin-proteasome system has been studied in more details during MHV infection and has also been highlighted in a genetic screen using infectious bronchitis coronavirus ( Wong et al . , 2015; Raaben et al . , 2010a; Raaben et al . , 2010b ) . Notably , numerous coronavirus RTC-proximal host proteins and pathways also have documented roles in the life cycle of other , more intensively studied , positive-stranded RNA viruses . Recent genome-wide CRISPR screens identified proteins involved in biosynthesis of membrane and secretory proteins , as well as in the ERAD pathway , as required for flavivirus replication ( Marceau et al . , 2016; Zhang et al . , 2016 ) , suggesting considerable commonalities and conserved virus-host interactions at the replication complexes of a broad range of RNA viruses ( Marceau et al . , 2016; Zhang et al . , 2016; Hsu et al . , 2010; Randall et al . , 2007 ) . Importantly , our list of RTC-proximal proteins by far exceeds the number of host cell proteins currently known to interact with viral replication complexes and the vast majority of MHV RTC-proximal proteins have not been described before . These likely include proteins with defined temporal roles during particular phases of the viral life cycle and proteins that did not yet attract our attention in previous screens because of functional redundancies . We therefore expect that this approach will find wide application in the field of virus-host interaction , target identification for virus inhibition , and provides a starting point to reveal similarities and differences between replication strategies of a broad range of viruses . One novel finding that arose immediately from our RTC-proximity screen is the demonstration of a close spatial association of host cell translation with the coronavirus RTC . Indeed , the biotin ligase-based proteomic screen identified a number of translation initiation factors , most prominently several eIF3 subunits that were found to have functional importance for viral replication , and numerous ribosome- and translation-associated proteins within the coronavirus RTC microenvironment ( Figure 3 , Figure 4 ) . These results are in line with a recent genome-wide siRNA screen where translation factors were suggested to play a role in the replication of avian infectious bronchitis coronavirus ( IBV ) ( Wong et al . , 2015 ) . The implication of this finding has , to our knowledge , not been further investigated . In addition , we noted the presence of subunits of the signal recognition particle in proximity to the coronavirus RTC and their functional relevance for viral replication , which is indicative of an importance for the translation of membrane proteins . Notably , the coronavirus RTC is translated as two polyproteins that contain nsp3 , 4 and 6 with multiple trans-membrane domains that are believed to anchor the RTC at ER-derived membranes ( Knoops et al . , 2008; Oostra et al . , 2007 ) . It is thus tempting to speculate that the coronavirus RTC is either attracting , or deliberately forming in proximity to , the ER-localized host translation machinery in order to facilitate replicase translation and insertion into ER membranes . This idea is also applicable to many other positive-stranded RNA viruses that express viral polyproteins with embedded trans-membrane domains to anchor the viral replication complex in host endomembranes . Recent experimental evidence for Dengue virus supports this hypothesis . By using cell fractionation and ribosomal profiling , it has been shown that translation of the Dengue virus ( family Flaviviridae ) genome is associated with the ER-associated translation machinery accompanied by ER-compartment-specific remodeling of translation ( Reid et al . , 2018 ) . Moreover , several recent genome-wide CRISPR screens demonstrated the functional importance of proteins involved in biosynthesis of membrane and secretory proteins , further supporting a pivotal role of the ER-associated translation machinery for virus replication ( Zhang et al . , 2016 ) . Compartmentalization of cellular translation to sites of viral RNA synthesis has been described for dsRNA viruses of the orthoreovirus family , which replicate and assemble in distinct cytosolic inclusions known as viral factories to which the host translation machinery is recruited ( Desmet et al . , 2014 ) . The data presented here indicate that coronaviruses have evolved a similar strategy by compartmentalizing and directing viral RNA synthesis to sites of ER-associated translation . Likewise , this strategy has a number of advantages . Coronaviruses would not require sophisticated transport mechanisms that direct viral mRNA to distantly located ribosomes . A close spatial association of viral RNA synthesis and translation during early post-entry events would rather allow for remodeling the ER-associated translation machinery to ensure translation of viral mRNA in a protected microenvironment . Viruses have evolved diverse mechanisms to facilitate translation of their mRNAs including highly diverse internal ribosomal entry sites , recruitment of translation-associated host factors to viral RNAs , and even transcript-specific translation ( Hashem et al . , 2013; Lee et al . , 2013 ) . Accordingly , by remodeling defined sites for viral mRNA translation , the repertoire and concentration of translation factors can be restricted to factors needed for translation of these viral mRNAs . A microenvironment that is tailored towards the translational needs of viral mRNAs in proximity to the viral replicase complex would also make virus replication tolerant to host- or virus-induced shut down of translation at distal sites within the cytosol ( Raaben et al . , 2007 ) . Notably , host translational shut down is well known for coronaviruses ( Raaben et al . , 2007 ) and specifically nsp1 has been implicated to play a role in this context by mediating host mRNA degradation ( Narayanan et al . , 2015; Kamitani et al . , 2006 ) . Coronaviruses may thus have evolved a two-pronged strategy to ensure efficient translation of viral proteins by establishing viral RNA synthesis in close proximity to actively translating ribosomes and by employing nsp1-mediated host cell mRNA degradation at RTC-distal sites . The strategy to assemble the viral RTC in close proximity to translation would also favor the coupling between genome translation and replication that has been proposed for picornaviruses and other positive-strand RNA viruses ( de Groot et al . , 1992; Novak and Kirkegaard , 1994 ) . Finally , host cells are equipped with fine-tuned mechanisms of foreign RNA recognition ( Gebhardt et al . , 2017 ) . As such , MDA5 has been identified as a key cytosolic pattern recognition receptor restricting coronavirus replication ( Züst et al . , 2011 ) . Likewise , the nonsense-mediated RNA decay pathway targets mRNAs of different origin containing aberrant features for degradation and has been newly demonstrated to also target cytosolic coronavirus mRNAs ( Wada et al . , 2018; Schweingruber et al . , 2013 ) . Therefore , proximity of viral mRNA synthesis and translation in a confined microenvironment protected from cytosolic surveillance factors can also be considered a mechanism to evade these cytosolic mRNA decay mechanisms and innate immune sensors of viral RNA . The novel finding of a close association of the host translation machinery with sites of viral RNA synthesis during coronavirus infection exemplifies the power of the MHV-BirAR118G-nsp2–mediated labeling approach to identify RTC-proximal cellular processes that significantly contribute to viral replication . Indeed , the ability of BirAR118G to label viral and host factors independently of high affinity and prolonged molecular interactions enables the establishment of a comprehensive repertoire reflecting the history of protein association with the viral RTC , recorded during the entire course of infection . In future studies it will be important to provide an ‘RTC-association map’ with temporal resolution . Like we have seen for translation initiation factors in this study , association of host cell proteins with the viral RTC might not persist throughout the entire replication cycle but might be of importance only transiently or during specific phases of the replication cycle . Given its short labeling time , APEX2 indeed offers this possibility to dissect protein recruitment to the viral RTC in a time-resolved manner , that is to detect RTC-associated host proteins at specific time points post infection . This will ultimately result in a dynamic , high resolution molecular landscape of virus-host interactions at the RTC and provide an additional impetus to elucidate critical virus-host interactions that take place at the site of viral RNA synthesis . These interactions should be exploited in the development of novel strategies to combat virus infection , based on conserved mechanisms of interactions at replication complexes of a broad range of positive-stranded RNA viruses .
Murine L929 fibroblasts ( ECACC 85011425 ) and murine 17Cl1 fibroblasts ( gift from S . G . Sawicki ) were cultured in MEM supplemented with 10% ( v/v ) heat-inactivated fetal bovine serum ( FBS ) , 100 μg/ml streptomycin and 100 IU/ml penicillin ( MEM+/+ ) . Huh-7 hepatocarcinoma cells ( gift from V . Lohnmann ) and Vero B4 cells ( kindly provided by M . Müller ) were propagated in Dulbecco’s Modified Eagle Medium-GlutaMAX supplemented with , 1 mM sodium pyruvate , 10% ( v/v ) heat-inactivated fetal bovine serum , 100 μg/ml streptomycin , 100 IU/ml penicillin and 1% ( w/v ) non-essential amino acids . 17Cl1 and Vero B4 are used routinely in our laboratory for the generation of virus stocks . L929 and Huh-7 , which were used in this study’s experiments , were newly purchased ( L929 ) or were verified by a Multiplex human cell line authentication test in the Lohmann laboratory ( Huh-7 ) . All cell lines were regularly tested to check they were free of mycoplasma contamination using a commercially available system ( LookOut Mycoplasma qPCR detection kit , Sigma ) . Recombinant MHV strain A59 ( WT ) , MHV-Gluc ( Lundin et al . , 2014 ) , which expresses a Gaussia luciferase reporter replacing accessory gene 4 of MHV strain A59 , and HCoV-229E were generated as previously described ( Coley et al . , 2005; Eriksson et al . , 2008; Thiel et al . , 2001 ) . Viruses were propagated on 17Cl1 cells ( MHV ) and Huh-7 cells ( HCoV-229E ) and their sequence was confirmed by RT-PCR sequencing . MERS-CoV ( van Boheemen et al . , 2012; Bermingham et al . , 2012 ) was propagated and titrated on Vero cells . Recombinant MHV viruses were generated using a vaccinia virus-based system as described before ( Eriksson et al . , 2008 ) . In short , a pGPT-1 plasmid encoding an Escherichia coli guanine phosphoribosyltransferase ( GPT ) flanked by MHV-A59 nt 447–950 and 1315–1774 was used for targeted homologous recombination with a vaccinia virus ( VV ) containing a full-length cDNA copy of the MHV-A59 genome ( Coley et al . , 2005 ) . The resulting GPT-positive VV was further used for recombination with a plasmid containing the EGFP coding sequence flanked by MHV-A59 nt 477–956 and 951–1774 for the generation of MHV-GFP-nsp2 , based on the strategy employed by Freeman et al . ( Freeman et al . , 2014 ) . Alternatively , a plasmid containing the BirAR118G coding sequence ( Roux et al . , 2012 ) or the APEX2 coding sequence ( Lam et al . , 2015 ) , with a N-terminal myc-tag or V5-tag , respectively , and a C-terminal ( SGG ) 3 flexible linker flanked by MHV-A59 nt 477–956 and 951–1774 was used for the generation of MHV- BirAR118G-nsp2 and MHV-APEX2-nsp2 . The resulting VV were used to generate full-length cDNA genomic fragments by restriction digestion of the VV backbone . Rescue of MHV-GFP-nsp2 , MHV-BirAR118G-nsp2 and MHV-APEX2-nsp2 was performed by electroporation of capped in vitro transcribed recombinant genomes into a BHK-21-derived cell line stably expressing the nucleocapsid ( N ) protein layered on permissive 17Cl1 mouse fibroblasts . Recombinant MHV viruses were plaque-purified three times and purified viruses were passaged three times for stock preparations . All plasmid sequences , VV sequences and recombinant MHV sequences were confirmed by PCR or RT-PCR sequencing . Viruses were propagated on 17Cl1 cells and virus stocks were titrated by plaque assay on L929 cells . L929 cells were infected with MHV-A59 , MHV-GFP-nsp2 , MHV-BirAR118G-nsp2 or MHV-APEX2-nsp2 in quadruplicate at an MOI = 1 . Virus inoculum was removed 2 h . p . i . , cells were washed with PBS and fresh medium was added . Viral supernatants were collected at the indicated time point and titrated by plaque assay on L929 cells . Titers reported are the averages of three independent experiments ± standard error of the mean ( SEM ) . Biotinylation assays were carried out as described before with minor modifications ( Roux et al . , 2013 ) . 106 L929 cells grown on glass coverslips were infected with MHV-A59 , MHV-BirAR118G-nsp2 or MHV-APEX2-nsp2 at an MOI = 1 , or non-infected in medium supplemented with 67 µM biotin ( Sigma B4501 ) . Cells were washed thrice with PBS at the indicated time points and fixed with 4% ( v/v ) neutral buffered formalin before being washed three additional times . Cells were permeabilized in PBS supplemented with 50 mM NH4Cl , 0 . 1% ( w/v ) Saponin and 2% ( w/v ) BSA ( CB ) for 60 min and incubated 60 min with the indicated primary antibodies diluted in CB ( polyclonal anti-MHV-nsp2/3 or nsp8 ( gift from S Baker ) , 1:200 ( Schiller et al . , 1998; Gosert et al . , 2002 ) ; anti-myc , 1:8000 Cell Signalling 2276 ) . Cells were washed three times with CB and incubated for 60 min with donkey-derived , AlexaFluor488-conjugated anti-mouse IgG ( H + L ) and donkey-derived , AlexaFluor647-conjugated anti-rabbit IgG ( H + L ) ( Jackson Immunoresearch ) . Cells were additionally labeled with streptavidin conjugated to AlexaFluor 594 ( Molecular Probes ) to detect biotinylated proteins . Coverslips were mounted on slides using ProLong Diamond Antifade mountant containing 4' , 6-diamidino-2-phenylindole ( DAPI ) ( Thermo Fisher Scientific ) . For indirect immunofluorescence detection of viral and host proteins , L929 cells were grown on glass coverslips in 24-well plates and infected with MHV-A59 or MHV-BirAR118G-nsp2 ( MOI = 1 ) . At the indicated time point , cells were fixed with 4% ( v/v ) formalin and processed using primary monoclonal antibodies directed against dsRNA ( J2 Mab , English Scientific and Consulting ) or myc-tab ( Cell signalling 2276 ) and polyclonal antibodies recognizing eIF3E ( Sigma , HPA023973 ) or RTN4 ( Nogo A + B , Abcam 47085 ) as well as secondary donkey-derived , AlexaFluor488-conjugated anti-mouse and AlexaFluor647-conjugated anti-rabbit IgG ( H + L ) , as described above . For proximity ligation assays , L929 cells were seeded in 24-well plates on glass coverslips and infected with MHV-A59 or MHV-BirAR118G-nsp2 ( MOI = 1 ) . At the indicated time point , cells were washed with PBS , fixed with 4% ( v/v ) formalin and permeabilized with 0 . 1% ( v/v ) Triton X-100 . Proximity ligation was performed as recommended by the manufacturer ( Duolink In Situ detection reagents Red , Sigma ) using monoclonal antibodies directed against dsRNA ( J2 , English and Scientific Consulting ) or myc-tag ( Cell Signaling 2276 ) and polyclonal antibodies recognizing eIF3E ( Sigma , HPA023973 ) or RTN4 ( Nogo A + B , Abcam 47085 ) . Coverslips were mounted using Duolink In Situ Mounting Media with DAPI ( Sigma ) . All samples were imaged by acquiring 0 . 2 µm stacks over 10 µm using a DeltaVision Elite High-Resolution imaging system ( GE Healthcare Life Sciences ) equipped with a 60x or 100x oil immersion objective ( 1 . 4 NA ) . Images were deconvolved using the integrated softWoRx software and processed using Fiji ( ImageJ ) . Brightness and contrast were adjusted identically for each condition and their corresponding control . Figures were assembled using the FigureJ plugin ( Mutterer and Zinck , 2013 ) . L929 cells were infected with MHV-A59 or MHV-BirAR118G-nsp2 , and for comparison MHVH277A and MHVH227A-BirAR118G-nsp2 , at an MOI = 1 in medium supplemented with 67 µM biotin ( Sigma B4501 ) . At 15 h . p . i . , cells were washed three times with PBS and lysed in ice-cold buffer containing 50 mM TRIS-Cl pH 7 . 4 , 500 mM NaCl , 0 . 2% ( w/v ) SDS , 1 mM DTT and 1x protease inhibitor ( cOmplete Mini , Roche ) . Cells were scraped off the flask and transferred to tubes . Cells were kept on ice until the end of the procedure . Triton X-100 was added to each sample to a final concentration of 2% . Samples were sonicated for two rounds of 20 pulses with a Branson Sonifier 250 ( 30% constant , 30% power ) . Equal volumes of 50 mM TRIS-Cl were added to each sample and samples were centrifuged at 4°C for 10 min at 18 , 000 x g . Supernatants were incubated with magnetic beads on a rotator at 4°C overnight ( 800 µl Dynabeads per sample , MyOne Streptavidin C1 , Life Technologies ) that were previously washed with lysis buffer diluted 1:1 with 50 mM TRIS-Cl . Beads were washed twice with buffer 1 ( 2% ( w/v ) SDS ) , once with buffer 2 ( 0 . 1% ( w/v ) deoxycholic acid , 1% ( v/v ) Triton X-100 , 1 mM EDTA , 500 mM NaCl , 50 mM HEPES pH 7 . 5 ) , once with buffer 3 ( 0 . 5% w/v deoxycholic acid , 0 . 5% NP40 , 1 mM EDTA , 250 mM LiCl , 10 mM TRIS-Cl pH 7 . 4 ) and once with 50 mM TRIS-Cl pH 7 . 4 . Proteins were eluted from beads by the addition of 0 . 5 mM biotin and Laemmli SDS-sample buffer and heating at 95°C for 10 min . For SDS-PAGE and western blot analysis , cells were cultured in six-well plates and lysates were prepared and affinity purified as described above . Proteins were separated on 10% ( w/v ) SDS-polyacrylamide gels ( Bio-Rad ) , and proteins were electroblotted on nitrocellulose membranes ( Amersham Biosciences , GE Healthcare ) in a Mini Trans-Blot cell ( Bio-Rad ) . Membranes were incubated in a protein-free blocking buffer ( Advansta ) and biotinylated proteins were probed by incubation with horseradish peroxidase-conjugated Streptavidin ( Dako ) . Proteins were visualized using WesternBright enhanced chemiluminescence horseradish peroxidase substrate ( Advansta ) according to the manufacturer's protocol . For mass spectrometry analysis , lysates and affinity purification were performed as described above from 4*107 cells cultured in 150 cm2 tissue culture flasks . Proteins were separated 1 cm into a 10% ( w/v ) SDS-polyacrylamide gel . A Coomassie stain was performed and 4 × 2 mm bands were cut with a scalpel . Proteins on gel samples were reduced , alkylated and digested with Trypsin ( Gunasekera et al . , 2012 ) . Digests were loaded onto a pre-column ( C18 PepMap 100 , 5 µm , 100 A , 300 µm i . d . x 5 mm length ) at a flow rate of 20 µL/min with solvent C ( 0 . 05% TFA in water/acetonitrile 98:2 ) . After loading , peptides were eluted in back flush mode onto the analytical Nano-column ( C18 , 3 μm , 100 Å , 75 μm x 150 mm , Nikkyo Technos C . Ltd . , Japan ) using an acetonitrile gradient of 5% to 40% solvent B ( 0 . 1% ( v/v ) formic acid in water/acetonitrile 4 , 9:95 ) in 40 min at a flow rate of 400 nL/min . The column effluent was directly coupled to a Fusion LUMOS mass spectrometer ( Thermo Fischer , Bremen; Germany ) via a nano-spray ESI source . Data acquisition was made in data-dependent mode with precursor ion scans recorded in the orbitrap with resolution of 120’000 ( at m/z = 250 ) parallel to top speed fragment spectra of the most intense precursor ions in the Linear trap for a cycle time of 3 s maximum . Spectra interpretation was performed with Easyprot on a local , server run under Ubuntu against a forward + reverse Mus musculus 2016_04 ) and MHV 2016_07 ) database , using fixed modifications of carboamidomethylated on Cysteine , and variable modification of oxidation on Methionine , biotinylation on Lysine and on protein N-term , and deamidation of Glutamine and Asparagine . Parent and fragment mass tolerances were set to 10 ppm and 0 . 4 Da , respectively . Matches on the reversed sequence database were used to set a Z-score threshold , where 1% false discoveries ( FDR ) on the peptide spectrum match level had to be expected . Protein identifications were only accepted , when two unique peptides fulfilling the 1% FDR criterion were identified . MS identification of biotinylated proteins was performed in three independent biological replicates . For label-free protein quantification , LC-MS/MS data was interpreted with MaxQuant ( version 1 . 5 . 4 . 1 ) using the same protein sequence databases and search parameters as for EasyProt . Match between runs was activated , however samples from different treatments were given non-consecutive fraction numbers in order to avoid over-interpretation of data . The summed and median normalized top3 peptide intensities extracted from the evidence table as a surrogate of protein abundance ( Braga-Lagache et al . , 2016 ) and LFQ values were used for statistical testing . The protein groups were first cleared from all identifications , which did not have at least two valid LFQ values . Protein LFQ levels derived from MaxQuant were log-transformed . Missing values were imputed by assuming a normal distribution between sample replicates . A two-tailed t-test was used to determine significant differences in protein expression levels between sample groups and p-values were adjusted for multiple testing using the Benjamini-Hochberg ( FDR ) test . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD009975 . Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) was used to perform GO enrichment analysis on the RTC-proximal cellular factors identified via mass spectrometry ( Huang et al . , 2009a; Huang et al . , 2009b; Ashburner et al . , 2000; The Gene Ontology Consortium , 2017 ) . GO BP terms with a p-value < 0 . 05 were considered to be terms that were significantly enriched in the dataset . Additional analysis of significant GO terms was conducted using AmiGO and revealed that the top 32 GO BP terms ( p-value < 0 . 005 ) were predominantly associated with five broad functional categories ( cell-cell adhesion , transport , cell organization , translation , and catabolic processes ) ( Carbon et al . , 2009 ) . Alternatively , enrichment analysis was performed using SetRank ( data not shown ) , a recently described algorithm that circumvents pitfalls of commonly used approaches and thereby reduces the amount of false-positive hits ( Simillion et al . , 2017 ) and the following databases were searched for significant gene sets: BIOCYC ( Krummenacker et al . , 2005 ) , GO ( Ashburner et al . , 2000 ) , ITFP ( Zheng et al . , 2008 ) , KEGG ( Kanehisa et al . , 2014 ) , PhosphoSitePlus ( Hornbeck et al . , 2012 ) , REACTOME ( Croft et al . , 2014 ) , and WikiPathways ( Kelder et al . , 2012 ) . Both independent approaches lead to highly similar results and consistently complement results obtained upon GO Cellular Components analysis . STRING functional protein association networks were generated using RTC-proximal host proteins found within each of the five broad functional categories . Default settings were used for active interaction sources and a high confidence interaction score ( 0 . 700 ) was used to maximize the strength of data support . The MCL clustering algorithm was applied to each STRING network using an inflation parameter of 3 ( Szklarczyk et al . , 2017; Szklarczyk et al . , 2015 ) . A custom siRNA library targeting each individual RTC-proximal factor ( On Target Plus , SMART pool , 96-well plate format , Dharmacon , GE Healthcare ) was ordered . Additionally , a deconvolved library of 4 individual siRNAs was purchased for selected targets . 10 nM siRNA were reverse transfected into L929 cells ( 8*103 cells per well ) using Viromer Green ( Lipocalyx ) according to the manufacturer’s protocol . Cells were incubated 48 hr at 37°C 5% CO2 and cell viability was assessed using the CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega ) . Cells were infected with MHV-Gluc ( MOI = 0 . 05 , 1000 plaque forming units/well ) , washed with PBS 3 h . p . i . and incubated in MEM+/+for additional 9 or 12 hr . Gaussia luciferase was measured from the supernatant using Pierce Gaussia Luciferase Glow Assay Kit ( ThermoFisher Scientific ) . Experiments were carried out in four independent replicates and both cytotoxicity values and luciferase counts were normalized to the corresponding non-targeting scrambled control of each plate . A one-way ANOVA ( Kruskal-Wallis test , uncorrected Dunn’s test ) was used to test the statistical significance of reduced viral replication ( mean <95% as compared to scramble control , n = 216 ) . The R package ggplot2 was used to create the bubble plot ( Figure 4b ) . L929 cells were transfected with 10 nM siRNA as described above . 48 hr post-transfection , cell viability was assessed using the CytoTox 96 Non-Radioactive Cytotoxicity Assay ( Promega ) and visually inspected by automated phase-contrast microscopy using an EVOS FL Auto 2 Imaging System equipped with a 4x air objective . Cells were infected with MHV-Gluc ( MOI = 0 . 05 ) , washed with PBS 3 h . p . i . and incubated for 9 additional hours . Gaussia luciferase activity , viral titers and cell viability were measured from the supernatant as described above . One-way ANOVAs ( ordinary one-way ANOVA , uncorrected Fisher’s LSD test ) were used to test the statistical significance . Total cellular RNA was isolated from cells using the NucleoMag RNA Kit ( Machery Nagel , Switzerland ) on a KingFisher Flex Purification System ( Thermo Fisher Scientific , Switzerland ) according to the manufacture’s instructions . The QuantiTect Probe RT-PCR Kit ( Qiagen , Switzerland ) was used according to the manufactures instructions for measuring the cell associated viral RNA levels with primers and probe specific to the MHV genome fragment coding the nucleocapsid gene ( Supplementary file 4 ) . Primers and Probe for mouse Glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) where obtained from ThermoFisher Scientific ( Mm03302249_g1 , Catalog Number: 4331182 ) . The MHV levels were normalized to GAPDH and shown as ΔΔCt over mock ( ΔCt values calculated as Ct reference - Ct target ) . The QuantiTect SYBR Green RT-PCR Kit ( Qiagen , Switzerland ) was used according to the manufactures instructions for measuring the expression levels of Rpl13a , eIF3E , eIF3I , eIF3F , eIF4G1 , eIF4G2 , eIF2ak3 , Rsl24d1 and Tbp . All primer pairs where placed over an exon intron junction ( Supplementary file 4 ) . All expression levels are displayed as ΔΔCt over non-targeting siRNA ( ΔCt values calculated as Ct target - Ct Tbp ) ( Livak and Schmittgen , 2001 ) . One-way ANOVA ( ordinary one-way ANOVA , uncorrected Fisher’s LSD test ) was used to test the statistical significance . Western blots were performed after a 48 hr transfection of 10 nM individual siRNAs as described before . Cells were lysed in M-PER Mammalian Protein Extraction Reagent ( ThermoFisher Scientific ) supplemented with cOmplete Mini Protease Inhibitor Cocktail ( Roche ) and Laemmli SDS-sample buffer . Samples were loaded on 4–12% Bolt Bis-Tris gels and run in MES SDS buffer ( Life Technolgies ) . Proteins were blotted on a nitrocellulose membranes using a power blotter system and power blotter select transfer stacks ( ThermoFisher Scientific ) . Membranes were blocked in 5% milk in PBS supplemented with 0 . 5% Tween20 ( PBST ) and incubated with primary antibodies ( anti-eIF3E , HPA023973; anti-eIF3F , ab176853; anti-eIF3I , HPA029939 ) and secondary HRP-conjugated donkey anti-rabbit antibodies ( Jackson ImmunoResearch ) in 0 . 5% milk in PBST . Proteins were visualized using WesternBright enhanced chemiluminescence horseradish peroxidase substrate ( Advansta ) according to the manufacturer's protocol . Subsequently , membranes were washed extensively in PBST and probed using an HRP-conjugated anti-actin antibody ( Sigma A3854 ) . siRNA-based silencing was performed as described above . 48 hr post-transfection , control cells were incubated with 355 μM cycloheximide ( Sigma ) and 208 μM Emetin ( Sigma ) for 30 min to block protein synthesis . Cells were treated with 3 μM puromycin for 60 min followed by three PBS washes ( Shen et al . , 2018 ) . Total cell lysates were prepared using M-PER mammalian protein extraction reagent ( Thermo Scientific ) supplemented with protease inhibitors ( cOmplete Mini , Roche ) . Lysates were separated on a 10% ( w/v ) SDS-PAGE and electroblotted as described above . Western blots were probed using a monclonal AlexaFluor647-conjugated anti-puromycin antibody ( clone 12D10 , Merk Millipore ) and a donkey-derived HRP-conjugated anti-mouse ( Jackson immunoresearch 715-035-151 ) . Actin was detected using a monoclonal HRP-conjugated anti-actin antibody ( Sigma A3854 ) and used to normalize input . Ribopuromycylation of actively translating ribosomes was performed as described before ( David et al . , 2012 ) . L929 , Huh-7 cells were seeded on glass coverslips and infected with MHV-A59 ( L929 ) , HCoV-229E ( Huh-7 ) and MERS-CoV ( Huh-7 ) at MOI = 1 . One hour after inoculation , cells were washed with PBS and incubated further for the indicated time . Cells were treated with 355 μM cycloheximide and 208 μM Emetin ( Sigma ) for 15 min at 37°C . Cells were further incubated in medium containing 355 μM cycloheximide , 208 μM Emetin and 182 μM puromycin ( Sigma ) for additional 5 min . Cells were washed twice in ice-cold PBS and fix on ice for 20 min in buffer containing 50 mM TRIS HCl , 5 mM MgCl2 , 25 mM KCl , 355 μM cycloheximide , 200 mM NaCl , 0 . 1% ( v/v ) TritonX-100 , 3% formalin and protease inhibitors ( cOmplete Mini , Roche ) . Cells were blocked for 30 min in CB , and immunostained as described above using polyclonal anti-MHV-nsp2/3 ( gift from S . Baker ) , polyclonal anti-HCoV-229E-nsp8 ( gift from J . Ziebuhr ) , or monoclonal anti-dsRNA ( J2 MAB , English and Scientific Consulting ) as primary antibodies to detect MHV and HCoV-229E replication complexes , respectively . Donkey-derived , AlexaFluor488-conjugated anti-mouse or anti-rabbit IgG ( H + L ) were used as secondary antibodies . Additionally , ribosome-bound puromycin was detected using a monoclonal AlexaFluor647-conjugated anti-puromycin antibody ( clone 12D10 , Merk Millipore ) . Slides were mounted , imaged and processed as described above . L929 fibroblasts were seeded in 24-well plates and infected with MHV-APEX2-nsp2 , MHV-A59 , or non-infected for 10 hr . 3 , 3-diaminobenzidine ( DAB ) stains were performed as described previously ( Martell et al . , 2017 ) . Briefly , cells were fixed at 10 h . p . i . using warm 2% ( v/v ) glutaraldehyde in 100 mM sodium cacodylate , pH 7 . 4 , supplemented with 2 mM calcium chloride ( cacodylate buffer ) and placed on ice for 60 min . The following incubations were performed on ice in ice-cold buffers unless stated otherwise . Cells were washed 3x with sodium cacodylate buffer , quenched with 20 mM glycine in cacodylate buffer for 5 min . before three additional washes with cacodylate buffer . Cells were stained in cacodylate buffer containing 0 . 5 mg/ml DAB and 10 mM H2O2 for 20 min until DAB precipitates were visible by light microscopy . Cells were washed 3x with cacodylate buffer to stop the staining reaction . Processing of samples for transmission electron microscopy ( TEM ) was performed as described previously ( Schätz et al . , 2013 ) . Briefly , cells were washed once with PBS prewarmed to 37°C and subsequently fixed with 2 . 5% ( v/v ) glutaraldehyde ( Merck , Darmstadt , Germany ) in 0 . 1 M cacodylate buffer ( Merck , Hohenbrunn , Germany ) pH 7 . 4 for 30 min at room temperature or overnight at 4°C . After three washes in cacodylate buffer for 10 min each , cells were post-fixed with 1% OsO4 ( Chemie Brunschwig , Basel , Switzerland ) in 0 . 1 M cacodylate buffer for 1 hr at 4°C and again washed three times with cacodylate buffer . Thereafter , cells were dehydrated in an ascending ethanol series ( 70% , 80% , 90% , 94% , 100% ( v/v ) for 20 min each ) and embedded in Epon resin , a mixture of Epoxy embedding medium , dodecenyl succinic anhydride ( DDSA ) and methyl nadic anhydride ( MNA ) ( Sigma Aldrich , Buchs , Switzerland ) . Ultrathin sections of 90 nm were then obtained with diamond knives ( Diatome , Biel , Switzerland ) on a Reichert-Jung Ultracut E ( Leica , Heerbrugg , Switzerland ) and collected on collodion-coated 200-mesh copper grids ( Electron Microscopy Sciences , Hatfield , PA ) . Sections were double-stained with 0 . 5% ( w/v ) uranyl acetate for 30 min at 40°C ( Sigma Aldrich , Steinheim , Germany ) and 3% ( w/v ) lead citrate for 10 min at 20°C ( Laurylab , Saint Fons , France ) in an Ultrastain ( Leica , Vienna , Austria ) and examined with a Philips CM12 transmission electron microscope ( FEI , Eindhoven , The Netherlands ) at an acceleration voltage of 80 kV . Micrographs were captured with a Mega View III camera using the iTEM software ( version 5 . 2; Olympus Soft Imaging Solutions GmbH , Münster , Germany ) . | Coronaviruses can infect the nose and throat and are a main cause of the common cold . Infections are usually mild and short-lived , but sometimes they can turn nasty . In 2002 and 2012 , two dangerous new coronaviruses emerged and caused diseases known as SARS and MERS . These viruses caused much more serious symptoms and in some cases proved deadly . The question is , why are some coronaviruses more dangerous than others ? Scientists know that the body's response to virus infection can make a difference to whether someone had mild or severe disease . So , to understand why some coronaviruses cause a cold and others kill , they also need to learn how people react to virus infection . Coronaviruses hijack membranes inside cells and turn them into virus factories . Within these factories , the viruses build molecular machinery called replicase complexes to copy their genetic code , which is needed for the next generation of virus particles . The viruses steal and repurpose proteins from their host cell that will assist in the copying process . However , scientists do not yet know which host proteins are essential for the virus to multiply . So , to find out , V’kovski et al . developed a way to tag any host protein that came near the virus factories . The new technique involved attaching an enzyme called a biotin ligase to the replicase complex . This enzyme acts as a molecular label gun , attaching a chemical tag to any protein that comes within ten nanometres . The label gun revealed that more than 500 different proteins come into contact with the replicase complex . To find out what these proteins were doing , the next step was to switch off their genes one by one . This revealed the key cell machinery that coronaviruses hijack when they are replicating . It included the cell's cargo transport system , the waste disposal system , and the protein production system . Using these systems allows the viruses to copy their genetic code next to machines that can turn it straight into viral proteins . These new results provide clues about which proteins viruses actually need from their host cells . They also do not just apply to coronaviruses . Other viruses use similar strategies to complete their infection cycle . These findings could help researchers to understand more generally about how viruses multiply . In the future , this knowledge could lead to new ways to combat virus infections . | [
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] | 2019 | Determination of host proteins composing the microenvironment of coronavirus replicase complexes by proximity-labeling |
Command-like descending neurons can induce many behaviors , such as backward locomotion , escape , feeding , courtship , egg-laying , or grooming ( we define ‘command-like neuron’ as a neuron whose activation elicits or ‘commands’ a specific behavior ) . In most animals , it remains unknown how neural circuits switch between antagonistic behaviors: via top-down activation/inhibition of antagonistic circuits or via reciprocal inhibition between antagonistic circuits . Here , we use genetic screens , intersectional genetics , circuit reconstruction by electron microscopy , and functional optogenetics to identify a bilateral pair of Drosophila larval ‘mooncrawler descending neurons’ ( MDNs ) with command-like ability to coordinately induce backward locomotion and block forward locomotion; the former by stimulating a backward-active premotor neuron , and the latter by disynaptic inhibition of a forward-specific premotor neuron . In contrast , direct monosynaptic reciprocal inhibition between forward and backward circuits was not observed . Thus , MDNs coordinate a transition between antagonistic larval locomotor behaviors . Interestingly , larval MDNs persist into adulthood , where they can trigger backward walking . Thus , MDNs induce backward locomotion in both limbless and limbed animals .
Animals typically execute one behavior to the exclusion of all other possible behaviors ( Briggman and Kristan , 2008 ) . For example , leeches can either crawl or swim , but cannot do both simultaneously ( Briggman and Kristan , 2006; Kristan , 2008 ) ; or using the same set of muscles , a locust is capable of either walking or flying but cannot execute both behaviors at the same time ( Ramirez and Pearson , 1988 ) . Such mutually exclusive choice of behavior has also been observed in several other systems , including Caenorhabditis elegans ( forward vs backward crawling ) , Tritonia ( crawling vs swimming ) , leech ( feeding vs swimming ) , tadpole ( struggling vs swimming ) , turtle ( swimming vs scratching ) , and zebrafish ( left vs right escape ) ( Berkowitz , 2002; Gaudry and Kristan , 2010; Koyama et al . , 2016; Popescu and Frost , 2002; Roberts et al . , 2016; Soffe , 1993 ) . The selection of a locomotor program to the exclusion of all others is necessary to prevent injury and escape predation . Despite the paramount importance of rapid transitions between antagonistic motor programs , the underlying circuitry is only beginning to be understood in C . elegans ( Lindsay et al . , 2011; Piggott et al . , 2011; Roberts et al . , 2016 ) . Command-like neurons can elicit specific behaviors , such as forward locomotion , backward locomotion , pausing , escape , flight , grooming , feeding , courtship , egg-laying or sleep ( Bidaye et al . , 2014; Bouvier et al . , 2015; Hägglund et al . , 2010; Hampel et al . , 2015; Hedwig , 2000; Hückesfeld et al . , 2015; Kallman et al . , 2015; Liu and Fetcho , 1999; Ohyama et al . , 2015; Pearson et al . , 1985; Sen et al . , 2017; Tanouye and Wyman , 1980; von Philipsborn et al . , 2011; Weber et al . , 2015; Wu et al . , 2015 ) . However , much less is known about how antagonistic motor programs are suppressed during command neuron-induced behavior . On one hand , there could be a high degree of reciprocal inhibition between neurons in antagonistic circuits; on the other hand , the command neurons that activate one behavior may also suppress antagonistic behaviors ( in which case there could be minimal reciprocal inhibition ) . Here , we use the Drosophila larva to characterize the neural circuits coordinately regulating two antagonistic behaviors: forward versus backward locomotion . Drosophila larva have many distinct behaviors ( Vogelstein et al . , 2014 ) , but forward locomotion is the default locomotor behavior ( Berni et al . , 2012 ) and consists of coordinated posterior-to-anterior waves of somatic body wall muscle contractions driven by corresponding waves of motor neuron activity within the segmented ventral nerve cord ( VNC ) ( Clark et al . , 2018; Heckscher et al . , 2012; Hughes and Thomas , 2007; Pulver et al . , 2015 ) . There are ~35 motor neurons per bilateral hemisegment , innervating 30 body wall muscles ( Landgraf and Thor , 2006 ) , about 250 interneurons per hemisegment ( Rickert et al . , 2011 ) , and an unknown number of ascending and descending neurons traversing each segment of the VNC . The circuits for motor wave propagation ( Fushiki et al . , 2016 ) , the coordination of muscle groups within each segment ( Zwart et al . , 2016 ) , and the bilateral adjustment of muscle contraction amplitude ( Heckscher et al . , 2015 ) have been recently investigated; however , much less is known about the circuits promoting backward locomotion , or the switching from forward to backward locomotion . Larvae initiate backward locomotion upon encountering a barrier or experiencing mild noxious stimulation to the anterior body ( Kernan et al . , 1994; Robertson et al . , 2013; Takagi et al . , 2017; Titlow et al . , 2014; Tracey et al . , 2003 ) . Backward locomotion consists of anterior-to-posterior waves of motor neuron and muscle activity ( Heckscher et al . , 2012; Pulver et al . , 2015 ) . A segmentally reiterated VNC neuron that triggers backward locomotion has been identified ( Takagi et al . , 2017 ) , but high-order command-like neurons for backward locomotion and the circuit for executing backward wave propagation while simultaneously suppressing forward waves remain unknown . Here , we identify a bilateral pair of Drosophila brain descending neurons that coordinately activate backward locomotion and suppress forward locomotion , and identify the downstream pre-motor circuitry effecting the switch . Surprisingly , immortalization of CsChrimson ( Chrimson ) expression in these larval command-like neurons reveals that they survive metamorphosis , have the exact morphology of previously described adult ‘moonwalker’ neurons ( Bidaye et al . , 2014 ) , and can induce backward walking in the adult . By analogy to the adult naming scheme , we refer to these larval brain neurons as ‘mooncrawler descending neurons’ ( MDNs ) . We reconstruct the larval MDNs in an electron microscopy volume comprising the whole central nervous system ( Ohyama et al . , 2015 ) , in which we also map its postsynaptic neuron partners . We identify the circuit motifs by which MDNs induce backward locomotion while simultaneously suppressing forward locomotion . The MDNs project their axons along the length of the nerve cord , where they directly activate an excitatory cholinergic pre-motor neuron ( A18b ) that is specifically active during backward waves . In parallel , the MDNs synapse onto a GABAergic inhibitory neuron ( Pair1 ) that directly inhibits cholinergic pre-motor neurons ( A27h ) active specifically during forward locomotion ( Fushiki et al . , 2016 ) ; optogenetic experiments showed that MDNs activate Pair1 neurons , which then inhibit A27h and block forward locomotion . The circuit structure therefore suggests that two behaviors such as forward and backward peristaltic locomotion can maintain mutually exclusive activity due to top-down excitation/inhibition , rather than reciprocal inhibition . We conclude that the MDNs promote backward locomotion at all stages of the Drosophila life cycle: from the limbless crawling maggot to the limbed walking adult .
We previously showed that activating neurons labeled by the Janelia R53F07-Gal4 line could induce backward larval locomotion , but this line has broad expression in the brain , subesophageal zone ( SEZ ) , and both motor neurons and interneurons of the VNC ( Clark et al . , 2016 ) . To identify the neurons within this population that can induce backward locomotion , we used intersectional genetics ( Dolan et al . , 2017; Luan et al . , 2006 ) to find lines labeling small subsets of the original population . We identified three lines called Split1 , Split2 , and Split3 labeling different subsets of the original pattern; the only neurons present in all three Split lines are a bilateral pair of neurons with cell bodies located in the ventral , anterior , medial brain with descending processes to A3-A5 in the VNC ( Figure 1A–C , arrowheads ) . All three Split lines could induce backward locomotion following Chrimson expression and activation ( Figure 1D , Videos 1 and 2 ) . Neuronal activation immediately switched locomotion from forward to backward ( Figure 1E , F ) , without a significant change in the number of peristaltic waves per second ( Split1 , 0 . 48; Split2 , 0 . 50; Split3 , 0 . 65 before activation; Split1 , 0 . 48; Split2 , 0 . 56; Split3 , 0 . 56 after activation ) . Conversely , using Split2 or Split3 to express the light-inducible neuronal silencer GtACR1 ( Mohammad et al . , 2017 ) significantly reduced backward locomotion induced by a noxious head poke ( Figure 1G , H ) . It is likely that these activation and silencing phenotypes arise from the pair of ventral , anterior , medial brain descending neurons common to all three lines , although it is possible that there are different neurons in each Split line that can induce backward locomotion . We distinguish between these alternatives in the next section . To determine whether Chrimson expression in just one or two of the ventral , anterior , medial brain neurons is sufficient to induce backward locomotion , we stochastically expressed Chrimson:Venus within the Split2 pattern via the ‘FLP-out’ method ( Figure 2A ) . We screened populations of larvae for Chrimson-induced backward locomotion ( obtaining 1–2 larvae per 100 screened ) , and stained the CNS to identify the Chrimson:Venus+ neurons that were sufficient to induce backward locomotion . All larvae with a backward locomotion phenotype ( n = 10 ) expressed Chrimson:Venus in one or both neurons from the anterior , medial pair that had descending projections to A3-A5 ( three examples shown in Figure 2B–D ) . Conversely , all larvae that lacked Chrimson-induced backward locomotion ( n = 20 ) never showed Chrimson:Venus expression in the ventral , anterior , medial descending neurons ( data not shown ) . Based on similarity to the ‘moonwalker’ neuron adult backward walking phenotype ( Bidaye et al . , 2014 ) , we name this bilateral pair of neurons the ‘mooncrawler’ descending neurons ( MDNa and MDNb ) , subsequently called MDNs . The MDNs are likely to be excitatory , as they are cholinergic ( Figure 2E ) . We conclude that activation of as few as two of the four MDNs ( either both in the same brain lobe or one in each brain lobe ) is sufficient to induce a behavioral switch from forward to backward locomotion . To determine if a short pulse of MDN activation can trigger one or more backward waves , we provided a brief 300 ms Chrimson activation of MDN and assayed the number of backward waves induced in both intact larvae and fictive CNS . We found that both the intact larvae and fictive CNS invariably performed a backward motor wave in response to a pulse of MDN activation , with the fictive CNS occasionally generating a second wave at variable times during the 25 s after Chrimson activation ( Figure 2F ) . We next asked , if forced activation of MDNs can induce backward locomotion , perhaps the MDNs are normally active specifically during backward locomotion . To test this hypothesis , we used CaMPARI to monitor MDN activity during forward versus backward locomotion within the intact crawling larva . CaMPARI undergoes an irreversible green-to-red conversion upon coincident exposure to elevated Calcium ( i . e . neuronal activity ) and 405 nm illumination ( Fosque et al . , 2015 ) . We used Split2 to express CaMPARI in MDNs and exposed crawling larvae to 405 nm illumination for 30 s while larvae moved either backward or forward . We detected little or no activity-induced red fluorescence during forward locomotion , but significant red fluorescence during backward locomotion ( Figure 2G ) . We conclude that MDNs are active during backward but not forward locomotion . To understand how MDNs induce backward locomotion , we next identified the MDN synaptic partners . To do this , we identified the MDNs in an existing serial section TEM reconstruction of the newly hatched larva ( Ohyama et al . , 2015 ) . Our first step was to determine the precise morphology of both MDN neurons . We generated individually labeled neurons within the Split2 pattern using MultiColor FlpOut ( MCFO ) ( Nern et al . , 2015 ) . These single neurons serve as the ‘ground truth’ for matching morphological features of individual neurons by light and electron microscopy ( Heckscher et al . , 2015; Schneider-Mizell et al . , 2016 ) . We identified single MDNs in Split2 MCFO preparations based on morphological similarity to the behavior flip-out neurons described in Figure 2 . Diagnostic features shared by both MDNs in the pair include ventral , anterior , medial somata , distinctive ipsilateral and contralateral arbors , a contralateral projection in the posterior commissure , and descending projections terminating in segments A3-A5 of the VNC ( Figure 3A–E ) . MDN descending projections run slightly lateral to the dorsal medial FasII+ bundle ( Landgraf et al . , 2003 ) ( Figure 3F ) . Each neuron in the pair share all these features , but the two MDNs can be distinguished from each other by their ipsilateral arbor , which is either linear ( Figure 3C , arrow ) or bushy ( Figure 3D , arrowhead ) . We next searched for the MDNs in the TEM volume using CATMAID ( Schneider-Mizell et al . , 2016 ) . We found two pair of neurons that showed an excellent morphological match to the MDNs in every distinctive feature ( Figure 3A'–D' ) ; we annotate them as MDNa and MDNb in the TEM volume . Hereafter , we call these neurons simply MDNs due to their similarity in morphology and connectivity ( see next section ) . Importantly , none of the 50 neurons with cell bodies nearest to the MDNs have a similar morphology ( data not shown ) . Thus , we can be certain that the MDNs in the TEM reconstruction are identical to the MDNs visualized by our Split-gal4 lines . This is also confirmed by functional optogenetics ( see below ) . We conclude that the MDNs can be uniquely identified by light microscopy and by TEM . Identification of the MDNs in the TEM volume is a prerequisite for identifying their pre- and post-synaptic partners ( next section ) . Annotation of the MDNs in the TEM reconstruction revealed bilateral arbors in the brain and descending processes to abdominal segments ( Figure 4A ) . Pre-synapses are restricted to the descending processes ( Figure 4A , green ) , whereas post-synapses are present in brain arbors and descending processes , suggesting information flow from brain to VNC . A representative MDN output synapse shown in Figure 4B; it is polyadic ( multiple postsynaptic neurons clustered around the MDN pre-synapse ) and electron dense with associated presynaptic vesicles . Due to the ability of the MDNs to induce backward locomotion when activated , we focused on identifying MDN post-synaptic partners , with the goal of understanding the relationship between the MDN activation and motor output . The post-synaptic partners with the most synapses with MDN are: ( 1 ) the Pair1 SEZ descending neuron; ( 2 ) the thoracic descending neuron ( ThDN ) ; ( 3 ) the premotor neuron A18b; and ( 4 ) the MDNs themselves ( Figure 4C–D ) . These are the top four MDN partners in both synapse number ( Figure 4C ) and percentage of total MDN output synapses ( Figure 4D ) . All four MDNs have similar connectivity ( Figure 4—figure supplement 1 ) . Most of the top MDN output neurons are either premotor neurons or have preferential input into known premotor neurons ( Figure 4D–G ) . For example , ThDN has a large number of synapses with A27l/A27k premotor neurons ( Figure 4C , E , H ) , as well as with A18g ( which is not a premotor neuron ) . Pair1 is connected to the previously described premotor neuron A27h ( Fushiki et al . , 2016 ) , both directly and indirectly ( Figure 4C , F , J ) . Lastly , A18b is a premotor neuron present in all abdominal segments , but it only receives MDN input in segment A1 ( Figure 4C , I ) . Thus , the MDNs provide mono- and di-synaptic connectivity to premotor neurons . The activity and function of the MDN-A18b and MDN-Pair1 pathways in locomotion will be addressed below; we lack genetic tools to investigate the MDN-ThDN pathway ( no known lines for ThDn or A27k , and the A27l line has many off-targets ) . There are numerous MDN inputs ( an average of 396 post-synapses per MDN neuron ) and we have not attempted to reconstruct them; this is beyond the scope of a single paper . However , we note that each MDN has similar inputs . We do not detect mono-synaptic sensory input into the MDNs ( data not shown ) , but based on the role of MDNs in generating a backward crawl in response to a noxious head touch , we predict that there will be , minimally , polysynaptic connections from head mechanoreceptors to the MDNs . The MDNs show anatomical connectivity to the A18b premotor neuron , which has not previously been characterized . We identified a LexA line that labels A18b within the VNC ( R94E10 , subsequently called A18b-LexA ) along with a small , variable number of brain and thoracic neurons ( Figure 5—figure supplement 1 ) . A18b has local , contralateral projections that match the morphology of A18b in the TEM reconstruction ( Figure 5A ) , is cholinergic ( Figure 5B ) , and is connected directly to the dorsal-projecting motor neurons aCC/RP2 and U1/U2 ( Figure 5C ) among other motor neurons . We showed above that MDNs are significantly more active during backward than forward locomotion , raising the question of whether the A18b neurons are also preferentially active during backward locomotion . To answer this question , we performed three experiments . First , we used dual color calcium indicators in a fictive CNS preparation to simultaneously monitor motor neuron activity ( GCaMP6m ) and A18b activity ( jRCaMP1b ) . We observed robust forward and backward motor waves ( Figure 5D , top ) , with A18b only active during backward motor waves , not forward motor waves ( Figure 5D , bottom; quantified in Figure 5E ) . Second , we performed dual color calcium imaging within intact larvae , and again observed that A18b was only active during backward motor waves ( Figure 5D;quantified in Figure 5E ) . Third , we used CaMPARI within intact larvae to determine if A18b was preferentially active during backward locomotion . We expressed CaMPARI in A18b and tested for activity-induced green-to-red photoconversion during either forward locomotion or backward locomotion . We found that illumination during forward locomotion generated minimal CaMPARI red fluorescence , whereas illumination during backward locomotion resulted in a significant increase in CaMPARI red fluorescence ( Figure 5F ) . We call the A18b neuron backward-active rather than backward-specific because we do not know its pattern of activity in rolling or other larval behaviors . We conclude that A18b neurons are preferentially active during backward not forward locomotion . To determine if MDNs activate A18b , we used Split1 to express Chrimson in MDNs and A18b-lexA to express GCaMP6f in A18b in fictive preparations . MDN stimulation led to a significant increase in GCaMP6f fluorescence in A18b , and this was not observed in controls lacking all-trans retinal ( ATR ) , an essential co-factor for Chrimson function ( Figure 5G ) . Interestingly , MDN activation triggered a backward wave of A18b activity from A2 to A6 ( Figure 5G ) . We propose that MDN activates A18b in segment A1 , which is the only segment we detect direct synaptic contacts , and this is transformed into an anterior-to-posterior wave of A18b activity . We showed above that A18b has direct synaptic connectivity to motor neurons and is cholinergic , indicating that is likely to be an excitatory pre-motor neuron . Consistent with this expectation , we observed co-activity of A18b and motor neurons during backward motor waves in fictive preparation ( Figure 5H ) , and A18b stimulation led to a significant increase in GCaMP6f fluorescence in motor neurons , which was not observed in controls lacking ATR ( Figure 5I ) . We wanted to test whether activation of A18b in segment A1 could induce backward waves of motor neuron activity . Unfortunately , the A18b-Gal4 line is not expressed in A1 ( only A2-A7 ) , precluding this experiment; moreover , it has ‘off-target’ expression in the brain and in the VNC; these off-target neurons do not prevent monitoring A18b activity because they do not overlap with A18b arbors , but they make it impossible to selectively activate or silence A18b . In conclusion , our data support the following model: MDN activates A18b in segment A1 , which initiates a coordinated anterior-to-posterior wave of A18b/motor neuron activity that drives backward locomotion . Connectomic data shows that MDNs have many synapses with the bilateral Pair1 neurons , which send a descending projection to the VNC where they form synapses with A27h in posterior abdominal segments . A27h neurons are only active during forward locomotion ( Fushiki et al . , 2013 ) . This leads to the hypothesis we test below: MDNs activate Pair1 to inhibit A27h , which terminates forward locomotion . To determine if MDNs activate Pair1 we used Split1 to express Chrimson in MDNs , and R75C02-lexA ( hereafter Pair1-lexA ) ( Figure 6A ) to express GCaMP6f specifically in Pair1 . Stimulation of MDNs led to a significant increase in Pair1 GCaMP6f fluorescence , and this was not observed in controls lacking ATR ( Figure 6B ) . We conclude that the MDNs activate Pair1 neurons . In addition , we observed that every time MDNs were active , the Pair1 neurons were co-active ( n = 5; Figure 6C ) , although Pair1 could be active alone ( n = 5; Figure 6—figure supplement 1 ) . We conclude that MDNs activate the Pair1 neurons , and that other mechanisms exist for activating Pair1 as well ( see Discussion ) . We next used two methods to determine whether Pair1 neurons are preferentially active during backward locomotion . First , we used GCaMP6m to simultaneously monitor Pair1 and motor neuron activity in a fictive CNS preparation; this is possible because Pair1 and motor neuron processes are in different positions within the neuropil . These preparations show rhythmic forward and backward waves of motor neuron activity , and Pair1 neurons were only active during backward waves ( Figure 6D; left , center ) . In cases where Pair1 activity is coupled with motor neuron activity , we find that Pair1 activity precedes motor neuron activity ( Figure 6D , right ) . Second , we expressed CaMPARI in Pair1 neurons and performed photoconversion during forward locomotion or backward locomotion . We found that illumination during forward locomotion generated a small amount of red fluorescence , whereas illumination during backward locomotion resulted in a significant increase in red fluorescence ( Figure 6E ) . Taking all our anatomical and functional data together , we conclude that MDNs activate the A18b and the Pair1 neurons , which are both active during backward but not forward locomotion . We confirm previous work ( Fushiki et al . , 2016 ) showing that A27h is active during forward not backward locomotion ( Figure 7—figure supplement 1 ) . This raises the interesting possibility that the MDNs coordinately switch locomotor behavioral states: concurrently promoting backward locomotion via A18b , and suppressing forward locomotion via Pair1 inhibition of A27h . To test whether Pair1 inhibits the A27h neuron , we expressed Chrimson in Pair1 and GCaMP6m in A27h . We used Chrimson to stimulate Pair1 just as A27h activity was rising as part of a forward motor wave , and observed a significant decrease in A27h GCaMP6m fluorescence; this was not observed in controls lacking ATR ( Figure 7A , B ) . Furthermore , we found that Pair1 neurons are GABAergic ( Figure 7A’’ ) , consistent with Pair1 direct repression of A27h activity . In addition , we found that Chrimson stimulation of Pair1 immediately and persistently blocked forward larval locomotion; control larvae lacking ATR briefly paused in response to illumination onset but rapidly resumed forward locomotion ( Figure 7C; Videos 3 and 4 ) . Consistent with an inhibitory relationship , we observed that Pair1 and A27h activity is anti-correlated , with A27h often rising in activity as Pair1 declines in activity ( Figure 7—figure supplement 1 ) . We conclude that activation of the GABAergic Pair1 neurons inhibit A27h and prevent forward locomotion . Our results suggest that Pair1 suppression of forward locomotion may be an essential component of MDN triggering a switch from forward to backward locomotion . If so , silencing Pair1 activity should reduce the effectiveness of MDN-induced backward locomotion; alternatively , MDN may be able to induce backward locomotion equally well without Pair1 function . Thus , we expressed Chrimson in MDNs and the neuronal silencer Shibirets in Pair1; Shibirets blocks vesicle release at 32°C but not at 25°C ( experiment summarized in Figure 7D ) . We observed that silencing Pair1 alone had no effect on forward locomotion ( Figure 7E , i–ii ) , but silencing Pair1 prior to low light or high light Chrimson-induced activation of MDN led to a loss in the effectiveness of MDN-induced backward locomotion ( Figure 7E , iii–vi ) . We conclude that MDN triggers robust backward locomotion by coordinately activating the backward locomotion program and suppressing the forward locomotion program; we find no evidence for direct , monosynaptic reciprocal inhibition between these pathways ( Figure 7—figure supplement 1 ) . Larval MDNs share several features with the moonwalker descending neurons characterized in the adult ( Bidaye et al . , 2014; Sen et al . , 2017 ) . Both larval and adult neurons have anterior , medial somata with ipsilateral and contralateral arbors , and descending projections into the VNC . Both have presynaptic output into the SEZ and VNC . Could they be the same neurons ? We tried to trace the MDNs through pupal stages using the Split1-Gal4 and observed the MDNs at early pupal stages ( Figure 8A ) and mid-pupal stages , where they began to prune their dendritic arbors ( Figure 8B ) . However , Split1 was down-regulated by adulthood ( data not shown ) , requiring us to use alternate methods to follow the larval MDNs into adulthood . To permanently mark the larval MDN neurons , trace their morphology , and test their gain-of-function phenotype in the adult brain , we used two distinct intersectional genetic methods . First , we generated an intersection between a larval MDN line and an adult MDN line to express the optogenetic activator ReaChr ( genetics schematized in Figure 8C ) . If the larval MDNs become adult moonwalker neurons , they will express ReaChr:citrine and show light-induced backward walking . We observed light-induced backward walking in 8 of 10 adult flies assayed ( Figure 8D; Video 5 ) ; all eight had ReaChr::citrine expression in neurons matching the moonwalker neuron morphology ( Figure 8E , F ) , whereas the two flies that did not walk backward also did not have ReaChr:citrine expression in moonwalker neurons ( data not shown ) . Second , we used ‘immortalization’ genetics ( Harris et al . , 2015 ) to permanently mark larval MDNs and assay their function in the larva and adult ( genetics schematized in Figure 8G ) . We used Split1 to express an RU486-inducible FLP recombinase ( hPR:FLP ) , allowing us to chemically induce FLP activity in first instar larva when Split1 is only expressed in the MDNs and a few off-targets . FLP activity resulted in permanent expression of lexA in the MDN neurons , which immortalizes expression of LexAop-Chrimson:Venus in these neurons . We identified larvae that crawled backward in response to Chrimson activation , and all grew into adults that showed Chrimson-induced backward walking ( n = 20; Figure 8H ) . Importantly , all the backward walking adults that were successfully stained showed expression in the adult moonwalker neurons ( n = 5; Figure 8I , I’ ) ; although each brain showed staining in a few additional neurons ( blue shading ) , only the MDNs were present in all the brains . We conclude that the larval MDNs are descending neurons that are born embryonically , persist throughout larval stages , and survive into the adult . Surprisingly , activation of MDNs can induce backward crawling in the limbless larva , as well as backward walking in the six-limbed adult ( Figure 9A ) . How much of the MDN larval circuitry persists into the adult is an interesting open question ( see Discussion ) .
We have shown that MDNs are brain descending interneurons that activate two neuronal pathways: one to stop forward locomotion and one to induce backward locomotion ( Figure 9B , C ) . This is similar to C . elegans , where in response to a head poke the ASH sensory neuron activates AVA , a command neuron for backward locomotion ( Lindsay et al . , 2011 ) , and indirectly inhibits AVB , a command neuron for forward locomotion ( Roberts et al . , 2016 ) , although AVB inhibition may also arise from reciprocal inhibition between AVA and AVB . It is also similar to the role of the eighth nerve in simultaneously exciting the ipsilateral Mauthner neuron while inhibiting , via a feed-forward inhibitory neuron , the contralateral Mauthner neuron ( Koyama et al . , 2016 ) . Our results raise the question of whether previously described command-like neurons in Drosophila ( Bidaye et al . , 2014; King and Wyman , 1980; Sen et al . , 2017 ) , leech ( Kristan , 2008 ) , lamprey ( Dubuc et al . , 2008 ) , zebrafish ( Kimura et al . , 2013; Medan and Preuss , 2014 ) , mouse ( Bouvier et al . , 2015; Grillner and El Manira , 2015; Hägglund et al . , 2010; Jordan et al . , 2008; Juvin et al . , 2016; Roberts et al . , 2008 ) and other animals may not only induce a specific behavior , but concurrently inhibit an antagonistic or incompatible behavior . MDNs can induce backward locomotion within intact larvae or isolated CNS . In each case , a pulse of Chrimson activation as short as 300 ms can induce a full , multi-second long backward wave , suggesting that MDN activity triggers a backward wave without persisting throughout the wave . These results are consistent with detection of MDN-A18b synapses only in segment A1 , and support our conclusion that MDNs trigger but do not persist throughout , a backward wave . Noxious stimuli that produce multiple waves of backward locomotion are likely to continuously activate MDNs . We do not know how sensory stimulation of MDNs produce a bilateral backward motor wave . It may be that noxious sensory stimuli typically activate both left/right MDNs . Alternatively , there is a single contralateral synapse in both directions between A18b left/right neurons in A1 , and the number could increase with the age of the larva . Perhaps , the single synapses between contralateral A18b neurons transform unilateral MDN activation into bilateral motor waves . Lastly , unilateral MDN activation may produce bilateral A18 activation via uncharacterized contralateral neurons . MDNs are necessary for a normal backward locomotor response following mild noxious touch to the head . It is unclear how the tactile sensory cue is transduced to the MDNs: we find no monosynaptic sensory inputs to the MDNs in the current TEM connectome ( data not shown ) . It is also unknown whether MDNs are used for backward crawling in response to other noxious sensory modalities , such as high salt , bright light , or bitter taste . MDNs may be dedicated to responding to noxious mechanosensation , or they may integrate multimodal inputs to initiate backward locomotion . The discovery of MDN command-like neurons that switch locomotion from forward to backward raises the question: are there command-like neurons that induce the opposite transition: from backward to forward locomotion ? Whereas the MDN descending projection extends to A3-A5 , and thus well past the thoracic and upper abdominal segments that initiate backward locomotion ( Berni , 2015; Heckscher et al . , 2012; Pulver et al . , 2015 ) , a descending command-like neuron that induces forward locomotion is likely to project into the posterior abdominal segments , where forward waves are initiated ( Berni , 2015; Heckscher et al . , 2012; Pulver et al . , 2015 ) . Exploring the function of the latter type of descending neuron would help answer this question , as would the characterization of inhibitory inputs into the Pair1 or A18b backward-active neurons . Our model is that the activation of A18b in A1 induces backward locomotion . This model is based on several observations . ( 1 ) A18b is only active during backward locomotion . ( 2 ) MDN forms excitatory synapses on A18b in A1 but not more posterior segments . ( 3 ) Stimulation of MDN produces an A18b backward activity wave . ( 4 ) The A18b backward wave is always concurrent with a motor neuron backward wave . Unfortunately , we are unable to directly test the function of A18b in triggering backward locomotion due to the A18b Gal4 line having off-target expression in the brain and in the VNC , and lacking expression in A1 or thoracic segments ( Figure 5—figure supplement 1 ) . Backward motor waves are initiated from the thorax ( Pulver et al . , 2015 ) , and it is likely that stimulation of A18b in A1 or thoracic segments would be required to induce a backward motor wave . We attempted to find A18b in the thoracic segments , but failed , either due to incomplete annotation , segmental differences in morphology , or lack of thoracic A18b neurons . Similarly , the A02o ‘wave’ neuron can only induce backward motor waves following stimulation in anterior abdominal segments ( Takagi et al . , 2017 ) . The relationship between A18b and A02o is unclear ( they are not directly connected ) , nor is it known how activation of either produces a backward motor wave . This level of understanding would require a comprehensive anatomical and functional analysis of larval premotor and motor circuits . We propose that MDNs directly excite Pair1 neurons to halt forward locomotion . But there are also additional mechanisms to induce Pair1 activity , as many Pair1 activity bouts occur without MDN activity . These alternate mechanisms are likely to be used for Pair1-induced pausing that is not followed by backward locomotion , for example during a pause-turn behavior . The MDN-independent inputs that activate Pair1 remain to be discovered . The least understood MDN output to motor neurons is the MDN-ThDN-A27k/l pathway . A27l is inhibitory ( AAZ and CQD , unpublished ) so if ThDN is also inhibitory , it would provide a disinhibitory circuit motif for activating A18b . This would be synergistic with MDN direct excitation of A18b . There are currently no genetic tools providing access to ThDN or A27k neurons , and the existing driver line for A27l has off-target neurons , precluding a functional analysis of this pathway . MDNs can induce backward crawling in the limbless Drosophila larva , and persist into adulthood where they can induce backward walking in the six-legged adult fly . This is remarkable because most mechanosensory neurons are completely different ( Kendroud et al . , 2018; Kernan , 2007 ) , although there are some gustatory and stomatogastric sensory neurons that survive from larva to adult ( Kendroud et al . , 2018 ) . Similarly , most or all the downstream motor neurons controlling crawling ( larva ) and walking ( adult ) are different: abdominal motor neurons in the larva and thoracic motor neurons in the adult . It will be interesting to see which , if any , interneurons in the larval MDN circuit remain connected in the adult , and whether they perform the same function in the adult . For example , does the larval Pair1-A27h circuit persist in the adult , but become restricted to thoracic segments ? It is also interesting to consider the evolution of the MDN circuit; some of the neurons we describe here may originally have been used to regulate adult walking , prior to becoming co-opted for regulating larval crawling .
pBDP-Gal4 in attP2 ( gift from B . D . Pfeiffer , JRC ) pBDP-LexA:p65Uw in attp40 ( gift from T . Shirangi , Villanova Univ ) R53F07-Gal4 ( BDSC# 50442 ) R53F07-Gal4DBD ( Doe lab ) R49F02-Gal4AD ( a gift from G . Rubin , JRC ) R94E10-Gal4 ( A18b line; BDSC# 40689 ) R94E10-lexA ( A18b line; Doe lab ) R36G02-Gal4 ( A27h line; BDSC# 49939 ) R75C02-Gal4 ( Pair1 line; BDSC# 39886 ) R75C02-lexA ( Pair1 line; a gift from M . Louis , UC Santa Barbara ) ss01613-Gal4 ( Split3; a gift from M . Louis , UC Santa Barbara and J . Truman , Univ . Washington ) CQ2-lexA ( U1-U5 motor neurons; Doe lab ) RRa-Gal4 ( aCC/RP2 motor neurons; a gift from M . Fujioka , Thomas Jefferson Univ . ) tsh-lexA ( a gift from J . Simpson , UC Santa Barbara ) UAS-Chrimson:mCherry ( a gift from V . Jayaraman , JRC ) UAS-Chrimson:mVenus ( BDSC# 55138 ) UAS . dsFRT . Chrimson:mVenus ( a gift from G . Rubin , JRC ) UAS-MCFO2 ( BDSC# 64086 ) UAS-GCaMP6m ( BDSC# 42748 ) UAS-GCaMP6f ( a gift from V . Jayaraman , JRC ) UAS-jRCaMP1b ( BDSC# 63793 ) lexAop-GCaMP6f ( gift from V . Jayaraman , JRC ) lexAop-Gal80 ( BDSC# 32213 ) lexAop-Chrimson:mCherry ( a gift from V . Jayaraman , JRC ) lexAop-KZip+:3xHA ( a gift from B . White , NIH ) UAS-CaMPARI ( BDSC# 58761 ) UAS-GtACR1 ( a gift from A . Claridge-Chang , Duke-NUS Med School ) lexAop-shibirets in attP2 ( a gift from G . Rubin , JRC ) VT044845-lexA ( adult moonwalker line; a gift from B . Dickson , JRC ) hsFlpG5 . PEST ( BDSC# 62118 ) pJFRC108-20XUAS-IVS-hPR:Flp-p10 ( a gift from J . Truman , Univ . Washington ) Actin5C-FRT>-dSTOP-FRT>-LexAp:65 ( a gift from J . Truman , Univ . Washington ) P[13XLexAop2-IVS-CsChrimson . mVenus] attP18 ( BDSC# 55137 ) lexAop- ( mCherry-STOP-FRT ) ReaChR:Citrine VK00005 ( BDSC #53744 ) Split1 ( R53F07-Gal4DBD R49F02-Gal4AD ) Split2 ( R53F07-Gal4DBD R49F02-Gal4AD tsh-lexA lexAop-KZip+:3xHA ) Split3 ( ss01613-Gal4 ) Immortalization stock: P[13XLexAop2-IVS-CsChrimson . mVenus]attP18; Actin5C-FRT-STOP-FRT-lexAop::65; pJFRC108-20XUAS-IVS-hPR::Flp-p10 Standard confocal microscopy , immunocytochemistry and MCFO methods were performed as previously described for larvae ( Clark et al . , 2016; Heckscher et al . , 2015 ) or adults ( Nern et al . , 2015; Pfeiffer et al . , 2008 ) . Primary antibodies used recognize: GFP or Venus ( rabbit , 1:500 , ThermoFisher , Waltham , MA; chicken 1:1000 , Abcam13970 , Eugene , OR ) , GFP or Citrine ( Camelid sdAB direct labeled with AbberiorStar635P , 1:1000 , NanoTab Biotech . , Gottingen , Germany ) , GABA ( rabbit , 1:1000 , Sigma , St . Louis , MO ) , mCherry ( rabbit , 1:1000 , Novus , Littleton , CO ) , Corazonin ( rabbit , 1:2000 , J . Veenstra , Univ Bordeaux ) , FasII ( mouse , 1:100 , Developmental Studies Hybridoma Bank , Iowa City , IA ) , HA ( mouse , 1:200 , Cell signaling , Danvers , MA ) , or V5 ( rabbit , 1:400 , Rockland , Atlanta , GA ) , Flag ( rabbit , 1:200 , Rockland , Atlanta , GA ) . Standard methods were used for pupal staging ( Bainbridge and Bownes , 1981 ) . Secondary antibodies were from Jackson Immunoresearch ( West Grove , PA ) and used according to manufacturer’s instructions . Confocal image stacks were acquired on Zeiss 700 , 710 , or 800 microscopes . Images were processed in Fiji ( https://imagej . net/Fiji ) , Adobe Photoshop ( Adobe , San Jose , CA ) , and Adobe Illustrator ( Adobe , San Jose , CA ) . When adjustments to brightness and contrast were needed , they were applied to the entire image uniformly . Mosaic images to show different focal planes were assembled in Fiji or Photoshop . We reconstructed neurons in CATMAID using a Google Chrome browser as previously described ( Ohyama et al . , 2015 ) . Figures were generated using CATMAID graph or 3D widgets . Embryos were collected for 4 hr on standard 3 . 0% agar apple juice collection caps with a thin layer of wet yeast , and transferred to standard cornmeal fly food supplemented with 0 . 5 mM all-trans retinal at 48 hr after collection . Following another 48 hr ( 96 ± 6 hr larval age ) , animals were collected and transferred to 3 . 0% agar apple juice caps and relocated to the room were behavioral data was collected . Five minutes after acclimation to the room , one animal at a time was transferred to of 3 . 0% agar apple juice square arenas , 2 cm thick with an area of 81 . 0 cm2 , and crawling was then recorded at 5 Hz using an Axiocam 506 mono under low transmitted light from below for 15 s follow by 15 s under 0 . 275 mW/mm2 561 nm green light . For the intact larvae experiment in Figure 2F , a 300 ms pulse of 0 . 275 mW/mm2 561 nm green light was followed by 5 s of recording in the absence of green light . For the fictive CNS experiment in 2F , a 300 ms pulse of 561 nm green light was followed by 25 s of recording in the absence of green light . Temperature of the room was kept at 24 ± 2C° . Number of forward waves and backward waves , and percent of time engaged in either forward , backward or paused were quantified using the recorded movies . Behavioral data was acquired , given an unique identifier , and scored blind; except Figure 7E , where it was a binary assay ( forward wave/backward wave ) that did not require blind scoring . Unpaired Student’s t-test was performed to determine significance in the number of waves over 15 s . The Chrimson together with Shibire silencing experiment ( Figure 7D–E ) was performed as the Chrimson only experiments described above except that the agar arena was placed on top of a heating plate which was kept at 25C° or at 32C° for Shibire Off or On groups respectively . Animals were individually placed on the arena . After 1 min to reach the desired temperature , we manually quantified the number of forward and backward waves with no light , under 0 . 07 mW/mm2 green light or 0 . 275 mW/mm2 green light . For GtACR1 experiments ( Figure 1G–H ) , instead of square arenas , animals were placed into a 0 . 75 mm wide agar lane to limit their movement to forward or backward locomotion only . To quantify backward wave probability ( Figure 1G ) larvae were gently poked in the most anterior part of their body and scored whether the animal responded with backward crawling ( regardless of how many backward peristaltic waves ) . We then calculated the probability by dividing the number of times the animal began backward crawling immediately after a poke by the total number of times that each animal was poked , which was always five times . For each animal , this was done with no light first and then under 0 . 96 mW/mm2 561 nm green light . We performed one-way ANOVA with Bonferroni post-hoc test between light ON groups . For panel 1H , we induced a backward run and turned on the 0 . 96 mW/mm2 green light immediately after the second backward wave . We define a backward run as two or more consecutive backward peristaltic waves after being poked in the most anterior part of the animal . We scored how many backward waves animals performed after the light was turned on . For dual-color and single-color calcium imaging in fictive preps , freshly dissected brains were mounted on 12 mm round Poly-D-Lysine Coverslips ( Corning BioCoat ) in HL3 . 1 saline , which were then were placed on 25 mm ×75 mm glass slides to be imaged with a 40 × objective on an upright Zeiss LSM-800 confocal microscopy . To do calcium imaging in intact animals ( e . g . Figure 5D’ ) , a second or third instar larva was washed with distilled water , then moved into a drop of halocarbon oil 700 ( Sigma , St . Louis , MO ) on the slide . A 22 mm × 40 mm cover glass was put on the larva and pressed gently to restrict larval locomotion . The larva was mounted ventral side up so that the ventral nerve cord could be imaged using 40 × objective on an upright Zeiss LSM800 confocal microscope . To simultaneously image two different neurons expressing GCaMP , we imaged neuron-specific regions of interest ( ROI ) . In addition , we imaged two neurons using neuron-specific GCaMP6m and jRCaMP1b . Image data were imported into FijI ( https://imagej . net/fiji ) and GCaMP6m and jRCaMP1b channels were separated . The ΔF/F0 of each ROI was calculated as ( F-F0 ) /F0 , where F0 was averaged over ~1 s immediately before the start of the forward or backward waves in each ROI . Freshly dissected brains were mounted in HL3 . 1 saline as described above , with the exception that the dissection was done under the minimum level of light possible to prevent activation of Chrimson . GCaMP6m or GCaMP6f signal in postsynaptic neurons were imaged using 2–4% power of the 488 nm laser with a 40 × objective on an upright Zeiss LSM800 confocal microscope . Chrimson in presynaptic neurons was activated with three pulses of 561 nm laser at 100% power delivered via the same 40 × objective using the bleaching function in the ZEN Zeiss software . The total length of the pulses carried was depend on the ROI size which was kept consistent across ATR +and ATR– samples within an experiment . For A18b activation ( Figure 5I ) , the light pulse was 700 ms; for activation of MDN ( Figure 6B ) or Pair1 ( Figure 7B ) , the light pulse was 440 ms . To quantify ΔF/F0 traces we used MATLAB . Before extracting any fluorescence , our script first performs rigid registration to correct for movement while recording . F0 was set as the average fluorescence of the three frames acquired before each Chrimson stimulus analyzed . For predicted excitatory connections ( Figures 5I and 6B ) , we first average ΔF/F0 traces for two consecutive 561 nm Chrimson stimuli separated by 20 488 nm acquisition frames . This was enough time to let GCaMP6f levels return to ground state . For predicted inhibitory connections ( Figure 7B ) , we gave multiple 440 msec Chrimson stimuli separated by 5 s . After recording , we then selected all events where the start of the Chrimson stimulus coincided with an A27h forward activity wave , which was necessary to elevate the GCaMP6m levels sufficiently to see subsequent Chrimson-induced inhibition . We selected the A27h segmental neuron with the highest mean fluorescent intensity in the frame before the Chrimson stimulus from segments A4-A7 ( where Pair1 synapses with A27h ) . For all Chrimson experiments , traces were averaged across animals . Larvae were collected 96 hrs days after egg laying and place in agar apple collection caps for at least 5 min to acclimate animals to the environment . Using a soft brush , larvae were placed into a 0 . 75 mm wide agar lane to limit their movement to forward or backward locomotion only . We let the animals start crawling forward for at least 5 s in the lanes . For forward data collection , the photoconverting 405 nm light was turned on at 0 . 5 mW/mm2 while the larvae crawled forward for 30 s . For backward , same light was turned on and backward locomotion was immediately induced by gentle touch on the most anterior part of the larva with a semi-blunt pin . Brains were dissected in HL3 . 1 , then green and red CaMPARI signals were imaged with a 40 × objective on Zeiss LSM-800 confocal microscope in the regions of interest . ROIs were manually selected using the green channel . Fluorescence within ROIs were quantified using Image J . After eclosion adults were transferred to standard cornmeal fly food supplemented with ATR ( 0 . 5 mM ) for 4 days changing to fresh food after two days . Wings were clipped and animals were placed in ring arenas made of 3 . 0% agar apple juice . The ring arena size was 1 . 4 cm outer diameter , 1 . 0 cm inner diameter and 0 . 2 cm height . After 5 min for environmental acclimation , animal behavior was recorded at 5 Hz using an Axiocam 506 mono under low transmitted light for 10 s followed by 10 s under 0 . 28 mW/mm2 red light . This was done three times for each animal . To quantify backward locomotion probability upon light stimulus , we divided the amount of times the animal began backward walking within 2 s after light stimulus over the total number of times the animals was presented with light . To calculate significance , we used Student’s t-test unpaired analysis . Adult flies were allowed to lay eggs on standard culture medium that was supplemented with 1 µM RU486 and 2 mM ATR . After 24 hr , light-induced backward crawling larvae were transferred to culture medium supplemented with 2 mM ATR and grown to adulthood . Two- to 6-day-old adult flies were individually transferred into a 10-ml serological pipette for walking assay . Red‐orange light from a 617 nm high‐power LED was fiber‐coupled to a 200 µm core optical cable that was triggered via a T-Cube LEDD1B driver ( ThorLabs , Newton , NJ ) . Optogenetic stimulation was measured via a photodiode power sensor ( S130VC , ThorLabs ) to be ~4 . 6 µW/mm2 . We performed the same analysis for the intersectional experiment ( above ) to quantify backward locomotion probability upon light stimulus . Statistical significance is denoted by asterisks: ****p<0 . 0001; ***p<0 . 001; **p<0 . 01; *p<0 . 05; n . s . , not significant . All statistical Student’s t-tests were performed using Graphpad Prism software . One way ANOVA with Bonferroni post-hoc test was done using http://astatsa . com/ . The results are stated as mean ± s . d . , unless otherwise noted . | When we choose to make one kind of movement , it often prevents us making another . We cannot move forward and backward at the same time , for example , and a horse cannot simultaneously gallop and walk . These ‘antagonistic’ behaviors often use the same group of muscles , but the muscles contract in a different order . This requires exquisite control over muscle contractions . Neurons located in the central nervous system form circuits to produce distinct patterns of muscle contractions and to switch between these patterns . Smooth , rapid switching between behaviors is important for animal escape and survival , as well as for performing fine movements . However , we know little about how the activity of the neuronal circuits enables this . Carreira-Rosario , Zarin , Clark et al . set out to identify the underlying neuronal circuitry that allows larval fruit flies to transition between crawling forward and backward . Results from a combination of genetics and microscopy techniques revealed that a neuron called the Mooncrawler Descending Neuron ( MDN ) induces a switch from forward to backward travel . MDN activates a neuron that stops the larvae crawling forward , and at the same time activates a different neuron that is only active when the larvae crawl backward . Carreira-Rosario et al . also found that MDN triggers backward crawling in the six-limbed adult fly . Understanding how a single neuron – in this case MDN – can trigger a smooth switch between opposing behaviors could be beneficial for the medical and robotics fields . In the medical field , understanding how movement is generated could help to improve therapies that fix damage to the relevant neuronal circuits . Understanding how behavioral transitions occur may also help to design autonomous robots that can navigate complex terrain . | [
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] | 2018 | MDN brain descending neurons coordinately activate backward and inhibit forward locomotion |
We subjectively perceive our visual field with high fidelity , yet peripheral distortions can go unnoticed and peripheral objects can be difficult to identify ( crowding ) . Prior work showed that humans could not discriminate images synthesised to match the responses of a mid-level ventral visual stream model when information was averaged in receptive fields with a scaling of about half their retinal eccentricity . This result implicated ventral visual area V2 , approximated ‘Bouma’s Law’ of crowding , and has subsequently been interpreted as a link between crowding zones , receptive field scaling , and our perceptual experience . However , this experiment never assessed natural images . We find that humans can easily discriminate real and model-generated images at V2 scaling , requiring scales at least as small as V1 receptive fields to generate metamers . We speculate that explaining why scenes look as they do may require incorporating segmentation and global organisational constraints in addition to local pooling .
Vision science seeks to understand why things look as they do ( Koffka , 1935 ) . Typically , our entire visual field looks subjectively crisp and clear . Yet our perception of the scene falling onto the peripheral retina is actually limited by at least three distinct sources: the optics of the eye , retinal sampling , and the mechanism ( s ) giving rise to crowding , in which our ability to identify and discriminate objects in the periphery is limited by the presence of nearby items ( Bouma , 1970; Pelli and Tillman , 2008 ) . Many other phenomena also demonstrate striking ‘failures’ of visual perception , for example change blindness ( Rensink et al . , 1997; O'Regan et al . , 1999 ) and inattentional blindness ( Mack and Rock , 1998 ) , though there is some discussion as to what extent these are distinct from crowding ( Rosenholtz , 2016 ) . Whatever the case , it is clear that we can be insensitive to significant changes in the world despite our rich subjective experience . Visual crowding has been characterised as compulsory texture perception ( Parkes et al . , 2001; Lettvin , 1976 ) and compression ( Balas et al . , 2009; Rosenholtz et al . , 2012a ) . This idea entails that we cannot perceive the precise structure of the visual world in the periphery . Rather , we are aware only of some set of summary statistics or ensemble properties of visual displays , such as the average size or orientation of a group of elements ( Ariely , 2001; Dakin and Watt , 1997 ) . One of the appeals of the summary statistic idea is that it can be directly motivated from the perspective of efficient coding as a form of compression . Image-computable texture summary statistics have been shown to be correlated with human performance in various tasks requiring the judgment of peripheral information , such as crowding and visual search ( Rosenholtz et al . , 2012a; Balas et al . , 2009; Freeman and Simoncelli , 2011; Rosenholtz , 2016; Ehinger and Rosenholtz , 2016 ) . Recently , it has even been suggested that summary statistics underlie our rich phenomenal experience itself—in the absence of focussed attention , we perceive only a texture-like visual world ( Cohen et al . , 2016 ) . Across many tasks , summary statistic representations seem to capture aspects of peripheral vision when the scaling of their pooling regions corresponds to ‘Bouma’s Law’ ( Rosenholtz et al . , 2012a; Balas et al . , 2009; Freeman and Simoncelli , 2011; Wallis and Bex , 2012; Ehinger and Rosenholtz , 2016 ) . Bouma’s Law states that objects will crowd ( correspondingly , statistics will be pooled ) over spatial regions corresponding to approximately half the retinal eccentricity ( Bouma , 1970; Pelli and Tillman , 2008; though see Rosen et al . , 2014 ) . While the precise value of Bouma’s law can vary substantially even over different visual quadrants within an individual ( see e . g . Petrov and Meleshkevich , 2011 ) , we refer here to the broader notion that summary statistics are pooled over an area that increases linearly with eccentricity , rather than the exact factor of this increase ( the exact factor becomes important in the paragraph below ) . If the visual system does indeed represent the periphery using summary statistics , then Bouma’s scaling implies that as retinal eccentricity increases , increasingly large regions of space are texturised by the visual system . If a model captured these statistics and their pooling , and the model was amenable to being run in a generative mode , then images could be created that are indistinguishable from the original despite being physically different ( metamers ) . These images would be equivalent to the model and to the human visual system ( Freeman and Simoncelli , 2011; Wallis et al . , 2016; Portilla and Simoncelli , 2000; Koenderink et al . , 2017 ) . Freeman and Simoncelli ( 2011 ) developed a model ( hereafter , FS-model ) in which texture-like summary statistics are pooled over spatial regions inspired by the receptive fields in primate visual cortex . The size of neural receptive fields in ventral visual stream areas increases as a function of retinal eccentricity , and as one moves downstream from V1 to V2 and V4 at a given eccentricity . Each visual area therefore has a signature scale factor , defined as the ratio of the receptive field diameter to retinal eccentricity ( Freeman and Simoncelli , 2011 ) . Similarly , the pooling regions of the FS-model also increase with retinal eccentricity with a definable scale factor . New images can be synthesised that match the summary statistics of original images at this scale factor . As scale factor increases , texture statistics are pooled over increasingly large regions of space , resulting in more distorted synthesised images relative to the original ( that is , more information is discarded ) . The maximum scale factor for which the images remain indistinguishable ( the critical scale ) characterises perceptually-relevant compression in the visual system’s representation . If the scale factor of the model corresponded to the scaling of the visual system in the responsible visual area , and information in upstream areas was irretrievably lost , then the images synthesised by the model should be indistinguishable while discarding as much information as possible . That is , we seek the maximum compression that is perceptually lossless:scrit ( I ) =maxs:d ( I^s , I ) =0s , where scrit ( I ) is the critical scale for an image I , I^s is a synthesised image at scale s and d is a perceptual distance . Larger scale factors discard more information than the relevant visual area and therefore the images should look different . Smaller scale factors preserve information that could be discarded without any perceptual effect . Crucially , it is the minimum critical scale over images that is important for the scaling theory . If the visual system computes summary statistics over fixed ( image-independent ) pooling regions in the same way as the model , then the model must be able to produce metamers for all images . While images may vary in their individual critical scales , the image with the smallest critical scale determines the maximum compression for appearance to be matched by the visual system in general , assuming an image-independent representation:ssystem=minIscrit ( I ) Freeman and Simoncelli showed that the largest scale factor for which two synthesised images could not be told apart was approximately 0 . 5 , or pooling regions of about half the eccentricity . This scaling matched the signature of area V2 , and also matched the approximate value of Bouma’s Law . Subsequently , this result has been interpreted as demonstrating a link between receptive field scaling , crowding , and our rich phenomenal experience ( e . g . Block , 2013; Cohen et al . , 2016 , Landy , 2013 , Movshon and Simoncelli , 2014 , Seth , 2014 ) . These interpretations imply that the FS-model creates metamers for natural scenes . However , observers in Freeman and Simoncelli’s experiment never saw the original scenes , but only compared synthesised images to each other . Showing that two model samples are indiscriminable from each other could yield trivial results . For example , two white noise samples matched to the mean and contrast of a natural scene would be easy to discriminate from the scene but hard to discriminate from each other . Furthemore , since synthesised images represent a specific subset of images , and the system critical scale ssystem is the minimum over all possible images , the ssystem estimated in Freeman and Simoncelli ( 2011 ) is likely to be an overestimate . No previous paper has estimated ssystem for the FS-model using natural images . Wallis et al . , 2016 tested the related Portilla and Simoncelli ( 2000 ) model textures , and found that observers could easily discriminate these textures from original images in the periphery . However , the Portilla and Simoncelli model makes no explicit connection to neural receptive field scaling . In addition , relative to the textures tested by Wallis et al . , 2016 , the pooling region overlap used in the FS-model provides a strong constraint on the resulting syntheses , making the images much more similar to the originals . It is therefore still possible that the FS-model produces metamers for natural scenes for scale factors of 0 . 5 .
We tested whether the FS-model can produce metamers using an oddity design in which the observer had to pick the odd image out of three successively shown images ( Figure 1E ) . In a three-alternative oddity paradigm , performance for metamerism would lie at 1/3 ( dashed horizontal line , Figure 1F ) . We used two comparison conditions: either observers compared two model syntheses to each other ( synth vs synth; as in Freeman and Simoncelli , 2011 ) or the original image to a model synthesis ( orig vs synth ) . As in the original paper ( Freeman and Simoncelli , 2011 ) we measured the performance of human observers for images synthesised with different scale factors ( using Freeman and Simoncelli’s code , see Materials and methods ) . To quantify the critical scale factor we fit the same nonlinear model as Freeman and Simoncelli , which parameterises sensitivity as a function of critical scale and gain , but using a mixed-effects model with random effects of participant and image ( see Materials and methods ) . We used 20 images to test the FS model . These images are split into two classes of ten images each , which we labelled ‘scene-like’ and ‘texture-like’ . The distinction of these two classes is based on the results of a pilot experiment with a model we developed , which is inspired by the FS model but based on a different set of image features ( those extracted by a convolutional neural network; see Materials and methods and Appendix 2—figure 1 ) . In this pilot experiment , we found that some images are easier to discriminate than others ( Appendix 2—figure 7—figure 9 ) . Easily-discriminable images tended to contain larger areas of inhomogenous structure , long edges , borders between different surfaces or objects , and angled edges providing perspective cues ( ‘scene-like’ ) . Difficult images tended to contain more visual textures: homogenous structure , patterned content , or materials ( ‘texture-like’“ ) . For example , images from the first class tended to contain more structure such as faces , text , skylines , buildings , and clearly segmented objects or people , whereas images from the second class tended to contain larger areas of visual texture such as grass , leaves , gravel , or fur . A similar distinction could also be made along the lines of ‘human-made’ versus ‘natural’ image structure , but we suspect the visual structure itself rather than its origin is of causal importance and so used that level of description . While our labelling of images in this way is debatable ( for example , ‘texture-like’ regions contain some ‘scene-like’ content and vice versa ) and to some degree based on subjective judgment , we hypothesised that this classification distinguishes the types of image content that are critical . If the visual system indeed created a texture-like summary in the periphery and the FS-model was a sufficient approximation of that process , then we should observe no difference in the average critical scale factor of images in each group ( because image content would be irrelevant to the distribution of scrit ( I ) ) . We start by considering the condition where participants compared synthesised images to each other—as in Freeman and Simoncelli ( 2011 ) . Under this condition , there was little evidence that the critical scale depended on the image content ( see curves in Figure 1F , synth vs synth ) . The critical scale ( posterior mean with 95% credible interval quantiles ) for scene-like images was 0 . 28 , 95% CI [0 . 21 , 0 . 36] and the critical scale for texture-like images was 0 . 37 , 95% CI [0 . 27 , 0 . 5] ( Figure 1G ) . Though these critical scales are lower than those reported by Freeman and Simoncelli ( 2011 ) , they are within the range of other reported critical scale factors ( Freeman and Simoncelli , 2013 ) . There was weak evidence for a difference in critical scale between texture-like and scene-like images , with the posterior distribution of scale differences being 0 . 09 , 95% CI [−0 . 03 , 0 . 24] , p ( β<0 ) =0 . 078 ( where p ( β<0 ) is the posterior probability of the difference being negative; symmetrical posterior distributions centered on zero would have p ( β<0 ) =0 . 5 ) . However , this evidence should be interpreted cautiously: because asymptotic performance never reaches high values , critical scale estimates are more uncertain than in the orig vs synth condition below ( Figure 1G ) . This poor asymptotic performance may be because we used more images in our experiment than Freeman and Simoncelli , so participants were less familiar with the distortions that could appear . To make sure this difference did not arise due to different experimental paradigms ( oddity vs . ABX ) , we repeated the experiment using the same ABX task as in Freeman and Simoncelli ( Appendix 1—figure 4 ) . This experiment again showed poor asymptotic performance , and furthermore demonstrated no evidence for a critical scale difference between the scene- and texture-like images . Taken together , our synth vs synth results are somewhat consistent with Freeman and Simoncelli , who reported no dependency of scrit ( I ) on image . It seems likely that this is because comparing synthesised images to each other means that the model has removed higher-order structure that might allow discrimination . All images appear distorted , and the task becomes one of identifying a specific distortion pattern . Comparing the original image to model syntheses yielded a different pattern of results . First , participants were able to discriminate the original images from their FS-model syntheses at scale factors of 0 . 5 ( Figure 1F ) . Performance lay well above chance for all participants . This result held for both scene-like and texture-like images . Furthermore , there was evidence that critical scale depended on the image type . Model syntheses matched the texture-like images on average with scale factors of 0 . 36 , 95% CI [0 . 29 , 0 . 43] . In contrast , the scene-like images were quite discriminable from their model syntheses even at the smallest scale we could generate ( 0 . 25 ) . The critical scale estimated for scene-like images was 0 . 22 , 95% CI [0 . 18 , 0 . 27] . Texture-like images had higher critical scales than scene-like images on average ( scale difference = 0 . 13 , 95% CI [0 . 06 , 0 . 22] , p ( β<0 ) =0 . 001 ) . This difference in critical scale was not attributable to differences in the success of the synthesis procedure between scene-like and texture-like images . Scene-like images had higher final loss ( distance between the original and synthesised images in model space ) than texture-like images on average ( see Materials and methods ) . This is a corollary of the importance of image content: since a texture summary model is a poor description of scene-like content , the model’s optimisation procedure is also more likely to find local minima with relatively high loss . We checked that our main result was not explained by this difference by performing a control analysis in which we refit the model after equating the average loss in the two groups by excluding images with highest final loss until the groups were matched ( resulting in four scene-like images being excluded; see Materials and methods ) . The remaining scene-like images had a critical scale of 0 . 24 , 95% CI [0 . 2 , 0 . 28] in the orig vs synth condition , texture-like images again showed a critical scale of 0 . 36 , 95% CI [0 . 3 , 0 . 42] and the difference distribution had a mean of 0 . 12 , 95% CI [0 . 06 , 0 . 19] , p ( β<0 ) <0 . 001 . Thus , differences in synthesis loss do not explain our findings . As noted above , the image with the minimum critical scale determines the largest compression that can be applied for the scaling model to hold ( ssystem ) . For two images ( Figure 2A and E ) the nonlinear mixed-effects model estimated critical scales of approximately 0 . 14 ( see Figure 1G , diamonds; the minimum critical scale after excluding high-loss images in the control analysis reported above was 0 . 19 ) . However , examining the individual data for these images ( Figure 2D and H ) reveals that these critical scale estimates are largely determined by the hierarchical nature of the mixed-effects model , not the data itself . Both images were easy to discriminate from the original even for the lowest scale factor we could generate . This suggests that the true scale factor required to generate metamers may be even lower than estimated by the mixed-effects model . Our results show that smaller pooling regions are required to make metamers for scene-like images than for texture-like images . Human observers can reliably detect relatively small distortions produced by the FS-model at scale factors of 0 . 25 in scene-like image content ( compare Figure 2B and F at scale 0 . 25 and C and G at scale 0 . 46 to images A and B ) . Thus , syntheses at these scales are not metamers for natural scenes . In our first experiment we found that scene-like images yielded lower critical scales than texture-like images . However , this categorisation is crude: ‘texture-ness’ in photographs of natural scenes is a property of local regions of the image rather than the image as a whole . In addition , the classification of images above was based in part on the difficulty of these images in a pilot experiment . We therefore ran a second experiment to test the importance of local image structure more directly ( Bex , 2010; Koenderink et al . , 2017; Valsecchi et al . , 2018; Wallis and Bex , 2012 ) , using a set of images whose selection was not based on pilot discrimination results . Participants detected a localised texture-like distortion ( generated by the texture model of Gatys et al . , 2015 ) blended into either a scene-like or texture-like region ( Figure 3A–C ) . These image regions were classified by author CF ( non-authors showed high agreement with this classification—see Materials and methods ) . The patches were always centered at an eccentricity of six degrees , and we varied the radius of the circular patch ( Figure 3D ) . This is loosely analogous to creating summary statistics in a single pooling region ( Wallis et al . , 2016 ) . Participants discriminated between the original image and an image containing a local distortion in a 2IFC paradigm ( Figure 3E ) . The results showed that the visibility of texture-like distortions depended strongly on the underlying image content . Participants were quite insensitive to even large texture-like distortions occurring in texture-like image regions ( Figure 3F ) . Performance for distortions of nearly five degrees radius ( i . e . nearly entering the foveal fixation point ) was still close to chance . Conversely , distorting scene-like regions is readily detectable for the three largest distortion patch sizes .
These segmentation and grouping mechanisms could be mediated by local interactions between nearby image features , global properties of the scene , or both . The present results do not allow us to distinguish these alternatives . In favour of the importance of local interactions , studies of contour integration in Gabor fields show that the arrangement of local orientation structure can influence the discrimination of contour shape ( Dakin and Baruch , 2009 ) and contour localisation ( Robol et al . , 2012 ) , and that these effects are consistent with crowding ( Robol et al . , 2012 ) . In these stimuli , crowding between nearby contour elements is the primary determinant of global contour judgments ( see also Dakin et al . , 2009 ) . Specifically , contours consisting of parallel Gabor elements ( ‘snakes’ ) were more easily perceived when adjacent Gabor elements were oriented perpendicularly to the main contour . A related study ( Van der Burg et al . , 2017 ) used an evolutionary algorithm to select dense line element displays that maximally alleviated crowding in an orientation discrimination task . Displays evolved using human responses showed that a substantial reduction of crowding was obtained by orienting the two line segments nearest the target ( separated by only 0 . 75∘ at 6∘ eccentricity ) to be perpendicular to the target’s mean orientation ( forming ‘T’ and/or ‘I’ junctions ) . In contrast , simulations based on Bouma’s Law predicted that much larger areas of the display ( relative to the human data ) would need to be adjusted . These results are consistent with our finding that humans can be far more sensitive to image structure in the periphery than predicted by Bouma-like scaling . The studies above suggest the possibility that T-junctions may be critical local cues to segmentation in the periphery . The potential importance of different junction types in segmentation and grouping has long been noted ( Biederman , 1987 ) . In real scenes , T-junctions usually signal occlusion edges between rigid surfaces , whereas Y- , L- and arrow-junctions are created by projecting the corners of 3D objects into 2D . Histograms of junction distributions are diagnostic of scene category ( Walther and Shen , 2014 ) , with human-made scenes such as city streets and offices tending to contain more T-junctions than more natural environments like beaches and mountains . A recent study also highlights the importance of local contour symmetry for scene categorisation ( Wilder et al . , 2019 ) . Finally , Loschky et al . ( 2010 ) found that participants were extremely poor at classifying scene category from Portilla and Simoncelli ( 2000 ) global textures of scene images . These results suggest that the Portilla and Simoncelli texture statistics ( used in the FS-model ) do not adequately preserve junction information . Taken together , these studies give rise to the following hypothesis: images with more junctions ( particularly T-junctions; Van der Burg et al . , 2017 ) will require smaller pooling regions to match and thus will show lower critical scale estimates in the FS-model . We applied the junction detection algorithm of Xia et al . ( 2014 ) to each of the 20 original images used in our first experiment . Consistent with the ( post-hoc ) hypothesis above , lower critical scales were associated with more frequent junctions , particularly if ‘less meaningful’ junctions ( defined by the algorithm ) were excluded ( T-junction correlation r=-0 . 54; L-junctions r=-0 . 63; Appendix 1—figure 3 ) . If confirmed by a targeted experiment ( and dissociated from general edge density ) , this relationship would suggest a clear avenue for future improvement of scene appearance models: they must successfully capture junction information in images . Other evidence supports the role of global information ( the arrangement and organisation of objects over large retinal areas ) in segmentation and grouping . In crowding , Manassi et al . ( 2013 ) found that configurations of stimuli well outside the region of Bouma’s law could modulate the crowding effectiveness of the same flankers ( see also Manassi et al . , 2012; Saarela et al . , 2009; Vickery et al . , 2009; Levi and Carney , 2009 ) . Neri ( 2017 ) reported evidence from a variety of experiments in support of a fast segmentation process , operating over large regions of space , that can strongly modulate the perceptual interpretation of—and sensitivity to—local edge elements in a scene according to the figure-ground organisation of the scene ( see also Teufel et al . , 2018 ) . Our findings could be explained by the fact that the texture summary statistic models we examine here do not include any such global segmentation processes . The importance of these mechanisms could be examined in future studies , and potentially dissociated from the local information discussed above , by using image manipulations thought to disrupt the activity of global grouping mechanisms such as polarity inversion or image two-toning ( Neri , 2017; Balas , 2012; Teufel et al . , 2018 ) . Our results do not undermine the considerable empirical support for the periphery-as-summary-statistic theory as a description of visual performance . Humans can judge summary statistics of visual displays ( Ariely , 2001; Dakin and Watt , 1997 ) , summary statistics can influence judgments where other information is lost ( Fischer and Whitney , 2011; Faivre et al . , 2012 ) , and the information preserved by summary statistic stimuli may offer an explanation for performance in various visual tasks ( Rosenholtz et al . , 2012b; Balas et al . , 2009; Rosenholtz et al . , 2012a; Keshvari and Rosenholtz , 2016; Chang and Rosenholtz , 2016; Zhang et al . , 2015; Whitney et al . , 2014; Long et al . , 2016; though see Agaoglu and Chung , 2016; Herzog et al . , 2015; Francis et al . , 2017 ) . Texture-like statistics may even provide the primitives from which form is constructed ( Lettvin , 1976 ) —after appropriate segmentation , grouping and organisation . However , one additional point merits further discussion . The studies by Rosenholtz and colleagues primarily test summary statistic representations by showing that performance with summary statistic stimuli viewed foveally is correlated with peripheral performance with real stimuli . This means that the summary statistics preserve sufficient information to explain the performance of tasks in the periphery . Our results show that these summary statistics are insufficient to match scene appearance , at least under the pooling scheme used in the Freeman and Simoncelli model at computationally feasible scales . This shows the usefulness of scene appearance matching as a test: a parsimonious model that matches scene appearance would be expected to also preserve enough information to show correlations with peripheral task performance; the converse does not hold . While it may be useful to consider summary statistic pooling in accounts of visual performance , to say that summary statistics can account for phenomenological experience of the visual periphery ( Cohen et al . , 2016; see also Block , 2013; Seth , 2014 ) seems premature in light of our results ( see also Haun et al . , 2017 ) . Cohen et al . ( 2016 ) additionally posit that focussed spatial attention can in some cases overcome the limitations imposed by a summary statistic representation . We instead find little evidence that participants’ ability to discriminate real from synthesised images is improved by cueing spatial attention , at least in our experimental paradigm and for our CNN-model ( Appendix 2—figure 6 ) . Our results show that the appearance of scenes in the periphery cannot be captured by the Freeman and Simoncelli ( 2011 ) summary statistic model at receptive field scalings similar to V2 . We suggest that peripheral appearance models emphasising pooling processes that depend on retinal eccentricity will instead need to explore input-dependent grouping and segmentation . We speculate that mechanisms of perceptual organisation ( either local or global ) are critical to explaining visual appearance and efficient peripheral encoding . Models of the visual system that assume image content is processed in feedforward , fixed pooling regions—including current convolutional neural networks—lack these mechanisms .
Eight observers participated in the first experiment ( Figure 1 ) : authors CF and TW , a research assistant unfamiliar with the experimental hypotheses , and five naïve participants recruited from an online advertisement pool who were paid 10 Euro per hr for two one-hour sessions . An additional naïve participant was recruited but showed insufficient eyetracking accuracy ( see below ) . Four observers participated in the second experiment ( Figure 3 ) ; authors CF and TW plus two naïve observers paid 10 Euro per hour . All participants signed a consent form prior to participating . Participants reported normal or corrected-to-normal visual acuity . All procedures conformed to Standard 8 of the American Psychological Association’s ‘Ethical Principles of Psychologists and Code of Conduct’ ( 2010 ) . Images were taken from the MIT 1003 scene dataset ( Judd et al . , 2012; Judd et al . , 2009 ) . A square was cropped from the center of the original image and downsampled to 512 × 512 px . The images were converted to grayscale and standardized to have a mean gray value of 0 . 5 ( scaled [0 , 1] ) and an RMS contrast ( σ/μ ) of 0 . 3 . For the first experiment , images were selected as described in the Results and Appendix 2—figure 7—figure 9 . Stimuli were displayed on a VIEWPixx 3D LCD ( VPIXX Technologies Inc , Saint-Bruno-de-Montarville , Canada; spatial resolution 1920 × 1080 pixels , temporal resolution 120 Hz , operating with the scanning backlight turned off in normal colour mode ) . Outside the stimulus image the monitor was set to mean grey . Participants viewed the display from 57 cm ( maintained via a chinrest ) in a darkened chamber . At this distance , pixels subtended approximately 0 . 025 degrees on average ( approximately 40 pixels per degree of visual angle ) . The monitor was linearised ( maximum luminance 260 cd/m2 ) using a Konica-Minolta LS-100 ( Konica-Minolta Inc , Tokyo , Japan ) . Stimulus presentation and data collection was controlled via a desktop computer ( Intel Core i5-4460 CPU , AMD Radeon R9 380 GPU ) running Ubuntu Linux ( 16 . 04 LTS ) , using the Psychtoolbox Library ( version 3 . 0 . 12 , Brainard , 1997; Kleiner et al . , 2007; Pelli , 1997 ) , the Eyelink toolbox ( Cornelissen et al . , 2002 ) and our internal iShow library ( http://dx . doi . org/10 . 5281/zenodo . 34217 ) under MATLAB ( The Mathworks Inc , Natick MA , USA; R2015b ) . Participants’ gaze position was monitored by an Eyelink 1000 ( SR Research ) video-based eyetracker . In the first experiment , participants were shown three images in succession on each trial . Two images were identical , one image was different ( the ‘oddball’ , which could occur first , second or third with equal probability ) . The oddball could be either a synthesised or a natural image ( in the orig vs synth condition; counterbalanced ) , whereas the other two images were physically the same as each other and from the opposite class as the oddball . In the synth vs synth condition ( as used in Freeman and Simoncelli ) , both oddball and foil images were ( physically different ) model synths . The participant identified the temporal position of the oddball image via button press . Participants were told to fixate on a central point ( Thaler et al . , 2013 ) presented in the center of the screen . The images were centred around this spot and displayed with a radius of 512 pixels ( i . e . images were upsampled by a factor of two for display ) , subtending ≈12 . 8° at the eye . Images were windowed by a circular cosine , ramping the contrast to zero in the space of 52 pixels . The stimuli were presented for 200 ms , with an inter-stimulus interval of 1000 ms ( making it unlikely participants could use motion cues to detect changes ) , followed by a 1200 ms response window . Feedback was provided by a 100 ms change in fixation cross brightness . Gaze position was recorded during the trial . If the participant moved the eye more than 1 . 5 degrees away from the fixation spot , the trial immediately ended and no response was recorded; participants saw a feedback signal ( sad face image ) indicating a fixation break . Prior to the next trial , the state of the participant’s eye position was monitored for 50 ms; if the eye position was reported as more than 1 . 5 degrees away from the fixation spot a recalibration was triggered . The inter-trial interval was 400 ms . Scene-like and texture-like images were compared under two comparison conditions ( orig vs synth and synth vs synth; see main text ) . Image types and scale factors were randomly interleaved within a block of trials ( with a minimum of one trial from another image in between ) whereas comparison condition was blocked . Participants first practiced the task and fixation control in the orig vs synth comparison condition ( scales 0 . 7 , 0 . 86 and 1 . 45 ) ; the same images used in the experiment were also used in practice to familiarise participants with the images . Participants performed at least 60 practice trials , and were required to achieve at least 50% correct responses and fewer than 20% fixation breaks before proceeding ( as noted above , one participant failed ) . Following successful practice , participants performed one block of orig vs synth trials , which consisted of five FS-model scale factors ( 0 . 25 , 0 . 36 , 0 . 46 , 0 . 59 , 0 . 86 ) plus the CNN 32 model , repeated once for each image to give a total of 120 trials . The participant then practiced the synth vs synth condition for at least one block ( 30 trials ) , before continuing to a normal synth vs synth block ( 120 trials; scale factors of 0 . 36 , 0 . 46 , 0 . 7 , 0 . 86 , 1 . 45 ) . Over two one-hour sessions , naïve participants completed a total of four blocks of each comparison condition in alternating order ( except for one participant who ran out of time to complete the final block ) . Authors performed more blocks ( total 11 ) . In the second experiment , observers discriminated which image contained the distortion in a 2IFC paradigm . Each image was presented for 200 ms with a 1000 ms inter-stimulus interval , after which the observer had 1200 ms to respond . The original , unmodified image could appear either first or second; the other image was the same but contained the circular distortion . Observers fixated a spot ( Thaler et al . , 2013 ) in the centre of the screen . Feedback was provided , and eyetracking was not used . All observers performed 389 trials . To avoid effects of familiarity with the distortion region , each observer saw each original image only once ( that is , each original image was randomly assigned to one of the four distortion scales for each observer ) . While authors were familiar with the images , naïve observers were not . The consistency of effects between authors and naïves suggests that familiarity does not play a major role in this experiment . In the first experiment , we discarded trials for which participants made no response ( N = 66 ) and broke fixation ( N = 239 ) , leaving a total of 7555 trials for further analysis . The median number of responses for each image at each scale for each subject in each condition was 4 trials ( min 1 , max 7 ) . The individual observer data for the FS-model averaged over images ( faint lines in Figure 1F ) were based on a median of 39 trials ( min 20 , max 70 ) for each scale in each condition . The individual observer performance as a function of condition ( each psychometric function of FS-scale ) was based on a median of 192 . 5 responses ( min 136 , max 290 ) . In the second experiment we discarded trials with no response ( N = 8 ) , and did not record eye movements , leaving 1548 trials for further analysis . To quantify the critical scale as a function of the scale factor s , we used the same 2-parameter function for discriminability d′ fitted by Freeman and Simoncelli:d′ ( s ) ={α ( 1−sc2s2 ) , s>sc0 , s≤scconsisting of the critical scale sc ( below which the participant cannot discriminate the stimuli ) and a gain parameter α ( asymptotic performance level in units of d′ ) . This d′ value was transformed to proportion correct using a Weibull function as in Wallis et al . , 2016:p ( correct ) =1m+ ( 1-1m ) ( 1-exp ( - ( d′/λ ) k ) with m set to three ( the number of alternatives ) , and scale λ and shape k parameters chosen by minimising the squared difference between the Weibull and simulated results for oddity as in Craven ( 1992 ) . The posterior distribution over model parameters ( sc and α ) was estimated in a nonlinear mixed-effects model with fixed effects for the experimental conditions ( comparison and image type ) and random effects for participant ( crossed with comparison and image type ) and image ( crossed with comparison , nested within image type ) , assuming binomial variability . Note that sc here is shorthand for a population-level critical scale and group-level offsets estimated for each participant and image; scrit ( I ) is the image-specific sc estimate . Estimates were obtained by a Markov Chain Monte Carlo ( MCMC ) procedure implemented in the Stan language ( version 2 . 16 . 2 , Stan Development Team , 2017; Hoffman and Gelman , 2014 ) , with the model wrapper package brms ( version 1 . 10 . 2 , Bürkner , 2017; Bürkner , 2018 ) in the R statistical environment . MCMC sampling was conducted with four chains , each with 20 , 000 iterations ( 10 , 000 warmup ) , resulting in 40 , 000 post-warmup samples in total . Convergence was assessed using the R^ statistic ( Brooks and Gelman , 1998 ) and by examining traceplots . The model parameters were given weakly-informative prior distributions , which provide information about the plausible scale of parameters but do not bias the direction of inference . Specifically , both critical scale and gain were estimated on the natural logarithmic scale; the mean log critical scale ( intercept ) was given a Gaussian distribution prior with mean −0 . 69 ( corresponding to a critical scale of approximately 0 . 5—that is centred on the result from Freeman and Simoncelli ) and sd 1 , other fixed-effect coefficients were given Gaussian priors with mean 0 and sd 0 . 5 , and the group-level standard deviation parameters were given positive-truncated Cauchy priors with mean 0 and sd 0 . 1 . Priors for the log gain parameter were the same , except the intercept prior had mean 1 ( linear gain estimate of 2 . 72 in d′ units ) and sd 1 . The posterior distribution represents the model’s beliefs about the parameters given the priors and data . This distribution is summarised above as posterior mean , 95% credible intervals and posterior probabilities for the fixed-effects parameters to be negative ( the latter computed via the empirical cumulative distribution of the relevant MCMC samples ) . | As you read this digest , your eyes move to follow the lines of text . But now try to hold your eyes in one position , while reading the text on either side and below: it soon becomes clear that peripheral vision is not as good as we tend to assume . It is not possible to read text far away from the center of your line of vision , but you can see ‘something’ out of the corner of your eye . You can see that there is text there , even if you cannot read it , and you can see where your screen or page ends . So how does the brain generate peripheral vision , and why does it differ from what you see when you look straight ahead ? One idea is that the visual system averages information over areas of the peripheral visual field . This gives rise to texture-like patterns , as opposed to images made up of fine details . Imagine looking at an expanse of foliage , gravel or fur , for example . Your eyes cannot make out the individual leaves , pebbles or hairs . Instead , you perceive an overall pattern in the form of a texture . Our peripheral vision may also consist of such textures , created when the brain averages information over areas of space . Wallis , Funke et al . have now tested this idea using an existing computer model that averages visual input in this way . By giving the model a series of photographs to process , Wallis , Funke et al . obtained images that should in theory simulate peripheral vision . If the model mimics the mechanisms that generate peripheral vision , then healthy volunteers should be unable to distinguish the processed images from the original photographs . But in fact , the participants could easily discriminate the two sets of images . This suggests that the visual system does not solely use textures to represent information in the peripheral visual field . Wallis , Funke et al . propose that other factors , such as how the visual system separates and groups objects , may instead determine what we see in our peripheral vision . This knowledge could ultimately benefit patients with eye diseases such as macular degeneration , a condition that causes loss of vision in the center of the visual field and forces patients to rely on their peripheral vision . | [
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Decidual remodelling of midluteal endometrium leads to a short implantation window after which the uterine mucosa either breaks down or is transformed into a robust matrix that accommodates the placenta throughout pregnancy . To gain insights into the underlying mechanisms , we established and characterized endometrial assembloids , consisting of gland-like organoids and primary stromal cells . Single-cell transcriptomics revealed that decidualized assembloids closely resemble midluteal endometrium , harbouring differentiated and senescent subpopulations in both glands and stroma . We show that acute senescence in glandular epithelium drives secretion of multiple canonical implantation factors , whereas in the stroma it calibrates the emergence of anti-inflammatory decidual cells and pro-inflammatory senescent decidual cells . Pharmacological inhibition of stress responses in pre-decidual cells accelerated decidualization by eliminating the emergence of senescent decidual cells . In co-culture experiments , accelerated decidualization resulted in entrapment of collapsed human blastocysts in a robust , static decidual matrix . By contrast , the presence of senescent decidual cells created a dynamic implantation environment , enabling embryo expansion and attachment , although their persistence led to gradual disintegration of assembloids . Our findings suggest that decidual senescence controls endometrial fate decisions at implantation and highlight how endometrial assembloids may accelerate the discovery of new treatments to prevent reproductive failure .
Upon embryo implantation , the cycling human endometrium transforms into the decidua of pregnancy to accommodate the placenta ( Gellersen and Brosens , 2014 ) . Transition between these physiological endometrial states requires intensive tissue remodelling , a process termed decidualization . Notwithstanding that decidualization in early pregnancy cannot be studied directly , a spectrum of prevalent reproductive disorders is attributed to perturbations in this process , including recurrent implantation failure and recurrent pregnancy loss ( Dimitriadis et al . , 2020; Macklon , 2017; Zhou et al . , 2019 ) . By contrast , the sequence of events that renders the endometrium receptive to embryo implantation has been investigated extensively , starting with obligatory oestrogen-dependent tissue growth following menstrual repair . As a consequence of rapid proliferation of stromal fibroblasts and glandular epithelial cells ( EpCs ) , which peaks in the upper third of the functional layer ( Ferenczy et al . , 1979 ) , endometrial volume and thickness increases multifold prior to ovulation ( Raine-Fenning et al . , 2004; Dallenbach-Hellweg , 1981 ) . After the postovulatory rise in progesterone levels , proliferation of EpCs first decreases and then ceases altogether in concert with the onset of apocrine glandular secretions , heralding the start of the midluteal window of implantation ( Dallenbach-Hellweg , 1981 ) . Concurrently , uterine natural killer ( uNK ) cells accumulate and endometrial stromal cells ( EnSCs ) start decidualizing in a process that can be described as ‘inflammatory programming’ ( Brighton et al . , 2017; Chavan et al . , 2021; Erkenbrack et al . , 2018; Salker et al . , 2012 ) . Morphological decidual cells , characterized by abundant cytoplasm and enlarged nuclei , emerge upon closure of the 4-day implantation window , meaning that the endometrium has become refractory to embryo implantation ( Gellersen and Brosens , 2014 ) . In pregnancy , decidual cells form a robust , tolerogenic matrix in which invading trophoblast cells cooperate with local immune cells to form a haemochorial placenta ( Aplin et al . , 2020; Vento-Tormo et al . , 2018 ) . In non-conception cycles , however , falling progesterone levels and influx of neutrophils lead to breakdown of the superficial endometrial layer and menstrual shedding ( Jabbour et al . , 2006 ) . Recently , we highlighted the importance of cellular senescence in endometrial remodelling during the midluteal implantation window ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) . Senescence denotes a cellular stress response triggered by replicative exhaustion or other stressors that cause macromolecular damage ( Muñoz-Espín and Serrano , 2014 ) . Activation of tumour suppressor pathways and upregulation of cyclin-dependent kinase inhibitors p16INK4a ( encoded by CDKN2A ) and p21CIP1 ( CDKN1A ) lead to permanent cell cycle arrest , induction of survival genes , and production of a bioactive secretome , referred to as the senescence-associated secretory phenotype ( SASP ) . The composition of the SASP is tissue-specific but typically includes proinflammatory and immunomodulatory cytokines , chemokines , growth factors , and extracellular matrix ( ECM ) proteins and proteases ( Birch and Gil , 2020 ) . Acute senescence , characterized by transient SASP production and rapid immune-mediated clearance of senescent cells , is widely implicated in processes involving physiological tissue remodelling , including during embryo development , placenta formation , and wound healing ( Muñoz-Espín and Serrano , 2014; Van , 2014 ) . By contrast , persisting senescent cells cause chronic inflammation or ‘inflammaging’ ( Birch and Gil , 2020 ) , a pathological state that underpins ageing and age-related disorders . We demonstrated that inflammatory reprogramming of EnSC burdened by replication stress leads to the emergence of acute senescent cells during the implantation window ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) . Upon successful implantation and continuous progesterone signalling , decidual cells co-opt uNK cells to eliminate their senescent counterparts through granule exocytosis ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) . Clearance of senescent decidual cells likely necessitates recruitment of bone marrow-derived decidual precursor cells , which confer tissue plasticity for rapid decidual expansion in early pregnancy ( Diniz-da-Costa et al . , 2021 ) . Importantly , lack of clonogenic decidual precursor cells and a pro-senescent decidual response are linked to recurrent pregnancy loss ( Lucas et al . , 2016; Lucas et al . , 2020; Tewary et al . , 2020 ) . Based on these insights , we hypothesized that acute senescence is integral to successful implantation by creating conditions for anchorage of the conceptus in an otherwise tightly adherent decidual matrix . To test this hypothesis , we developed an ‘assembloid’ model , consisting of endometrial gland-like organoids and primary EnSC , which recapitulates the complexity in cell states and gene expression of the midluteal implantation window , improving resemblance to endometrial tissue in comparison with existing co-culture models ( Cheung et al . , 2021; Rawlings , 2021 ) . We used this model to establish co-cultures with human blastocysts and demonstrate that aspects of different pathological states associated with implantation failure and miscarriage can be recapitulated in endometrial assembloids by modulating decidual senescence .
Organoids consisting of gland-like structures are established by culturing endometrial EpCs seeded in Matrigel in a chemically defined medium containing growth factors and signal transduction pathway modulators ( Supplementary file 1: Table 1; Turco et al . , 2017; Boretto et al . , 2017 ) . Gland-like organoids grown in this medium , termed expansion medium , are genetically stable , easily passaged , and can be maintained in long-term cultures ( Boretto et al . , 2017; Turco et al . , 2017 ) . Oestradiol ( E2 ) promotes proliferation of gland-like organoids and cooperates with NOTCH signalling to activate ciliogenesis in a subpopulation of EpC ( Haider et al . , 2019 ) . Further , treatment with a progestin ( e . g . medroxyprogesterone acetate [MPA] ) and a cyclic AMP analogue ( e . g . 8-bromo-cAMP ) induces secretory transformation of gland-like organoids in parallel with expression of luteal-phase marker genes ( Turco et al . , 2017; Boretto et al . , 2017 ) . We modified the gland-like organoid model to incorporate EnSC . To this end , midluteal endometrial biopsies ( Supplementary file 1: Table 2 ) were digested and gland-like organoids established from isolated EpC ( Figure 1A ) . In parallel , purified EnSC were propagated in standard monolayer cultures . At passage 2 , single-cell suspensions of EnSC were combined with organoid EpC , seeded in hydrogel , and cultured in expansion medium supplemented with E2 ( Figure 1A ) . The hydrogel matrix comprised 97% type I and 3% type III collagens , which are both present in midluteal endometrium ( Oefner et al . , 2015; Aplin et al . , 1988; Aplin and Jones , 1989; Iwahashi et al . , 1996 ) , and has a predicted in-use elastic modulus ( Pa ) of comparable magnitude to non-pregnant endometrium ( Abbas et al . , 2019; Bagley , 2019 ) . As shown in Figure 1B , gland formation was unperturbed by the presence of EnSC and assembloids resembled the architecture of native endometrium more closely than organoids . Further , decidualization of assembloids with 8-bromo-cAMP and MPA for 4 days ( Figure 1C ) resulted in robust secretion of decidual prolactin ( PRL ) and C-X-C motif chemokine ligand 14 ( CXCL14 ) ( Figure 1D ) . Immunofluorescence microscopy provided further evidence that decidualizing assembloids mimic luteal phase endometrium , exemplified by laminin deposition by decidualizing EnSC , induction of osteopontin ( SPP1 ) and accumulation of glycodelin ( encoded by PAEP ) in the lumen of secretory glands , and downregulation of the progesterone receptor ( PGR ) in both stromal and glandular compartments ( Figure 1E ) . We reasoned that once established assembloids may no longer require exogenous growth factors and pathway modulators for differentiation because of the presence of EnSC . To test this hypothesis , parallel gland-like organoids and assembloids were established from three endometrial biopsies and decidualized with E2 , 8-bromo-cAMP , and MPA for 4 days in either expansion medium , base medium ( Supplementary file 1: Table 1 ) , or base medium with each exogeneous factor added back individually . Induction of PAEP and SPP1 was used to monitor the glandular differentiation response . As shown in Figure 2 , differentiation of gland-like organoids in base medium markedly blunted the induction of PAEP and SPP1 when compared to expansion medium . Add-back of individual factors did not restore the glandular response , with the exception of N-acetyl-L-cysteine ( NAC ) . Addition of NAC at low concentration ( 1 . 25 mM ) to base medium resulted in a robust glandular response in assembloids . Thus , in subsequent experiments assembloids were grown in expansion medium supplemented with E2 and then decidualized in minimal differentiation medium ( MDM ) , consisting of base medium containing NAC , E2 , 8-bromo-cAMP , and MPA . We hypothesized that , depending on the level of replicative stress ( Lucas et al . , 2020; Abbas et al . , 2019 ) , individual EpC and EnSC adopt distinct cellular states upon decidualization of endometrial assembloids . Based on previous time-course experiments in 2D cultures , we further speculated that divergence of cells into distinct subpopulations would be apparent by day 4 of differentiation ( Brosens et al . , 1999; Lucas et al . , 2020 ) . To test this hypothesis , we performed single-cell RNA sequencing ( scRNA-seq ) on undifferentiated assembloids grown for 4 days in expansion medium and assembloids decidualized in MDM for four additional days ( Figure 3A ) . Eleven distinct cell clusters were identified by Shared Nearest Neighbour ( SNN ) and Uniform Manifold Approximation and Projection ( UMAP ) analysis , segregating broadly into epithelial and stromal populations within the UMAP-1 dimension and into undifferentiated and differentiated subpopulations within the UMAP-2 dimension ( Figure 3B ) . Each cell cluster was annotated based on expression of curated marker genes , which were cross-referenced with a publicly available data set ( GEO: GSE4888 ) to determine their relative expression across the menstrual cycle in vivo ( Talbi et al . , 2006 ) . We identified five unambiguous EpC subsets . The glandular component of undifferentiated assembloids harboured actively dividing EpC ( EpS1; n = 198 ) as well as EpC-expressing marker genes of E2-responsive proliferative phase endometrium ( EpS2; n = 692 ) , including PGR and CPM ( Figure 3C ) . EpS3 ( n = 29 ) consisted of ciliated EpC , expressing an abundance of genes involved in cilium assembly and organization , including DNAI1 and TUBA4B ( Figure 3C ) . Ciliated cells are the only glandular subpopulation present in both undifferentiated and decidualized assembloids . In vivo , EpS3 marker genes transiently peak during the early-luteal phase ( Figure 3C ) . Decidualization of endometrial assembloids led to the emergence of two distinct EpC subsets , EpS4 ( n = 434 ) and EpS5 ( n = 208 ) . Both clusters expressed canonical endometrial ‘receptivity genes’ ( annotated in green in Figure 3C ) , that is , genes used in a clinical test to aid the timing of embryo transfer to the window of implantation in IVF patients ( Díaz-Gimeno et al . , 2011 ) . In agreement , induction of EpS4 and EpS5 marker genes in vivo coincides with the transition from early- to midluteal phase . However , while expression of EpS4 marker genes , including SOD2 , MAOA , and PTGS1 , generally peaks during the midluteal window of implantation , EpS5 genes tend to persist or peak during the late-luteal phase ( Figure 3C ) . Additional mining of the data revealed that transition from EpS4 to EpS5 coincides with induction of p16INK4a and p21CIP1 in parallel with upregulation of 56 genes encoding secretory factors ( Figure 3—figure supplement 1 ) . Notably , several canonical implantation factors secreted by this subpopulation are also well-characterized SASP components , including dipeptidyl peptidase 4 ( DPP4; Kim et al . , 2017 ) , growth differentiation factor 15 ( GDF15; Basisty et al . , 2020 ) , and insulin-like growth factor binding protein 3 ( IGFBP3; Elzi et al . , 2012 ) . Thus , EpS5 consists of senescent EpC producing an implantation-specific SASP . Decidualized endometrial assembloids also harboured a sizable population of ambiguous cells expressing both epithelial and stromal genes ( Figure 3C and Figure 3—figure supplement 2 ) . A hallmark of this subset , termed ‘transitional population’ ( TP; n = 472 ) , is the induction of long non-coding RNAs involved in mesenchymal-epithelial and epithelial-mesenchymal transition ( MET/EMT ) , such as NEAT1 ( nuclear paraspeckle assembly transcript 1 ) and KCNQ1OT1 ( KCNQ1 opposite strand/antisense transcript 1 ) ( Bian et al . , 2019; Chen et al . , 2021 ) . GO analysis showed that both EpS5 and the transitional population comprised secretory cells involved in ECM organization ( Figure 3D ) . However , while EpS5 genes are implicated in neutrophil activation ( a hallmark of premenstrual endometrium ) , genes expressed by the transitional population are uniquely enriched in GO terms such as ‘wound healing’ , ‘regulation of stem cell proliferation’ , ‘blood coagulation’ , and ‘blood vessel development’ ( Figure 3D ) , which points towards a putative role in tissue repair and regeneration . The stromal fraction of undifferentiated assembloids consisted of actively dividing EnSC ( stromal subpopulation 1 [SS1]; n = 434 ) and E2-responsive EnSC ( SS2; n = 874 ) expressing proliferative phase marker genes , such as PGR , MMP11 , and CRABP2 ( Figure 3E ) . As anticipated , decidualization of assembloids for 4 days led to a preponderance of pre-decidual cells ( SS3; n = 495 ) as well as emerging decidual cells ( SS4; n = 87 ) and senescent decidual cells ( SS5; n = 118 ) ( Figure 3E ) . Each of these subpopulations expressed marker genes identified previously by scRNA-seq reconstruction of the decidual pathway in standard primary EnSC cultures ( Lucas et al . , 2020 ) . Pre-decidual cells in SS3 express HAND2 , a key decidual transcription factor ( Marinić et al . , 2021 ) , as well as previously identified genes encoding secreted factors , including VEGFA ( vascular endothelial growth factor A ) , CRISPLD2 ( a progesterone-dependent anti-inflammatory response gene coding cysteine-rich secretory protein LCCL domain containing 2 ) , IL15 ( interleukin 15 ) , and TIMP3 ( TIMP metallopeptidase inhibitor 3 ) ( Lucas et al . , 2020 ) . Novel candidate pre-decidual genes were also identified , such as DDIT4 ( DNA damage-inducible transcript 4 ) , encoding a stress response protein intimately involved in autophagy , stemness , and antioxidative defences ( Ho et al . , 2020; Miller et al . , 2020 ) . Decidual cells ( SS4 ) and senescent decidual cells ( SS5 ) express SCARA5 and DIO2 , respectively ( Figure 3E ) , two stroma-specific marker genes identified by scRNA-seq analysis of mid- and late-luteal endometrial biopsies ( Lucas et al . , 2020 ) . SS3 and SS4 genes mapped to the early- and midluteal phase of the cycle , whereas SS5 genes peak in the late-luteal phase , that is , prior to menstrual breakdown . Notably , the transcriptomic profiles of SS3 and SS5 are enriched in GO terms such as ‘Wound healing’ , ‘Response to hypoxia’ , and ‘Inflammatory response’ , suggesting that both clusters comprise stressed cells ( Figure 3F ) . However , the nature of the cellular stress response differs between these populations with only senescent decidual cells ( SS5 ) expressing genes enriched in categories such as ‘Embryo implantation’ , ‘Cellular senescence’ , ‘Aging’ , and ‘Leukocyte activation’ . By contrast , few notable categories were selectively enriched in decidual cells ( e . g . ‘Mesenchymal cell differentiation’ ) , rendering the lack of GO terms that pertain to stress , inflammation , or wound healing perhaps the most striking observation . In keeping with the GO analysis , senescent decidual cells ( SS5 ) express a multitude of SASP-related genes ( Figure 3—figure supplement 3 ) , including matrix metallopeptidases ( e . g . MMP3 , 7 , 9 , 10 , 11 , and 14 ) , insulin-like growth factor binding proteins ( e . g . IGFBP1 , 3 , 6 , and 7 ) , growth factors ( e . g . AREG , FGF2 , FGF7 , HGF , and VEGFA ) and growth factor receptors ( PDGFRA and PDGFRB ) , cytokines ( e . g . LIF , IL6 , IL1A , and IL11 ) , chemokines ( e . g . CXCL8 and CXCL1 ) , and members of the TGF-β superfamily of proteins ( e . g . GDF15 , INHBA , and BMP2 ) . By contrast , decidual cells are characterized by expression of a unique network of secretory genes , some encoding ECM proteins ( e . g . COL1A1 , COL3A1 , and LAMA4 ) and other known decidual markers ( e . g . PRL , PROK1 , and WNT4 ) as well as factors involved in uNK cell chemotaxis and activation ( e . g . CCL2 , CXCL14 , and IL15 ) ( Figure 3—figure supplement 3 ) . Taken together , single-cell analysis of undifferentiated and decidualized assembloids revealed a surprising level of cellular complexity . Each epithelial and stromal subpopulation appears functionally distinct and maps to a specific phase of the menstrual cycle . Transition between cellular states is predicated on changes in cell cycle status , ranging from actively dividing cells in proliferating assembloids to the emergence upon differentiation of highly secretory senescent epithelial and decidual subpopulations , resembling premenstrual endometrium . However , the dominant subpopulations on day 4 of decidualization are EpS4 and SS3 , which map to the midluteal implantation window in vivo . We used CellPhoneDB , a publicly available online repository of highly curated receptor-ligand interactions , to explore putative interactions between subpopulations in decidualizing assembloids . This computational tool also takes into account the subunit architecture of both ligands and receptors in heteromeric complexes ( Efremova et al . , 2020; Vento-Tormo et al . , 2018 ) . The number of predicted interactions is depicted in Figure 4A , showing a conspicuous lack of crosstalk between the transitional population and any other populations . Conversely , the most abundant interactions centre around the secretory subpopulations , EpS5 and SS5 . A total of 270 significantly enriched ( non-integrin ) receptor-ligand interactions ( FDR-corrected p<0 . 05 ) were identified between epithelial and stromal subsets in decidualizing assembloids ( Figure 4—source data 1 ) , a representative selection of which are shown in Figure 4B . Within the multitude of predicted complex interactions , three broad categories can be discerned . First , there are non-selective interactions involving ligands produced by all subpopulations in one compartment acting on receptors expressed by all subsets in the other compartment . Second , there are semi-selective stromal-epithelial interactions involving three or four subpopulations across both compartments . For example , binding of WNT5A secreted by all decidual stromal subsets to FZD3 ( frizzled class receptor 3 ) expressed on all EpCs represents a non-selective receptor-ligand interaction , whereas binding of WNT5A or WNT4 to FZD6 is a predicted semi-selective interaction , involving all stromal subsets ( SS3-5 ) and EpS4 but not EpS5 ( Figure 4B ) . While FZD3 activates the canonical β-catenin pathway , FZD6 functions as a negative regulator of this signalling cascade ( Corda and Sala , 2017 ) . Finally , we identified only three highly selective receptor-ligand interactions ( Figure 4B ) , two of which involved secretion of decidual ligands , prolactin ( PRL ) and C-X-C motif chemokine ligand 12 ( CXCL12 ) , acting on their cognate receptors expressed on receptive EpC ( EpS4 ) . CXCL12-dependent activation of C-X-C motif chemokine receptor 4 ( CXCR4 ) has been shown to promote motility of EpC ( Zheng et al . , 2020 ) , whereas PRL is a lactogenic hormone that stimulates glandular secretion in early pregnancy ( Burton et al . , 2020 ) . In contrast to stromal-epithelial communication , non-selective interactions are predicted to be rare between decidual subsets . Instead , communication appears governed largely by a combinatorial network of receptor-ligand interactions ( Figure 4C ) . For example , colony stimulating factor 3 ( CSF3 ) and vascular endothelial growth factor A ( VEGFA ) produced by senescent decidual cells ( SS5 ) are predicted to impact selectively on pre-decidual cells ( SS3 ) , whereas secretion of inhibin A ( INHBA ) may engage both pre-decidual and decidual cells ( SS4 ) . Other interactions are predicted to govern crosstalk between SS3 and SS4 , such as modulation of the WNT pathway in response to binding of R-spondin 3 ( RSPO3 ) to leucine-rich repeat-containing G protein-coupled receptor 4 ( LGR4 ) . A striking observation is the overrepresentation of receptor tyrosine kinases implicated in SS3 and SS5 signal transduction as well as the involvement of receptors that signal through downstream cytoplasmic tyrosine kinases , including CSF3 receptor ( CSF3R ) and CD44 ( Figure 4C; Corey et al . , 1998; van der Voort et al . , 1999 ) . The CellPhoneDB analysis inferred that epithelial-stromal crosstalk in assembloids is robust , buffered by numerous non-selective interactions , whereas decidual subsets are reliant on selective receptor-ligand interactions and activation of distinct signal transduction pathways . For example , the predicted tyrosine kinase dependency of pre-decidual ( SS3 ) and senescent decidual cells ( SS5 ) raised the possibility that these subpopulations can be targeted by tyrosine kinase inhibitors , such as dasatinib ( Brighton et al . , 2017; Zhu et al . , 2015 ) , a second-generation , broad-spectrum ATP-competitive protein tyrosine kinase inhibitor ( Aguilera and Tsimberidou , 2009; Li et al . , 2010 ) . To test this supposition , we generated single-cell transcriptomic profiles of assembloids decidualized for 4 days in the presence of dasatinib ( Figure 5A ) . We found that decidualization in the presence of dasatinib had a dramatic impact on stromal subpopulations , virtually eliminating senescent decidual cells ( SS5 , n = 7 ) and increasing the abundance of decidual cells ninefold ( SS4 , n = 882; Figure 5B ) . Apart from a modest reduction in pre-decidual cells ( SS3 ) , dasatinib also impacted markedly on transitional cells , reducing their numbers by 76% . By contrast , the effect on epithelial populations was confined to a modest reduction in senescent EpC ( EpS5 ) ( Figure 5B ) . Further , relatively few genes were perturbed significantly ( FDR-corrected p<0 . 05 ) upon dasatinib treatment in epithelial populations ( Figure 5C ) . In the stroma , dasatinib triggered a conspicuous transcriptional response in pre-decidual ( SS3 ) and transitional cells , whereas gene expression in decidual cells ( SS4 ) and the few remaining senescent decidual cells ( SS5 ) was largely unaffected ( Figure 5C ) . In transitional cells , dasatinib simultaneously upregulated genes encoding canonical mesenchymal markers ( e . g . SNAI2 , TWIST2 , ZEB1 , COL1A1 , and FBN1; Owusu-Akyaw et al . , 2019 ) and decidual factors ( e . g . SCARA5 , FOXO1 , GADD45A , IL15 , CXCL14 , and SGK1; Gellersen and Brosens , 2014 ) , suggesting that MET accounts for the emergence of this population upon decidualization ( Figure 5—source data 1 ) . In pre-decidual cells , dasatinib inhibited the expression of a network of genes enriched in GO categories such as ‘Response to wounding’ ( FDR-corrected p=3 . 5 × 10–5 ) , ‘Response to stress’ ( FDR-corrected p=3 . 8 × 10–5 ) , and ‘Response to oxidative stress’ ( FDR-corrected p=1 . 3 × 10–4 ) , indicative of a blunted stress response . To substantiate this finding , we measured the secreted levels of CXCL8 ( IL-8 ) , a potent inflammatory mediator implicated in autocrine/paracrine propagation of cellular senescence ( Acosta et al . , 2008; Kuilman et al . , 2008 ) , in assembloids decidualized with or without dasatinib . CXCL14 , IL-15 , and TIMP3 levels were also measured to monitor the decidual response . As shown in Figure 5D , dasatinib completely abrogated the release of CXCL8 by pre-decidual cells while markedly enhancing subsequent secretion of CXCL14 , IL-15 , and TIMP3 , which are involved in effecting immune clearance of senescent decidual cells ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) . Together , these observations not only support the CellPhoneDB predictions but also indicate that the amplitude of the cellular stress response during the pre-decidual phase determines the subsequent decidual trajectory , with low levels accelerating differentiation and high levels promoting cellular senescence and MET . We postulated that decidual invasion by human embryos that have breached the luminal endometrial epithelium depends on an acute cellular senescence and transient SASP production , rich in growth factors and proteases . Conversely , we reasoned that lack of senescent decidual cells or unconstrained SASP should simulate pathological implantation environments associated with implantation failure and early pregnancy loss , respectively . To test this hypothesis , we constructed a simple implantation model by embedding human embryos in endometrial assembloids . To this end , assembloids were first decidualized for 96 hr in the presence or absence of dasatinib , washed and cultured in embryo medium , consisting of MDM with added supplements ( Figure 6A and Supplementary file 1: Table 1 ) . Day 5 human blastocysts were placed into small pockets created in the decidualized assembloids ( Figure 6B ) , one embryo per assembloid , and individual co-cultures imaged using time-lapse microscopy over 72 hr . Co-cultured blastocysts ( n = 5 ) expanded markedly when placed in decidualized assembloids that were not pre-treated with dasatinib ( Figure 6C and D ) . Time-lapse microscopy revealed intense cellular movement in the stromal compartment as well as evidence that interaction between migratory decidual cells and polar trophectoderm promotes adherence and early invasion of the embryo ( SI Video 1 and Figure 6—figure supplement 1 ) . Retrieval and processing of one attached embryo demonstrated proliferating polar trophectoderm and expression of OCT4 and GATA6 in the epiblast and hypoblast , respectively ( Figure 6E ) . A major limitation of this implantation model is that persistence of senescent decidual cells also causes gradual disintegration of the assembloids ( Figure 6—figure supplement 2 ) . By contrast , pre-treatment with dasatinib , which accelerates decidualization and all but eliminates decidual senescence , resulted in much more robust assembloids . However , all embedded blastocysts ( n = 5 ) failed to expand in this model ( Figure 6C and D ) . Further , movement of the decidual matrix was greatly reduced and directed migration or attachment of decidual cells to the blastocyst was not observed ( SI Video 2 ) . Secreted levels of human chorionic gonadotropin ( hCG ) did not differ between co-cultures ( Figure 6E ) , suggesting that all embryos remained viable over the 72 hr observation period . Thus , while our experimental design precluded modelling of physiological embryo implantation , aspects of different pathological endometrial states underlying reproductive failure , that is , implantation failure and miscarriage , were recapitulated in assembloids .
Here we report on the development of endometrial assembloids , consisting of gland-like organoids surrounded by a matrix rich in primary EnSC , as novel model to parse the cellular dynamics that govern embryo implantation in cycling human endometrium . While assembloids complement and advance other recently described endometrial organoid models ( Boretto et al . , 2017; Cheung et al . , 2021; Fitzgerald et al . , 2019; Luddi et al . , 2020; Turco et al . , 2017 ) , they still lack the cellular complexity of native endometrium , including uNK cells , macrophages , and vascular cells . Nevertheless , we demonstrated that aspects of pathological implantation events can be recapitulated in assembloids , rendering them useful as novel models to study mechanisms of reproductive failure and evaluate potential therapeutic interventions . Single-cell analysis of differentiating endometrial assembloids indicates that the sequence of events leading up to the implantation window , and beyond , requires divergence of both glandular EpC and EnSC into differentiated and senescent subpopulations , a process likely determined by the level of replication stress incurred by individual cells in the preceding proliferative phase ( Brighton et al . , 2017 ) . Importantly , we demonstrate that acute senescence in glandular EpC ( EpS5 ) underpins production of an implantation-specific SASP , comprising canonical implantation factors and growth factors , such as amphiregulin ( AREG ) and epiregulin ( EREG ) , implicated in transforming cytotrophoblasts into extravillous trophoblasts ( Cui et al . , 2020; Yu et al . , 2019 ) . On the other hand , the transcriptome profile of differentiated EpC ( EpS4 ) revealed a pivotal role for this subpopulation in prostaglandin and glycodelin synthesis . Prostaglandins , and specifically PGE2 , are indispensable for implantation ( Ruan et al . , 2012 ) , whereas glycodelin is an abundantly secreted , multifaceted glycoprotein involved in blastocyst attachment , trophoblast differentiation , and immune modulation in early pregnancy ( Lee et al . , 2016 ) . Further , differentiated EpC highly express SLC2A1 , encoding the major glucose transporter GLUT1 . Glucose is required for glycogen synthesis , an essential component of glandular secretions that nourishes the conceptus prior to the onset of placental perfusion around 10 weeks of pregnancy ( Burton et al . , 2020 ) . The fate and function of senescent EpC in pregnancy are unknown . Arguably , localized secretion of proteinases by senescent EpC may promote breakdown of the surrounding basement membrane , thereby facilitating endoglandular trophoblast invasion and access to histotrophic nutrition in early gestation ( Huppertz , 2019; Moser et al . , 2010 ) . In non-conception cycles , the abundance of p16INK4-positive glandular EpC rises markedly during the late-luteal phase ( Brighton et al . , 2017 ) , indicating that senescent EpC are progesterone-independent and likely responsible for glandular breakdown in the superficial endometrial layer at menstruation . Decidual transformation of EnSC in assembloids unfolded largely as anticipated from previous studies , that is , starting with an acute pre-decidual stress response and leading to the emergence of both decidual and senescent decidual subpopulations ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) . Like their epithelial counterparts , senescent decidual cells have a conspicuous secretory phenotype . We identified 56 and 72 genes encoding secreted factors upregulated in senescent epithelial and decidual subpopulations , respectively . However , only 15 genes were shared , indicating that the SASP generated in both cellular compartments is distinct . As glandular secretions drain into the uterine cavity , the embryonic microenvironment is therefore predicted to change abruptly upon breaching of the luminal epithelium . Recent comparative metabolomics of apical and basolateral endometrial gland-like organoid secretomes also supports the prediction of an asymmetrical profile of glandular secretions in the pre- and post-implantation microenvironments ( Simintiras et al . , 2021 ) . Based on computational predictions of ligand-receptor interactions , we demonstrated that the decidual response in assembloids can be targeted pharmacologically with only modest impact on glandular function and , by extension , the preimplantation embryo milieu . Specifically , dasatinib , a tyrosine kinase inhibitor , was highly effective in blunting the pre-decidual stress response , leading to a dramatic expansion of anti-inflammatory decidual cells and near-total elimination of senescent decidual cells . Dasatinib also inhibited the emergence of TP and shifted the transcriptional profile of the remaining transitional cells towards a decidual phenotype . An analogous population of ambiguous cells expressing both epithelial and mesenchymal marker genes was recently identified in midluteal endometrium by scRNA-seq analysis ( Lucas et al . , 2020 ) . Further , based on CellPhoneDB and GO analyses , transitional cells are predicted to be highly autonomous and involved in tissue regeneration , in line with experimental evidence that MET drives re-epithelization of the endometrium following menstruation and parturition ( Owusu-Akyaw et al . , 2019; Patterson et al . , 2013 ) . Thus , the level of endogenous cellular stress generated by the endometrium during the window of implantation calibrates the subsequent decidual trajectory , either promoting the formation of a robust decidual matrix or facilitating tissue breakdown and repair . Further , an in-built feature of both trajectories is self-enforcement as decidual cells recruit and activate uNK cells to eliminate their senescent counterparts ( Brighton et al . , 2017; Lucas et al . , 2020; Kong et al . , 2021 ) , whereas senescent decidual cells induce secondary senescence in neighbouring decidual ( Brighton et al . , 2017; Ozaki et al . , 2017 ) and , plausibly , uNK cells ( Rajagopalan and Long , 2012 ) . Clinically , recurrent pregnancy loss , defined as multiple miscarriages , is associated with loss of endometrial clonogenicity ( Lucas et al . , 2016; Diniz-da-Costa et al . , 2021 ) , uNK cell deficiency and excessive decidual senescence ( Lucas et al . , 2020; Tewary et al . , 2020 ) , and rapid conceptions ( also referred to as ‘superfertility’ ) ( Dimitriadis et al . , 2020; Ticconi et al . , 2020 ) . Conversely , lack of a proliferative gene signature in midluteal endometrium and premature expression of decidual PRL have been linked to recurrent implantation failure ( Berkhout et al . , 2020b; Koler et al . , 2009; Koot et al . , 2016 ) , a pathological condition defined by a failure to achieve a pregnancy following transfer of one or more high-quality embryos in multiple IVF cycles ( Polanski et al . , 2014 ) . We reasoned that these aberrant implantation environments can be recapitulated in assembloids by manipulating the level of decidual senescence . In line with these predictions , the presence of senescent decidual cells created a permissive environment in which migratory decidual cells interacted with expanding blastocysts , although continuous SASP production also promoted breakdown of the assembloids . Conversely , in the absence of senescent decidual cells , non-expanding embryos became entrapped in a robust but stagnant decidual matrix . These observations are in keeping with previous studies demonstrating that implantation of human embryos depends critically on the invasive and migratory capacities of decidual cells ( Berkhout et al . , 2020a; Gellersen et al . , 2010; Grewal et al . , 2008; Weimar et al . , 2012 ) . Our co-culture experiments also highlighted the shortcomings of assembloids as an implantation model , including the lack of a surface epithelium to create distinct pre- and post-implantation microenvironments and the absence of key cellular constituents , such as innate immune cells . In summary , parsing the mechanisms that control implantation has been hampered by the overwhelming complexity of factors involved in endometrial receptivity . Our single-cell analysis of decidualizing assembloids suggests that this complexity reflects the reliance of the human endometrium on rapid E2-dependent proliferation and replicative exhaustion to generate both differentiated and senescent epithelial and stromal subpopulations in response to the postovulatory rise in progesterone . We demonstrate that senescent cells in both cellular compartments produce distinct bioactive secretomes , which plausibly prime pre-implantation embryos for interaction with the luminal epithelium and then stimulate encapsulation by underlying decidual stromal cells . Based on our co-culture observations , we predict that a blunted pre-decidual stress response causes implantation failure because of a lack of senescence-induced tissue remodelling and accelerated decidualization . Conversely , a heightened stress response leading to excessive decidual senescence may render embryo implantation effortless , albeit in a decidual matrix destined for breakdown and repair . Finally , we demonstrated that pre-decidual stress responses can be modulated pharmacologically , highlighting the potential of endometrial assembloids as a versatile system to evaluate new or repurposed drugs aimed at preventing reproductive failure .
Endometrial biopsies were obtained from women attending the Implantation Research Clinic , University Hospitals Coventry and Warwickshire National Health Service Trust . Written informed consent was obtained in accordance with the Declaration of Helsinki 2000 . The study was approved by the NHS National Research Ethics Committee of Hammersmith and Queen Charlotte’s Hospital NHS Trust ( 1997/5065 ) and Tommy’s Reproductive Health Biobank ( Project TSR19-002E , REC Reference: 18/WA/0356 ) . Timed endometrial biopsies were obtained 6–11 days after the post-ovulatory LH surge using a Wallach Endocell Endometrial Cell Sampler . Patient demographics for the samples used in each experiment are detailed in Supplementary file 1: Table 2 . The use of vitrified human blastocysts was carried out under a Human Fertilisation and Embryology Authority research licence ( HFEA: R0155 ) with local National Health Service Research Ethics Committee approval ( 04/Q2802/26 ) . Spare blastocysts were donated to research following informed consent by couples who had completed their fertility treatment at the Centre for Reproductive Medicine , University Hospitals Coventry and Warwickshire National Health Service Trust . Briefly , women underwent ovarian stimulation and oocytes were collected by transvaginal ultrasound-guided aspiration and inseminated with prepared sperm ( day 0 ) . All oocytes examined 16–18 hr after insemination and classified as normally fertilized were incubated under oil in 20–25 µl drops of culture media ( ORIGIO Sequential Cleav and Blast media , CooperSurgical , Denmark ) at 5% O2 , 6% CO2 , 89% N2 at 37°C . Following culture to day 5 of development , the embryo ( s ) with the highest quality was selected for transfer , whereas surplus embryos considered top-quality blastocysts were cryopreserved on day 5 or 6 by vitrification using Kitazato vitrification media ( Dibimed , Spain ) and stored in liquid nitrogen . Prior to their use in the co-culture , vitrified blastocysts were warmed using the Kitazato vitrification warming media ( Dibimed , Spain ) and underwent zona pellucida removal using a Saturn 5 Laser ( CooperSurgical ) . Blastocysts were then incubated for 1 hr under oil in 20 µl drops of culture media ( ORIGIO Sequential Blast media , CooperSurgical ) at 5% O2 , 6% CO2 , 89% N2 at 37°C and allowed to re-expand . Unless otherwise stated , reagents were obtained from Life Technologies ( Paisley , UK ) . Cell cultures were incubated at 37°C , 5% CO2 in a humidified incubator . Centrifugation and incubation steps were performed at room temperature unless stated otherwise . Fresh endometrial biopsies were processed as described previously ( Barros et al . , 2016 ) . Briefly , tissue was finely minced for 5 min using a scalpel blade . Minced tissue was then digested enzymatically with 0 . 5 mg/ml collagenase I ( Sigma-Aldrich , Gillingham , UK ) and 0 . 1 mg/ml deoxyribonuclease ( DNase ) type I ( Lorne Laboratories , Reading , UK ) in 5 ml phenol red-free Dulbecco’s Modified Eagle Medium ( DMEM ) /F12 for 1 hr at 37°C , with regular vigorous shaking . Dissociated cells were washed with growth medium ( DMEM/F12 containing 10% dextran-coated charcoal stripped FBS [DCC-FBS] , 1% penicillin-streptomycin , 2 mM L-glutamine , 1 nM E2 [Sigma-Aldrich] and 2 mg/ml insulin [Sigma-Aldrich] ) . Samples were passed through a 40 µm cell sieve . EnSCs were collected from the flowthrough , while epithelial clumps remained in the sieve and were collected by backwashing into a 50 ml Falcon tube . Samples were resuspended in growth medium and centrifuged at 400× g for 5 min . EnSC pellets were resuspended in 10 ml growth medium and plated in tissue culture flasks . To isolate EnSC from other ( non-adherent ) cells collected in the flowthrough , medium was refreshed after 24 hr . Thereafter , medium was refreshed every 48 hr . Sub-confluent monolayers were passaged using 0 . 25% Trypsin-EDTA and split at a ratio of 1:3 . Endometrial gland-like organoids were established as described previously ( Turco et al . , 2017 ) , with adaptations . Freshly isolated endometrial gland fragments were resuspended in 500 µl phenol red-free DMEM/F12 medium in a microcentrifuge tube and centrifuged at 600× g for 5 min . The medium was aspirated and ice-cold , growth factor-reduced Matrigel ( Corning Life Sciences B . V . , Amsterdam , Netherlands ) was added at a ratio of 1:20 ( cell pellet: Matrigel ) . Samples mixed in Matrigel were kept on ice until plating at which point the suspension was aliquoted in 20 µl volumes to a 48-well plate , one drop per well , and allowed to cure for 15 min . Expansion medium supplemented with E2 ( Supplementary file 1: Table 1; Turco et al . , 2017 ) was then added and samples cultured for up to 7 days . For passaging , Matrigel droplets containing gland-like organoids were collected into microcentrifuge tubes and centrifuged at 600× g for 6 min at 4°C . Samples were resuspended in ice-cold , phenol red-free DMEM/F12 and subjected to manual pipetting to disrupt the organoids . Suspensions were centrifuged again , resuspended in ice-cold additive-free DMEM/F12 , and then subjected to further manual pipetting . Suspensions were centrifuged again and either resuspended in Matrigel and plated as described above for continued expansion or used to establish assembloid cultures . At passage 2 , EnSC and gland-like organoid pellets were mixed at a ratio of 1:1 ( v/v ) and ice-cold PureCol EZ Gel ( Sigma-Aldrich ) added at a ratio of 1:20 ( cell pellet: hydrogel ) . Samples were kept on ice until plating . The suspension was aliquoted in 20 µl volumes using ice-cold pipette tips into a 48-well plate , one droplet per well , and allowed to cure in the cell culture incubator for 45 min . Expansion medium supplemented with 10 nM E2 was overlaid and the medium was refreshed every 48 hr . For decidualization experiments , assembloid cultures were grown in expansion medium supplemented with E2 for 4 days to allow for growth and expansion . Assembloids were then either harvested or decidualized using different media as tabulated in Supplementary file 1: Table 1 for a further 4 days . Again , the medium was refreshed every 48 hr and spent medium stored for further analysis . For tyrosine kinase inhibition , MDM was supplemented with 250 nM dasatinib ( Cell Signaling Technology , Leiden , NL ) . For fluorescent microscopy , assembloids were removed from culture wells and transferred into tubes for fixation . Samples were washed in PBS and fixed in 10% neutral buffered formalin in the tube for 15 min , then washed three times with PBS , and stored for use . Samples were dehydrated in increasing concentrations of ethanol ( 70% then 90% for 1 hr each , followed by 100% for 90 min ) , then incubated in xylene for 1 hr . After paraffin wax embedding , 5 µm sections were cut and mounted , then incubated overnight at 60°C . Slides were then stored at 4°C until further processing . De-paraffinization and rehydration were performed through xylene , 100% isopropanol , 70% isopropanol , and distilled water incubations . Following antigen retrieval , permeabilization was performed where appropriate by incubation with 0 . 1% Triton X-100 for 30 min . Slides were then washed , blocked , and incubated in primary antibodies overnight at 4°C . Antibody details are presented in Supplementary file 1: Table 3 . After washing three times , slides were incubated with secondary antibodies for 2 hr , then washed as before and mounted in ProLong Gold Antifade Reagent with DAPI ( Cell Signaling Technology ) . Slides were visualized using the EVOS Auto system , with imaging parameters maintained throughout image acquisition . Images were merged in ImageJ and any adjustments to brightness or contrast were applied equally within comparisons . After removal of spent medium , gland-like organoid cultures were washed in PBS and harvested in 200 µl Cell Recovery Solution ( Corning ) . Gel droplets were transferred to nuclease-free microcentrifuge tubes and placed at 4°C for 30 min . Samples were then washed in PBS , centrifuged at 600× g for 6 min twice , and snap frozen as cell pellets . Assembloid cultures were washed with PBS and then recovered by directly scraping the samples into nuclease-free microcentrifuge tubes . Samples were centrifuged at 600× g for 6 min . The cellular pellet was resuspended in 500 µl of 500 µg/ml collagenase I diluted in additive-free DMEM/F12 and incubated at 37°C for 10 min with regular manual shaking . Samples were washed twice in PBS , with centrifugation at 600× g for 6 min , then cell pellets were snap frozen . RNA extraction was performed using the RNeasy Micro Kit ( QIAGEN , Manchester , UK ) according to the manufacturer’s instructions . RNA concentration and purity were determined using a NanoDrop ND-1000 . All RNA samples were stored at –80°C until use . Reverse transcription was performed using the QuantiTect Reverse Transcription ( RT ) Kit according to the manufacturer’s protocol ( QIAGEN ) . Input RNA was determined by the sample with lowest concentration within each experiment . Genes of interest were amplified using PrecisionPlus SYBR Green Mastermix ( PrimerDesign , Southampton , UK ) . Amplification was performed in 10 µl reactions containing 5 μl PrecisionPlus 2× master mix , 300 nM each of forward and reverse primer , nuclease-free water , and 1 µl of cDNA or water control . Amplification was performed for 40 cycles on an Applied Biosystems QuantStudio 5 Real-Time PCR System ( qPCR ) . Data were analysed using the Pffafl method ( Pfaffl , 2001 ) and L19 was used as a reference gene . Primer sequences were as follows: L19 forward: 5′-GCG GAA GGG TAC AGC CAA T-3′ , L19 reverse: 5′-GCA GCC GGC GCA AA-3′ , PAEP forward: 5′-GAG CAT GAT GTG CCA GTA CC-3′ , PAEP reverse: 5′-CCT GAA AGC CCT GAT GAA TCC-3′ , SPP1 forward: 5′-TGC AGC CTT CTC AGC CAA A-3′ , SPP1 reverse: 5′-GGA GGC AAA AGC AAA TCA CTG-3′ . Spent medium was collected every two days during a 4-day decidual time course , with or without dasatinib treatment . Duoset solid-phase sandwich enzyme-linked immunosorbent assay ( ELISA ) kits ( Bio-Techne , Abingdon , UK ) were used for the detection of PRL ( DY682 ) , TIMP3 ( DY973 ) , IL-8 ( DY208 ) , IL-15 ( DY247 ) , CXCL14 ( DY866 ) , and HCG ( DY9034 ) . Assays were performed according to the manufacturer’s instructions . Absorbance at 450 nm was measured on a PheraStar microplate reader ( BMG LABTECH Ltd , Aylesbury , UK ) , with background subtraction from absorbance measured at 540 nm . Protein concentration was obtained using a four-parameter logistic regression analysis and interpolation from the curve . As medium was collected at different timepoints in a time-course culture , secreted levels were not normalized to total cell or protein contents . Assembloids were dissociated to single cells by incubation of gel droplets with 0 . 5 mg/ml collagenase I for 10 min in a 37°C water bath for 10 min with regular vigorous shaking . Samples were washed with additive-free DMEM/F12 phenol-free medium and incubated with 5× TrypLE Select diluted in additive-free DMEM/F12 phenol-free medium for 5 min in a 37°C water bath . Cell clumps were disrupted by manual pipetting , then suspended in 0 . 1% bovine serum albumin ( BSA ) in PBS and passed through a 35 µm cell sieve . Droplet generation was performed using a Nadia Instrument ( Dolomite Bio , Cambridge , UK ) according to the manufacturer’s guidelines and using reagents as described by Macosko et al . , 2015 and the scRNAseq v1 . 8 protocol ( Dolomite Bio ) . Pooled beads were processed as described previously ( Lucas et al . , 2020 ) and sequenced using a NextSeq 500 with high-output 75-cycle cartridge ( Illumina , Cambridge , UK ) by the University of Warwick Genomics Facility . Initial single-cell RNAseq data processing was performed using Drop-Seq_tools-2 . 3 . 0 ( DropseqAlignmentCookbook_v2Sept2018 , http://mccarrolllab . com/dropseq ) and as described previously ( Lucas et al . , 2020 ) . To select high-quality data for analysis , cells were included when at least 200 genes were detected , while genes were included if they were detected in at least three cells . Cells with more than 5000 genes were excluded from the analysis as were cells with more than 5% mitochondrial gene transcripts to minimize doublets and low quality ( broken or damaged ) cells , respectively . The Seurat v3 standard workflow ( Stuart et al . , 2019 ) was used to integrate datasets from biological replicates . Clustering and nearest-neighbour analysis was performed on the full integrated dataset using principal components 1:15 and a resolution of 0 . 6 . The ‘subset’ function was applied for interrogation of specific experimental conditions and timepoints . Gene Ontology ( GO ) analysis was performed on differentially expressed genes from specified ‘FindMarkers’ comparisons in Seurat v3 using the Gene Ontology Consortium database ( Ashburner , 2000; THE GENE ONTOLOGY , 2019; THE GENE ONTOLOGY , 2019; Mi et al . , 2013 ) . Dot plots of significantly enriched GO terms ( FDR-adjusted p<0 . 05 ) were generated in RStudio ( version 1 . 2 . 5042 ) . CellPhoneDB was used to predict enriched receptor-ligand interactions between subpopulations in decidualizing assembloids ( Efremova et al . , 2020; Vento-Tormo et al . , 2018 ) . Significance was set at p<0 . 05 . Annotated tyrosine kinase interactions were curated manually . Prior to co-culture , decidualized assembloids were washed in PBS and medium was replaced with embryo medium ( Supplementary file 1: Table 1 ) . Assembloids were lightly punctured with a needle to create a small pocket , to enable one re-expanded day 5 human blastocyst to be co-cultured per assembloid . The plate was transferred to a pre-warmed and gassed ( humidified 5% CO2 in air ) environment chamber placed on an automated X-Y stage ( EVOS FL Auto Imaging System with onstage incubator ) for time-lapse imaging . Brightfield images were captured every 60 min over 72 hr . Captured images were converted into videos using ImageJ . For fixation , assembloid co-cultures were removed from culture wells and transferred into tubes . Samples were washed in PBS and fixed in 10% neutral buffered formalin in the tube for 15 min , then washed three times with PBS . Assembloids were permeabilized for 30 min in PBS containing 0 . 3% Triton X-100 and 0 . 1 M glycine for 30 min at room temperature . Samples were incubated overnight at 4°C in primary antibodies diluted in PBS containing 10% FBS , 2% BSA , and 0 . 1% Tween-20 . Samples were then washed in PBS ( 0 . 1% Tween-20 ) and incubated for 2 hr at room temperature protected from light in fluorescently conjugated Alexa Fluor secondary antibodies 1:500 ( ThermoFisher Scientific ) and DAPI ( D3571 , ThermoFisher Scientific , dilution 1/500 ) , diluted in PBS containing 10% FBS , 2% BSA , and 0 . 1% Tween-20 . Samples were imaged on a Leica SP8 confocal microscope using a ×25 water objective , with a 0 . 6 µm z-step and 2× line averaging . Data were analysed using GraphPad Prism . Pairwise comparison of non-parametric data was performed using Mann–Whitney U test . For paired , non-parametric significance testing between multiple groups , the Friedman test , and Dunn’s multiple comparisons post hoc test were performed . Only values of p<0 . 05 were considered statistically significant . | At the beginning of a human pregnancy , the embryo implants into the uterus lining , known as the endometrium . At this point , the endometrium transforms into a new tissue that helps the placenta to form . Problems in this transformation process are linked to pregnancy disorders , many of which can lead to implantation failure ( the embryo fails to invade the endometrium altogether ) or recurrent miscarriages ( the embryo implants successfully , but the interface between the placenta and the endometrium subsequently breaks down ) . Studying the implantation of human embryos directly is difficult due to ethical and technical barriers , and animals do not perfectly mimic the human process , making it challenging to determine the causes of pregnancy disorders . However , it is likely that a form of cellular arrest called senescence , in which cells stop dividing but remain metabolically active , plays a role . Indeed , excessive senescence in the cells that make up the endometrium is associated with recurrent miscarriage , while a lack of senescence is associated with implantation failure . To study this process , Rawlings et al . developed a new laboratory model of the human endometrium by assembling two of the main cell types found in the tissue into a three-dimensional structure . When treated with hormones , these ‘assembloids’ successfully mimic the activity of genes in the cells of the endometrium during implantation . Rawlings et al . then exposed the assembloids to the drug dasatinib , which targets and eliminates senescent cells . This experiment showed that assembloids become very robust and static when devoid of senescent cells . Rawlings et al . then studied the interaction between embryos and assembloids using time-lapse imaging . In the absence of dasatinib treatment , cells in the assembloid migrated towards the embryo as it expanded , a process required for implantation . However , when senescent cells were eliminated using dasatinib , this movement of cells towards the embryo stopped , and the embryo failed to expand , in a situation that mimicks implantation failure . The assembloid model of the endometrium may help scientists to study endometrial defects in the lab and test potential treatments . Further work will include other endometrial cell types in the assembloids , and could help increase the reliability of the model . However , any drug treatments identified using this model will need further research into their safety and effectiveness before they can be offered to patients . | [
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] | 2021 | Modelling the impact of decidual senescence on embryo implantation in human endometrial assembloids |
As the general population ages , more people are affected by eye diseases , such as retinopathies . It is therefore critical to improve imaging of eye disease mouse models . Here , we demonstrate that 1 ) rapid , quantitative 3D and 4D ( time lapse ) imaging of cellular and subcellular processes in the mouse eye is feasible , with and without tissue clearing , using light-sheet fluorescent microscopy ( LSFM ) ; 2 ) flat-mounting retinas for confocal microscopy significantly distorts tissue morphology , confirmed by quantitative correlative LSFM-Confocal imaging of vessels; 3 ) LSFM readily reveals new features of even well-studied eye disease mouse models , such as the oxygen-induced retinopathy ( OIR ) model , including a previously unappreciated ‘knotted’ morphology to pathological vascular tufts , abnormal cell motility and altered filopodia dynamics when live-imaged . We conclude that quantitative 3D/4D LSFM imaging and analysis has the potential to advance our understanding of the eye , in particular pathological , neurovascular , degenerative processes .
Eye diseases , such as diabetic retinopathy , age-related macular degeneration , cataracts , and glaucoma are becoming increasingly common with the increased age of the general population . Although advances in understanding and treating eye diseases have been made , the cellular and molecular mechanisms involved are still not fully understood . We believe that is partially due to the inadequate ability to image eye tissue in its natural , spherical state , to reveal the many distinct layers with interacting cell types oriented differentially within or between the layers . Optical coherence tomography ( OCT ) is an established medical imaging diagnostic tool that uses light to capture micrometre-resolution , three-dimensional images , non-invasively ( Srinivasan et al . , 2006; Huber et al . , 2009 ) . Its main strength lies in revealing information on tissue depth preserving the eyes natural state . However , its limitation lies in not being able to provide a wide field of view , cellular or molecular information . Furthermore , being a non-fluorescent method , specific proteins cannot be labelled and tracked to investigate mechanisms . Currently , only confocal microscopy can deliver this detailed fluorescently labelled information ( del Toro et al . , 2010 ) , but the 3D nature of the tissue is likely distorted during flat-mounting and it is currently not known to what extent this might impact the obtained results . For instance , the vascular biology field is one clear example where these limitations can have a substantial impact . The mouse retina is a common model used to study vascular development and disease; confocal imaging approaches have been used to measure vessel morphology , vascular malformations , junctional organisation , and pathological tuft formation ( Gerhardt et al . , 2003; Bentley et al . , 2014; Stahl et al . , 2010 ) . Moreover , vessel diameters are now being used to predict blood flow ( Bernabeu et al . , 2014; Baeyens et al . , 2016 ) . Distortions arising from confocal flat-mounting could therefore have important ramifications for the overall conclusions of several studies . Changes in cellular and tissue morphology are a hallmark of many eye diseases . For instance , retinopathy of prematurity and diabetic retinopathy are characterised by excessive , bulbous and leaky blood vessels that protrude out of their usual layered locations . These malformed vessels cause many problems including the generation of abnormal mechanical traction , which pulls on the different layers , eventually leading to detachment of the retina ( Nentwich and Ulbig , 2015; Hartnett , 2015 ) . Yet , very limited information has arisen on the conformation and morphogenesis mechanisms of these vascular tuft malformations , despite a wealth of confocal studies of the related oxygen- induced retinopathy ( OIR ) mouse model ( Connor et al . , 2009 ) . Another limitation of confocal microscopy for imaging of mouse retinal angiogenesis is the inability to perform live imaging of endothelial cell dynamics . Endothelial cells move and connect in highly dynamic , complex ways to generate the extensive vascular networks required to perfuse the retina over time ( angiogenesis ) . Live , in vivo imaging of murine intraocular vasculature has been reported using confocal microscopy ( Ritter et al . , 2005 ) and holds great promise for dynamic longitudinal studies of the growth/regression of large vessel such as hyaloid vessels . However , it does not as yet suit studies of smaller more dynamic cell and subcellular structures as being reliant on confocal currently limits such studies to slow frame rates ( 5–10 min intervals ) , limited z stack resolution with photobleaching issues and an apparent limited field of view . There are a small number of reports on ex vivo live-imaging of the retinal vasculature with confocal microscopy , but which clearly entails challenges as dissection of the retina for culture is time consuming , and moreover , the flatmounting is likely to disturb local tissue arrangement and mechanics ( Sawamiphak et al . , 2010; Rezzola et al . , 2013 ) . Furthermore , photobleaching , phototoxicity and long acquisition times continue to remain an issue . A growing number of reports show that neurovascular interactions in the eye are important during development and disease progression ( Akula et al . , 2007; Narayanan et al . , 2014; Nentwich and Ulbig , 2015; Usui et al . , 2015; Verheyen et al . , 2012 ) . Neurons and vessels are however currently imaged with physical sectioning of paraffin or cryo-embedded retinas , which precludes concurrent visualisation of the vasculature , due to the orthogonal arrangement of neurons and vessels within or between retinal layers respectively . Likewise , current methods have limited potential for quantitative 3D and live imaging of fluorescently labelled neurons in neurodegenerative mouse models . Recent advances in light-sheet fluorescence microscopy ( LSFM ) have demonstrated its strength for allowing the rapid acquisition of optical sections through thick tissue samples such as mouse brains ( Stelzer , 2015 ) . Instead of illuminating or scanning the whole sample through the imaging objective , as in wide-field or confocal microscopy , the sample is illuminated from the side with a thin sheet of light . Thus , in principle LSFM would require little interference with the original spherical eye tissue structure , avoiding distortion of the tissue with flat-mounting . Moreover , LSFM is becoming a gold-standard technique to perform live-imaging in whole organs/organisms because it permits imaging of thick tissue sections without disturbing the local environment , while also reducing photobleaching and phototoxicity ( Stelzer , 2015; Reynaud et al . , 2015 ) . Thus , here we investigate the feasibility , advantages and disadvantages of LSFM for imaging the mouse eye for development or disease studies . We present an optimised LSFM protocol to rapidly image neurovascular structures , across scales from the entire eye to subcellular components in mouse retinas . We investigate the pros and cons of LSFM imaging of vessels over standard confocal imaging techniques in early mouse pup retinas . Importantly , we also demonstrate the benefits of LSFM using the OIR mouse model , where we discover previously unappreciated new spatial arrangements of endothelial cells in the onset of vascular tuft malformations due to the improved undistorted , 3D and 4D imaging capabilities of LSFM . We conclude that LSFM quantitative 3D/4D imaging and analysis has the potential to advance our understanding of healthy and pathological processes in the eye , with a particular relevance for the vascular and neurovascular biology fields , as well as ophthalmology .
To visualise the retinal vasculature using epifluorescence or confocal microscopes , the retina is flat-mounted by making four incisions before adding a cover slip containing mounting medium onto glass slides ( Figure 1a , upper panel ) . To image samples using LSFM , however , samples are suspended in their natural state in low-melting agarose ( Figure 1a , lower panel ) . This enables imaging of the vasculature of large and intact samples such as the whole eyeball ( minus the sclera and cornea ) ( Figure 1b ) , the iris ( Figure 1c ) , or the optic nerve ( Figure 1d ) . Using LSFM , it was possible to observe the superficial , intermediate and deep vascular plexus ( Figure 1e and Figure 1—figure supplement 1b ) of a retina in its native conformation ( Figure 1f ) . Acquiring a stack of the entire retina using LSFM contains 200–300 images , still , yet the imaging time is much shorter than it would be using confocal ( ~1 min ) . Imaging the iris microvasculature ( Figure 1c ) revealed that the vasculature network is immature at P15 ( Figure 1b , Figure 1—figure supplement 1a ) , and that it remodels into a mature network in adulthood ( Figure 1c ) . A network of capillaries was visible at P15 , whereas the adult microvasculature consisted of radial branches of small vessels and capillaries in a relatively linear pattern . The major arterial circles ( MICs ) around the iris root were developed in both P15 and adult mice ( Figure 1c and Figure 1—figure supplement 1a ) . The images generated from adult mice using LSFM are consistent with a previous report using OCT to image the iris microvasculature ( Choi et al . , 2014 ) . Using LSFM , the vessels appeared straighter , and the MICs were not as close to the iris root , which could be because OCT involves live-imaging of the vasculature , without mechanically removing the sclera and cornea . We next tested whether LSFM could resolve subcellular structures in the retinal vasculature such as the Golgi apparatus , which has recently been shown to be important for inferring cell polarity during vessel regression ( Franco et al . , 2015 ) . We found it feasible to stain and image the Golgi organelle ( Golph4 , Alexa 647 ) and the collagen IV-containing basement membrane around the vessels ( Figure 1—figure supplement 1c , d ) . Moreover , quantification of the nucleus-Golgi polarity axis was amenable when imaging the GNrep mouse ( Barbacena et al . , 2019 ) , which expresses Golgi-localised mCherry and nucleus-localised eGFP upon Cre-mediated recombination , enabling visualisation of endothelial specific nuclei and Golgi apparatus . We measured the polarity of cells in 3D by drawing lines from the centre of their nuclei to the nearest Golgi body . We observed endothelial cells collectively polarising against the flow direction along an arterial network ( Figure 1—figure supplement 1e ) , as previously described ( Franco et al . , 2016 ) . The ability to 3D rotate the undistorted vascular image stacks obtained with LSFM revealed hidden cells whose polarity could be analysed , not visible when analysing the same image stack using standard confocal 2D imaging ( i . e . viewed only from above ) ( Figure 1—figure supplement 1f–h ) . On static images of mice expressing Lifeact-enhanced green fluorescent protein ( EGFP ) ( Riedl et al . , 2010 ) we performed deconvolution to reduce the light scattering effects and found this gave a marked improvement to the resolution of actin bundles within endothelial cells ( Figure 1—figure supplement 1i–l ) . Taken together , we concluded that LSFM can rapidly generate 3D images of the murine eye in its native form across scales , with tissue , cellular and subcellular resolution . Neurons are currently imaged by making vertical sections , orthogonal to the three vascular layers ( the superficial , intermediate and deep plexus ) ( Figure 2a , b , ‘side view’ ) , which necessarily means losing the ability to observe vascular branching in the horizontal layer in the same tissue . Likewise , studies focused on the retinal vasculature use whole mount images of the retina viewed from above ( Figure 2b , ‘top view’ ) using horizontal optical sections ( Usui et al . , 2015 ) , which does not allow proper imaging of retinal neurons spanning between the layers because of insufficient z-resolution in confocal microscopy . Thus , we next investigated whether concurrent imaging of neurons and vessels in the same sample might be achieved with the optical sectioning and rotational viewing capacity of LSFM . We found that eye cups from P3 C57BL/6 Thy1-YFP mice , labelling retinal ganglion cells in yellow combined with IsolectinB4 labelled vasculature provided 3D high resolution images without the need for tissue clearing ( Figure 2c , d , Video 1 ) . However , we found that including lipid removal/permeabilisation as part of a full tissue clearing protocol further improves resolution for eye cups at later stages of development , when more of the retinal vascular layers have formed ( Figure 2e–h ) , as it decreases the scattered light caused by imaging thicker tissue with the light sheet ( Richardson and Lichtman , 2015 ) . In order to establish whether LSFM could be used to quantify neuronal changes in a retinal degeneration model we imaged retinal cups from the Rho KO degeneration model ( Figure 2—figure supplement 1; Humphries et al . , 1997 ) . The LSFM images were easily segmented and quantification showed a significant decrease in neuronal density in the outer nuclear layer ( ONL ) at 4 weeks for Rho KO compared to control retinas , which worsened in the 8 weeks Rho KO ( Figure 2—figure supplement 1c ) . Measuring ONL thickness showed no notable difference between control and Rho KO at 4 weeks , however , there was a significant reduction in thickness in the Rho KO at 8 weeks relative to both strains at 4 weeks ( Figure 2—figure supplement 1d ) . The ONL had almost entirely lost its stable convex curvature by 8 weeks in the KO retina and the inner nuclear layer ( INL ) also appeared ruffled when viewed in 3D which may be due to the unevenness of dropout of photoreceptors ( Figure 2—figure supplement 1a , b ) . We tested several different clearing methods to see which was better suited to retinal tissue . Using the aqueous-based clearing methods ScaleA2 and FRUIT ( Hou et al . , 2015; Hama et al . , 2011 ) did not result in higher quality images and made tissue-handling very difficult during imaging due to the high viscosity of the FRUIT clearing agent . We also tested the passive aqueous-based methods CUBIC-R ( Kubota et al . , 2017 ) and PROTOS ( Murray et al . , 2015 ) , but again found little improvement . Since many studies use animals genetically engineered to express fluorescent markers such as Tomato or GFP , we decided not to pursue solvent-based clearing methods such as iDISCO , which do not maintain fluorescent protein emission for more than a few days after the clearing process ( Renier et al . , 2014 ) . Overall , we found PACT was the most efficient and effective clearing method for retinal tissue , likely because it is relatively thin ( Yang et al . , 2014; Treweek et al . , 2015 ) . PACT cleared adult retinas with Draq5 staining , which stains all nuclei , visualising the INL and ONL ( Figure 2f , Video 2 ) . The deep vascular plexus , visualised by IsolectinB4 staining could be seen between the ONL and INL , whereas the intermediate vascular plexus bordered the INL as expected . The superficial vascular plexus is located on the inner retinal surface together with nuclei of the retinal ganglion cells ( Figure 2f , Video 2 ) . Adult retinas were co-immunostained for Tuj1 and Calbindin , markers for retinal ganglion cells and horizontal cells , respectively . This immunostaining made it possible to appreciate the distance between these two cell types in the fully developed retina ( Figure 2g , Video 3 ) . 3D-rendered images of co-staining for smooth muscle actin and collagenIV moreover showed arteries of the superficial vascular plexus covered with smooth muscle cells ( Figure 2h , Video 4 ) . Overall , LSFM holds great promise for concurrent studies of how different cell types interact during eye development and disease . As vascular measurements taken from confocal images are used as the standard for inferring the actual sizes of vascular structures in the retina , we next aimed to systematically quantify the 3D distortion of vascular structures incurred by flat-mounting and confocal imaging . In order to make direct , quantitative comparisons of the relatively small vessels in the superficial plexus , we used a correlative LSFM-confocal approach: we first imaged the retinal tissue with LSFM , which retains the natural tissue curvature , then we melted the agarose gel and flat-mounted the same retina onto a coverslip and imaged it again using confocal microscopy ( Figure 3a ) . We first analysed the largest vessels near the optic nerve and then smaller capillaries in the sprouting vascular front from P4 WT retinas . Images obtained with our correlative LSFM-confocal approach were then brightness/contrast adjusted and cropped and surface rendered using Imaris to focus on small regions of same vessel segments in the corresponding confocal and LSFM images . Dramatically shallower side views and cross-sections of vessels were evident in the confocal images compared to LSFM ( Figure 3b ) . We next quantified this shift in aspect ratio by measuring the diameter taken across the vessel in XY ( hereafter ‘width’ ) and down through the Z-axis ( hereafter ‘depth’ ) in the confocal ( Figure 3c ) . For LSFM images , given the tissue can be at any orientation in the agarose with respect to the objective , the XYZ coordinate system of the image stack is not indicative of the equivalent width/depth measurement in confocal . Instead , the orientation of the surrounding vascular plexus at the point of the vessel segment was used as a reference surface ‘plexus plane’ to make the corresponding ‘width’ diameter measurement , as it is equivalent to the XY plane in the corresponding confocal image . Similarly , the ‘depth’ diameter in LSFM was defined as perpendicular to the plexus plane and width measurement ( equivalent to the diameter through the z-stack in confocal ) . Vessels were significantly more elliptical ( wider and shallower ) under the confocal than LSFM , indicative of being compressed during flat-mounting ( Figure 3d , e ) . Overall , vessels from retinas flat-mounted for confocal displayed significant distortion , and not in a simple ratio of depth to width changes , indicating LSFM as more reliable for quantitative 3D morphometric studies . Ex vivo live-imaging could be a useful tool to study tip cell guidance during the angiogenic sprouting process in the mouse retina , but it has proven to be challenging with conventional microscopy . Existing ex vivo confocal methods to live-image retinal vasculature , tissue handling leads to damage of the tissue , as it involves either flat-mounting the retinas onto a membrane and then submerging it in medium ( Sawamiphak et al . , 2010 ) , or cutting the retina into fragments and embedding them in fibrin gels prior to imaging ( Rezzola et al . , 2013 ) . We therefore established a protocol for live-imaging of the growing retinal vasculature in ex vivo prepared retinas using LSFM . We first crossed mT/mG mice with Cdh5 ( PAC ) -CreERT2 mice and injected them with tamoxifen to induce endothelial GFP expression ( Muzumdar et al . , 2007; Wang et al . , 2010 ) . Surprisingly , connections between ECs formed very rapidly ( within 20 min ) and regressed just as rapidly ( Figure 4a , Video 5 ) . Such transient ‘kiss and run’ adhesion and release style interactions between ECs ( as opposed to full adhesions or anastomoses , where the connections stably remain ) have only been previously reported in glycolysis-deficient ECs in vitro ( Schoors et al . , 2014 ) . The dynamics in vivo were assumed to be slower and more stable than in vitro live-imaging , however our new ex vivo observations indicate a very different set of dynamics and inter-cellular behaviors may be at work in the complex in vivo tissue . Timing is crucial , as the VEGF gradient dissipates after the retinas are dissected and submerged in agarose , at room air and the tissue is therefore no longer hypoxic . However , the directed growth of the filopodia towards the vascular front in our LSFM Videos suggests that this gradient remains intact for at least the first few hours after dissection . Further back from the sprouting front , in the vascular plexus ( Figure 4b , Video 6 ) , we occasionally observed the formation of connections over the course of a few hours , however , branch formation was a rare occurrence . Notably , we did not observe EC apoptosis under these imaging conditions indicating conditions are viable . We next assessed the feasibility of using LSFM to live-image intracellular processes in ex vivo prepared retinas . We dynamically imaged Lifeact-EGFP mice ( Riedl et al . , 2010 ) with LSFM and quantified the movements of actin-enriched bundles within the endothelial cell bodies in the sprouting front during developmental angiogenesis . Quantitative subcellular actin live-imaging was found feasible ( n = 6 retinas ) with the average distance travelled by each bundle found to be 2 . 56 µm ( Figure 4c , Videos 7 , 8 and 9 ) . Taken together , our LSFM permits the visualisation in real-time of cellular movements with subcellular resolution in the mouse retina , with minimal distortion . We next sought to image vessels that have grown pathologically , in order to determine whether this imaging method could be used to gain greater insights into eye disease . To this end , we used the OIR model , where mouse pups are placed in 75% oxygen from P7 to P12 , and are then kept at room air from P12 to P17 ( Connor et al . , 2009 ) . During the hyperoxia phase , the vasculature regresses , and in the subsequent normoxia phase , new vessels grow in an abnormally enlarged and tortuous manner ( Connor et al . , 2009 ) . Furthermore , vessels also start to grow into the vitreal space forming bulbous vessels , known as ‘vascular tufts’ , above the superficial vascular layer ( Figure 5a ) . In the past , it has been difficult to analyse and characterise the growth of these tufts because they are large formations , which appear to be distorted by the flat-mounting process . By performing IsolectinB4 and ERG immunostaining to visualise endothelial cells ( ECs ) and their nuclei , we obtained 3D-reconstructions of the tufts and were able to first classify them into different groups by quantifying both volume and number of nuclei ( Figure 5a , b ) . As expected , we found that the number of nuclei increased with the size of the tuft ( R2 = 0 . 83 ) . Interestingly , however we found many small tufts , and only very few large tufts . The smallest tuft we could identify had two nuclei parallel to each other , the cells growing straight up into the vitreous ( Figure 5a , upper panel , Video 10 ) . We found that most tufts have between 4 and 20 nuclei ( ‘Medium tufts’ , Figure 5a , second panel row , Video 11 ) . We also identified a few very ‘large tufts’ with over 20 nuclei ( Figure 5a , third panel row , Video 12 ) . Next , we quantified the number of connections between the vasculature and the tuft ( Figure 5b ) . The large tufts had a higher number of connections to the existing vasculature ( R2 = 0 . 61 ) , Within the medium tuft class there is a linear increase of volume with nuclei number up to approximately 10 nuclei per tuft , but then the volume remained constant despite a doubling of the nuclei number to 20 at the top of this class . Within the large tuft class the volume remained unchanged despite a three-fold increase in nuclei ( Figure 5b ) . Intriguingly , the number of connections to the plexus was approximately constant despite the increasing number of nuclei within these classes ( Figure 5c ) . However , the number of connections and tuft volume transitioned sharply , to ~2 . 5 fold and ~3 fold respectively , when the number of nuclei in the tuft exceeded twenty . This indicates that proliferation or an influx of cells to the tuft does not increase tuft volume , but rather , tuft volume only significantly increases when the number of connections to the plexus also increases . Based on this observation , we propose that large tufts are in fact formed by fusion of 2 or three medium tufts . We observed that some of the vascular tufts contained highly curved nuclei ( Figure 5a , fourth panel row , yellow arrow , Video 13 ) . Quantification of the number of curved nuclei/total nuclei in a tuft showed that in small and medium tufts , the number of curved nuclei correlated well with the number of total nuclei ( Figure 5d ) . In large tufts ( over 20 total nuclei ) , the number of curved nuclei was stable suggesting actually a decline in curved nuclei as the number of cells in the tuft increased . Thus , the relative number of curved nuclei per tuft could also be used as a clear marker to distinguish medium and large tufts . As curved nuclei indicate cells are under severe mechanical strain , twisting or turning them around ( Xia et al . , 2018 ) , this suggests that larger tufts may be more stable and mature , whereas the small and medium ones are under more tension , still forming with significant forces curving and pulling the cells around in the tuft . Interestingly , highly curved nuclei have been shown to result in rupture of the nucleus and DNA damage ( Xia et al . , 2018 ) , which may further exacerbate dysfunctional cell behaviour in tuft formation . It should be noted that care should be taken to rotate the image stack to confirm nuclear curvature , as two nuclei parallel to each other can look like only one nucleus ( Figure 5a , fourth panel row , blue arrow ) , emphasising the importance of 3D imaging with LSFM as rotating and viewing tufts from the side without distortion is not possible with confocal . Finally , to quantify the level of distortion of vascular tufts incurred by flat-mounting and confocal imaging , we compared tuft depth measurements between retinas imaged with confocal and LSFM ( depth defined the tuft length orientated perpendicular to plexus plane ) . The change in depth was particularly striking and more pronounced for larger tuft structures ( Figure 5a bottom panels , e ) . Taken together , this further confirmed that LSFM is superior to confocal to image larger structures in the eye . In order to gain better resolution to characterise the specific morphology of the different sized tufts we performed computational image deconvolution on cropped LSFM images of vascular tufts ( see Materials and methods ) , which helped to decrease the scattered light caused by imaging thicker tissue with the light sheet without the need to clear the tissue ( Richardson and Lichtman , 2015 ) . Upon deconvolution a previously unappreciated ‘knotted’ morphology of the tufts was evident across all tuft classes; often tufts had one or more holes going through ( Figure 6a , b; Figure 6—figure supplement 1a , b for more rotational views and original rotational Videos 14 , S15 ) . To describe these 3D tuft structures in detail , we first explored three systematic image analysis approaches: 1 ) by slowly shifting clipping planes through the tuft from the vitreous , facing side to the plexus-connecting side of the tuft , it was possible to better appreciate the upper and lower 3D organisation of the tuft; 2 ) carefully rotating and hand-drawing the tufts surface rendered structures from every angle and 3 ) comparing the colour-labelled positions of nuclei to indicate their depth position in the tuft . The first approach revealed that the tuft shown in Figure 5a fourth panel row , had a figure of eight knot , with two clear holes through the tuft and an unexpected vessel connecting the upper vitreous surface of the tuft to the plexus ( Figure 6c and Figure 2—figure supplement 1b , Video 13 ) . A combination of the second two approaches revealed a swirl structure to two tufts ( small and medium in size ) , akin to a snake coiling upon itself in layers , with several highly curved nuclei ( Figure 6d–i , Videos 16 and 17 ) . We noted a central hole either through the entire tuft or evident in the upper vitreous facing portion , akin to a depression or invagination . A sprout-like protrusive tip with filopodia was often also evident ( Figure 7a , Figure 6—figure supplement 1c ) . Overall , all three , 3D rotational image processing/analysis approaches were extremely useful for better interpreting these complex 3D structures , providing a much deeper understanding of tuft morphology than would be possible using a 2D analysis of distorted tufts , viewed from above in standard flat mount confocal microscopy . To further validate these unexpected tuft morphologies with an independent high-resolution 3D imaging method , we performed microCT on intact health control and OIR retinas . We found microCT of mouse retinas entirely feasible and the OIR vascular tufts readily amenable to analysis by microCT , as they protrude into the vitreous ( Figure 6—figure supplement 2a ) . On close inspection we indeed found tufts also appear to have holes/invaginations ( Figure 6—figure supplement 2b–c ) indicating further study of these complex 3D structures is warranted . Next , we investigated whether LSFM could provide added benefits for OIR drug study quantifications , when compared to confocal microscopy . We therefore reproduced an OIR drug-treatment study using Everolimus , an inhibitor of the mammalian target of rapamycin ( mTOR ) and compared the feasibility of quantifications ( primarily quantifying 2D avascular and/or tuft area/numbers ) between LSFM and confocal microscopy ( Yagasaki et al . , 2014 ) . In accordance to published data , we observed an evident increased avascular area and smaller tufts with drug treatment ( Figure 6—figure supplement 3a ) . However , quantification of avascular area in LSFM was not feasible due to a lack of available computational tools to take account of the natural 3D curved retinal tissue surface , which suggests that confocal imaging is a more suitable imaging modality to quantify this 2D parameter . Yet , we found that LSFM was very practical to measure tuft volume and found that tufts in drug-treated retinas were markedly smaller than in untreated retinas , despite having comparable nuclear counts ( Figure 6—figure supplement 3b , c ) . Furthermore , drug-treated retinal tufts showed a particular small swirl/ordered two-layer cup morphologies with distinctive wide-reading filopodia all around the tuft , suggesting they are highly active ( Figure 6—figure supplement 3d–j ) , similar to our previous observations in OIR untreated retinas ( e . g . Figure 6d ) . Thus , we concluded that LSFM is more suitable for 3D volume and tuft morphology characterisation to understand the mechanism of action of OIR drug treatments than confocal microscopy . To gain insights into endothelial cell behaviour in vascular tufts , we next imaged the OIR-induced tufts dynamically with LSFM . Thereby , we observed that filopodia extended/retracted from abnormal vascular tufts , similar to what is seen in the extending vascular front during development of the retina vasculature . However , filopodia formed from vascular tufts remained very short ( mean 4 . 3 µm ) as compared to normoxia ( mean 14 . 84 µm ) ( Figure 7a , b ) . In OIR , filopodia more rapidly extended and retracted , without making connections ( Figure 7a , c , d , Video 18 ) . As the VEGF gradient is expected to be disrupted in the OIR model , timing from dissection to imaging is not as crucial . However , most filopodia movements occurred in the first few hours under this pathological condition . When imaging other parts of the OIR retinas to the tufts , we observed intriguing , abnormal EC behaviour . Their movements were undirected and appeared to involve blebbing-based motility ( Figure 7e , f , Video 19 ) . We observed both cells that were dividing , and undergoing apoptosis ( Figure 7e , Video 20 ) , which was not observed during normal conditions . This first live imaging of altered cell behaviour in the OIR mouse model further highlights the potential of LSFM for new insights into disease processes .
The OIR model is a commonly used to study retinopathies . The three-dimensional nature of vascular tufts makes them ideal for LFSM and though this is a widely studied mouse model , the improved three-dimensional imaging allowed us to identify several new features of the important pathological vessels it generates . Our observations of small , medium and large tuft classes with distinct properties and the observation of more complex knotted , swirling and looping morphologies than previously reported , suggest a new mechanistic explanation is required to understand how and why vessels twist and turn on themselves and why it appears that medium tufts reach a critical size then stop twisting and instead coalesce into larger more stable structures , akin to the development of blood islands in retinal development ( Goldie et al . , 2008 ) . Nuclei with unusual shapes have previously been identified in abnormally growing tissues , such as cancer ( Hida et al . , 2004; Kondoh et al . , 2013; Versaevel et al . , 2012 ) , and to reflect mitotic instability ( Gisselsson et al . , 2001 ) . It is remarkable that we observed the dramatically curved shape of EC nuclei in tufts . Although it remains unclear whether their unusual shape has consequences for EC function in the tuft , it is tempting to speculate that it would have some bearing on , or is at least be an indicator of abnormal cell behavior . Overall , the ability to rotate the tufts in 3D and view from the side , not just the top , gave a much clearer view of their structure potentiating a detailed analysis of their complex knotted structure in the future . It was particularly interesting that tufts in the Everolimus-treated OIR retinas appeared to conform to a specific swirl structure with many filopodia , suggesting that LSFM imaging could help reveal much greater information of the mechanism of action of many drugs targeting these or other complex 3D structures in the eye . LSFM therefore could greatly improve our understanding of these abnormal vascular formations , already opening up avenues for future studies . Current retinal studies must infer dynamics from static images by hypothesising what might have happened in real-time to generate the retina’s phenotype . For example , CollagenIV-positive and IsolectinB4-negative vessels are considered to be empty membrane sleeves where the vasculature has regressed . It is therefore important to establish reproducible live-imaging methods . It will be interesting to investigate in future live-imaging studies how pervasive the kiss and run behaviors are across the plexus and under different conditions , in order to fully elucidate their functional role . We furthermore demonstrated the potential to quantify diverse subcellular level movements in the cells and altered cell movements in the OIR disease model as proof of concept . Previously undirected vascular movements have been indicated as due to the loss of the underlying astrocyte template ( Dorrell et al . , 2010 ) , LSFM now permits mechanisms involving multiple cell types to be investigated and confirmed live with fluorescent co-labelling studies of neurons/glial cells with vessels in the same retina . The LSFM live imaging protocol is sturdy as indicated from the testing in three different laboratories in three different countries ( US , Sweden and Portugal ) with different scientists performing the dissections and imaging , on different instruments . As such we can confirm that though challenging , the live imaging protocol has been optimised and is reproducible in different hands .
mT/mG mice ( Wang et al . , 2010 ) were crossed with cdh5 ( PAC ) CreERT2 mice . For live-imaging of retinal angiogenesis during development , mice were injected with 50 µg tamoxifen at postnatal day ( P ) 1 , P2 and P3 , and imaged at P4 ( Wang et al . , 2010 ) . For live-imaging of oxygen-induced retinopathy ( OIR ) experiments , mice were injected with 100 µg tamoxifen at P13 , P14 and P15 . The retinal vasculature was imaged at P17 unless otherwise stated . Recombination was confirmed by GFP expression in ECs . GNrep mice ( Barbacena et al . , 2019 ) were injected with 4OH-tamoxifen at P3 and P4 ( 20 ug/g ) and fixed in PFA 2% . Lifeact mice ( Riedl et al . , 2010 ) were a kind gift from Dr . Wedlich-Söldner , University of Münster , Germany . Mice used in experiments at Beth Israel Deaconess Medical Center were held in accordance with Beth Israel Deaconess Medical Center IACUC guidelines ( protocol #009–2014 ) . Animal work performed at Uppsala University was approved by the Uppsala University board of animal experimentation ( ethics approval reference C134/14 and C116/15 ) . Animal work performed at SERI was IACUC approved ( protocol S467-1019 ) . Animal work performed at FAS Harvard was IACUC approved ( protocol 14-02-191 ) . Transgenic mice were maintained at the Instituto de Medicina Molecular ( iMM ) under standard husbandry conditions and under national regulations ( DGAV project license 0421/000/000/2016 . IsolectinB4 directly conjugated to Alexa488 and all corresponding secondary alexa conjugated antibodies were obtained from Invitrogen . Isolectin IB4 conjugated with an Alexa Fluor 568 dye was purchased from Thermo Fisher Scientific , MA . Anti-calretinin ( ab702 ) and anti-ERG ( ab2513 ) antibodies were obtained from Abcam . The antibody directed against Calbindin ( AB1778 ) was acquired from Millipore . Anti-Glial Fibrillary Acidic Protein ( GFAP ) antibody was purchased from Dako ( Z0334 ) , anti-CollagenIV from AbD Serotec ( 2150–1470 ) , biotinylated anti-neuron-specific b-III Tubulin from R and D Systems ( Clone TuJ-1 , BAM1195 ) , and Cy3-conjugated anti-smooth muscle actin ( SMA ) antibody was obtained from Sigma Life Science ( C6198 ) . Draq5 was obtained from ThermoScientific . Anti-GOLPH4 ( ab28049 ) from Abcam . GNrep mice were co-stained with CD31 ( R and D , AF3628 , 1/200 ) and anti-RFP antibody coupled to mCherry ( Alfagene , M11240 , 1/100 ) to further increase the signal . TO-PRO-3 stain for Rho KO neurodegeneration study ( Thermo Fisher Scientific; diluted 3000x in PBS ) . Retinas were dissected as previously described ( del Toro et al . , 2010 ) . In brief , eyeballs were fixed for 18 min in 4% paraformaldehyde at room temperature . After dissection , retinas were blocked for 1 hr in blocking buffer ( TNBT ) or Claudio’s Blocking Buffer ( CBB ) for retinas with Golgi stained . CBB consists of 1% FBS ( ThermoFisher Scientific ) , 3% BSA ( Nzytech ) , 0 . 5% Triton X100 ( Sigma ) , 0 . 01% Sodium deoxycholate ( Sigma ) , 0 , 02% Sodium Azide ( Sigma ) in PBS pH = 7 . 4 for 2 hr in a rocking platform ) for retinas stained for golgi . Thereafter , retinas were incubated overnight in primary antibody in blocking buffer . After extensive washing , retinas were incubated in the corresponding secondary antibody for 2 hr at room temperature . For confocal microscopy , retinas were mounted on glass slides , and for LSFM , retinas were mounted in 2% low-melting agarose . Agarose was melted at >65 °C , and then maintained at 42 °C before adding the tissue . To minimise curling of the retina , apply 1–2 drops of low melting agarose on retina and start uncurling the retina before the gel is solidified . It can then be transferred to the cylinder for imaging . PACT clearing was performed as previously described ( Treweek et al . , 2015 ) . Retinas were dissected and fixed with 4% PFA at 4 °C overnight . Samples were incubated overnight at 4 °C in ice cold A4P0 ( 40% acrylamide , Photoinitiator in PBS ) . The following day , samples were degassed on ice by applying a vacuum to the tube for 30 min , followed by purging with N2 for 30 min . Thereafter , samples were incubated at 37 °C for 3 hr to allow hydrogel polymerisation . Excess gel was then removed from the samples , the samples washed in PBS , and incubated at 37 °C for 6 hr in 8% SDS/PBS , pH 7 . 5 . Samples were then washed in PBST for 1–2 days , changing wash buffer 4–5 times to remove all of the SDS . Immunostaining was then performed following the same protocol without PACT clearing . Thereafter , the tissue was cleared by at least 48 hr incubation in RIMS ( 40 g histodenz in 30 ml of sterile-filtered 0 . 02 M phosphate buffer , 0 . 01% sodium azide ) . Cleared retinas were mounted in 5% low-melting agarose/RIMS for LSFM imaging . Wild-type control and Rho knockout eye cups were incubated overnight in TO-PRO-3 stain ( Thermo Fisher Scientific; diluted 3000x in PBS ) . Rho KO eye cups were then washed thoroughly with PBS for 24 hr prior to clearing with a modified iDISCO+ protocol ( Renier et al . , 2016 ) . Briefly , eye cups were dehydrated through a methanol/water gradient ( 20% , 40% , 60% , 80% , 100% , 100% ) . Incubations were for 30 min at each concentration . Next , eye cups were incubated twice in 100% dichloromethane for 30 min . Finally , eye cups were transferred to 100% ethyl cinnamate and incubated for at least 1 hr prior to imaging . Eye cups were imaged in ethyl cinnamate using a Lightsheet microscope ( Zeiss , Jena Germany ) with modified optics designed for imaging 1 . 56 refractive index solutions . A 20 × 1 . 0 NA objective ( RI = 1 . 56 corrected ) was used for detection . For live-imaging , mice were imaged at P4 or P5 . The sample chamber was filled with DMEM without phenol red containing 50% FBS and P/S and heated to 37 °C . Retinas were quickly dissected in prewarmed HBSS containing penicillin and streptomycin . After dissection , retinas were rapidly cut into quarters ( mainly to minimise the datafile size created , the curved form was preserved ) and immediately mounted in 1% low melting agarose in DMEM without phenol red containing 50% Fetal Bovine Serum ( FBS ) and 1x penicillin and streptomycin ( P/S ) . To minimise curling of the retina , as with static imaging , apply 1–2 drops of low melting agarose on retina and start uncurling the retina before the gel is solidified . It can then be transferred to the cylinder for imaging . Eyes were dissected and fixed for one hour in 4% PFA in 0 . 1 M PB pH 7 . 4 . The retinas were isolated , and post-fixed overnight in 4% PFA plus 2 . 5% GA in 0 . 1 M PB pH 7 . 4 prior to storage in 1% PFA in 0 . 1 M PB . After washing in 0 . 1 M PB to remove fixative residues , secondary fixation was performed in 2% reduced osmium tetroxide ( aqueous ) , followed by washes in H2O and storage at 4°C . For hydrated microCT imaging , individual retinas were mounted in CyGEL ( Biostatus , Shepshed UK ) , and scanned using an Xradia 510 Versa ( Zeiss ) . Scans were performed at 40 kV/3 W using an exposure of 10 or 20 s and 3001 projections ( OIR overviews , 1 . 89 µm voxels , ~3 mm field of view; WT overviews 3 . 78 µm voxels , ~3 mm field of view ) . The data was reconstructed into 16-bit TIFF image sequences using Scout-and-Scan Control System Reconstructor software ( Zeiss ) . For visualisation of retina overviews , the OIR datasets were binned in XYZ to reach a voxel resolution of 3 . 78 µm , thereby matching the WT datasets , and rendered in three dimensions using Drishti ( Limaye , 2012 ) . To visualise individual epiretinal tufts , the full resolution OIR datasets were cropped to smaller regions of interest , and rendered in three dimensions using the volume viewer plugin in Fiji , and Imaris . Pups ( P7 ) were put into the OIR chamber with the dam and exposed to 75% of O2 during 5 days ( P11 ) . At P11 animals were passed to normoxia conditions and injected with Everolimus ( P11-P12-P13-P14 ) during four consecutive days . Sacrificed at P15 and retinas collected . Eyes were fixed with 2% of PFA for 5 hr . Everolimus ( Selleckchem ) treatment administered with subcutaneous injections of 5 ug/g of Everolimus and the Vehicle ( DMSO + 30% PEG300 ) . All LSFM images were acquired with a Zeiss Z . 1 light sheet microscope except for those detailed below . The Zeiss objectives used for uncleared tissue and live imaging were Zeiss , RI = 1 . 33 , 5x/0 . 16 , and 20x/1 . 0 . For PACT cleared tissue , RI = 1 . 45 , 20x/1 . 0 ( 5 . 5mm working distance ) was used . The Luxendo-Bruker MuVi-SPIM was used for specialised subcellular imaging of Golgi ( Figure 1—figure supplement 1e–h ) and tuft morphology in Figure 6—figure supplement 3d–j . The Miltenyi-LaVision BioTec Ultramicroscope II light sheet microscopes is particularly good for larger samples and used for the overview images in Figure 6—figure supplement 3a . The Luxendo objectives used were Olympus , RI = 1 . 33 , 20x/1 . 0 in combination with a 1 . 5x magnification changer . The LaVision objective used was a Olympus MV PLAPO 2XC/0 . 5 in combination with a 2x zoom . All raw data were handled on a high-end DELL workstation ( Dual 8-core Xeon Processors , 196 GB RAM , NVIDIA Titan Black GPU , Windows 7 64 bit ) running ZEISS ZEN ( Light sheet edition ) or equivalent . Confocal images were taken with the LSM 880 Confocal Microscope . Tracking was performed manually using ImageJ/Fiji . For filopodia tracking , each filopodia was tracked between each frame of imaging and different analysis was performed . For tracking the actin-rich bundles , the Manual Tracking plugin in Fiji was used to manually select the ROI ( =region of interest ) and follow the pathway of each trajectory . The trajectories and the pathway were overlaid . Each trajectory could be visualised using Montage function . Nuclei Density Method: Using freehand selection in FIJI to define the ONL area in a given image slice , we performed a particle analysis after thresholding to obtain an estimate of nuclei density ( n = 1 retina per condition ) . This was performed at multiple points ( n = approx . eight areas per condition ) in the retina by incrementing the Z-dimension 50 slices and retaking the measurements , before averaging across the densities across slices . ONL Thickness Method: To measure average ONL thickness ( n = 1 retina per condition ) , we used straight line selection in FIJI at three points along the ONL in a given slice and averaged the lengths . This was performed at multiple points ( n = approx . eight slices per condition ) in the retina by incrementing the Z-dimension 50 slices , before retaking the measurements and averaging across slices to obtain an estimate for the overall ONL thickness . | Eye diseases affect millions of people worldwide and can have devasting effects on people’s lives . To find new treatments , scientists need to understand more about how these diseases arise and how they progress . This is challenging and progress has been held back by limitations in current techniques for looking at the eye . Currently , the most commonly used method is called confocal imaging , which is slow and distorts the tissue . Distortion happens because confocal imaging requires that thin slices of eye tissue from mice used in experiments are flattened on slides; this makes it hard to accurately visualize three-dimensional structures in the eye . New methods are emerging that may help . One promising method is called light-sheet fluorescent microscopy ( or LSFM for short ) . This method captures three-dimensional images of the blood vessels and cells in the eye . It is much faster than confocal imaging and allows scientists to image tissues without slicing or flattening them . This could lead to more accurate three-dimensional images of eye disease . Now , Prahst et al . show that LSFM can quickly produce highly detailed , three-dimensional images of mouse retinas , from the smallest parts of cells to the entire eye . The technique also identified new features in a well-studied model of retina damage caused by excessive oxygen exposure in young mice . Previous studies of this model suggested the disease caused blood vessels in the eye to balloon , hinting that drugs that shrink blood vessels would help . But using LSFM , Prahst et al . revealed that these blood vessels actually take on a twisted and knotted shape . This suggests that treatments that untangle the vessels rather than shrink them are needed . The experiments show that LSFM is a valuable tool for studying eye diseases , that may help scientists learn more about how these diseases arise and develop . These new insights may one day lead to better tests and treatments for eye diseases . | [
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] | 2020 | Mouse retinal cell behaviour in space and time using light sheet fluorescence microscopy |
The human gastrointestinal tract is immature at birth , yet must adapt to dramatic changes such as oral nutrition and microbial colonization . The confluence of these factors can lead to severe inflammatory disease in premature infants; however , investigating complex environment-host interactions is difficult due to limited access to immature human tissue . Here , we demonstrate that the epithelium of human pluripotent stem-cell-derived human intestinal organoids is globally similar to the immature human epithelium and we utilize HIOs to investigate complex host-microbe interactions in this naive epithelium . Our findings demonstrate that the immature epithelium is intrinsically capable of establishing a stable host-microbe symbiosis . Microbial colonization leads to complex contact and hypoxia driven responses resulting in increased antimicrobial peptide production , maturation of the mucus layer , and improved barrier function . These studies lay the groundwork for an improved mechanistic understanding of how colonization influences development of the immature human intestine .
The epithelium of the gastrointestinal ( GI ) tract represents a large surface area for host-microbe interaction and mediates the balance between tolerance of mutualistic organisms and the exclusion of potential pathogens ( Peterson and Artis , 2014 ) . This is accomplished , in part , through the formation of a tight physical epithelial barrier , in addition to epithelial secretion of antimicrobial peptides and mucus ( Veereman-Wauters , 1996; Renz et al . , 2011 ) . Development and maturation of the epithelial barrier coincides with the first exposure of the GI tract to microorganisms and the establishment of a microbial community within the gut ( Palmer et al . , 2007; Koenig et al . , 2011 ) . Although microorganisms have long been appreciated as the primary drivers of the postnatal expansion of adaptive immunity ( Renz et al . , 2011; Shaw et al . , 2010; Hviid et al . , 2011; Abrahamsson et al . , 2014; Arrieta et al . , 2015 ) , and more recently as key stimuli in the development of digestion ( Erkosar et al . , 2015 ) , metabolism ( Cho et al . , 2012 ) , and neurocognitive function ( Diaz Heijtz et al . , 2011; Clarke et al . , 2014; Borre et al . , 2014; Desbonnet et al . , 2014 ) , it remains unclear how the human epithelial surface adapts to colonization and expansion of microorganisms within the immature GI tract . Studies in gnotobiotic mice have improved our understanding of the importance of microbes in normal gut function since these mice exhibit profound developmental defects in the intestine ( Round and Mazmanian , 2009; Gensollen et al . , 2016; Bry et al . , 1996; Hooper et al . , 1999 ) including decreased epithelial turnover , impaired formation of microvilli ( Abrams et al . , 1963 ) , and altered mucus glycosylation at the epithelial surface ( Bry et al . , 1996; Goto et al . , 2014; Cash et al . , 2006 ) . However , evidence also suggests that the immature human intestine may differ significantly from the murine intestine , especially in the context of disease ( Nguyen et al . , 2015 ) . For example , premature infants can develop necrotizing enterocolitis ( NEC ) , an inflammatory disease with unknown causes . Recent reports suggest a multifactorial etiology by which immature intestinal barrier function predisposes the preterm infant to intestinal injury and inflammation following postpartum microbial colonization ( Neu and Walker , 2011; Morrow et al . , 2013; Greenwood et al . , 2014; Hackam et al . , 2013; Afrazi et al . , 2014; Fusunyan et al . , 2001; Nanthakumar et al . , 2011 ) . Rodent models of NEC have proven to be inadequate surrogates for studying human disease ( Tanner et al . , 2015 ) . Therefore , direct studies of host-microbial interactions in the immature human intestine will be important to understand the complex interactions during bacterial colonization that lead to a normal gut development or disease . Important ethical and practical considerations have limited research on the immature human intestine . For example , neonatal surgical specimens are often severely damaged by disease and not conducive for ex vivo studies . We and others have previously demonstrated that human pluripotent stem-cell-derived human intestinal organoids ( HIOs ) closely resemble immature intestinal tissue ( Spence et al . , 2011; Finkbeiner et al . , 2015; Watson et al . , 2014; Forster et al . , 2014; Dedhia et al . , 2016; Aurora and Spence , 2016; Chin et al . , 2017 ) and recent work has established gastrointestinal organoids as a powerful model of microbial pathogenesis at the mucosal interface ( Leslie et al . , 2015; McCracken et al . , 2014; Forbester et al . , 2015; Hill and Spence , 2017 ) . In the current work , we used HIOs as a model immature intestinal epithelium and a human-derived non-pathogenic strain of E . coli as a model intestinal colonizer to examine how host-microbe interactions affected intestinal maturation and function . Although the composition of the neonatal intestinal microbiome varies between individuals , organisms within the genera Escherichia are dominant early colonizers ( Gosalbes et al . , 2013; Bäckhed et al . , 2015 ) and non-pathogenic E . coli are widely prevalent and highly abundant components of the neonatal stool microbiome ( Palmer et al . , 2007; Koenig et al . , 2011; Bäckhed et al . , 2015; Morrow et al . , 2013 ) . Microinjection of E . coli into the lumen of three-dimensional HIOs resulted in stable bacterial colonization in vitro , and using RNA-sequencing , we monitored the global transcriptional changes in response to colonization . We observed widespread , time-dependent transcriptional responses that are the result of both bacterial contact and luminal hypoxia resulting from bacterial colonization in the HIO . Bacterial association with the immature epithelium increased antimicrobial defenses and resulted in enhanced epithelial barrier function and integrity . We observed that NF-κB is a central downstream mediator of the transcriptional changes induced by both bacterial contact and hypoxia . We further probed the bacterial contact and hypoxia-dependent epithelial responses using experimental hypoxia and pharmacological NF-κB inhibition , which allowed us to delineate which of the transcriptional and functional responses of the immature epithelium were oxygen and/or NF-κB dependent . We found that NF-κB-dependent microbe-epithelial interactions were beneficial by enhancing barrier function and protecting the epithelium from damage by inflammatory cytokines . Collectively , these studies shed light on how microbial contact with the immature human intestinal epithelium can lead to modified function .
Previous work has demonstrated that stem-cell-derived human intestinal organoids resemble immature human duodenum ( Watson et al . , 2014; Finkbeiner et al . , 2015; Tsai et al . , 2017 ) . Moreover , transplantation into immunocompromised mice results in HIO maturation to an adult-like state ( Watson et al . , 2014; Finkbeiner et al . , 2015 ) . These analyses compared HIOs consisting of epithelium and mesenchyme to whole-thickness human intestinal tissue , which also possessed cellular constituents lacking in HIOs such as neurons , blood vessels and immune cells ( Finkbeiner et al . , 2015 ) . Thus , the extent to which the HIO epithelium resembles immature/fetal intestinal epithelium remained unclear . To address this gap and further characterize the HIO epithelium relative to fetal and adult duodenal epithelium , we isolated and cultured epithelium from HIOs grown entirely in vitro , from fetal duodenum , adult duodenum , or HIOs that had been transplanted into the kidney capsule of NSG immuno-deficient mice and matured for 10 weeks . These epithelium-only derived organoids were expanded in vitro in uniform tissue culture conditions for 4–5 passages and processed for RNA-sequencing ( RNA-seq ) ( Figure 1—figure supplement 1 ) . Comparison of global transcriptomes between all samples in addition to human embryonic stem cells ( hESCs ) used to generate HIOs ( Finkbeiner et al . , 2015; E-MTAB-3158 ) revealed a clear hierarchy in which both in vitro grown HIO epithelium ( p=5 . 06 × 10-9 ) and transplanted epithelium ( p=7 . 79 × 10-14 ) shares a substantially greater degree of similarity to fetal small intestinal epithelium ( Figure 1—figure supplement 1A ) . While unbiased clustering demonstrated that transplanted epithelium closely resembles fetal epithelium , we noted a shift toward the adult transcriptome that resulted in a relative increase in the correlation between transplanted HIO epithelium and adult duodenum-derived epithelium grown in vitro ( Figure 1—figure supplement 1B , p=1 . 17 × 10-4 ) . Principle component analysis ( PCA ) of this multi-dimensional gene expression dataset ( Figure 1—figure supplement 1C ) corroborated the correlation analysis , and indicated that PC1 was correlated with developmental stage ( PC1 , 27 . 75% cumulative variance ) and PC2 was correlated with tissue maturation status ( PC2 , 21 . 49% cumulative variance ) ; cumulatively , PC1 and PC2 accounted for 49 . 24% of the cumulative variance between samples , suggesting that developmental stage and tissue maturation status are major sources of the transcriptional variation between samples . HIO epithelium clustered with fetal epithelium along PC2 , whereas transplanted HIO epithelium clustered with adult epithelium . We further used differential expression analysis to demonstrate that in vitro grown HIO epithelium is similar to the immature human intestine , whereas in vivo transplanted HIO epithelium is similar to the adult epithelium . To do this , we identified differentially expressed genes through two independent comparisons: ( 1 ) human fetal vs . adult epithelium; ( 2 ) HIO epithelium vs . transplanted HIO epithelium . Genes enriched in transplanted HIO epithelium relative to the HIO epithelium were compared to genes enriched in the adult duodenum relative to fetal duodenum ( Figure 1—figure supplement 1D ) . There was a highly significant correlation between log2-transformed expression ratios where transplanted HIOs and adult epithelium shared enriched genes while HIO and fetal epithelium shared enriched genes ( p=2 . 6 × 10-28 ) . This analysis supports previously published data indicating that the epithelium from HIOs grown in vitro recapitulates the gene expression signature of the immature duodenum and demonstrates that the HIO epithelium is capable of adopting a transcriptional signature that more strongly resembles adult duodenum following transplantation into mice . Given that the HIO epithelium recapitulates many of the features of the immature intestinal epithelium , we set out to evaluate the effect of bacterial colonization on the naïve HIO epithelium . Previous studies have established that pluripotent stem-cell-derived intestinal organoids can be injected with live viral ( Finkbeiner et al . , 2012 ) or bacterial pathogens ( Leslie et al . , 2015; Engevik et al . , 2015; Forbester et al . , 2015 ) ; however , it was not known if HIOs could be stably co-cultured with non-pathogenic microorganisms . We co-cultured HIOs with the non-motile human-derived Esherichia coli strain ECOR2 ( Ochman and Selander , 1984 ) . Whole genome sequencing and phylogentic analysis demonstrated that E . coli str . ECOR2 is closely related to other non-pathogenic human E . coli and only distantly related to pathogenic E . coli and Shigella isolates ( Figure 1—figure supplement 3 ) . We developed a microinjection technique to introduce live E . coli into the HIO lumen in a manner that prevented contamination of the surrounding media ( Figure 1—figure supplement 2 ) . HIOs microinjected with 105 live E . coli constitutively expressing GFP exhibit robust green fluorescence within 3 hr of microinjection ( Figure 1A and Video 1 ) . Numerous E . coli localized to the luminal space at 48 hr post-microinjection and are present adjacent to the HIO epithelium , with some apparently residing in close opposition to the apical epithelial surface ( Figure 1B ) . In order to determine the minimum number of colony-forming units ( CFU ) of E . coli required to establish short term colonization ( 24 hr ) , we microinjected increasing numbers of live E . coli suspended in PBS into single HIOs and collected and determined the number of bacteria in the luminal contents at 24 hr post-microinjection ( Figure 1C ) . Single HIOs can be stably colonized by as few as 5 CFU E . coli per HIO with 77 . 8% success ( positive luminal culture and negative external media culture at 24 hr post-injection ) and 100% success at ≥100 CFU per HIO ( Figure 1C ) . Increasing the number of CFU E . coli microinjected into each HIO at t = 0 did result in a significant increase in the mean luminal CFU per HIO at 24 hr post-microinjection at any dose ( ANOVA p=0 . 37; Figure 1C ) . Thus , the 24 hr growth rate of E . coli within the HIO lumen ( CFU×HIOt=24−1CFU×HIOt=0−1 ) was negatively correlated with the CFU injected ( r2 = 0 . 625 , p=3 . 1 × 10-12; Figure 1C ) . Next , we examined the stability of HIO and E . coli co-cultures over time in vitro . HIOs were microinjected with 10 CFU E . coli and maintained for 24–72 hr ( Figure 1D ) . Rapid expansion of E . coli density within the HIO lumen was observed in the first 24 hr , with relatively stable bacterial density at 48–72 hr . A 6 . 25-fold increase in bacterial density was observed between 24 and 72 hr post-microinjection ( p=0 . 036 ) . Importantly , samples taken from the external HIO culture media were negative for E . coli growth . Finally , we examined the stability of HIO cultures following E . coli microinjection ( Figure 1E ) . A total of 48 individual HIOs were microinjected with 104 CFU E . coli each . Controls were microinjected with sterile PBS alone . We found that external culture media was sterile in 100% of control HIOs throughout the entire experiment , and in 100% of E . coli injected HIOs on days 0–2 post-microinjection . On days 3–9 post-microinjection some cultured media was positive for E . coli growth; however , 77 . 08% of E . coli injected HIOs were negative for E . coli in the external culture media throughout the timecourse . Additional control experiments were conducted to determine if the HIO growth media had any effect on E . coli growth . E . coli-inoculated HIO growth media showed that the media itself allowed for robust bacterial growth , and therefore the absence of E . coli growth in external media from HIO cultures could not be attributed to the media composition alone ( Figure 1—figure supplement 3 ) . Thus , the large majority of E . coli colonized HIOs remain stable for an extended period when cultured in vitro and without antibiotics . Colonization of the immature gut by microbes is associated with functional maturation in both model systems ( Kremer et al . , 2013; Sommer et al . , 2015; Broderick et al . , 2014; Erkosar et al . , 2015 ) and in human infants ( Renz et al . , 2011 ) . To evaluate if exposing HIOs to E . coli led to maturation at the epithelial interface , we evaluated the transcriptional events following microinjection of live E . coli into the HIO lumen . PBS-injected HIOs ( controls ) and HIOs co-cultured with E . coli were collected for transcriptional analysis after 24 , 48 and 96 hr ( Figure 2 ) . At 24 hr post-microinjection , a total of 2018 genes were differentially expressed ( adjusted-FDR < 0 . 05 ) , and the total number of differentially expressed genes was further increased at 48 and 96 hr post-microinjection relative to PBS-injected controls ( Figure 2A ) . Principle component analysis demonstrated that global transcriptional activity in HIOs is significantly altered by exposure to E . coli , with the degree of transcriptional change relative to control HIOs increasing over time ( Figure 2B ) . Gene set enrichment analysis ( GSEA ) ( Subramanian et al . , 2005 ) using the GO ( Ashburner et al . , 2000; Gene Ontology Consortium , 2015 ) and REACTOME ( Croft et al . , 2014; Fabregat et al . , 2016 ) databases to evaluate RNA-seq expression data revealed coordinated changes in gene expression related to innate anti-microbial defense , epithelial barrier production , adaptation to low oxygen , and tissue maturation ( Figure 2C ) . Innate antimicrobial defense pathways , including genes related to NF-κB signaling , cytokine production , and Toll-like receptor ( TLR ) signaling were strongly up-regulated at 24 hr post-microinjection and generally exhibited decreased activation at later time points . GSEA also revealed changes in gene expression consistent with reduced oxygen levels or hypoxia , including the induction of pro-angiogenesis signals . A number of pathways related to glycoprotein synthesis and modification , including O-linked mucins , glycosaminoglycans , and proteoglycans , were up-regulated in the initial stages of the transcriptional response ( Syndecans , integrins ) , exhibited a somewhat delayed onset ( O-linked mucins ) , or exhibited consistent activation at all time points post-microinjection ( Keratan sulfate and glycosaminoglycan biosynthesis ) . Finally , genes sets associated with a range of processes involved in tissue maturation and development followed a distinct late-onset pattern of expression . This included broad gene ontology terms for organ morphogenesis , developmental maturation , and regionalization as well as more specific processes such as differentiation of mesenchymal and muscle cells , and processes associated with the nervous system ( Figure 2C ) . We also made correlations between upregulated genes in the RNA-seq data ( Figure 2D ) and protein factors present in the organoid culture media following E . coli microinjection ( Figure 2E ) . β-defensin 1 ( DEFB1 ( gene ) ; BD-1 ( protein ) ) and β-defensin 2 ( DEFB4A ( gene ) ; BD-2 ( protein ) ) exhibited distinct patterns of expression , with both DEFB1 and its protein product BD-1 stable at 24 hr after E . coli microinjection but relatively suppressed at later time points , and DEFB4A and BD-2 strongly induced at early time points and subsiding over time relative to PBS-injected controls . By contrast , inflammatory regulators IL-6 and IL-8 and the pro-angiogenesis factor VEGF were strongly induced at the transcriptional level within 24–48 hr of E . coli microinjection . Secretion of IL-6 , IL-8 , and VEGF increased over time , peaking at 5–9 days after E . coli association relative to PBS-injected controls ( Figure 2E ) . Taken together , this data demonstrates a broad-scale and time-dependent transcriptional response to E . coli association with distinct early- and late-phase patterns of gene expression and protein secretion . While the transcriptional analysis demonstrated strong time-dependent changes in the cells that comprise the HIO following E . coli colonization , we hypothesized that exposure to bacteria may also alter the cellular behavior and/or composition of the HIO . Previous studies have demonstrated that bacterial colonization promotes epithelial proliferation in model organisms ( Bates et al . , 2006; Cheesman et al . , 2011; Neal et al . , 2013; Kremer et al . , 2013; Ijssennagger et al . , 2015 ) . We examined epithelial proliferation in HIOs over a timecourse of 96 hr by treating HIOs with a single 2 hr exposure of 10 μM EdU added to the culture media from 22 to 24 hr after microinjection with 104 CFU E . coli or PBS alone . HIOs were subsequently collected for immunohistochemistry at 24 , 48 , and 96 hr post-microinjection ( Figure 3 ) . The number of proliferating epithelial cells ( Edu\+ and E-cadherin\+ ) was elevated by as much as three-fold in E . coli-colonized HIOs relative to PBS-treated HIOs at 24 hr ( Figure 3A–B ) . However , at 48 hr post-microinjection , the proportion of EdU + epithelial cells was significantly decreased in E . coli colonized HIOs relative to control treated HIOs . This observation was supported by another proliferation marker , KI67 ( Gerdes et al . , 1984 ) ( Figure 3B ) , as well as RNA-seq data demonstrating an overall suppression of cell cycle genes in E . coli colonized HIOs relative to PBS-injected HIOs at 48 hr post-microinjection ( Figure 3—figure supplement 1 ) . By 96 hr post-microinjection the proportion of EdU+ epithelial cells was nearly identical in E . coli and PBS-treated HIOs ( Figure 3B ) . Collectively , these results suggest that E . coli colonization is associated with a rapid burst of epithelial proliferation , but that relatively few of the resulting daughter cells are retained subsequently within the epithelium . The transcription factor Sox9 is expressed by progenitor cells in the murine intestinal epithelium ( Bastide et al . , 2007; Mori-Akiyama et al . , 2007 ) , and several epithelial subtypes are derived from a Sox9-expressing progenitor population in the mature intestinal epithelium ( Bastide et al . , 2007; Furuyama et al . , 2011 ) . We examined SOX9 expression in HIOs following microinjection with E . coli or PBS alone over a 96 hr time course ( Figure 3C ) . In the PBS-treated HIOs , the majority of epithelial cells exhibited robust nuclear SOX9 expression at all time points examined . However , SOX9 expression was dramatically reduced in E . coli-colonized HIOs at 48–96 hr after microinjection and was notably distributed in nuclei farthest from the lumen and adjacent to the underlying mesenchyme , mirroring the altered distribution of EdU + nuclei seen in Figure 3B . This observation suggests that there is a reduction in the number of progenitor cells in the HIO epithelium following E . coli colonization and implies that other epithelial types may account for a greater proportion of the HIO epithelium at later time points post-colonization . We saw no appreciable staining for epithelial cells expressing goblet , Paneth , or enteroendocrine cell markers ( MUC2 , DEFA5 , and CHGA , respectively; negative data not shown ) . However , expression of the small intestinal brush border enzyme dipeptidyl peptidase-4 ( DPPIV ) was found to be robustly expressed in the E . coli-colonized HIOs at 48 and 96 hr post-microinjection ( Figure 3D ) . DPPIV was not detected in any of the PBS-injected HIOs at any timepoint . Lysozyme ( LYZ ) , an antimicrobial enzyme expressed by Paneth-like progenitors in the small intestinal crypts Bevins and Salzman ( 2011 ) , was widely distributed throughout the epithelium of PBS-treated HIOs as we have previously described ( Spence et al . , 2011 ) ( Figure 3D ) . However , in E . coli-colonized HIOs , LYZ expression was restricted to distinct clusters of epithelial cells and , notably , never overlapped with DPPIV staining ( Figure 3D ) . Given that bona fide Paneth Cell markers ( i . e . DEFA5 ) were not observed in any HIOs , it is likely that the LYZ expression is marking a progenitor-like population of cells . Taken together , these experiments indicate that E . coli colonization induces a substantial but transient increase in the rate of epithelial proliferation followed by a reduction and redistribution of proliferating epithelial progenitors and differentiation of a population of cells expressing small intestinal enterocyte brush boarder enzymes over a period of 2–4 days . The mature intestinal epithelium is characterized by a steep oxygen gradient , ranging from 8% oxygen within the bowel wall to <2% oxygen in the lumen of the small intestine ( Fisher et al . , 2013 ) . Reduction of oxygen content in the intestinal lumen occurs during the immediate perinatal period ( Gruette et al . , 1965 ) , resulting in changes in epithelial physiology ( Glover et al . , 2016; Kelly et al . , 2015; Colgan et al . , 2013; Zeitouni et al . , 2016 ) that helps to shape the subsequent composition of the microbiota ( Schmidt and Kao , 2014; Espey , 2013; Albenberg et al . , 2014; Palmer et al . , 2007; Koenig et al . , 2011 ) . Analysis of the global transcriptional response to E . coli association in the immature intestinal tissue revealed pronounced and coordinated changes in gene expression consistent with the onset of hypoxia ( Figure 2C–E ) . We therefore measured oxygen concentration in the lumen of control HIOs and following microinjection of live E . coli using a 50 μm diameter fiberoptic optode ( Figure 4A–B ) . Baseline oxygen concentration in the organoid lumen was 8 . 04 ± 0 . 48% , which was significantly reduced relative to the external culture media ( 18 . 86 ± 0 . 37% , p=3 . 6 × 10-11 ) . At 24 and 48 hr post-microinjection , luminal oxygen concentration was significantly reduced in E . coli-injected HIOs relative to PBS-injected HIOs ( p=0 . 04 and p=5 . 2 × 10-05 , respectively ) reaching concentrations as low as 1 . 67 ± 0 . 62% at 48 hr ( Figure 4A ) . E . coli injected HIOs were collected and CFU were enumerated from luminal contents at 24 and 48 hr post-microinjection . We observed a highly significant negative correlation between luminal CFU and luminal oxygen concentration where increased density of luminal bacteria was correlated with lower oxygen concentrations ( r2 = 0 . 842 , p=6 . 86 × 10-5; Figure 4B ) . Finally , in order to assess relative oxygenation in the epithelium itself , we utilized a small molecule pimonidazole ( PMDZ ) , which forms covalent conjugates with thiol groups on cytoplasmic proteins only under low-oxygen conditions ( Arteel et al . , 1998 ) . Fluorescent immunochemistry demonstrated enhanced PMDZ uptake in E . coli associated HIO epithelium , and in HIOs grown in 1% O2 as a positive control when compared to to PBS-injected HIOs , or HIOs injected with heat killed E . coli at 48 hr post-microinjection ( Figure 4C ) . Thus , luminal and epithelial oxygen is reduced following microinjection of E . coli into the HIO , consistent with data in mice showing that the in vivo epithelium is in a similar low-oxygen state in normal physiological conditions ( Schmidt and Kao , 2014; Kelly et al . , 2015; Kim et al . , 2017 ) . E . coli colonization elicits a robust transcriptional response in immature intestinal tissue ( Figure 2 ) that is associated with the onset of luminal oxygen depletion and relative tissue hypoxia ( Figure 4 ) . We set out to determine whether we could assign portions of the transcriptional response to direct interaction with microbes or to the subsequent depletion of luminal oxygen . In the RNA-seq analysis ( Figure 2 ) , NF-κB signaling emerged as a major pathway involved in this complex host-microbe interaction , and NF-κB has been shown by others to act as a transcriptional mediator of both microbial contact and the response to tissue hypoxia ( Rius et al . , 2008; Gilmore , 2006; Wullaert et al . , 2011 ) . Gene Ontology and REACTOME pathway analysis showed that NF-κB signaling components are also highly up-regulated following microinjection of E . coli into HIOs ( Figure 2C and Figure 5—figure supplement 1A ) . Thus , we assessed the role of NF-κB signaling in the microbial contact-associated transcriptional response and the hypoxia-associated response using the highly selective IKKβ inhibitor SC-514 ( Kishore et al . , 2003; Litvak et al . , 2009 ) to inhibit phosphorylation and activation of the transcription factor p65 ( Figure 5—figure supplement 1B ) . Another set of HIOs was simultaneously transferred to a hypoxic chamber and cultured in 1% O2 with and without SC-514 . At 24 hr post-treatment , HIOs were harvested for RNA isolation and RNA-seq . We devised an experimental scheme that allowed us to parse out the relative contributions of microbial contact and microbe-associated luminal hypoxia in the transcriptional response to association with live E . coli ( Figure 5A and Figure 5—figure supplement 1C ) . First , we identified a set of genes significantly up-regulated ( log2FC > 0 and FDR-adjusted p-value < 0 . 05 ) by microinjection of either live E . coli or heat-inactivated E . coli ( contact dependent genes ) . From this gene set , we identified a subset that was suppressed by the presence of NF-κB inhibitor SC-514 during association with either live or heat-inactivated E . coli ( log2FC < 0 and FDR-adjusted p-value < 0 . 05; Gene Set I , Figure 5B ) . Thus , Gene Set I represents the NF-κB dependent transcriptional response to live or dead E . coli . Genes induced by live or heat-inactivated E . coli but not suppressed by SC-514 were considered NF-κB independent ( Gene Set III , Figure 5B ) . Likewise , we compared genes commonly up-regulated by association with live E . coli and those up-regulated under 1% O2 culture conditions . A subset of genes induced by either live E . coli or 1% O2 culture but suppressed by the presence of NF-κB inhibitor was identified as the NF-κB-dependent hypoxia-associated transcriptional response ( Gene Set II , Figure 5B ) . Genes induced by live E . coli or hypoxia but not inhibited by the presence of NF-κB inhibitor were considered NF-κB independent transcriptional responses to microbe-associated hypoxia ( Gene Set IV ) . Gene lists for each gene set are found in Supplementary file 1 . Following the identification of these four gene sets , we then applied over-representation analysis using the GO and REACTOME pathway databases to identify enriched pathways for each of the four gene sets , resulting in four clearly distinguishable patterns of gene pathway enrichment ( Figure 5C ) . Contact with either live or heat-inactivated E . coli is sufficient to promote expression of genes involved in maintaining epithelial barrier integrity and mucin production , an effect that is suppressed in the presence of NF-κB inhibitor . Additionally , key developmental pathways including epithelial morphogenesis , digestive tract development , and expression of digestive enzymes appear to be driven primarily by bacterial association and are largely NF-κB dependent . Robust innate and adaptive defense requires both bacterial contact and hypoxia , with some genes associated with antigen processing and cytokine signaling being NF-κB dependent ( Gene Set II ) and others associated with NF-κB-independent gene sets ( Gene Sets III and IV ) . Genes associated with antimicrobial defensin peptides were enriched only in the hypoxia-asociated , NF-κB-independent gene set ( Gene Set IV ) , suggesting that antimicrobial peptides are regulated by mechanisms that are distinct from other aspects of epithelial barrier integrity such as mucins and epithelial junctions ( Gene Set I ) . TLR signaling components were is broadly enhanced by live E . coli and associated with both microbial contact and hypoxia were largely NF-κB independent ( Gene Sets III and IV ) . There was a notable transcriptional signature suggesting metabolic and mitochondrial adaptation to bacteria that was independent of NF-κB and primarily driven by bacterial contact rather than hypoxia ( Gene Set III ) . To interrogate the transcriptional changes influenced by SC-514 exposure , we examined over-represented genes sets from the GO and REACTOME databases in genes that were significantly up- or down-regulated by treatment with SC-514 alone ( Figure 5—figure supplement 1C and D ) . Notably , SC-514 alone does not appear to have a strong effect on the pathways identified in Figure 5C as key NF-κB-dependent responses to bacterial contact and/or hypoxia . In Figure 5—figure supplement 1E , we examined the degree of overlap between Gene Set I , Gene Set II , and the set of genes that are significantly down-regulated in PBS-injected HIOs treated with SC-514 . This analysis demonstrates that the majority of genes in Set I and Set II are not significantly down-regulated in PBS-injected HIOs treated with SC-514 . The most significant effects of SC-514 alone among Gene Set I and Gene Set II genes are related to metabolism , redox state , and ribosomal dynamics ( Figure 5—figure supplement 1F ) . Thus , the effect of SC-514 alone cannot account for the NF-κB-dependent changes in innate and adaptive defense , epithelial barrier integrity , angiogenesis and hypoxia signaling , or intestinal development following bacterial contact and/or hypoxia during colonization . Finally , we also examined the role of microbial contact and hypoxia in colonization-induced changes in AMP , cytokine , and growth factor secretion using ELISA ( Figure 5—figure supplement 2 ) . Consistent with findings from the RNA-seq data , these results indicate that there are diverse responses to bacterial contact and hypoxia . We observed cases where cytokines were induced by either microbial contact or hypoxia alone ( IL-6 ) , other cases where hypoxia appeared to be the dominant stimuli ( BD-1 ) , and a third regulatory paradigm in which the response to live E . coli evidently results from the cumulative influence of bacterial contact and hypoxia ( BD-2 , IL-8 , VEGF ) . Taken together , this analysis demonstrates that association of immature intestinal epithelium with live E . coli results in a complex interplay between microbial contact and microbe-associated hypoxia-induced gene expression and protein secretion . Antimicrobial peptides ( AMPs ) are key effectors for innate defense of epithelial surfaces ( Muniz et al . , 2012 ) and act to inhibit microbial growth through direct lysis of the bacterial cell wall and modulation of bacterial metabolism ( Ganz , 2003; Bevins and Salzman , 2011; O'Neil and O’Neil , 2003; Vora et al . , 2004; Brogden , 2005 ) . Defensin gene expression is highly up-regulated following microinjection of E . coli into HIOs ( Figures 2D–E and and 4C ) . Using an annotated database of known AMPs ( Wang et al . , 2016 ) to query our RNA-seq datasets , we found that several AMPs are up-regulated in the immature intestinal epithelium following E . coli association ( Figure 6A ) . Among these , DEFB4A and DEFB4B , duplicate genes encoding the peptide human β-defensin 2 ( Harder et al . , 1997 ) , were the most highly up-regulated; other AMPs induced by E . coli association included multi-functional peptides CCL20 , CXCL2 , CXCL1 , CXCL6 , CXCL3 , REG3A ( Cash et al . , 2006 ) , and LTF ( Figure 6A ) . Analysis of RNA-seq data from HIOs microinjected with live or heat-killed E . coli with and without NF-κB inhibitor or culture of HIOs under hypoxic conditions had indicated that defensin genes were enriched among the set of NF-κB-independent genes induced by hypoxia ( Figure 5C ) . We examined DEFB4A expression specifically ( Figure 6B ) and found that relative to control treatment , microinjection of live E . coli resulted in a 7 . 38-fold increase in normalized DEFB4A expression . Consistent with the notion that DEFB4A expression is induced by hypoxia and is not dependent on NF-κB signaling , NF-κB inhibitor treated HIOs injected with E . coli still showed an ~8-fold increase in gene expression and hypoxia-cultured HIOs showed a ~5 . 5-fold induction ( Figure 6B ) . On the other hand , microinjection with heat-inactivated E . coli resulted in DEFB4A induction that was significantly lower relative to microinjection with live E . coli ( p=0 . 007 . A similar pattern of expression was observed for DEFB4B ( Figure 6—figure supplement 1 ) . We also examined secretion of human β-defensin 2 peptide ( BD-2 ) in the supernatant of E . coli-associated HIOs ( Figure 2E and Figure 6C ) . BD-2 secretion was increased 3 . 4-fold at 24 hr following E . coli microinjection ( p=2 . 7 × 10-8 ) . However , heat-inactivation of E . coli or addition of NF-κB inhibitor resulted in suppression of BD-2 secretion relative to live E . coli ( p=0 . 00051 and 1 . 6 × 10-6 , respectively ) . To determine if the levels of BD-2 produced by HIOs and secreted into the media were sufficient to retard bacterial growth , we tested the effect of BD-2 at concentrations recapitulating the baseline state in the HIO ( ~0 . 1 μg/ml ) and following microinjection with E . coli ( ~1 μg/ml ) on in vitro growth of E . coli over 18 hr ( Figure 6D ) . Although there was little effect on E . coli density during initial log-phase growth , BD-2 reduced the amount of time bacteria spent in log-phase growth , and E . coli density was significantly decreased over time in bacterial growth media supplemented BD-2 ( p=0 . 001 ) , resulting in a significant decrease in the effective in vitro carrying capacity , or maximum population density ( Figure 6E , p=8 × 10-4 ) . Furthermore , concentrations of BD-2 consistent with those found in HIO/E . coli supernatant ( 1 μg/ml ) was significantly more inhibitory than low concentration BD-2 ( 0 . 1 μg/ml ) in our in vitro growth assay ( p=0 . 013 ) . Additional data suggest that the inhibitory activity of BD-2 in vitro is not specific to E . coli str . ECOR2 and is dependent upon maintenance of BD-2 protein structure , since BD-2 similarly inhibited growth of E . coli str . K12 , and heat-inactivated BD-2 lost these inhibitory effects ( Figure 6—figure supplement 2 ) . From this set of experiments , we conclude that E . coli colonization promotes enhanced expression of AMPs , including BD-2 , at concentrations that are sufficient to suppress microbial growth . Mucins are an essential component of epithelial integrity , serving as a formidable barrier to microbial invasion and repository for secreted AMPs ( Bergstrom and Xia , 2013; Cornick et al . , 2015; Johansson and Hansson , 2016; Kim and Ho , 2010 ) . Mucin synthesis requires a complex series of post-translational modifications that add high-molecular-weight carbohydrate side chains to the core mucin protein ( Varki , 2017 ) . Our RNA-seq data suggested that mucin gene expression is dependent on both bacterial contact and NF-κB signaling ( Figure 5C ) . Therefore , we examined expression of genes in control and E . coli microinjected HIOs that encode mucin core proteins as well as the glycotransferases that generate the wide variety of post-translational mucin modifications ( Figure 7A ) . Although some glycotransferases were increased at 24 hr after E . coli microinjection , expression of mucin core proteins and many glycotransferases reached peak levels at 48 hr after the introduction of E . coli to the HIO lumen ( Figure 7A ) . Periodic Acid-Schiff and Alcian blue staining ( PAS/AB ) of sections taken from HIOs at 48 hr after E . coli microinjection reveal the formation of a robust mucin layer at the apical epithelial surface consisting of both acidic ( AB-positive ) and neutral ( PAS-positive ) glycoprotein components , suggesting a rich matrix of O-linked mucins , glycosaminoglycans , and proteoglycans ( Figure 7B–C ) . Interestingly , we observed that E . coli association caused an initial induction of MUC5AC at 48 hr that was reduced by 96 hr ( Figure 7A ) . MUC5AC is most highly expressed within the gastric mucosa but has also been reported in the duodenal epithelium ( Buisine et al . , 1998 , 2001; Rodríguez-Piñeiro et al . , 2013 ) . On the other hand , MUC2 is more commonly associated with the duodenum , and increased more slowly , showing peak expression after 96 hr of association with E . coli ( Figure 7A ) . Co-staining of control HIOs and E . coli microinjected HIOs demonstrated colocalization with Ulex europaeus agglutinin I ( UEA1 ) , a lectin with high specificity for the terminal fucose moiety Fucα1-2Gal-R ( Figure 7D ) . This suggests that following E . coli association , HIOs produce mucins with carbohydrate modifications associated with bacterial colonization in vivo ( Cash et al . , 2006; Hooper et al . , 1999; Goto et al . , 2014 ) . RNA-seq data suggested that O-linked mucins were highly enriched among the subset of genes induced by bacterial contact in an NF-κB-dependent manner ( Figure 5 ) . We examined this phenomenon at the level of individual glycosyltransferase and mucin genes ( Figure 7E ) . E . coli induced transcription of mucins and glycosyltransferases ( Figure 7E ) and mucin secretion ( Figure 7—figure supplement 1 ) was suppressed in the presence of NF-κB inhibitor SC-514 . Furthermore , culture of HIOs under hypoxia conditions was not sufficient to promote transcription of genes involved in mucin synthesis ( Figure 7E ) . This result was confirmed with PAS/AB staining of HIOs microinjected with PBS , live or heat-inactivated E . coli , or cultured under hypoxic conditions for 24 hr , where bacterial contact promoted formation of a mucus layer while PBS microinjection or culture under hypoxic conditions did not ( Figure 7F ) . Taken together , these results indicate that association of the immature intestinal epithelium with E . coli promotes robust mucus secretion through an NF-κB-dependent mechanism and that hypoxia alone is not sufficient to recapitulate E . coli-induced mucus production . Having established that the immature intestinal epithelium in HIOs ( Figure 1—figure supplement 1 ) can be stably associated with non-pathogenic E . coli ( Figure 1 ) , resulting in broad changes in transcriptional activity ( Figure 2 ) and leading to elevated production of AMPs ( Figure 6 ) and epithelial mucus secretion ( Figure 7 ) , we hypothesized that these changes in gene and protein expression would have functional consequences for the immature epithelial barrier . RNA-seq analysis demonstrated broad up-regulation of transcription in genes involved in the formation of the adherens junction and other cell-cell interactions in HIOs after microinjection with live E . coli that was inhibited in the presence of NF-κB inhibitor SC-514 ( Figure 8A ) . We utilized a modified FITC-dextran permeability assay ( Leslie et al . , 2015 ) and real-time imaging of live HIO cultures to measure epithelial barrier function in HIOs microinjected with PBS , live E . coli , or live E . coli +SC-514 at 24 hr after microinjection ( Figure 8B ) . While HIOs microinjected with PBS or E . coli retained 94 . 1 0 . 3% of the FITC-dextran fluorescence over the 20-hr assay period , E . coli microinjected HIOs cultured in the presence of SC-514 retained only 45 . 5 ± 26 . 3% of the fluorescent signal ( p=0 . 02; Figure 8B ) . We also measured the rate of bacterial translocation across the HIO epithelium , which resulted in contaminated culture media ( Figure 8C ) . HIOs microinjected with E . coli and treated with SC-514 were compared to E . coli microinjected HIOs treated with vehicle ( DMSO controls ) and PBS microinjected controls over 7 days in culture . HIOs associated with E . coli +SC-514 exhibited a rapid onset of bacterial translocation by days 2–3 , with bacterial translocation detected in 96% of SC-514-treated HIOs by day 7 compared to 23% of HIOs microinjected with E . coli and cultured in DMSO ( P=<2 × 10-16; Figure 8C ) . Therefore , blocking NF-κB signaling inhibited epithelial barrier maturation resulting in increased bacterial translocation during E . coli association with the immature epithelium . Finally , we assayed epithelial barrier function under circumstances recapitulating physiologic inflammation . TNFα and IFNγ are key cytokines mediating innate and adaptive immune cell activity in the gut ( Turner , 2009 ) during bacterial infection ( Rhee et al . , 2005; Emami et al . , 2012 ) and in necrotizing enterocolitis ( Tan et al . , 1993; Ford et al . , 1996 , 1997; Halpern et al . , 2003; Upperman et al . , 2005 ) . The combination of TNFα and IFNγ has been previously demonstrated to induce barrier permeability in a dose-dependent manner in Transwell epithelial cultures ( Wang et al . , 2005; Wang et al . , 2006 ) . Thus , HIOs were microinjected with PBS or live E . coli and cultured for 24 hr and were subsequently microinjected with FITC-dextran and treated with PBS or a cocktail of TNFα and IFNγ added to the external media to expose the basolateral epithelium ( Figure 8D ) . Loss of FITC-dextran fluorescence was observed using live-imaging and indicated that treatment with TNFα and IFNγ alone resulted in a rapid and sustained decrease in luminal fluorescence relative to PBS or E . coli injected HIOs ( p=5 × 10-4 , Figure 8D ) . However , HIOs associated with E . coli prior to addition of the TNFα and IFNγ cocktail retained significantly more fluorescent signal relative to treatment with TNFα and IFNγ alone ( p=0 . 042 , Figure 8D ) . We examined expression and distribution of the tight junction protein ZO-1 , and the basal-lateral protein E-cadherin ( ECAD ) in histological sections taken from PBS and E . coli-associated HIOs subjected to TNFα and IFNγ treatment ( Figure 8E ) . Compared to controls , the epithelial layer is highly disorganized in HIOs treated with TNFα and IFNγ , with cytoplasmic ZO-1 staining and disorganized ECAD . By contrast , HIOs associated with E . coli prior to TNFα and IFNγ treatment retain and organized columnar epithelium with robust apical ZO-1 and properly localized ECAD staining ( Figure 8E ) . Similarly , proper localization of additional markers of epithelial barrier integrity occludin ( OCLN ) and acetylated-tubulin are retained in HIOs associated with E . coli during TNFα and IFNγ treatment relative to HIOs treated with TNFα and IFNγ alone ( Figure 8—figure supplement 1 ) . These results suggest that colonization of the immature epithelium with E . coli results in an epithelium that is more robust to challenge by potentially damaging inflammatory cytokines .
The work presented here demonstrates that HIOs represent a robust model system to study the initial interactions between the gastrointestinal epithelium and colonizing microbes that occurs in the immediate postnatal period . Microorganisms introduced into the digestive tract at birth establish an intimate and mutualistic relationship with the host over time ( Costello et al . , 2012; Palmer et al . , 2007; Koenig et al . , 2011; Bäckhed et al . , 2015; Wopereis et al . , 2014 ) . However , the expansion of bacterial populations in the gut also presents a major challenge to intestinal homeostasis through the exposure to potentially inflammatory MAMPs ( Tanner et al . , 2015; Renz et al . , 2011 ) , consumption of tissue oxygen ( Glover et al . , 2016; Espey , 2013; Albenberg et al . , 2014 ) , digestion of the mucus barrier ( Marcobal et al . , 2013; Desai et al . , 2016 ) , and competition for metabolic substrates ( Rivera-Chávez et al . , 2016; Kaiko et al . , 2016 ) . The mature intestinal epithelium serves as a crucial barrier to microbes that inhabit the lumen and mucosal surfaces ( Artis , 2008; Turner , 2009; Desai et al . , 2016; Kelly et al . , 2015; Cornick et al . , 2015; Peterson and Artis , 2014; Hackam et al . , 2013; Turner , 2009 ) . The specific function of the epithelium in adapting to initial microbial colonization , independent of innate and adaptive immune systems , remains unclear due to the lack of appropriate model systems that recapitulate host-microbe mutualism . Clarifying the role of the epithelium in colonization of the digestive tract by microorganisms is essential to understanding the molecular basis of the stable host-microbe mutualism in the mature intestine . To examine the establishment of host-microbe mutualism , we chose to examine the interaction between the immature epithelium of HIOs and a non-pathogenic strain of E . coli . Enterobacteriaceae , including E . coli , are abundant in the newborn gut ( Palmer et al . , 2007; Koenig et al . , 2011; Bäckhed et al . , 2015; DIABIMMUNE Study Group et al . , 2016 ) . Several large-scale surveys of microbial composition have demonstrated that E . coli are among the most prevalent and abundant organisms in stool samples from newborns ( Bäckhed et al . , 2015; Koenig et al . , 2011 ) and in meconium ( Gosalbes et al . , 2013 ) . Non-pathogenic E . coli strains may represent ideal model organisms for examining the impact of bacterial colonization of the immature epithelium due to their prevalence in the neonatal population and relevance to natural colonization , extensive characterization , and ease of laboratory manipulation . Microinjection of non-pathogenic E . coli into the lumen of HIOs resulted in stable , long-term co-cultures ( Figure 1 ) . E . coli grows rapidly within the HIO lumen ( Figure 1 ) , reaching densities roughly comparable to populations found in the human small intestine ( Donaldson et al . , 2016 ) within 24 hr . Furthermore , the HIO is able to sustain this internal microbial population for several days while retaining the integrity of the epithelial barrier ( Figure 1 ) . Implicit is this observation is the conclusion that immature epithelium , along with a loosely structured mesenchymal layer , is intrinsically capable of adapting to the challenges imposed by colonization with non-pathogenic gut bacteria . To more closely examine these epithelial adaptations of microbial colonization , we performed transcriptional analysis of this response . HIOs colonized by E . coli exhibit widespread transcriptional activation of innate bacterial recognition pathways , including TLR signaling cascades and downstream mediators such as NF-κB ( Figure 2 ) . The cellular composition of the HIO epithelium is refined following E . coli colonization , with a rapid but transient increase in epithelial proliferation preceding a general reduction in the number of immature epithelial progenitor cells and the emergence of mature enterocytes expressing brush border digestive enzymes ( Figure 3 ) . Together , these results suggest that bacterial stimuli exert a broad influence on the molecular and cellular composition of the immature epithelium . Indirect stimuli resulting from microbial activity can also shape epithelial function ( Buffie and Pamer , 2013 ) , and the transcriptome of E . coli-colonized HIOs reflects a cellular response to reduced oxygen availability ( Figure 2 ) . Reduction of luminal O2 concentration occurs in the neonatal gut ( Gruette et al . , 1965; Fisher et al . , 2013; Zheng et al . , 2015 ) , possibly as a result of the consumption of dissolved O2 by the anaerobic and facultative anaerobic bacteria that predominate in the intestinal microbiome in early life ( Espey , 2013; Fanaro et al . , 2003; Favier et al . , 2002; Palmer et al . , 2007 ) , and the mature intestinal epithelium is hypoxic relative to the underlying lamina propria due to the close proximity to the anaerobic luminal contents ( Glover et al . , 2016; Kelly et al . , 2015; Zheng et al . , 2015 ) . We measured luminal oxygen content and epithelial hypoxia in HIOs microinjected with live E . coli , finding that luminal oxygen concentration is reduced more than 10-fold relative to the surrounding media . This state of relative hypoxia extends into the epithelium itself and is correlated with increased microbial density ( Figure 4 ) . Thus , although HIOs lack the network of capillaries that play an essential role in tissue oxygen supply in the intestine , E . coli-colonized HIOs recapitulate in vitro the oxygen gradient present at the epithelial interface . Colonization of the HIO by E . coli therefore comprises two broad stimuli: immediate exposure to contact-mediated signals such as MAMPs , and the onset of limiting luminal oxygen and epithelial hypoxia . Although the potential significance of exposure to microbial products in the context of tissue hypoxia is widely recognized in the setting of necrotizing enterocolitis ( Tanner et al . , 2015; Afrazi et al . , 2014; Hackam et al . , 2013; Neu and Walker , 2011; Upperman et al . , 2005; Nanthakumar et al . , 2011 ) , this two factor signaling paradigm has not been well studied as a component of normal intestinal colonization and development . Using the HIO model system , it was possible to design experiments which separately examine the relative impact of microbial contact-mediated signals from microbe-associated hypoxic signals ( Figure 5 and Figure 5—figure supplement 2 ) . This approach reveals that the full transcriptional response generated by the HIO following E . coli colonization is the product of both contact-dependent and hypoxia-dependent signals , with heat-inactivated E . coli or hypoxia alone recapitulating distinct subsets of the changes in gene expression observed in HIOs colonized with live E . coli ( Figure 5 ) . Future studies may examine the role of additional hypoxia-independent live microbe-associated stimuli , such as metabolic products ( Kaiko et al . , 2016 ) and viability-associated MAMPs ( Sander et al . , 2011 ) , in mediating the epithelial response to initial bacterial colonization . NF-κB signaling has been implicated in the downstream response to both microbial contact-mediated signals ( Zhang and Ghosh , 2001; Xiao and Ghosh , 2005; Kawai and Akira , 2007 ) and tissue hypoxia ( Koong et al . , 1994; Rius et al . , 2008; Arias-Loste et al . , 2015; Oliver et al . , 2009; Zeitouni et al . , 2016; Colgan et al . , 2013; Grenz et al . , 2012 ) . Pharmacologic inhibition of NF-κB resulted in the suppression of both microbial contact- and hypoxia-associated gene expression in HIOs , inhibiting both contact-mediated epithelial barrier defense pathways and hypoxia-associated immune activation ( Figure 5 ) . NF-κB appears to play a key role in integrating the complex stimuli resulting from exposure to microbial products and the onset of localized hypoxia in the immature intestinal epithelium during bacterial colonization . The molecular and cellular maturation of the intestine that occurs during infancy ultimately results in enhanced functional capacity ( Lebenthal and Lebenthal , 1999; Sanderson and Walker , 2000; Neu , 2007 ) . Bacterial colonization is associated with enhanced epithelial barrier function in gnotobiotic animals , including changes in the production of antimicrobial peptides and mucus ( Vaishnava et al . , 2008; Cash et al . , 2006; Goto et al . , 2014; García-Lafuente et al . , 2001; Malago , 2015; Ménard et al . , 2008 ) . Defensins produced in the intestinal epithelium are critical mediators of the density and composition of microbial populations in the gut and protect the epithelium from microbial invasion ( Kisich et al . , 2001; Ostaff et al . , 2013; Cullen et al . , 2015; Salzman et al . , 2003; Salzman et al . , 2010 ) . Production of BD-2 is dramatically increased in HIOs immediately following E . coli colonization ( Figure 2 , Figure 5—figure supplement 2 and Figure 6 ) , reaching concentrations that are sufficient to limit overgrowth of E . coli ( Figure 6 and Figure 6—figure supplement 2 ) without completely precluding potentially beneficial bacterial colonization ( Figure 1 ) . Secreted and cell-surface associated mucins form a physical barrier to microbes in the gut , act as local reservoirs of antimicrobial peptide , and serve as substrates for the growth of beneficial microorganisms ( Desai et al . , 2016; Johansson and Hansson , 2016; Cornick et al . , 2015; Hansson , 2012; Li et al . , 2015; Dupont et al . , 2014; Bergstrom and Xia , 2013 ) . The immature HIO epithelium produces a robust mucus layer consisting of both neutral and acidic oligosaccharides with terminal carbohydrate modifications following colonization with E . coli ( Figure 7 ) . Importantly , hypoxia alone does not result in the production of mucus while the introduction of heat-inactivated E . coli induces mucus secretion at the apical epithelium ( Figure 7 ) , suggesting that microbial contact is the major stimulus eliciting mucus secretion in HIOs . Epithelial barrier permeability is an important parameter of intestinal function reflecting the degree of selectivity in the transfer of nutrients across the epithelial layer and the exclusion of bacteria and other potentially harmful materials ( Bischoff et al . , 2014 ) . Increases in epithelial barrier permeability occur in the setting of inflammation ( Ahmad et al . , 2017; Michielan and D'Incà , 2015 ) and infectious disease ( Shawki and McCole , 2017 ) . Colonization of HIOs with E . coli results in increased transcription of genes associated with the formation of the adherens junction and other cell-cell interactions in the epithelium ( Figure 8 ) . However , inhibition of NF-κB signaling dramatically increases both epithelial barrier permeability and the rate of bacterial translocation ( Figure 8 ) , suggesting that NF-κB signaling is critical to maintaining epithelial barrier integrity following colonization . Expression of genes involved in the formation of the cell junction and the production of antimicrobial defensins and mucus are NF-κB dependent ( Figures 6–8 , Figure 7—figure supplement 1 , Tsutsumi-Ishii and Nagaoka , 2002; Ahn et al . , 2005 ) . The inability to mount an effective innate defense response in the presence of NF-κB inhibition results in the failure of the HIO epithelial barrier and the loss of co-culture stability ( Figure 8 ) . This result underscores the critical role of NF-κB signaling in the formation of a stable host-microbe mutualism at the immature epithelial interface . Dysregulated production of pro-inflammatory cytokines contributes to the loss of epithelial barrier integrity in NEC ( Tanner et al . , 2015; Hackam et al . , 2013; Neu and Walker , 2011; Nanthakumar et al . , 2011; Halpern et al . , 2003; Ford et al . , 1997 , 1996; Tan et al . , 1993 ) ; this is recapitulated in HIOs , as exposure to pro-inflammatory cytokines results in the rapid loss of epithelial barrier integrity and the dissolution of epithelial tight junctions ( Figure 8 ) . Probiotics may promote epithelial barrier integrity in NEC ( Robinson , 2014; Alfaleh et al . , 2011; Underwood et al . , 2014; Khailova et al . , 2009 ) and HIOs colonized by E . coli exhibit enhanced epithelial barrier resilience ( Figure 8 ) . Functional maturation resulting from colonization of the immature intestinal epithelium may therefore play an essential role in promoting the resolution of physiologic inflammation . While great progress has been made in characterizing the composition of the gut microbiota in health and disease ( Shreiner et al . , 2015; Costello et al . , 2012 ) , this approach has a limited ability to discern the contributions of individual bacteria to the establishment of host-microbe symbiosis . Our work establishes an approach that recapitulates host-microbe mutualism in the immature human intestine in an experimentally tractable in vitro model system . Application of this approach may facilitate the development of mechanistic models of host-microbe interactions in human tissue in health and disease . For example , one of the major limitations in our understanding of NEC has been the lack of an appropriate model system to study colonization of the immature intestine ( Neu and Walker , 2011; Balimane and Chong , 2005; Tanner et al . , 2015; Nguyen et al . , 2015 ) . Our results suggest that colonization of the HIO with a non-pathogenic gut bacteria results in functional maturation of the epithelial barrier . Future work which examines the effects of organisms associated with the premature gut ( Morrow et al . , 2013; Greenwood et al . , 2014; Ward et al . , 2016 ) on the molecular , cellular , and functional maturation of the immature epithelium may be instrumental in elucidating mechanisms of microbiota-associated disease pathogenesis in the immature intestine .
Human ES cell line H9 ( NIH registry #0062 , RRID:CVCL_9773 ) was obtained from the WiCell Research Institute . H9 cells were authenticated using Short Tandem Repeat ( STR ) DNA profiling ( Matsuo et al . , 1999 ) at the University of Michigan DNA Sequencing Core and exhibited an STR profile identical to the STR characteristics published by ( Josephson et al . , 2006 ) . The H9 cell line was negative for Mycoplasma contamination . Stem cells were maintained on Matrigel ( BD Biosciences , San Jose , CA ) in mTeSR1 medium ( STEMCELL Technologies , Vancouver , Canada ) . hESCs were passaged and differentiated into human intestinal organoid tissue as previously described ( Spence et al . , 2011; McCracken et al . , 2011 ) . HIOs were maintained in media containing EGF , Noggin , and R-spondin ( ENR media , see [McCracken et al . , 2011] ) in 50 μl Matrigel ( 8 mg/ml ) without antibiotics prior to microinjection experiments . For hypoxic culture experiments , HIOs were transferred to a hydrated and sealed Modular Incubator Chamber ( MIC-101 , Billups-Rothenburg , Inc . Del Mar CA ) filled with 1% O2 , 5% CO2 , and balance N2 and maintained at 37 for 24 hr . HIO transplantations: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All animal experiments were approved by the University of Michigan Institutional Animal Care and Use Committee ( IACUC; protocol # PRO00006609 ) . HIO transplants into the kidney capsule were performed as previously described ( Finkbeiner et al . , 2015; Dye et al . , 2016 ) Briefly , mice were anesthetized using 2% isofluorane . The left flank was sterilized using Chlorhexidine and isopropyl alcohol , and an incision was made to expose the kidney . HIOs were manually placed in a subcapsular pocket of the kidney of male 7- to 10-week-old NOD-SCID IL2Rgnull ( NSG ) mice using forceps . An intraperitoneal flush of Zosyn ( 100 mg/kg; Pfizer Inc . ) was administered prior to closure in two layers . The mice were sacrificed and transplant retrieved after 10 weeks . Human Tissue: Normal , de-identified human fetal intestinal tissue was obtained from the University of Washington Laboratory of Developmental Biology . Normal , de-identified human adult intestinal tissue was obtained from deceased organ donors through the Gift of Life , Michigan . All human tissues used in this work were obtained from non-living donors , were de-identified and were conducted with approval from the University of Michigan IRB ( protocol # HUM00093465 and HUM00105750 ) . Isolation and culture of HIO epithelium , transplanted HIO epithelium , fetal and adult human duodenal epithelium was carried out as previously described ( Finkbeiner et al . , 2015 ) , and was cultured in a droplet of Matrigel using L-WRN conditioned medium to stimulate epithelial growth , as previously described ( Miyoshi et al . , 2012; Miyoshi and Stappenbeck , 2013 ) Escherichia coli strain ECOR2 ( ATCC 35321 ) was cultured in Luria broth ( LB ) media or 1 . 5% LB agar plates at 37 under atmospheric oxygen conditions . Glycerol stock solutions are available upon request . The assembled and annotated genome for the isolate of Escherichia coli strain ECOR2 used in these studies is available at https://www . patricbrc . org/view/Genome/562 . 18521 . E . coli strain K-12 MG1655 ( CGSC #6300 ) was obtained from the Coli Genetic Stock Center at Yale University ( http://cgsc2 . biology . yale . edu/ ) and was used only in the in vitro BD-2 activity experiments . Whole genome sequencing of E . coli strain ECOR2 was performed by the University of Michigan Host Microbiome Initiative Laboratory using the Illumina MiSeq platform . Microinjections were performed using a protocol modified from Leslie et al . , 2015 . Briefly , HIOs were injected using thin wall glass capillaries ( TW100F-4 , World Precision Instruments , Sarasota , FL ) shaped using a P-30 micropipette puller ( Sutter Instruments , Novato , CA ) . Pulled microcapilaries were mounted on a Xenoworks micropipette holder with analog tubing ( BR-MH2 and BR-AT , Sutter Instruments ) attached to a 10-ml glass syringe filled with sterile mineral oil ( Fisher Scientific , Hampton , NH ) . Fine control of the micropippette was achieved using a micromanipulator ( Narishge International Inc . , East Meadow , NY ) and microinjection was completed under 1-2X magnification on an SX61 stereo dissecting scope ( Olympus , Tokyo , Japan ) . HIOs suspended in Matrigel ( Corning Inc . , Corning , NY ) were injected with approximately 1 μl solution . A detailed and up-to-date HIO microinjection protocol is available at Hill , 2017b; a copy is archived at https://github . com/elifesciences-publications/HIO_microinjection ) . In bacterial microinjection experiments , the HIO culture media was removed immediately following microinjection and the cultures were rinsed with PBS and treated with ENR media containing penicillin and streptomycin to remove any bacteria introduced to the culture media during the microinjection process . After 1 hr at 37 , the HIOs were washed again in PBS and the media was replaced with fresh antibiotic-free ENR . Luminal oxygen content was measured in HIOs using an optically coated implantable microsensor with a tip tapered at <50 μm ( IMP-PSt1 , PreSens Precision Sensing GmbH ) attached to a micro fiber optic oxygen meter ( Microx TX3 , PreSens Precision Sensing GmbH , Regensburg , Germany ) . The oxygen probe was calibrated according to the manufacturer’s instructions and measurements of the external media and HIO luminal oxygen content were collected by mounting the microsensor on a micromanipulator ( Narishge International Inc . , East Meadow , NY ) and guiding the sensor tip into position using 1-2X magnification on a stereo dissecting scope ( Olympus , Tokyo , Japan ) . All oxygen concentration readings were analyzed using PreSens Oxygen Calculator software ( TX3v531 , PreSens Precision Sensing GmbH , Regensburg , Germany ) . For measurement of relative cytoplasmic hypoxia , HIO cultures were treated with 100 μM pimonidazol HCl ( Hypoxyprobe , Inc . , Burlington , MA ) added to the external culture media and incubated at 37% and 5% CO2 for 2 hr prior to fixation in 4% parafomaldehyde . Pimonidazole conjugates were stained in tissue sections using the Hypoxyprobe-1 mouse IgG monoclonal antibody ( Hypoxyprobe , Inc . , Burlington , MA , RRID:AB_2335667 ) with appropriate secondary antibody ( see antibody dilutions table ) . Immunostaining was carried out as previously described ( Finkbeiner et al . , 2015 ) . Antibody information and dilutions can be found in Supplementary file 2 . All images were taken on a Nikon A1 confocal microscope or an Olympus IX71 epifluorescent microscope . CarboFree blocking buffer ( SP-5040; Vector Laboratories , Inc . Burlingame , CA ) was substituted for dilute donkey serum in PBS in staining for mucins and carbohydrate moieties . EdU treatment and EdU fluorescent labeling using Click-iT chemistry was applied according to the manufacturer’s instructions ( #C10339 Thermo Fisher , Waltham , MA ) . Supplementary file 2 contains a table of all primary and secondary antibodies , blocking conditions , and product ordering information . The NF-κB inhibitor SC-514 ( Kishore et al . , 2003; Litvak et al . , 2009 ) ( Tocris Cookson , Bristol , UK ) was re-suspended in DMSO at a concentration of 25 mM . HIOs were treated with SC-514 suspended in DMSO added to the external ENR culture media at a final concentration of 1 μM . Efficacy of SC-514 was verified by Western blot of lysates from HIOs injected with PBS or live E . coli or injected with live E . coli in the presence of 1 μM SC-514 added to the external media . HIOs were collected after 24 hr in lysis buffer composed of 300 mM NaCl , 50 mM Tris base , 1 mM EDTA , 10% glycerol , 0 . 5% NP-40 , and 1X Halt Phosphatase Inhibitor Cocktail ( Pierce Biotechnology , Rockford , IL ) . Lysates were separated on a 10% Bis-Tris polyacrylamide gel under reducing conditions ( Invitrogen , Carlsbad , CA ) and transferred to PVDF using a wet transfer apparatus ( Bio-Rad Laboratories , Hercules , CA ) overnight at 4 . The PVDF membrane was blocked in Odyssey TBS blocking buffer ( LI-COR Biosciences , Lincoln , NE ) . The membrane was submerged in blocking buffer containing primary rabbit monoclonal antibodies against phosphorylated NF-κB p65 ( 1:200 , Cell Signaling Technology #3033S ) or total NF-κB p65 ( 1:400 , Cell Signaling Technology #8242S ) and incubated at room temperature for 2 hr . All washes were conducted in Tris-buffered saline with 1% Tween-20 ( TBST ) . The secondary goat anti-rabbit IgG IRDye 800CW was diluted 1:15 , 000 in TBST and exposed to the washed membrane for 1 hr at room temperature . After additional washes , the PVDF membrane was imaged using an Odyssey Scanner ( LI-COR Biosciences , Lincoln , NE ) . Incidence of bacterial translocation was determined in HIOs plated individually in single wells of 24-well plates and microinjected with E . coli . The external culture media was collected and replaced daily . The collected media was diluted 1:10 in LB broth in 96 well plates and cultured at 37 overnight . Optical density ( 600 nm ) was measured in the 96-well LB broth cultures using a VersaMax microplate reader ( Molecular Devices , LLC , Sunnyvale , CA ) . OD600> sterile LB broth baseline was considered a positive culture . For epithelial permeability assays , HIOs were microinjected with 4 kDa FITC-dextran suspended in PBS at a concentration of 2 mg/ml as described previously ( Leslie et al . , 2015 ) using the microinjection system detailed above . Images were collected at 10 min intervals at 4X magnification on an Olympus IX71 epifluorescent microscope using a Deltavision RT live cell imaging system with Applied Precision softWoRx imaging software ( GE Healthcare Bio-Sciences , Marlborough , MA ) . Cultures were maintained at 37% and 5% CO2 throughout the imaging timecourse . For experiments involving cytokine treatment , recombinant TNF-α ( #210-TA-010 , R and D Systems ) and INF-γ ( #AF-300–02 , Peprotech ) were added to the external culture media at a concentration of 500 ng/ml at the start of the experiment . A detailed and up-to-date HIO microinjection and live imaging protocol is available at ( Hill , 2017b ) . Recombinant human BD-2 ( Abcam , Cambridge , MA ) was reconstituted in sterile LB broth and diluted to 0 . 1–1 μg/ml . E . coli cultures were diluted 1:1000 in sterile LB containing 0–1 μg/ml BD-2 and transferred to a 96-well microplate . A VersaMax microplate reader ( Molecular Devices , LLC , Sunnyvale , CA ) was used to measure OD600 at 10 min intervals in microplates maintained at 37°C with regular shaking over a 18 hr timecourse . For stationary phase antimicrobial assays , overnight cultures of E . coli str . ECOR2 were diluted in PBS containing 1 μg/ml BD-2 or heat-inactivated BD-2 ( heated at 120 for > 60 min ) and placed in a 37 bacterial incubator for 5 hr . Cultures were then spread on LB agar plates and cultured overnight . The number of CFU was counted manually . Secreted cytokine , antimicrobial peptide , and growth factor concentrations were determined by ELISA ( Duosets , R and D Systems , Minneapolis , MN ) using the manufacturer’s recommended procedures at the Immunological Monitoring Core of the University of Michigan Cancer Center . RNA was isolated using the mirVana RNA isolation kit and following the 'Total RNA’ isolation protocol ( Thermo-Fisher Scientific , Waltham MA ) . RNA library preparation and RNA-sequencing ( single-end , 50 bp read length ) were performed by the University of Michigan DNA Sequencing Core using the Illumina Hi-Seq 2500 platform . All sequences were deposited in the EMBL-EBI ArrayExpress database ( RRID:SCR_004473 ) using Annotare 2 . 0 and are cataloged under the accession number E-MTAB-5801 . Transcriptional quantitation analysis was conducted using 64-bit Debian Linux stable version 7 . 10 ( 'Wheezy’ ) . Pseudoalignment of RNA-seq data was computed using kallisto v0 . 43 . 0 ( Bray et al . , 2016 ) and differential expression of pseudoaligned sequences was calculated using the R package DEseq2 ( Love et al . , 2014 ) ( RRID:SCR_000154 ) . Unless otherwise indicated in the figure legends , differences between experimental groups or conditions were evaluated using an unpaired Student’s t-test . A p-value <0 . 05 was considered to represent a statistically significant result . All statistical analyses were conducted using R version 3 . 4 . 1 ( 2017-06-30 ) ( Core Team , 2017 ) and plots were generated using the R package ggplot2 ( Wickham , 2009 ) with the ggstance expansion pack ( Henry et al . , 2016 ) . The multiple testing-adjusted FDR was calculated using the DESeq2 implementation of the Wald test ( Love et al . , 2014 ) . Gene pathway over-representation tests and Gene Set Enrichment Analysis ( Subramanian et al . , 2005 ) were implemented using the R packages clusterProfiler ( Yu et al . , 2012 ) and ReactomePA ( Yu and He , 2016 ) . Analyses conducted in R were collated using Emacs v25 . 2 ( Stallman , 1981 ) with Org-mode v8 . 3 . 5 and the paper was written in LaTeX using Emacs . Complete analysis scripts are available on the Hill , 2017a GitHub repository ( copy archived at https://github . com/elifesciences-publications/Hill_HIO_Colonization_2017 ) . | Human newborns are exposed to large numbers of bacteria at birth . They must transition from the protective , sterile environment of the womb into the bacteria-rich world . The gut , in particular , must adapt as bacteria colonize it . Many of the first bacteria found in the newborn gut form the basis of the bacterial communities needed for a healthy intestine throughout life . In some premature infants , bacterial colonization of the intestine may trigger harmful inflammation and a serious illness called necrotizing enterocolitis . It is not known exactly how the immature intestine first responds to bacteria . It is also unclear what goes wrong that causes illness in some premature infants . Learning more about how a healthy newborn intestine becomes colonized and responds to this colonization may help medical professionals to better care for normal infants and those who are at risk of intestinal disease . But it has been difficult to study because there is not much newborn intestinal tissue available for scientific research . One solution would be to grow tissue in the laboratory that is like tissue found in the newborn intestine and see how it adapts to bacteria . Now , Hill et al . use stem cells to grow a tissue in the laboratory that is very like immature newborn intestine and show that bacterial colonization helps it to mature . In the experiments , stem cells were grown into an intestine-like tissue . Analyses showed that this laboratory-grown tissue had the same patterns of gene expression as newborn intestines . Then , a type of bacteria called Escherichia coli that is normally found in the intestines of healthy babies was introduced to the intestine-like tissue . Hill et al . show that the initial contact with bacteria and changes in oxygen levels due to bacterial activity cause shifts in gene expression . These in turn stimulate the release of mucus and other protective responses . A protein called NF-κB plays a central role in these normal bacteria-intestine interactions . Hill et al . show that using a drug to block NF-κB interferes with these processes . The experiments show that contact with bacteria encourages the immature intestine to protect itself from potential harm . More experiments like these may help scientists understand normal bacteria-intestine interactions in early life and how they may go wrong in disease . These studies might also help identify new treatments for babies with necrotizing enterocolitis . | [
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The design of synthetic optogenetic tools that allow precise spatiotemporal control of biological processes previously inaccessible to optogenetic control has developed rapidly over the last years . Rational design of such tools requires detailed knowledge of allosteric light signaling in natural photoreceptors . To understand allosteric communication between sensor and effector domains , characterization of all relevant signaling states is required . Here , we describe the mechanism of light-dependent DNA binding of the light-oxygen-voltage ( LOV ) transcription factor Aureochrome 1a from Phaeodactylum tricornutum ( PtAu1a ) and present crystal structures of a dark state LOV monomer and a fully light-adapted LOV dimer . In combination with hydrogen/deuterium-exchange , solution scattering data and DNA-binding experiments , our studies reveal a light-sensitive interaction between the LOV and basic region leucine zipper DNA-binding domain that together with LOV dimerization results in modulation of the DNA affinity of PtAu1a . We discuss the implications of these results for the design of synthetic LOV-based photosensors with application in optogenetics .
Light-sensing is essential for the survival of organisms from all kingdoms of life and plays an important role in their adaptation to different habitats . Prokaryotes , higher plants , fungi , animals and algae use light-sensing systems that encompass a variety of sensory photoreceptors that respond to different wavelengths of light . Recently , a new type of blue light photoreceptor termed Aureochrome ( Aureo ) was discovered in the photosynthetic stramenopile alga Vaucheria frigida that has been suggested to function as blue light-regulated transcription factor . Originally , two Aureo homologs , named Aureo 1 and 2 , were identified , but only Aureo1 was shown to bind DNA in a light-dependent manner ( Takahashi et al . , 2007 ) . Since the discovery of the first Aureos , several orthologs from other stramenopile algae such as Ochromonas danica , Fucus distichus , Saccharina japonica and Phaeodactylum tricornutum have been identified ( Ishikawa et al . , 2009; Deng et al . , 2014; Schellenberger Costa et al . , 2013 ) . The diatom P . tricornutum has four genes encoding aureochromes: three orthologs of type 1 ( PtAu1a , b and c ) and one of type 2 ( Depauw et al . , 2012 ) . Only PtAu1a has been functionally characterized so far and is shown to be involved in light-dependent mitosis regulation ( Huysman et al . , 2013 ) and repress high-light acclimation ( Schellenberger Costa et al . , 2013 ) . Aureos typically consist of an N-terminal domain with unknown function , a basic region leucine zipper ( bZIP ) DNA-binding domain , and a C-terminal light-oxygen-voltage ( LOV ) sensing domain . LOV domains are a subgroup of the Per-Arnt-Sim ( PAS ) superfamily that sense blue light using a noncovalently bound flavin cofactor ( Zoltowski and Gardner , 2011; Herrou and Crosson , 2011; Losi and Gartner , 2012; Conrad et al . , 2014 ) . Photon absorption of the flavin results in formation of a flavin-C4 ( a ) -cysteinyl adduct with a conserved cysteine residue ( Salomon et al . , 2000 ) , which initiates a cascade of structural rearrangements within the LOV core that are propagated to the domain boundaries . LOV domains can be found as isolated entities , but are often part of multidomain proteins where they are coupled to a variety of different effector domains . The effector-sensor topology observed in Aureos differs from the domain topology found in most other LOV photoreceptors where the sensory LOV domain is located N-terminally to the effector domain . This rare domain topology raises the question of how light signaling is achieved in Aureos compared with other LOV proteins . Recent biochemical and spectroscopic experiments on V . frigida Aureo1 ( VfAu1 ) and PtAu1a led to the hypothesis that DNA binding of Aureos might be influenced by light-induced LOV domain dimerization and that structural changes of the N– ( A´α ) and C-terminal ( Jα ) helices flanking the LOV core play a key role in this process ( Toyooka et al . , 2011; Herman et al . , 2013; Mitra et al . , 2012; Herman and Kottke , 2015 ) . This hypothesis was supported to some extent by the crystal structure of the VfAu1 LOV domain that showed an unexpected dimeric arrangement ( Mitra et al . , 2012 ) . However , this structure was determined from crystals grown in the dark and it remains unclear whether the observed dimeric LOV arrangement represents the biologically relevant light state dimer . To obtain insights into structural rearrangements within VfAu1 LOV upon light activation , dark state crystals were illuminated to study light-induced conformational changes ( Mitra et al . , 2012 ) . However , crystal lattice constraints can prevent large conformational changes that limit this approach . Therefore , the mechanism of light-induced LOV dimerization and its consequences on DNA binding in Aureos remain unclear . Here , we present crystal structures of a fully light-adapted LOV dimer as well as of a dark state LOV monomer of PtAu1a . We combine these results with hydrogen/deuterium-exchange coupled to mass spectrometry ( HDX-MS ) and small-angle X-ray scattering ( SAXS ) experiments of full-length PtAu1a and of a truncated construct that lacks the N-terminal domain , respectively . Together with functional studies , this integrative structural approach enabled us to establish a model for light signaling in Aureos where , in the dark , the LOV domain directly interacts with the bZIP domain and thereby impedes its DNA binding function . Illumination with blue light triggers intramolecular bZIP–LOV dissociation and subsequent LOV dimerization , thus enhancing the affinity of PtAu1a for its target DNA sequence . Together , these results provide insight into the molecular mechanism of Aureo function and implicate a new model of light-dependent gene regulation by Aureos in stramenopiles . In addition , they offer new design strategies for synthetic Aureo–LOV based photosensors for applications in optogenetics .
To understand how blue light-sensing of the LOV domain influences PtAu1a DNA binding , we used the full-length protein ( PtAu1afull ) and additionally generated N- and C-terminally truncated PtAu1a variants encompassing the bZIP and LOV domain ( PtAu1abZIP-LOV ) , the bZIP domain ( PtAu1abZIP ) as well as the LOV domain ( PtAu1aLOV ) containing its N- and C-terminal α-helical extensions A´α ( in the context of PAS domains often referred to as N-cap ) and Jα , respectively ( Figure 1a ) . UV-vis spectra of dark adapted LOV domain containing PtAu1a variants show the typical signature of an oxidized flavin mononucleotide ( FMN ) chromophore with a main absorption maximum at 448 nm and several subsidiary peaks ( Figure 1b ) . Upon light activation , the intensities of these absorption bands decrease and a new maximum appears at 390 nm indicating FMN-cysteine adduct formation . Investigation of the dark state recovery kinetics of PtAu1afull , PtAu1abZIP-LOV and PtAu1aLOV yielded traces that were analyzed by fitting an exponential function ( Figure 1c ) . For PtAu1afull , PtAu1abZIP-LOV and PtAu1aLOV , time constants of 826 ± 19 s , 811 ± 4 s and 1500 ± 7 s were determined , respectively , indicating that the presence of the bZIP DNA binding domain accelerates the recovery kinetics of the LOV domain about 1 . 8-fold . 10 . 7554/eLife . 11860 . 003Figure 1 . Absorption spectra of PtAu1afull and dark state recovery kinetics of the LOV domain-containing PtAu1a variants . ( a ) Schematic representation of the PtAu1a constructs used in this study . ( b ) Absorption spectrum of PtAu1afull in the dark ( black ) and after illumination with blue light ( blue ) . In the dark , the typical signature of an oxidized FMN chromophore can be detected . Upon light activation , these maxima decrease and a new absorption band appears at 390 nm , indicating FMN-cysteine adduct formation . ( c ) Recovery kinetics of the absorbance at 445 nm of PtAu1afull , PtAu1abZIP-LOV and PtAu1aLOV after light-activation . The red ( PtAu1afull ) , cyan ( PtAu1abZIP-LOV ) and orange ( PtAu1aLOV ) lines in the plot represent an exponential fit to the data ( black squares ) . Measurements were performed at a protein concentration of 20 µM and time constants represent the mean of three independent measurements . LOV , light-oxygen-voltage; FMN , flavin mononucleotide . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 003 To investigate the effect of blue light illumination on the oligomerization of PtAu1afull , PtAu1abZIP-LOV , PtAu1aLOV we performed size-exclusion chromatography coupled to multi-angle light scattering ( MALS ) in the dark and under continuous blue light illumination . Quantification of the average molar mass of PtAu1afull ( Figure 2a ) and PtAu1abZIP-LOV ( Figure 2b ) in the dark and light state yielded similar values of 55 . 1 , 53 . 3 , 31 . 7 and 33 . 7 kDa , respectively , which are between the theoretically expected molar masses of dimers ( 83 . 6 kDa for PtAu1afull and 52 . 6 for PtAu1abZIP-LOV ) and monomers ( 41 . 8 kDa for PtAu1afull and 26 . 3 kDa for PtAu1abZIP-LOV ) . Peak tailing and a continuous decrease of the molar mass signal in the dark and light state of PtAu1afull and PtAu1abZIP-LOV suggested an equilibrium between dimers and monomers irrespective of the light conditions . Illumination-induced small differences in the elution volumes , indicating conformational changes of both protein variants have occurred as also reported for VfAu1 ( Toyooka et al . , 2011; Hisatomi et al . , 2013 ) . In contrast to PtAu1afull and PtAu1abZIP-LOV , light activation of PtAu1aLOV shifts the oligomerization state from monomers to dimers ( Figure 2c ) . The change in oligomerization state is reflected by a decrease of the elution volume from 16 . 2 ml ( dark ) to 15 . 2 ml ( light ) and an increase of the calculated average molar mass from 20 kDa ( dark ) to 28 kDa ( light ) . Quantification of the monomer–dimer transition using microscale thermophoresis ( MST ) revealed a Kd of 13 . 6 ± 1 . 4 µM for the dark adapted protein ( Figure 2d ) and pre-illumination of PtAu1aLOV increased the dimerization ability as expected from the MALS measurements ( data not shown ) . However , it was not possible to determine a reliable Kd value for the monomer–dimer transition of light-activated PtAu1aLOV , as continuous illumination of the protein is not possible during the measurements . Together , our results are in line with previous reports on the oligomerization states of the LOV domain of PtAu1a ( Herman et al . , 2013; Herman and Kottke , 2015 ) as well as truncated and full-length variants of VfAu1 in their dark and light states ( Toyooka et al . , 2011; Hisatomi et al . , 2013 ) . It was recently reported that VfAu1 dimerizes in a light-dependent manner and that the redox potential influences the oligomerization state by formation of disulfide bonds between the bZIP domains and the bZIP–LOV linker regions ( Hisatomi et al . , 2014 ) . We did not observe such light-dependent oligomerization in our experiments and can also rule out an influence of the redox potential on the oligomerization state of PtAu1a , as PtAu1a does not possess cysteine residues outside the LOV domain . 10 . 7554/eLife . 11860 . 004Figure 2 . Domains involved in PtAu1a dimerization . Normalized MALS detection of PtAu1afull ( a ) , PtAu1abZIP-LOV ( b ) and PtAu1aLOV ( c ) fractionated by size-exclusion chromatography in the dark ( black traces ) and light ( blue traces ) . The MALS-derived molar-mass signals are shown in green ( dark runs ) and blue–green ( light runs ) . Additional experiments performed for PtAu1afull and PtAu1abZIP-LOV in the light at varying protein concentrations are shown in Figure 2—figure supplement 1 . ( d ) Quantification of the monomer-dimer equilibrium of PtAu1aLOV in the dark by MST . Error bars represent the standard deviation of three individual experiments . MALS , multi-angle light scattering; MST , microscale thermophoresis . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 00410 . 7554/eLife . 11860 . 005Figure 2—figure supplement 1 . Concentration dependent elution profiles of PtAu1afull and PtAu1abZIP-LOV in the light . Normalized MALS detection of PtAu1afull ( a ) and PtAu1abZIP-LOV ( b ) fractionated by size-exclusion chromatography at protein concentrations of 200 µM ( black traces ) and 100 µM ( dashed gray traces ) in the light . The MALS-derived molar mass signals are shown in green ( 200 µM ) and yellow–green ( 100 µM ) . PtAu1afull and PtAu1abZIP-LOV were pre-incubated at 20°C under continuous blue light illumination ( 400 μW cm-2 at 450 nm ) from a royal blue ( 455 nm ) collimated LED lamp ( Thorlabs ) for 5 min . 100 μl protein solution was subjected to size-exclusion chromatography at RT on a Superdex 200 Increase 10/300 GL column ( GE Healthcare , Uppsala , Sweden ) equilibrated in buffer C . LED , light emitting diode; MALS , multi-angle light scattering; RT , room temperature . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 005 To characterize the effect of blue light on the DNA binding properties of PtAu1afull we performed electrophoretic mobility shift assays ( EMSAs ) in the dark as well as under continuous blue light illumination and measured the binding of PtAu1a to a 24-base pair ( bp ) DNA fragment of the diatom-specific cyclin 2 ( dsCYC2 , GenBank XM_002179247 ) promoter sequence of P . tricornutum containing the TGACGT binding motif reported for VfAu1 ( Takahashi et al . , 2007 ) ( Figure 3a , b ) . Semi-quantitative data evaluation using the Hill equation revealed an effective concentration for 50% response ( EC50 ) of 860 nM in the dark and a Hill coefficient of 1 . 35 ( Figure 3—figure supplement 1 ) . Illumination with blue light results in a decrease of the EC50 to 90 nM ( Hill coefficient of 1 . 65 ) , revealing a 9 . 6-fold higher affinity of PtAu1afull to DNA in its light compared with its dark state . To verify sequence-specific DNA binding of PtAu1afull , we performed the same experiments with a 24-bp DNA fragment lacking the PtAu1a target sequence ( Figure 3—figure supplement 2 ) . In the dark as well as light experiments , PtAu1afull displayed no or only weak binding to the DNA probe lacking the target sequence , confirming sequence-specific DNA binding of PtAu1afull . The presence of MgCl2 in the EMSA experiments is essential for PtAu1afull sequence specificity , as also described for other bZIP transcription factors ( Moll , 2002 ) . In the absence of MgCl2 , sequence specificity and light-dependence of DNA binding are negligible ( Figure 3—figure supplement 3 ) . 10 . 7554/eLife . 11860 . 006Figure 3 . Blue light illumination enhances DNA binding of PtAu1afull to its target DNA sequence . EMSAs of PtAu1afull under dark ( a ) and light ( b ) conditions in the presence of 50 nM dsCYC2 promoter DNA . Quantification of the gels in Figure 3—figure Supplement 1 . EMSAs , electrophoretic mobility shift assays . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 00610 . 7554/eLife . 11860 . 007Figure 3—figure supplement 1 . DNA binding curves of PtAu1afull under dark and light conditions . DNA binding curves of PtAu1afull under dark ( black ) and light ( blue ) conditions obtained by quantification of the amount of free DNA in the EMSAs shown in Figure 3 revealed EC50 values of 860 nM ( Hill coefficient of 1 . 35 ) in the dark and 90 nM ( Hill coefficient of 1 . 65 ) in the light , indicating a 9 . 6-fold higher affinity of PtAu1afull to DNA in its light compared with its dark state . EMSAs , electrophoretic mobility shift assays . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 00710 . 7554/eLife . 11860 . 008Figure 3—figure supplement 2 . PtAu1afull binds DNA in a sequence-specific manner . EMSAs of PtAu1afull under dark ( a ) and light ( b ) conditions in the presence of a 24-bp DNA probe ( c = 50 nM ) lacking the bZIP target sequence . Under dark conditions , no binding of PtAu1afull to the DNA probe can be detected . Upon light activation , weak binding of PtAu1afull to the DNA probe can be detected at protein concentrations above 5–10 µM . The significantly decreased affinity of PtAu1afull to the DNA probe lacking the TGACGT binding motif reported for bZIP transcription factors confirms sequence-specific DNA binding of PtAu1afull ( cf . Figure 3 ) . bZIP , basic region leucine zipper; EMSAs , electrophoretic mobility shift assays . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 00810 . 7554/eLife . 11860 . 009Figure 3—figure supplement 3 . MgCl2 is essential for sequence-specific DNA binding of PtAu1afull . EMSAs of PtAu1afull in the dark in the absence and presence of 10 mM MgCl2 in the polyacrylamide gels as well as in the reaction and running buffers . The 35-bp DNA probe used in the EMSAs was the one used for DNA binding studies of VfAu1 in the publication by Takahashi et . al . ( Takahashi et al . , 2007 ) ( 5'-GGAGTATCCAGCTCCGTAGCTGACGTG GCCTCTGG-3' , the bZIP target sequence is underlined ) . The DNA probe ( 388 nM ) was incubated in buffer D without MgCl2 ( a ) and with MgCl2 ( b ) with varying amounts of purified PtAu1afull ( 2 , 4 , 7 . 9 , 15 . 9 , 31 . 8 , 63 . 5 , 125 , 250 , 500 , 1000 , 2000 , 4000 nM ) . EMSA runs were performed as described in the methods section . In the EMSA in the absence of MgCl2 , the formation of a PtAu1afull-DNA complex is already observed at the lowest protein concentration of 2 nM . At protein concentrations above 250 nM , a second PtAu1afull-DNA band appears , which was also observed in gel shift experiments performed by Takahashi et . al . ( Takahashi et al . , 2007 ) and in DNA binding studies of the CREB bZIP domain ( Moll et al . , 2002 ) . This slower migrating band most likely represents two PtAu1afull dimers bound to the 35-bp DNA probe , indicating the ability of unspecific DNA binding of PtAu1afull at high protein concentrations . In the EMSAs performed in the presence of MgCl2 , the formation of a stable PtAu1afull-DNA complex is observed at much higher protein concentrations compared with the experiments without MgCl2 . Additionally , the slower migrating PtAu1afull-DNA band disappeared , indicating sequence-specific DNA binding of a single PtAu1afull dimer to the target DNA sequence . Therefore , it can be concluded that the presence of MgCl2 is required for sequence-specific DNA binding of PtAu1afull . bZIP , basic region leucine zipper; EMSAs , electrophoretic mobility shift assays . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 009 To investigate the underlying molecular mechanism for light-regulated gene transcription by PtAu1a and the effect of LOV dimerization , we solved the crystal structure of PtAu1aLOV in its dark and light state ( Figure 4a and b and Table 1 ) . The dark state structure revealed a LOV monomer that adopts a typical PAS fold consisting of a five-stranded antiparallel β-sheet flanked by several helices . The LOV core forms the chromophore binding pocket and closely resembles the structure of VfAu1 LOV ( Mitra et al . , 2012 ) ( root-mean-square deviation ( r . m . s . d ) between 0 . 42 and 0 . 58 Å for 101 Cα atoms and molecules A and B PtAu1a and A , B , C , D , E , F for VfAu1a ) . The LOV core is flanked at the N- and C-termini by prominent α-helical extensions denoted A´α and Jα , respectively . As observed for VfAu1 LOV ( Mitra et al . , 2012 ) , the C-terminal Jα helix partially folds back onto the surface of the β-sheet and interacts with the LOV core via hydrogen bonds between the conserved residue Gln365 and the carbonyl and amine group of Cys316 as well as the side chains of Tyr357 and Gln330 . A´α forms an amphipathic three-turn helix and interacts with the LOV core through a highly conserved 4 amino acid linker with Ala248 , Glu249 , Glu250 and Gln251 in the hinge region . In addition to Jα , A´α also folds back across the surface of the β-sheet and covers a large hydrophobic patch ( Figure 4c ) . The chromophore binding pocket is mainly formed by hydrophobic residues that stabilize the FMN chromophore together with Gln350 , Gln291 , Asn319 , Asn329 , which form hydrogen bonds with the heteroatoms of the isoalloxazine ring . FMN is additionally stabilized by Arg304 and Arg288 , which interact with the phosphate group of the ribityl chain . The conserved photoreactive Cys287 , which forms a covalent bond with FMN upon illumination , is located in the Eα helix on the opposite site of the core β-sheet . 10 . 7554/eLife . 11860 . 010Figure 4 . Structural characterization of PtAu1aLOV in its dark and light state . ( a ) Crystal structure of the PtAu1aLOV dark state monomer with the N- and C-terminal A'α and Jα helices flanking the LOV core colored in light gray and dark gray , respectively . ( b ) Blue light illumination induces formation of a parallel PtAu1aLOV dimer . ( c ) In the dark , A'α covers the hydrophobic dimerization site on the LOV β-sheet . ( d ) Illumination results in a release of A'α from the LOV β-sheet and exposes the dimerization site . The PtAu1aLOV molecules in ( c and d ) are colored according to the Eisenberg hydrophobicity scale ( Eisenberg , et al . , 1984 ) . Reddish regions correspond to high and white regions to low hydrophobicity . ( e ) The PtAu1aLOV light state dimer colored according to differences in deuterium incorporation in the dark and light state after 10 s of labeling . Shades of red and blue correspond to regions with increased and decreased deuterium uptake in the light , respectively . A peptide map that shows the differences in relative deuteration of dark and light experiments for all time points is shown in ( Figure 4—figure supplement 3 ) . All evaluated peptides for PtAu1aLOV and their individual deuteration plots are shown in ( Figure 4—figure supplement 4 ) . ( f ) PtAu1aLOV dark state monomer colored according to deuterium incorporation in the dark after 10 s labeling . Elements in ( e ) and ( f ) colored in dark gray represent regions that are not covered by peptides generated by pepsin digestion . Since rapid back-exchange of the two N-terminal residues prevents precise measurement of deuterium incorporation , these residues of all peptides are shown in dark gray , if not covered by an overlapping peptide . LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01010 . 7554/eLife . 11860 . 011Figure 4—figure supplement 1 . Light-induced structural changes of PtAu1aLOV ( protomer A ) . ( a ) Rotamer changes of Tyr266 , Cys287 , Met313 , Leu317 , Phe331 , Ile333 , Gln350 and Cys351 are observed in protomer A and B upon light activation . Additional rotamer changes detected in either protomer A or B are not shown , but might be also functional relevant . ( b ) In the dark ( blue ) , Gln365 located on Jα forms hydrogen bonds with the carbonyl and amine group of Cys316 , which are broken upon illumination ( wheat ) and results in undocking of Jα from the LOV β-sheet . ( c ) Fo-Fc omit map ( green mesh ) of the photoreactive Cys287 and the FMN cofactor upon light activation contoured at 2 . 5 σ and superimposed on the final model . The covalent Cys287-FMN adduct is significantly reduced due to radiation damage , as also observed for other LOV proteins ( Fedorov et al . , 2003; Zoltowski et al . , 2007 ) . ( d ) Overlay of 2Fo-Fc map in the dark ( blue sticks and mesh ) and light ( wheat sticks and orange mesh ) . Both maps are contoured at 1 . 5 σ . FMN , flavin mononucleotide; LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01110 . 7554/eLife . 11860 . 012Figure 4—figure supplement 2 . Interdomain interactions and crystal lattice contacts . ( a ) Interactions between the Jα and A'α helices observed in the PtAu1aLOV light state dimer . The two protomers are related by two-fold non-crystallographic symmetry that does not apply to the A´α helices . ( b ) Crystal lattice contacts observed for the PtAu1aLOV light-state dimer . The N-terminus of protomer A of a symmetry related molecule ( indicated by a * ) interacts with elements of the light-state dimer interface , which slightly affects the relative positioning of the two protomers . LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01210 . 7554/eLife . 11860 . 013Figure 4—figure supplement 3 . Effect of illumination on PtAu1aLOV . Each box reflects one peptide and contains five different colors that correspond to the differences in relative deuteration ( ΔDrel ) of dark and light ( ΔDrel of PtAu1aLOV , light -PtAu1aLOV , dark ) experiments according to the legend on the left for the incubation times of 10 , 45 , 180 , 900 and 3600 s ( bottom up ) . MS/MS confirmed peptides are marked with diamonds . Secondary structure elements are taken from DSSP ( Kabsch and Sander , 1983 ) analysis of the PtAu1aLOV dark state crystal structure . DSSP , define secondary structure of proteins; LOV , light-oxygen-voltage; MS/MS , tandem mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01310 . 7554/eLife . 11860 . 014Figure 4—figure supplement 4 . Overview of all 39 PtAu1aLOV peptides evaluated during HDX-MS analysis . Please zoom in on the region of interest for full details . Individual plots show the time-dependent increase in deuterium incorporation . Drel values represent the mean of three independent measurements and error bars correspond to the standard deviation . A software-estimated abundance distribution of deuterated species is presented in the lower sub-panel on a scale from undeuterated to all exchangeable amides deuterated . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01410 . 7554/eLife . 11860 . 015Table 1 . Data collection and refinement statistics . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 015PtAu1aLOV darkPtAu1aLOV lightData collectionSpace groupP212121P3221Cell dimensionsa , b , c ( Å ) 64 . 4 , 69 . 2 , 74 . 6108 . 6 108 . 6 104 . 7α , β , γ ( ° ) 90 . 0 , 90 . 0 , 90 . 090 . 0 90 . 0 120 . 0Resolution ( Å ) 50–2 . 5 ( 2 . 5–2 . 6 ) *50–2 . 7 ( 2 . 7–3 . 2 ) Rmeas13 . 2 ( 55 . 1 ) 10 . 0 ( 63 . 7 ) I / σI 15 . 57 ( 4 . 50 ) 13 . 01 ( 2 . 46 ) Completeness ( % ) 99 . 9 ( 100 ) 98 . 9 ( 98 . 1 ) Redundancy13 . 1 ( 13 . 5 ) 5 . 0 ( 5 . 0 ) RefinementResolution ( Å ) 48 . 7-2 . 545 . 7-2 . 7No . reflections12 , 01619 , 785Rwork / Rfree0 . 196/0 . 2540 . 209/0 . 243No . atoms22372164Protein21082100Ligand/ion7462Water552B factors33 . 374 . 7Protein33 . 474 . 9Ligand/ion32 . 669 . 1Water32 . 272 . 3r . m . s . deviationsBond lengths ( Å ) 0 . 0070 . 009Bond angles ( ° ) 1 . 1091 . 090*Values in parentheses are for highest-resolution shell . One crystal was used for data measurements of PtAu1aLOV in its dark and light-adapted state , respectively . Light-induced dimerization of PtAu1aLOV requires tertiary and quaternary structural rearrangements that cannot be induced by illumination of dark state crystals due to crystal lattice restraints . Therefore , to detect the full extent of light-induced structural changes , crystals of the light state need to be grown under continuous blue light illumination of the setup . The increase in structural dynamics and aggregation tendency upon illumination of most photoreceptors as well as potential photobleaching makes crystallization of photoreceptors difficult and has so far only been achieved for the single LOV domain proteins VIVID ( Vaidya et al . , 2011 ) and PpsB1 ( Circolone et al . , 2012 ) as well as for a truncated phytochrome construct ( Takala et al . , 2014 ) . These photoreceptors revert slowly back into their dark conformation or , as in the case of VIVID , have been modified to do so . To obtain the structure of a fully light-adapted PtAu1aLOV dimer , we set up crystallization screens of wildtype PtAu1aLOV under continuous blue light illumination and obtained colorless crystals overnight . Their lack of color is indicative of formation of the flavin-C4 ( a ) -cysteinyl adduct . As expected from the MALS measurements , light-activated PtAu1aLOV crystallized as a dimer that features an assembly similar to other N-cap-comprising PAS dimers ( Figure 4b ) ( Heintz et al . , 2014; Ayers and Moffat , 2008 ) . The two PtAu1aLOV protomers are related by a 2-fold non-crystallographic symmetry that does not apply to the A´α helices . The observed parallel dimer arrangement differs significantly from the antiparallel dimer arrangement observed for the dark state structure of VfAu1 LOV ( Mitra et al . , 2012 ) . Analysis of the contact area using the PISA web server revealed a buried surface area ( BSA ) of 1524 Å2 , which is similar to that observed for the VIVID light state dimer ( 1342 Å2 ) , supporting the observed dimer arrangement as biologically relevant . The main dimerization interface is formed by a network of hydrophobic residues on the core β-sheet and A´α . Superposition of the dark and light state structures not only reveals a variety of light-induced side chain rearrangements ( Figure 4—figure supplement 1 ) , but also a significant change of the position of the A´α helix that is released from the hydrophobic patch on the LOV core , thus exposing the dimerization site ( Figure 4d ) . Compared with the dark state structure , the C-terminal part of Jα is unstructured and the hydrogen bonds between Gln365 and the carbonyl and amine group of Cys316 are broken , indicating light-induced Jα undocking from the LOV β-sheet and subsequent unfolding as suggested previously ( Herman et al . , 2013 ) ( Figure 4—figure supplement 1 ) . Interestingly , the side chains of Cys316 and Leu317 located on strand Gβ also undergo conformational changes upon illumination , which may promote light-induced Jα undocking from the LOV core . Jα seems to contribute directly to the stability of the light state dimer by interacting with A'α of the second protomer . However , the effect of these interactions needs to be interpreted with caution since the conformations of the A´α helices differ between the two protomers ( Figure 4—figure supplement 2 ) . This could either originate from intrinsic asymmetry of the PtAu1aLOV light-state dimer or from asymmetry induced by crystal contacts ( Figure 4—figure supplement 2 ) . The formation of the covalent Cys287–FMN adduct and the resulting doming of the isoalloxazine ring at position C4a are supported by the Fo-Fc omit map and the 2Fo-Fc map ( Figure 4—figure supplement 1 ) . Light-induced rotamer changes were observed for a variety of residues predominantly in the vicinity of the FMN cofactor ( Leu317 , Phe331 and Ile333 ) or on the outer side of strand Iβ ( Cys 351 ) and Gβ ( Met 313 ) . Additionally , the highly conserved Tyr266 located on strand Bβ shows a light-induced rotation out of the dimer interface , which is prerequisite for LOV dimerization . To relate light-induced LOV dimerization to the enhanced DNA binding of PtAu1afull in the light and to identify structural elements involved in light signaling , we performed HDX-MS measurements comparing deuterium uptake of dark- and light-adapted PtAu1afull in the absence and presence of DNA as well as of dark- and light adapted PtAu1aLOV . Measurements on PtAu1aLOV confirmed the important role of A´α for dimer formation and revealed slightly reduced deuterium incorporation into fast-exchanging amides of A´α in light-adapted PtAu1aLOV ( Figure 4e ) . After longer exchange times , all except one of the A´α peptides show the opposite effect and exhibit slightly increased deuterium incorportation . ( Figure 4—figure supplement 3 ) . Analysis of the peptide overlap showed that the observed destabilization originates from an increased exchange of the amide proton of Phe252 that is located on strand Aβ and is part of the PtAu1aLOV dimerization site that is covered by A´α in the dark state crystal structure ( Figure 4c ) . In the dark as well as light state structure , the Phe252 amide proton is in hydrogen bonding distance to the hydroxyl group of Ser268 located between strand Bβ and helix Cα that is also destabilized at later time points , suggesting interaction of these elements . Decreased conformational dynamics is observed for helix Eα encompassing the photoreactive cysteine that forms the covalent Cys287-FMN adduct upon illumination . Structural stabilization is observed for the end of strand Hβ with its adjacent loop that is in hydrogen bonding distance to Bβ upon dimer formation . Only negligible light-induced differences in deuterium uptake are observed for Jα that was shown to unwind upon illumination ( Herman et al . , 2013 ) and that is also partially unstructured in the light state crystal structure . This originates from the fact that Jα is highly dynamic already in the dark and amide hydrogen exchange is too rapid to detect a further light-induced increase of the hydrogen exchange rate of Jα due to the limited time resolution of the chosen HDX-MS approach . Similar results were also reported for HDX-MS and nuclear magnetic resonance ( NMR ) measurements on the isolated LOV2 domain of photoropin 1 from Avena sativa ( Winkler et al . , 2015; Harper et al . , 2003 ) . In addition to Jα , A´α is also highly dynamic and shows a high degree of deuterium incorporation in the dark and light ( Figure 4f ) . The moderate protection observed for the crystallographic PtAu1aLOV light state dimer interface upon illumination is in line with HDX-MS measurements performed on VIVID ( Lee et al . , 2014 ) and suggests a rapid monomer–dimer interconversion . Thus , the HDX-MS data obtained for PtAu1aLOV highlight the important role of A´α for light-induced dimerization and support the observed dimer arrangement in the crystal structure also in solution . In contrast to the weak light-induced changes observed for PtAu1aLOV , measurements on PtAu1afull revealed pronounced destabilization of several elements of the LOV domain upon illumination including Jα and A´α ( Figure 5a–c and Figure 5—figure supplement 1 ) . Additionally , elements within the bZIP DNA-binding domain are significantly destabilized , whereas the N-terminal domain and bZIP–LOV linker region are mainly unaffected by LOV activation . The deuterium exchange behavior determined for LOV domain peptides of light-activated PtAu1afull and free PtAu1aLOV are nearly identical , indicating no differences in the conformation or interaction state of the free or effector-coupled LOV domain in the light state ( Figure 5—figure supplement 2 ) . Therefore , the pronounced differences in deuterium exchange of the LOV and bZIP domains between dark- and light-adapted protein can be explained by a direct interaction between the two domains in the dark , which is broken upon illumination and subsequent LOV dimerization . Closer inspection of the HDX-MS data suggests the N-terminal part of the leucine zipper as the potential interaction site of the two domains in the dark . Peptides within this region revealed bimodal exchange behavior , indicative of two distinct protein populations with slow interconversion and significantly different deuterium exchange kinetics ( EX1 kinetics ( Konermann , Pan , and Liu , 2011 ) ) , which might reflect bZIP–LOV dissociation during labeling ( Figure 5d ) . Interestingly , A´α as well as elements of Iβ and the beginning of Jα show a significant increase in deuterium uptake upon illumination and exhibit exchange kinetics similar to those observed for the leucine zipper peptides around residues 176-184 , suggesting functional interplay or direct interaction of these elements ( Figure 5b–d ) . Structural destabilization is observed for helix Fα and its adjacent linker regions as well as Hβ and the loop between Gβ and Hβ . Since slowly exchanging amides are involved in the exchange of the aforementioned elements , the observed destabilization can be interpreted as an increase in structural dynamics . The N-terminal domain as well as the linker connecting the bZIP and LOV domain show rapid deuterium uptake in the dark and light , which identifies these regions as highly dynamic and probably unstructured as also suggested from secondary structure prediction ( Figure 5—figure supplement 3 ) . Subtle light-induced structural destabilization was observed for the region between residues 28 and 44 of the N-terminal domain , which suggests that illumination also affects the function of the N-terminal domain . 10 . 7554/eLife . 11860 . 016Figure 5 . HDX-MS data of PtAu1afull in the absence and presence of DNA . ( a ) Changes in deuterium incorporation of PtAu1afull mapped onto the structure of the PtAu1aLOV light state dimer and a model of the bZIP domain . ( b–d ) Deuterium uptake plots of Jα-Iβ , A'α and leucine zipper peptides with Drel plotted against the labeling time for three independent experiments . The estimated abundance distribution of individual deuterated species is shown at the bottom . ( e ) Differences in deuterium incorporation of PtAu1afull in the dark and light state in the presence of DNA mapped onto the PtAu1aLOV light state dimer and a model of the bZIP domain . All evaluated peptides for PtAu1afull in the absence and presence of DNA and their individual deuteration plots are shown in Figure 5—figure supplement 6 and Figure 5—figure supplement 7 , respectively . HDX-MS , hydrogen/deuterium-exchange coupled to mass spectrometry; LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01610 . 7554/eLife . 11860 . 017Figure 5—figure supplement 1 . Effect of illumination on PtAu1afull in the absence of DNA . Each box reflects one peptide and contains five different colors that correspond to the differences in relative deuteration ( ΔDrel ) between light and dark ( ΔDrel of PtAu1afull , light - PtAu1afull , dark ) experiments according to the legend on the left for the incubation times of 10 , 45 , 180 , 900 and 3600 s ( bottom up ) . MS/MS confirmed peptides are marked with diamonds . Secondary structure elements are taken from DSSP ( Kabsch and Sander , 1983 ) analysis of the PtAu1aLOV dark state crystal structure and PSIPRED ( Psi-blast based ) secondary structure prediction ( Jones , 1999 ) . DSSP , define secondary structure of proteins; MS/MS , tandem mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01710 . 7554/eLife . 11860 . 018Figure 5—figure supplement 2 . Comparison of HDX characteristics of LOV domain peptides of PtAu1afull and PtAu1aLOV in the light . Each box reflects one peptide and contains five different colors that correspond to the differences in relative deuteration ( ΔDrel ) of light ( ΔDrel of PtAu1afull , light - PtAu1aLOV , light ) experiments according to the legend on the left for the incubation times of 10 , 45 , 180 , 900 and 3600 s ( bottom up ) . The deuterium exchange rates of LOV domain peptides of PtAu1aLOV and PtAu1afull in the light are nearly identical , confirming that the pronounced differences in deuterium exchange rates of LOV domain peptides of PtAu1afull in the dark and light ( Figure 5—figure supplement 1 ) originate from an interaction of the LOV and bZIP domains in the dark . HDX , hydrogen/deuterium-exchange; LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01810 . 7554/eLife . 11860 . 019Figure 5—figure supplement 3 . Normalized relative deuterium incorporation ( Dnorm ) of PtAu1afull in the dark . Each box reflects one peptide and contains five different colors that indicate deuterium incorporation after 10 , 45 , 180 , 900 and 3600 s from HDX-MS measurements performed in the dark , normalized to the number of exchangeable amides in each peptide . Data were not corrected for back-exchange . The back-exchange rates for individual peptides under the chosen experimental conditions are in the range of ~30-–35% . Blue colors indicate low deuterium incorporation and reflect stable secondary structure elements , while red colors indicate high deuterium incorporation and flexible elements . Most of the peptides within the N-terminal domain and the bZIP–LOV linker region reach their highest deuteration level already after 10 s of labeling , indicating a significant fraction of highly dynamic or unstructured elements within these regions . MS/MS confirmed peptides are marked with diamonds . Secondary structure elements are taken from DSSP ( Kabsch and Sander , , 1983 ) analysis of the PtAu1aLOV dark state crystal structure and PSIPRED secondary structure prediction ( Jones , 1999 ) . DSSP , define secondary structure of proteins; HDX-MS , hydrogen/deuterium-exchange coupled to mass spectrometry; MS/MS , tandem mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 01910 . 7554/eLife . 11860 . 020Figure 5—figure supplement 4 . Effect of illumination on PtAu1afull in the presence of DNA . Each box reflects one peptide and contains five different colors that correspond to the differences in relative deuteration ( ΔDrel ) between light and dark ( ΔDrel of PtAu1afull , light-DNA - PtAu1afull , dark-DNA ) experiments according to the legend on the left for the incubation times of 10 , 45 , 180 , 900 and 3600 s ( bottom up ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02010 . 7554/eLife . 11860 . 021Figure 5—figure supplement 5 . Effect of DNA on LOV domain peptides of PtAu1afull in the dark . Each box reflects one peptide and contains five different colors that correspond to the differences in relative deuteration ( ΔDrel ) between dark ( ΔDrel of PtAu1afull , dark-DNA - PtAu1afull , dark ) experiments according to the legend on the left for the incubation times of 10 , 45 , 180 , 900 and 3600 s ( bottom up ) . DNA binding of PtAu1afull in the dark induces similar effects within the LOV domain as illumination ( cf . Figure 5—figure supplement 1 ) , which suggests DNA-induced bZIP–LOV dissociation . The subtle differences in deuterium incorporation of Jα peptides observed after 3600 s in both comparisons most likely originate from slight differences in the experimental conditions of the HDX-MS measurements and have no functional implications . MS/MS confirmed peptides are marked with diamonds . Secondary structure elements are taken from DSSP ( Kabsch and Sander , 1983 ) analysis of the PtAu1aLOV dark state crystal structure . DSSP , define secondary structure of proteins; HDX-MS , hydrogen/deuterium-exchange coupled to mass spectrometry; MS/MS , tandem mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02110 . 7554/eLife . 11860 . 022Figure 5—figure supplement 6 . Overview of all 80 PtAu1afull peptides evaluated during HDX-MS analysis . Please zoom in on the region of interest for full details . Individual plots show the time-dependent increase in deuterium incorporation . Drel values represent the mean of three independent measurements and error bars correspond to the standard deviation . A software-estimated abundance distribution of deuterated species is presented in the lower sub-panel on a scale from undeuterated to all exchangeable amides deuterated . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02210 . 7554/eLife . 11860 . 023Figure 5—figure supplement 7 . Overview of all 80 peptides evaluated during HDX-MS analysis of PtAu1afull in the presence of DNA . Please zoom in on the region of interest for full details . Individual plots show the time-dependent increase in deuterium incorporation . Drel values represent the mean of three independent measurements and error bars correspond to the standard deviation . A software-estimated abundance distribution of deuterated species is presented in the lower sub-panel on a scale from undeuterated to all exchangeable amides deuterated . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 023 To study the effect of illumination on the DNA binding affinity of PtAu1afull and identify regions affected by protein–DNA complex formation , we performed HDX-MS measurements in the presence of DNA . As expected from the EMSA experiments , illumination results in an increased DNA binding affinity of PtAu1afull , reflected by a pronounced protection of the basic DNA binding region and a decrease in structural dynamics of leucine zipper peptides ( Figure 5e and Figure 5—figure supplement 4 ) . DNA binding and illumination not only affect the bZIP domain but also several elements within the LOV domain , indicating bidirectional allosteric signaling as also reported for the blue light-regulated phosphodiesterase 1 ( BlrP1 ) of Klebsiella pneumoniae ( Winkler et al . , 2014 ) . Stabilization of the light-adapted state is observed for A´α , Aβ , Fα , Eα encompassing the photoreactive cysteine and Hβ with its adjacent loop , whereas destabilization is detected for helix Cα , as also observed for the measurements without DNA and in isolated PtAu1aLOV . Interestingly , the presence of DNA in the dark measurements induces similar effects within the LOV domain as illumination in the absence of DNA , suggesting DNA-induced bZIP–LOV dissociation ( Figure 5—figure supplement 5 ) . The N-terminal domain of PtAu1afull does not show significant differences in deuterium incorporation in the presence of DNA and seems to play a negligible role for light-dependent DNA binding ( Figure 5—figure supplement 4 ) . We obtained an independent confirmation of the suggested bZIP–LOV interaction in the dark from MALS experiments . We incubated PtAu1aLOV together with PtAu1abZIP upon which the elution volume shifted to a lower volume and the molar mass signal increased slightly compared with PtAu1aLOV alone ( Figure 6 ) . Although the detected effects were only subtle and indicate nearly complete complex dissociation during size-exclusion chromatography , they were reproducible in several experiments . 10 . 7554/eLife . 11860 . 024Figure 6 . Normalized MALS detection of PtAu1aLOV ( solid line ) alone and PtAu1aLOV together with PtAu1abZIP ( dashed line ) fractionated by size-exclusion chromatography in the dark . The MALS-derived molar-mass signals are shown in green . PtAu1aLOV and PtAu1abZIP interact in the dark , which is reflected by a slight decrease of the elution volume of PtAu1aLOV and an increase of the calculated molar mass signal . LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 024 To analyze light and oligonucleotide induced global conformational changes of PtAu1afull and PtAu1abZIP-LOV in solution , we performed SAXS measurements . The data obtained for PtAu1afull was of poor quality and did not allow shape reconstructions . Estimation of the radius of gyration ( Rg ) using the Guinier approximation was possible and revealed a light-induced increase of Rg in the measurements without DNA ( Figure 7—source data 1 ) . Since the SAXS data collected for PtAu1abZIP-LOV was of high quality and the protein variant encompasses all important structural elements affected by light-activation and DNA binding , we focused on the analysis of the PtAu1abZIP-LOV data ( Figure 7—figure supplement 1 ) . As observed for PtAu1afull , PtAu1abZIP-LOV also exhibited a slight increase in the Rg and the maximum particle diameter ( Dmax ) upon illumination , indicating light-induced protein elongation ( Figure 7—source data 2 ) . To obtain information on the overall shape of PtAu1abZIP-LOV in its light and dark state , we performed ab initio modeling using DAMMIN ( Svergun , 1999 ) . The low-resolution structure reconstructed from the dark measurements matches the length of the bZIP dimer and shows additional density that originates from the two LOV monomers . Rigid body modeling of the PtAu1abZIP-LOV dark state shows the LOV monomers arranged close to the potential bZIP–LOV interaction site identified by HDX-MS and confirms an interaction of the LOV domain with the leucine zipper in the dark ( Figure 7a ) . 10 . 7554/eLife . 11860 . 025Figure 7 . SAXS-derived shape reconstructions of PtAu1abZIP-LOV . ( a ) DAMMIN low-resolution envelope calculated for PtAu1abZIP-LOV in the dark superimposed with an atomic PtAu1abZIP-LOV dark state model calculated by CORAL . Modelled loops are indicated as dots . The model is colored according to the HDX-MS data obtained for PtAu1afull in the absence of DNA and shows differences in deuterium incorporation in the dark and light state after 10 s of labeling . The results obtained from SAXS-based rigid body modeling , ab initio modeling and HDX-MS data agree perfectly and support an interaction between the LOV domain and the leucine zipper of the bZIP domain . ( b ) Shape reconstruction of the PtAu1abZIP-LOV-DNA complex calculated using DAMMIN . The PtAu1aLOV light state dimer and a model of the DNA bound bZIP domain was placed in the envelope by visual inspection . HDX-MS , hydrogen/deuterium-exchange coupled to mass spectrometry; LOV , light-oxygen-voltage; SAXS , small-angle X-ray scattering . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02510 . 7554/eLife . 11860 . 026Figure 7—source data 1 . Rg values calculated for PtAu1afull from SAXS data DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02610 . 7554/eLife . 11860 . 027Figure 7—source data 2 . Structural parameters calculated for PtAu1abZIP-LOV from SAXS data DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 02710 . 7554/eLife . 11860 . 028Figure 7—figure supplement 1 . SAXS data for PtAu1abZIP-LOV . ( a ) Scattering curves of dark-adapted PtAu1abZIP-LOV ( c = 5 mg/ml , black dots ) and PtAu1abZIP-LOV in the absence ( c = 5 mg/ml , blue triangles ) and presence of DNA ( c = 6 . 2 mg/ml , green squares ) pre-illuminated with blue light . ( b ) Guinier and ( c ) Kratky plots of the SAXS data obtained for the different PtAu1abZIP-LOV states . The red lines in the Guinier plot indicate regions of the fit . All linear fits fulfil the criteria of qmax•· Rg ≤ 1 . 3 . ( d ) Plots of the pair distance distribution functions ( p ( r ) ) calculated using GNOM ( Svergun , 1992 ) . Blue light illumination alone and in combination with the presence of DNA results in PtAu1abZIP-LOV elongation . ( e ) Merged SAXS data of PtAu1abZIP-LOV used for ab initio and rigid body reconstructions of the PtAu1abZIP-LOV dark state conformation . For the low q region , data from the measurements performed at a protein concentration of 5 mg/ml was merged at q = 0 . 13 A-1 with the high q region of data collected at 9 mg/ml . The fits for the best DAMMIN ( Svergun , 1999 ) and CORAL ( Petoukhov et al . , 2012 ) models are shown as red and gray dashed lines , respectively . ( f ) Merged SAXS data of PtAu1abZIP-LOV in the presence of DNA used for DAMMIN ( Svergun , 1999 ) ab initio reconstructions . The fit for the best model is shown as red dashed line . LOV , light-oxygen-voltage; SAXS , small-angle X-ray scattering . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 028 Shape reconstructions of PtAu1abZIP-LOV in the light resulted in a similar but slightly more elongated envelope with less pronounced bulges in the center ( data not shown ) . Since light measurements were performed by short pre-illumination of the protein , a substantial part of PtAu1abZIP-LOV might not have been light-activated and/or reverted from its light back to its dark conformation during the measurements . Additionally , light-adapted PtAu1abZIP-LOV exhibits a high degree of flexibility and populates a variety of different conformations . Therefore , it is likely that the obtained envelope represents a mixture of different dark- and light-adapted protein conformations that contribute simultaneously to the measured SAXS data . For this reason , shape reconstructions of light-activated PtAu1abZIP-LOV are more difficult to interpret than those of dark-adapted protein . However , light measurements of PtAu1abZIP-LOV in the presence of DNA clearly show that illumination together with DNA binding induces bZIP–LOV dissociation and results in an elongated protein–DNA complex with Rg and Dmax values that are significantly larger than those observed for dark-adapted PtAu1abZIP-LOV ( Figure 7b ) .
Aureos are blue light-regulated transcription factors that exhibit an unusual effector–sensor domain topology that is opposite to the domain order found in most LOV proteins and raises the question how signal transmission and light-dependent DNA binding are achieved in these photoreceptors . Our studies on PtAu1a in the presence of its cognate DNA provide structural and functional insights into light-dependent DNA binding of Aureos with implications not only for their biological function , but also for a better understanding of allosteric light-signaling in multidomain LOV proteins . Crystal structures of the activated light state dimer as well as of the dark state PtAu1aLOV monomer reveal the molecular mechanism of blue light-dependent Aureo LOV dimerization . The N- and C-terminal A'α and Jα helices flanking the LOV core play a central role in this process and are directly affected by illumination as also observed for phototropin-LOV2 from Av . sativa and A . thaliana ( Zayner , Antoniou , and Sosnick , 2012; Takeda et al . , 2013; Harper , Neil , and Gardner , 2003 ) . In the dark , A'α covers the hydrophobic dimerization site of the LOV β-sheet that becomes exposed upon illumination and thus enables LOV dimerization . These results provide a molecular mechanism for recent size-exclusion chromatography experiments performed on isolated PtAu1a LOV constructs lacking A'α and/or Jα that also imply that A'α covers the LOV dimerization site in the dark ( Herman and Kottke , 2015; Herman et al . , 2013 ) . However , not only A'α , but also Jα is affected by blue light illumination . It undocks from the LOV β-sheet and partially unfolds . The light-induced structural changes observed for A'α and Jα are in line with Fourier transform infrared ( FT-IR ) spectroscopy studies that also indicate light-induced structural changes in both helices ( Herman and Kottke , 2015; Herman et al . , 2013 ) . Interestingly , it was reported that A'α is only affected by illumination in the presence and after unfolding of Jα , whereas Jα unfolds even in the absence of A'α ( Herman and Kottke , 2015 ) . Since a direct interaction between the two helices is not observed in the dark state PtAu1aLOV crystal structure , the interdependence between A'α and Jα indicates an allosteric interplay between the two helices as suggested previously ( Herman and Kottke , 2015 ) . Allosteric signaling occurs via the LOV β-sheet and involves the central strand Iβ , which is covalently coupled to Jα and also interacts with A'α in the dark . The important role of the LOV β-sheet in light-signaling is supported by the fact that several β-sheet residues show light-induced rotamer changes . Since the dimer arrangement observed in the PtAu1aLOV light state structure differs from the dimeric assembly observed in the recently determined dark state crystal structure of VfAu1 LOV ( Mitra et al . , 2012 ) , it can be concluded that a combination of light-induced structural rearrangements within the LOV core together with undocking of Jα from the β-sheet are required to trigger the release of A'α from the LOV core , which ultimately induces the formation of the biologically relevant light state dimer . In contrast to the consecutive structural changes reported for the flanking helices of PtAu1a LOV , A'α and Jα of phototropin-LOV2 exhibit structural changes independently of each other ( Zayner et al . , 2012; Takeda et al . , 2013 ) , which implies differences in the signaling mechanism of Aureos and Phototropins . Two models for light-dependent DNA binding of VfAu1 have been proposed in the literature where LOV dimerization plays a central role . According to the first model , VfAu1 is dimeric regardless of the light conditions and additional light-induced LOV dimerization enhances its affinity for DNA ( Toyooka et al . , 2011 ) . In the second model , DNA binding not only depends on the light , but also on the redox conditions ( Hisatomi et al . , 2014 ) . Under reducing conditions , VfAu1 exists as a monomer and blue light illumination changes the oligomerization state from monomers to dimers , thus increasing the affinity for its target DNA sequence . Under oxidizing conditions , intermolecular disulfide bonds are formed between the bZIP domains and the bZIP–LOV linker regions , which induce VfAu1 dimerization and enable light-independent DNA binding . We point out that PtAu1a does not possess cysteine residues outside the LOV domain and is therefore unable to form such disulfide bonds . Consequently , an influence of the redox conditions on the DNA binding of PtAu1a can be ruled out . Concerning light-induced changes in the oligomerization state , we could not detect indications for light-induced PtAu1afull and PtAu1abZIP-LOV oligomerization in any of our experiments . Instead , our data suggests a mechanism where the LOV domain directly interacts with the leucine zipper region of the bZIP domain in the dark , impeding its DNA binding function . Our HDX-MS data suggest that the LOV–bZIP interaction might occur via the LOV β-sheet and directly involves A'α as well as Jα . Upon blue light illumination , structural changes occur within the LOV core that are transmitted to the flanking helices and result in bZIP–LOV dissociation and subsequent LOV dimerization , thus increasing the affinity of PtAu1a for its target DNA sequence ( Figure 8 ) . Such an allosteric signaling mechanism not only explains the absence of changes in structural dynamics of the bZIP–LOV linker region upon illumination , but also explains the need for the > 30 amino acids long linker in Aureos , which is required to enable direct bZIP–LOV interaction . Our model is further supported by recent data on N-terminally truncated and modified VfAu1 constructs that also indicate an interaction between the leucine zipper and the LOV domain in the dark , although it was hypothesized that this interaction stabilizes the monomeric form of the synthetic proteins ( Nakatani and Hisatomi , 2015 ) . Moreover , a mechanism involving light-induced release of the LOV domain from its interaction site on the bZIP domain is consistent with the general concept of LOV and PAS domain signaling via the β-sheet surface ( Harper et al . , 2003; Zoltowski et al . , 2007; Möglich et al . , 2009; Nash et al . , 2011; Rivera-Cancel et al . , 2014 ) . Since the cellular concentration of PtAu1a is not known , we cannot completely exclude a regulatory mechanism where light-induced LOV dimerization additionally influences the oligomerization state and thus DNA binding of PtAu1a in vivo . 10 . 7554/eLife . 11860 . 029Figure 8 . Model for light-regulated gene expression by PtAu1a . ( 1 ) In the dark , PtAu1a is dimeric and the LOV and bZIP domains interact directly thus inhibiting DNA binding of PtAu1a . 2-4: close up of the LOV domain . ( 2 ) A'α and Jα are attached to the surface of the LOV β-sheet and are highly dynamic even in the dark . ( 3 ) Illumination with blue light causes Cys287–FMN adduct formation and results in undocking of Jα from the LOV core and increases its structural dynamics . ( 4 ) Structural changes within the LOV core together with the destabilization of Jα trigger the release of A'α from the dimerization site and also increase its flexibility . ( 5 ) The LOV domain dissociates from the leucine zipper of the bZIP domain and dimerizes , which results in an increased structural dynamics of the bZIP domain and ( 6 ) increases the affinity of PtAu1a for its target DNA sequence . The model depicted includes results from FT-IR experiments on PtAu1a-LOV that revealed Jα-dependent A'α release from the LOV core ( Herman and Kottke , 2015 ) . FT-IR , Fourier transform infrared; FMN , flavin mononucleotide; LOV , light-oxygen-voltage . DOI: http://dx . doi . org/10 . 7554/eLife . 11860 . 029 So far , the very limited number of light state and multi-domain LOV protein crystal structures has hampered our understanding of allosteric light signaling in this class of photoreceptors . Detailed structural and functional characterization of the light- and dark-adapted states are required for elucidating how the light signal is transmitted from the light-sensing LOV to the effector domain . Our data for PtAu1a not only contribute to the understanding of the modularity and allosteric light signaling in LOV proteins , but also provides important information for rational and structure-guided design of new optogenetic tools . In fact , our data also explain the underlying working mechanism of recently engineered Aureo LOV containing light-activatable synthetic transcription factors and receptor tyrosine kinases that are based on light-dependent homo-dimerization ( Yang et al . , 2013; Grusch et al . , 2014 ) . These successful applications of Aureo LOVs highlight their efficacy as photodimerizers . However , the full potential of Aureo LOV domains for the design of optogenetic devices has not been exploited yet . In addition to their dimerization ability , the concerted undocking of A'α , Jα as well as of the bZIP domain from the LOV core can be used for the design of new optogenetic tools . The light-sensitive bZIP–LOV interaction observed in Aureos allows combining the engineering strategies developed for leucine zippers and coiled coils with the light-sensing function of LOV domains . This unique combination of different functionalities provided by Aureos offers new design strategies for robust and tightly controllable molecular switches that allow precise spatial and temporal control of biological processes .
The pETM-11 plasmids encoding Escherichia coli codon optimized PtAu1afull ( Epoch Life Science ) and PtAu1aLOV were kindly provided by H . Janovjak , IST Austria . We generated PtAu1a constructs representing PtAu1abZIP-LOV and PtAu1abZIP by polymerase chain reaction ( PCR ) amplification using pETM-11 PtAu1afull as template and the following primer pairs: 148_fw ( 5'-ATATCCATGGGAATGTCTGAGCAGCAGAAAGTGG-3' ) and 378_rv ( 5'-ATATGCGGCCGCTTAGTCTTCATCGTCATTGGCTG-3' ) for PtAu1abZIP-LOV and 148_fw and 212_rv ( 5'- ATATGCGGCCGCTTAAGCGGAATCGATCAGGGTG-3' ) for PtAu1abZIP . . The PCR products were cloned into the pETM-11 vector using NcoI and NotI restriction sites . The resulting PtAu1abZIP-LOV and PtAu1abZIP as well as the PtAu1afull and PtAu1aLOV constructs carry an N-terminal hexahistidine tag followed by a Tobacco Etch Virus ( TEV ) protease cleavage site . Chemically competent E . coli BL21 ( DE3 ) cells ( Invitrogen ) were transformed with the respective PtAu1a plasmid DNA . Protein expression was induced with 0 . 2 mM isopropyl β-D-1-thiogalactopyranoside at an optical density of 0 . 8 . All protein constructs were expressed overnight at 18°C in the dark in Lysogeny broth medium supplemented with 30 µg ml-1 kanamycin . Cells were harvested by centrifugation and the cell pellets were resuspended in buffer A ( 20 mM 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( HEPES ) pH 7 . 5 , 300 mM NaCl , 40 mM imidazole , 5% ( w/v ) glycerol ) including cOmplete Protease-Inhibitor Cocktail ( Roche ) . The cells were lysed using a microfluidizer ( Microfluidics ) and the lysates were clarified by ultracentrifugation at 185 , 500 g at 4°C for 1 hr . The supernatant was loaded onto an Ni2+–NTA Superflow ( Qiagen ) affinity column pre-equilibrated with buffer A . The resin was washed with 10 column volumes ( CV ) of buffer A and the bound proteins were eluted using 5 CV of buffer A supplemented with 160 mM imidazole . The protein containing fractions of PtAu1afull , PtAu1abZIP-LOV and PtAu1aLOV were dialyzed at 4°C overnight against 1 L buffer B ( 20 mM HEPES pH 7 . 5 , 50 mM NaCl , 2 mM dithioerythritol , 2 mM ethylenediaminetetraacetic acid ( EDTA ) , 5% ( w/v ) glycerol ) and in parallel the hexahistidine tag was removed from the protein using TEV protease ( 1:30 molar ratio of TEV:protein ) . The cleaved tag and the histidine-tagged TEV protease were removed from the PtAu1a solutions by rechromatography on Ni2+–NTA resin and the flow through was used for further purification . After Ni2+–NTA chromatography , PtAu1abZIP was directly loaded on a HiTrap heparin column ( GE Healthcare ) using buffer C ( 20 mM HEPES pH 7 . 5 , 100 mM NaCl , 10 mM MgCl2 , 5% ( w/v ) glycerol ) as running buffer and eluted in a gradient to 100% buffer C supplemented with 950 mM NaCl . PtAu1afull and PtAu1abZIP-LOV were loaded onto a MonoS column ( GE Healthcare ) using buffer D ( 50 mM 2- ( N-morpholino ) ethansulfonic acid ( MES ) pH 6 . 0 , 50 mM NaCl ) as running buffer and eluted in a gradient to 100% buffer D supplemented with 950 mM NaCl . All PtAu1a variants were concentrated using centrifugal filter units ( Amicon , Millipore ) and the LOV domain containing variants were reconstituted with FMN ( Sigma-Aldrich ) by incubation with a five-fold excess of FMN for 1 h at 4°C in the dark . Subsequently , PtAu1afull , PtAu1abZIP-LOV and PtAu1aLOV were subjected to gel filtration on a Superdex 200 ( PtAu1afull ) or Superdex 75 ( PtAu1abZIP-LOV and PtAu1aLOV ) ( GE Healthcare ) column equilibrated in buffer C . For SAXS analysis , PtAu1afull and PtAu1abZIP-LOV as well as their DNA complexes were buffer exchanged on a Superdex 200 Increase 10/300 GL column ( GE Healthcare ) equilibrated in buffer C . To efficiently form the ( PtAu1afull ) 2 -DNA and ( PtAu1abZIP-LOV ) 2-DNA complex the proteins were incubated for 5 min under blue light illumination together with a 1 . 5-fold molar excess of 21-bp DNA and subsequently subjected to gel-filtration . The peak fractions of the eluting protein–DNA complexes were pooled and incubated at 4°C overnight to allow back conversion to the dark state . 24 bp blunt end DNA probes encompassing a TGACGT bZIP binding motif ( 5'- TGTAGCGTCTGACGTGGTTCCCAC-3' , the binding motif is underlined ) of the P . triccornutum dsCYC2 promoter region or a random DNA sequence ( 5'-AGTGGGTCATTGCAAGTAGTCGAT-3' ) as well as a 21 bp DNA probe with single base-pair overhangs encompassing the binding sequence ( fw: 5'- ATAGCGTCTGACGTGGTTCCC-3' , rv: 5'-TGGGAACCACGTCAGACGCTA-3' ) were ordered as single strands and resuspended in annealing buffer containing 10 mM Tris pH 7 . 0 , 500 mM NaCl , 2 . 5 mM MgCl2 , 1 mM EDTA . Complementary DNA strands were annealed together in equimolar amounts by heating to 95°C and gradual cooling to room temperature overnight . For purification , the annealed DNA probes were diluted in buffer E ( 20 mM HEPES pH 7 . 5 , 50 mM NaCl , 5% ( w/v ) glycerol ) and loaded on a MonoQ column ( GE Healthcare ) equilibrated in buffer E . The DNA was eluted from the column driving a gradient to 100% buffer E supplemented with 950 mM NaCl . Subsequently , the DNA probes were subjected to size-exclusion chromatography on a Superose 6 ( GE Healtcare ) column equilibrated in buffer C . The DNA probes were concentrated by ultrafiltration using a 10-kDa cut off centrifugal filter unit ( Amicon Ultra-4 , Millipore ) . Individual LOV domain containing PtAu1a variants were pre-incubated at 20°C in the dark or under continuous blue light illumination ( 400 µW cm–2 at 450 nm ) from a royal blue ( 455 nm ) collimated LED lamp ( Thorlabs ) for 20 min . 100 µl of a 150 µM protein solution was subjected to size-exclusion chromatography at RT on a Superdex 200 10/300 GL column ( GE Healthcare ) equilibrated in buffer C . For dark and light experiments , the column was kept in the dark or continuously illuminated with blue light during the gel-filtration runs . To investigate the interaction between PtAu1aLOV and PtAu1abZIP , the proteins were mixed in a molar ratio of 1:1 and concentrated using centrifugal filter units ( Amicon , 3 kDa cut off ) . 100 µl of a 75 µM complex solution ( based on a theoretical ( PtAu1aLOV-PtAu1abZIP ) 2 complex ) was subjected to size-exclusion chromatography at RT on a Superdex 200 10/300 GL column ( GE Healthcare ) equilibrated in buffer C . The high performance liquid chromatography ( HPLC ) ( Waters ) setup was connected to a MALS detector ( Dawn Heleos , Wyatt Technology ) combined with a refractive-index detector ( Waters ) . Data analysis was performed using the ASTRA software ( Wyatt Technology ) , providing estimates for the molar mass of the different PtAu1a variants and the ( PtAu1aLOV-PtAu1abZIP ) 2 complex . 24 bp DNA probes ( 50 nM ) were incubated in buffer C supplemented with 5% ( w/v ) glycerol and varying amounts of purified PtAu1afull in a total volume of 5 µl . The protein DNA mixtures were incubated at RT for 20 min in the dark or under continuous blue light illumination ( 400 µW cm–2 at 450 nm ) and then separated on 10% Tris-glycine-EDTA ( TGE ) gels , pH 9 . 0 , supplemented with 10 mM MgCl2 using buffer containing 25 mM Tris pH 9 . 0 , 190 mM glycine , 10 mM MgCl2 and 1 mM EDTA . Gel runs were performed at 4°C for 90 min ( 15 V cm–1 ) in the dark or under continuous blue light illumination ( 30 µW cm–2 ) . Gels were stained with GelRed ( Biotinum ) for DNA visualization . To exclusively visualize DNA and cancel out flavin fluorescence in the EMSA experiments , the gels were imaged using green excitation light and a 605/50 nm emission filter ( ChemiDoc MP , Biorad ) . Crystallization of PtAu1aLOV was performed at 20°C . Dark state crystals were grown in sitting-drop geometry with unreconstituted PtAu1aLOV protein ( chromophore occupancy ~55% , protein buffer: 20 mM Tris pH 8 . 0 , 100 mM NaCl , 10% ( w/v ) glycerol ) using a protein concentration of 16 mg ml–1 and 40% ( v/v ) ethylene glycol and 0 . 1 M sodium acetate pH 4 . 5 as reservoir solution . Orthorhombic crystals appeared after 1 day and were harvested after 5 days . Dark state crystals were flash-cooled in liquid nitrogen under safe-light conditions without further cryoprotection . PtAu1aLOV light state crystals were grown with FMN-reconstituted protein overnight in hanging-drop vapour-diffusion geometry under continuous blue light illumination ( 50 µW cm–2 at 450 nm ) . Trigonal crystals started to grow from a mixture of 0 . 66 µl protein solution ( 12 mg ml–1 ) , 0 . 66 µl reservoir solution consisting of 3 . 5 M sodium formate pH 7 . 0 and 0 . 66 µl 0 . 1 M hexamine cobalt ( III ) chloride . Prior to flash-cooling in liquid nitrogen , crystals were incubated in reservoir solution supplemented with 20% ( v/v ) glycerol . Diffraction data of PtAu1aLOV dark and light state crystals were collected on beamline P11 at PETRA III , DESY ( Hamburg , Germany ) and beamline X10SA at the Swiss Light Source ( Villigen , Switzerland ) at 100 K and wavelengths of 0 . 9780 and 0 . 9795 Å , respectively . Data were processed using the XDS program suite ( Kabsch , 2010 ) . To minimize X-ray-induced reduction of the covalent adduct formed in the light-adapted protein , PtAu1aLOV light state data was collected from three different spots on a single crystal and merged to obtain a complete data set . PtAu1aLOV dark and light state data were phased by molecular replacement using Phaser ( McCoy et al . , 2007 ) in CCP4 and Phaser in PHENIX , respectively , with the Aureochrome 1 LOV domain ( residues 219-317 ) from Vaucheria frigida ( PDB code 3UE6 , molecule A ) and the PtAu1aLOV dark state structure without A'α helix ( residues 251-372 ) as the search models . The missing segments flanking the LOV core were manually built in Coot ( Emsley et al . , 2010 ) and the structure refined in cycles of phenix . refine ( Afonine et al . , 2012 ) refinement and manual re-building . Rfree values for the PtAu1aLOV dark and light state data sets were computed from 8% and 5% randomly chosen reflections not used during the refinement , respectively . The topology and parameter files for the covalent Cys287-FMN adduct were obtained by quantum chemical calculations ( Fedorov et al . , 2003 ) . Model quality was analyzed using the MolProbity ( Emsley et al . , 2010 ) validation tool as implemented in PHENIX . Both final models contained no outliers in the Ramachandran plot , with 100% of the residues in the favored region for the dark state structure of PtAu1aLOV and 96 . 2% for the light state structure . Atomic coordinates of the structures and structure-factor amplitudes have been deposited in the Protein Data Bank as entries 5DKK and 5DKL . Analysis of the PtAu1aLOV light state dimer interface and the BSAs was performed using the Protein Interfaces , Surfaces and Assemblie service ( PISA; v1 . 51 ) ( Krissinel and Henrick , 2007 ) . Aliquots of 2 µl at final concentrations of 250 µM PtAu1afull , 250 µM PtAu1afull together with a 1 . 2-fold excess of 24 bp DNA on the basis of a ( PtAu1afull ) 2-DNA stoichometry and 250 µM PtAu1aLOV were pre-incubated at 20°C for 60 s in the dark or under continuous blue light illumination ( 400 µW cm–2 at 450 nm ) in buffer D . The corresponding light conditions were maintained throughout the labeling reactions , which were prepared in triplicates for all experiments . Hydrogen-deuterium exchange was initiated by 1:20 dilution of the samples in buffer D prepared with D2O and glycer ( ol-d3 ) pD 7 . 5 at 20°C . Aliquots of 6 µl were removed after 10 and 45 s , and 3 , 15 and 60 min and the labeling reaction terminated by quenching with 56 µl ice cold buffer containing 200 mM ammonium formic acid pH 2 . 6 and 2 . 8 M urea . Deuterated samples were injected into a cooled HPLC setup and digested on an immobilized pepsin column ( Poroszyme , Life Technologies ) kept at 10°C . All subsequent steps were carried out in a water bath at 0 . 5 ± 0 . 1°C . The generated peptides were desalted on a 2 cm C18 guard column ( Discovery Bio C18 , Sigma ) and separated during a 7 min acetonitrile gradient ( 15–50% ) in the presence of 0 . 6% ( v/v ) formic acid on a reversed phase column ( XR ODS 75 × 3 mm , 2 . 2 µM; Shimadzu ) . Eluting peptides were infused into a maXis electrospray ionization-ultra high resolution-time-of-flight mass spectrometer ( Bruker ) and deuterium incorporation was analyzed and quantified using the Hexicon 2 software package ( Lindner et al . , 2014 ) . SAXS measurements were performed at the X12SA cSAXS beamline at the Swiss Light Source ( Villigen , Switzerland ) . Measurements were performed in buffer C at protein concentrations of 10 , 5 , and 2 . 5 mg/ml for PtAu1afull; 9 , 5 , and 2 . 5 mg/ml for PtAu1abZIP-LOV; 5 . 8 and 2 . 9 mg/ml for the ( PtAu1afull ) 2-DNA complex and 6 . 2 and 3 . 1 mg/ml for the ( PtAu1abZIP-LOV ) 2-DNA complex . Samples were filled and mounted in Ø 1-mm-quartz capillaries in the dark and kept at 10°C throughout the experiments . For light activation of the LOV domain , the samples were briefly pre-illuminated with a blue LED ( λmax = 455 nm; Thorlabs ) . Data acquisition using 11 . 2 keV photons was performed in 500 µm steps along the capillary with 10 × 0 . 5 s exposure at 20 different positions . Scattered X-rays were recorded with a Pilatus 2M detector . Data was collected from the buffer alone and subsequently from the protein and protein–DNA complexes from the identical position on the same capillary . Measurements were performed in the dark with and without pre-illumination of the samples . The scattering vector q is defined as q = 4π sin ( θ ) λ-1 . For data analysis , all diffraction images were azimuthally integrated , averaged and the buffer signal was subtracted from that of the buffered protein solution . For DAMMIN ab initio and CORAL ( Petoukhov et al . , 2012 ) rigid body modeling PtAu1abZIP-LOV dark state data from the 5 and 9 mg/ml measurements was merged at q = 0 . 13 A-1 and ( PtAu1abZIP-LOV ) 2-DNA complex light data was merged at q = 0 . 10 A–1 . Distance distribution functions p ( r ) and maximum particle diameters Dmax were calculated using the program GNOM ( Svergun , 1992 ) . Shape reconstructions were performed using the ab initio bead-modelling program DAMMIN ( Svergun , 1999 ) . Ten independent models generated for the dark state of PtAu1abZIP-LOV and the ( PtAu1abZIP-LOV ) 2-DNA light complex without enforcing symmetry ( P1 ) were superimposed and averaged using the programs DAMSUP ( Volkov and Svergun , 2003 ) and DAMAVER ( Volkov and Svergun , 2003 ) . Finally , the averaged shapes were filtered using the program DAMFILT ( Volkov and Svergun , 2003 ) . Molecular modelling was performed using the program CORAL ( Petoukhov et al . , 2012 ) and the high resolution structure of PtAu1aLOV in its dark state and a homology model of the bZIP domain on the basis of the c-Fos-c-Jun heterodimer ( PDB: 1FOS ) . Modelling was applied keeping the bZIP dimer fixed and allowing free positioning of the two LOV monomers as well as building of the missing parts . Dimerization of PtAu1aLOV in the dark was quantified by microscale thermophoresis using a Monolith NT . 115 ( Nanotemper ) . The protein was randomly labeled at the amine positions using the NHS-reactive red fluorescent dye DY-647 ( MoBiTec ) according to the labeling protocol of Nanotemper . A 1:2 dilution series of unlabeled PtAu1aLOV was prepared over an appropriate concentration range using a buffer containing 20 mM HEPES pH 7 . 5 , 100 mM NaCl and 10% ( w/v ) glycerol and were mixed with equivalent volumes of labeled PtAu1aLOV ( final concentration 20 nM ) . Measurements were performed in the dark using standard treated capillaries . Data of three individual experiments were averaged and evaluated using the quadratic equation of the law of mass action with the constraint of a fixed labelled species concentration . Dark state recovery kinetics of the different LOV domain containing PtAu1a variants were measured at 25°C in buffer D using a Varioskan Flash multimode reader ( Thermo Scientific ) . Samples were pre-illuminated with a blue LED ( λmax = 455 nm; Thorlabs ) for 3 min and subsequently the absorbance was measured at 445 nm . Measurements for all LOV domain containing PtAu1a variants were performed at a protein concentration of 20 µM . Data of three independent measurements were averaged and evaluated by fitting exponential functions . | The ability to react to sunlight is important for the survival of a wide range of lifeforms . Many organisms , including humans , plants , bacteria and algae , sense light using specialized proteins called photoreceptors . These proteins are able to translate the information transported by light into various biological activities . The structure of a photoreceptor can be broken down into different parts , each with a specialized role . For example , the light-sensing region of a photoreceptor typically binds to small molecules called chromophores that are able to absorb light . This light absorption causes changes in the photoreceptor that are ultimately transmitted to a part of the protein that can bind to DNA or perform some other type of biological activity . This activity triggers further processes that build up to the organism’s reaction to the incoming light . Aureochromes are photoreceptors that detect blue light and are found in algae . The light-sensing and DNA-binding parts of aureochromes are arranged in a different way to the arrangement seen in most related photoreceptors . This raises questions about how the light signal is transmitted to the DNA-binding part of the protein and how this affects the DNA binding of aureochromes . By using a combination of biophysical and structural methods , Heintz and Schlichting now provide detailed information about the structural changes that blue light causes in the Aureochrome 1a photoreceptor found in the algae Phaeodactylum tricornutum . This shows that when exposed to light , the light-sensing part of the photoreceptor , called LOV domain , detaches from the DNA binding part and binds to the LOV region of a second molecule . This helps the protein to bind to DNA . Recently , synthetic photoreceptors have been engineered that use the light-sensing part of aureochromes . Therefore , as well as contributing to the fundamental understanding of light signaling in photoreceptors , Heintz and Schlichting’s findings can be used to help develop light-controllable artificial proteins for use in research , medicine or industry . | [
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] | 2016 | Blue light-induced LOV domain dimerization enhances the affinity of Aureochrome 1a for its target DNA sequence |
Transcriptional pausing underlies regulation of cellular RNA biogenesis . A consensus pause sequence that acts on RNA polymerases ( RNAPs ) from bacteria to mammals halts RNAP in an elemental paused state from which longer-lived pauses can arise . Although the structural foundations of pauses prolonged by backtracking or nascent RNA hairpins are recognized , the fundamental mechanism of the elemental pause is less well-defined . Here we report a mechanistic dissection that establishes the elemental pause signal ( i ) is multipartite; ( ii ) causes a modest conformational shift that puts γ-proteobacterial RNAP in an off-pathway state in which template base loading but not RNA translocation is inhibited; and ( iii ) allows RNAP to enter pretranslocated and one-base-pair backtracked states easily even though the half-translocated state observed in paused cryo-EM structures rate-limits pause escape . Our findings provide a mechanistic basis for the elemental pause and a framework to understand how pausing is modulated by sequence , cellular conditions , and regulators .
During the first step in gene expression , transcription by RNA polymerase ( RNAP ) at ~30 nt/s or faster is interrupted by ≥1 s pause events every 100–200 bp ( Landick , 2006; Larson et al . , 2014; Chen et al . , 2015 ) . These pauses underlie diverse mechanisms that regulate gene expression in both prokaryotes and eukaryotes ( Figure 1A ) , including attenuation , antitermination , and promoter-proximal pausing ( Jonkers and Lis , 2015; Zhang and Landick , 2016; Mayer et al . , 2017 ) . Pausing also couples transcription to translation in bacteria or to mRNA splicing in eukaryotes ( Landick et al . , 1985; Proshkin et al . , 2010; Mayer et al . , 2017 ) ; defines temporal and positional windows for binding of small molecules , regulatory proteins , or regulatory RNAs to the nascent RNA transcript ( Wickiser et al . , 2005; Artsimovitch and Landick , 2002 ) ; mediates nascent RNA folding ( Pan et al . , 1999; Pan and Sosnick , 2006; Steinert et al . , 2017 ) ; and enables termination ( Gusarov and Nudler , 1999; Proudfoot , 2016 ) . Conversely , RNA folding and the interactions of cellular molecules and complexes ( e . g . , ribosomes ) with the elongating transcription complex ( EC ) modulate pausing ( Toulokhonov et al . , 2001; Artsimovitch and Landick , 2002; Yakhnin et al . , 2016; Zhang and Landick , 2016 ) . Despite its crucial role in cellular information processing , the biophysical mechanism of pausing remains incompletely defined . Multiple pause mechanisms exist , but most pauses that mediate gene regulation are triggered initially by sequence-specific interactions of DNA and RNA with RNAP . An increasingly accepted view is that these initial interactions interrupt the nucleotide addition cycle by promoting entry of RNAP into a state termed the elemental pause ( Landick , 2006 ) , creating an elemental paused elongation complex ( ePEC; Figure 1B ) . The ePEC can then rearrange into long-lived pause states by backtracking ( reverse translocation of RNA and DNA ) , by pause hairpin ( PH ) formation in the RNA exit channel that alters RNAP conformation ( at least in bacteria ) , or by interactions of diffusible regulators with the ePEC . Recent cryoEM structures of artificially assembled PECs suggest the ePEC is half-translocated with a tilted RNA–DNA hybrid , meaning that the RNA but not the DNA is translocated and the next template base is still sequestered in the downstream DNA duplex ( Kang et al . , 2018a; Guo et al . , 2018; Vos et al . , 2018 ) . The hairpin-stabilized PEC is additionally inhibited by a rotation of the swivel module ( including the clamp , shelf , and SI3 ) that inhibits trigger-loop folding ( Kang et al . , 2018a; Guo et al . , 2018 ) . High-throughput sequencing of nascent RNAs from bacteria ( NET-seq ) reveals a consensus elemental pause sequence conserved among diverse bacterial RNAPs and mammalian RNAPII whose effects on pausing in vitro are consistent with a block in template DNA translocation sometimes accompanied by modest backtracking ( Larson et al . , 2014; Vvedenskaya et al . , 2014; Imashimizu et al . , 2015 ) . Although earlier work establishes contributions of multiple pause signal components ( upstream RNA , RNA–DNA hybrid , downstream fork junction , and downstream DNA ) to hairpin-stabilized pausing ( Chan and Landick , 1993; Wang et al . , 1995; Chan et al . , 1997 ) , the definition of the consensus elemental pause signal has varied and it is unknown if the discrete components affect a common step in the elemental pause mechanism . Additionally , questions remain about the structure and properties of the ePEC . A longstanding debate is whether the ePEC is an on-pathway state unable to translocate DNA or RNA in a largely unchanged RNAP due to the thermodynamic properties of the RNA–DNA scaffold ( i . e . , a pretranslocated pause; Bai et al . , 2004; Bochkareva et al . , 2012 ) or if it represents an offline state generated by conformational rearrangement of RNAP that forms in kinetic competition with the on-pathway steps ( Landick , 2006; Herbert et al . , 2006; Kireeva and Kashlev , 2009; Imashimizu et al . , 2013; Kang et al . , 2018a ) . Uncertainty also exists as to whether the elemental pause is non-backtracked ( Landick , 2006; Herbert et al . , 2006; Kireeva and Kashlev , 2009; Kang et al . , 2018a ) or must be backtracked one or more registers ( Dangkulwanich et al . , 2013; Forde et al . , 2002; Galburt et al . , 2007; Mejia et al . , 2015; Tadigotla et al . , 2006; Ó Maoiléidigh et al . , 2011 ) . To address these questions , we combined kinetic analyses of pausing using elemental pause sequence variants or mutant RNAPs with precise structural probes of translocation , trigger-loop folding , and clamp conformation . Our results lead us to propose a multistate model of elemental pausing in which template-base loading in a half-translocated offline intermediate limits pause escape .
To probe the elemental pause mechanism , we used kinetic analyses of ECs reconstituted on a synthetic RNA-DNA scaffold encoding a consensus elemental pause sequence ( Figure 1C and Figure 1—figure supplement 1A; Larson et al . , 2014 ) . We first asked if the pause signal always causes ECs to bifurcate into paused and rapidly elongating ( bypass ) fractions . We measured C17 pause RNA as a function of time when radiolabeled G16 ECs were extended with 100 µM each CTP and GTP ( Figure 1D , E , F ) . Most ECs ( 80% at 37°C ) entered a paused state ( lifetime 5 s; a , Figure 1D , F ) , whereas some ECs ( 20% ) transcribed past C17 rapidly ( lifetime 0 . 1 s; bypass , Figure 1D , F ) . Invariably , a minor ePEC population ( typically 15% ) escaped more slowly ( lifetime 100 s; b , Figure 1D , F ) , requiring double-exponential fitting of the escape rate . Pause bypass was evident from y-intercepts <1 in the double-exponentials fits ( Figure 1F ) . Interestingly , the amounts of slow ePEC and bypass fractions varied among RNAP preparations ( Figure 1—figure supplement 1B ) . This kinetic malleability of ePECs differed from the reproducible kinetics typically observed for hairpin-stabilized pauses like the hisPEC ( Toulokhonov et al . , 2001; Kyzer et al . , 2007; Kang et al . , 2018a ) . To confirm that the ePEC is an offline state formed in competition with bypass nucleotide addition ( i . e . , in a branched kinetic mechanism; Figure 1D ) , we performed three additional tests . First , we asked whether the fraction of ECs entering the ePEC state increased if GTP ( the NTP required for pause escape ) were withheld to allow more time for ePEC formation . Unlike for the hairpin-stabilized hisPEC ( Toulokhonov et al . , 2007 ) , halting ECs at C17 for 15 s before GTP addition did not change the 10–20% EC that bypassed the pause ( Figure 1G ) . However , the bypass fraction was shifted from ~22% at 100 µM GTP to ~7% at 10 µM GTP , suggesting that GTP can affect a partition between the on-pathway EC and offline elemental pause state . Second , using data from Larson et al . , 2014 , we found that the bypass fraction varied but appeared to plateau at high GTP ( Figure 1—figure supplement 1C ) . Because extrapolation of bypass from bi-exponential fits is an approximation , we next used numerical integration of rate equations as a third test to ask if reaction progress curves are better fit by an unbranched or branched kinetic mechanism at saturating GTP ( 10 mM; Figure 1—figure supplement 1A , F; Materials and methods ) . The unbranched ( on-pathway pause ) mechanism did not fit the data well , thus favoring a branched mechanism ( Figure 1—figure supplement 1F ) . We also used kinetic modeling to reexamine the proposed on-pathway pausing reported by Bochkareva et al . , 2012 . Using their optimal pause scaffold sequence , we replicated the reported high-efficiency pausing ( Figure 1—figure supplement 1D , E ) . However , this scaffold positions the downstream DNA end ( +17 ) within RNAP , so that forward translocation reduces downstream DNA-RNAP interaction . Consistent with a prior finding that downstream DNA truncation increases pausing ( Kyzer et al . , 2007 ) , pause bypass was readily detected when the downstream DNA end was positioned outside RNAP as in natural ECs ( Figure 1—figure supplement 1D , E ) . This result highlights why highly efficient pauses , even if they isomerize to an offline state , may appear to be on-pathway when bypass falls below ~5% . We confirmed the branched kinetic pause mechanism on the Bochkareva et al . scaffold at saturating GTP by kinetic modeling ( Figure 1—figure supplement 1G ) . In agreement with most prior ensemble ( Kassavetis and Chamberlin , 1981; Kyzer et al . , 2007; Toulokhonov et al . , 2007; Strobel and Roberts , 2015 ) and single-molecule ( Herbert et al . , 2006; Larson et al . , 2014; Gabizon et al . , 2018 ) analyses of pausing , we conclude that the elemental pause is an offline state that forms in competition with on-pathway nucleotide addition ( i . e . , in a branched kinetic mechanism ) . The existence of a minor , variable , slowly escaping ePEC population has been a consistent and puzzling feature of pause assays using synthetic scaffolds ( Figure 1F ) . We wondered if the slow pause population could be explained by backtracking , an EC conformational change , or a subpopulation of chemically altered RNAP . Variation of the slow ePEC fraction among RNAP preparations ( Figure 1—figure supplement 1B ) seemed to favor a chemically altered subpopulation . We found that absence of neither the ω subunit nor the αCTDs changed the slow ePEC fraction ( not shown ) . More definitively , transcription through tandem consensus pause sequences revealed formation of the slow fraction from all RNAPs arriving at the second pause site rather than a chemically altered , slow subpopulation of RNAP that would be filtered out by the first pause site ( Figure 1—figure supplement 2 ) . We next used GreA- or GreB-induced cleavage to ask if the minor slow ePEC fraction was backtracked . Either or both GreA or GreB had little effect on pause lifetimes , even though 2-nt cleavage products , indicative of a 1 bp backtracked state , appeared much faster than the rate of ePEC escape and 3-nt cleavage products , indicative of a 2 bp backtracked state , were detectable ( Figure 1H , I; Figure 1—figure supplement 3B , D ) . Thus , even though the scaffold structure disfavors ≥2 bp backtracking due to a –12 rU–dG mismatch , ePECs may shift from the 1 bp backtracked state to ≥2 bp backtracked states , at least in the presence of GreA , GreB , or both . We next tested the effect on both pausing and intrinsic cleavage of a −11rU-dC mismatch that would disfavor even 1 bp backtracking using an RNAP that formed a high level of slow fraction ( Figure 1C , K , J; Figure 1—figure supplement 3E , F ) . The −11rU-dC mismatch virtually eliminated ≥2 nt cleavage indicative of backtracking , reduced 1-nt cleavage indicative of the pretranslocated register , and modestly decreased the slow pause fraction . The relative ease with which ePECs entered backtracked registers is notable given the half-translocated state observed in ePEC cryo-EM structures ( Kang et al . , 2018a; Guo et al . , 2018; Vos et al . , 2018 ) . Backtracking of ePECs is detectable both in vivo and in vitro ( Larson et al . , 2014; Imashimizu et al . , 2015 ) , but the half-translocated or pretranslocated states appears to be predominant ( Figure 1J; Larson et al . , 2014 ) . Our GreA/B and intrinsic cleavage results establish that slow escape from a 1 bp backtrack state explains neither the major nor the minor pause fractions even though the 1 bp backtrack state is accessible . A parsimonious explanation is that the ePEC readily equilibrates among several active-site states , including half-translocated , pretranslocated , and backtracked , with the half-translocated state being the dominant species in which the kinetic block to pause escape is manifest . The slow pause state may be backtracked by ≥2 bp ( since 0 . 007 s−1 3-nt cleavage is close to the 0 . 002 s−1 escape rate; Figure 1H , I ) , but its persistence in the −11U mutant suggests other changes to the ePEC must also contribute ( see Discussion ) . Available NET-seq data have been interpreted to suggest an elemental pause signal involving just the upstream fork-junction ( usFJ ) and downstream fork-junction ( dsFJ ) sequences or a multipartite signal that additionally depends on the hybrid ( Hyb ) and downstream DNA ( dsDNA ) sequences ( Larson et al . , 2014; Vvedenskaya et al . , 2014; Imashimizu et al . , 2015 ) . To test the proposed roles of Hyb and dsDNA sequences and additionally to ask if the different pause signal elements combine additively to define a single energetic barrier to pause escape , as observed for the hairpin-stabilized his pause signal ( Wang et al . , 1995; Chan et al . , 1997 ) , we analyzed effects on pausing of substitutions in each element separately and in combination ( Figure 2A , B ) . We measured pause lifetimes and the bypass , pause , and slow pause fractions using two-exponential fits of C17 RNA vs . time at 10 µM GTP ( Figure 2C; Figure 2—figure supplement 1A ) . Because a translocation barrier would affect both pause formation and escape , we plotted pause strength ( PS; pause efficiency times the lifetime [τ] of the major pause species ) vs . pause bypass fraction ( Figure 2D ) . Alone , the usFJ , dsFJ , and dsDNA mutants reduced PS by a factor of ~5 , whereas the Hyb mutant decreased PS by a factor or ~1 . 4 fold ( but see larger effects in Bochkareva et al . , 2012 ) . Combinations of mutants produced additive effects on pause strength ( Figure 2D , magenta brackets; additive effects on an energy barrier are multiplicative in τ or PS; e . g . , reduction of PS by factors of 1 . 4 and 3 . 7 for the Hyb and dsDNA substitutions predicts a combined reduction by a factor of 5 . 1 vs . the factor of 5 . 2 observed ) . Additive effects are expected if pause signal components independently affect the same energetic barrier to pause escape ( e . g . , translocation of the template base ) . The lifetimes of the major and minor pause states were highly correlated for the pause-sequence variants ( Figure 2—figure supplement 1B ) . In contrast , the slow pause fraction ( Eb; Figure 2—figure supplement 1C ) was uncorrelated with the major pause escape rate ( k-spapp ) , the major pause fraction ( Ea ) , and the total ePEC fraction ( E ) . These results are consistent with a model in which both the major pause species and the minor , slowly escaping pause species pass through a common barrier for pause escape ( e . g . , template-base loading ) . The significant variation in the amount of slow pause species provides additional evidence that the slow species arises by kinetic partitioning of a single RNAP population and not from a chemically distinct ‘slow’ subpopulation of RNAP . We conclude that the elemental pause signal is indeed multipartite with significant contributions from sequences in the usFJ , Hyb , dsFJ , and dsDNA to a common kinetic barrier to escape from multiple paused states . To ask if the barrier to elemental pause escape corresponds to the half-translocated intermediate identified by cryoEM ( Kang et al . , 2018a; Guo et al . , 2018 ) , we next tested for ePEC translocation at the usFJ and dsFJ using a fluorescence-based translocation assay developed by Belogurov and co-workers ( Figures 3A and 4A; Malinen et al . , 2012 ) . In this assay , the fluorescent guanine analog 6-methylisoxanthopterin ( 6-MI ) is located in the template DNA ( usFJ ) or nontemplate DNA ( dsFJ ) such that an adjacent guanine unstacks from 6-MI upon translocation of the hybrid or the dsDNA; this unstacking increases 6-MI fluorescence ( Figure 3A ) . We compared translocation of the hybrid or the dsDNA leading to increased 6-MI fluorescence on ePEC scaffolds to a control scaffold previously found to translocate rapidly upon 3′-CMP addition ( Figure 3B; Malinen et al . , 2012; Hein et al . , 2014 ) . Because both the control and ePEC scaffold encode C as the RNA 3′ nucleotide and G as the next nucleotide after translocation ( Figures 3B , C and and 4A , B ) , differences in their behavior can be attributed to the usFJ , Hyb , and dsDNA . To calibrate the 6-MI signal change from the posttranslocated hybrid , we compared the effects of CMP , 3′dCMP , or 2′dCMP ( Figure 3D–F ) . A 3′deoxy ribonucleotide shifts the hybrid toward the pretranslocated register , whereas a 2′deoxy ribonucleotide shifts the hybrid toward the posttranslocated register ( Malinen et al . , 2012 ) . For both scaffolds , 2′dCMP gave a 6-MI signal increase comparable to CMP , whereas 3′dCMP reduced the signal increase . These results suggested that addition of CMP shifts both hybrids toward the posttranslocated register , although the absolute fluorescence change for the ePEC scaffold was about half that of the control scaffold . Rapid-quench and stopped-flow kinetic measurements revealed that CMP added rapidly ( ~300 s−1 ) followed by a rapid ( ~200 s−1 ) rise in fluorescence signal indicating translocation on both scaffolds . In contrast , subsequent extension to G18 of the ePEC but not the control EC was slow ( ~0 . 2 s−1 vs . 40 s−1; Figure 3D , E ) . Because the 6-MI and –10 substitutions in the ePEC scaffold could affect pausing , we verified that the 6-MI ePEC scaffold produced pause fractions ( ~85% ) and lifetimes similar to the unmodified consensus scaffold ( Figure 3G , H ) . Thus , the ePEC fluorescence signal could not be explained by the ~15% bypass fraction and must have arisen in significant part from the paused species . We also verified that CMP addition occurred fully in the fluorescence assay ( Figure 3—figure supplement 2 ) . We conclude that the ePEC hybrid translocates rapidly after CMP addition , resembling the control scaffold . The reduced level of unquenching ( ~50% of the control scaffold ) could reflect lower 6-MI fluorescence in a half-translocated hybrid , since the ePEC cryoEM structure suggests –10 dC ( vs . –10 dG in the assay scaffold ) remains at least partially paired in the hybrid with an altered interaction with the lid ( Kang et al . , 2018a ) . Alternatively , –10 dG may unstack in our assay at 37°C but the pretranslocated and half-translocated hybrids may be in dynamic equilibrium ( consistent with intrinsic hydrolysis readout; Figure 1J ) . These results also confirm that most ePECs are not backtracked , which would not unquench 6-MI ( Figure 1I and Figure 1—figure supplement 3 ) . Together the data are fully consistent with a view that the ePEC , once formed , may easily fluctuate among multiple translocation states . To test whether dsDNA fails to translocate in the ePEC ( as predicted from the ePEC structure ) , we used an ePEC scaffold for which translocation of the +1 dC–dG bp would generate a +2 6-MI fluorescence signal ( +1 dG would shift into an RNAP pocket that aids pause escape; Vvedenskaya et al . , 2014 ) . Placement of a dT–dA bp at +3 was necessary to generate a strong +2 6-MI signal . The changes needed for the 6-MI assay weakened the elemental pause signal but still allowed ECs to enter the pause state ( Figure 3H ) , possibly to greater extent prior to GTP addition ( e . g . , see effect of GTP in Figure 1G ) . For the control scaffold , addition of CMP or 2′dCMP or extension with CTP + GTP to G19 gave a large 6-MI signal indicating translocation after C17 nucleotide addition ( Figure 4C ) . In contrast , the ePEC scaffold gave minimal 6-MI signal after CMP or 2′dCMP addition ( consistent with translocation in only a bypass fraction ) compared to extension to G19 , which produced a strong 6-MI signal ( Figure 4D ) . These data confirm that the dsDNA in the ePEC does not translocate . Since the dsDNA in the ePEC did not translocate , we used GTP binding to 3′dCMP-ePEC and control 3′dCMP-EC to assess translocation propensity . For the control EC , a clear increase in 6-MI signal was evident as GTP bound in the active site with apparent Kd ≈ 400 µM ( Figure 4D , inset ) . This signal , which was reduced by interference from the high levels of GTP present , indicated that the post-translocated register was stabilized by GTP binding to the control EC ( Figure 4C; Malinen et al . , 2014 ) . However , even a high GTP concentration was unable to shift the 3′dC17 ePEC to the post-translocated register ( Figure 4D ) . We conclude that even the weakened elemental pause signal in the dsFJ ePEC assay scaffold prevented dsDNA translocation and thus template-base loading . Taken together , the usFJ and dsFJ 6-MI data confirm that the half-translocated state detected in ePEC cryoEM structure also forms in actively transcribing complexes , and that template-base loading is the relevant barrier to nucleotide addition in the ePEC . As a second approach to interrogate the status of the RNAP active site in the ePEC , we tested the effect of the consensus pause sequence on formation of disulfide bonds between Cys substitutions engineered to report TL conformation ( Cys-pair reporters , CPRs; Nayak et al . , 2013 ) . Previous studies established that a β′ 937 Cys substitution in the trigger helix near the active site crosslinks efficiently to a Cys substitution at β′ 736 when the trigger helices formed ( folded CPR , F937-736; Figure 5A ) . Other CPRs ( P937-687 , U937-1135 , or U937-1139 ) report the partially folded ( P ) or unfolded ( U ) conformations of the TL ( Figure 5A ) . The CPRs are sensitive to TL conformation when oxidized by cystamine ( CSSC ) because the CPR disulfide competes with formation of mixed disulfides ( Figure 5B ) . These CPRs combined with other probes and a cryoEM structure revealed that the hairpin-stabilized hisPEC fails to add the next nucleotide because it forms a swiveled PEC conformation that inhibits TL folding ( Hein et al . , 2014; Nayak et al . , 2013; Kang et al . , 2018a ) . In contrast , the ePEC , whose cryoEM structure is not swiveled , did not exhibit constraints on TL conformation ( compare to control EC; Figure 5C , D , E ) . If anything , the ePEC accessed the folded TH conformation more readily than did the control EC , consistent with ePEC access to the pretranslocated register ( Figure 1J ) that is thought to increase TL folding ( Malinen et al . , 2014; Liu et al . , 2016 ) . Thus , inhibition of TL folding does not appear to limit escape from the elemental pause . Incorporation of a 3′-dNMP in ECs and PECs allows the CPRs to detect TL folding stimulated by NTP binding in the EC and its inhibition in the hisPEC ( Nayak et al . , 2013 ) . Consistent with results of the 6-MI dsFJ translocation assay , F937-736 and P937-687 crosslinking in 3′dCMP-ePEC , and therefore TL conformation , were unaffected by high GTP concentration even though the crosslinks formed readily . In contrast , CPR crosslinking in control 3′dCMP-EC exhibited an obvious shift toward TL folding upon ATP binding ( Figure 5E , F; Nayak et al . , 2013 ) . These results are consistent with the ePEC cryoEM structure and translocation assay results , supporting a view that inability to load the template base inhibits NTP binding in the ePEC . The role of clamp conformation in elemental pausing is uncertain . Although a crystal structure of TthRNAP on a partial ePEC scaffold suggested the clamp could open in the ePEC ( Weixlbaumer et al . , 2013 ) , more recent cryoEM structures and biochemical probing suggested the clamp remains closed in the ePEC but can swivel upon pause hairpin formation ( Guo et al . , 2018; Kang et al . , 2018a ) . To probe the role of clamp conformation in elemental pausing , we examined the effect of stabilizing the clamp in the closed ( unswiveled ) conformation using an engineered disulfide bond between the lid and flap ( β′258i-β1044; Figure 6A and B; Kang et al . , 2018a ) . In contrast to suppression of pausing for the hairpin-stabilized hisPEC ( Figure 6C; Hein et al . , 2014; Kang et al . , 2018a ) , the closed-clamp disulfide had minimal effect on the ePEC lifetime ( Figure 6D ) . We conclude that , unlike for hairpin-stabilized pausing and consistent with the cryoEM structure , clamp swiveling ( or opening ) is not required in the ePEC . Even though full clamp swiveling is not required for elemental pausing , we wondered if any change in clamp conformation accompanied formation of the ePEC . To investigate this question , we used a variant of the disulfide bond probing strategy in which three Cys residues were positioned in RNAP such that either the closed-clamp disulfide ( β′258i-β1044 ) or the swiveled-clamp disulfide ( β′258i-β843 ) could form ( Kang et al . , 2018a ) . This Cys-triplet reporter ( CTR ) enabled a convenient measure of the energetic balance between closed and swiveled clamp conformations because the different crosslinked β′-β polypeptides could be readily distinguished by denaturing gel electrophoresis ( Figure 6B; Kang et al . , 2018a ) . The ratio of closed-to-swiveled crosslinks shifts during oxidation with cystamine likely because the mixed disulfide intermediates destabilize the closed-clamp conformation ( Figure 6E ) . Thus , cystamine oxidation shifts the unswiveled-to-swiveled equilibrium toward the swiveled conformation , making it a sensitive assay of clamp conformation . This shift is greater for the hisPEC ( which favors clamp swiveling; Kang et al . , 2018a ) , but intermediate between the EC and hisPEC for the ePEC ( Figure 6F ) . We conclude that formation of the ePEC is accompanied by a loosening of clamp contacts . We wondered if specific amino acids in RNAP inhibit template base translocation in the ePEC . Two candidates were β′K334 and βR542 ( Figure 7A ) . β′K334 in switch two contacts the template DNA backbone adjacent to a dC blocked from active-site loading in half-translocated SceRNAPII EC and TthEC crystal structures ( Brueckner and Cramer , 2008; Weixlbaumer et al . , 2013 ) and the ePEC cryoEM structure . βR542 in fork loop two appears to contact the +1dC–dG bp in the ePEC cryoEM structure . Although the modest resolution of the cryoEM structure makes this assignment tentative and apparent H-bonding patterns differ , interaction of this conserved Arg with a pretranslocated template +1C also is seen in the half-translocated TthRNAP crystal structure ( Weixlbaumer et al . , 2013 ) , in a crystal structure of a TthRNAP open complex formed on the B . subtilis pyrG promoter ( Murakami et al . , 2017 ) , and in a 1 bp backtracked TthRNAP PEC ( Sekine et al . , 2015 ) . To ask if either β′K334 or βR542 played a key role in inhibiting template-base loading in the ePEC , we generated Ala substitution mutants and compared their pausing behaviors to wild-type RNAP . Because the strong consensus pause sequence could mask the effect of a single amino-acid contact , we also assayed the Ala mutants on the usFJ and dsFJ mutant templates that display weaker pausing behavior ( Figure 2B ) . Strikingly , β′K334A resembled the wild-type enzyme on both consensus and mutant pause scaffolds , whereas βR542A decreased pausing by a factor of 2 on the consensus pause and dsFJ templates , but significantly more on the usFJ template ( 13X effect of usFJ vs . 6X for wild-type RNAP; Figure 7B ) . Given that βR542 interacts with the dsFJ , these data suggest that βR542 contributes to elemental pausing but less on a mutant template altered near its contact . We conclude that βR542 may help inhibit template dC loading in the ePEC .
To explain how RNAP responds to an elemental pause signal , we propose a multi-state pause mechanism in which pause escape is principally inhibited by inability to load the template base into the active site of RNAP in a half-translocated , off-line intermediate ( Figure 8A , B ) . When RNAP encounters an elemental pause signal , a modest shift in the mobile modules of RNAP that are in contact with RNA and DNA ( the clamp , shelf , lid , rudder , switch regions , lobe , protrusion , fork loop 2 , and βDloopII ) occurs during translocation . This shift creates or reinforces an energetic barrier to completion of translocation from a half-translocated state in which the RNA transcript has translocated but the DNA template has not , corresponding to the tilted hybrid intermediate observed in cryo-EM structures ( Kang et al . , 2018a; Guo et al . , 2018; Vos et al . , 2018 ) . Comparison of ePEC and posttranslocated EC cryoEM structures reveals movements that slightly reposition these key mobile modules ( compare green EC to magenta ePEC positions , Figure 8A ) . Key contacts preventing DNA translocation involve the lid , rudder , and switch 2 ( Kang et al . , 2018a; Guo et al . , 2018 ) as well as apparent H-bonds of R542 in fork loop two to the +1dC–dG bp that may hinder its translocation into the active site ( Figures 7A and 8; compare to the conserved Arg in a TthEC , which contacts the backbone phosphate of a template nucleotide loaded into the active site; Vassylyev et al . , 2007 ) . Notably the apparent R542 H-bonding contacts to the +1 dC–dG bp as well as to an unpaired but pretranslocated +1 dC in an initiation complex ( Murakami et al . , 2017 ) are not feasible for other +1 bases , possibly suggesting multiple ways R542 could inhibit template DNA translocation in an ePEC . The fraction of EC that partitions into the ePEC state and the height of the energetic barrier to completion of translocation and pause escape are both functions of the specific sequences present in the elemental pause signal ( i . e . , a suboptimal signal will capture fewer ECs for a shorter overall dwell time ) . DNA and RNA in the ePEC can move backwards to form pretranslocated , frayed , 1 bp backtracked , and , much more slowly , ≥2 bp backtracked states ( Figure 8B ) . At least at the consensus pause , the half-translocated , pretranslocated , and 1 bp backtracked states equilibrate but these equilibria are biased toward the half-translocated state . This bias explains why we observed a fast translocation signal for the RNA:DNA hybrid ( Figure 3 ) and why the half-translocated intermediate forms in reconstituted ePECs analyzed by cryo-EM ( Kang et al . , 2018a; Guo et al . , 2018; Vos et al . , 2018 ) . The fast equilibria explain why GreA rapidly cleaves a 1 bp backtracked ePEC state ( Figure 1H and I; Figure 1—figure supplement 3 ) ; the 1 bp backtracked state quickly repopulates after cleavage even though it is only a small fraction of the total ePEC states . It is possible that GreA shifts the bias toward the 1 bp backtracked state , but mass action ‘pulling’ of ePECs into the 1 bp backtracked state is a sufficient explanation . Because the cleaved ePEC re-encounters a strong pause signal , we observed little effect of GreA cleavage on pause lifetime . Our Gre A/B cleavage data also suggest a much slower entry into ≥2 bp backtracked states , which may be related to the minor but variable slow fraction of ePECs ( see below ) . This multistate model of the elemental pause predicts that the pretranslocated and 1 bp backtracked states can contribute significantly to pause lifetime at some pause sequences despite being in rapid equilibrium with the half-translocated intermediate in which the kinetic barrier to pause escape ( template-base loading ) is manifest . If an ePEC spends only 50% of the time half-translocated and 50% pre-translocated or backtracked , then the pause dwell time will increase by a factor of ~2 relative to an ePEC that spends >95% of the time half-translocated . This is true despite the rapid equilibria because only the half-translocated intermediate can escape the pause and the other states diminish its effective concentration . Shifting to 25% or 10% half-translocated would lengthen the pause by factors of ~3 or~9 , respectively , for the same reason . Thus , an elemental pause signal that favors the 1 bp backtracked state , although different from the consensus signal we studied , would increase pause lifetime . These behaviors have been observed in vitro directly ( Gabizon et al . , 2018 ) or by biasing ePECs using opposing force ( Galburt et al . , 2007; Dangkulwanich et al . , 2013 ) , as well as in vivo ( Imashimizu et al . , 2015 ) . Further , multiple locations of the 1 bp backtracked RNA 3′ nucleotide have been observed or proposed ( Figure 8B; Sekine et al . , 2015; Wang et al . , 2009; Sosunova et al . , 2013; Turtola et al . , 2018 ) . From a mechanistic standpoint , possible entry into multiple 1 bp backtracked states means that their contribution to pause dwell times may be both increased and highly variable as a function of sequence . The multistate elemental pause model makes the distinction between backtrack pausing and non-backtrack pausing a matter of degree rather than a clear-cut division . Our results do not address the important question of whether the half-translocated state also is a significant kinetic intermediate during on-pathway nucleotide addition , although other studies suggest this possibility . Translocation can affect overall elongation rate ( Imashimizu et al . , 2013; Gabizon et al . , 2018 ) , and a half-translocated intermediate has been directly observed during in crystallo nucleotide addition by RNA-dependent RNAP ( Shu and Gong , 2016 ) . On-pathway translocation may proceed via initial RNA translocation then template DNA loading; the second step may be naturally slower at a pause signal , allowing time for formation of the ePEC conformation in which template-base loading becomes strongly inhibited ( Gabizon et al . , 2018 ) . The multistate model ( Figure 8 ) has important regulatory implications . Just as different regulators can exert distinct effects on the multistep mechanism of transcription initiation by stabilizing or destabilizing different intermediates ( Hubin et al . , 2017 ) , the existence of multiple elemental pause states affords multiple targets for regulators even when these intermediates are in equilibrium . For example , ppGpp is known to stimulate pausing ( Kingston et al . , 1981 ) and to promote backtracking ( Kamarthapu et al . , 2016 ) ; ppGpp could stimulate pausing by stabilizing a pretranslocated or backtracked PEC . Further , ≥1 bp backtracking may become more significant for other sequence contexts , conditions , or RNAPs ( e . g . , eukaryotic RNAPII ) . Our results ( Figure 2 ) confirm that , like the hairpin-stabilized hisPEC ( Chan et al . , 1997 ) , the elemental pause signal is multipartite and involves significant contributions from the usFJ , hybrid , dsFJ , and dsDNA . Although strong evidence of significant contributions by the hybrid and dsDNA exists ( Bochkareva et al . , 2012; Larson et al . , 2014; Palangat and Landick , 2001; Palangat et al . , 2004 ) , some descriptions of pausing focus only on the usFJ and dsFJ ( Vvedenskaya et al . , 2014; Imashimizu et al . , 2015 ) . The inhibitory contributions of the usFJ and dsFJ to hybrid unwinding and NTP binding , respectively , have been known since the earliest studies of pausing ( Gilbert et al . , 1974; Aivazashvili et al . , 1981 ) but alone are inadequate to predict pause strength . At least three factors may contribute to underestimation of the importance of hybrid and downstream DNA sequences in pausing . First , the widespread use of sequence logos to represent nucleic acid signals places undue weight on simple , independent interactions and underweights contributions of complex sequence interactions that affect energetics through nucleic-acid conformation or alternative side-chain contacts . Thus , a logo representing information content as single bp can cause complex , multipartite sequences to appear less important ( Figure 2A ) . Although more sophisticated algorithms hold promise to characterize complex , multipartite signals like the elemental pause ( Siebert and Söding , 2016 ) , direct analyses of sequence variants remain the most reliable way to define relevant contributions . Direct mutational analyses establish the key contributions of the hybrid and downstream DNA in the elemental pause signal ( Figure 2; Bochkareva et al . , 2012; Larson et al . , 2014; Palangat and Landick , 2001; Palangat et al . , 2004 ) . Second , using NET-seq to identify a consensus pause signal requires rapid capture of PECs by quick-freezing actively growing cells in liquid N2 ( Churchman and Weissman , 2012; Larson et al . , 2014 ) . Quick-freezing reveals the modest sequence signatures of the hybrid and dsDNA in sequence logos and energetic analyses ( Figure 2A; Larson et al . , 2014 ) . However , some studies that detected little if any hybrid or dsDNA contribution recovered cells by centrifugation before freezing , which may allow RNAP to escape all but the strongest pause sites and thus could over-represent contributions of the usFJ and dsFJ components ( Vvedenskaya et al . , 2014; Imashimizu et al . , 2015 ) . Finally , the hybrid and dsDNA contact multiple RNAP modules ( e . g . , rudder , switch 2 , clamp , etc . ) and may principally affect the complex and modest conformational rearrangement into the ePEC state . Because this conformation is still incompletely understood , the contributions of the hybrid and dsDNA , which may involve specific duplex conformations , may be difficult to characterize . Our findings taken together with earlier demonstrations of usFJ , hybrid , dsFJ , and dsDNA sequences that contribute to pausing should solidify a model of elemental pausing that depends on a multipartite pause signal . The elemental pause is a distinct offline state , not an on-pathway elongation intermediate . Our study uncovered evidence for a modestly rearranged multistate ePEC in which the clamp is loosened and template-base loading is inhibited as well as a slower ePEC state entered by a minor but variable fraction of ECs ( Figures 1 and 2 , and Figure 2—figure supplement 1 ) . The lack of effect of rapid GreA cleavage ruled out 1 bp backtracking as an explanation of the slow ePEC state , but the GreA/B experiments could not definitively rule out ≥2 bp backtracking because the maximal ≥3 nt cleavage rate ( Zhang et al . , 2010; Sosunova et al . , 2013 ) is too close to slow ePEC escape rate . Alternatively or additionally , the slow ePEC state could involve RNAP swiveling , which is observed in the hairpin-stabilized PEC ( Kang et al . , 2018b ) . Swiveling involves a near-rigid body rotation of the clamp , shelf , SI3 , and jaw , and inhibits nucleotide addition by a factor of ~10 by interfering with TL folding . Interestingly , the lifetime of the minor slow ePEC is ~10 times that of the majority ePEC state . Variations in susceptibility to swiveling , ≥2 bp backtracking , or both could explain why the slow fraction varies among RNAP preparations . Further studies will be needed to establish the structure and importance of the slow ePEC fraction , including detection with non-reconstituted transcription complexes or in vivo . The multistate model of elemental pausing involving small changes in RNAP conformation and half-translocated , pretranslocated , and multiple 1 bp backtracked states described here will be difficult to test fully using ensemble biochemistry , single-molecule biochemistry , or crystallography . These methods are encumbered by the perturbing effects of probes and experimental configurations , the number of intermediates involved , and the rapid time-scale of their interchange . Time-resolved cryo-EM ( Frank , 2017 ) promises an attractive approach if methods to distinguish intermediates during particle classification can be developed .
Plasmids and oligonucleotides are listed in Supplementary file 1 . RNA and DNA oligonucleotides were obtained from Integrated DNA Technologies ( IDT; Coralville , IA ) and purified by denaturing polyacrylamide gel electrophoresis ( PAGE ) before use . GreA , GreB , and RNAPs were purified as described previously ( Larson et al . , 2014; Windgassen et al . , 2014 ) . Briefly , His-tagged RNAPs were overexpressed in E . coli BL21 λDE3 and cells were lysed by sonication . RNAPs were enriched by PEI and ammonium sulfate precipitation , then purified by sequential nickel ( 5 mL HisTrap ) and heparin ( 5 mL HiTrap ) column chromatography , dialyzed into storage buffer ( 20 mM Tris-Cl , pH 8 , 250 mM NaCl , 20 μM ZnCl2 , 1 mM MgCl2 , 0 . 1 mM EDTA , 1 mM DTT , and 25% glycerol ) , and stored in small aliquots at –80° C . PAGE-purified 15-mer RNA with 3′ end two nt upstream from the pause site ( 5 µM ) and template DNA ( 10 µM ) were annealed in transcription buffer 1 ( TB1; 20 mM Tris-OAc pH 7 . 7 , 5 mM Mg ( OAc ) 2 , 40 mM KOAc , 1 mM DTT; sequences of RNAs and DNAs are in Supplementary file 1 ) . Scaffolds were incubated with RNAP for 15 min at 37°C in TB1 , then non-template DNA was added and incubation continued for 15 min at 37°C . The ratio of RNA:tDNA:RNAP:ntDNA was 1:2:3:5 ( 0 . 5 µM , 1 µM , 1 . 5 µM , 2 . 5 µM , respectively ) . ECs were diluted to 0 . 1 µM with TB1 + heparin ( 0 . 1 mg/ml ) , incubated for 3 min at 37°C , labeled by the incorporation of [α-32P]GMP at 10 µM total GTP for 1 min at 37°C , and then placed on ice for 30–60 min . ECs were incubated for 3 min at 37°C before initiating the pause assay by addition of CTP to 100 µM and GTP to 10 or 100 µM in TB1 at 37°C . Reaction samples were removed at various time points and quenched with an equal volume of 2X stop buffer ( 8 M urea , 50 mM EDTA , 90 mM Tris-borate buffer , pH 8 . 3 , 0 . 02% each bromophenol blue and xylene cyanol ) . All remaining active ECs were chased to product by incubation with GTP at 1 mM for 1 min at 37°C . RNAs in each quenched reaction sample were separated by PAGE ( 15%; 19:1 acrylamide:bis-acrylamide ) in 44 mM Tris-borate , pH 8 . 3 , 1 . 25 mM Na2EDTA , 8 M urea . The gel was exposed to a PhosphorImager screen , and the screen was scanned using Typhoon PhosphorImager software and quantified in ImageQuant . The averaged fraction of RNA at the position of the pause over time was fit to single- or double-exponential decay functions in KaleidaGraph to estimate pause efficiencies ( amplitudes ) and rate constants of pause escape . All pause kinetic parameters were determined from replicate ( n ≥ 3 ) assays using error-weighted ( SD ) fits . For experiments comparing RNAPs , wild-type and variant RNAPs were purified side-by-side to avoid variable effects of different RNAP preparations on pausing kinetics . To measure rates of C17 and G18 addition , G16 ECs were formed essentially as described for the pause transcription assay , but with 5′-[32P]RNA limiting such that RNA:tDNA:RNAP:ntDNA was 1:1 . 3:2:3 . 3 . To obtain nucleotide addition rates using a quench-flow apparatus ( RQF-3; KinTek Corporation , Snow Shoe , PA ) , 400 nM G16 ECs were injected in one sample loop and 200 µM each CTP and GTP in TB1 , in the other sample loop . Reactions were performed at 37°C for the designated times and quenched with 2 M HCl , then neutralized immediately to pH 7 . 8 with 3 M Tris base ( supplemented with 250 µg torula yeast RNA/mL ) . RNA products were purified by phenol:chloroform extraction followed by ethanol precipitation , and resuspended in 1X stop buffer to a constant specific activity . RNA products from all timepoints were resolved by denaturing PAGE and quantified as described for pause transcription assays . Reaction progress curves were generated for each RNA length ( G16 , C17+ , and G18+ ) using KaleidaGraph ( Synergy Software ) by calculating the fraction of total RNA for each condition as a function of time . C17 and all RNAs longer than C17 were combined to give the C17+ fraction; G18 and all RNAs longer than G18 were combined to give the G18+ fraction . The averaged fraction at each time-point was then fit to a single-exponential equation . To test whether the elemental pause is on online or offline state ( i . e . , involves a linear or branched kinetic mechanism; Figure 1—figure supplement 1F , G ) and to test whether the slow fraction of ePECs was evident at only the first or at both pause sites on the tandem pause scaffold ( Figure 1—figure supplement 2 ) , we used kinetic modeling by numerical integration of pre-steady state rate equations using the program KinTek Explorer v6 . 1 ( KinTek Corp . , Snow Shoe , PA; Johnson et al . , 2009 ) . In both cases , to test whether the simpler mechanism was adequate to explain the data , we used replicate datasets ( triplicate or greater ) to generate a kinetic model for the rates of arrival at the pause site using the rate at which all RNAs before the pause site converted to RNAs at the pause site and beyond . We then held these rates constant and tested the simple kinetic models ( linear , online pause ( Figure 1—figure supplement 1F , G ) or two populations of RNAP , fast and slow pausing ( Figure 1—figure supplement 2D , E ) , including error for replicates to obtain the best fit and the residuals between the best fit and the observed data . We concluded that the more complex models ( branched for Figure 1—figure supplement 1F , G or dynamic formation of the slow pause species for Figure 1—figure supplement 2D , E ) were favored because the residuals for the simpler mechanism exhibited large , systematic variations whereas the more complex mechanisms exhibited smaller , random variations . We did not attempt to determine which mechanism best fit the data , and limited our conclusion to rejection of the simpler mechanism . For the dataset using the template from Bochkareva et al . ( 2012 ) ( Figure 1—figure supplement 1G ) , for which we had six replicates , we determined error in the fits and residuals by individually fitting each dataset and calculating the average and error for the six fits . GreA- and GreB-stimulated cleavage and effects on pausing were assayed essentially as described earlier ( Larson et al . , 2014 ) . Briefly , for pause assays ( Figure 1H and Figure 1—figure supplement 3B , C ) , [α-32P]GTP ( 10 µM ) was maintained at constant specific activity throughout the labeling and pause assay GreA , GreB , or both ( 1 µM each , final ) were added concurrently with the CTP and UTP ( 100 µM each , final ) . The assays were performed in triplicate and analyzed as described above . To assay the rate of cleavage product accumulation ( Figure 1I and Figure 1—figure supplement 3B , D ) , accumulating small RNAs were quantified from short phosphorimager screen exposures to avoid saturating the signal . Intrinsic cleavage was assayed essentially as described earlier ( Mishanina et al . , 2017 ) . Briefly , 3′-end labeled C17 ePECs ( #9563 NT DNA , #8334 T DNA , #8401 RNA; Supplementary file 1 ) were formed and immobilized on Ni2+-NTA beads , then washed to remove unincorporated [α-32P]CTP . Cleavage was initiated at 37°C by resuspending washed ePECs with Cleavage Buffer ( CB; 25 mM Tris·HCl pH 9 . 0 , 50 mM KCl , 20 mM MgCl2 , 1 mM DTT , 5% glycerol , and 25 μg acetylated BSA/mL ) , and samples were collected at designated timepoints by mixing with 2X stop buffer . Cleavage products were separated by denaturing PAGE as described for transcription pause assays . To measure translocation rates of the hybrid ( Figure 3 ) , we used the assay developed by Belogurov and co-workers ( Malinen et al . , 2012; Malinen et al . , 2014 ) . PAGE-purified template DNA and RNA were annealed in TB1 ( see Supplementary file 1 ) . This scaffold was incubated with RNAP for 15 min at 37°C in TB , then non-template DNA was added such that the final ratio of tDNA:RNA:RNAP:ntDNA was 1:2:3:5 ( 2 µM: 4 µM: 6 µM: 10 µM , respectively ) . This solution was diluted to 0 . 4 µM RNAP with TB1 . ECs were then injected into one loading syringe of a stopped-flow apparatus ( SF-300X; KinTek Corporation , Snow Shoe , PA ) and 200 µM CTP in TB1 was loaded in the other syringe . Upon initiating rapid mixing at 37°C , 6-MI fluorescence was excited at 340 nm ( 2 . 4 nm bandwidth ) , and emission was monitored in real time through a 400 nm long-pass filter ( Edmund Optics Inc . , Barrington , NJ ) . The kinetics of 6-MI fluorescence unquenching was determined by fitting the average fluorescence ( n ≥ 6 traces ) , normalized from 0 to 1 , to a double exponential Equation 1: ( 1 ) Ft=A ( 1−e−k1 , obst ) +B ( 1−e−k2 , obst ) where , t = time ( s ) ; A = fast kinetic species signal amplitude; B = slow kinetic species signal amplitude; k1 , obs = observed rate of fluorescence increase for the fast kinetic species; k2 , obs = observed rate of fluorescence increase for the slow kinetic species . The fast species rate is reported as the translocation rate ( Malinen et al . , 2012 ) . To measure equilibrium translocation of downstream DNA ( Figure 4 ) , G16 ECs were formed in TB1 as described for the quench-flow experiment but without Mg ( OAc ) 2 to stabilize ECs and with the fluorescent ntDNA as the limiting component ( ntDNA:tDNA:RNA:RNAP = 1:1 . 5:2:3 with ntDNA at 300 nM ) . For equilibrium measurements of hybrid translocation ( Figure 3 ) , G16 ECs were formed similarly but with the tDNA as the limiting component ( see description in stopped-flow assay section ) . Fluorescence measurements were conducted using a PTI-spectrofluorometer ( Model QM-4/2003 , Photon Technology International ) with 5 mM path length and 45 uL quartz cuvettes ( Hellma Analytics , Müllheim , Germany ) . Emission spectra were obtained by exciting at 340 nm ( 5 nm bandwidth ) and monitoring fluorescence between 360 and 500 nm ( 5 nm bandwidth ) . For each substrate addition experiment , 60 µL EC was added to the cuvette and incubated for 2 min at 37°C in the cuvette holder before performing an emission scan ( average of 3 traces at 0 . 25 nm step size ) . Then , NTPs were added ( concurrently with 5 mM Mg ( OAc ) 2 plus additional Mg ( OAc ) 2 equal to the NTP concentration in the assay ) and the incubation was continued for 1 min ( 5 min for 2′dCTP ) at 37°C before performing an emission scan . Between fluorescence measurements , an aliquot was removed to 2X stop buffer to subjected to denaturing PAGE to confirm nucleotide addition . For the GTP titration condition , aliquots were only removed to confirm initial G16 RNA , 3′dC addition , and failure of 20 mM GTP to incorporate following 3′dC addition . 6-MI fluorescence was quantified at 425 nm . Background fluorescence from a GTP contaminant was subtracted using signal from GTP alone in buffer . We found that the level of fluorescence from the contaminant varied significantly among vendors and GTP lot , with GTP from GE Healthcare containing the least amount . Reported increases in fluorescence are fold changes relative to the initial signal in G16 EC . CPR crosslinking assays ( Figure 5 ) were performed as described previously ( Nayak et al . , 2013 ) . Nucleic acid scaffolds were prepared by annealing RNA , template DNA ( T-DNA ) and 15 μM non-template DNA ( NT-DNA ) at 10 µM , 12 µM , and 15 µM final concentrations , respectively , in reconstitution buffer ( RB; 20 mM Tris-HCl pH 8 , 20 mM NaCl , and 1 mM EDTA ) . ECs , ePECs , and his PEC were formed by incubating 1 μM RNAP and scaffold ( 2 μM , based on RNA ) in buffer A ( 50 mM Tris-HCl pH 8 , 20 mM NaCl , 10 mM MgCl2 , 1 mM EDTA , and 2 . 5 ug acetylated bovine serum albumin/mL for 15 min at room temperature ( RT ) . For crosslinking reactions with NTP , 3′deoxyECs formed by reaction with 3′dNTP were incubated for 15 min at RT with 0 , 0 . 005 , 0 . 01 , 0 . 025 , 0 . 05 , 0 . 1 , 0 . 25 , 0 . 5 , 1 , 2 . 5 , 5 , and 10 mM GTP or ATP . Next , EC , ePEC , or his-PEC ( final RNAP 0 . 8 uM and scaffold 1 . 6 uM ) were incubated for 60 min with 2 . 5 mM CSSC and 0 . 05 mM DTT ( E = –0 . 16 ) and stopped with 50 mM iodoacetamide . Samples were separated by native PAGE to verify reconstitution efficiency and by sodium dodecyl sulfate ( SDS ) -PAGE using 4–15% GE Healthcare PhastGel to quantify formation of crosslinks . Gels were stained with Coomassie Blue and imaged with a CCD camera . The fraction cross-linked was quantified with ImageJ software . For crosslinked CPR transcription assays , nucleic-acid scaffolds containing RNA and template DNA ( 1:2 ratio of RNA to DNA ) were used to reconstitute ePECs or control hisPECs ( Figure 6C and D ) as described in Kang et al . , 2018a . The U15 ePECs containing limiting CPR RNAP ( 1 μM ) were reconstituted on 2 µM scaffold ( based on RNA ) for 15 min at 37°C in Elongation Buffer ( EB; 25 mM HEPES-KOH , pH 8 . 0 , 130 mM KCl , 5 mM MgCl2 , 1 mM DTT , 0 . 15 mM EDTA , 5% glycerol , and 25 μg acetylated bovine serum albumin/mL ) , followed by addition of 6 μM non-template DNA and further incubation for 10 min at 37°C to complete assembly of the transcription complexes . Wild-type RNAP was tested as a control side-by-side with CPR RNAPs . Crosslinking of 1 μM ePECs was performed in the presence of 1 mM cystamine as the oxidant and 0 . 8 mM DTT , for 15 min at 37°C . An aliquot of the crosslinking reaction was quenched with 15 mM iodoacetamide ( final concentration ) and analyzed by non-reducing SDS-PAGE to confirm formation of the crosslink . The crosslinked U15 ePECs were diluted to 0 . 2 μM with EB ( without DTT , for crosslinked samples ) and incubated with heparin ( 0 . 1 mg/mL final ) for 3 min at 37°C . The U15 ePECs were then radiolabeled by extension with 20 μM [α-32P]GTP for 1 min at 37°C , to poise the complexes one nucleotide before the pause sequence . The resulting G16 ePECs were further diluted to 0 . 1 μM ( based on RNAP ) and assayed at 37°C for pause-escape kinetics at 10 µM GTP by addition of CTP in EB to 100 µM ( without DTT , for crosslinked samples ) . Reaction samples were removed at various time points and quenched with an equal volume of 2X stop buffer . All active ePECs were chased out of the pause with 500 μM GTP and CTP , each , for 5 min at 37°C . RNAs in each quenched reaction sample were separated on a 15% urea-PAGE gel . Gels were visualized and quantified as described for in vitro transcription assays . For Cys triplet reporter ( CTR ) cross-linking assays ( Figure 6B , E and F ) , ECs and PECs were assembled on purified DNA and RNA scaffolds specified in the figure legend and as described previously ( Kang et al . , 2018a ) . Briefly , 10 μM RNA , 12 μM template DNA , and 15 μM non-template DNA ( Supplementary file 1 ) were annealed in RB . To assemble complexes , scaffold ( 2 μM ) was mixed with limiting CTR RNAP ( 1 μM; CTR RNAP: β′1045iC 258iC , β843C ) in 50 mM Tris-HCl , pH 7 . 9 , 20 mM NaCl , 10 mM MgCl2 , 0 . 1 mM EDTA , 5% glycerol , and 2 . 5 μg of acetylated bovine serum albumin/mL , and added to mixtures of cystamine and DTT to generate redox potentials that ranged from −0 . 314 to −0 . 424 . Complexes were incubated for 60 min at room temperature and then were quenched with the addition of iodoacetamide to 15 mM . The formation of cysteine-pair cross-links was then evaluated by non-reducing SDS-PAGE ( 4%–15% gradient Phastgel; GE Healthcare ) as described previously ( Nayak et al . , 2013 ) . Gels were stained with Coomassie Blue and imaged with a CCD camera . The fraction cross-linked was quantified with ImageJ software . The experimental error was determined as the standard deviation of measurements from three or more independent replicates . | The information a cell needs to create a specific protein is encoded in a sequence of precisely organized DNA ‘letters’ . Unlocking these instructions requires an enzyme known as RNA polymerase ( RNAP for short ) , which reads the DNA segment and faithfully copies the information to form a strand of RNA . This molecule then relays the genetic message to the machinery that pieces together a protein . An RNAP works by reading a DNA segment and building a matching RNA strand at the same time . The enzyme clamps onto DNA , and threads it letter-by-letter through its reading and building site . For each DNA letter that RNAP reads , the enzyme adds a matching RNA building block onto the budding RNA strand , with DNA and RNA segments then being moved away from the active site . However , RNAP does not usually read a whole gene in one go: there are several ‘pause sites’ in the sequence where it stops and waits for instruction . If the cell needs this protein immediately , it sends signals that encourage RNAP to carry on and even ignore further pause sites; if the protein is not needed at the time , the enzyme is instructed to terminate the RNA-making process . This mechanism is present in species across the tree of life , and is key so that a cell fine-tunes its protein production . Once RNAP has stopped , several well-studied mechanisms kick in to stabilize the enzyme in its waiting position . Yet , it is still unclear how the enzyme , which normally reads 50 to 100 DNA letters per second , is able to come to a halt in the first place . To dissect this mechanism , Saba et al . made targeted changes to RNAP or to the DNA segment it was reading , and then closely monitored the movement of the protein under these conditions . The experiments revealed that when RNAP interacts with multiple signals in the DNA , such as particular sequences just before or inside the segment being read , the enzyme changes its structure slightly , and loosens its grip on DNA and RNA . With the enzyme’s new shape , the RNA strand is ready to be extended , but the DNA segment is trapped and cannot move into the reading site . This prevents a new RNA letter to be added onto the growing strand , stopping RNAP in its tracks . Knowing how RNAP pauses may help researchers to understand how its activity is regulated , for example by antibiotics . Ultimately , this could allow us to manipulate the activity of the enzyme so that we could control how and when a cell creates specific proteins . | [
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] | 2019 | The elemental mechanism of transcriptional pausing |
The vertebrate eye originates from the eye field , a domain of cells specified by a small number of transcription factors . In this study , we show that Tcf7l1a is one such transcription factor that acts cell-autonomously to specify the eye field in zebrafish . Despite the much-reduced eye field in tcf7l1a mutants , these fish develop normal eyes revealing a striking ability of the eye to recover from a severe early phenotype . This robustness is not mediated through genetic compensation at neural plate stage; instead , the smaller optic vesicle of tcf7l1a mutants shows delayed neurogenesis and continues to grow until it achieves approximately normal size . Although the developing eye is robust to the lack of Tcf7l1a function , it is sensitised to the effects of additional mutations . In support of this , a forward genetic screen identified mutations in hesx1 , cct5 and gdf6a , which give synthetically enhanced eye specification or growth phenotypes when in combination with the tcf7l1a mutation .
The paired optic vesicles originate from the eye field , a single , coherent group of cells located in the anterior neural plate ( Cavodeassi , 2018 ) . During early neural development , the specification and relative sizes of prospective forebrain territories , including the eye field , depend on the activity of the Wnt/β-Catenin and other signalling pathways ( Beccari et al . , 2013; Cavodeassi , 2014; Wilson and Houart , 2004 ) . Enhanced Wnt/β-Catenin activity leads to embryos with small or no eyes ( Cavodeassi et al . , 2005; Kim et al . , 2000; Heisenberg et al . , 2001; Houart et al . , 2002 ) . In contrast , decreasing activity of Wnt/β-Catenin signalling generates embryos with bigger forebrain and eyes ( Cavodeassi et al . , 2005; Glinka et al . , 1998; Lekven et al . , 2001; Houart et al . , 2002 ) . Although much research has focused on the molecular mechanisms involved in the specification of the eye field , little is known about what happens to the eyes if eye field size is disrupted . A number of genes have been identified as encoding a transcription factor network that specifies the eye field ( Beccari et al . , 2013; Zuber et al . , 2003 ) . These genes have been defined based on conserved cross species expression patterns in the anterior neuroectoderm and on phenotypes observed when overexpressed or when function is compromised ( Beccari et al . , 2013 ) . Perhaps surprisingly , to date , there are relatively few mutations that lead to complete loss of eyes suggesting that early stages of eye development are robust to compromised function of genes involved in eye development . Indeed , in humans , eye phenotypes are often highly variable in terms of penetrance and expressivity even between left and right eyes ( Reis and Semina , 2015; Williamson and FitzPatrick , 2014 ) . This again raises the possibility that the developing eye is robust and can sometimes cope with mutations in genes involved in eye formation . Genetic robustness is the capacity of organisms to withstand mutations , such that they show little or no phenotype , or compromised viability ( Félix and Barkoulas , 2015; Wagner , 2005 ) . This inherent property of biological systems is wired in the genetic and proteomic interactomes and enhances the chance of viability of individuals in the face of mutations . High-throughput reverse mutagenesis projects and the emergence of CRISPR/Cas9 gene editing techniques have highlighted the fact that homozygous loss of function mutations in many genes generate viable mutants with no overt phenotype ( Varshney et al . , 2015; Dickinson et al . , 2016; Meehan et al . , 2017 ) . Across phyla , mutations in single genes are more likely to give rise to viable organisms than to show overt or lethal phenotypes . For instance , it is estimated that zygotic homozygous null mutations in just ~7% of zebrafish genes compromise viability before 5 days post-fertilisation ( Kettleborough et al . , 2013 ) and 8–10% between day 5 and 3 months ( Shawn Burgess , personal communication ) ; and compromised viability is predicted following loss of function for about 35% of mice genes ( Dickinson et al . , 2016; Meehan et al . , 2017 ) . Furthermore , apparently healthy viable homozygous or compound heterozygous ‘gene knockouts’ have been found for 1171 genes in the Icelandic human population ( Sulem et al . , 2015 ) and for 1317 genes in the Pakistani population ( Saleheen et al . , 2017 ) . In some cases , the lack of overt phenotype may be due to redundancy in gene function based on functional compensation by paralogous or related genes ( Barshir et al . , 2018; Hurles , 2004; Wagner , 1996 ) . We can assume that genes that do not express a phenotype when mutated are not lost to genetic drift because in some way they enhance the fitness of the species . For instance , even though two paralogous Lefty genes encoding Nodal signalling feedback effectors have been shown to be individually dispensable for survival , they do make embryonic development robust to signalling noise and perturbation ( Rogers et al . , 2017 ) . Genetic compensation for deleterious mutations is a cross-species feature ( El-Brolosy and Stainier , 2017 ) , and mRNAs that undergo nonsense-mediated decay due to mutations that lead to premature termination codons can upregulate the expression of paralogous and other related genes ( El-Brolosy et al . , 2018 ) . However , only a fraction of genes have paralogues and other compensatory mechanisms must contribute to the ability of the embryo to cope with potentially deleterious mutations . One such mechanism is distributed robustness , which can emerge in gene regulatory networks ( Wagner , 2005 ) . This kind of robustness relies on the ability of the network to regulate the expression of genes and/or the activity of proteins within the network , such that homeostasis is preserved when one of its components is compromised ( Davidson , 2010; Peter and Davidson , 2016 ) . Maternal-zygotic tcf7l1a mutant zebrafish have been previously described as lacking eyes ( Kim et al . , 2000 ) . In this study , we show that expression of this phenotype is dependent on the genetic background . We find that tcf7l1a mutants can develop functional eyes and are viable , and that this is not due to compensatory upregulation of other lef/tcf genes . Despite the presence of functional eyes , the eye field in tcf7l1a mutants is only half the size of the eye field of wildtype embryos , indicating an early requirement for tcf7l1a during eye field specification . We further show that this requirement is cell autonomous , revealing a striking dissociation between the early role and requirement for Tcf7l1a in eye field specification and the later absence of an overt eye phenotype . Subsequent to compromised eye field specification , tcf7l1a mutant eyes recover their size by delaying neurogenesis and prolonging growth in comparison to wildtype eyes . This compensatory ability of the developing eye was also observed when cells were removed from wild-type optic vesicles . Altogether , our study suggests that the loss of Tcf7l1a does not trigger any genetic compensation or signalling pathway changes that restore eye field specification; instead , the developing optic vesicle shows a remarkable ability to subsequently modulate its development to compensate for the early , severe loss of eye field progenitors . The penetrance and expressivity of eye phenotypes appears to be dependent on complex genetic and environmental interactions ( Gestri et al . , 2009; Kaukonen et al . , 2018; Prokudin et al . , 2014 ) . Thus , we speculated that tcf7l1a mutant eyes may be sensitised to the effects of additional mutations . Here , we show this is indeed the case and describe the isolation of three mutations from a recessive synthetic modifier screen in tcf7l1a homozygous mutant zebrafish that lead to enhanced/novel eye phenotypes when in combination with loss of tcf7l1a function . In summary , our work shows that zebrafish eye development is robust to the effects of a mutation in tcf7l1a due to compensatory growth mechanisms that may link eye size and neurogenesis . Our study adds to a growing body of research revealing a variety of mechanisms by which the developing embryo can cope with the effects of deleterious genetic mutations .
The headless ( hdl ) m881 mutation in tcf7l1a ( tcf7l1a-/- from here onwards ) was identified because embryos lacking maternal and zygotic ( MZ ) gene function lacked eyes ( Kim et al . , 2000 ) . However , no overt defects were observed in zygotic ( Z ) tcf7l1a-/- mutants , due to functional redundancy with the paralogous tcf7l1b gene ( Dorsky et al . , 2003 ) . In our facility , MZtcf7l1a-/- embryos initially showed a variable eye phenotype , ranging from eyeless , to small and overtly normal eyes , with proportions that varied in clutches from different pairs of fish ( not shown ) . We hypothesised that genetic background effects could be responsible for either enhancing or suppressing the eyeless phenotype . To test this idea , we outcrossed tcf7l1a-/- fish to ekkwill ( EKW ) or AB wildtype fish and identified tcf7l1+/- carriers by PCR genotyping . After three generations of outcrossing to EKW or AB fish , we incrossed tcf7l1+/- carriers to grow Ztcf7l1a-/- adults . All MZtcf7l1a-/- embryos coming from six pairings of Ztcf7l1a-/- mutant fish developed eyes only slightly reduced in size compared to eyes of wildtype embryos of the same EKW or AB strain ( 100% , n > 100; Figure 1A , B ) . The tcf7l1am881 mutation creates a splice acceptor site in intron 7 , which leads to a seven nucleotide insertion in tcf7l1a mRNA that gives rise to a truncated protein due to a premature termination codon ( Kim et al . , 2000 ) . Given that the wildtype splice site in intron seven is still present in tcf7l1a mutants , we assessed whether the lack of phenotype in MZtcf7l1a-/- mutants could be due to incomplete molecular penetrance as a result of expression of mRNA from both wildtype and mutant splice sites . The chromatogram sequence of the RT-PCR product amplifying exons 7 and 8 in wildtype , mutant and heterozygous embryos showed that only wildtype tcf7l1a mRNA was detected in wildtype embryos and only mutant mRNA containing the seven nucleotide insertion was observed in mutants , while heterozygous embryos produced both wildtype and mutant mRNAs ( Figure 1—figure supplement 1; Kim et al . , 2000 ) . This suggests that the mutant splice site is the only one used in tcf7l1a-/- embryos . In addition , while overexpression of wildtype tcf7l1a mRNA rescued eye formation in embryos in which both tcf7l1a and tcf7l1b are knocked down , tcf7l1am881 mutant mRNA did not , confirming that protein arising from the tcf7l1am881 allele is not functional ( not shown; Kim et al . , 2000 ) . These observations suggest that the m881 allele is indeed a null mutation and that tcf7l1a is not essential for eye formation . Supporting a requirement for tcf7l1a to form eyes , antisense morpholino knockdown of tcf7l1a ( mo1tcf7l1a ) leads to eyeless embryos ( Dorsky et al . , 2003 ) comparable to the originally described headless MZtcf7l1a-/- mutant phenotype ( Kim et al . , 2000 ) . However , the target site for the morpholino used in that study shows considerable sequence homology to the translation start ATG region of other tcf gene family members ( 56–76%; Figure 1—figure supplement 2A ) . This suggests that the mo1tcf7l1a phenotype may be due to the morpholino knocking down expression of other tcf genes , as has been described for other morpholinos targeting paralogous genes ( Kamachi et al . , 2008 ) . Indeed , injection of a different tcf7l1a morpholino ( mo2tcf7l1a ) with low homology to other tcf genes ( 36–45% , Figure 1—figure supplement 2B ) did not lead to an eyeless phenotype ( 0 . 4 pMol/embryo , 100% , n > 100; Figure 1C , D ) . tcf7l1b morpholino injection on its own showed no overt phenotype ( Dorsky et al . , 2003 ) but co-injection of mo2tcf7l1a and motcf7l1b gave rise to eyeless embryos ( each at 0 . 2 pMol/embryo , 78 . 26% , n = 92 , over three experiments; Figure 1E and see Dorsky et al . , 2003 ) . This suggests that even though mo2tcf7l1a injection alone resulted in no phenotype , the morpholino does knockdown tcf7l1a . Together , these results suggest that even though tcf7l1a-/- is a fully penetrant null mutation , lack of maternal and zygotic tcf7l1a function alone does not lead to loss of eyes in all genetic backgrounds . Ztcf7l1a-/- and MZtcf7l1a-/- embryos develop eyes , whereas embryos lacking both Ztcf7l1a and Ztcf7l1b do not ( Dorsky et al . , 2003 ) . Thus , we hypothesised that enhanced expression of the paralogous tcf7l1b , or other lef/tcf genes may compensate for the absence of tcf7l1a function , as shown for other mutations ( El-Brolosy et al . , 2018; Rossi et al . , 2015 ) . To test this idea , we assessed the expression of all lef/tcf genes by RT-qPCR in sibling wildtype and Ztcf7l1a-/- mutant embryos at the stage when the eye field has been specified ( 10 hr post-fertilisation; hpf ) . Expression levels of lef/tcf genes did not increase in Ztcf7l1a-/- mutant embryos suggesting that there is no compensatory upregulation ( Figure 2A , Supplementary file 1A ) . As previously shown , tcf7l1a undergoes nonsense-mediated decay in mutants resulting in reduced expression levels ( Kim et al . , 2000; Figure 2A; Supplementary file 1A ) . lef1 and tcf7 levels did not change significantly in mutants and tcf7l1b ( tcf3b ) and tcf7l2 ( tcf4 ) expression was actually reduced to 63 ± 6% and 62 ± 8% respectively of wildtype levels ( Figure 2A; Supplementary file 1A ) . The otx1b and otx2 genes , which are expressed in the anterior neural plate , also showed slightly reduced expression ( otx1b , reduced to 81 ± 11% and otx2 , 79 ± 10% ) suggesting that the anterior neural plate may be slightly reduced in size in tcf7l1a mutants . Indeed , the domain of the neural plate encompassed by expression of emx3 around the anterior margin of the neural plate up to the mesencephalic marker pax2a ( Figure 2D , E ) was reduced to 76% of wildtype size in tcf7l1a mutants ( n = 11 , p=0 . 0041 , Figure 2B; Supplementary file 1B ) . This indicates that a reduction in the size of the prospective forebrain of Ztcf7l1a-/- embryos may contribute to the reduced levels of expression of tcf7l1b , tcf7l2 and otx genes . Overall , these results suggest that tcf genes do not show compensatory regulation in response to loss of tcf7l1a function . More remarkable than the modest changes in tcf and otx gene expression was the finding that RT-qPCR showed very reduced expression of eye field genes in Ztcf7l1a-/- mutant embryos ( Figure 2A; rx3 reduced to 26 ± 1% , p=0 . 0002 and six3b reduced to 44 ± 5% , p=0 . 0091 of wildtype levels ) . Consequently , the presence of overtly normal looking eyes in both Ztcf7l1a-/- and MZtcf7l1a-/- embryos is surprising given that rx3-/- mutant embryos lack eyes due to impaired specification/evagination of the optic vesicles ( Loosli et al . , 2003; Stigloher et al . , 2006 ) . We confirmed that expression of six3b and rx3 is reduced in the anterior neural plate by in situ hybridisation in Ztcf7l1a-/- and tcf7l1a morphant embryos ( 100% , n > 40; Figure 2F–I; Figure 2—figure supplement 1A , B; similar changes seen in MZtcf7l1a-/- mutants , data not shown ) . The expression of six3b was reduced in the eye field but not in the prechordal plate of Ztcf7l1a-/- mutants , likely explaining why RT-qPCR showed a greater reduction in rx3 than six3b expression ( Figure 2F–I; Supplementary file 1A ) . Analysis of eye field volume by fluorescent in situ hybridisation of rx3 revealed a reduction to 54 . 7% of wildtype size ( n = 10 , Figure 2C , J–M; Supplementary file 1C ) and intensity of expression within the reduced eye field also appeared reduced ( Figure 2H , I ) . Further in situ hybridasation analysis suggests that it is the caudal region of the eye field that is most affected in Ztcf7l1a-/- mutants . emx3 expression directly rostral to the eye field was slightly broader in Ztcf7l1a-/- mutants than wildtypes but expression did not encroach into the reduced eye field ( Figure 2D , E; Figure 2—figure supplement 2A , B , n = 5 each condition ) . Conversely , expression of the prospective diencephalic marker barhl2 caudal to the reduced eye field was expanded rostrally at 10hpf ( Figure 2—figure supplement 2C , D n = 5 each condition ) and even more evidently at 9hpf ( Figure 2—figure supplement 2E , F , 13/13 Ztcf7l1a-/- ) . These observations suggest a caudalisation of the anterior neural plate in Ztcf7l1a-/- mutants leading to reduced eye field specification consistent with phenotypes observed in conditions in which Wnt pathway repression is reduced ( Heisenberg et al . , 2001; van de Water et al . , 2001 ) . RNAseq analysis of wildtype , Ztcf7l1a-/- and Ztcf7l1a-/-/Ztcf7l1b+/- embryos at 8 . 5hpf ( 80–90% epiboly stage ) , when the eye field is first specified confirmed and extended RT-qPCR and in situ hybridisation analyses ( Supplementary file 1D ) . In Ztcf7l1a-/- , hesx1 ( Kazanskaya et al . , 1997 ) , rx3 , tcf7l1a , and fezf2 ( Sun et al . , 2006 ) which are expressed in the prospective forebrain/eyefield were downregulated , whereas her5 and irx1b which are expressed more caudally in the neural plate ( Müller et al . , 1996 , Wang et al . , 2001b ) were upregulated consistent with mild caudalisation of the neural plate . Additionally , in Ztcf7l1a-/-/tcf7l1b+/- embryos , which to not form eyes , the expression of tcf7l1b was enhanced by about 40% , suggesting transcriptional compensation but evidently this was not sufficient to rescue eye formation . Despite the small size of eye field in tcf7l1a-/- mutants , optic vesicles appear to evaginate normally . Time lapse analysis of optic vesicle evagination using the Tg ( rx3:GFP ) zf460Tg transgene to visualise eye field cells ( Brown et al . , 2010 ) showed that optic vesicle morphogenesis in Ztcf7l1a-/- embryos proceeded as in heterozygous sibling embryos ( Figure 2—figure supplement 3A , B; tcf7l1a+/- , n = 6 and Ztcf7l1a-/- n = 6; Video 1 and Video 2 ) . Although Tcfs regulate the balance between activation and repression of the Wnt/βCatenin pathway during anterior neural plate regionalisation ( Kim et al . , 2000; Dorsky et al . , 2003 ) , it is unclear if Tcf function is required for cells to adopt the eye field fate . To address this , we determined whether Tcf7l1a function is required cell-autonomously during eye formation by transplanting wildtype and MZtcf7l1a-/- GFP labelled ( GFP+ ) cells into wildtype and tcf7l1a mutant hosts and analysing the expression of rx3 when eye specification has occurred ( 10hpf , 100% epiboly; Figure 3 ) . Transplants of wildtype cells to MZtcf7l1a-/- mutant embryos led to the recovery of rx3 expression exclusively restricted to the wildtype GFP+ cell clones ( 13/13 transplants , Figure 3A–C ) . However , the border of the GFP+ wildtype clones showed less rx3 expression , suggesting that cells at the edge of the graft are subject to cell non-autonomous signalling effects from cells surrounding the clone . Conversely , MZtcf7l1a-/- mutant GFP+ cells expressed much lower levels of rx3 than wildtype neighbours when positioned in the eye field of wildtype embryos ( 9/9 transplants , Figure 3D–F ) . The reduction in rx3 expression was limited to the MZtcf7l1a-/- GFP+ mutant cells . Control transplants of cells from wildtype donor embryos to wildtype hosts showed no effect on rx3 expression ( not shown ) . Consistent with a cell autonomous role for Tcf7l1a in eye formation , overexpression of the Wnt inhibitor Dkk1 ( Hashimoto et al . , 2000 ) expanded the anterior neural plate in both wildtype and tcf7l1a mutants , but rx3 expression and eye field size remained much smaller in the enlarged anterior plate of tcf7l1a mutants ( Figure 3G–J ) . All together , these results support a cell-autonomous role for Tcf7l1a in promoting eye field specification . Despite a much-reduced eye field , eyes in Ztcf7l1a-/- fry and adults seem indistinguishable from those in wildtype siblings . Indeed , optokinetic responses of Ztcf7l1a-/- and wildtype 5dpf larvae showed no significant differences at any of the four tested spatial frequencies ( Figure 4—figure supplement 1 , Supplementary file 1E ) , suggesting that by this stage , Ztcf7l1a-/- eyes are functional and have visual acuity comparable to that of wildtype siblings . Consequently , although Ztcf7l1a-/- embryos show a robust and severe neural plate patterning phenotype , eye formation recovers over time . To explore how this recovery happens , we measured eye size in Ztcf7l1a-/- embryos from 24 to 96hpf ( Figure 4A , C–L; Supplementary file 1F ) , estimating eye volumes from retinal profiles ( see Materials and methods ) . At 24hpf , eye volume in mutants was about 57% of the estimated volume of wildtype eyes at the same stage ( Figure 4A , C , H; Supplementary file 1F ) . However , by 48hpf mutant eyes were about 85% of the size of eyes in wildtype/heterozygous siblings ( Figure 4A , G , L; Supplementary file 1F ) . Ztcf7l1a-/- eye size did not further recover beyond this time point and up to 5dpf ( Figure 4B ) . Eye growth in both wildtypes and Ztcf7l1a-/- mutants showed similar growth kinetics ( Figure 4A ) . This suggests that even though Ztcf7l1a-/-eyes are smaller , they follow a comparable developmental time-course as wildtype eyes in the early growth phase between 24 and 36hpf but with about 8 hr delay ( for instance , a 32hpf Ztcf7l1a-/- eye is about the same size as a wild-type 24hpf eye ) . The temporal shift in eye growth in Ztcf7l1a-/- mutants is not explained by an overall developmental delay as the position of the posterior lateral line primordium ( pLLP ) was similar to wildtype at all stages tested ( Figure 4—figure supplement 2 , Supplementary file 1G ) . Volumes of eye cells in Ztcf7l1a-/- mutants and siblings were not significantly different at 24 or 36hpf and consequently cell size changes likely play no role in eye size compensation in Ztcf7l1a mutants ( Figure 4—figure supplement 3 , Supplementary file 1H ) . To assess if size recovery is a general feature of eye development , we physically removed optic vesicle cells in wildtype embryos and assessed the effect on eye growth . Cells were aspirated from one of the two nascent optic vesicles at 12hpf ( six somite stage ) , leaving approximately the medial half of the vesicle intact ( Figure 4M ) . At 36hpf , there was still a clear size difference between the experimental and control eyes ( Figure 4N , Figure 4—figure supplement 4 , Supplementary file 1I ) . However , by 4dpf we observed little or no size difference between control and experimental eyes ( three independent experiments , n=20/20 , Figure 4O , Figure 4—figure supplement 4 , Supplementary file 1I ) . Three eyes from partially ablated optic vesicles which were ~90% the size of their control contralateral eyes at 30hpf , recovered to 100% by 3dpf ( Figure 4—figure supplement 4K , Supplementary file 1I ) and five eyes which were between ~65 and~75% of control size at 30hpf recovered to 90% by 3dpf with little or no further recovery by 4dpf ( Figure 4—figure supplement 4A–I , K , Supplementary file 1I ) . Consequently , the forming eye can effectively recover from either genetic or physical reduction in the size of the eye field/evaginating optic vesicle . The observation that wildtype and Ztcf7l1a-/- mutant eyes display similar , but temporally offset , growth kinetics led us to speculate that that retinal neurogenesis might be delayed in Ztcf7l1a-/- eyes to extend the period of proliferative growth prior to retinal precursors undergoing neurogenic divisions . In the zebrafish eye , neurogenesis can be visualised by tracking expression of atoh7 ( ath5 ) in retinal neurons starting in the ventronasal retina at ~28 hpf and spreading clockwise across the central retina until it reaches the ventrotemporal side ( Masai et al . , 2000; Hu and Easter , 1999; Figure 5A–E , Q , Supplementary file 1J ) . Although atoh7 was induced at a similar time in Ztcf7l1a-/- as in wildtype eyes , the subsequent progression of expression was delayed ( Figure 5F–J , Q; Supplementary file 1J ) . Classifying the expression of atoh7 in six categories according to its progression across the neural retina ( see legend to Figure 5 ) revealed that atoh7 expression in mutant retinas was slow to spread and remained restricted to the ventro-nasal or nasal retina for longer ( Figure 5Q , Supplementary file 1J ) . Indeed , between 36 and 40hpf , Ztcf7l1a-/- retinas expressed atoh7 exclusively in the nasal half of the retina ( Figure 5H , Q ) , a phenotype we did not see at any stage in sibling embryo eyes . These data indicate that progression of atoh7 expression and neurogenesis is delayed by about 8–12 hr in Ztcf7l1a-/- retinas compared to siblings , a timeframe comparable to the delays seen in optic vesicle growth . In line with our results in Ztcf7l1a-/- embryos , eye vesicle ablated wildtype retinas also showed delayed neurogenesis compared to control non-ablated contralateral eyes at 36hpf ( Figure 5K , L; 6/6 ablated eyes , two independent experiments ) . Our results suggest that retinal precursors in Ztcf7l1a-/- eyes remain proliferative at stages when precursors in wildtype eyes are already producing neurons . Our results are consistent with the idea that neurogenesis may be triggered when the optic vesicle reaches a critical size . To explore this possibility , we generated embryos with larger optic vesicles by overexpressing the Wnt antagonist Dkk1 ( Hashimoto et al . , 2000 ) . Heatshocking tg ( hsp70:dkk1-GFP ) w32 transgenic embryos at 6hpf led to eyes ~ 34% bigger than control heat-shocked embryos by 28hpf ( Figure 5M , N , R , n = 12; Supplementary file 1K ) . After 36hpf , wildtype eyes gradually caught up in size as growth slowed in eyes in dkk1-overexpressed embryos ( Figure 5R; Supplementary file 1K ) . Neurogenesis was prematurely triggered by 28hpf in the eyes of dkk1 overexpressing embryos , with many more cells expressing atoh7 compared to eyes in heat-shocked control embryos ( Figure 5M , N , n = 7 out of 9 embryos ) . This result is unlikely to be due to a direct effect of dkk1 overexpression on neurogenesis as premature neurogenesis was not triggered in tg ( hsp70:dkk1-GFP ) w32 retinas heat-shocked at 24hpf ( Figure 5O , P , n = 10 , 100% ) . These results further support a link between eye size and the onset of neurogenesis and the size self-regulating ability of the forming eye . The delayed production of neurons in tcf7l1a mutant eyes suggests that more retina progenotor cells ( RPCs ) may continue proliferating and contribute to growth compensation . To address this possibility , we counted mitotic phosphohistone3 ( PH3 ) positive cells in wildtype and tcf7l1a mutant embryos carrying the Tg ( atoh7:GAP-RFP ) cu2Tg transgene that is expressed from the last mitotic event prior to neuronal birth ( Zolessi et al . , 2006 ) . The proportion of PH3-positive ( PH3+ ) cells standardised to the total number of cells in confocal sections at 36hpf was about 20% higher in Ztcf7l1a mutants than wildtypes ( Figure 6A , B , C , Supplementary file 1L , wildtype , n = 7; Ztcf7l1a-/- , n = 8 , p=0 . 021 , unpaired t-test ) . The proportion of PH3+ cells was similar between nasal and temporal retina and similar between wildtypes and Ztcf7l1a mutants ( Figure 6—figure supplement 1 , Supplementary file 1L ) . Furthermore , the percentage of PH3+ RPCs co-expressing atoh7:GAP-RFP was 3 . 6 times higher in wildtypes compared to tcf7l1a mutants ( Figure 6A , B , D , Supplementary file 1L , wildtype , 35 . 21 ± 3 . 5 , n = 7 , Ztcf7l1a-/- , 9 . 67 ± 1 . 58 , n = 8 , unpaired t-test p<0 . 0001 ) . Wildtype nasal RPCs were about three times more likely to be atoh7:GAP-RFP positive than nasal RPCs in mutants ( Figure 6D , Supplementary file 1L , wildtype , 45 . 91 ± 3 . 71 , n = 7 , Ztcf7l1a-/- , 15 . 44 ± 2 . 25 , n = 8 , unpaired t-test p<0 . 0001 ) , and PH3+ RPCs in the temporal retina of tcf7l1a mutants almost never showed atoh7:GAP-RFP co-expression ( Figure 6D , Supplementary file 1L , wildtype , 20 . 97 ± 3 . 67 , n = 7 , Ztcf7l1a-/- , 0 . 48 ± 0 . 48 , n = 8 , unpaired t-test p<0 . 0001 ) . These results suggest that at a stage when many wildtype RPCs are undergoing neurogenic divisions , more RPCs in tcf7l1a mutants are still proliferating , likely contributing to compensatory growth of the mutant eye . Although eye formation can recover in tcf7l1a-/- mutants despite a much smaller eye field , we speculated that eye development in these embryos might be sensitised to showing the effects of additional mutations . To test this , we performed an ENU mutagenesis screen on fish carrying the tcf7l1a mutation ( Figure 7A ) . Homozygous Ztcf7l1a mutant adult male fish ( F0 founders ) were treated with four rounds of ENU ( van Eeden et al . , 1999 ) and then crossed with Ztcf7l1a-/- adult females to generate F1 families ( Figure 7A ) . However , possibly because of cellular stress or the synergistic cumulative effect of many mutations induced by ENU , we observed many eyeless F1 embryos . To circumvent this problem , we injected 10 pg/embryo of zebrafish tcf7l1a mRNA to rescue any Tcf-dependent eyeless phenotypes in the F1 embryos ( Figure 7A ) . Adult F1 fish were outcrossed to EKW wildtype fish . All F2 fish were tcf7l1a+/- and half carried unknown mutations ( m ) in heterozygosity ( Figure 7A ) . To screen , we randomly crossed F2 pairs from each family aiming for at least 6 clutches of over 100 embryos . The probability of finding double Ztcf7l1a-/-/m-/- embryos for independently segregating mutations is 1/16 , hence we would expect to find ~6 double mutants in 100 embryos . Here , we describe examples of synthetic lethal mutations that lead to microphthalmia/anophthalmia ( U910; Figure 7B-K ) or eyes that fail to grow ( U762 , U768; Figures 8 and 9 ) . tcf7l1a-/- embryos homozygous for the U910 mutation were eyeless ( Figure 7B , C ) whereas homozygous U910 mutants with one or no mutant tcf7l1a alleles showed no eye phenotype . U910 was mapped by SSLP segregation analysis ( Kelly et al . , 2000 ) to a 1 . 46 Mb interval between 41 . 36 Mb ( four recombinants/276meioses ) and 42 . 82 Mb ( two recombinants/276meioses ) on chromosome 11 in GRCz10 assembly ( Figure 6D , Supplementary file 1M ) . Within this interval is hesx1 , which morpholino knock-down experiments had previously suggested to genetically interact with tcf7l1a ( Andoniadou et al . , 2011 ) . Primers for hesx1 cDNA failed to amplify in U910/Ztcf7l1a-/- eyeless embryo cDNA samples . Using a primer set that spans the hesx1 locus , we found that all U910/Ztcf7l1a-/- eyeless embryos had a ~ 2700 bp deletion that covers hesx1 exons 1 and 2 ( hesx1Δex1/2; Figure 7—figure supplement 1 ) ; this was unexpected as deletions are not normally induced by ENU ( see below ) . Sequencing of the hesx1 locus revealed that there is a polyA stretch of approximately 80 nucleotides followed by a 33 AT microsatellite repeat on the 3’ end of intron two that may have generated a chromosomal instability that led to the deletion of exons 1 and 2 ( Figure 7—figure supplement 1 ) . As a consequence of the deletion , hesx1 mRNA was not detected by RT-PCR or in situ hybridisation in U910 homozygous embryos ( Figure 6E , H , I ) . We further confirmed that only U910-F2 embryos that were homozygous for both the tcf7l1a mutation and hesx1U910/U910 were eyeless ( Figure 7B , C ) . As ENU usually generates point mutations , we speculated that the deletion in hesx1Δex1/2 was not caused by our mutagenesis but was already present in one or more fish used to generate the mutant lines . Indeed , we found the same deletion in wildtype fish not used in the mutagenesis project . To confirm that the eyeless phenotype in hesx1U910/U910/Ztcf7l1a-/- double mutants is not caused by another mutation induced by ENU , we crossed Ztcf7l1a-/- fish to one such wildtype TL fish carrying hesx1Δex1/2 . Incrossing of hesx1Δex1/2/Δex1/2/tcf7l1a+/- adult fish led to embryos with a very small rudiment of eye pigment with no detectable lens ( Figure 7J , K ) . Genotyping of eyeless and sibling embryos confirmed that only double homozygosity for hesx1Δex1/2/Ztcf7l1a-/- led to the eyeless embryo phenotype ( Four independent experiments , n = 53 , Supplementary file 1N ) . The interaction between hesx1 and tcf7l1a mutations strikingly illustrates how the developing eye can fully cope with loss of function of either gene alone but fails to form in absence of both gene activities . Additional eyeless families that do not carry the hesx1 deletion were identified but they remain to be validated and mutations cloned . U762 mutant eyes showed no significant size difference compared to wildtypes at 36hpf ( Figure 8A , C , K , Supplementary file 1O ) ; neither did Ztcf7l1a-/- compared to double U762/Ztcf7l1a-/- eyes ( Figure 8B , D , K , Supplementary file 1O ) . However , by 52hpf U762 mutants showed slightly reduced eye size and this phenotype was considerably more severe in embryos additionally homozygous for the Ztcf7l1a mutation ( Figure 8F–I , K , Supplementary file 1O ) . The U762 mutation was mapped by SSLP segregation analysis to a 1 . 69 Mb interval between 15 . 50 Mb and 17 . 19 Mb on chromosome 24 ( Figure 8—figure supplement 1A ) and through sequencing candidate genes in this interval ( Figure 8—figure supplement 1A; Supplementary file 1P ) , we identified a mutation in the splice donor of cct5 ( chaperonin containing TCP-1 epsilon ) intron 4 ( GT >GC , Figure 8—figure supplement 1B ) . The mutation leads to the usage of an alternative splice donor in the 3’ most end of cct5 exon 4 , which induces a two nucleotide deletion in the mRNA ( Figure 8—figure supplement 1C ) . This deletion changes the reading frame of the protein after amino acid 176 , encoding a 29aa nonsense stretch followed by a stop codon ( Figure 8—figure supplement 1C , D ) . The mutation also induces nonsense-mediated decay of the mRNA ( not shown ) . U762 and cct5hi2972bTg mutations failed to complement ( not shown ) supporting the conclusion that the cct5 mutation in U762 is responsible for the tcf7l1a modifier phenotype . Cct5 is one of the eight subunits of the chaperonin TRiC/TCP-1 protein chaperone complex , which assists the folding of actin , tubulin and many proteins involved in cell cycle regulation ( Sternlicht et al . , 1993; Dekker et al . , 2008; Yam et al . , 2008 ) . To assess if the phenotype in double cct5U762/tcf7l1a mutants is likely due to TRiC/TCP-1 chaperone activity or an independent function of Cct5 we knocked down cct3 , another member of the chaperonin complex , in tcf7l1a mutants . Morpholino knockdown of cct3 abrogated eye growth in Ztcf7l1a mutants as in cct5U762/Ztcf7l1a mutants ( Figure 8K , last two bars; Supplementary file 1O ) , suggesting that the genetic interaction is between TRiC/TCP-1 and Tcf7l1a function . Compared to single cct5U762 or tcf7l1a mutants , double cct5U762/Ztcf7l1a homozygous mutant eyes did not grow beyond 36hpf ( Figure 8 compare D , I to B . G , Figure 8K , dotted line , Supplementary file 1O ) . As described for other cct gene mutants ( Matsuda and Mishina , 2004 ) , we observed dying cells in 36hpf and 48hpf cct5U762and cct5U762/Ztcf7l1a mutant eyes and tecta ( Figure 8—figure supplement 2C ) , whereas dying cells were rarely detected in these regions at these times in wildtype or Ztcf7l1a-/- mutant siblings ( Figure 8—figure supplement 2 ) . To assess if apoptosis contributes to the lack of compensatory eye growth in cct5U762/Ztcf7l1a mutants , we inhibited cell death by knocking down tp53 ( Figure 8E , J , K , Supplementary file 1O ) . Double cct5U762/Ztcf7l1a mutants with abrogated Tp53 function showed little or no apoptosis in the eye or tectum ( Figure 8 , compare J to I , arrows; Figure 8—figure supplement 2E , J , O ) but still showed eye size reduced similarly to cct5/Ztcf7l1a mutants at 36hpf and 52hpf ( Figure 8E , J , K; Supplementary file 1O ) . As Cct5 is implicated in the folding of cell cycle related proteins , we assessed the presence of proliferative RPCs and neurons in cct5U762/Ztcf7l1a mutants ( Figure 8L–P , Supplementary file 1Q ) . cct5U762 mutants showed no significant difference in PH3 +cells compared to wildtype siblings ( Figure 8L , N , P , Supplementary file 1Q , n = 9 ) , whereas double cct5U762/Ztcf7l1a-/- mutants showed a 48% increase compared to single Ztcf7l1a mutants ( Figure 8M , O , P , Supplementary file 1Q , n = 10 , p<0 . 0051 , unpaired t-test , two experiments ) . By 48hpf , wildtype eyes show strong atoh7:GFP expression in the retinal ganglion cell layer ( Figure 8—figure supplement 3A , n = 4 , arrow head; Masai et al . , 2003 ) , whereas although cct5U762/U762 eye cells do express GFP , lamination of neurons is abnormal ( Figure 8—figure supplement 3B , n = 5 ) . In cct5U762/U762/Ztcf7l1a-/- eyes , GFP-expressing neurons are almost completely restricted to the ventro-nasal retina ( Figure 8—figure supplement 3C , n = 6 ) . Homozygous U768 mutants show slightly smaller ( Figure 9A , C , M , Supplementary file 1R , n = 6 , p=0 . 0143 , unpaired t-test ) and misshapen eyes; this mutation was mapped to gdf6a ( Valdivia et al . , 2016 ) . Eyes in gdf6aU768/U768/Ztcf7l1a-/- embryos at 36hpf were reduced to 52% the size of eyes in Ztcf7l1a-/- mutants ( Figure 9B , D , M , Supplementary file 1R , n = 3 , p=0 . 0036 , unpaired t-test ) . Unlike Ztcf7l1a-/- mutants in which eye size recovered , eyes in gdf6aU768/U768/Ztcf7l1a-/- embryos remained smaller than in single mutants or wildtypes at 52hpf ( Figure 9E–H ) . This suggests that the ability to compensate eye size is compromised in absence of both gdf6a and tcf7l1a function . The smaller eye in gdf6aU768/U768/Ztcf7l1a-/- mutants at 36hpf suggested that early eye development and maybe eye field specification in these mutants is compromised . Volumetric analysis of rx3 expression by fluorescent in situ hybridisation showed that gdf6a-/- eye fields were reduced to 63% of wildtype size at 10hpf ( Figure 9I , K , N , arrowheads; Supplementary file 1S , n = 8 , p=0 . 0001 , unpaired t-test ) . Moreover , gdf6aU768/U768/Ztcf7l1a-/- eye fields were about ~68% of the size of Ztcf7l1a-/- mutants ( Figure 9I–L , N , arrowheads; Supplementary file 1S , n = 4 , p=0 . 0143 , unpaired t-test ) . Altogether , analysis of the interacting mutations reveals that although abrogation of Tcf7l1a function alone has little effect on formation of eyes , it can lead to complete loss of eye formation or more severe eye phenotypes in combination with additional mutations . Consequently , although eye development is sufficiently robust to cope with loss of Tcf7l1a , mutant embryos are sensitised to the effects of additional mutations .
Tcf7l1 is a core Wnt/β-catenin pathway transcription factor that can activate or repress genes dependent upon the status of the Wnt signalling cascade ( Cadigan and Waterman , 2012 ) . Homozygous tcf7l1 mutant mice present severe mesodermal and ectodermal patterning defects ( Merrill et al . , 2004 ) , but the duplication of tcf7l1 into tcf7l1a and tcf7l1b in zebrafish has led to functional redundancy ( Dorsky et al . , 2003 ) . Although Ztcf7l1a embryos have a severe eye field specification phenotype , they still develop normal eyes . We confirmed that the tcf7l1am881 mutant allele is null , generates no wildtype transcript and that morpholino knock-down specifically of Tcf7l1a does not give an eyeless phenotype . Hence , the originally described MZtcf7l1a eyeless phenotype ( Kim et al . , 2000 ) may have been due to genetic background effects modifying the outcome of the tcf7l1am881 allele . The fact that we were able to recover an eyeless modifier of the tcf7l1a phenotype in our own mutagenesis pilot screen lends support to this idea . At the stage of eye specification , we did not find genetic compensation in tcf7l1a mutants by other tcf genes . Even though tcf7l1a mutants develop eyes , they do so from an eye field that is ~50% smaller than wild-type . Although we did not find evidence for genetic compensation , and despite tcf7l1 being duplicated in fish , the fact that neither gene has been lost due to genetic drift suggests that having both genes may confer enhanced fitness and robustness to zebrafish . As an example , paralogous Lefty proteins make Nodal signalling more stable to noise and perturbations during early embryogenesis ( Rogers et al . , 2017 ) . Tcf7l1a is cell-autonomously required for the expression of rx3 and consequently is a bona fide eye field gene regulatory network transcription factor that functions upstream to rx3 . tcf7l1a is expressed very early in the anterior neural plate and so may work alongside otx , sox , six and pax genes to regionalise the eye-forming region of the neural plate ( Beccari et al . , 2013; Zuber et al . , 2003 ) . Considering that it is the repressor activity of Tcf7l1a that promotes eye formation ( Kim et al . , 2000 ) , the most likely role for Tcf7l1a is to repress transcription of a gene that suppresses eye field formation . We show that despite the small eye field in tcf7l1a mutants , the optic vesicles evaginate and undergo overtly normal morphogenesis . Although tcf7l1a mutant eye vesicles are still much smaller than wild-type at 24hpf , we found that their eye growth kinetics and cell volumes are similar . This suggests that the mechanisms that regulate overall growth of the retina in both conditions are comparable albeit delayed in the tcf7l1a mutant retina . Although atoh7 expression is initiated in the ventronasal retina in tcf7l1a mutants at the same stage as in wild-type eyes , the wave of atoh7 expression that spreads across the retina is delayed by approximately 8–12 hr in mutants . atoh7 is required for the first wave of neurogenesis in the retina ( Brown et al . , 2001; Kay et al . , 2001; Wang et al . , 2001a ) and thus , the delay we see in tcf7l1a mutants suggests that RPCs continue proliferating in mutants at stages when they are already generating neurons in wild-type eyes . Indeed , we show that the tcf7l1a mutant eye has more mitotic RPCs , fewer of which are undergoing neurogenic divisions . This suggests that the extended period of proliferative growth due to delayed neurogenesis enables the forming eye to continue growing and recover its size . We observed a similar phenomenon of delayed neurogenesis and prolonged growth when cells were removed from one optic vesicle . Conversely , atoh7 spreads precociously in experimentally enlarged optic vesicles . The premature neurogenesis of RPCs in these conditions may contribute to eyes achieving a final size similar to wild-type . Altogether , our data suggest that the timing of the spread of neurogenesis across the retina may be coupled to size of the eye , thereby providing a mechanism to buffer eye size . It is intriguing that the compensatory changes in growth seen in tcf7l1a mutant and optic-vesicle ablated eyes seem to occur prior to the establishment of the ciliary marginal zone , which accounts for the vast majority of eye growth ( Fischer et al . , 2013 ) . Our results support classical embryology experiments from Ross Harrison , Victor Twitty and others ( Harrison , 1929; Twitty and Schwind , 1931; Twitty and Elliott , 1934 ) . These investigators showed that when eye primordia from small-eyed salamander species ( A . punctatum ) were transplanted to larger-eyed salamanders ( A . tigrinum ) or vice-versa , the eye derived from the grafted tissue formed an eye of a size corresponding to the donor salamander species . Species-specific size differences are also observed in self-organising in vitro cultured eye organoids derived from mouse or human embryonic stem cells ( Nakano et al . , 2012 ) . Our work , together with the experiments in salamanders and organoids , suggests that the developing eye has intrinsic size-determining mechanisms . Size regulatory mechanisms have been previously described in other species and perhaps most extensively studied in the fly wing imaginal disc ( Potter and Xu , 2001 ) . Indeed , many models have been put forward to explain imaginal disk size control ( Eder et al . , 2017; Irvine and Shraiman , 2017; Vollmer et al . , 2017 ) . It is evident that the final size of paired structures within individuals is remarkably similar supporting the idea that the mechanisms that control the size of such organs/tissues are highly robust . Our results indicate that tcf7l1a mutant eyes are sensitised to the effects of additional mutations . Indeed , a homozygous deletion of the two first exons of hesx1 leads to eyeless embryos when in combination with tcf7l1a . This result also confirms our previous observations suggesting a genetic interaction between hesx1 and tcf7l1a based upon morpholino knock-down experiments ( Andoniadou et al . , 2007 ) . Furthermore , both hesx1 and tcf7l1a are expressed in the anterior neural plate including the eye field , and as observed in tcf7l1a zebrafish mutants , hesx1 mutant mice also show a posteriorised forebrain ( Andoniadou et al . , 2007; Martinez-Barbera et al . , 2000 ) . These and our results suggest that Tcf7l1a and Hesx1 have similar , overlapping functions in the anterior neural plate such that the eyeless phenotype is expressed in zebrafish only when both genes are abrogated . Mutations in hesx1 lead to anophthalmia , microphthalmia , septo-optic dysplasia ( SOD ) and pituitary defects in humans and mice ( Dattani et al . , 1998; Gaston-Massuet et al . , 2008; Martinez-Barbera et al . , 2000; Thomas et al . , 2001 ) . Interaction of hesx1 mutations with other genetic lesions may also occur in patients carrying Hesx1 mutations , as the phenotypes in these individuals show variable expressivity ( McCabe et al . , 2011 ) . In these patients , tcf7l1a should be considered as a candidate modifier for hesx1-related genetic conditions . Gdf6a is a TGFβ pathway member ( David and Massagué , 2018 ) that when mutated in zebrafish results in small mis-patterned eyes , neurogenesis defects and retino-tectal axonal projection errors ( Gosse and Baier , 2009; French et al . , 2009 ) . In humans , mutations in GDF6 have been identified in anophthalmic , microphthalmic and colobomatous patients ( Asai-Coakwell et al . , 2009 ) as well as in some cases of Leber congenital Amaerurosis ( Asai-Coakwell et al . , 2013 ) . Double gdf6aU768/tcf7l1a mutant eye fields are smaller than both single mutants and their eyes fail to recover their size at later stages . This suggests that gdf6a/tcf7l1a double mutant eye fields have more severe specification defects compared to either individual mutant , and that double mutant optic vesicles lack the compensatory growth seen in tcf7l1a mutants . Given the phenotypes we describe , it is perhaps surprising that gdf6a appears not to be expressed in the eye field ( Rissi et al . , 1995 ) , although it is expressed in neighbouring tissues and also prior to eye specification ( Sidi et al . , 2003 ) . Consequently , we presume that Gdf6 acting prior to eye field formation or arising from outside of the eye field impacts eye field specification . It is also intriguing that gdf6a mutants show premature expression of atoh7 and neurogenesis ( Valdivia et al . , 2016 ) . If this phenotype is epistatic to the compensatory growth mechanisms , then this may contribute to the lack of growth in double mutant eyes . Mutations in cct5 in combination with tcf7l1a also led to phenotypes in which eye size failed to recover . cct5 codes for the epsilon subunit of the TCP-1 Ring Complex ( TRiC ) chaperonin that is composed of eight different subunits that form a ring , the final complex organised as a stacked ring in a barrel conformation ( Yébenes et al . , 2011 ) . In vitro studies indicate TRiC chaperonin mediates actin and tubulin folding ( Sternlicht et al . , 1993 ) ; however , it also assists in the folding of cell cycle-related and other proteins ( Dekker et al . , 2008; Yam et al . , 2008 ) . A mutation in cct2 has been found in a family with Leber congenital ameurosis retinal phenotype ( Minegishi et al . , 2016; Minegishi et al . , 2018 ) and mutations in cct4 and cct5 have been related to sensory neuropathy ( Pereira et al . , 2017; Lee et al . , 2003; Hsu et al . , 2004; Bouhouche et al . , 2006 ) . Similar to our cct5 mutant , cct1 , cct2 , cct3 , cct4 and cct8 mutant zebrafish show retinal degeneration ( Berger et al . , 2018; Matsuda and Mishina , 2004; Minegishi et al . , 2018 ) , suggesting that the cct5/tcf7l1a double mutant phenotype is due to abrogation of TRiC chaperonin function , a conclusion supported by cct3 knockdown in tcf7l1a mutants . Double cct5/tcf7l1a homozygous mutant eyes degenerate prematurely and to a greater extent than cct5 single mutants , and neurogenesis is also severely compromised . However , the lack of compensatory growth is not solely due to cell death as blocking apoptosis in cct5/tcf7l1a mutants fails to restore eye size . Our results show that the consequence of cct5 loss of function is exacerbated by the lack of tcf7l1a function , although it is currently unclear how such an interaction might occur . However , this genetic interaction does highlight that in some conditions a gene of pleiotropic function , like cct5 , can lead to a specific phenotype in the eye . Surprisingly many eyeless embryos were observed in the F1 clutches of embryos used to establish homozygous tcf7l1a fish carrying new mutations . These phenotypes were suppressed by providing exogenous wildtype tcf7l1a . It is not unusual to see mutant phenotypes in F1 embryos in ENU screens and we suspect that heavy mutational load may impact developmental processes such that phenotypic penetrance is enhanced due to cellular or tissue level stress . Additionally , we now think it likely that the hesx1 mutant allele was in the background of some parent fish and this may also have contributed to enhancing the homozygous tcf7l1a phenotype in combination with the many newly induced ENU mutations . Anophthalmia and microphthalmia are generally associated with eye field specification defects ( Reis and Semina , 2015 ) , but given that normal eyes can still develop from a much reduced eye field , further analysis of the genetic and developmental mechanisms that lead to small or absent eyes is warranted . Our isolation and identification of modifiers of tcf7l1a highlights the utility of genetic modifier screens to identify candidate genes underlying congenital abnormalities of eye formation . Indeed , given that Tcf7l1a itself can now be classified as a bona fide gene in the eye transcription factor regulatory network , it should be considered when screening patients with inherited morphological defects in eye formation .
Adult zebrafish were kept under standard husbandry conditions and embryos were obtained by natural spawning . Wildtype and mutant embryos were raised at 28 . 5°C and staged according to Kimmel et al . ( 1995 ) . To minimise variations in staging , embryos were collected every 30 min and kept separate clutches according to their time of fertilisation . Fish lines used were tcf7l1a/headless ( hdl ) m881 ( Kim et al . , 2000 ) , cct5hi2972bTg ( Amsterdam et al . , 2004 ) , cct5U762 , gdf6aU768 ( Valdivia et al . , 2016 ) , hesx1U910 , tcf7l1bzf157Tg ( Gribble et al . , 2009 ) , Tg ( atoh7:GFP ) rw021Tg ( Masai et al . , 2000 ) , Tg ( atoh7:GAP-RFP ) cu2Tg ( Zolessi et al . , 2006 ) , Tg ( hsp70:dkk1-GFP ) w32 ( Stoick-Cooper et al . , 2007 ) and Tg ( rx3:GFP ) zf460Tg ( Brown et al . , 2010 ) . All the alleles except for cct5hi2972bTg were genotyped by KASP assays ( K Biosciences , assay barcodes: 1077647141 ( cct5U762 ) , 1077647146 ( gdf6aU768 ) , 172195883 ( this assay discriminates a SSLP 500 bp from the 3’ end of the deletion in hesx1U910 ) , 1145062619 ( tcf7l1am881 ) ) using 1 µl of genomic DNA for 8 µl of reaction volume PCR as described by K Biosciences . For heatshock ( HS ) gene induction , embryos from a heterozygous Tg ( hsp70:dkk1-GFP ) w32 to wild type cross were moved from embryo media at 28 . 5°C to 37°C at 6hpf or 24hpf for 45 min , and then back to 28 . 5°C embryo media . Three hours post HS , embryos were separated in controls ( GFP- ) and HS experimental ( GFP+ ) groups , and fixed at the stages described in results . Homozygous male tcf7l1am881 fish were exposed to four rounds of ENU according to van Eeden et al . ( 1999 ) . Details of the mutagenesis pipeline are in the results section . Embryos from incrosses of carriers of the cct5U762 or gdf6aU768 mutations , which show a phenotype as homozygous embryos independently of mutations in tcf7l1a , were identified for the described eye phenotype at 3dpf to avoid ambiguity and false positives . For rough mapping , batches of 30 mutants and 30 siblings were fixed in methanol and genomic DNA was extracted by proteinase K protocol . This gDNA was then used for bulk segregant analysis PCR to test a library of 245 polymorphic SSLP variants spanning the whole zebrafish genome ( Stickney et al . , 2002 ) . SSLP markers heterozygous in the sibling samples and homozygous in the mutant sample were confirmed on gDNA samples of 12 mutant and 12 sibling individuals . Markers that showed linkage to a locus were tested on additional mutant samples , and more SSLP markers were tested for the mapped region until a genomic interval was defined . Homozygous tcf7l1a/hesx1U910 mutant carriers were incrossed , and eyeless embryos and siblings were fixed in methanol . Rough mapping was carried out as above but in this case sibling embryos used for bulk segregant analysis were genotyped for tcf7l1am881 and only homozygous mutants with eyes were included in the sibling pool . mRNA for overexpression was synthesised using RNA mMessage mMachine transcription kits ( Ambion ) . One- to two-cell stage embryos were co-injected with 10 nl of 5 pg of GFP mRNA and morpholinos or in vitro synthesised mRNA at the indicated concentrations . Only embryos with an even distribution of GFP fluorescence were used for experiments . For cell volume analysis , one-cell stage embryos from a tcf7l1a ± incross were injected with 5 pg pCS2-GFP DNA and 10 pg lyn-cherry mRNA . The following day , embryos were sorted for GFP mosaicism in the eye and mounted in PTU/Tricaine-containing 1% low-melt agarose and were imaged at 24 and 36hpf in a Leica SP8 confocal microscope . Morpholino sequences: mo2 tcf7l1a ( 5’ AGG CAT GTT GGC ACT TTA AAT G 3’ ) , motcf7l1b ( 5’-CAT GTT TAA CGT TAC GGG CTT GTC T-3’; Dorsky et al . , 2003 ) and moC ( TGT TGA AAT CAG CGT GTT CAA G ) . tcf7l1am881/m881 embryos injected with motcf7l1b phenocopy the loss of eye phenotype seen in tcf7l1am881/m881/tcf7l1b+/zf157tg double mutants ( Young and Wilson , unpublished ) . Total RNA and genomic DNA were isolated from individual embryos at 10hpf following Life Technologies Trizol protocol . cDNA was synthesised by reverse transcription using SuperscriptII ( Life Technologies ) with 200 ng of total RNA to a final volume of 40 µl and oligo dT for priming . The cDNA reaction was diluted 10 times and 5 µl were used in 25 µl final volume reactions using GoTaq qPCR Master mix ( Biorad ) . Each experimental condition was processed in technical and biological triplicates . All primers used had PCR efficiencies within 90–100% range: gapdh ( F-ACC CGT GCT GCT TTC TTG , R-CTG CCT TAA CCT CAC CCT TG ) ; hprt1 ( F-AAC AGT GAT CGC TCC ATT CC , R-GGA CAG ATC ATC TCC ACC AAT C ) ; lef1 ( F-GCT TCA GGT ACA GGC CAG AG , R-AAA GAC GTC CGC TTT CCT CC ) ; otx1a ( F-GGT GTT TCT TGG CTT TGT GG , R-GGG CTT GCT TGA GGT ATG A ) ; otx2 ( F-TAC ACG GTC AAC GGG CTA A , R-CTC GTC TCT GGT TTC GAG GA ) ; rx3 ( F-TCC GAG TAC AGG TGT GGT TCC , R-CTC CTG TCG CCG CCA TTT A ) ; six3b ( F-TGC CAA AAA CAG GCT TCA GCA , R-CTG ACA TGG AGC GCA GAC T ) ; tcf7l1a ( F- AGC ACA CGA ACG TAT CTC CA , R-GAG TCT TTA AGA GCC GCC GA ) ; tcf7 ( F-TGC TGC CGT ATG AAC ACT TC , R-TCT CCT GCG TCT GAT GTC TG ) ; tcf7l1b ( F-GGC TAA AGT AGT GGC CGA GTG , R-CTG GCC AGC TCG TAG TAT TTG ) ; tcfl2 ( F-GCC TCC GCC TAG ATC TGA AA , R-CTT GCC TTT TTG CAG CCT CC ) . Wildtype and tcf7l1am881 mutant cDNA fragments spanning the tcf7l1a exon 7/8 border for DNA sequencing were amplified with primers P2 and P3 ( Kim et al . , 2000 ) . Total RNA was extracted from zebrafish embryos at 80% epiboly by Trizol extraction and gDNA was genotyped for tcf7l1a to identify wildtype , heterozygous and homozygous embryos . RNA from six wild-type and six tcf7l1a-/-mutant embryos was DNase treated for 20 min at 37°C followed by addition of 1 ml 0 . 5M EDTA and inactivation at 75°C for 10 min to remove residual DNA . RNA was then cleaned using 2 volumes of Agencourt RNAClean XP ( Beckman Coulter ) beads under the standard protocol . Stranded RNA-seq libraries were constructed using the Illumina TruSeq Stranded RNA protocol with oligo dT pulldown . Libraries were pooled and sequenced on two lanes of Illumina HiSeq 2000 in 75 bp paired-end mode . Sequence data were deposited in ENA under accession PRJEB9957 . FASTQ files were aligned to the GRCz11 reference genome using TopHat ( v2 . 0 . 13 , options: --library-type fr-firststrand , Kim et al . , 2013 ) . The data were assessed for technical quality ( GC-content , insert size , proper pairs etc . ) using QoRTs ( Hartley and Mullikin , 2015 ) . Counts for genes were produced using htseq-count ( v0 . 6 . 0 options: --stranded=reverse , Anders et al . , 2015 ) with the Ensembl v93 annotation as a reference . Sequence data were deposited in ENA under accession PRJEB9957 . Differential gene expression was analysed using DESeq2 ( Love et al . , 2014 ) . Whole mount in situ hybridisation was performed using digoxigenin ( DIG ) and fluorescein ( FLU ) -labelled RNA probes according to standard protocols ( Thisse and Thisse , 2008 ) . Probes were synthesized using T7 or T3 RNA polymerases ( Promega ) according to manufacturers’ instructions and supplied with DIG or FLU labelled UTP ( Roche ) . Probes were detected with anti-DIG-AP ( 1:5000 , Roche ) , anti-FLU-AP ( 1:10000 , Roche ) , or anti-DIG-POD ( 1:1000 , Roche ) antibodies and developed with NBT/BCIP mix ( Roche ) , for regular microscopy or Fast Red ( Sigma ) or CY-3 tyramide ( Lauter et al . , 2011 ) substrate for confocal analysis . For Tunel assays , embryos were processed as for in situ hybridisation up to the washing of PK stage . After this , embryos were incubated in an acetone:ethanol ( 2:1 ) –20°C prechilled solution at −20°C for 10 min . After PBS tween 0 . 5% washes , embryos were incubated for 1 hr in the equilibration buffer ( Millipore ApopTag kit ) at room temperature . The buffer was removed and 20 µl of fresh equilibration buffer was added plus 15 µl of TdT mix ( 12 µl reaction buffer , 6 µl TdT enzyme , 0 . 5 µl of 10% triton X100 ) , and embryos incubated at 37°C over night . Samples were washed for 1 hr at 37°C and 1 hr at room temperature , and the protocol was continued as for in situ hybridisation . The eye profile and eye volume were calculated from confocal imaging of vsx2 in situ hybridisation stained embryos at 24hpf . The eye volume/eye profile ratio average from 10 embryos was 53 . 24 . This ratio was used to estimate eye volume from eye profile area as the profile area to eye volume ratio is approximately constant after 24hpf ( Matejčić et al . , 2018 ) . The sizes of eye profiles were quantified from lateral view images of PFA-fixed embryos by delineating the eye using Adobe Photoshop CS5 magic wand tool and measuring the area of pixels included in the delineated region . The surface area was then transformed from px2 to µm2 . For estimation of cell volume , 3d stacks were first contrast enhanced to increase the intensity of the labelled cells in the entire volume . Subsequently , a 3d median filter was applied to filter out high intensity noise . Next , a fixed threshold was applied to segment individual cells in the volume and their surface area and volume were calculated . For each imaged and analysed image stack , we also manually inspected the processed data to ensure that post processing did not result in partial segmentation of cell volumes . Cells that were undergoing division or were within ~20 µm from the dorsal or ventral surface of the imaged volume were excluded from the analysis . Image processing and analysis was carried out using ImageJ . For PH3 quantification , embryos were oriented to yield a lateral view of the retina , and the widest plane of the retina was imaged . The z-series was defined as 2 μm above and 2 μm below the widest plane of the retina . Stacks were taken at a step size of 1 μm for a total of 5 stacks per imaged volume . The number of nuclei undergoing mitosis , marked by α-PH3 , were counted . The total number of nuclei for each eye was estimated by counting the DAPI labeled nuclei in a standardized area of 40 × 40 pixels . This result was multiplied by the total area of the retina ( in pixels ) , obtained using the freehand tool in ImageJ and the software’s measuring capabilities , and then divided by 1600 . To normalize the data , the number of α-PH3 positive cells was presented as a percentage of the total number of nuclei per retina . pLLP migration was measured by analysing the position of the posterior end of the primordium relative to the somite boundary labelled by in situ hybridisation with eya1 and xirp2a respectively . Confocal imaging was performed on a Leica TCS SP8 confocal microscope . For time lapse analyses , the stage was set in an air chamber heated to 28 . 5°C . Live embryos were immobilized in 1% low melting point agarose ( Sigma ) and 0 . 016% Tricaine ( Sigma ) to anesthetize . Image volume analysis measurement was performed on Imaris 7 . 7 . 0 and Fuji . WIldtype or MZtcf7l1a-/- embryos used as donors were injected with 50 pg of GFP mRNA at 1 cell stage . At 3-4hpf , blastula stage , dechorionated donor and host embryos were mounted in 3% methylcellulose in fish water supplemented with 1% v/v penicillin/streptomycin ( 5000 units penicillin and 5 mg streptomycin per ml ) and viewed with a fixed-stage compound microscope ( Nikon Optiphot ) . Approximately 30–40 cells were taken from the animal pole of donors and transplanted to approximately the same position in hosts by suction using an oil-filled manual injector ( Sutter Instrument Company ) . Embryos were moved to 1% penicillin/streptomycin supplemented fish media and fixed at 10hpf . Embryos were mounted in 1% low melting point agarose in Ringer’s solution supplemented with 1% v/v penicillin/streptomycin . A slice of set agarose was removed to expose one of the eyes and a drop of mineral oil ( sigma ) was placed over the target eye to dissolve the epidermis ( Picker et al . , 2009 ) . After 2 min , the oil drop was removed and optic vesicle cells were sucked out with a capillary needle filled with mineral oil . Embryos were left to recover for half an hour before being released from the agarose . Optokinetic responses were examined using a custom-built rig to track horizontal eye movements ( optokinetic nystagmus ) in response to whole-field motion stimuli . Larvae at 4dpf were mounted in 1% low melting point agarose in fish water and analysed at 5dpf . The agarose surrounding the eyes was removed to allow normal eye movements . Sinusoidal gratings with spatial frequencies of 0 . 05 , 0 . 1 , 0 . 13 and 0 . 16 cycles/degree were presented on a cylindrical diffusive screen 25 mm from the centre of the fish’s head with a MicroVision SHOWWX + projector . Gratings had a constant velocity of 10 degrees/s and changed direction and/or spatial frequency every 20 s . Eye movements were tracked under infrared illumination ( 720 nm ) at 60 Hz using a Flea3 USB machine vision camera controlled with custom-written LABVIEW software . MATLAB scripts were used to extract slow phase eye velocity from recorded eye position data ( degrees per second ) . | Left and right eyes develop independently , yet they consistently grow to roughly the same size in humans and other creatures . How they do this remains a mystery , though scientists have learned that both eyes originate from a single group of cells in the developing nervous system called the eye field . As development progresses , the eye field splits in two , and buds into the two separate compartments from which each eye forms . As the eyes grow , the cells in each compartment specialize , or ‘differentiate’ , to make working left and right eyes . Scientists often study eye development in zebrafish embryos because it is easy to see each step in the process . Now , Young at al . show that zebrafish with a mutation that causes the eye field to be half its normal size go on to form normal-sized eyes . Somehow these developing embryos overcome this deleterious mutation . It turns out that the eyes of zebrafish with this mutation grow for a longer period of time than typical zebrafish eyes . This change allows the mutant fish’s eyes to catch up and reach normal size . When Young et al . removed some cells from one of the forming eyes of normal zebrafish embryos they found that same thing happened . The smaller eye developed for a longer time and delayed its differentiation until both eyes were the same size . Conversely , when eyes developed from a larger than normal eye field , growth stopped prematurely and differentiation began early preventing the eyes from ending up oversized . Though the fish were able to overcome the effects of one mutation to develop normal-sized eyes , adding a second mutation that affected eye development led to unusual sized eyes or absence of eyes . Together the experiments identify genes and mechanisms essential for the formation and size of the eyes . Given that the processes underlying eye formation are very similar in many animals , this new information should help scientists to better understand eye abnormalities in humans . | [
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] | 2019 | Compensatory growth renders Tcf7l1a dispensable for eye formation despite its requirement in eye field specification |
Virtually all mitochondrial matrix proteins and a considerable number of inner membrane proteins carry a positively charged , N-terminal presequence and are imported by the TIM23 complex ( presequence translocase ) located in the inner mitochondrial membrane . The voltage-regulated Tim23 channel constitutes the actual protein-import pore wide enough to allow the passage of polypeptides with a secondary structure . In this study , we identify amino acids important for the cation selectivity of Tim23 . Structure based mutants show that selectivity is provided by highly conserved , pore-lining amino acids . Mutations of these amino acid residues lead to reduced selectivity properties , reduced protein import capacity and they render the Tim23 channel insensitive to substrates . We thus show that the cation selectivity of the Tim23 channel is a key feature for substrate recognition and efficient protein import .
Double membrane bounded mitochondria import over 1000 different proteins synthesized on cytosolic ribosomes ( Endo and Yamano , 2009; Neupert and Herrmann , 2007; Schmidt et al . , 2010 ) . Different targeting signals direct the proteins into one of the four mitochondrial sub-compartments: outer membrane ( OM ) , intermembrane space ( IMS ) , inner membrane ( IM ) and matrix . Approximately , 70% of these mitochondrial proteins are synthesized with an N-terminal presequence ( Vögtle et al . , 2009 ) , which directs them across the OM . Once threaded through the OM , the presequence directs preproteins to the presequence translocase ( TIM23 complex ) , located in the inner boundary membrane ( Barbot and Meinecke , 2016; Chacinska et al . , 2005 ) . The TIM23 complex transports precursor proteins across the inner membrane , or , if they contain additional sorting signals , inserts them into the IM ( Neupert and Herrmann , 2007; van der Laan et al . , 2007 ) . The membrane potential ( ΔΨ ) across the energy coupling inner membrane exerts an electrophoretic force on the positively charged presequences , thereby providing energy for the translocation of preproteins . ΔΨ is necessary and sufficient for membrane insertion of IM proteins ( van der Laan et al . , 2007 ) , whereas full translocation of proteins into the mitochondrial matrix depends on additional energy provided by the ATP consuming presequence translocase-associated import motor PAM ( Neupert and Brunner , 2002; Schendzielorz et al . , 2017 ) . The TIM23 complex consists of the channel forming Tim23 subunit and its homolog Tim17 ( Lohret et al . , 1997; Maarse et al . , 1994; Meinecke et al . , 2006; Ryan et al . , 1998; Truscott et al . , 2001 ) . Additionally , the receptor protein Tim50 as well as Mgr2 are constitutive subunits of the presequence translocase , whereas Tim21 is specific to the TIM23 complex in the absence of the PAM motor ( Chacinska et al . , 2005; Ieva et al . , 2014 ) . Tim23 was identified as the central pore-forming component of the TIM23 complex by electrophysiological characterization of purified Tim23 as well as patch-clamp analyses of inner membrane derived vesicles , depleted of Tim17 ( Martinez-Caballero et al . , 2007; Truscott et al . , 2001 ) . Tim23 forms a voltage-activated , water-filled pore with a diameter of 1 . 3–2 . 4 nm . To maintain the permeability barrier of the inner membrane it is voltage-regulated by the Tim50 receptor and shows sensitivity towards presequence peptides and full-length preproteins ( Meinecke et al . , 2006; Truscott et al . , 2001 ) . Many electrophysiological features of purified Tim23 , such as voltage-gating , substrate sensitivity and selectivity , were also found in measurements of the TIM23 complex . The role of Tim17 is less clear , though recent studies suggest it might be involved in channel regulation within the complex ( Martinez-Caballero et al . , 2007; Ramesh et al . , 2016 ) . Despite its channel dimension , which would allow the simultaneous passage of multiple ions , Tim23 shows a clear preference to conduct cations over anions . Since its discovery this selectivity was speculated to be important to recognize and transport positively charged presequences through the channel . The lack of high-resolution 3D structures on the one hand , and the missing amphipathic character of the predicted transmembrane helices hindered the possibility to construct structure based mutants to investigate the molecular nature and physiological importance of the basic electrophysiological characteristics of the Tim23 channel . In recent years efforts have been made to overcome this issue . Fluorescent mapping has allowed for the first time to show which amino acid residues of the transmembrane helices of Tim23 are likely facing the aqueous channel lumen ( Alder et al . , 2008; Malhotra et al . , 2013 ) . In this study , we identify pore-lining amino acids of the Tim23 channel that contribute to ion selectivity . Mutations of these highly conserved amino acids specifically affect the channels selective properties while leaving other electrophysiological characteristics intact . Yeast cells expressing mutant Tim23 channels with decreased selectivity show growth defects and are impaired in the import of mitochondrial proteins . On the protein level , selectivity reduction leads to a highly decreased sensitivity towards substrates . Our data provide evidence for the idea that the biophysical properties of protein-conducting Tim23 channel are essential for its physiological functions .
To investigate the physiological function of highly conserved , pore-lining amino acid residues of Tim23 ( Alder et al . , 2008; Malhotra et al . , 2013 ) from Saccharomyces cerevisiae in vivo , we employed mutants based on substitution of amino acids in the second transmembrane helix ( Figure 1A ) . S . cerevisiae cells with chromosomal deletion of TIM23 , rescued by a URA3-containing plasmid carrying wild type TIM23 , were transformed with plasmids carrying the HIS3 gene as a selection marker and either a wild type copy of TIM23 or TIM23 mutant alleles . Transformants were selected on medium lacking Histidine ( Figure 1—figure supplement 1A ) . The ability of TIM23 mutants to complement Tim23 function was monitored by plasmid shuffle on 5-fluoroorotic acid ( 5-FOA ) -containing medium ( Figure 1B ) . Transformation was successful for all constructs , while 5-FOA selection showed that Tim23G153L exhibited a lethal phenotype as published previously ( Demishtein-Zohary et al . , 2015 ) . TIM23 mutants that grew on 5-FOA were subsequently analyzed for growth on fermentable ( glucose ) and non-fermentable ( glycerol ) carbon sources ( Figure 1C ) . Four mutants , encoding Tim23N150A , Tim23L155A , Tim23A156L and Tim23Y159A , exhibited a significant growth defect on non-fermentable media at 37°C , with Tim23A156L showing the strongest phenotype ( Figure 1C ) . To analyze whether the growth defects could be explained by changed channel characteristics of Tim23 , we expressed wild type and mutant forms of Tim23 in E . coli . The proteins were purified from inclusion bodies to homogeneity , incorporated into preformed large unilamellar vesicles ( LUVs ) and subjected to single-channel planar lipid bilayer experiments ( Krüger et al . , 2012; Montilla-Martinez et al . , 2015 ) . Interestingly , in a wide screen for basic electrophysiological parameters we found that a number of mutants ( Tim23N150A , Tim23A156L , Tim23Y159A ) that showed growth defects exhibited a significantly reduced reversal potential ( Figures 1D and 3D and Figure 1—figure supplement 1B ) , which translates to a severe reduction of the channels cation preference ( Figure 1E ) , while other parameters remained unaffected ( Figure 1—figure supplement 1B–D ) . The strongest reduction was observed for Tim23N150A , where the selectivity dropped down to 33% of wild type level . A slightly weaker reduction in cation preference ( between 50–70% of wild type level ) was observed for Tim23G153A , Tim23A156G , Tim23A156L , Tim23Y159A and Tim23N160A . All residues with decreased selectivity are highly conserved between Tim23 in different species ( Figure 1—figure supplement 2 ) . To analyze if the observed growth defects could be directly linked to altered channel characteristics or if they were secondary effects , we examined the integrity of the TIM23 complex in the inner membrane . Mitochondrial lysates of all mutants and wild type were analyzed for steady state protein levels of Tim23 ( Figure 2A ) . Reduced levels of Tim23 were found for the mutants Tim23L155A , Tim23A156L , Tim23Y159A and Tim23N160A ( Figure 2A , lanes 5 , 7 , 8 and 9 ) . Tim23L155A , Tim23A156L , Tim23Y159A all showed impaired growth phenotypes , which might result from decreased Tim23 levels . To gain more insight into TIM23 complex integrity of the mutants we performed co-immunoprecipitation of wild type and all mutants using antibodies against Tim23 ( Figure 2B ) . Interestingly , TIM23 and PAM subunits could be efficiently co-purified . The altered levels of some subunits ( for example Tim17 and Tim50 ) can probably be attributed to decreased Tim23 levels in mitochondria . As an alternative approach , we analyzed TIM23 complex integrity of selected mutants by size exclusion chromatography . To this end , mitochondrial extracts carrying Tim23 , Tim23N150A , or Tim23Y159A were generated and subjected to chromatographic separation of protein complexes . In agreement with the results of the immunoisolation analyses , the TIM23 complex apparently remained intact and associated with the import motor ( Figure 2—figure supplement 1 ) . Hence , after carefully testing the suitability of Tim23 mutants for subsequent analysis , Tim23N150A was the only mutation that led to impaired growth , decreased ion-selectivity and exhibited normal protein levels and complex assembly and was therefore further analyzed in in organello assays . Mitochondrial steady state levels of selected proteins were analyzed , that is , TIM23 complex components , PAM complex subunits and mitochondrial marker proteins ( Figure 2C ) . Here , all protein levels in mitochondria from Tim23N150A expressing cells were unchanged compared to wild type . To assess that the inner membrane potential was not affected in mitochondria containing Tim23N150A , we tested the ΔΨ in organello , using the membrane-permeable fluorophore DiSC3 ( 5 ) ( Figure 2D ) . The measurements showed that ΔΨ was not significantly altered in Tim23N150A-expressing cells compared to the wild type control ( Figure 2D and E ) . In agreement with this unchanged membrane potential , in single-channel measurements the IMS domain of Tim50 exhibited the same voltage-regulation on wild type and Tim23N150A that we reported before ( Figure 3E and F ) ( Meinecke et al . , 2006 ) . In our initial screen for altered electrophysiological characteristics , we found that specifically the ion-selectivity of Tim23N150A was decreased ( Figure 1E and 3D ) . We next performed an in-depth analysis of this mutant form of the channel to confirm that no other channel parameters were affected . Wild type as well as Tim23N150A channels exhibited complex voltage-dependent gating patterns ( Figure 3A and B ) . Both pores gated with the same main-conductance state of ~460 pS ( at 250 mM KCl ) and showed similar sub-conductance states of ~170 pS and ~60 pS ( Figure 3C ) . Again Tim23N150A displayed a reduced reversal potential , while the wild type and mutant Tim23 showed the same voltage-dependent open probability ( Figure 3E and F ) and were efficiently voltage-regulated by Tim50IMS as published before ( Figure 3E and F ) ( Meinecke et al . , 2006 ) . In summary , Tim23N150A is found in wild type levels in mitochondria , integrates properly into the TIM23 complex , and has no effect on the integrity of the inner membrane . In addition , it displays wild type-like channel characteristics except for a significantly reduced cation preference . We next asked whether the reduced selectivity for cations impacted the import capabilities of the presequence translocase . To this end , isolated mitochondria were incubated with radiolabeled matrix proteins bearing typical , positively charged presequences: F1β ( Figure 4A ) , a subunit of the F1FO-ATP synthase , Cox4 ( Figure 4B ) , a subunit of the cytochrome c oxidase , and the model fusion proteins b2 ( 167 ) Δ-DHFR ( Figure 4C ) and b2 ( 220 ) -DHFR ( Figure 4D ) which is sorted into the inner membrane . The import reaction was stopped after 10 , 20 or 30 min by dissipation of ΔΨ and mitochondria were subsequently treated with Proteinase K to remove non-imported precursor proteins . Even at permissive temperature , quantified import efficiency in the linear phase revealed significant reductions for both types of imported substrates ( Figure 4E ) , showing that Tim23N150A is clearly affected in protein import . Import experiments conducted at 37°C show the same trend with an even more pronounced reduction ( Figure 4—figure supplement 1 ) , while import experiments using the ADP/ATP carrier ( AAC ) and Cox12 revealed that other import pathways into mitochondria ( TIM22 and MIA ) were not impaired by the mutation ( Figure 4F and G ) . In fact , a slightly increased import efficiency for AAC is frequently observed when transport along the TIM23 pathway is affected ( Geissler et al . , 2002; Schulz et al . , 2011 ) . These observations led us to hypothesize that the reduced import capabilities of Tim23N150A were linked to the altered cation selectivity , which could be explained if selectivity defects lead to changed sensitivity of the mutant channel towards substrates . To test this , we analyzed the channel response of wild type Tim23 and Tim23N150A to presequences in single-channel experiments . As a substrate we used a peptide corresponding to the presequence of Cox4 ( Allison and Schatz , 1986 ) , a subunit of the cytochrome c oxidase , which is well characterized to study import processes and signal recognition biochemically ( Chacinska et al . , 2005; Lytovchenko et al . , 2013; Schulz et al . , 2011 ) and channel excitation electrophysiologically ( Lohret et al . , 1997; Martinez-Caballero et al . , 2007; Meinecke et al . , 2006; Ramesh et al . , 2016; Truscott et al . , 2001; van der Laan et al . , 2007 ) . The presequence peptide was titrated in increasing concentrations to the intermembrane space corresponding side of bilayer-incorporated wild type or Tim23N150A channels and current was recorded at constant holding potentials after each titration step . Tim23 reacted to higher holding potentials by partial or complete closing ( Figure 3A , B , E and F ) , which could mask presequence-induced activity increase or change channel behavior . Hence , we aimed to minimize such secondary effects by recording at lower holding potentials . We applied voltages of +80 mV , where the channel stayed primarily in an open state even during prolonged exposure but reacts to presequence activation . Wild type Tim23 showed a distinct activity increase , characterized by fast gating ( flickering ) , after addition of Cox4 ( Figure 5A , B and C ) . While Tim23N150A also responded with an increased gating frequency to Cox4 addition , the effect was drastically reduced in comparison to wild type Tim23 . Where wild type Tim23 channels could be activated to a relative increase in gating frequency of factor 50 ( Figure 5D , black curve ) , the gating frequency of Tim23N150A only changed by a factor 6 ( Figure 5D , red curve ) , resulting in an 88% reduction of voltage-activated gating from wild type to mutant Tim23 . To further prove that the relative reduction in gating frequency did not originate from e . g . a higher baseline of gating frequency of Tim23N150A compared to wild type Tim23 , we analyzed absolute gating frequencies in the absence of substrate peptides ( Figure 5E ) . Both proteins have near identical average gating frequencies in unstimulated conditions , excluding pre-activation effects . In summary , our data show that a decreased selectivity of Tim23 leads to reduced substrate sensitivity , which explains the impaired protein import capacity of mitochondria expressing a Tim23 selectivity mutant .
Tim23 , the eponymous core subunit of the TIM23 complex , was identified as the import channel for presequence carrying substrates more than 15 years ago ( Lohret et al . , 1997; Truscott et al . , 2001 ) . Although the basic channel characteristics have been described to some extend ( Martinez-Caballero et al . , 2007; Meinecke et al . , 2006; Truscott et al . , 2001 ) , the physiological relevance of these parameters as well as the molecular mode of function of the import pore are still enigmatic . Due to lack of structural information the current understanding is little more than that at the heart of the TIM23 complex a water-filled pore facilitates the passage of preproteins across the inner membrane . In this study , we investigated the molecular basis of Tim23’s ion-selectivity to link biophysical properties of the import channel to its physiological function . We report the identification of pore-lining amino acids that contribute to the channels cation selectivity . Interestingly , we found that a number of different residues are involved in discriminating between ions . All amino acids detected to be important for channel selectivity are strictly conserved in evolution , suggesting an essential role for the selectivity in protein transport across the inner membrane . In recent studies these residues were successfully cross-linked to preproteins in transit , showing their accessibility and exposure to the channel lumen ( Alder et al . , 2008; Malhotra et al . , 2013 ) . Our results imply that selectivity is not necessarily provided by a confined restriction zone within the channel but rather by specific channel surface characteristics throughout the length of the pore , which facilitate the passage of certain ions over others . Such a mechanism was proposed for other large pores to explain their selective properties ( Im and Roux , 2002; Kutzner et al . , 2011 ) . Even though these reports were mainly made for β-barrel proteins , the similarity of the electrophysiological properties between some β-barrel pores , like Tom40 or Sam50 , and α-helical import pores , like Tim23 or Tim22 , makes it appealing to speculate that the selectivity of these pores has a similar molecular nature . Most of the detected selectivity mutants in our study showed impaired growth . As an outlier Tim23G153A shows a mediocre reduction in selectivity while growth is relatively unaffected and we therefore hypothesize that a certain impairment of the selective properties can be compensated . We rigorously excluded mutant forms of Tim23 that showed decreased mitochondrial steady state levels or with a compromised TIM23 complex assembly and hence could not be used to link in vitro single-channel results with in vivo and in organello experiments . This led to the identification of Tim23N150A , which is expressed and assembled as wild type Tim23 . The potential across the inner membrane is unaffected in cells expressing Tim23N150A . In line with the uncompromised mitochondrial fitness , all channel parameters , especially Tim50IMS voltage-regulation , but selectivity and substrate sensitivity , are comparable with wild type Tim23 . Importantly , mitochondria containing Tim23N150A channels showed significantly reduced import capacity for matrix proteins . We therefore conclude that the drastically reduced selectivity renders Tim23 channels insensitive towards positively charged substrate peptides , which in turn explains the reduced import rates of preproteins . The position of the amino acid substitution N150A is found close to the beginning of transmembrane helix 2 and is therefore located at the matrix side of the channel . This suggests that the decreased substrate sensitivity characterized by an inactive , slowly gating channel in the presence of prepeptides cannot be explained by binding and activation of the substrate to the IMS side of the channel . Instead the substrate has to reach deep into the channel to be affected by a mutation at the matrix side . Similar to what was described for Tom40 ( Mahendran et al . , 2012 ) , the active , fast-gating Tim23 is therefore likely a transport competent state of the channel , triggered by peptides in transit within the channel that are discriminated by the channels selective properties . A decreased selectivity of Tim23 leads to an inability to be activated and therefore directly to decreased import rates . The strategy used in this study allowed us to link the biophysical properties of a protein translocase to its physiological function . While water-filled pores were identified at the heart of most organellar translocation complexes ( du Plessis et al . , 2011; Harsman et al . , 2010; Meinecke et al . , 2016; Neupert and Herrmann , 2007; Schmidt et al . , 2010; Sjuts et al . , 2017 ) , only basic channel characteristics were described in most cases . A correlation between the fascinating electrophysiological properties of these large pores and their physiological function remained circumstantial evidence . It was for example speculated for almost 20 years , that the cation selectivity of the Tim23 channel is important for recognition or transport of positively charged presequences , though no direct evidence was reported . Interestingly , many translocation pores show partially similar electrophysiological properties . Although structurally diverse , Tim23 , Tim22 , Tom40 and Sam50 all display comparable channel diameters and a preference for cations . The molecular nature of these characteristics as well as their physiological importance will be highly important problems to tackle in the future .
ScTim23 wild type and mutants were expressed from the plasmid pET10N containing an N-Terminal His10-Tag in E . coli strain BL21 ( DE3 ) . All mutants were generated from the wild type plasmid by site-directed mutagenesis . Inoculated cultures in LB-medium were grown to OD600 ≈ 0 . 7 , after expression ( induced by 1 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) , 37°C , 3 hr ) cells were lysed and inclusion bodies were purified ( Meinecke et al . , 2006; Tarasenko et al . , 2017 ) . Inclusion bodies were then denatured by 8 M Urea , 150 mM NaCl , 10 mM Tris-HCl , 50 mM Imidazole , pH 8 . 0 , applied to NiNTA-Agarose and eluted by the same buffer with 500 mM Imidazole . Isolated Tim23 was further subjected to size exclusion chromatography using a HiLoad 16/600 Superdex 75 column ( GE Healthcare , NJ , USA ) and single band purity was confirmed by SDS-PAGE . The presequence-peptide Cox4 ( MLSLRQSIRFFKPATRTLCSSRYLL ) was purchased from JPT Peptide Technologies ( DE ) as N-terminal amine and C-terminal amide . The IMS domain of Tim50 ( aa 132–476 ) was recombinantly expressed and purified to single band purity as described elsewhere ( Geissler et al . , 2002; Schulz et al . , 2011 ) . Lipids were purchased as L-α-Phosphatidylcholine ( PC ) , L-α-Phosphatidylethanolamine ( PE ) , L-α-Phosphatidylinositol ( PI ) , L-α-Phosphatidylserine ( PS ) and Cardiolipin ( CL ) from Avanti Polar Lipids ( AL , USA ) . The lipid mixture of 45:20:15:5:15 mol% PC:PE:PI:PS:CL in CHCl3 , closely resembling inner mitochondrial lipid composition ( van Meer et al . , 2008 ) , was dried with a nitrogen stream and resuspended in 100 mM KCl , 10 mM MOPS-Tris , pH 7 . 0 . Lipid suspension was thoroughly vortexed , subjected to seven freeze-thaw cycles and extruded through 200 nm membranes ( Whatman plc , UK ) to ensure unilamellarity and defined size distribution . Both liposomes and protein in urea were incubated with the mild detergent MEGA-9 ( Glycon , DE ) above CMC at 80 mM first separately then combined , at room temperature . Subsequently the liposome-protein-detergent mixture was subjected to dialysis against 5 L of liposome buffer to remove both urea and MEGA-9 . Incorporation success was monitored by Histodenz flotation assay and sodium carbonate extractions as described elsewhere ( Barbot et al . , 2015 ) . Electrophysiological experiments with Tim23 were carried out using the planar lipid bilayer technique , described in detail before ( Harsman et al . , 2011; Reinhold et al . , 2012 ) . Briefly , Tim23-containing proteoliposomes were added next to the bilayer in the cis chamber to enable fusion of liposomes with the bilayer . Asymmetrical buffer conditions for osmotically-driven fusion were 250 mM KCl , 10 mM MOPS-Tris , pH 7 . 0 in the cis chamber and 20 mM KCl , 10 mM MOPS-Tris , pH 7 . 0 , for a 12 . 5-fold KCl-gradient over the bilayer . The electric recordings were performed using two Ag/AgCl electrodes in glass tubes , embedded in a 2 M KCl agar-bridge to minimize junction potentials , with one electrode per chamber . The electrode in the trans chamber was the reference electrode as it was connected to the headstage ( CV-5-1GU ) of a Geneclamp 500B current amplifier ( both Molecular Devices , CA , USA ) , with the cis-electrode acting as ground . Currents were digitized by a Digidata 1440A A/D converter and recorded using the software AxoScope 10 . 3 and Clampex 10 . 3 ( all Molecular Devices ) . Analysis of the data was carried out using R-packages stepR ( Hotz et al . , 2013 ) and dbacf ( Tecuapetla-Gómez and Munk , 2015 ) and OriginPro 8 . 5 ( OriginLab , MA , USA ) . After incorporation of Tim23 into the lipid bilayer , symmetrical conditions were set by perfusion with 20x chamber volume of 250 mM KCl , 10 mM MOPS-Tris , pH 7 . 0 . These symmetrical buffer conditions were used for constant-voltage recordings and current-voltage relations . Asymmetrical buffers identical to the fusion conditions were used for reversal potential measurements . Tim23 typically inserts unidirectionally into the bilayer , with the IMS-domain of Tim23 exposed to trans . For concentration-dependent quantification of gating events , the synthetic peptide representing the presequence of the TIM23-substrate cytochrome c oxidase subunit 4 was titrated to the trans chamber in increasing concentrations . After addition , the buffer in the chamber was stirred for 2 min and then rests for 2 min before current recordings start . Constant-voltage currents were recorded for one minute , all gating events were counted and used to determine the gating frequency , i . e . events per minute . The open probability was calculated by dividing the mean by the maximum current . All yeast strains were grown in YP medium ( 1% yeast extract , 2% peptone ) with 2% glucose ( YPD ) or 3% glycerol ( YPG ) medium at 30°C . For plate growth test , synthetic medium containing 3% glycerol or 2% glucose was used . For generation of a Tim23 shuffling strain MB29 ( Geissler et al . , 2002 ) , endogenous TIM23 was replaced by homologous recombination with a LYS2 cassette in a strain expressing TIM23 from a URA3 containing plasmid . Wild type and mutant Tim23 were expressed by cloning TIM23 gene +1 kb upstream and downstream of the gene into pRS413 . Point mutations were introduced by side-directed mutagenesis . After transformation of these plasmids into the shuffling strain , 5-Fluoroorotic acid ( 5-FOA ) was used to select against URA3 containing plasmids harboring wild type TIM23 . For subsequent isolation of mitochondria , yeast cells were first grown in YPD medium at 30°C overnight , then diluted to OD600 = 0 . 2 in YPG medium and continued to grow for 24 hr at 30°C . Cells were then transferred to a bigger culture at OD600 = 0 . 2 in YPG and grown at 30°C for 16 hr , reaching a final OD600 of 2–3 . Isolation of mitochondria was handled as described before ( Schendzielorz et al . , 2017 ) . For import of [35S]-methionine labeled precursors into isolated mitochondria , proteins were translated using rabbit reticulocyte lysate ( Promega , WI , USA ) . Reaction was performed in import buffer ( 250 mM sucrose , 10 mM MOPS/KOH pH 7 . 2 , 80 mM KCl , 2 mM KH2PO4 , 5 mM MgCl2 , 5 mM methionine and 3% fatty acid-free BSA ) supplemented with 2 mM ATP and 2 mM NADH . To stop the import reaction , membrane potential was disrupted using final concentration of 1 µM valinomyin , 8 µM antimycin A and 20 µM oligomycin and samples were Proteinase K ( PK , 20 µg/ml ) treated for 10 min on ice . PK was inhibited with 2 mM phenylmethylsulphonyl fluoride ( PMSF ) for 10 min on ice; mitochondria were pelleted , washed with SEM ( 250 mM sucrose , 20 mM MOPS pH 7 . 2 , 1 mM EDTA ) and further analyzed by SDS-PAGE and autoradiography . Import and assembly of the ADP/ATP carrier protein ( AAC ) via TIM22 was performed using the standard protocol and further analyzed by Blue Native PAGE and autoradiography as described before ( Schulz et al . , 2011 ) . Import of Cox12 via MIA was performed as described ( Gornicka et al . , 2014 ) . Briefly , in addition to the standard protocol the reticulocyte lysate was diluted 1:2 in saturated ammonium sulfate ( ( NH4 ) 2SO4 ) and precipitated on ice . The pellet was resuspended in 8 M urea , 10 mM DTT , 30 mM MOPS , pH 7 . 2 and then added to mitochondria in import buffer without BSA . The Cox12 import reaction was stopped with 25 mM iodoacetamide ( IAA ) and PK . Quantifications were performed using ImageQuant TL ( GE Healthcare , NJ , USA ) using a rolling ball background subtraction . TIM23 complex isolation was carried out essentially as described ( Herrmann et al . , 2001 ) . Briefly , mitochondria were resuspended to 1 mg/ml in solubilization buffer ( 20 mM Tris/HCl pH 7 . 4 , 150 mM NaCl , 10% glycerol ( w/v ) , 1 mM PMSF , 1% digitonin ) and kept on ice for 20 min . Insoluble parts were removed by centrifugation at 14000 x g for 10 min and supernatant was incubated with Tim23-specific antibodies cross-linked to Protein A sepharose beads . After 30 min of binding on a rotating wheel at 4°C and 5x washing with 500 µl washing buffer ( solubilization buffer with 0 . 3% digitonin ) , samples where eluted with 50 µl 0 . 1 mM glycine pH 2 . 8 ( neutralized with 5 µl 1 M Tris base ) . Membrane potential was measured using 3 , 3’-dipropylthiadicarbocyanine iodide ( DiSC3 ( 5 ) ) . Mitochondria were resuspended in buffer containing 600 mM sorbitol , 1% ( wt/vol ) BSA , 10 mM MgCl2 and 20 mM KPi , pH 7 . 4 to a concentration of 166 µg/ml . Changes in fluorescence were assessed with a F-7000 fluorescence spectrophotometer ( Hitachi , JP ) at room temperature with excitation at 622 nm , emission at 670 nm and slits of 5 nm . Components were added to the cuvette in the fowling order: 500 µl of buffer , DiSC3 ( 5 ) , 83 µg of mitochondria , 1 µM valinomycin . To compare relative differences in membrane potential , the difference in fluorescence before and after addition of valinomycin was used . Mitochondria were solubilized as described for protein complex isolation , with 2 mg/ml instead of 1 mg/ml , and 50 µl were loaded to a Superose 6 increase 3 . 2/300 and eluted in solubilization buffer containing 0 . 3% digitonin . The first 1250 µl were discarded , the following 750 µl were collected in 50 µl fractions and subjected to SDS-PAGE and Western blot . Quantifications were performed using ImageQuant TL ( GE Healthcare , NJ , USA ) using a rolling ball background subtraction . | The cells of animals , plants and other eukaryotic organisms contain compartments known as organelles that play many different roles . For example , compartments called mitochondria are responsible for supplying the chemical energy cells need to survive and grow . Two membranes surround each mitochondrion and energy is converted on the surface of the inner one . Mitochondria contain over 1 , 000 different proteins , most of which are produced in the main part of the cell and have to be transported into the mitochondria . A transport protein called Tim23 is part of a larger group or ‘complex’ of proteins that helps to import many other proteins into the mitochondria . This complex sits in the inner membrane , with the Tim23 protein forming a large , water-filled pore through its core that provides a route for proteins to pass through the membrane . Proteins are made of building blocks called amino acids . The proteins transported by the complex containing Tim23 all have a short chain of amino acids at one end known as an N-terminal presequence . However , it is not clear how the inside of the Tim23 channel identifies and transports this presequence to allow the right proteins to pass through the inner membrane . Denkert , Schendzielorz et al . studied the normal and mutant versions of a Tim23 channel from yeast to find out which parts of the protein are involved in detecting the N-terminal presequence after it enters the pore . The experiments show that there are several amino acids in Tim23 that play important roles in this process . Furthermore , mitochondria containing mutant Tim23 channels , that are less able to identify the N-terminal presequence , are impaired in their ability to import proteins . Tim23 proteins in humans and other organisms also contain most or all of the specific amino acids identified in this study , suggesting that the findings of Denkert , Schendzielorz et al . will also apply to other species . Furthermore , the experimental strategy used in this study could be adapted to investigate transport proteins in other cell compartments . | [
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] | 2017 | Cation selectivity of the presequence translocase channel Tim23 is crucial for efficient protein import |
Exercise-induced cognitive improvements have traditionally been observed following aerobic exercise interventions; that is , sustained sessions of moderate intensity . Here , we tested the effect of a 6 week high-intensity training ( HIT ) regimen on measures of cognitive control and working memory in a multicenter , randomized ( 1:1 allocation ) , placebo-controlled trial . 318 children aged 7-13 years were randomly assigned to a HIT or an active control group matched for enjoyment and motivation . In the primary analysis , we compared improvements on six cognitive tasks representing two cognitive constructs ( N = 305 ) . Secondary outcomes included genetic data and physiological measurements . The 6-week HIT regimen resulted in improvements on measures of cognitive control [BFM = 3 . 38 , g = 0 . 31 ( 0 . 09 , 0 . 54 ) ] and working memory [BFM = 5233 . 68 , g = 0 . 54 ( 0 . 31 , 0 . 77 ) ] , moderated by BDNF genotype , with met66 carriers showing larger gains post-exercise than val66 homozygotes . This study suggests a promising alternative to enhance cognition , via short and potent exercise regimens . Funded by Centre for Brain Research . NCT03255499 .
Possibly the most reliable means to induce cognitive improvements behaviorally , physical exercise has become known for its effects on the brain in addition to its well-documented impact on the body ( see for a review Moreau and Conway , 2013 ) . Individuals with higher cardiovascular fitness typically show higher performance on a wide range of cognitive measures , from cognitive control ( Pontifex et al . , 2011 ) to working memory ( Erickson et al . , 2013 ) and executive functioning ( Colcombe and Kramer , 2003; Hillman et al . , 2008 ) . In addition , long-term sport practice is associated with higher working memory capacity ( Moreau , 2013 ) , spatial ability ( Moreau , 2012 ) , and more efficient visual processing of movements ( Güldenpenning et al . , 2011 ) . In neuroimaging studies , greater fitness indices have also been linked to differences in white matter integrity ( Chaddock-Heyman et al . , 2014; Voss et al . , 2013 ) , as well as with larger hippocampal ( Weinstein et al . , 2012 ) and cortical areas ( Erickson et al . , 2009; Makizako et al . , 2015 ) . These results are corroborated by increased long-term potentiation ( LTP ) in the visual system of physically fit individuals compared to the non-fit ( Smallwood et al . , 2015 ) , a key finding given the primary role of LTP in major cognitive processes such as learning and memory ( Bliss and Collingridge , 1993 , Bliss and Lomo , 1973 ) . Importantly , these correlational findings are further supported by longitudinal designs . Exercise interventions can elicit cognitive improvements in diverse populations ranging from children ( Davis et al . , 2011 ) to the elderly ( Fabre et al . , 2002 ) , as well as in individuals with various clinical conditions such as developmental coordination disorders ( Tsai et al . , 2012 ) and schizophrenia ( Pajonk et al . , 2010 ) . Furthermore , improvements appear to be dose-dependent ( Davis et al . , 2007; Vidoni et al . , 2015 ) , and are not restricted to atypical or clinical populations—adults at their cognitive peak show similar benefits ( Gomez-Pinilla and Hillman , 2013; Moreau and Conway , 2013; Voss et al . , 2011 ) . In school settings , exercise interventions have shown to be associated with higher levels of academic achievement ( Coe et al . , 2006 ) , and exercise regimens in children typically lead to improvements in various aspects of cognition , including executive function , cognitive control and memory ( see for a review Tomporowski et al . , 2015a ) . Interventions implemented in early stages of life allow capitalizing on higher cortical plasticity , potentially maximizing their impact ( Cotman and Berchtold , 2002 ) . The appeal of early interventions has motivated a whole line of research exploring the effect of physical exercise regimens on behavior , cognitive function , and scholastic performance ( Castelli et al . , 2007; Davis et al . , 2007 , Davis et al . , 2011; Donnelly et al . , 2016; Jackson et al . , 2016; Pontifex et al . , 2013 ) . Consistent with these findings , Sibley et al . found a robust effect of physical exercise on cognitive function in children , in a meta-analytic assessment of the literature at the time ( Sibley and Etnier , 2003 ) . Most studies in this line of research have evaluated the impact of a rather specific type of regimen , based on aerobic exercise . Usually defined as a sustained regimen performed at moderate intensity ( e . g . , McArdle et al . , 2006 ) , aerobic exercise has come to be accepted as the form of exercise typically associated with neural changes and cognitive enhancement ( Hillman et al . , 2008; Thomas et al . , 2012 ) , for at least two reasons . First , current interventions are rooted in early findings in the animal literature , which typically investigated the effects of physical exercise in rodents—animals who naturally favor aerobic forms of exercise ( Gould et al . , 1999; Shors et al . , 2001 ) . Second , the most dramatic gains in cognition have been observed in the elderly ( Erickson et al . , 2015; but see also Etnier et al . , 2006; Young et al . , 2015 , for a more nuanced view ) , a population for which moderate-intensity exercise is seemingly most adequate . Subsequent studies have stemmed from this line of research , therefore expanding the initial paradigm to a wider range of populations . Yet current trends of research suggest other promising directions . For example , regimens based on resistance training have shown sizeable effects on cognition ( Best et al . , 2015; Liu-Ambrose et al . , 2012; van Uffelen et al . , 2007 ) , despite underlying mechanisms of improvements being potentially different from those elicited by aerobic exercise ( Goekint et al . , 2010 ) . More complex forms of motor training that combine high physical and cognitive demands also appear encouraging ( Moreau et al . , 2015; Tomporowski et al . , 2015b ) . Moreover , a compelling body of research in the field of exercise physiology indicates that interventions based on short , intense bursts of exercise can induce physiological changes that mirror those following aerobic exercise on a wide variety of outcomes . These include measures of cardiovascular function ( Chrysohoou et al . , 2015; Gayda et al . , 2016 ) , overall fitness ( Benda et al . , 2015 ) , and general health ( Milanović et al . , 2015 ) . In some cases , physiological improvements following high-intensity training ( HIT ) can even go beyond those typically following aerobic regimens ( Rognmo et al . , 2004 ) . For example , HIT appears to be particularly effective to increase the release of neurotrophic factors essential to neuronal transmission , modulation and plasticity—potentially surpassing aerobic exercise regimens ( Ferris et al . , 2007 ) . This body of research is promising , as it suggests a plausible mechanism by which intense bursts of exercise could meaningfully influence cognitive function and behavior . A few studies have partially tested this idea . Short bouts of exercise have been shown to alleviate some of the difficulties typically associated with Attention-deficit/hyperactivity disorder ( ADHD ) in children ( Piepmeier et al . , 2015 ) , demonstrating the potential of this type of intervention to enhance cognitive abilities . The benefits reported in this study were not limited to children diagnosed with ADHD , however—typical children also exhibited cognitive improvements . More strikingly perhaps , Pontifex and colleagues found that a single 20 min bout of exercise was sufficient to improve cognitive function and scholastic performance in children ( Pontifex et al . , 2013 ) . This is an impressive finding , given that exercise-induced cognitive improvements typically occur after longer training periods ( Etnier et al . , 2006; Sibley and Etnier , 2003 ) . Importantly , these effects should be distinguished from short-term improvements immediately following acute bouts of exercise ( Tomporowski , 2003 ) , which typically dissipate after a few hours . The two types of outcomes ( short-term consequences of single acute sessions vs . more durable benefits ) are sometimes conflated , resulting in misleading conclusions ( Jackson et al . , 2016 ) . The hypothesized mechanisms are , however , different—heightened state of alertness induced by neurotransmitter increases for the former ( Tomporowski , 2003 ) , and slower but more durable neurophysiological adaptations in the case of the latter ( Erickson et al . , 2011 , Erickson et al . , 2015; Moreau and Conway , 2013; Voss et al . , 2013 ) . One aspect that remains to be formerly investigated relates to the specific influence of exercise intensity . Although focused on short sessions , the aforementioned studies utilized regimens of moderate intensity—a reported 62–72% ( Piepmeier et al . , 2015 ) and 65–75% ( Pontifex et al . , 2013 ) of individual maximum heart rate , respectively . Yet based on findings from the physiological literature ( e . g . , Rognmo et al . , 2004 ) , there are clear mechanisms via which exercising at a high intensity could influence cognition in a meaningful manner . Arguably , HIT could elicit improvements above and beyond those typically following short sessions of moderate intensity , and provide a legitimate , time-efficient alternative to longer aerobic exercise regimens ( Costigan et al . , 2015 ) . Together , the conjunction of promising early findings and clear mechanisms of action has prompted discussions to implement HIT interventions more systematically within the community ( Gray et al . , 2016 ) . In an effort to better understand and predict individual responses to physical exercise interventions , several studies have investigated the role of specific genetic polymorphisms on the magnitude of exercise-induced improvements ( Erickson et al . , 2008 , Erickson et al . , 2013 ) . Among these , many have focused on the brain-derived neurotrophic factor ( BDNF ) val66met polymorphism , given its direct influence on serum BDNF concentration ( Lang et al . , 2009 ) . BDNF is known to support neuronal growth and has been shown to facilitate learning , a process that in turn induces BDNF production ( Berchtold et al . , 2001; Cotman and Berchtold , 2002; Kesslak et al . , 1998 ) . This dynamic coupling makes BDNF an important underlying factor of exercise-induced cognitive improvements . Consistent with this idea , it has been proposed that individuals whose particular BDNF polymorphism is associated with lower activity-dependent BDNF levels ( met66 carriers ) might benefit from exercise interventions to a greater extent than individuals whose activity-dependent BDNF levels are higher ( val66 homozygotes ) . Similarly , a few studies have shown that individuals with lower cardiovascular function might maximize benefits from physical exercise interventions designed to improve cognitive function ( Sofi et al . , 2011; Strong et al . , 2005 ) . The implicit assumption is that although lack of physical exercise might be a limiting factor for individuals whose fitness level is low , more active individuals might be less impacted by an exercise intervention program ( Heyn et al . , 2004; Lautenschlager et al . , 2008; Sniehotta et al . , 2006 ) . Despite the intuitive appeal of this assumption , several studies , including one from our group , have failed to find a positive correlation between exercise-induced cognitive improvements and the associated physiological changes ( Moreau et al . , 2015; Tsai et al . , 2014 ) . Arguably , the absence of a clear link between the hypothesized physiological mechanisms of improvements and tangible cognitive gains might stem from the plurality and complexity of variables underlying these changes , rendering coherent associations elusive . Despite similarities in the physiological mechanisms linking aerobic exercise and HIT on cognition , the precise impact of the latter on cognitive performance remains to be confirmed experimentally . In the present study , we tested the viability of HIT as a substitute for aerobic exercise to induce cognitive improvements in school populations . In particular , we postulated that HIT would result in improvements in measures of cognitive control and working memory , as both constructs have been linked to fitness levels ( Pontifex et al . , 2011 ) and appear to be malleable via aerobic regimens ( Erickson et al . , 2013 ) . The choice of these constructs was also motivated by previous research showing the malleability of both cognitive control and working memory in training studies , thus providing theoretical and empirical support for the plausibility of expected improvements ( Hampshire et al . , 2012; Mishra et al . , 2014 ) . Consistent with recent efforts to better model and understand the mechanisms of cognitive improvement ( Young et al . , 2015; Moreau and Waldie , 2015 ) , the present study also intended to address interindividual variability so as to isolate the underlying factors of improvement . Based on previous literature ( Erickson et al . , 2013; Moreau et al . , 2015 ) , we hypothesized that exercise training would elicit substantially larger cognitive benefits in individuals whose cardiovascular fitness is low , and in BDNF met66 carriers , whose activity-dependent BDNF levels are naturally limited . Finally , we expected physiological improvements with exercise , as typically induced from aerobic interventions ( see for a review Gomez-Pinilla and Hillman , 2013 ) .
Participants in the exercise group saw a greater decrease in resting heart rate than controls , as demonstrated by a Bayesian ANCOVA with Condition ( HIT vs . Control ) as a fixed factor and baseline heart rate as a covariate . The full model was preferred to the model with baseline resting heart rate only: BFM = 40 . 45 , and was the most likely given our data: P ( M | Data ) =0 . 93 , assessed from equal prior probabilities ( Figure 1A ) . In-depth analyses focused on individuals with elevated resting heart rate at baseline allowed further insights into the potency of our exercise intervention . Specifically , a Bayesian t-test on resting heart rate change showed a sizeable difference between the two groups , with larger gains for the HIT group ( BF10 = 3 . 47 , with Mgain = 6 . 11 , SDgain = 11 . 64 and Mgain = 1 . 89 , SDgain = 8 . 63 , for HIT and Control , respectively; Hedges’ g = 0 . 41 ( 0 . 09 , 0 . 73 ) . Test-retest reliability—assessed via a comparison between pretest and posttest resting heart rate for controls only—was acceptable ( r = 0 . 77 , BF10 = 1 . 75 e+51 ) . Physiological data also provided important indications about workout intensity idiosyncrasies . We used resting heart rate at pretest to determine target intensities for each individual , such that: ( 1 ) HRTarget=HRReset+δ ( HRMax−HRRest ) where HRMax = 220 Age , and δ is set to . 80 . This yielded an individual target range ( HRTarget or above ) while exercising . We then compared this range with the maximum intensity measured during each workout , to obtain an index of accuracy , or agreement , between target zone and actual effort . Results showed that participants did exercise at a suitable intensity overall , as expressed by the deviation from individual target heart rate values ( MDev = 1 . 65 , SDDev = 5 . 87; Figure 1B ) . Importantly , effort intensity was maintained stable across time , as demonstrated by moderate evidence favoring the null model over an alternate model that included time as a predictor of maximum heart rate in a Bayesian linear regression analysis [BFM = 2 . 87 , P ( M | Data ) =0 . 74 , Figure 1C] . Because individual resting heart rates tended to decrease throughout the intervention , sustained effort indicates that individuals incrementally increased workout volume , which was confirmed by additional measures such as step count [BFM = 2 . 979e + 10 , P ( M | Data ) ≈ 1 , for the model that included Session as a covariate , Figure 1D] . Together , these results support the notion that the intervention was adaptive , allowing workout intensities tailored to each individual . Physiological improvements are informative in two key aspects: they provide corroborating evidence for the hypothesized changes associated with exercise , and they allow identifying idiosyncratic parameters often characteristic of training interventions . However , the main goal of a cognitive intervention is to elicit cognitive gains , which were the primary outcomes of the present intervention . In the following sections , we first identify latent constructs from cognitive assessments , before discussing the impact of the intervention on these two constructs . An exploratory factor analysis using principal component extraction and promax rotation was performed on all six cognitive measures at pretest . Although less common than orthogonal rotations , oblique rotations such as promax allow factors to correlate; this property is especially appropriate when the factors extracted are assumed to be correlated to some degree−a reasonable assumption given our design . The corresponding scree plot and eigenvalues ( i . e . the variance in all variables accounted for by each factor ) suggested a two-component solution ( see factor loadings in Table 1 and Table 1—source data 1 and 2 ) . Subsequent test of the two-factor model confirmed that the number of factors was sufficient ( χ2 ( 4 ) =0 . 59 , p=0 . 96; Bayesian Information Criterion , BIC = −22 . 05 ) . We refer to these two components hereafter as Cognitive Control and Working Memory . The correlation between the two factors was r = 0 . 32 . Uniqueness values indicated that the tasks spanned an adequate range within the sample space of each construct ( Table 1 ) . Here , we report cognitive improvements broken down by constructs , defined based on the factors extracted from the exploratory factor analysis . A Bayesian repeated measures ANOVA on Cognitive Control scores , with Session ( pretest vs . posttest ) as a within factor and Condition ( HIT vs . Control ) as a between factor , showed moderate evidence for the interaction model over the main effect model [BFM = 3 . 38 , p ( M | Data ) =0 . 46; Table 2] . Participants in the HIT group showed larger improvements than controls from pretest to posttest ( Mgain = 0 . 25 , SDgain = 0 . 6 and Mgain = 0 . 08 , SDgain = 0 . 47 , respectively , Hedges’ g = 0 . 31 [0 . 09 , 0 . 54]; Figure 2A , see also Figures 4–6 ) . A Bayesian repeated measures ANOVA on Working Memory scores , with Session ( pretest vs . posttest ) as a within factor and Condition ( HIT vs . Control ) as a between factor showed strong evidence for the interaction model over the main effect model [BFM = 5233 . 68 , p ( M | Data ) ≈1; Table 3] . Participants in the HIT group showed larger improvements than controls from pretest to posttest ( Mgain = 0 . 48 , SDgain = 0 . 83 and Mgain = 0 . 12 , SDgain = 0 . 44 , respectively , Hedges’ g = 0 . 54 [0 . 31 , 0 . 77]; Figure 2B ) . Because the cognitive improvements we reported are presumably based on physiological changes , we directly tested the relationship between the two types of variables . A Bayesian regression analysis showed that change in resting heart rate was a reliable predictor of cognitive gains in the HIT group , with respect to Cognitive Control ( BF10 = 6 . 34 , p ( M | Data ) =0 . 86 ) . This was not the case in the Control group ( BF10 = 0 . 20 , p ( M | Data ) =0 . 16 ) . The contrast was weaker when comparing Working Memory gains in the HIT group ( BF10 = 0 . 56 , p ( M | Data ) =0 . 36 ) with those of the Control group ( BF10 = 0 . 18 , p ( M | Data ) =0 . 15 ) . Additional Bayesian regression analyses showed that lower resting heart rate at pretest did not predict improvements in either Cognitive Control or Working Memory in the HIT group ( BF10 = 0 . 41 , p ( M | Data ) =0 . 29 . and BF10 = 0 . 18 , p ( M | Data ) =0 . 15 , respectively ) . This was also the case when the analyses were restricted to the . 75 quantile of individuals with the lowest resting heart rate at baseline ( BF10 = 0 . 34 , p ( M | Data ) =0 . 25 . and BF10 = 0 . 62 , p ( M | Data ) =0 . 38 , respectively ) . Overall , baseline resting heart rate was a fairly noisy measure ( M = 85 . 2 , SD = 14 . 77 , over the entire sample ) and this might have contributed to the lack of clear impact of resting heart rate change on cognitive function . A subsample of our data allowed for a better understanding of individual differences in exercise-induced cognitive improvements . Specifically , we looked at the effect of variations in the BDNF polymorphism on cognitive gains in the HIT group , via a comparison between met66 carriers ( i . e . met66/ met66 or val66/ met66 ) and non-carriers ( val66 homozygotes ) . Separate Bayesian repeated measures ANOVAs on Cognitive Control and Working Memory scores , with Session ( pretest vs . posttest ) as a within factor and BDNF polymorphism ( val66 homozygotes vs . met66 carriers ) as a between factor showed strong evidence for the interaction model in both cases [BFM = 31 . 17 , p ( M | Data ) =0 . 89 , and BFM = 675 . 92 , p ( M | Data ) =0 . 99 , for Cognitive Control and Working Memory , respectively , see Table 4 and Table 5] . These findings suggest that met66 carriers benefited to a greater extent than non-carriers from the exercise intervention ( Cognitive Control: Mgain = 0 . 93 , SDgain = 1 . 20 and Mgain = 0 . 05 , SDgain = 0 . 13 , Hedges’ g = 1 . 36 [0 . 52 , 2 . 2]; Working Memory , Mgain = 0 . 87 , SDgain = 0 . 64 and Mgain = 0 . 14 , SDgain = 0 . 24 , Hedges’ g = 1 . 83 [0 . 94 , 2 . 72]; Figure 3 ) . Unequal baseline scores cannot fully account for this effect since evidence for differences in Cognitive Control was limited at pretest ( BF10 = 4 . 03 , Error ( % ) =1 . 55 e −6 from a Bayesian independent samples t-test ) and more substantial , but unable to account for the full effect , for Working Memory ( BF10 = 17 . 47 , Error ( % ) =1 . 22 e −6 from a Bayesian independent samples t-test ) . Together , these findings indicate that although genetic variations in the BDNF polymorphism are associated with cognitive differences , the latter are malleable and can be reduced with physical exercise . All priors used in the reported analyses are default prior scales ( Morey and Rouder , 2015 ) . For Bayesian repeated measures ANOVA and ANCOVA , the prior scale on fixed effects is set to 0 . 5 , the prior scale on random effects to 1 , and the prior scale on the covariate to 0 . 354 . The latter is also used in Bayesian Linear Regression . The Bayesian t-test uses a Cauchy prior with a width of √2/2 ( ~0 . 707 ) , that is half of parameter values lies within the interquartile range [−0 . 707; 0 . 707] . It is worth pointing out that the Bayesian repeated measures ANOVA that showed only moderate evidence for the effect of our HIT intervention on Cognitive Control shows stronger evidence with a slight variation on the prior scale . Although this variation in priors is consistent with our data and provides stronger evidence for our claim , we chose to report analyses with default prior scales , as these were the intended parameters a priori . For transparency , we plotted below the prior and posterior distribution for the comparison between Conditions ( HIT vs . Control ) for Cognitive Control ( Figure 4 ) , as well as the Bayes Factor robustness check ( Figure 5 ) . Both indicate that our findings are robust and supported by a wide range of priors , as corroborated by a sequential analysis ( Figure 6 ) . Broadly speaking , MCMC methods approximate the true posterior density p ( θ | y ) by constructing a Markov chain on the state space θ ∈ Θ . The probability of the subsequent state in a given chain can be defined as: ( 2 ) P ( Xn+1=in+1|Xn=In ) , I ∈ θwhere {X0 , X1 , . . } is a sequence of random variables and θ is the state space . Accordingly , the state at time step n + 1 is dependent only on the state at time n . This process is best represented with a random walk where each vertex is defined by θ , and weighted by the transition probabilities: ( 3 ) pij=P ( Xn+1=j|Xn=i ) , i , j∈θ In the analyses reported in the paper , MCMC was used to generate posterior samples via the Metropolis-Hastings algorithm ( see for details Rubinstein and Kroese , 2011 ) . All analyses were set at 10 , 000 iterations , with diagnostic checks for convergence . One chain per analysis was used for all analyses reported in the paper , with a thinning interval of 1 ( i . e . , no iteration was discarded ) . We reported Bayesian analyses throughout the paper . Because we understand that some readers may wish to compare these results with the equivalent frequentist analyses , we are providing all of these herein , in the order of presentation in the paper . Note that an a priori power analysis based on previous studies ( Erickson et al . , 2013; Moreau et al . , 2015 ) indicated the need for a minimum N of 129 participants per group to detect an effect of d = 0 . 35 on the primary outcome measures , with 1 – β = 0 . 80 and α = 0 . 05 . The actual sample size of the present study ( N = 152 and N = 153 , for HIT and control groups , respectively ) allowed an a priori power of . 86 , given d and α constant . We present below analyses for each cognitive tasks included in this study . Descriptive statistics are reported in Table 6 . Here , we report analyses for which our a priori hypotheses were null effects . These variables were collected either to control for potential confounds , or for exploratory purposes . There was no difference between groups regarding self-reported enjoyment or motivation ( W = 12058 , p=0 . 54 and W = 11497 , p=0 . 86 , respectively ) . This finding allows controlling for expectation effects , and thus stronger causal claims ( Boot et al . , 2013; Stothart et al . , 2014 ) . In addition , participants’ self-reported belief about cognitive malleability ( i . e . , mindset ) indicated a statistically significant difference ( p<0 . 03 ) in favor of the control group ( M = 7 . 11 , SD = 2 . 65 and M = 6 . 42 , SD = 2 . 74 , respectively , Hedges’ g = 0 . 26 [0 . 03 , 0 . 48] ) . There was no statistically significant difference between groups at either time points ( pretest or posttest ) in terms of ethnic background , age , gender , handedness , height , weight , diagnosis of learning disorder , brain trauma or epileptic seizures , current or past enrolment in a remediation or a cognitive training program , English as first language , videogaming experience , physical exercise , self-reported happiness , sleep quality , or general health .
The present study reported the first experimental evidence that HIT can elicit robust cognitive improvements in children . We confirmed the main hypothesis that exercise could induce gains in both cognitive control and working memory , as assessed from multiple measures . This finding is particularly promising given that the two constructs are reliable predictors of success in many domains , including professional and academic ( Deary et al . , 2007 ) ; in the classroom , cognitive control and working memory have been associated with effective learning and overall achievement ( Rohde and Thompson , 2007 ) . Importantly , these effects are also meaningful at the level of single tasks . Our main findings thus emphasize the potency of short but intense exercise interventions to enhance cognition , and suggests that aerobic exercise is not the sole means to elicit cognitive gains , in line with a growing body of research ( Liu-Ambrose et al . , 2012; Moreau et al . , 2015; Pesce et al . , 2016; Tomporowski et al . , 2015b ) . HIT appears to be a viable and promising alternative to longer workouts to enhance cognition . In addition to the main effect of training , we also postulated that specific genetic profiles would be correlated with different responses to training , with BDNF met66 carriers ( i . e . met/met or val/met ) benefiting from exercise to a greater extent . This hypothesis , confirmed by the present findings , was based on previous literature showing a relationship between BDNF polymorphism and serum BDNF ( Lang et al . , 2009 ) , and the influence of exercise interventions on the latter ( Leckie et al . , 2014 ) . BDNF met66 carriers showed greater gains from pretest to posttest on both cognitive constructs . As the substitution from valine to methionine at codon 66 typically results in decreases of activity-dependent secretion of BDNF at the synapse ( Egan et al . , 2003 ) , BDNF met66 carriers are thought to be particularly impacted by post-exercise BDNF increases ( Nascimento et al . , 2015 ) . Conversely , val66 homozygotes might benefit less from BDNF increases given above-average baseline levels . This finding is interesting because of its predictive power−controlling for BDNF polymorphism can allow more accurate forecasting of individual training responses , and better estimates of effect size . Given the current trend toward more personalized interventions ( Medalia and Richardson , 2005; Moreau and Waldie , 2015 ) , factoring in genetic information has the potential to refine and improve regimens , for each individual . Importantly , the main effect of exercise on cognitive function is unlikely to be explained by the placebo effect . Self-reported measures of enjoyment and motivation did not differ between groups , supporting the notion that both types of intervention were equally appealing to children , or at least not fundamentally different with respect to intrinsic motivating factors . In addition , we also controlled for mindset—the degree to which individuals believe their cognitive abilities can change over time ( Dweck et al . , 1995 ) . Previous research suggests that individual mindsets may be of influence in cognitive growths: individuals with a more ‘malleable’ mindset are thought to be more likely to improve in cognitive and academic domains than individuals with a ‘fixed’ mindset ( Paunesku et al . , 2015 ) . Although we did find a difference between conditions , it was to the advantage of children in the control group , who held a more malleable view about the dynamic properties of cognitive function than did the HIT group . This finding was reported as an additional analysis in the Results section , rather than in the main section , because the effect was not hypothesized a priori . In any case , and with warranted caution regarding post hoc interpretation , this effect would suggest that controls were more likely to improve over time , an assumption that was not corroborated by our main finding . If anything , this strengthens our main claim—greater improvements in the HIT condition are inconsistent with a differential placebo effect , a critical point in light of recent findings in the field of cognitive training ( Foroughi et al . , 2016 ) . Effort-dependent variables provided additional insight about the mechanism underlying improvements . Consistent with previous interventional studies that have investigated the influence of exercise on cognition , we actively monitored several variables throughout the intervention . We could thus ensure that training was adequate , performed at a suitable intensity , and could test directly the dynamic coupling of these variables and their effects on cognitive outcomes . Indeed , we found that almost all participants stayed within the overall targeted range of effort required by the design of the workout and by initial individual measurements . This indicates a high degree of agreement , or fidelity , to the intended protocol—an essential component of the intervention given the underlying assumption that participants would exercise at a high intensity . Performed at a more moderate intensity , the same regimen becomes an aerobic training program , for which substantially longer time commitment might be required to elicit improvements . In addition , it is important to note that participants incrementally increased workout load , thus maintaining appropriate intensity throughout the intervention . Arguably , this adaptive property emerged from the design of the intervention , whereby participants were encouraged to exercise at maximum intensity at the time of the workout—an intrinsically individual and dynamic variable by definition . In the present study , the effect of exercise could also be appreciated on changes in resting heart rate from pretest to posttest , a finding that confirms previous research in the field ( e . g . , Moreau et al . , 2015 ) . More specifically , exercise appears to be a valuable regulating mechanism , with elevated resting heart rate values likely to normalize as a result of HIT . This effect could not be attributed to regression toward the mean , as it was not found in controls , thus suggesting that the intervention is especially beneficial to individuals who need it most . Together with the finding that HIT benefited more individuals with a genetic polymorphism ( BDNF met66 carriers ) that was associated with lower cognitive performance , this idea emphasizes the relevance of exercise interventions to individuals with specific genetic or physiological attributes to reduce interindividual differences ( Gómez-Pinilla et al . , 2001; Leckie et al . , 2014 ) . Disparities are genuine , yet targeted interventions allow low-performing individuals to improve dramatically . Our design allowed delving deeper into the relationship between exercise and cognitive improvements . In particular , we found that change in resting heart rate was predictive of cognitive control , but not working memory , gains . This finding is of interest because we postulated that the mechanisms of improvement were physiological , given that HIT has been shown to elicit neurophysiological changes similar to those following aerobic exercise regimens ( Ferris et al . , 2007 ) . However , we should point out that baseline resting heart rate did not predict gains in cognitive control or working memory in the HIT group , emphasizing the inherent noise associated with the relationship between physiological and cognitive measures . This lack of clear association between both types of variables is fairly common in the literature , suggesting that the hypothesized mechanisms of improvements are difficult to elucidate ( Moreau et al . , 2015; Tsai et al . , 2014 ) . Potentially , this also suggests that other variables may be of importance , a question we attempted to address with additional measurements , in an exploratory manner ( see Additional Analyses in the Results section ) . Regardless of the strength of the evidence reported in the present study , one might question the genuine impact of such a short training regimen . Although perhaps counterintuitive , the extreme potency of short , intense bursts of exercise has come to light in recent years ( Lucas et al . , 2015; Rognmo et al . , 2004 ) . In a review of the impact of HIT on public health , Biddle and Batterham , 2015 go as far as to premise their entire argument on the idea that the cardio-metabolic health outcomes are not to be questioned—rather , their only concern was about whether or not such regimens can be widely implemented and sustained over time ( Biddle and Batterham , 2015 ) . In a similar vein , Costigan and colleagues concluded that HIT is a time-efficient approach for improving cardiorespiratory fitness in adolescent populations ( Costigan et al . , 2015 ) . These are strong , unequivocal statements , which reflect current views in exercise physiology—HIT has tremendous health benefits , with little , if any , disadvantages . More direct evidence for the impact of HIT on brain function comes from neurophysiological studies . In an experiment that directly assessed the impact of short bursts of exercise on BDNF levels , Ferris et al . found that exercise leads to BDNF increases , and that the magnitude of the increase is intensity-dependent ( Ferris et al . , 2007 ) . This result emphasizes the importance of controlling exercise intensity in HIT studies , given that the main determinant of improvement appears to be the intensity of the workout . Indeed , previous studies have looked at the effect of short , but not intense , bursts of exercise on cognition , and found no clear evidence of improvement ( Craft , 1983 ) . The high-intensity component of this type of exercise regimen is intended to allow for higher workout intensity than traditional workouts , despite shorter overall volume . The brevity of exercise , on the other hand , is simply a byproduct of intensity—one cannot maintain a near-maximal exercise intensity for long periods of time , given that this type of regimen depletes energetic resources rapidly ( Parolin et al . , 1999 ) . In terms of practical implications , this aspect is critical , as it allows designing shorter , more potent workouts . Despite our findings being in line with previous literature showing that short bursts of exercise can elicit potent cognitive improvements in children ( Piepmeier et al . , 2015; Pontifex et al . , 2013 ) , and , more generally , with a wider , more general literature linking physical exercise and cognition in children ( Jackson et al . , 2016; Sibley and Etnier , 2003 ) , we should also point out a few limitations of the present study . These open up interesting avenues and directions for future research . First , the duration of training was not experimentally manipulated , and therefore the specific question of dose-dependence was not directly assessed . Therefore , we cannot claim that a 6 week regimen is optimal—larger cognitive improvements could possibly be elicited with longer durations , or , alternatively , similar improvements could be induced with a shorter intervention . Similarly , no follow-up tests were performed to assess durability of improvements post-training; although it should be noted that maintaining benefits is arguably less important with short , potent interventions such as the HIT regimen we proposed . In addition , both the duration of the intervention and the specific experimental protocol were constrained by external factors ( e . g . feasibility , academic schedule ) . With respect to the potency of the intervention , however , this is also promising—as little as six weeks of training can induce noticeable improvements , with possible larger effects if physical exercise is sustained . Related to this idea , we had to work around constraints typically imposed by interventional studies; namely , the necessity to keep testing sessions time-efficient . Beyond logistic considerations , this was also intended to minimize the influence of cognitive fatigue on our results . Despite time constraints , we aimed to preserve testing diversity ( i . e . , number of tasks per construct ) for a few reasons . First , we strived to provide estimates of constructs that minimize task-specific components and extract meaningful domain-general scores . In psychometric testing of working memory capacity , Foster and colleagues demonstrated that the majority of the variance explained by a single WM task is accounted for in the first few blocks , and that the predictive nature of the task remains largely unchanged for practical purposes when tasks are shortened ( Foster et al . , 2015 ) . Second , simulation studies have shown that incorporating more tasks within constructs leads to a better signal-to-noise ratio , resulting in more meaningful measures of an underlying ability ( Moreau et al . , 2016 ) . To further validate this approach , we piloted different versions of our testing tasks and determined that the validity of the versions we retained for the present study was acceptable , given appropriate reliability across task lengths . Second , because training occurred in schools , the environment was possibly less standardized and controlled than laboratory settings , highlighting typical tradeoffs between impoverished but highly reliable environments and ecological but less controlled settings . Our view remains that ecological validity is of primary importance when training cognitive abilities , because of the wide range of applications stemming from this line of research ( Moreau and Conway , 2014 ) . Accordingly , fixed , predictable training regimens are unlikely to favor durable improvements of cognitive function ( Moreau and Conway , 2014; Moreau et al . , 2015; Posner et al . , 2015 ) . We have previously stressed the importance of novelty and variety in cognitive training interventions ( Moreau and Conway , 2014 ) , and this limitation applies to exercise regimens as well . Importantly , this echoes similar views across research groups worldwide , which suggest that training-induced cognitive improvements are often restricted to specific activities ( Harrison et al . , 2013; Tomporowski et al . , 2012 ) , and are best nurtured within complex , dynamic environments ( Diamond and Lee , 2011 ) . Therefore , the regimen we have presented in this paper constitutes a potent short-term intervention , but more variety might be required to elicit long-lasting improvements . In a field that has suffered from setbacks such as lack of replication ( e . g . Redick et al . , 2013; Thompson et al . , 2013 ) or common methodological flaws ( Moreau et al . , 2016 ) , it is wise to remain cautious about preliminary studies and emphasize the need for replication . Despite these limitations , the present findings represent a promising first step toward reliable and affordable exercise-based cognitive interventions , highlighting effective alternatives to aerobic exercise . Together with complementary findings ( e . g . Moreau et al . , 2015 ) , the type of physical exercise regimen we described in this paper could pave the way for novel exercise interventions particularly suited to school environments , which are often constrained by time and equipment . The high-intensity workout we designed did not require any special equipment or any instructors training , and each 10 min session was all-inclusive with warm-up and stretching . Therefore , this type of regimen could also be generalized to other populations; for instance , individuals whose schedule allows little time for exercise , or those who do not intrinsically enjoy exercising , could appreciate opportunities to shorten workouts while preserving the typical benefits of exercise . Adaptations to older populations also represent interesting opportunities considering the benefits typically associated with exercise regimens in these communities ( Colcombe and Kramer , 2003 ) . The present regimen might require adjustments , given that practicality in a specific context ( e . g . managing time constraints in school or professional schedules ) potentially differs from practical considerations in another ( e . g . mitigating risks of injury in older populations ) . In any case , generalization is of the rationale , not necessarily of the specific workout that was designed for this intervention . Finally , it is important to acknowledge that physical exercise , regardless of the specific training regimens considered , is not a panacea when it comes to addressing cognitive deficits−in some cases , especially in the presence of specific conditions or disorders , more targeted or individualized interventions might be required ( e . g . , Moreau and Waldie , 2015 ) , and the ability for exercise regimens to remediate core cognitive deficits might appear inherently limited . However , it remains that physical exercise is one of the most potent and wide-ranging means currently available to enhance cognition non-invasively , with a myriad of positive side effects .
A total of 318 children participated in this study . Thirteen participants were not included in the analyses because of dropouts ( N = 7 ) , extensive missing data ( N = 4 ) or problems in data collection ( N = 2 , see CONSORT flow diagram for details ) . Our final sample consisted of 305 children ( Mage = 9 . 9 ( 7–13 ) , SDage = 1 . 74 , 187 female , MBMI = 18 . 3 , SDBMI = 6 . 26 ) . They were recruited from six schools across New Zealand , providing a sample of various socioeconomic deciles ( three public institutions , three private ) , locations ( three urban institutions , three rural ) , and ethnic backgrounds representative for the country ( 70% New Zealand European , 20% Pacific , 7% Asian , 3% Other ) . The number of students involved per school ranged from 5 to 83 ( M = 50 . 8 , SD = 31 ) . All participants reported no history of brain trauma or epilepsy , and all had self-reported normal or corrected-to-normal vision . A subset of 22 children reported a learning disability diagnosis ( dyslexia: 14 , ADHD: 3 , Autism spectrum disorder: 3 , mild developmental delay: 2 , Irlen syndrome: 2 , dyscalculia: 1 , dyspraxia: 1 ) . Respective subsets of 284 , 99 and 32 participants underwent all assessments , measurements and genotyping described below . All the variables measured in the experiment are reported hereafter . Testing was conducted on school premises . All cognitive assessments were computer-based , administered in groups of a maximum of 15 students . This limit on the number of participants tested at a given time was implemented to minimize potential averse effects of group testing . These assessments have shown to be adequate measures of both cognitive control and working memory ( Anderson-Hanley et al . , 2012; Aron and Poldrack , 2005; Kane et al . , 2004; Nee et al . , 2007; Pajonk et al . , 2010; Rudebeck et al . , 2012; Unsworth and Engle , 2007 ) . For each task , we measured accuracy and response time . Different stochastic variations of all tasks were used at pretest and posttest . Unless specified otherwise , the number of trials varied based on individual performance to allow reaching asymptotes , with a minimum and a maximum specified for each task . The reliability of this method for each task was assessed from a separate sample ( N = 34 , Mage = 10 . 3 ( 8–12 ) , 15 females ) , and deemed acceptable ( all ρs > 0 . 65 ) based on Spearman-Brown prophecy formula ( Brown , 1910; Spearman , 1910 ) . Specifically , reliability was calculated by comparing test scores on the asymptotic version vs , the maximal-length version , for each task ( see trial length details below and online repository for source code data ) . The order below was the order of presentation for every participant at both pretest and posttest ( i . e . , Flanker – Go/no-go – Stroop – Backward digit span – Backward Corsi blocks – Visual 2-back ) . Both testing sessions were scheduled at the same time of the day , and lasted approximately one hour . Physiological measures were collected using FitbitChargeHRTM , powered by the MEMS tri-axial accelerometer . This multisensory wristband has shown adequate accuracy and reliability in previous studies for the measures of interest in the present study ( e . g . , de Zambotti et al . , 2016 ) . Measures included minutes of activity , calories burned , intensity , intensity range ( sedentary , lightly active , fairly active , very active ) , steps and heart rate ( measured by changes in blood volume using PurePulseTM LED lights ) . Participants provided information about the following: ethnic background , age , gender , handedness , height , weight , diagnosis of learning disorder , brain trauma or epileptic seizures , current or past enrolment in a remediation or a cognitive training program , and whether English was their first language . In addition , self-reported information was gathered to quantify videogaming and physical exercise habits ( 4-point Likert scale in both cases ) , as well as to evaluate overall health , happiness , sleep quality , and mindset ( 6-point Likert scale for each item ) . The latter was intended to capture beliefs about the malleability of cognitive ability in the context of schoolwork , that is , the extent to which students perceive academic achievement in a predominantly fixed or malleable manner ( see for example Paunesku et al . , 2015 ) . All measures were collected prior to the intervention , but variables susceptible to change over time were reassessed post-intervention . DNA collection was performed using Oragene-DNA Self-Collection kits , in a manner consistent with the manufacturer's instructions . DNA was subsequently extracted from all saliva samples according to a standardized procedure ( Nishita et al . , 2009 ) . All resultant DNA samples were resuspended in Tris-EDTA buffer and were quantified used Nanodrop ND-1000 1-position spectrophotometer ( Thermo Scientific , Waltham , MA , USA ) . DNA samples were diluted to 50 ng/μL . A modified version of the method described by Erickson et al . , 2008 was used for DNA amplification . Amplification was carried out on the 113 bp polymorphic BDNF fragment , using the primers BDNF-F 5-GAG GCT TGC CAT CAT TGG CT-3 and BDNF-R 5-CGT GTA CAA GTC TGC GTC CT-3 . Polymerase chain reaction ( PCR ) was conducted using 10X Taq buffer ( 2 . 5 L μL ) , Taq polymerase ( 0 . 125 μL ) , dNTPs ( 5 nmol ) , primers ( 10 pmol each ) , Q solution ( 5 μL ) , and DNA ( 100 ng ) made up to 25 μL with dH2O . The PCR conditions consisted of denaturation at 95°C for 15 min , 30 cycles on a ThermoCycler ( involving denaturation at 94°C for 30 s , annealing at 60°C for 30 s , and extension at 72°C for 30 s ) and a final extension at 72°C . PCR product ( 6 . 5 μL ) was incubated with Pm1l at 37°C overnight . The digestion products were analyzed using a high-resolution agarose gel ( 4% ) with a Quick Load 100 bp ladder ( BioLabs ) and a GelPilot Loading Dye ( QIAGEN ) . After immersion in an ethidium bromide solution for 10 min , DNA was visualized under ultraviolet light . Enzyme digestion resulted in a 113 bp fragment for the BDNF met66 allele , and 78 and 35 bp fragments for the val66 allele . This procedure is consistent with the one described by Erickson et al . ( 2008 ) . Participants were randomly assigned to either an exercise group ( N = 152 ) or a control ( N = 153 ) group ( see Table 7 ) . Randomization was computer-based , generated in R ( Core Team R , 2016 ) by one of the authors ( D . M . ) . Group allocation was performed at the individual level . Testers were blind to group allocation . The exercise intervention consisted of a high-intensity workout including the following: warm-up ( 2 min ) , short bursts ( 5 × 20 s , interleaved with incremental breaks ( 30 s , 40 s , 50 s , 60 s , and a shorter 20 s break after the last workout period ) , and stretching ( 2 min ) . The video-based workout did not require previous experience or knowledge , as it included basic fitness movements . All movements were designed so that participants could maintain their gaze fixed on the screen at all times . All instructions were provided both verbally ( audio recording ) and visually ( on-screen captions ) . Complete details and script can be found in the online repository . A complete session lasted 10 min , and was scheduled every morning on weekdays . The control condition consisted of a blend of board games , computer games , and trivia quizzes , consistent with current recommendations regarding active control groups ( Boot et al . , 2013 ) and findings showing that aerobic exercise interventions typically do not differ from other regimens with respect to participants’ expectations ( Stothart et al . , 2014 ) . Consistent with this assumption , self-reported feedback indicated no difference in enjoyment or motivation between conditions , and no difference in mindsets regarding cognitive malleability ( Paunesku et al . , 2015 ) . Frequency and duration were matched between conditions . The intervention was 6 weeks long , with five sessions per week , for a total of 30 sessions . This translates to 300 min of actual exercise . There was no difference between groups regarding the number of sessions completed ( M = 29 . 05 , SD = 1 . 63 , overall ) . Due to the nature of the intervention , class size was limited to 20 participants in both conditions . Participants were supervised at all times , to ensure a high degree of fidelity to the intended protocol . Participants did not differ between groups in any of the self-reported measures described previously , which include physical exercise habits . Note that participants did not exercise on the days of pretest and posttest , to prevent acute effects of physical exercise on cognitive performance ( see Tomporowski , 2003 ) . | Exercise has beneficial effects on the body and brain . People who perform well on tests of cardiovascular fitness also do well on tests of learning , memory and other cognitive skills . So far , studies have suggested that moderate intensity aerobic exercise that lasts for 30 to 40 minutes produces the greatest improvements in these brain abilities . Recently , short high-intensity workouts that combine cardiovascular exercise and strength training have become popular . Studies have shown that these brief bouts of strenuous exercise improve physical health , but do these benefits extend to the brain ? It would also be helpful to know if the effect that exercise has on the brain depends on an individual’s genetic makeup or physical health . This might help to match people to the type of exercise that will work best for them . Now , Moreau et al . show that just 10 minutes of high-intensity exercise a day over six weeks can boost the cognitive abilities of children . In the experiments , over 300 children between 7 and 13 years of age were randomly assigned to one of two groups: one that performed the high-intensity exercises , or a ‘control’ group that took part in less active activities – such as quizzes and playing computer games – over the same time period . The children who took part in the high-intensity training showed greater improvements in cognitive skills than the children in the control group . Specifically , the high-intensity exercise boosted working memory and left the children better able to focus on specific tasks , two skills that are important for academic success . Moreau et al . further found that the high-intensity exercises had the most benefit for the children who needed it most – those with poor cardiovascular health and those with gene variants that are linked to poorer cognitive skills . This suggests that genetic differences do alter the effects of exercise on the brain , but also shows that targeted exercise programs can offer everyone a chance to thrive . Moreau et al . suggest that exercise need not be time consuming to boost brain health; the key is to pack more intense exercise in shorter time periods . Further work could build on these findings to produce effective exercise routines that could ultimately form part of school curriculums , as well as proving useful to anyone who wishes to improve their cognitive skills . | [
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] | 2017 | High-intensity training enhances executive function in children in a randomized, placebo-controlled trial |
The time-dependent rate I ( t ) of origin firing per length of unreplicated DNA presents a universal bell shape in eukaryotes that has been interpreted as the result of a complex time-evolving interaction between origins and limiting firing factors . Here , we show that a normal diffusion of replication fork components towards localized potential replication origins ( p-oris ) can more simply account for the I ( t ) universal bell shape , as a consequence of a competition between the origin firing time and the time needed to replicate DNA separating two neighboring p-oris . We predict the I ( t ) maximal value to be the product of the replication fork speed with the squared p-ori density . We show that this relation is robustly observed in simulations and in experimental data for several eukaryotes . Our work underlines that fork-component recycling and potential origins localization are sufficient spatial ingredients to explain the universality of DNA replication kinetics .
Eukaryotic DNA replication is a stochastic process ( Hyrien et al . , 2013; Hawkins et al . , 2013; Hyrien , 2016b ) . Prior to entering the S ( ynthesis ) -phase of the cell cycle , a number of DNA loci called potential origins ( p-oris ) are licensed for DNA replication initiation ( Machida et al . , 2005; Hyrien et al . , 2013; Hawkins et al . , 2013 ) . During S-phase , in response to the presence of origin firing factors , pairs of replication forks performing bi-directional DNA synthesis will start from a subset of the p-oris , the active replication origins for that cell cycle ( Machida et al . , 2005; Hyrien et al . , 2013; Hawkins et al . , 2013 ) . Note that the inactivation of p-oris by the passing of a replication fork called origin passivation , forbids origin firing in already replicated regions ( de Moura et al . , 2010; Hyrien and Goldar , 2010; Yang et al . , 2010 ) . The time-dependent rate of origin firing per length of unreplicated DNA , I ( t ) , is a fundamental parameter of DNA replication kinetics . I ( t ) curves present a universal bell shape in eukaryotes ( Goldar et al . , 2009 ) , increasing toward a maximum after mid-S-phase and decreasing to zero at the end of S-phase . An increasing I ( t ) results in a tight dispersion of replication ending times , which provides a solution to the random completion problem ( Hyrien et al . , 2003; Bechhoefer and Marshall , 2007; Yang and Bechhoefer , 2008 ) . Models of replication in Xenopus embryo ( Goldar et al . , 2008; Gauthier and Bechhoefer , 2009 ) proposed that the initial I ( t ) increase reflects the progressive import during S-phase of a limiting origin firing factor and its recycling after release upon forks merge . The I ( t ) increase was also reproduced in a simulation of human genome replication timing that used a constant number of firing factors having an increasing reactivity through S-phase ( Gindin et al . , 2014 ) . In these three models , an additional mechanism was required to explain the final I ( t ) decrease by either a subdiffusive motion of the firing factor ( Gauthier and Bechhoefer , 2009 ) , a dependency of firing factors’ affinity for p-oris on replication fork density ( Goldar et al . , 2008 ) , or an inhomogeneous firing probability profile ( Gindin et al . , 2014 ) . Here , we show that when taking into account that p-oris are distributed at a finite number of localized sites then it is possible to reproduce the universal bell shape of the I ( t ) curves without any additional hypotheses than recycling of fork components . I ( t ) increases following an increase of fork mergers , each merger releasing a firing factor that was trapped on DNA . Then I ( t ) decreases due to a competition between the time tc to fire an origin and the time tr to replicate DNA separating two neighboring p-ori . We will show that when tc becomes smaller than tr , p-ori density over unreplicated DNA decreases , and so does I ( t ) . Modeling random localization of active origins in Xenopus embryo by assuming that every site is a ( weak ) p-ori , previous work implicitly assumed tr to be close to zero ( Goldar et al . , 2008; Gauthier and Bechhoefer , 2009 ) forbidding the observation of a decreasing I ( t ) . Licensing of a limited number of sites as p-ori thus appears to be a critical property contributing to the observed canceling of I ( t ) at the end of S-phase in all studied eukaryotes .
In our modeling of replication kinetics , a bimolecular reaction between a diffusing firing factor and a p-ori results in an origin firing event; then each half of the diffusing element is trapped and travels with a replication fork until two converging forks merge ( termination , Figure 1a ) . A molecular mechanism explaining the synchronous recruitment of firing factors to both replication forks was recently proposed ( Araki , 2016 ) , supporting the bimolecular scenario for p-ori activation . Under the assumption of a well-mixed system , for every time step dt , we consider each interaction between the NFD ( t ) free diffusing firing factors and the Np−ori ( t ) p-oris as potentially leading to a firing with a probability kondt . The resulting simulated firing rate per length of unreplicated DNA is then: ( 1 ) IS ( t ) =Nfired ( t , t+dt ) LunrepDNA ( t ) dt , where Nfired ( t , t+dt ) is the number of p-oris fired between times t and t+dt , and LunrepDNA ( t ) is the length of unreplicated DNA a time t . Then we propagate the forks along the chromosome with a constant speed v , and if two forks meet , the two half firing complexes are released and rapidly reform an active firing factor . Finally , we simulate the chromosomes as 1D chains where prior to entering S-phase , the p-oris are precisely localized . For Xenopus embryo , the p-ori positions are randomly sampled , so that each simulated S-phase corresponds to a different positioning of the p-oris . We compare results obtained with periodic or uniform p-ori distributions ( Materials and methods ) . For S . cerevisiae , the p-ori positions , identical for each simulation , are taken from the OriDB database ( Siow et al . , 2012 ) . As previously simulated in human ( Löb et al . , 2016 ) , we model the entry in S-phase using an exponentially relaxed loading of the firing factors with a time scale shorter than the S-phase duration Tphase ( 3 min for Xenopus embryo , where Tphase∼30 min , and 10 min for S . cerevisiae , where Tphase∼60 mins ) . After the short loading time , the total number of firing factors NDT is constant . As shown in Figure 1b ( see also Figure 2 ) , the universal bell shape of the I ( t ) curves ( Goldar et al . , 2009 ) spontaneously emerges from our model when going from weak to strong interaction , and decreasing the number of firing factors below the number of p-oris . The details of the firing factor loading dynamics do not affect the emergence of a bell shaped I ( t ) , even though it can modulate its precise shape , especially early in S-phase . In a simple bimolecular context , the rate of origin firing is i ( t ) =konNp−ori ( t ) NFD ( t ) . The firing rate by element of unreplicated DNA is then given by ( 2 ) I ( t ) =konNFD ( t ) ρp−ori ( t ) , where ρp−ori ( t ) =Np−ori ( t ) /LunrepDNA ( t ) . In the case of a strong interaction and a limited number of firing factors , all the diffusing factors react rapidly after loading and NFD ( t ) is small ( Figure 1 ( c ) , dashed curves ) . Then follows a stationary phase where as long as the number of p-oris is high ( Figure 1 ( c ) , solid curves ) , once a diffusing factor is released by the encounter of two forks , it reacts rapidly , and so NFD ( t ) stays small . Then , when the rate of fork mergers increases due to the fact that there are as many active forks but a smaller length of unreplicated DNA , the number of free firing factors increases up to NDT at the end of S-phase . As a consequence , the contribution of NFD ( t ) to I ( t ) in Equation ( 2 ) can only account for a monotonous increase during the S phase . For I ( t ) to reach a maximum Imax before the end of S-phase , we thus need that ρp−ori ( t ) decreases in the late S-phase . This happens if the time to fire a p-ori is shorter than the time to replicate a typical distance between two neighboring p-oris . The characteristic time to fire a p-ori is tc=1/konNFD ( t ) . The mean time for a fork to replicate DNA between two neighboring p-oris is tr=d ( t ) /v , where d ( t ) is the mean distance between unreplicated p-oris at time t . So the density of origins is constant as long as: ( 3 ) d ( t ) v<1konNFD ( t ) , or ( 4 ) NFD ( t ) <NFD∗=vkond ( t ) . Thus , at the beginning of the S-phase , NFD ( t ) is small , ρp−ori ( t ) is constant ( Figure 1 ( c ) , solid curves ) and so IS ( t ) stays small . When NFD ( t ) starts increasing , as long as Equation ( 4 ) stays valid , IS ( t ) keeps increasing . When NFD ( t ) becomes too large and exceeds NFD∗ , then Equation ( 4 ) is violated and the number of p-oris decreases at a higher rate than the length of unreplicated DNA , and ρp−ori ( t ) decreases and goes to zero ( Figure 1 ( c ) , red solid curve ) . As NFD ( t ) tends to NDT , IS ( t ) goes to zero , and its global behavior is a bell shape ( Figure 1 ( b ) , red ) . Let us note that if we decrease the interaction strength ( kon ) , then the critical NFD∗ will increase beyond NDT ( Figure 1 ( c ) , dashed blue and green curves ) . IS ( t ) then monotonously increase to reach a plateau ( Figure 1 ( b ) , green ) , or if we decrease further kon , IS ( t ) present a very slow increasing behavior during the S-phase ( Figure 1 ( b ) , blue ) . Now if we come back to strong interactions and increase the number of firing factors , almost all the p-oris are fired immediately and IS ( t ) drops to zero after firing the last p-ori . Another way to look at the density of p-oris is to compute the ratio of the number of passivated origins by the number of activated origins ( Figure 1 ( d ) ) . After the initial loading of firing factors , this ratio is higher than one . For weak and moderate interactions ( Figure 1 ( d ) , blue and green solid curves , respectively ) , this ratio stays bigger than one during all the S-phase , where IS ( t ) was shown to be monotonously increasing ( Figure 1 ( b ) ) . For a strong interaction ( Figure 1 ( b ) , red solid curve ) , this ratio reaches a maximum and then decreases below one , at a time corresponding to the maximum observed in IS ( t ) ( Figure 1 ( d ) , red solid curve ) . Hence , the maximum of I ( t ) corresponds to a switch of the balance between origin passivation and activation , the latter becoming predominant in late S-phase . We have seen that up to this maximum ρp−ori ( t ) ≈cte≈ρ0 , so IS ( t ) ≈konρ0NF ( t ) . When NFD ( t ) reaches NFD∗ , then IS ( t ) reaches its maximum value: ( 5 ) Imax=konρ0NFD∗≈ρ0vd ( t ) ≈vρ02 , where we have used the approximation d ( t ) ≈d ( 0 ) =1/ρ0 ( which is exact for periodically distributed p-oris ) . Imax can thus be predicted from two measurable parameters , providing a direct test of the model .
To summarize , we have shown that within the framework of 1D nucleation and growth models of DNA replication kinetics ( Herrick et al . , 2002; Jun and Bechhoefer , 2005 ) , the sufficient conditions to obtain a universal bell shaped I ( t ) as observed in eukaryotes are a strong bimolecular reaction between localized p-oris and limiting origin firing factors that travel with replication forks and are released at termination . Under these conditions , the density of p-oris naturally decreases by the end of the S-phase and so does IS ( t ) . Previous models in Xenopus embryo ( Goldar et al . , 2008; Gauthier and Bechhoefer , 2009 ) assumed that all sites contained a p-ori implying that the time tr to replicate DNA between two neighboring p-oris was close to zero . This clarifies why they needed some additional mechanisms to explain the final decrease of the firing rate . Moreover , our model predicts that the maximum value for I ( t ) is intimately related to the density of p-oris and the fork speed ( Equation ( 5 ) ) , and we have shown that without free parameter , this relationship holds for five species with up to a 300-fold difference of Imax and vρ02 ( Table 1 , Figure 2 ( c ) ) . Our model assumes that all p-oris are governed by the same rule of initiation resulting from physicochemically realistic particulars of their interaction with limiting replication firing factors . Any spatial inhomogeneity in the firing rate I ( x , t ) along the genomic coordinate in our simulations thus reflects the inhomogeneity in the distribution of the potential origins in the genome . In yeast , replication kinetics along chromosomes were robustly reproduced in simulations where each replication origin fires following a stochastic law with parameters that change from origin to origin ( Yang et al . , 2010 ) . Interestingly , this heterogeneity between origins is captured by the Multiple-Initiator Model where origin firing time distribution is modeled by the number of MCM2-7 complexes bound at the origin ( Yang et al . , 2010; Das et al . , 2015 ) . In human , early and late replicating domains could be modeled by the spatial heterogeneity of the origin recognition complex ( ORC ) distribution ( Miotto et al . , 2016 ) . In these models , MCM2-7 and ORC have the same status as our p-oris , they are potential origins with identical firing properties . Our results show that the universal bell-shaped temporal rate of replication origin firing can be explained irrespective of species-specific spatial heterogeneity in origin strength . Note , however , that current successful modeling of the chromosome organization of DNA replication timing relies on heterogeneities in origins’ strength and spatial distributions ( Bechhoefer and Rhind , 2012 ) . To confirm the simple physical basis of our modeling , we used molecular dynamics rules as previously developed for S . cerevisiae ( Arbona et al . , 2017 ) to simulate S-phase dynamics of chromosomes confined in a spherical nucleus . We added firing factors that are free to diffuse in the covolume left by the chain and that can bind to proximal p-oris to initiate replication , move along the chromosomes with the replication forks and be released when two fork merges . As shown in Figure 2 ( a , b ) for Xenopus embryo and S . cerevisiae , results confirmed the physical relevance of our minimal modeling and the validity of its predictions when the 3D diffusion of the firing factors is explicitly taken into account . Modeling of replication timing profiles in human was recently successfully achieved in a model with both inhibition of origin firing 55 kb around active forks , and an enhanced firing rate further away up to a few 100 kb ( Löb et al . , 2016 ) as well as in models that do not consider any inhibition nor enhanced firing rate due to fork progression ( Gindin et al . , 2014; Miotto et al . , 2016 ) . These works illustrate that untangling spatio-temporal correlations in replication kinetics is challenging . 3D modeling opens new perspectives for understanding the contribution of firing factor transport to the correlations between firing events along chromosomes . For example in S . cerevisiae ( Knott et al . , 2012 ) and in S . pombe ( Kaykov and Nurse , 2015 ) , a higher firing rate has been reported near origins that have just fired ( but see Yang et al . ( 2010 ) ) . In mammals , megabase chromosomal regions of synchronous firing were first observed a long time ago ( Huberman and Riggs , 1968; Hyrien , 2016b ) and the projection of the replication program on 3D models of chromosome architecture was shown to reproduce the observed S-phase dynamics of replication foci ( Löb et al . , 2016 ) . Recently , profiling of replication fork directionality obtained by Okazaki fragment sequencing have suggested that early firing origins located at the border of Topologically Associating Domains ( TADs ) trigger a cascade of secondary initiation events propagating through the TAD ( Petryk et al . , 2016 ) . Early and late replicating domains were associated with nuclear compartments of open and closed chromatin ( Ryba et al . , 2010; Boulos et al . , 2015; Goldar et al . , 2016; Hyrien , 2016b ) . In human , replication timing U-domains ( 0 . 1–3 Mb ) were shown to correlate with chromosome structural domains ( Baker et al . , 2012; Moindrot et al . , 2012; Pope et al . , 2014 ) and chromatin loops ( Boulos et al . , 2013 , Boulos et al . , 2014 ) . Understanding to which extent spatio-temporal correlations of the replication program can be explained by the diffusion of firing factors in the tertiary chromatin structure specific to each eukaryotic organism is a challenging issue for future work .
Each model simulation allows the reconstruction of the full replication kinetics during one S-phase . Chromosome initial replication state is described by the distribution of p-oris along each chromosomes . For Xenopus embryo , p-ori positions are randomly determined at the beginning of each simulation following two possible scenarios: For yeast , the p-ori positions are identical in each S-phase simulations and correspond to experimentally determined positions reported in OriDB ( Siow et al . , 2012 ) . The simulation starts with a fixed number NDT of firing factors that are progressively made available as described in Results . At every time step t=ndt , each free firing factor ( available factors not bound to an active replication fork ) has a probability to fire one of the Np−ori ( t ) p-oris at unreplicated loci given by: ( 6 ) 1− ( 1−kondt ) Np−ori ( t ) . A random number is generated , and if it is inferior to this probability , an unreplicated p-ori is chosen at random , two diverging forks are created at this locus and the number of free firing factors decreases by 1 . Finally , every fork is propagated by a length vdt resulting in an increase amount of DNA marked as replicated and possibly to the passivation of some p-oris . If two forks meet they are removed and the number of free firing factors increases by 1 . Forks that reach the end of a chromosome are discarded . The numbers of firing events ( Nfired ( t ) ) , origin passivations , free firing factors ( NFD ( t ) ) and unreplicated p-oris ( Np−ori ( t ) ) as well as the length of unreplicated DNA ( LunrepDNA ( t ) ) are recorded allowing the computation of IS ( t ) ( Eq . ( 1 ) ) , the normalized density of p-oris ( ρp−ori ( t ) ) /ρ0 ) , the normalized number of free firing factors ( NFD ( t ) /NFD∗ ( t ) ) and the ratio between the number of origin passivations and activations . Simulation ends when all DNA has been replicated , which define the replication time . Replication kinetics simulation for the 3D model follows the same steps as in the well-mixed model except that the probability that a free firing factor activates an unreplicated p-ori depends on their distance d obtained from a molecular dynamic simulation performed in parallel to the replication kinetics simulation . We used HOOMD-blue ( Anderson et al . , 2008 ) with parameters similar to the ones previously considered in Arbona et al . ( 2017 ) to simulate chromosome conformation dynamics and free firing factor diffusion within a spherical nucleus of volume VN . The details of the interaction between the diffusing firing factors and the p-oris is illustrated in Figure 2—figure supplement 1 . Given a capture radius rc set to two coarse grained chromatin monomer radiuses , when a free firing factor is within the capture volume Vc=43πrc3 around an unreplicated p-ori ( d<rc ) , it can activate the origin with a probability p . In order to have a similar firing activity as in the well-mixed model , rc and p were chosen so that pVc/VN takes a value comparable to the kon values used in the well-mixed simulations . For each set of parameters of the well-mixed and 3D models , we reported the mean curves obtained over a number of independent simulations large enough so that the noisy fluctuations of the mean IS ( t ) are small compared to the average bell-shaped curve . The complete set of parameters for each simulation series is provided in Supplementary file 1 . The scripts used to extract yeast I ( t ) from the experimental data of Alvino et al . ( 2007 ) can be found here https://github . com/ jeammimi/ifromprof/blob/master/notebooks/exploratory/Alvino_WT . ipynb ( yeast in normal growth conditions ) and here https://github . com/jeammimi/ifromprof/blob/master/notebooks/exploratory/Alvino_H . ipynb ( yeast grown grown in Hydroxyurea ) ( Arbona and Goldar , 2018 ) . A copy is archived at https://github . com/elifesciences-publications/ifromprof . | Before a cell can divide , it must duplicate its DNA . In eukaryotes – organisms such as animals and fungi , which store their DNA in the cell’s nucleus – DNA replication starts at specific sites in the genome called replication origins . At each origin sits a protein complex that will activate when it randomly captures an activating protein that diffuses within the nucleus . Once a replication origin activates or “fires” , the complex then splits into two new complexes that move away from each other as they duplicate the DNA . If an active complex collides with an inactive one at another origin , the latter is inactivated – a phenomenon known as origin passivation . When two active complexes meet , they release the activating proteins , which diffuse away and eventually activate other origins in unreplicated DNA . The number of origins that activate each minute divided by the length of unreplicated DNA is referred to as the “rate of origin firing” . In all eukaryotes , this rate – also known as I ( t ) – follows the same pattern . First , it increases until more than half of the DNA is duplicated . Then it decreases until everything is duplicated . This means that , if plotted out , the graph of origin firing rate would always be a bell-shaped curve , even for organisms with genomes of different sizes that have different numbers of origins . The reason for this universal shape remained unclear . Scientists had tried to create numerical simulations that model the rate of origin firing . However , for these simulations to reproduce the bell-shape curve , a number of untested assumptions had to be made about how DNA replication takes place . In addition , these models ignored the fact that it takes time to replicate the DNA between origins . To take this time into account , Arbona et al . instead decided to model the replication origins as discrete and distinct entities . This way of building the mathematical model succeeded in reproducing the universal bell curve shape without additional assumptions . With this simulation , the balance between origin activation and passivation is enough to achieve the observed pattern . The new model also predicts that the maximum rate of origin firing is determined by the speed of DNA replication and the density of origins in the genome . Arbona et al . verified this prediction in yeast , fly , frog and human cells – organisms with different sized genomes that take between 20 minutes and 8 hours to replicate their DNA . Lastly , the prediction also held true in yeast treated with hydroxyurea , an anticancer drug that slows DNA replication . A better understanding of DNA replication can help scientists to understand how this process is perturbed in cancers and how drugs that target DNA replication can treat these diseases . Future work will explore how the 3D organization of the genome affects the diffusion of activating proteins within the cell nucleus . | [
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] | 2018 | The eukaryotic bell-shaped temporal rate of DNA replication origin firing emanates from a balance between origin activation and passivation |
Dysfunction of the noradrenergic ( NE ) neurons is implicated in the pathogenesis of bipolar disorder ( BPD ) . ErbB4 is highly expressed in NE neurons , and its genetic variation has been linked to BPD; however , how ErbB4 regulates NE neuronal function and contributes to BPD pathogenesis is unclear . Here we find that conditional deletion of ErbB4 in locus coeruleus ( LC ) NE neurons increases neuronal spontaneous firing through NMDA receptor hyperfunction , and elevates catecholamines in the cerebrospinal fluid ( CSF ) . Furthermore , Erbb4-deficient mice present mania-like behaviors , including hyperactivity , reduced anxiety and depression , and increased sucrose preference . These behaviors are completely rescued by the anti-manic drug lithium or antagonists of catecholaminergic receptors . Our study demonstrates the critical role of ErbB4 signaling in regulating LC-NE neuronal function , reinforcing the view that dysfunction of the NE system may contribute to the pathogenesis of mania-associated disorder .
Bipolar disorder ( BPD ) , diagnosed on the basis of manic episodes with or without depression , is a severely debilitating psychiatric disorder ( Holden , 2008 ) . Though risk genes and rodent models of BPD have been reported ( Arey et al . , 2014; Craddock and Sklar , 2009; Gouvea et al . , 2016; Han et al . , 2013; Roybal et al . , 2007; Saul et al . , 2012 ) , the underlying pathogenic mechanism has not yet been clearly defined due to the phenotypic and genotypic complexity of this disorder ( Harrison et al . , 2018 ) . Several lines of evidence implicate the noradrenergic ( NE ) system in the pathology of BPD . For instance , the concentrations of norepinephrine and its metabolites are significantly upregulated in the cerebrospinal fluid ( CSF ) of BPD patients during the manic state ( Kurita , 2016; Manji et al . , 2003; Post et al . , 1973; Post et al . , 1978 ) . In contrast , norepinephrine is downregulated in patients with depressive disorder ( Maas et al . , 1971; Moret and Briley , 2011; Wiste et al . , 2008 ) and associated with mood transition in BPD patients ( Kurita , 2016; Salvadore et al . , 2010 ) . However , how the NE system is involved in the pathology of BPD remains uncertain . ErbB4 , a receptor tyrosine kinase , plays a vital role in a number of biological processes , including neural development , excitability , and synaptic plasticity ( Mei and Nave , 2014 ) . In parvalbumin-positive ( PV ) interneurons , ErbB4 is involved in the etiology of schizophrenia and epilepsy ( Chen et al . , 2010; Del Pino et al . , 2013; Fisahn et al . , 2009; Kx et al . , 2012; Tan et al . , 2011 ) . ErbB4 mRNA is also prominently expressed in locus coeruleus ( LC ) NE neurons ( Gerecke et al . , 2001 ) , and coding variants of ERBB4 are genetically associated with BPD susceptibility ( Chen et al . , 2012; Bipolar Genome Study et al . , 2011 ) . However , how ErbB4 regulates NE neuronal function and whether NE neuron-specific ErbB4 signaling participates in the pathogenesis of BPD is unknown . In this study , we achieved ErbB4 deletion primarily in NE neurons by crossing Th-Cre mice ( Gelman et al . , 2003 ) , in which Cre recombinase is mainly expressed in NE neurons of the LC ( see Figure 1A–E , Figure 1—figure supplement 1 and Discussion section ) , with mice carrying the loxP-flanked Erbb4 allele ( Erbb4loxp/loxp ) . ErbB4 deletion increases the spontaneous firing of LC-NE neurons in an NMDA receptor-dependent manner , and elevates the concentrations of norepinephrine and dopamine in the CSF . Furthermore , Th-Cre;Erbb4loxp/loxp mice manifest a mania-like behavioral profile that can be recapitulated by Erbb4loxp/loxp mice with region-specific ablation of ErbB4 in the LC . In addition , treatment with lithium , a commonly used clinical anti-manic drug , or antagonists against dopamine or norepinephrine receptors all rescue the mania-like behaviors in Th-Cre;Erbb4loxp/loxp mice . Taken together , our study linked ErbB4 physiological function with NE system homeostasis and demonstrated the pathogenic effect of ErbB4 dysregulation in NE neurons in mania-associated psychiatric diseases .
To determine Cre distribution in our specific Th-Cre mouse line , we crossed Th-Cre mice with Ai9 mice to label Cre-positive neurons with red fluorescent protein tdTomato ( Madisen et al . , 2010 ) . We examined Cre expression in the LC , ventral tegmental nucleus ( VTA ) , and substantia nigra pars compacta ( SNC ) of Ai9;Th-Cre mice at postnatal day ( P ) 50 because tyrosine hydroxylase ( TH ) , the key enzyme for the synthesis of norepinephrine and dopamine , is mainly expressed in these three areas . Colocalization analysis of TH staining and tdTomato suggested that in the rostral part of the LC , approximately 40% of TH-positive ( TH+ ) neurons were Cre/Tomato-positive ( Cre/Tomato+ ) and 92% of Cre/Tomato+ neurons were TH+ , whereas in the caudal part of the LC , Cre/Tomato+ neurons only constituted 14% of TH+ neurons , with 82% of Cre/Tomato+ neurons being TH+ ( Figure 1A , B ) . The VTA and SNC contain approximately 70% of the dopaminergic neurons in the brain ( Björklund and Dunnett , 2007 ) . Unexpectedly , in contrast to the LC , there were very few Cre/Tomato+ neurons in the VTA and SNC . Cre/Tomato was only expressed in approximately 1 . 6% and 0 . 9% of neurons in the rostral and caudal VTA , respectively . Moreover , only 2 . 1% of neurons in the rostral and caudal parts of the SNC were Cre/Tomato+ . In the rostral and caudal parts of the VTA and SNC , only 8% and 12% of Cre/Tomato+ neurons , respectively , were TH+ ( Figure 1A , B ) . To exclude possible false-positive signals introduced by the reporter mouse line , we took advantage of Ai3 mice , another reporter mouse strain that labels Cre-positive neurons with green fluorescent protein ( GFP ) , to confirm these results . Consistently , we observed very little Cre expression in the VTA or SNC ( Figure 1—figure supplement 1 ) . These data suggest that Cre recombinase was primarily expressed in the NE neurons of the LC in our Th-Cre mouse line . To investigate its role in NE neurons , we deleted ErbB4 in NE neurons by crossing Th-Cre with Erbb4loxp/loxp mice ( Figure 1C ) . Immunoblotting analysis showed that ErbB4 was significantly decreased in the LC of Th-Cre;Erbb4loxp/loxp mice ( Figure 1D , E , and Figure 1—figure supplement 2 ) with no significant change in the midbrain ( VTA and SNC ) ( Figure 1D , E ) , which is consistent with our previous observation that Cre was mainly expressed in LC-NE neurons in Th-Cre mice . Immunohistochemical analysis also confirmed the deletion of ErbB4 in the LC ( Figure 1F , G ) . In addition , we observed no obvious changes in cell density or soma size of LC neurons in Th-Cre;Erbb4loxp/loxp mice compared to control mice ( Figure 1F and Figure 1—figure supplement 3 ) . To analyze the influence of ErbB4 deficiency on LC-NE neuronal physiology , we measured the spontaneous activity of LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice in cell-attached configuration . Consistent with previous studies , LC-NE neurons recorded from slices of the control mice exhibited a firing rate of 1 . 94 ± 0 . 18 Hz ( Chandler et al . , 2014; Jedema and Grace , 2004 ) ( Figure 2A , C ) . However , the spontaneous firing rate of LC-NE neurons in the Th-Cre;Erbb4loxp/loxp mice was significantly increased ( 2 . 87 ± 0 . 18 Hz ) ( Figure 2B , C ) , and the inter-spike interval was significantly decreased ( Figure 2D ) . Previous studies have demonstrated that neuronal excitability can affect the expression and phosphorylation of TH , the rate limiting factor in catecholamine synthesis ( Aumann et al . , 2011; Chevalier et al . , 2008; Lew et al . , 1999; Zigmond et al . , 1989 ) . Using protein extracts of LC tissues from controls and mutants , we measured the expression of phosphorylated TH ( TH-Ser40 ) , an active form of TH required for norepinephrine synthesis , along with other enzymes involved in norepinephrine homeostasis , including dopamine beta-hydroxylase ( DBH ) , norepinephrine transporter ( NET ) , and catechol-O-methyltransferase ( COMT ) . We observed a marked increase in TH-Ser40 but not in total TH ( Figure 2E , F , and Figure 2—figure supplement 1 ) . In contrast , DBH , another enzyme involved in norepinephrine synthesis , and NET and COMT , which regulate norepinephrine degradation , were unchanged ( Figure 2E , F ) . Thus , changes in the neuronal excitability of LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice may specifically increase TH phosphorylation . Using lysates from the midbrain , none of these proteins showed any changes ( Figure 2G , H ) , suggesting region-specific norepinephrine synthetic activity influenced in mutant animals . As LC-NE neurons are the major source of norepinephrine in the forebrain ( Sara , 2009 ) , we hypothesized that the increase in NE neuronal and TH activities in the LC might increase the level of norepinephrine in the brain . Therefore , we examined the norepinephrine level in Th-Cre;Erbb4loxp/loxp mice using in vivo microdialysis in the lateral ventricle of anaesthetized mice , followed by high-performance liquid chromatography ( HPLC ) . Results showed that norepinephrine concentration was significantly increased in the CSF of Th-Cre;Erbb4loxp/loxp mice ( Figure 2I and Figure 2—figure supplement 2 ) . Given that dopamine , the precursor of norepinephrine , is coupled with changes in norepinephrine level and can be co-released with norepinephrine by NE neurons ( Devoto et al . , 2005; Guiard et al . , 2008; Pozzi et al . , 1994; Yamamoto and Novotney , 1998 ) , we also examined the concentration of dopamine in the CSF in Th-Cre;Erbb4loxp/loxp mice . Remarkably , the concentration of dopamine was also obviously increased compared with that in the control mice ( Figure 2J and Figure 2—figure supplement 2 ) . Taken together , the increased excitability of LC-NE neurons may increase TH phosphorylation , resulting in the increase of norepinephrine and dopamine observed in the CSF of Th-Cre;Erbb4loxp/loxp mice . An increase in glutamatergic synaptic input ( Jodo and Aston-Jones , 1997; Somogyi and Llewellyn-Smith , 2001 ) , or decrease in feedback inhibition from α-2-adrenoceptor , an autoreceptor ( Langer , 1980; Starke , 2001 ) , may contribute to the increased firing rate of LC-NE neurons . Moreover , in studies on the hippocampus and prefrontal cortex , the NMDA receptor ( NMDAR ) , especially its subunit isoform NR2B , is reported to be regulated by ErbB4 ( Hahn et al . , 2006; Pitcher et al . , 2011 ) . Therefore , we examined the expression of NMDAR subunits ( NR2B , NR1 , and NR2A ) and autoreceptors α-2A ( A2A ) and α-2C ( A2C ) using protein samples from the LC of the controls and Th-Cre;Erbb4loxp/loxp mice . Results showed that the expression of NR2B was significantly increased in Th-Cre;Erbb4loxp/loxp mice , whereas no changes were detected in the expressions of NR1 , A2A , A2C or NR2A ( Figure 3A , B ) . NR2B overexpression may alter NMDAR activity ( Galliano et al . , 2018 ) . Therefore , we recorded evoked NMDAR-mediated current of LC-NE neurons in acute brain slices from Ai9;Th-Cre mice and Ai9;Th-Cre;Erbb4loxp/loxp mice and compared the current amplitude . NMDAR-mediated current showed significantly increased amplitude in Ai9; Th-Cre;Erbb4loxp/loxp mice compared with Ai9; Th-Cre mice ( Figure 3C , D ) , thus indicating NMDAR hyperfunction in the LC-NE neurons in the absence of ErbB4 . Meanwhile , to test whether the balance between excitatory synapses and inhibitory synapses and the intrinsic excitability of the LC-NE neurons were altered by ErbB4 deletion , we recorded spontaneous EPSC ( sEPSC ) and spontaneous IPSC ( sIPSC ) of LC-NE neurons from the control Ai9;Th-Cre mice and ErbB4-deficient Ai9;Th-Cre;Erbb4loxp/loxp mice . Neither sEPSC nor sIPSC presented any changes in their amplitude or frequency ( Figure 3—figure supplement 1 ) . In addition , intrinsic properties of LC-NE neurons measured by analysis of action potential ( AP ) threshold , AP amplitude , AP half-width , afterhyperpolarization ( AHP ) , rheo-based current , Cm , Rin , and τ were unchanged compared with those of the control mice ( Figure 3—figure supplement 2 ) . We hypothesized that the increase in NE neuronal activity might be attributed to strengthened NMDAR function in Th-Cre;Erbb4loxp/loxp mice . Using the patch clamp technique , we found that the spontaneous firing rates and inter-spike intervals of LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice were rescued by the NMDAR antagonist APV ( 50 μM ) ( Figure 3E–G ) . Thus , NMDARs appear to mediate the hyperexcitability of LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice . The LC is involved in mood , reward , and motor ability ( Borodovitsyna et al . , 2017; Bouret and Sara , 2005; Sara , 2009 ) . As ErbB4 was mainly deleted from the LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice , we hypothesized that Th-Cre;Erbb4loxp/loxp mice might exhibit LC-related behavioral abnormalities . We first examined the motor ability of Th-Cre;Erbb4loxp/loxp mice using the open field test . Th-Cre;Erbb4loxp/loxp mice traveled longer distances and at higher speeds than control ( ErbB4loxp/loxp ) mice ( Figure 4A–E ) and spent less time immobile ( Figure 4F ) . To examine anxiety- and depression-related behaviors of Th-Cre;Erbb4loxp/loxp mice , we conducted the elevated plus maze test ( EPM ) and forced swim test . In the EPM , Th-Cre;Erbb4loxp/loxp mice spent more time in and presented more entries into the open arms compared with the control mice ( Figure 4G–I ) . In the forced swim test , Th-Cre;Erbb4loxp/loxp mice showed less immobility ( Figure 4J ) and longer latency to first surrender compared with the control mice ( Figure 4K ) . To examine the responses of Th-Cre;Erbb4loxp/loxp mice to a natural reward , we performed the sucrose preference test . During this test , Th-Cre;Erbb4loxp/loxp mice displayed increased preference for sucrose compared with the control mice ( Figure 4L ) . No significant deficits in body weight or prepulse inhibition were observed in Th-Cre;Erbb4loxp/loxp mice ( Figure 4—figure supplement 1 ) . In addition , no significant change was detected between control mice and Th-Cre; ErbB4loxp/loxp mice in the distance travelled in center and time spent in center area in open field test ( Figure 4—figure supplement 2 ) . These data indicate that Th-Cre;Erbb4loxp/loxp mice exhibited hyperactivity , decreased anxiety and depression , and increased sucrose preference , thus resembling the phenotypes of rodent mania models ( Arey et al . , 2014; Cosgrove et al . , 2016; Han et al . , 2013; Kirshenbaum et al . , 2011; Nestler and Hyman , 2010; Prickaerts et al . , 2006; Roybal et al . , 2007; Shaltiel et al . , 2008 ) . To exclude the possibility that the Th-Cre;Erbb4loxp/loxp mouse phenotypes were attributed to Cre-expressing neurons in other brain areas , we tested whether region-specific deletion of ErbB4 in the LC was sufficient to induce mania-like behaviors . We injected an adeno-associated virus ( AAV ) expressing Cre and GFP ( AAV-Cre-GFP ) into the LC of Erbb4loxp/loxp mice bilaterally . Cre/GFP was expressed abundantly in the LC ( Figure 5A ) , with 52% of LC neurons being Cre/GFP-positive ( Cre/GFP+ ) and 77 . 7% of Cre/GFP + neurons being TH+ ( Figure 5—figure supplement 1 ) . Of the left 22 . 3% Cre/GFP+ but TH-negative ( TH- ) neurons , 55 . 5% among them showed ErbB4 expression ( Figure 5—figure supplement 2 ) . Using immunoblotting , we confirmed the efficiency of ErbB4 deletion in the LC of Erbb4loxp/loxp mice after AAV-Cre-GFP injection ( Figure 5B ) . Behavioral tests were carried out 4 weeks after viral injection . In the open field test , viral-mediated region-specific deletion of ErbB4 in the LC significantly increased locomotor activity and traveling speed ( Figure 5C , D ) , and both the distance and time traveled at high speed were significantly increased ( Figure 5E , F ) . Moreover , the immobility time in the open area was significantly reduced ( Figure 5G ) . In the EPM , ErbB4 deletion in the LC significantly increased both the time in and number of entries into the open arms ( Figure 5H , I ) . In the forced swim test , mice with LC ErbB4 deletion exhibited decreased immobility time and increased latency to first surrender ( Figure 5J , K ) . In the sucrose preference test , LC ErbB4 deletion significantly increased sucrose preference of the injected mice ( Figure 5L ) . In addition to behavioral performance , AAV-mediated LC-specific ErbB4 deletion mice showed similar molecular and electrophysiological abnormalities to Th-Cre;Erbb4loxp/loxp mice . Spontaneous excitability ( Figure 6A–C ) , NR2B expression ( Figure 6D , E ) , and NMDAR-mediated current amplitude ( Figure 6F , G ) were increased in LC-NE neurons infected by AAV-Cre-GFP viruses in Erbb4loxp/loxp mice . In summary , the results showed that region-specific deletion of ErbB4 in the LC is sufficient to induce similar electrophysiological , biochemical , and mania-like behavioral phenotypes as those manifested in Th-Cre;Erbb4loxp/loxp mice , thus indicating that ErbB4 deletion in LC-NE neurons plays the prominent role in abnormalities of Th-Cre;Erbb4loxp/loxp mice as opposed to potential off-target ErbB4 deletions in non-LC regions . Lithium was the first medicine approved by the Food and Drug Administration for BPD treatment . Testing the effect of lithium treatment on the behavioral abnormalities of Th-Cre;Erbb4loxp/loxp mice may validate those behaviors to be mania-like and also reveal whether NE neurons are involved in the mechanism of lithium . After mice were treated for 10 d with lithium chloride ( 600 mg L−1 ) dissolved in drinking water , as described previously ( Roybal et al . , 2007 ) , the behavioral performance of Th-Cre;Erbb4loxp/loxp mice was rescued in the open field test , EPM , forced swim test , and sucrose preference test ( Figure 7 ) . In the open field test , lithium decreased locomotor activity , traveling speed , and distance and time traveled at high speed , and increased immobility time of Th-Cre;Erbb4loxp/loxp mice ( Figure 7A–E ) . In addition , lithium decreased both time in and entries into the open arms by Th-Cre;Erbb4loxp/loxp mice in the EPM ( Figure 7F–G ) . The treated Th-Cre;Erbb4loxp/loxp mice also exhibited significantly increased immobility time and decreased latency to first surrender in the forced swim test ( Figure 7H , I ) , and reduced sucrose preference in the sucrose preference test ( Figure 7J ) . To better understand the mechanisms underlying the effect of lithium on mania-like behaviors of Th-Cre;Erbb4loxp/loxp mice , Western blotting and patch clamp recordings were performed to detect NR2B expression , TH phosphorylation , and spontaneous firing of LC-NE neurons after lithium treatment . The NR2B protein level and phosphorylation of TH were significantly decreased in Th-Cre;Erbb4loxp/loxp mice receiving lithium ( Figure 7K , L ) , whereas no change was observed in the protein levels of TH or the membrane-bound and soluble forms of COMT ( MB-COMT and S-COMT , respectively ) ( Figure 7K , L ) . In addition , the spontaneous firing rates and inter-spike intervals of LC-NE neurons in Th-Cre;Erbb4loxp/loxp mice were both rescued after lithium treatment ( Figure 7M , N ) . These results indicate that NE neurons may be a potential target of lithium in the treatment of mania . In addition , the rescuing effect of lithium on the behavioral abnormalities of Th-Cre;Erbb4loxp/loxp mice further indicated that the behaviors induced by ErbB4 deletion in LC-NE neurons are mostly mania-like phenotypes . Our previous results showed that both norepinephrine and dopamine were increased in Th-Cre;Erbb4loxp/loxp mice ( Figure 2I , J ) . To identify which system contributes to the mania-like behaviors of Th-Cre;Erbb4loxp/loxp mice , norepinephrine α1 receptor antagonist prazosin ( 1 mg/kg , i . p . ) and dopamine D1 receptor antagonist SCH23390 ( 0 . 125 mg/kg , i . p . ) were used to inhibit the effects of norepinephrine and dopamine , respectively . Both prazosin and SCH23390 decreased the locomotor activity and traveling speed of Th-Cre;Erbb4loxp/loxp mice in the open field test ( Figure 8A , B ) . Furthermore , both distance and time traveled at high speed decreased ( Figure 8C , D ) , and the immobility time was markedly increased ( Figure 8E ) after prazosin and SCH23390 treatment . In the EPM test , prazosin and SCH23390 treatment in Th-Cre;Erbb4loxp/loxp mice decreased the time spent in the open arms , although no effect on the number of entries was observed ( Figure 8F , G ) . Moreover , Th-Cre;Erbb4loxp/loxp mice treated with prazosin or SCH23390 exhibited increased immobility time and decreased latency to first surrender in the forced swim test ( Figure 8H , I ) . In the sucrose preference test , prazosin and SCH23390 both significantly decreased the sucrose preference of Th-Cre;Erbb4loxp/loxp mice ( Figure 8J ) . Similar to their effect on Th-Cre;Erbb4loxp/loxp mice , prazosin and SCH23390 reduced locomotor activity , elevated depression and anxiety , and induced anhedonia in the control animals ( Figure 8 ) . The effect of prazosin and SCH23390 on control animals implies that a basal physiological level of norepinephrine and dopamine receptor activities is required for the mediation of those behaviors , while results from prazosin and SCH23390 treatment on mutant animals demonstrate that increases in norepinephrine and dopamine contribute to the mania-like behaviors of Th-Cre;Erbb4loxp/loxp mice .
We show that disruption of ErbB4 in LC-NE neurons causes NMDA receptor-mediated hyperactive spontaneous firing of LC-NE neurons and elevates CSF norepinephrine and dopamine concentrations , which induce mania-like behaviors that could be rescued by lithium or noradrenergic and dopaminergic receptor antagonists . This is the first study to demonstrate the function of ErbB4 in the regulation of behavior and mood by LC-NE neurons and of catecholamine dyshomeostasis in the pathogenesis of mania-associated disorders such as BPD . BPD is a severe psychiatric disorder with a long-term global disease burden; however , its pathogenic mechanisms remain unknown ( Harrison et al . , 2018 ) . Despite the increase of norepinephrine in BPD patient brains in mania episodes observed since early in the twentieth century ( Manji et al . , 2003; Post et al . , 1973; Post et al . , 1978 ) , a clear description of a causal role played by norepinephrine in the pathophysiology of BPD is lacking . Here , for the first time , we demonstrates direct causality between catecholamine dyshomeostasis and mania behavior , as well as the important role of ErbB4 in BPD pathogenesis . Conditional ErbB4 deletion in LC-NE neurons increased the concentration of both norepinephrine and dopamine in the CSF , which is consistent with clinical observations of BPD patients . By specifically blocking the function of norepinephrine or dopamine , we restored the mania-like behaviors of Th-Cre;Erbb4loxp/loxp mice , providing strong evidence that elevated norepinephrine directly contributes to BPD pathogenesis . These findings will facilitate a better understanding of the pathophysiology of diseases associated with mania beyond BPD . The increased dopamine level may be attributable to the co-release of dopamine in NE neurons and the regulation of dopamine by NE neuronal terminals ( Carboni et al . , 1990; Devoto et al . , 2005; Pozzi et al . , 1994; Yamamoto and Novotney , 1998 ) . The mechanism underlying lithium treatment for BPD is complicated and unresolved ( Jope , 1999; Schloesser et al . , 2012 ) . Here we showed that lithium rescued the abnormal spontaneous firing activity and NR2B expression of LC-NE neurons and alleviated mania-like behaviors in Th-Cre;Erbb4loxp/loxp mice ( Figure 7 ) . These observations suggest that LC-NE neurons may be a target of lithium and thus provide a possible mechanism for lithium treatment of BPD . In contrast to the manifestation in mutant mice , lithium treatment led to increased movement and decreased immobile time in the forced swim test in control mice ( Figure 7 ) , suggesting different functions of lithium in physiological and pathological statuses . The LC is a nucleus consisting of most of the NE neurons in the brain , and its impairment has been implicated in many severe neurodegenerative diseases and affective disorders ( Benarroch , 2009; Berridge and Waterhouse , 2003; Aston-Jones and Cohen , 2005; Mather and Harley , 2016; Pamphlett , 2014; Ross et al . , 2015 ) . Though norepinephrine has been linked with BPD , direct evidence of how the LC functions in the pathogenesis of BPD is still unclear ( Bernard et al . , 2011; Bielau et al . , 2012; Kato , 2008 ) . Here , we provide direct evidence demonstrating the crucial role of the LC in BPD pathogenesis . Work by D'Andrea et al . ( 2015 ) showed that LC neuronal dysfunction by CREB signaling hyperactivity cause ADHD-like behavior in PI3Kγ conventional knockout ( PI3Kγ KO ) mice . Since ADHD and mania animal models have overlapped behavioral phenotypes ( Beyer and Freund , 2017; Itohara et al . , 2015 ) , we treated ErbB4-deficient mice with methylphenidate ( MHP ) , a clinical ADHD medication which alleviates hyperactivity of ADHD animal models but aggravates hyperactivity of mania models ( D'Andrea et al . , 2015; Souza et al . , 2016; Sumitomo et al . , 2018 ) . Our results showed that MHP aggravated hyperactivity of ErbB4-deficient ( Erbb4loxp/loxp + AAV-Cre-GFP ) mice ( Figure 5—figure supplement 3 ) . In addition , the CREB signaling in the LC was unaltered in our LC ErbB4-deficient mice ( Figure 5—figure supplement 4 ) . Thus , these results suggest that our LC ErbB4-deficient mice should be a mania model . Using ELISA kit , D'Andrea et al . showed that PI3Kγ KO mice showed increased norepinephrine and decreased dopamine levels in prefrontal cortex and striatum ( D'Andrea et al . , 2015 ) , while both norepinephrine and dopamine were elevated in the CSF of LC ErbB4-deficient mice in our study . The possible explanation might be that the specimens and measurement methods of their and our studies were different and that the decrease of dopamine in PI3Kγ KO mice may be caused by the regulation of other PI3Kγ KO non-catecholaminergic neurons . Meanwhile , the dysregulation of different molecular players ( e . g . ErbB4 , PI3Kγ ) in LC-NE neurons may lead to different cellular states that causes diverse psychiatric–like behaviors . Erbb4 is a genetic susceptibility gene for schizophrenia ( Mei and Xiong , 2008; Pitcher et al . , 2011 ) , with many studies reporting the crucial role of ErbB4 in the pathogenesis of schizophrenia ( Chong et al . , 2008; Del Pino et al . , 2013; Hahn et al . , 2006; Shamir et al . , 2012 ) . While coding variants of Erbb4 have also been genetically associated with BPD ( Chen et al . , 2012; Bipolar Genome Study et al . , 2011 ) , direct evidence remains limited . We reveal that functional deficiency of ErbB4 in LC-NE neurons facilitates the paroxysm of mania-like behaviors and increases spontaneous firing of LC-NE neurons ( Figure 2A–D ) . In contrast , conditional ErbB4 deletion in parvalbumin-positive GABAergic neurons in the frontal cortex decreases the excitability of these neurons via KV1 . 1 ( Kx et al . , 2012 ) . These lines of evidence suggest that ErbB4 may function differently in different neurons . The NMDA receptor , especially its subunit isoform NR2B , is regulated by ErbB4 in the hippocampus and prefrontal cortex ( Bjarnadottir et al . , 2007; Pitcher et al . , 2011 ) . Consistent with previous studies on the influence of ErbB4 on NR2B , we observed NR2B overexpression and enhanced NMDAR function in LC tissue of ErbB4-deficient mice ( Figure 3 ) . However , how ErbB4 deletion increases NR2B protein expression and how NR2B overexpression participates in the strengthening of NMDA receptor function in the hyperexcitability of ErbB4-deficient NE neurons requires further investigation . Past studies have reported ErbB4 mRNA to be highly expressed in the LC area ( Gerecke et al . , 2001 ) . We confirmed ErbB4 protein expression in LC-NE neurons ( Figure 1F , G ) . In addition , we functionally validated the presence of ErbB4 in LC-NE neurons by showing increased spontaneous firing and enhanced NMDAR function upon ErbB4 deletion . However , a previous report failed to detect the expression of Cre/Tomato in LC neurons in ErbB4::CreERT2;Rosa::LSL-tdTomato mice ( Bean et al . , 2014 ) . This discrepancy may arise from the different experimental methods adopted by the different research groups . Earlier research has shown that Cre is abundantly expressed in Th-Cre mouse lines in the NE and dopaminergic neurons of the LC and midbrain , respectively ( Lindeberg et al . , 2004; Savitt et al . , 2005 ) . Recent research used the Th-Cre line ( Gong et al . , 2007 ) to drive ErbB4 selective deletion , with gene loss mainly observed in dopaminergic neurons in the midbrain ( Gong et al . , 2007; Skirzewski et al . , 2017 ) . In contrast , very low Cre expression was detected in the midbrain dopaminergic neurons in our Th-Cre mice ( Gelman et al . , 2003 ) compared with the abundant Cre expression observed in the LC-NE neurons . Though mice of the same genotype ( both Th-Cre;Erbb4loxp/loxp ) were used , the varied Cre expression in the distinct Th-Cre lines in our research and that of Skirzewski et al . ( 2017 ) yielded different findings on ErbB4 function in different neuronal types . ErbB4 deletion in dopaminergic neurons in the midbrain led to deficits in spatial/working memory but had no influence on locomotion or anxiety ( Skirzewski et al . , 2017 ) . In comparison , our mutant mice with ErbB4 deletion in LC-NE neurons presented significant hyperactivity and reduced anxiety ( Figure 4 ) . The reason underlying the discrepancy between different Th-Cre lines is currently unknown ( Lammel et al . , 2015 ) . One probable explanation may be that Cre is inserted into different chromosomal loci and the surrounding genetic or epigenetic elements may modify the spatial and temporal regulation of Cre gene expression . Nevertheless , discrepancy of behaviors presented by ErbB4 mutants used by Skirzewski et al . and by us is an additional line of evidence supporting that ErbB4 deletion in LC-NE neurons , instead of off-target ErbB4 deletion in catecholaminergic neurons in other brain regions , is the primary cause for abnormalities of our Th-Cre;Erbb4loxp/loxp mice . Together , our findings demonstrate the importance of ErbB4 in LC-NE neurons in behavior and mood regulation and reveal the participation of catecholamine homeostasis modulated by ErbB4 in the pathogenesis of mania-associated disorders . Future studies aimed at identifying ErbB4 downstream signals in LC-NE neurons may provide new insights into therapies for mania-associated disorders .
Four mouse lines were used . We first crossed Th-Cre mice ( kindly provided by Yuqiang Ding , Tongji University School of Medicine , Shanghai , China ) , which have been described previously ( Gelman et al . , 2003 ) , with Erbb4loxp/loxp mice ( Mutant Mouse Regional Resource Center from North America ) , generating a Th-Cre;Erbb4loxp/loxp mouse line in which ErbB4 was mainly deleted in the noradrenergic neurons ( NE neurons ) . For Immunohistochemical analysis and electrophysiological recordings , we then crossed Th-Cre;Erbb4loxp/loxp mice with Ai9 mice or Ai3 mice , which are used as a Cre reporter strain ( purchased from Jackson Laboratory ) . Th-Cre mice were in C57BL/6N genetic background and Erbb4loxp/loxp mice were in the C57BL/6 genetic background ( No substrain information was available from Mutant Mouse Regional Resource Center from North America , Stock Number: 010439 ) . The control mice used in behavior experiments were littermates to the Th-Cre; Erbb4loxp/loxp mice . Additionally , we have compared the behavioral performance between Th-Cre mice and Erbb4loxp/loxp mice , which were littermates to the Th-Cre; Erbb4loxp/loxp mice , and there was no significant difference between these two mouse lines ( data not shown ) . Only male mice ( 8 – 12 weeks old ) with normal appearance and weight were used in experiments and were divided into different groups randomly . All mice were housed under a 12 hr light/dark cycle ( lights were on from 7:00 am-7:00 pm everyday ) and had access to food and water ad libitum . Mice were anesthetized with 10% chloral hydrate and perfused with ice-cold saline followed by paraformaldehyde ( PFA ) ( 4% ) in phosphate-buffered saline ( PBS ) . Brains were removed and fixed in the same 4% PFA solution at 4°C overnight and transferred to 30% sucrose in PBS for 2 d . Frozen brains were sectioned at 30 μm with a sliding microtome ( Leica CM3050 S , Leica biosystems ) in the coronal plane . Slices were immersed in PBS with 0 . 02% sodium azide and stored at 4°C until further use . After incubation in blocking buffer containing 5% goat serum and 3% bovine serum albumin ( BSA ) in PBST ( 0 . 5% Triton X-100 in PBS ) for 1 hr at room temperature , slices were incubated with primary antibodies ( rabbit tyrosine hydroxylase ( TH ) -specific antibody ( 1:700 , Abcam ) , mouse ErbB4-specific antibody ( 1:300 , Abcam ) ) in blocking buffer at 4°C overnight . The slices were washed three times in PBST and incubated with Alexa Fluor 488- or Alexa Fluor 543-conjugated secondary antibodies at 25°C for 1 hr . All slices were counterstained with DAPI during final incubation . Fluorescent image acquisition was performed with an Olympus FluoView FV1000 confocal microscope using a 20 × objective lens and analyzed using ImageJ software . In western blotting experiments , our controls were ErbB4loxP/loxP mice or ErbB4loxP/loxP + AAV-GFP mice . Brain tissues from control and Th-Cre;Erbb4loxp/loxp mice or Erbb4loxp/loxp + AAV-Cre-GFP mice were homogenized in RIPA lysis buffer containing 50 mM Tris ( pH 7 . 4 ) , 150 mM NaCl , 1% Triton X-100 , 1% sodium deoxycholate , 0 . 1% SDS , 1 mM PMSF , and phosphatase inhibitor cocktail ( Cell Signaling Technology ) . Protein samples were loaded on 10% acrylamide SDS-PAGE gels and then transferred to nitrocellulose membranes . After incubation with 4% BSA for 1 hr at 25°C , membranes were incubated with primary antibodies at 4°C overnight ( sheep TH-specific antibody , 1:2 , 000 , Millipore; rabbit TH-Ser40-specific antibody , 1:1 , 000 , Millipore; rabbit ErbB4-specific antibody , 1:2 , 000 , Abcam; rabbit norepinephrine transporter ( NET ) -specific antibody , 1:300 , Millipore; rabbit dopamine beta-hydroxylase ( DBH ) -specific antibody , 1:300 , Abcam; rabbit GAPDH-specific antibody , 1:5 , 000 , Cell Signaling Technology; mouse catechol-o-methyltransferase ( COMT ) -specific antibody , 1:5 , 000 , BD Biosciences; and rabbit actin-specific antibody , 1:2 , 000 , Cell Signaling Technology ) . The membranes were washed three times and then incubated for 1 hr with horseradish peroxidase-conjugated secondary antibodies in 4% BSA at 25°C . Immunoreactive bands were visualized clearly by X-ray film exposure ( ECL kit , Thermo Scientific ) and analyzed using NIH ImageJ software . Each experiment was repeated at least three times . Mice were deeply anesthetized with isoflurane ( 0 . 15% in oxygen gas ) and mounted on a stereotaxic frame ( RWD Life Science ) . A stainless steel guide cannula with a dummy probe was implanted into the lateral ventricle ( anteroposterior ( AP ) = −0 . 6 mm; mediolateral ( ML ) =± 1 . 2 mm; dorsoventral ( DV ) = −2 . 0 mm ) . After 7 d of recovery , the dummy probe was replaced with a microdialysis probe ( membrane length: 4 mm , molecular weight cut-off: 18 , 000 Da , outer diameter: 0 . 2 mm ) . For balance , artificial cerebrospinal fluid ( ACSF ) , which contained ( in mM ) 125 NaCl , 2 . 5 KCl , 2 CaCl2 , 1 MgCl2 , 1 . 25 NaH2PO4 , 25 NaHCO3 , and 11 D-glucose , was perfused continuously by syringe pump at a speed of 2 µl min−1 for 2 hr before sample collection . Samples ( 60 µl each ) were automatically collected from each mouse for 2 hr and analyzed by high-performance liquid chromatography ( HPLC ) with an electrochemical detector ( 5014b , ESA , USA ) . The concentrations of norepinephrine and dopamine were detected by HPLC ( Coulochem III , ESA , USA ) using a C18 column ( MD150 3 mm × 150 mm , 5 μm , ESA , USA ) . Mice were deeply anesthetized and decapitated . The brain was quickly removed and immersed in ice-cold high-sucrose ACSF bubbled with 95% O2/5% CO2 to maintain a pH of 7 . 4 . High-sucrose ACSF contained the following ( in mM ) : 200 sucrose , 3 KCl , 2 CaCl2 , 2 MgCl2 , 1 . 25 NaH2PO4 , 26 NaHCO3 , and 10 D-glucose . Coronal slices ( 250 µm ) were prepared with a vibratome ( Leica , VT 1000S , Germany ) , allowed to rest for 1 hr at 34°C in oxygenated ACSF , and then maintained at 25°C before transfer to the recording chamber . Acute slices from adult control mice ( Ai9;Th-Cre mice or Erbb4loxp/loxp mice injected with AAV-GFP virus ) , or Ai9;Th-Cre;Erbb4loxp/loxp mice , or Erbb4loxp/loxp mice injected with AAV-Cre-GFP virus were transferred to a recording chamber and fully submerged in ACSF at 25°C , which was continuously perfused ( 2 ml/min ) with oxygen . Fluorescent neurons were visually identified under an upright microscope ( Nikon , Eclipse FN1 ) equipped with an infrared-sensitive CCD camera . Electrophysiological recordings were performed in cell-attached mode for spontaneous firing recording and in whole-cell mode for detection of NMDAR current , sEPSC , sIPSC , and intrinsic membrane properties by MultiClamp 700B Amplifier equipped with Digidata 1440A analog-to-digital converter . For intrinsic membrane properties and spontaneous firing recordings , microelectrodes ( 3 – 5 MΩ ) were filled with a solution containing 130 mM potassium gluconate , 20 mM KCl , 10 mM HEPES buffer , 2 mM MgCl·6 H2O , 4 mM Mg-ATP , 0 . 3 mM Na-GTP , and 10 mM EGTA; the pH was adjusted to 7 . 25 with 10 M KOH . 3 mins for stabilization . AP-V ( 50 μM , Tocris Bioscience ) , DNQX ( 30 μM , Tocris Bioscience ) and picrotoxin ( 50 mM ) were present in the bath solution for intrinsic membrane properties recordings . Spontaneous firing was recorded for at least 4 min for each neuron . For NMDAR current and sEPSC recording , microelectrodes ( 3 – 5 MΩ ) were filled with a solution containing 140 mM Cs-methanesulfonate , 5 mM NaCl , 1 mM MgCl2 . 6H2O , 10 mM HEPES , 0 . 2 mM EGTA , 2 mM MgATP , 0 . 5 mM NaGTP , 0 . 5 mM spermine , and 5 mM QX314 Chloride; the pH was adjusted to 7 . 25 with 10 M CsOH . To isolate sEPSC , picrotoxin ( 50 mM ) was present in the bath solution . AMPA-sEPSC were recorded at −60 mV with DL-2-amino-5-phosphonopentanoic acid ( AP-V; 50 μM , Tocris Bioscience , to block NMDA receptors ) in the bath solution . NMDAR-EPSC were induced using bipolar electrodes and recorded at +40 mV with 6 , 7-dinitroquinoxaline-2 , 3 ( 1H , 4H ) -dione ( DNQX; 30 μM , Tocris Bioscience , to block AMPA receptors ) in the bath solution . For sIPSC recording , microelectrodes ( 3 – 5 MΩ ) were filled with a solution containing 120 mM CsCl , 20 mM Cs-methanesulfonate , 5 mM NaCl , 1 mM MgCl2 . 6H2O , 10 mM HEPES , 0 . 2 mM EGTA , 2 mM MgATP , 0 . 5 mM NaGTP , 0 . 5 mM spermine , and 5 mM QX314 Chloride; the pH was adjusted to 7 . 25 with 10 M CsOH . To isolate sIPSC , AP-V ( 50 μM , Tocris Bioscience ) and DNQX ( 30 μM , Tocris Bioscience ) were present in the bath solution . Electrophysiological recordings were performed at the same time of day for control ( Erbb4loxp/loxp mice ) and Th-Cre;Erbb4loxp/loxp mice from 13:00 to 17:00 on the each experimental day . All analyses were performed using Clampfit 10 . 2 ( Axon Instruments/Molecular Devices ) , Minianalysis , and Matlab software . AAV-GFP and AAV-Cre-GFP carrying human synaptophysin promotor for gene expression were purchased from Shanghai SBO Medical Biotechnology Company , Shanghai . For viral injection , 1-month-old Erbb4loxp/loxp mice were anesthetized with chloral hydrate ( 400 mg/kg of body weight ) by intraperitoneal ( i . p . ) injection and placed in a stereotactic frame , with their skulls then exposed by scalpel incision . Glass microelectrodes were used to bilaterally inject 0 . 15 μl of purified and concentrated AAV ( ~1011 infections units per ml ) into the locus coeruleus ( LC ) ( coordinates from bregma: anterior-posterior , 5 . 25 mm; lateral-medial , 1 . 00 mm; dorsal-ventral , –4 . 5 mm ) at 100 nl min–1 . The injection microelectrode was slowly withdrawn 2 min after virus infusion . All experiments were performed in quiet rooms ( <40 dB ) equipped with dumboard between 13:00 and 16:00 and analyzed in a double-blind fashion . According to the principal of beginning with the test with minimized stress stimulation , each mouse was subjected to behavioral tests in the following order: open field test , elevated plus maze test , 6 min forced swim test , and lastly , the most time-consuming sucrose preference test . For the first three tests , mice were rested in their home cage for 1 – 2 days between two behavior tests . For the last one , mice were rested in their home cage for 3 days before the test . Mice were treated for 10 d with lithium chloride ( 600 mg L−1 ) in drinking water and were then subjected to behavioral tests or sacrificed for Western blotting or patch clamp experiments ( Dehpour et al . , 2002; Roybal et al . , 2007 ) . Methylphenidate ( MHP ) ( 10 mg/kg ) , prazosin ( 1 mg/kg ) , or SCH23390 ( 0 . 125 mg/kg ) were injected ( i . p . ) 30 min before behavioral experiments . The samples were randomly assigned to each group and restricted randomization was applied . The investigator was blinded to group allocation and when assessing outcome in the all behavioral tests and immunocytochemistry tests . For the electrophysiology experiments , the investigator was blinded when assessing outcome . For quantification , values from three independent experiments with at least three biological replicates were used . For behavioral assays , all population values appeared normally distributed , and variance was similar between groups . Sample size was calculated according to the preliminary experimental results and the formula: N = [ ( Zα / 2 + Zβ ) σ / δ]2 ( Q1– 1 + Q2− 1 ) , where α = 0 . 05 significance level , β = 0 . 2 , power = 1-β , δ is the difference between means of two samples , and Q is the sample fraction . All data are presented as means ± s . e . m . and were analyzed using two-tailed Student's t-test , one-way analysis of variance ( ANOVA ) , two-way ANOVA , or two-way repeated-measures ANOVA . The Kolmogorov–Smirnov test ( K–S test ) was used to compare the interspike interval distributions , as specified in each figure legend and source data of Figure 1–7 . Grubbs' test are used to detect an outlier . All data were analyzed using Origin8 . 0 ( OriginLab ) . Data were exported into Illustrator CS5 ( Adobe Systems ) for preparation of figures . | Bipolar disorder is a mental illness that affects roughly 1 in 100 people worldwide . It features periods of depression interspersed with episodes of mania – a state of delusion , heightened excitation and increased activity . Evidence suggests that changes in a brain region called the locus coeruleus contribute to bipolar disorder . Cells within this area produce a chemical called norepinephrine , whose levels increase during mania and decrease during depression . But it is unclear exactly how norepinephrine-producing cells , also known as noradrenergic cells , contribute to bipolar disorder . The answer may lie in a protein called ErbB4 , which is found within the outer membrane of many noradrenergic neurons . ErbB4 is active in both the developing and adult brain , and certain people with bipolar disorder have mutations in the gene that codes for the protein . Might changes in ErbB4 disrupt the activity of noradrenergic neurons ? And could these changes increase the risk of bipolar disorder ? To find out , Cao , Zhang et al . deleted the gene for ErbB4 from noradrenergic neurons in the locus coeruleus of mice . The mutant mice showed mania-like behaviors: compared to normal animals , they were hyperactive , less anxious , and consumed more of a sugary solution . Treating the mice with lithium , a medication used in bipolar disorder , reversed these changes and made the rodents behave more like non-mutant animals . Further experiments revealed that noradrenergic neurons in the mutant mice showed increased spontaneous activity . These animals also had more of the chemicals noradrenaline and dopamine in the fluid circulating around their brains and spinal cords . The results thus suggest that losing ErbB4 enhances the spontaneous firing of noradrenergic neurons in the locus coeruleus . This increases release of noradrenaline and dopamine , which in turn leads to mania-like behaviors . Future research should examine whether drugs that target ErbB4 could treat mania and improve the lives of people with bipolar disorder and related conditions . | [
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Infection by Toxoplasma gondii leads to massive changes to the host cell . Here , we identify a novel host cell effector export pathway that requires the Golgi-resident aspartyl protease 5 ( ASP5 ) . We demonstrate that ASP5 cleaves a highly constrained amino acid motif that has similarity to the PEXEL-motif of Plasmodium parasites . We show that ASP5 matures substrates at both the N- and C-terminal ends of proteins and also controls trafficking of effectors without this motif . Furthermore , ASP5 controls establishment of the nanotubular network and is required for the efficient recruitment of host mitochondria to the vacuole . Assessment of host gene expression reveals that the ASP5-dependent pathway influences thousands of the transcriptional changes that Toxoplasma imparts on its host cell . All these changes result in attenuation of virulence of Δasp5 tachyzoites in vivo . This work characterizes the first identified machinery required for export of Toxoplasma effectors into the infected host cell .
The phylum Apicomplexa comprises a group of obligate intracellular parasites that cause a range of diseases by actively invading and replicating within host cells . Like all intracellular pathogens , these parasites extensively modify their host cells in order to prevent immune clearance , while permitting nutrient acquisition for growth . Toxoplasma , one of the most common human pathogens infecting 10–80% of individuals within a population , imparts a multitude of phenotypic changes on the infected host cell in order to promote survival and dissemination , including modulation of the inflammatory response ( Fischer et al . 1997; Braun et al . , 2013 ) , hyper-migration of infected dendritic cells ( Lambert et al . , 2006 ) , down-regulation of major histocompatibility complex ( MHC ) class II ( Lüder et al . , 2003 ) , induction of c-Myc expression ( Franco et al . , 2014 ) , activation of inflammasomes ( Ewald et al . , 2014 ) , and recruitment of host endoplasmic reticulum ( ER ) ( Goldszmid et al . , 2009 ) and mitochondria ( Pernas et al . , 2014 ) to the parasitophorous vacuole membrane ( PVM ) . Over the last decade , the mechanisms of host cell modification by Toxoplasma have been explored . The first exported Toxoplasma effectors were identified through genetic quantitative trait loci mapping between progeny of crosses between virulent and avirulent strains . These proteins were shown to be protein kinases that are injected from the rhoptries into host cells during invasion ( Saeij et al . , 2006; 2007; Taylor et al . , 2006; Peixoto et al . , 2010 ) . Two canonical effector rhoptry proteins , ROP16 and ROP18 , are only known to be injected into the host cell at the onset of invasion , where ROP16 levels peak within the host cell nucleus between 10 min and 4 hr post infection . ROP16 phosphorylates signal transducers and activators of transcription 1/3/5/6 ( Rosowski et al . , 2012; Yamamoto et al . , 2009; Jensen et al . , 2013; Ong et al . , 2010 ) , thus skewing the immediate-early immune response to limit parasite clearance ( Saeij et al . , 2007 ) . While ROP16 and ROP18 were shown to be required for virulence differences between the three canonical Toxoplasma strains , they did not explain many other known phenotypic changes that occur during Toxoplasma infection of host cells . Recently , an additional class of Toxoplasma effector proteins was identified as coming from the dense granules . These include dense granule protein 16 ( GRA16 ) , which is exported to the host cell nucleus post invasion via the dense granules , where it contributes to cell cycle arrest , potentially as a mechanism to prevent apoptosis ( Bougdour et al . , 2013 ) . Other parasite processes and host pathways now known to be impacted by the GRA proteins include: a skewing of the immune response through the effector GRA24 ( Braun et al . , 2013 ) , influencing nuclear factor kappa-light-chain-enhancer of activated B cells nuclear translocation in some strains via GRA15 ( Rosowski et al . , 2011 ) , transport of small molecules across the PVM via GRA17 and GRA23 ( Gold et al . , 2015 ) , generation of the nanotubular network ( NTN , thought to aid nutrient acquisition [Mercier , 2002] ) via GRA2 ( and others ) as well as recruitment of the host mitochondria to the PVM through the dense granule protein mitochondrial association factor 1 ( MAF1 ) ( Pernas et al . , 2014 ) . The recent and rapid discovery of these effectors suggests that there may be many more proteins that are exported via the dense granules and that they may use a conserved export pathway to mediate changes in the infected host cell . While some exported proteins in Toxoplasma have been identified , there is currently little information about how these proteins are transported across the PVM and into the host cell . In the related malaria-causing parasites , Plasmodium spp . , some of the mechanisms of protein export into the host erythrocyte have been revealed . Protein export by P . falciparum occurs almost immediately after invasion ( Riglar et al . , 2013 ) , and cargo proteins traffic via the parasite’s secretory pathway through the ER to the parasitophorous vacuole ( PV ) and across the PVM into the host cell ( Wickham , 2001 ) . In the majority of cases , a conserved pentameric motif , RxLxE/Q/D , referred to as the Plasmodium export element ( PEXEL ) or vacuolar transport signal ( VTS ) , is required for export to the host cell ( Marti , 2004 , Hiller , 2004 ) . In all published cases involving Plasmodium proteins , the PEXEL resides ~15–30 amino acids after the signal peptide ( SP ) , where it acts as a proteolytic cleavage site ( Chang et al . , 2008; Boddey et al . , 2009 ) for the ER-resident aspartyl protease plasmepsin V ( PMV ) ( Boddey et al . , 2010; Russo et al . , 2010 ) . PEXEL processing occurs after the leucine ( RxL↓xE/Q/D ) , which reveals a new N-terminus that is acetylated in the ER ( Chang et al . , 2008; Boddey et al . , 2009 ) . The current hypothesis is that the exposed new N-terminus ( Ac-xE/Q/D ) permits cargo selection for targeting to a parasite translocon located at the PVM , known as PTEX ( for Plasmodium translocon of exported proteins ) ( de Koning-Ward et al . , 2009; Elsworth et al . , 2014; Beck et al . , 2014 ) . Effectors must be unfolded for translocation ( Gehde et al . , 2009 ) through PTEX into the host cell before refolding and trafficking to their final destination in the host cell . Given that several dense granule proteins are exported by Toxoplasma , we investigated whether a conserved pathway is used and whether it shares any similarities with the Plasmodium system . Here , we identify the novel Golgi-resident aspartyl protease 5 ( ASP5 ) that is the first known component of the dense granule export machinery in Toxoplasma . Our study of ASP5 has revealed a novel mechanism of protein export in this parasite and extended our understanding of the importance of this pathway in inducing changes to the infected host cell . This work highlights similarities and important differences between mechanisms of protein export in the agriculturally and medically important Apicomplexan phylum .
Several hundred P . falciparum proteins contain a pentameric amino acid motif , also known as the PEXEL , that is essential for export into the infected erythrocyte ( Marti , 2004; Hiller , 2004 ) . Within the N-terminus of GRA16 ( Bougdour et al . , 2013 ) , we observed a PEXEL-like motif ( RRLAE ) after the SP , at amino acid positions 63 to 67 ( Figure 1A ) . To determine whether the PEXEL-like motif was involved in protein trafficking in Toxoplasma , we undertook a mutational analysis of GRA16 at the endogenous locus . This was achieved through double-homologous recombination whereby the endogenous GRA16 gene was replaced with either wild-type ( WT ) gra16 encoding the native PEXEL-like motif RRLAE and fused to a C-terminal hemagglutinin ( HA ) tag ( GRA16WT-HA ) , or a version of gra16 with its PEXEL-like motif mutated from RRLAE to AAAAE ( GRA16AAAAE-HA ) . The resulting lines were analyzed for proteolytic processing and trafficking . Immunoblot analysis showed that GRA16WT-HA is represented by a strong signal at ~57 kDa and two minor species at approximately 60 kDa and 54 kDa , respectively ( Figure 1B ) . Following mutation of the PEXEL-like motif ( GRA16AAAAE-HA ) , the two lower molecular weight species were not observed , demonstrating that the mutated protein was no longer processed in the same way . The result is consistent with the slowest migrating species representing signal peptidase-cleaved GRA16 , while the size shift of the dominant ~57 kDa species is consistent with cleavage of the PEXEL-like motif located ~45 residues beyond the SP . Interestingly , while both proteins were expressed from the endogenous locus , the amount of GRA16AAAAE-HA protein was dramatically reduced ( Figure 1B ) , suggesting the mutant protein was degraded in the absence of appropriate N-terminal processing . These results are consistent with the PEXEL-like motif being a proteolytic cleavage site similar to that observed in Plasmodium spp . ( Boddey et al . , 2010; Russo et al . , 2010 ) . 10 . 7554/eLife . 10809 . 003Figure 1 . GRA16 contains a PEXEL-motif that is required for processing and export . ( A ) Schematic representation of GRA16 containing an N-terminal SP for entry into the secretory pathway and a PEXEL-like ( TEXEL ) motif RRLAE found at residues 63–67 . Arrows relate to predicted sizes of bands seen by Western blot . ( B ) Western blot of GRA16WT-HA and GRA16AAAAE-HA . GRA16WT-HA has three molecular weight species , the uppermost ( black arrow ) being consistent with SP cleaved , the middle ( red arrow ) consistent with TEXEL cleavage and the lowest band , which is a potential degradation product . GRA16AAAAE-HA is present as only the slowest migrating species , consistent with the expected size of signal peptide cleaved , TEXEL uncleaved . αCatalase antibodies are used as a loading control . ( C ) Localization of GRA16WT-HA and GRA16AAAAE-HA . ( i ) As previously reported , GRA16WT-HA is exported into the host cell where it accumulates in the nucleus ( arrowheads ) while also being present within tachyzoites and the PV space . ( ii ) GRA16AAAAE-HA is exported far less efficiently ( a small amount can be observed in the host cell nucleus ) while the majority of this protein accumulates within tachyzoites and the PV space . Scale bar is 5 μm . HA , hemagglutinin; PEXEL , Plasmodium export element; PV , parasitophorous vacuole; SP , signal peptide; TEXEL , Toxoplasma export element . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 003 We next sought to determine whether the PEXEL-like motif was required for GRA16 trafficking to the host cell , as is true for the PEXEL motif in Plasmodium spp . ( Hiller , 2004 , Marti , 2004 ) . Human foreskin fibroblasts ( HFFs ) were infected with parasites expressing GRA16WT-HA and GRA16AAAAE-HA for 24 hr and the localization of the proteins was determined by immunofluorescence assay ( IFA ) using anti-HA antibodies . GRA16WT-HA was observed within the host cell nucleus , as previously reported ( Bougdour et al . , 2013 ) , as well as at the PV and within parasites ( Figure 1C-i ) . In contrast , GRA16AAAAE-HA was observed either within parasites , in small punctate structures reminiscent of the Golgi or in the PV space between parasites ( Figure 1C-ii ) . In a minority of cells , a small amount of exported GRA16AAAAE-HA could be observed within the host nuclei ( Figure 1C-ii , panel 2 ) ; however , there was a large and clear defect in export in the mutant line . Taken together , this demonstrates that the PEXEL-like motif is required for correct proteolytic processing of GRA16 and efficient export to the host cell . We therefore termed this motif the Toxoplasma export element ( TEXEL ) . In Plasmodium spp . , the PEXEL is cleaved by the ER-resident aspartyl protease plasmepsin V ( PMV [Boddey et al . , 2010; Russo et al . , 2010] ) . We hypothesized that an orthologous protease in Toxoplasma is required for cleavage of the TEXEL in GRA16 and potentially other Toxoplasma proteins . We searched ToxoDB ( http://toxodb . org ) using P . falciparum PMV ( PfPMV ) as a query and the top Basic Local Alignment Search Tool ( BLAST ) hit was aspartyl protease 5 ( ASP5 , TGME49_242720 ) , consistent with previous phylogenetic analysis of this group of proteases in Apicomplexa ( Shea et al . , 2007 ) . An alignment of the two proteins revealed that they share approximately 33% similarity and 14% identity across the full-length alignment ( Figure 2—figure supplement 1 ) . The proteins shared several key features , including an N-terminal SP , a core aspartyl protease domain ( with DTG and DSG residues defining the catalytic dyad ) , a plant-like nepenthesin fold , as well as a C-terminal transmembrane domain ( Hodder et al . , 2015 ) . While ASP5 contains a significantly longer SP and C-terminal tail sequence than PfPMV , it lacks the helix-turn-helix motif found in PMV from all Plasmodium spp . that is hypothesized to interact with other ER proteins ( Hodder et al . , 2015 ) . To characterize ASP5 within parasites , we tagged the 3’ end of the endogenous gene with a triple-hemagglutinin ( HA3 ) tag in the RHΔku80 background ( Huynh and Carruthers , 2009 ) . Immunoblot analysis with anti-HA antibodies revealed ASP5WT-HA3 is present as two major species of approximately 90 and 55 kDa ( Figure 2A ) , consistent with a signal peptidase-cleaved species and possibly an activated form , respectively ( Figure 2A ) . A mutant form of ASP5 , where the conserved aspartic acid catalytic residues were mutated to alanine ( ASP5D431A , D682A-HA3; herein referred to as ASP5MUT-HA3 ) , was observed predominantly as the ~90 kDa form , suggesting that ASP5 may auto-activate to produce the ~55 kDa species ( Figure 2A , Figure 2—figure supplement 2 ) . ASP5 was previously localized to the Golgi when tagged with a Ty1 epitope tag ( Shea et al . , 2007 ) . Using immunofluorescence microscopy with anti-HA antibodies , we observed ASP5WT-HA3 in apical puncta that co-localized with GalNAc-YFP , a known Golgi marker ( Nishi et al . , 2008 ) ( Figure 2B ) . ASP5MUT-HA3 also localized to discrete puncta adjacent to the nucleus , representative of the Golgi ( Figure 2B ) . 10 . 7554/eLife . 10809 . 004Figure 2 . ASP5 specifically cleaves the GRA16 TEXEL . ( A ) Western blot of endogenously tagged ASP5 ( ASP5WT-HA3 ) and ectopic ASP5D431A , D682A-HA3 ( ASP5MUT-HA3 ) in parasites shows two predominant species . The upper band ( red arrow ) is consistent with a signal peptidase-cleaved species and the lower ( blue arrow ) may be auto-activation , as it is greatly diminished for ASP5MUT-HA3 . αGAP45 antibodies are used as a loading control . ( B ) Endogenously-expressed ASP5WT-HA3 co-localizes with the Golgi marker GalNAc-YFP ( upper panel ) and this localization is unaffected for the catalytic mutant enzyme ( ASP5MUT-HA3 ) ( lower panel ) . ( C ) Immunoprecipitated ASP5WT-HA3 , but not ASP5MUT-HA3 , cleaves GRA16 TEXEL ( DABCYL-R-VSRRLAEEP-E-EDANS ) but not the RRL>AAA peptide . ( D ) LC chromatogram ( 214 nm ) of the fluorogenic GRA16 TEXEL peptide ( upper left ) incubated in buffer alone ( -ASP5-HA3 ) with MS analysis showing the parent ion of the unprocessed fluorogenic peptide ( lower left ) . LC chromatogram ( 214 nm ) of the fluorogenic GRA16 TEXEL peptide after incubation at 37°C for 48 hr with ASP5 ( +ASP5WT-HA3 ) ( upper right ) , showing the N-terminal product of processing within the TEXEL after leucine ( DABCYL-R-VSRRL ) at 15 . 5 min while the remaining unprocessed fluorogenic peptide is observed at 14 . 3 min . MS analysis showing the parent ion of the processed N-terminal cleavage product DABCYL-R-VSRRL ( lower right ) . ( E ) Structure of WEHI-586 ( RRLStatine ) . ( F ) Dose response curve showing inhibition of ASP5WT-HA3 activity by WEHI-586 with IC50 of 63 ± 15 nM . Data shown are mean ± standard deviation from three experiments . Scale bar is 5 μm . ASP5 , Aspartyl Protease 5; HA3 , triple-hemagglutinin; LC; liquid chromatography , MS; mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 00410 . 7554/eLife . 10809 . 005Figure 2—figure supplement 1 . Alignment of PfPMV and TgASP5 sequences . ClustalW alignment of the full-length predicted amino acid sequences of PfPMV ( PF3D7_1323500 ) and TgASP5 ( TGME49_242720 ) . The alignment of the two proteins shows they share 14 . 4% identity and 32 . 6% similarity across the full-length alignment , including gaps . The predicted positions of signal peptidase cleavage are shown with black arrows , the catalytic dyads are indicated by red boxes , with ‘*’ signifying each catalytic aspartic acid residue , the ‘nepenthesin 1-type’ aspartyl protease ( NAP1 ) fold is indicated with a gray box ( NAP1 insert ) , the enzyme ‘flap’ that sits over the substrate binding pocket is indicated with a green box including the unique cysteine residue in PMV that is absent from ASP5 , the helix-turn-helix of PMV that is absent from ASP5 is shown with a blue box , and the putative C-terminal transmembrane domain highlighted with a pink box ( Hodder et al . , 2015 ) . The mutations of catalytic aspartic acid residues D>A in ASP5MUT-HA3 are shown on the right ( red , D431A , D682A ) and the orange arrow denotes the point of the frame shift mutation in Δasp5CRISPR parasites , which leads to a premature stop codon in the protein sequence ( upper right ) . ASP5 , Aspartyl Protease 5; PMV , plasmepsin V . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 00510 . 7554/eLife . 10809 . 006Figure 2—figure supplement 2 . ASP5 may undergo auto proteolysis . Immunoblot of ASP5-HA3 in ( lane 1 ) endogenously tagged parasites , ( lane 2 ) WT parasites ectopically expressing ASP5MUT-HA3 , ( lane 3 ) Δasp5CRISPR ( right ) parasites ectopically expressing ASP5MUT-HA3 . A longer exposure is also shown ( right panel ) . The ~55 kDa species is diminished in WT parasites expressing ASP5MUT-HA3 and absent in Δasp5CRISPR:ASP5MUT-HA3 parasites . The blot shown is the same as in Figure 2A , with extended panels . ASP5 , Aspartyl Protease 5; HA3 , triple-hemagglutinin . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 00610 . 7554/eLife . 10809 . 007Figure 2—figure supplement 3 . Enzymological Characterization of ASP5 . ( A ) ASP5WT-HA3 cleavage of a fluorogenic peptide containing the TEXEL motif from GRA16 has a pH optimum of 5 . 5 . ( B ) Kinetics showing cleavage of the fluorogenic GRA16 TEXEL peptide at varying concentrations over time . ( C ) Michaelis–Menten curves showing the rate of cleavage ( Rate = relative fluorescence units per min ) of increasing concentrations of fluorogenic GRA16 TEXEL peptide by ASP5WT-HA3 . The data were used to derive Km values reported in the text . ( D ) Burk-Lineweaver or a double reciprocal plot of the velocity of ASP5WT-HA3 as a function of the fluorogenic GRA16 TEXEL substrate concentration . Data are mean ± standard deviation of triplicate experiments . ASP5 , Aspartyl Protease 5; HA3 , triple-hemagglutinin; TEXEL , Toxoplasma export element . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 00710 . 7554/eLife . 10809 . 008Figure 2—figure supplement 4 . Synthesis scheme for generation of WEHI-586 . Details of WEHI-586 synthesis is outlined in Materials and methods section . Reagents and conditions: a ) N , N , N′ , N′-Tetramethyl-O- ( 1H-benzotriazol-1-yl ) uronium hexafluorophosphate , O- ( Benzotriazol-1-yl ) -N , N , N′ , N′-tetramethyluronium hexafluorophosphate ( HBTU ) , Et3N , dimethylformamide ( DMF ) , HCl . NH2-Orn ( N-Boc ) -OMe; b ) Pd/C , H2 , MeOH; c ) PhCH2SO2Cl , Et3N , dichloromethane ( DCM ) ; d ) LiOH . H2O , tetrahydrofuran ( THF ) , H2O; e ) HBTU , Et3N , DMF , Ph ( CH2 ) 2NH2; f ) 4N HCl , dioxane; g ) HBTU , Et3N , DMF , 5; h ) 4N HCl , dioxane; i ) Et3N , N , N'-bis-Boc-1-guanylpyrazole; j ) TFA , DCM . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 008 To determine whether ASP5 could cleave the TEXEL motif , we immunopurified ASP5WT-HA3 from transgenic parasites using anti-HA agarose and incubated it with a fluorogenic peptide containing the TEXEL sequence RRLAE from GRA16 , as previously performed for PMV with PEXEL substrates ( Boddey et al . , 2010; Russo et al . , 2010 ) . ASP5WT-HA3 efficiently cleaved the GRA16 peptide with Km 47 . 8 ± 18 . 4 μM ( mean ± SD ) ( Figure 2C and Figure 2—figure supplement 3C ) . However , only minimal cleavage was observed when the TEXEL peptide was mutated from RRLAE to AAAAE ( Figure 2C ) , similar to the specificity observed for PMV ( Boddey et al . , 2010; Russo et al . , 2010 ) . ASP5WT-HA3 activity was optimal at pH 5 . 5 ( Figure 2—figure supplement 3A ) , in contrast to pH 6 . 4 for PMV ( Boddey et al . , 2010; Russo et al . , 2010 ) , consistent with the Golgi being a more acidic environment than the ER ( Wu et al . , 2000 ) . To control against proteolysis by potentially contaminating enzymes in the WT ASP5 preparation , we immunopurified ectopic ASP5MUT-HA3 from otherwise WT parasites , as above , and incubated it with the GRA16 TEXEL peptides . No cleavage was observed ( Figure 2C ) , demonstrating that the GRA16 TEXEL peptide is specifically cleaved by ASP5 and that this is dependent on the catalytic residues D431 and D682 . To determine the amino acid position of substrate processing by ASP5 , we used liquid chromatography ( LC ) combined with tandem mass spectrometry ( MS/MS ) to examine the GRA16 peptide cleavage products ( Figure 2D ) . Peptides incubated in buffer alone remained intact in contrast to peptides incubated with ASP5WT-HA3 , which resulted in the generation of a product corresponding to processing within the TEXEL after leucine ( DABCYL-R-VSRRL↓ ) . This processing event after the leucine residue , hereafter referred to as the P1 position , is the identical site of processing of the PEXEL by PMV in both P . falciparum and P . vivax ( Boddey et al . , 2009; 2010; Russo et al . , 2010; Sleebs et al . , 2014b ) . To further examine the specificity of ASP5 for the TEXEL sequence , we designed a peptide-like inhibitor that directly mimics the TEXEL sequence RRLAE from GRA16 but that contains the non-cleavable amino acid , statine ( RRLStatine; WEHI-586 , Figure 2E ) . This compound is predicted to bind the active site of ASP5 and mimic the transition state of GRA16 TEXEL cleavage , thus inhibiting the enzyme . Incubation of ASP5WT-HA3 with WEHI-586 blocked cleavage of the GRA16 peptide with IC50 of 63 ± 15 nM ( mean ± standard error of the mean ) ( Figure 2F ) , demonstrating the potent affinity of the TEXEL sequence for ASP5 . Taken together , these results demonstrate that ASP5 is a Golgi-resident protease that cleaves the GRA16 TEXEL motif after the leucine residue and can be potently inhibited by a TEXEL-mimetic small molecule . To investigate the substrate selectivity of ASP5 and directly compare it with PMV , we incubated ASP5WT-HA3 and PfPMV-HA with peptides containing different point mutations at the TEXEL and PEXEL motifs , based on RRLAE from GRA16 and RTLAQ from the P . falciparum exported protein , knob associated histidine rich protein ( KAHRP ) , respectively . PfPMV-HA behaved as expected ( Boddey et al . , 2010; 2013 ) , cleaving peptides containing the WT KAHRP PEXEL but not P3 ( R>K ) or P1 ( L>I ) point mutations ( Figure 3A-i ) . This Plasmodium enzyme also cleaved the peptide containing the WT GRA16 TEXEL , with notably higher efficiency than it cleaved the peptide KAHRP; however , it did not cleave GRA16 peptides containing TEXEL mutations at P3 ( R>A ) or P1 ( L>A ) , as expected based on the known specificity of this protease ( Figure 3A-i ) . Similarly , ASP5 cleaved the GRA16 TEXEL peptide but did not cleave peptides containing mutations of the TEXEL , P3 ( R>A ) or P1 ( L>A ) , demonstrating that the P3 and P1 positions of the substrate ( i . e . arginine and leucine , respectively ) are important for ASP5 activity , as is the case for PMV ( Figure 3A-ii ) ( Boddey et al . , 2010; Russo et al . , 2010; Sleebs et al . , 2014b ) . In contrast to PMV , ASP5 did not cleave KAHRP peptides above background levels ( Figure 3A-ii ) . Replacement of the P2 residue in the KAHRP PEXEL ( threonine ) with the corresponding residue in GRA16 ( arginine ) ( i . e . P2 T>R ) resulted in a 3-fold increase in cleavage , demonstrating the importance of the P2 position for ASP5 activity , although cleavage of this peptide was still well below that seen for the native GRA16 TEXEL peptide ( Figure 3A-ii ) . This demonstrates that while ASP5 and PMV both cleave peptides containing RxL sequences , they do not share identical substrate specificity . To further investigate the specificity of ASP5 , point mutations were introduced at different positions of the GRA16 TEXEL substrate . This demonstrated that ASP5 does not well tolerate conservative and non-conservative changes at the P1 , P2 or P3 positions . It appeared that ASP5 could cleave RKL at ~35–40% of WT , yet the physiological relevance of this is not known ( Figure 3B ) . Interestingly , mutation of the GRA16 TEXEL P2’ residue E>A resulted in enhanced processing , illustrating that this position in ASP5 substrates may not be essential for activity , similar to PMV , but can alter cleavage efficiency ( Boddey et al . , 2009; 2010; Sleebs et al . , 2014a; 2014b ) . 10 . 7554/eLife . 10809 . 009Figure 3 . ASP5 is highly selective for ‘RRL’ substrates . ( A ) ( i ) Activity of immunoprecipitated PfPMV-HA against KAHRP- and GRA16-based fluorogenic DABCYL/EDANS peptides . PfPMV-HA is able to cleave peptides containing KAHRP PEXEL and GRA16 TEXEL sequences but not corresponding mutants ( red amino acids ) . Note the GRA16 ‘RRLAE’ TEXEL is cleaved approximately twice as efficiently as the KAHRP ‘RTLAQ’ PEXEL . ( ii ) Cleavage of substrates by immunoprecipitated TgASP5-HA3 , as in ( i ) . ASP5 cleaves the wild type GRA16 TEXEL but is unable to efficiently process the corresponding point mutants in GRA16 or any KAHRP peptides . Mutation of the P2 threonine in KAHRP for arginine ( T>R ) marginally increases processing . ( B ) Substrate specificity of ASP5-HA3 in relation to the P1 , P2 , P3 and P2’ positions . This protease is unable to tolerate conservative and non-conservative changes at P1 , P2 or P3; however , this constriction appears to be more relaxed at P2’ . ( C ) ASP5WT-HA3 cleaves the GRA16 TEXEL , as well as the TEXEL from the dense granule protein GRA19 , but not a similar motif in GRA21 , or peptides containing RRL>AAA mutations . ( D ) Preferred TEXEL consensus with the position of cleavage by ASP5 indicated ( arrow ) , color-coded according to ( B ) . ( E ) ( i ) Structural model of ASP5 in complex with the TEXEL from GRA16 ( SRRLAEE ) colored gold; or ( ii ) with a point mutant of GRA16 containing threonine at P2 ( SRTLAEE ) colored blue to explain why arginine is preferred at P2 . Arrowheads denote the P2 position in each substrate . Heteroatoms are colored white: hydrogen , blue: nitrogen and red: oxygen . Several backbone groups in ASP5 are highlighted as pink spheres . Hydrogen bonds between the GRA16 peptides and ASP5 are shown as dotted lines; colored lines highlight the hydrogen bond interactions that differ between the two substrates . ASP5 , Aspartyl Protease 5; HA , hemagglutinin; KAHRP , knob associated histidine rich protein; PEXEL , plasmodium export element; PfPMV , P . falciparum PMV; TEXEL , Toxoplasma export element . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 009 PEXEL-like sequences have previously been identified in GRA19 ( RRLSD ) and GRA21 ( RRLAE and RELLD ) ( Hsiao et al . , 2013 ) . To examine whether ASP5 can cleave these sequences , peptides were synthesized containing RRLSD ( GRA19 ) and RELLD from GRA21 . These peptides were incubated with ASP5WT-HA3 , as above , alongside corresponding RRL>AAA or REL>AAA mutants ( Figure 3C ) . The WT GRA19 TEXEL peptide was processed efficiently and this was inhibited when the TEXEL was mutated from RRL to AAA ( Figure 3C ) . In contrast , the RELLD sequence in GRA21 , and the corresponding AAALD mutant , were not processed by ASP5 ( Figure 3C ) . Since the RRLAE in the N-terminus of GRA21 is the same sequence as the TEXEL in GRA16 , it is highly likely that ASP5 processes GRA21 at this position . Taken together , this work demonstrates that ASP5 has relatively strict requirement for arginine at P3 and P2 , and leucine at P1 of its substrates ( Figure 3D ) , and that residues at P1’ and P2’ are dispensable for processing but can influence the efficiency of cleavage by this enzyme . To investigate the structural basis for substrate selection by ASP5 , we modeled the tertiary structure of this enzyme bound to the GRA16 substrate using the crystal structure of PMV from P . vivax complexed with the PEXEL mimetic inhibitor WEHI-842 ( Hodder et al . , 2015 ) and P . vivax plasmepsin IV in complex with Pepstatin A ( Bernstein et al . , 2003 ) as templates ( Figure 3E ) . The model shows that the guanidyl side-chain of arginine at P3 ( the first position in the TEXEL sequence ) forms interactions with the side-chains of E506 and Q549 in a manner completely analogous to that observed in the structure of PMV in complex with the statine inhibitor , WEHI-842 ( Hodder et al . , 2015 ) ( Figure 3E-i ) . Furthermore , the leucine at P1 of the TEXEL is surrounded by hydrophobic residues I429 , Y503 , F546 and I554 of ASP5; the isoleucine at position 554 in ASP5 is a valine in PMV , while the other residues , I , Y , F are identical between ASP5 and PMV ( Figure 3E-i ) . Our TEXEL cleavage data described above revealed that , unlike PMV , arginine is strongly preferred at the P2 position for ASP5 activity and that this Toxoplasma enzyme could not efficiently process the PEXEL motif from KAHRP , which contains threonine at P2 . This was supported using our model , as the AutoDock potential predicted that ASP5 binds the GRA16 peptide ( SRRLAEE ) 5 kJ/mol more tightly than an SRTLAEE mutant form of GRA16 ( Figure 3E-ii ) . In this mutant substrate , the side-chain guanidine of arginine at P3 is still clamped by the side-chain carboxylate and amide of ASP5 residues E506 and Q549 , respectively , and the backbone carbonyl oxygen of the arginine residue also forms a hydrogen bond with the backbone amide of T677 of ASP5 . The backbone carbonyl of leucine at P1 in the GRA16 TEXEL forms a hydrogen bond with the side-chain hydroxyl and the backbone amide of S505 . The guanidine side-chain of arginine at P2 of native GRA16 forms hydrogen bonds with the side-chain hydroxyl of S505 and the backbone carbonyl of A776 ( Figure 3E-i ) , whereas the mutated GRA16 substrate containing threonine at P2 forms only a single hydrogen bond with the side-chain hydroxyl of S505 ( Figure 3E-ii ) . Taken together , these differences in binding interactions accounts for ~50% of the total difference in the calculated binding affinity between the two substrates , providing a clear structural explanation for the substrate specificity ( i . e . RRL ) observed for ASP5 . We also used structural modeling to understand the substrate preference at other sites within the TEXEL motif . Mutation of leucine at P1 of the GRA16 TEXEL to valine reduces the calculated binding energy by 6 kJ/mol , which is in line with our observations that mutations at this position significantly reduce ASP5 activity . The major source of the reduction in binding energy in the L>V mutation arises from a reduction in electrostatic interaction , similar to that seen for the R>T mutation above . Mutation of alanine at P1’ ( position 4 of the GRA16 TEXEL , RRLAE ) to valine is predicted to slightly increase the binding energy , but by less than 1 kJ/mol . The small change in calculated binding energy is consistent with a lack of sensitivity at this position in the TEXEL sequence recognized by ASP5 . Interestingly , mutation of glutamine at P2’ ( position 5 of the GRA16 TEXEL ) to alanine causes an 11 kJ/mol reduction in calculated binding affinity in the model , in contrast to the increase in ASP5 activity observed in vitro ( Figure 3B ) . It is possible the glutamine reside at position 6 ( i . e . RRLAEE ) can act as a surrogate for the loss of glutamine at position 5 in this interaction ( Figure 3E-ii ) . Following validation of ASP5 as the TEXEL-cleaving protease in vitro , we sought to determine whether this occurs in parasites in vivo through deletion of the ASP5 gene in parasites expressing GRA16-HA . Utilizing a double homologous recombination strategy combined with an ASP5-targeted CRISPR approach , we were able to successfully disrupt the ASP5 gene , where the 3’ flank underwent homologous recombination , while apparent lack of NotI cleavage and the presence of a Cas9-induced cut site resulted in the whole plasmid integrating non-homologously at that site , meaning that a green fluorescent protein ( GFP ) expression cassette also integrated ( Figure 4—figure supplement 1A-i ) . This integration was confirmed through polymerase chain reaction ( PCR ) and sequencing of the ASP5 locus ( Figure 4—figure supplement 1A-ii and data not shown ) . To determine overall qualitative changes in asexual growth rate , WT and Δasp5 tachyzoites were grown for 7 days in a plaque assay ( Figure 4A-i ) and we observed that the plaques of the Δasp5 parasites were smaller than those generated by WT parasites , demonstrating that Δasp5 parasites have a clear growth disadvantage under simple in vitro growth conditions . We subsequently generated a second Δasp5 mutant in the RHΔhxgprt background using CRISPR/Cas9 to yield Δasp5CRISPR ( Figure 4—figure supplement 1B ) , which had a similar growth defect to the Δasp5:GRA16-HA parasites . This defect was restored following complementation with a stably-integrated copy of ASP5 ( Δasp5CRISPR:ASP5WT-HA3 ) driven from the tubulin promoter ( Figure 4A-ii . ) 10 . 7554/eLife . 10809 . 010Figure 4 . ASP5 is required for cleavage and export of GRA16 . ( A ) ( i ) A plaque assay on confluent HFF monolayers , stained with crystal violet at 7 days post infection where plaques produced by Δku80:GRA16-HA:Δasp5 parasites are smaller than those made by WT ( Δku80:GRA16-HA ) parasites . ( ii ) As in ( i ) , where the plaques formed by Δasp5CRISPR parasites are diminished in comparison to parental wildtype ( RHΔhxgprt ) and Δasp5CRISPR:ASP5WT-HA3 parasites . ( B ) Replication assay . Tachyzoites were grown in HFFs and fixed at 16 hr post infection . Samples were stained with αGAP45 antibodies and counted . n = 3 independent experiments where > 50 vacuoles were counted , values are mean ± standard error of the mean . ( C ) Western blot of GRA16-HA in Δku80:GRA16-HA ( lane 1 ) and Δku80:GRA16-HA:Δasp5 ( lane 2 ) parasites . The black arrow corresponds to the predicted signal peptidase cleaved-species and the red arrow to the TEXEL cleaved product , as outlined in Figure 1A . Catalase antibodies are used as a loading control . ( D ) IFA showing GRA16-HA is exported into the host cell nucleus in otherwise WT parasites ( top panel ) but not in Δasp5 parasites ( GFP-positive , signal diminished in comparison to the strong GRA16-HA in the 488 nm channel ) . White arrowheads indicate host nuclei . Scale bar is 5 μm . GFP , green fluorescent protein; HA , hemagglutinin; HFF , human foreskin fibroblasts; IFA , immunofluorescence assay; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 01010 . 7554/eLife . 10809 . 011Figure 4—figure supplement 1 . Generation and complementation of Δasp5 parasites . ( A ) ( i ) Schematic representation of the ASP5 knockout strategy in the RHΔku80:GRA16-HA line using clustered regularly interspaced short palindromic repeats ( CRISPR ) /Cas9 and plasmid-based recombination . ( ii ) PCR confirmation of a resulting Δku80Δasp5 clonal line . Sequencing of PCR products confirmed atypical integration topology ( data not shown ) . ( B ) Generation of the Δasp5CRISPR parasites , where parental RHΔhx tachyzoites were transfected with pU6-Universal-mCherry-sgASP5-2 ( see Materials and methods ) and a clone was chosen with an insertion of ‘TT’ at the predicted Cas9 cleavage site , resulting in a frameshift mutation in the coding region of ASP5 . ( C ) Following transfection , two clones were chosen with stably-integrated ASP5WT-HA3 in the Δasp5CRISPR line , generating the Δasp5CRISPR:ASP5WT-HA3 parasites . Δasp5CRISPR:ASP5WT-HA3 is exclusively used to refer to clone 1 in this manuscript , with the exception of Figure 7C , where both clones are used . ASP5 , Aspartyl Protease 5; HA , hemagglutinin; HA3 , triple-hemagglutinin; PCR , polymerase chain reaction . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 011 To assess whether the loss of ASP5 resulted in a reduced intracellular growth rate , we assessed replication of parasites 16 hr after infection ( Figure 4B ) . From this analysis , it is clear that Δasp5CRISPR tachyzoites have no major difference in intracellular replication to either WT or Δasp5CRISPR:ASP5WT-HA3 parasites . This suggests that smaller plaque size of Δasp5CRISPR tachyzoites , compared to WT and Δasp5CRISPR:ASP5WT-HA3 parasites , is not due to retardation in replication . To assess whether ASP5 is the TEXEL cleaving protease in tachyzoites , we examined the processing and trafficking of GRA16-HA in the presence and absence of ASP5 . Western blot analysis of GRA16-HA in otherwise WT parasites yielded the same three bands as seen in Figure 1B , consistent with cleavage within the TEXEL motif , whereas in the Δasp5 parasites , GRA16-HA migrated as a larger protein that had not been processed correctly , mirroring the GRA16AAAAE-HA profile ( Figure 4C , Figure 1B ) . The localization of GRA16-HA was then investigated by IFA in the presence and absence of ASP5 . While GRA16-HA produced by WT parasites was observed in the host nucleus as expected , this effector was no longer exported into the host cell during infection with Δasp5 parasites and instead appeared to localize to an internal structure reminiscent of the Golgi and in the PV space ( Figure 4D ) , similar to the GRA16AAAAE-HA mutant ( Figure 1C-ii ) . This confirms that processing by ASP5 is essential for correct trafficking of GRA16 from the parasite into the infected host cell . We sought to determine the importance of ASP5 in controlling other cellular phenotypes that Toxoplasma imparts on its host cell . It has recently been shown that Toxoplasma tachyzoites , but not the related Neospora species , actively induce expression of host c-Myc following infection ( Franco et al . , 2014 ) . This activation of c-Myc was not induced in response to parasite invasion or injection of rhoptry proteins ( Franco et al . , 2014 ) , suggesting that one or more dense granule proteins may be responsible . To determine whether ASP5 is involved in up-regulation of host c-Myc , HFFs were infected with WT , Δasp5 or Δasp5CRISPR:ASP5WT-HA3 parasites , and c-Myc expression was measured by IFA and immunoblot . While uninfected HFFs showed little c-Myc expression by IFA , cells infected with WT Toxoplasma , or parasites with complemented ASP5 expression , had almost universal induction of c-Myc in their nuclei , as previously reported ( Figure 5A ) ( Franco et al . , 2014 ) . Upon infection with Δasp5 parasites , a sharp reduction in c-Myc expression within the nuclei was observed ( Figure 5A ) . Quantification by IFA showed that HFFs infected with parasites lacking ASP5 expressed approximately 6 . 4-fold less c-Myc than those infected with WT parasites ( normalized ratio of c-Myc to 4' , 6-diamidino-2-phenylindole [DAPI] ) ( Figure 5B ) . To confirm this , c-Myc induction was measured by immunoblot of whole cell protein fractions . While c-Myc expression was induced in host cells infected with WT parasites , the signal was dramatically reduced in HFFs infected with the same number of Δasp5 parasites ( Figure 5C ) , confirming that ASP5 is required for Toxoplasma to induce c-Myc in infected cells . Together , this work suggests that the up-regulation of c-Myc induced by tachyzoites is controlled by one or more ASP5-dependent proteins . 10 . 7554/eLife . 10809 . 012Figure 5 . Induction of host c-Myc is ASP5-dependent . ( A ) Representative IFAs 14 hr after infection of c-Myc expression in confluent HFFs . Mock-infected cells express very little c-Myc while infection with WT parasites leads to a dramatic up-regulation of this transcription factor . Δasp5CRISPR-infected cells express marginally more c-Myc than mock-infected , which is complemented by the re-introduction of ASP5 ( Δasp5CRISPR:ASP5WT-HA3 ) . ( B ) Quantitation of c-Myc signal ( as a ratio of DAPI signal ) in cells from ( A ) , P = 0 . 0001 , values are mean ± standard deviation , unpaired t-test , n ≥ 20 nuclei from cells infected with single vacuoles . ( C ) Western blot showing up-regulation of c-Myc upon wild type infection , which is drastically decreased following deletion of ASP5 . αSAG1 and αGAPDH serve as parasite and host loading controls , respectively . Scale bars are 20 μm . HA3 , triple-hemagglutinin; HFFs , human foreskin fibroblasts; IFA , immunofluorescence assay; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 012 Very recently , a novel dense granule protein was identified by the Boothroyd laboratory that localizes to the PV and is processed approximately two-thirds along its sequence , revealing a C-terminal fragment that migrates at ~32 kDa and an N-terminal fragment that migrates at ~80 kDa ( Figure 6A ) ( Franco et al . , in press ) . Analysis of this protein , MYR1 ( TGGT1_254470 ) , revealed a TEXEL-like RRLSE sequence approximately 230 residues from the C-terminus , the approximate position where cleavage is predicted to occur ( Figure 6C ) . We hypothesized that MYR1 is a substrate of ASP5 and to test this , we probed WT- and Δasp5-infected HFF lysates with antibodies derived to the N-terminal region of MYR1 . In the Δasp5 mutants , MYR1 is no longer processed and instead migrates at ~105 kDa ( Figure 6A ) . Further analysis of the ~80 kDa bands in the left panel using a 3–8% Tris-Acetate gel ( right panel ) revealed that this is a doublet , where the lower molecular weight species ( * ) is likely a cross-reactive protein often observed , even in knockout lines of this gene , by Franco et al . when using MYR1 antisera , but never when detecting this protein by C-terminal epitope tagging . This strongly suggests that ASP5 is required for cleavage of MYR1 . Note that the predicted pI of MYR1 is ~5 . 0 , which may contribute to the somewhat retarded mobility of the full-length and cleaved N-terminal species that have predicted masses of ~87 and 61 kDa , respectively ) . 10 . 7554/eLife . 10809 . 013Figure 6 . ASP5 processes the novel dense granule protein MYR1 near the C-terminus . ( A ) Antibodies to the N-terminus of MYR1 show that it is processed in wild-type parasites ( migrating at ~80 kDa ) and a loss of processing in Δasp5 parasites resulting in the appearance of a larger molecular weight species ( migrating at ~105 kDa ) . αSAG1 serves as the parasite loading control . For increased resolution , the samples from the left panel were separated on a 3–8% Tris-acetate gel ( right panel ) , which revealed that the ~80 kDa band migrates at ~70 kDa on this gel and comprises a doublet , with the upper band absent in Δasp5 parasites , confirming lack of cleavage . * = suspected cross-reactive species . As a consequence of increased running time , SAG1 migrated off the 3–8% gel and was not transferred . ( B ) ASP5WT-HA3 cleavage of DABCYL/EDANS peptides containing the TEXEL of MYR1 and associated mutations ( red residues ) . Peptides containing RRLSE from MYR1 and RRLAE from GRA16 are cleaved , but peptides with point mutations in P1 , P2 or P3 are not cleaved . The serine at P1’ ( compared to Ala in GRA16 ) does not interfere with cleavage by ASP5 . ( C ) Schematic of MYR1 with an N-terminal SP , the RRLSE TEXEL at AA 557–581 and a C-terminal HA tag . ( D ) Immunoblot using αHA antibodies against Δmyr1:MYR1-HA parasites where Δmyr1:MYR1WT-HA runs at ~32 kDa , whereas Δmyr1:MYR1ARLSE-HA and Δmyr1:MYR1ARASA-HA mutants run at ~105 kDa . αSAG1 serves as a loading control . ASP5 , Aspartyl Protease 5; HA3 , triple-hemagglutinin; TEXEL , Toxoplasma export element . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 013 We then tested whether MYR1 is a substrate of ASP5 by incubating immunoprecipitated ASP5WT-HA3 in combination with fluorogenic peptides containing RRLSE or mutations of this sequence . We found that ASP5WT-HA3 efficiently cleaved the MYR1 TEXEL peptide , similar to the GRA16 RRLAE control , whereas mutations of the conserved RRL residues abolished this activity ( Figure 6B ) . This demonstrates that MYR1 contains a TEXEL sequence that can be processed by ASP5 . It also suggests that the P1’ residue , which naturally differs between GRA16 ( alanine ) and MYR1 ( serine ) , is not as constrained as the P1-3 positions , and confirms that the P2’ residue is not essential for processing , since the MYR1 TEXEL P2’ ( E>A ) mutant peptide was efficiently processed . To test whether the TEXEL motif is necessary for cleavage in vivo , we generated lines that express MYR1 TEXEL mutants under control of the GRA1 promoter . While ectopic expression of the C-terminally tagged MYR1WT-HA resulted in detection of the expected ~32 kDa species , mutation of either RRLSE>ARLSE or RRLSE>ARASA prevented cleavage , leaving only the unprocessed species migrating at ~105 kDa ( Figure 6D ) . Thus , this sequence of MYR1 is necessary for processing in parasites . Taken together , these results demonstrate that ASP5 cleaves the TEXEL motif of MYR1 , and that the TEXEL can function in a novel location near the C-terminus of the protein , in contrast to Plasmodium spp . where all known PEXEL sequences are located ~15–30 amino acids from the SP cleavage site ( Sargeant et al . , 2006 ) . A striking feature of Toxoplasma infection is host mitochondrial association ( HMA ) , whereby the parasite recruits host mitochondria to the PVM using the dense granule protein MAF1 that localizes to the PVM ( Pernas et al . , 2014 ) . To examine whether ASP5 contributes to this phenotype , the ultrastructure of HFFs infected with WT and Δasp5 parasites was investigated by transmission electron microscopy ( TEM ) . While the overall morphology of WT and Δasp5 tachyzoites appeared normal , there was a reduction in host mitochondria associated with the PVM of Δasp5-infected HFFs ( Figure 7A ) . Quantification of HMA by TEM showed that the percentage of the PVM associated with host mitochondria was reduced by 4 . 3-fold in Δasp5 parasites ( Figure 7B ) . 10 . 7554/eLife . 10809 . 014Figure 7 . ASP5 influences efficient host mitochondrial recruitment and assembly of the NTN . ( A ) Electron micrographs of intracellular WT ( Δku80 ) ( i and iii ) and Δasp5 ( ii and iv ) tachyzoites within HFFs . Bars represent 1 µm ( i , ii ) and 200 nm ( iii , iv ) . ( i , ii ) Low-power image showing WT ( i ) and Δasp5 ( ii ) tachyzoites containing a nucleus ( N ) , rhoptries ( R ) , micronemes ( M ) , dense granules ( D ) and a Golgi body ( G ) located within a PV . Note the large number of host cell mitochondria ( arrowheads ) associated with the PVM and the large NTN within the PV in wild-type parasites compared to Δasp5 parasites . ( iii , iv ) Details from the periphery of the PV showing a large host cell mitochondrion ( HM ) closely applied to the PVM in the wild type ( iii ) compared to the smaller mitochondrion ( HM ) associated with the Δasp5 PV ( iv ) . ( B ) Quantitation of percentage of the PVM associated with host mitochondria , 5 . 59 ± 2 . 08% for Δasp5 parasites versus 24 . 3 ± 6 . 98% for wild-type parasites , mean ± standard error of the mean , P < 0 . 0001 , n = 20 vacuoles . ( C ) ( i ) Mouse embryonic fibroblasts expressing MTS-GFP infected for 4 hr with wild type ( Δhx ) , Δasp5CRISPR ( a non-GFP positive knock out ) or two independent ASP5 complemented clones ( Δasp5CRISPR:ASP5WT-HA3 ) . Localization of MAF1 at the PVM ( top panel and bottom two panels ) and mislocalized in intraparasitic puncta , potentially dense granules ( panels 2 and 5 ) , are shown in red . Mitochondria ( MTS-GFP ) are localized at the PVM in wild-type parasites ( panel 1 ) and Δasp5CRISPR:ASP5WT-HA3 clones 1 and 2 ( panels 5–6 ) to a large extent , but less so in the Δasp5CRISPR parasites ( panels 2–4 ) . ( ii ) Immunoblot using αHA antibodies against parasites expressing ASP5WT-HA3 and complemented mutants Δasp5CRISPR:ASP5WT-HA3 clones 1 and 2 shows the parasites express similar levels of HA-tagged ASP5 ( as in Figure 2A ) , αGAP45 serves as a loading control . ( D ) Western blot of MAF1 species in wild-type and Δku80Δasp5 parasites . Blue arrow shows non-specific labeling ( NS ) , αCatalase serves as a loading control . ( E ) Electron micrographs of intracellular wild type ( i and ii ) and Δku80Δasp5 ( iii and iv ) tachyzoites . Bars represent 1 µm ( i , iii ) and 200 nm ( ii , iv ) . ( i , ii ) Low-power image showing wild-type ( i ) and Δasp5 ( iii ) tachyzoites containing a nucleus ( N ) , rhoptries ( R ) , micronemes ( M ) , and dense granules ( D ) located within the PV . The large number of host cell mitochondria ( arrowheads ) associated with the PVM and the large NTN within the PV in the wild type compared to the Δasp5 parasites is noteworthy . ( ii ) Detail of the PV of a WT parasite showing the intertwining tubules of the NTN . HM – host cell mitochondrion . ( iv ) Detail of the PV surrounding a Δasp5 parasite showing granular material and a few vesicles ( V ) but absence of the tubular network . HM – host cell mitochondrion . Scale bar is 5 μm . ASP5 , Aspartyl Protease 5; GFP , green fluorescent protein; HFFs , human foreskin fibroblasts; MTS , mitochondrial targeting sequence; NTN , nanotubular network; PV , parasitophorous vacuole; PVM , parasitophorous vacuole membrane; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 014 To confirm that the reduction in HMA observed by TEM in Δasp5-infected HFFs was due to the loss of ASP5 , we used our Δasp5CRISPR parasites ( Figure 4A-ii ) . These parasites were incubated for 4 hr on 60% confluent mouse embryonic fibroblasts ( MEFs ) engineered to express GFP fused to the mitochondrial targeting sequence ( MTS ) of DIABLO ( MTS-GFP ) ( Verhagen et al . , 2000 ) . WT ( parental RH∆hx ) parasites efficiently associated with host mitochondria ( MTS-GFP ) and MAF1 was correspondingly observed at the PVM , as expected ( Figure 7C , panel 1 ) ( Pernas et al . , 2014 ) . In contrast , Δasp5CRISPR parasites exhibited reduced HMA , and MAF1 was incorrectly localized , appearing predominantly in punctate structures rather than at the PVM ( Figure 7C , panels 2–4 ) . In contrast , the Δasp5CRISPR:ASP5WT-HA3 parasites exhibited correct trafficking of MAF1 and recruitment of host mitochondria to the PVM , which was validated in two independent complemented clones that expressed ASP5 at levels close to the endogenous expression of the enzyme ( Figure 7C , panels 5–6 , and Figure 4—figure supplement 1C ) . While we observe changes in HMA in two independent Δasp5 mutants , observed by TEM and immunofluorescence , it should be noted that this phenotype appears to be somewhat variable when assayed in different labs . MAF1 is not known to be proteolytically processed , beyond removal of its SP ( Pernas et al . , 2014 ) . Nevertheless , to determine whether ASP5 affects the biosynthesis or processing of MAF1 , we performed immunoblots with αMAF1 antibodies using WT and Δasp5 parasites . There were no differences in MAF1 expression or processing between the two lines by Western blot ( Figure 7D ) , consistent with a lack of any TEXEL motif within MAF1 . This result suggests that the function of MAF1 is not directly dependent on ASP5 , but rather , ASP5 may act on as yet unidentified protein ( s ) that interact with MAF1 to facilitate efficient HMA . Another characteristic of Toxoplasma infection is the formation of the NTN that likely aids nutrient acquisition across the PVM and within the PV through an increase in surface area . The ultrastructure of the NTN was examined in HFFs infected with WT and Δasp5 parasites by TEM ( Figure 7E ) . Vacuoles containing WT parasites displayed extensive structures , extending from near the posterior of parasites to the PVM , typical of the NTN ( Figure 7E-i and ii ) . In stark contrast , the NTN in Δasp5 vacuoles was vastly diminished and disorganized in all cells examined , suggesting that one or more components involved in the biogenesis of this network requires processing by ASP5 ( Figure 7E-iii and iv ) . Following the identification of GRA24 as an exported effector protein that traffics to the host nucleus ( Braun et al . , 2013 ) , we sought to determine whether its translocation into the host cell is also ASP5-dependent . WT and Δasp5 parasites were transfected with an ectopic copy of GRA24 fused to 3xMyc tags ( GRA24-Myc3 ) , which was integrated into the uracil phosphoribosyltransferase ( URPT ) locus . GRA24-Myc3 was expressed in parasites and exported to the host cell nucleus by WT parasites as previously described ( Braun et al . , 2013 ) ; however , export was completely blocked in Δasp5 parasites ( Figure 8A , Figure 8—figure supplement 1 ) . Complementation of Δasp5 parasites with ASP5WT-HA3 restored the export of GRA24-Myc3 ( Figure 8—figure supplement 1 ) . Despite the requirement of ASP5 for GRA24 export , assessment of processing by Western blot did not reveal any size difference in GRA24-Myc3 between WT and Δasp5 parasites ( Figure 8B ) . While GRA24 lacks a canonical TEXEL sequence ( RRLxx ) , it does contain the non-canonical TEXEL-like sequences RGYHG , RGGLQ and RSLGM , and so we assessed whether these might be cleaved by ASP5 using synthetic peptides; however , none were efficiently processed ( Figure 8C ) . Collectively , this suggests that GRA24 is not a direct substrate of ASP5 but its export is dependent on this protease . 10 . 7554/eLife . 10809 . 015Figure 8 . GRA24 requires ASP5 for export but not processing . ( A ) Localization of GRA24-Myc3 in both WT ( Δku80 ) and Δku80Δasp5 tachyzoites . GRA24 can be observed in the host nucleus and in the PV in WT:GRA24-Myc3 parasites , whereas this export is lost in the Δasp5:GRA24-Myc3 parasites . Arrows signify the position of host nuclei ( DAPI ) . ( B ) The size of GRA24-Myc3 appears unchanged in the absence of ASP5 . αCatalase serves as a loading control . ( C ) ASP5 cannot cleave peptides containing non-canonical TEXEL-like motifs found within GRA24 , compared with cleavage of the GRA16 RRLAE peptide and AAAAE controls . Scale bar is 5 μm . ASP5 , Aspartyl Protease 5; DAPI , 4' , 6-diamidino-2-phenylindole; PV , parasitophorous vacuole; TEXEL , Toxoplasma export element; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 01510 . 7554/eLife . 10809 . 016Figure 8—figure supplement 1 . Complementation of Δasp5 parasites restores export of GRA24 . Localisation of transiently transfected GRA24-Myc3 of in both Δasp5CRISPR and Δasp5CRISPR:ASP5WT-HA3 ( Clone 1 ) tachyzoites . Filled arrowheads signify the position of host nuclei ( DAPI ) , open arrowheads identify non-transfected parasites and GAP45 marks the periphery of tachyzoites . Scale bar is 5 μm . DAPI , 4' , 6-diamidino-2-phenylindole; HA3 , triple-hemagglutininDOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 016 Given our above findings , we wondered how important the ASP5-dependent pathway is to the transcriptional changes that Toxoplasma imparts on its host cell . Given that we determined there is little to no change in replication rates between WT and Δasp5 parasites ( Figure 4B ) , we harvested all samples 20 hr after infection and used RNA sequencing ( RNA-seq ) to profile gene expression in HFFs that were either uninfected ( UI ) , infected with WT ( RH∆ku80 ) parasites , or infected with Δasp5 parasites . To make sure that all the changes that we observed were due to loss of ASP5 and not differences in tachyzoites numbers , we first compared the proportion of reads ( rpkm ) from parasite versus host cell origin as a readout of relative parasite numbers per sample . We saw equal amounts of reads mapping to human genes between all samples ( 24–27 × 106 reads ) , while infection with WT saw parasite RNA proportions of 27% ( replicate 1 ) , 24% ( replicate 2 ) and 23% ( replicate 3 ) . Infection with our ASP5 deficient line saw parasite RNA to be 18% ( replicate 1 ) , 35% ( replicate 2 ) and 36% ( replicate 3 ) of the total reads , therefore suggesting that , in 2 out of the 3 samples , we have slightly more ASP5-deficient parasites per sample . Therefore , any loss of gene expression in ASP5-deficient cells must be due to loss of this protease and not lower amounts of overall parasites per sample . The expression changes due to infection by the Δasp5 parasites were generally smaller than those for the WT parasites . The log-fold change during infection with Δasp5 parasites was , on average , only 60% of the log-fold change during infection with WT parasites ( Figure 9A-i ) . This suggests that most genes responding to parasite infection do so , at least partly , due to an ASP5-dependent pathway . At a false discovery rate of 5% , 3402 genes were significantly up-regulated and 3369 genes were significantly down-regulated in response to the WT infection , whereas only 1033 genes were significantly up- and 817 were significantly down-regulated in response to Δasp5 parasites . Of the 3402 genes up-regulated during WT infection , only 862 ( 25% ) remained significantly up-regulated upon deletion of ASP5 ( Figure 9A-ii ) . Of the 3269 genes down-regulated during WT infection , only 742 ( 22% ) remained significantly down-regulated upon deletion of ASP5 ( Figure 9A-ii ) . This identifies genes ( color-coded red and blue in Figure 9A ) that are potentially unaffected by ASP5-dependent pathways . 10 . 7554/eLife . 10809 . 017Figure 9 . ASP5 plays a major role in changing the host cell transcriptional response induced by Toxoplasma infection . ( A ) ( i ) Scatterplot of expression fold changes . The Y-axis shows log2-fold changes in HFFs infected with Δasp5 parasites versus uninfected HFFs ( UI ) , while the X-axis shows log2-fold changes in HFFS infected with WT parasites ( WT ) vs . UI . The dashed line shows x=y . The solid line shows the least squares regression line through the origin . The regression has slope 0 . 6 , showing that log fold changes for the Δasp5 parasites are only 60% of those for the wild-type parasites . Differentially expressed genes are color coded in the plot according to whether they change in both the WT and Δasp5 infections or only in the WT ( false discovery rate < 0 . 05 ) . Non-differentially expressed genes are shown in black . ( ii ) Numbers of genes corresponding to highlighted groups in the scatterplot . ( B ) Heat map of expression values for the 100 most differentially expressed genes for WT-infected HFFs versus uninfected . Z-scores are log2 counts per million , scaled to have mean 0 and standard deviation 1 for each gene . The plot shows that expression after Δasp5 infection tends to be intermediate between that of uninfected and WT-infected HFFs . ( C ) Barcode enrichment plot showing enrichment of Δgra16 regulated genes in the Δasp5 parasite infection expression changes . Genes are ordered from left to right in the plot from most up to most down during Δasp5 parasite infection . Specifically , genes are ranked from largest to smallest t-statistic for the Δasp5 versus WT comparison ( X-axis ) . Genes up-regulated by Δgra16 versus WT in an independent experiment ( Bougdour et al . , 2013 ) are marked with vertical red bars . Similarly , genes down-regulated by Δgra16 in the independent experiment are marked with vertical blue bars . The worms show relative enrichment ( Y-axes ) . The plot shows that Δasp5 up-regulated genes are strongly enriched for Δgra16 up-regulated genes ( red ) and Δasp5 down-regulated genes are strongly enriched for Δgra16 down-regulated genes ( blue ) . HFFs , human foreskin fibroblasts; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 017 To further analyze the role of ASP5-dependent export pathways on transcriptional changes , we extracted the 100 most differentially expressed genes upon WT infection ( compared with UI ) and generated a heat map to reveal the contribution of ASP5 to the expression of these genes by comparing with the dataset derived using Δasp5 parasites ( Figure 9B ) . As expected , the three biological replicates cluster together well for each condition . The plot shows that expression in HFFS infected with Δasp5 parasites tends to be intermediate between uninfected cells and the WT infection ( Figure 9B ) . Overall , this work suggests that ASP5-dependent pathways contribute significantly to the amount and magnitude of expression of host cell genes during tachyzoite infection . To assess the role the ASP5-dependent pathways in modifying the host cell , we performed Gene Ontology ( GO ) analysis on gene subsets as listed above ( Table 1; color-coded as in Figure 9A-ii ) . We observed that ASP5 controlled the up-regulation of gene sets implicated in cell cycle , nucleic acid metabolism and binding , nucleopore association and chromatin binding . Furthermore , ASP5 played a key role in the down-regulation of genes implicated in autophagy , peroxisome fission , vacuole organization , protein trafficking ( i . e . syntaxin binding ) and intracellular signaling processes ( Table 1 ) . This outlines that ASP5-dependent pathways play an important role in controlling specific cellular processes that may facilitate parasite persistence within the cell . 10 . 7554/eLife . 10809 . 018Table 1 . Gene ontology analysis of expression changes following infection with WT and Δasp5 parasites . The four columns of the table correspond to the color-coded gene groups shown in Figure 8A . For each group of genes , the table gives the top 10 BP and MF represented in the differentially expressed genes . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 018 Host gene expression significantly affected by loss of ASP5 DE genes that are up-regulated in wild type versus uninfected only DE genes that a down-regulated in wild type versus uninfected only Biological process ( BP ) GO ID Term Ont N DE P . DE GO ID Term Ont N DE P . DE GO:0000278 mitotic cell cycle BP 884 284 2 . 97E-26 GO:0006914 autophagy BP 295 79 1 . 40E-05 GO:0090304 nucleic acid metabolic process BP 3990 918 8 . 82E-23GO:0010927 cellular component assembly involved in morphogenesis BP 192 56 1 . 85E-05GO:0022402 cell cycle process BP 1096 323 1 . 08E-22 GO:0000045 autophagic vacuole assembly BP 60 24 2 . 03E-05GO:0007049 cell cycle BP 1446 401 1 . 15E-22 GO:0016559 peroxisome fission BP 10 8 2 . 26E-05GO:1903047 mitotic cell cycle process BP 772 243 3 . 91E-21 GO:0042594 response to starvation BP 173 51 3 . 13E-05GO:0006139 nucleobase-containing compound metabolic process BP 4441 995 5 . 21E-21GO:0044782 cilium organization BP 145 44 5 . 00E-05 GO:1901360 organic cyclic compound metabolic process BP 4710 1040 5 . 98E-20 GO:0007033 vacuole organization BP 110 35 1 . 00E-04 GO:0022613 ribonucleoprotein complex biogenesis BP 310 123 8 . 72E-20 GO:0051146 striated muscle cell differentiation BP 169 48 1 . 45E-04GO:0006725 cellular aromatic compound metabolic process BP 4559 1009 1 . 84E-19 GO:0030031 cell projection assembly BP 269 69 1 . 97E-04GO:0006396 RNA processing BP 532 180 2 . 08E-19GO:1903008 organelle disassembly BP 162 46 1 . 98E-04Molecular function ( MF ) GO ID Term Ont N DE P . DE GO ID Term Ont N DE P . DE GO:0044822 poly ( A ) RNA binding MF 1114 380 4 . 06E-42 GO:0017049 GTP-Rho binding MF 14 9 0 . 000104 GO:0003723 RNA binding MF 1445 452 2 . 22E-39 GO:0033743 peptide-methionine ( R ) -S-oxide reductase activity MF 4 4 0 . 000837 GO:0003676 nucleic acid binding MF 3243 795 7 . 02E-28 GO:0004030 aldehyde dehydrogenase [NAD ( P ) +] activity MF 5 4 0 . 003616 GO:1901363 heterocyclic compound binding MF 4739 1043 1 . 67E-19 GO:0030553 cGMP binding MF 5 4 0 . 003616 GO:0097159 organic cyclic compound binding MF 4780 1048 5 . 08E-19 GO:0004499 N , N-dimethylaniline monooxygenase activity MF 5 4 0 . 003616 GO:0003682 chromatin binding MF 383 110 1 . 05E-07 GO:0019905 syntaxin binding MF 65 20 0 . 004494 GO:0043566 structure-specific DNA binding MF 217 67 2 . 22E-06 GO:0031697 beta-1 adrenergic receptor binding MF 3 3 0 . 004923 GO:0017056 structural constituent of nuclear pore MF 9 8 8 . 09E-06 GO:0045159 myosin II binding MF 3 3 0 . 004923 GO:0005488 binding MF 10573 1974 8 . 11E-06 GO:0047555 3' , 5'-cyclic-GMP phosphodiesterase activity MF 3 3 0 . 004923 GO:0008094 DNA-dependent ATPase activity MF 76 30 8 . 31E-06GO:0031210 phosphatidylcholine binding MF 8 5 0 . 00505 Host gene expression not affected by loss of ASP5 DE genes that are up-regulated in both wild type versus uninfected and ? asp5 versus uninfected DE genes that are down-regulated in both wild type versus uninfected and ? asp5 versus uninfected Biological process ( BP ) GO ID Term Ont N DE P . DE GO ID Term Ont N DE P . DE GO:0044699 single-organism process BP 9400 689 1 . 02E-20GO:0003008 system process BP 888 87 5 . 74E-10GO:0044763 single-organism cellular process BP 8542 642 5 . 49E-20GO:0032501 multicellular organismal process BP 4364 286 6 . 37E-09GO:0050896 response to stimulus BP 5333 443 4 . 11E-17GO:0044707 single-multicellular organism process BP 4228 277 1 . 49E-08GO:0032501 multicellular organismal process BP 4364 365 2 . 73E-13GO:0006928 movement of cell or subcellular component BP 1295 104 4 . 60E-07GO:0044707 single-multicellular organism process BP 4228 354 8 . 10E-13GO:0045216 cell-cell junction organization BP 176 26 5 . 96E-07GO:0006950 response to stress BP 2675 246 1 . 84E-12GO:0034330 cell junction organization BP 205 28 1 . 13E-06GO:0051716 cellular response to stimulus BP 4476 367 4 . 63E-12GO:0048731 system development BP 2914 195 1 . 93E-06GO:0032502 developmental process BP 3950 331 8 . 70E-12GO:0048513 organ development BP 2054 143 1 . 01E-05GO:0065007 biological regulation BP 7817 570 2 . 04E-11GO:0034329 cell junction assembly BP 182 24 1 . 20E-05GO:0042221 response to chemical BP 2547 232 3 . 00E-11GO:0044767 single-organism developmental process BP 3888 243 1 . 25E-05Molecular function ( MF ) GO ID Term Ont N DE P . DE GO ID Term Ont N DE P . DE GO:0005125 cytokine activity MF 103 21 9 . 63E-07GO:0008092 cytoskeletal protein binding MF 635 61 5 . 31E-07GO:0000982 RNA polymerase II core promoter proximal region sequence-specific DNA binding transcription factor activity MF 201 31 1 . 93E-06GO:0003779 actin binding MF 299 36 7 . 99E-07GO:0008009 chemokine activity MF 22 9 2 . 92E-06GO:0022836 gated channel activity MF 164 24 1 . 91E-06GO:0005515 protein binding MF 8144 560 6 . 08E-06GO:0004872 receptor activity MF 592 53 2 . 29E-05GO:0043565 sequence-specific DNA binding MF 567 62 6 . 57E-06GO:0005216 ion channel activity MF 202 25 2 . 40E-05GO:0000981 sequence-specific DNA binding RNA polymerase II transcription factor activity MF 357 44 7 . 96E-06GO:0022838 substrate-specific channel activity MF 204 25 2 . 84E-05GO:0004857 enzyme inhibitor activity MF 226 32 8 . 47E-06GO:0015267 channel activity MF 217 25 7 . 97E-05GO:0044212 transcription regulatory region DNA binding MF 457 52 1 . 24E-05GO:0022803 passive transmembrane transporter activity MF 217 25 7 . 97E-05GO:0000975 regulatory region DNA binding MF 459 52 1 . 40E-05GO:0038023 signaling receptor activity MF 440 41 8 . 14E-05GO:0001067 regulatory region nucleic acid binding MF 459 52 1 . 40E-05GO:0005230 extracellular ligand-gated ion channel activity MF 25 7 1 . 60E-04ASP5 , Aspartyl Protease 5; BP , biological processes; DE = number of those genes that are differentially expressed genes; GO , Gene Ontology; MF , molecular functions; N = number of expressed genes annotated by the GO term; P = p-value; WT , wild type . As ASP5 affects the processing and translocation of GRA16 , we hypothesized that transcriptional changes induced by the loss of ASP5 would encompass the changes caused by this single effector . We obtained a list of genes that are differentially expressed in HFFs infected with Δgra16 versus WT parasites from a previously published study ( Bougdour et al . , 2013 ) . We found that the transcriptional profile of Δgra16 parasite infection is strongly correlated with the transcriptional profile that we observed in Δasp5 parasite infection . Genes up-regulated in the Δasp5 parasite infection were strongly enriched for up-regulated Δgra16 genes , and similarly , the down-regulated Δasp5 were strongly enriched for Δgra16 genes ( ROAST P-value =5×10–5 ) . Figure 9C shows the enrichment as a barcode plot . This shows that transcriptional changes induced by GRA16 mostly represent a subset of all genes influenced by ASP5 . Given the multiple effects that ASP5 plays on the cellular changes and transcriptional output of the infected host cell , we sought to determine whether this Golgi-resident protein , and the export pathway that it controls , are important virulence mechanisms in Toxoplasma . To determine this , we injected groups of 6 C57BL/6 mice with either phosphate buffered saline ( PBS ) or 100 WT , Δasp5CRISPR or Δasp5CRISPR:ASP5WT-HA3 parasites ( Figure 10A ) , which equated to 15 ± 3 live tachyzoites ( as determined by in vitro plaque assay ) . All mice were tested for sero-conversion to confirm the administration of parasites ( Figure 10—figure supplement 1A ) and any that did not elicit a response were discounted from the study ( this equated to two mice for each group ) . Over a 20-day period , we found that all mice infected with either WT or Δasp5CRISPR:ASP5WT-HA3 tachyzoites succumbed to infection by day 8 and dropped weight accordingly ( Figure 10A ) . Strikingly , all mice infected with Δasp5CRISPR parasites were still alive at day 20 , they maintained body weight and appeared healthy , despite sero-converting by day 14 ( Figure 10—figure supplement 1 ) . We also confirmed prior infection by performing a re-challenge experiment , where mice were injected with 200 wild-type parasites , equating to ~50 live parasites ( as determined by plaque assay ) . While ‘naïve’ PBS ( vehicle ) -injected mice succumbed to infection by day 10 , all those previously injected with Δasp5CRISPRall survived and maintained normal body weight ( Figure 10B ) . To determine whether the attenuation of Δasp5CRISPR parasites in mice was dependent on the infectious dose , an additional cohort of C57/BL6 mice was injected with ~50 live parasites of each strain , as determined by in vitro plaque assay ( Figure 10C ) . All mice infected with wild type or Δasp5CRISPR:ASP5WT-HA3 tachyzoites succumbed to infection by day 10 , whereas those injected with Δasp5CRISPR parasites exhibited a delay to death , including one mouse that survived the experiment and was seropositive for anti-Toxoplasma antibodies when tested at day 14 ( Figure 10—figure supplement 1B ) . 10 . 7554/eLife . 10809 . 019Figure 10 . ASP5 is an important virulence factor . ( A ) Four groups of six C57BL/6 mice were intraperitoneally injected with a live dose of 15 ± 3 tachyzoites or PBS alone and survival measured over a 20-day period . Mice infected with wild type ( RHΔhx ) and Δasp5CRISPR:ASP5WT-HA3 all succumbed to infection within 8 days , whereas all PBS-injected mice and those infected with Δasp5CRISPR parasites survived the 20 day experiment . At day 14 ( # ) , all mice were bled and tested for antibodies against tachyzoites . Animals were weighed daily throughout the course of the experiment ( lower panel ) and bodyweights were compared for statistical analysis while all animals were alive . Mice injected with PBS alone maintained a stable body weight , while those infected with wild type and Δasp5CRISPR:ASP5WT-HA3 parasites lost weight beginning at day 6 and day 4 , respectively , with significant weight loss evident in comparison to those injected with Dasp5CRISPR parasites by day 7 . ( B ) At 24 days post infection , surviving mice ( from A ) that were injected with PBS or Δasp5CRISPR tachyzoites were re-challenged with 50 live RHΔhx parasites . The naïve PBS-injected mice all succumbed to infection by day 10 , whereas those that had been injected with Δasp5CRISPR parasites were protected from death . Bodyweight was also monitored daily ( lower panel ) where mice previously injected with Δasp5CRISPR parasites maintained a stable bodyweight , while the naïve PBS mice began losing weight on approximately day 6 . ( C ) A separate cohort of C57/BL6 mice was also injected with 50 live parasites to assess the effect of parasite number on virulence during infection . All mice infected with WT or and Δasp5CRISPR:ASP5WT-HA3 parasites again succumbed to infection by days 8-10 , whereas there was a delay in death for the and Δasp5CRISPR-infected mice . One of these mice survived the 15-day experiment and was seropositive for antibodies against Toxoplasma ( serum collected at day 14 , # ) . Bodyweights were measured daily ( lower panel ) . Log-rank ( Mantel-Cox ) testing was used to derive statistical significance for survival curves while two-way analysis of variance testing was used for bodyweight data . Values are mean ± SD . * P < 0 . 05 , ** P < 0 . 005 , **** P <0 . 0001 . ASP5 , Aspartyl Protease 5; WT , Wild Type . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 01910 . 7554/eLife . 10809 . 020Figure 10—figure supplement 1 . Seroconversion of Δasp5 parasites . All mice that were alive 14 days after infection were tested for Toxoplasma sero-conversion . Toxoplasma tachyzoite and uninfected host cell lysates were probed using mouse serum at a 1:500 dilution . DOI: http://dx . doi . org/10 . 7554/eLife . 10809 . 020 Thus , ASP5-deficient tachyzoites exhibit attenuation even in the hypervirulent RH strain at a dose of 50 parasites , while injection with 15 parasites resulted in parasite clearance and provided protective immunity following re-challenge with a lethal dose of wild type tachyzoites . This work strongly suggests that the ASP5-dependent export pathway is necessary for virulence of Toxoplasma in a mouse model .
Toxoplasma gondii has the remarkable capacity to persist within almost any nucleated host cell in a vast array of organisms . Central to this is the ability to manipulate host cellular pathways using exported effector proteins to circumvent the host response to allow for intracellular parasite growth and survival . Previous work has demonstrated that Toxoplasma effectors can be delivered to the host cell by secretion from the rhoptry organelles ( Koshy et al . , 2010; Saeij et al . , 2007; Boothroyd and Dubremetz , 2008 ) . These effectors largely consist of a family of kinases and appear to be exclusively delivered to the host cell during the short time frame of host cell invasion , potentially limiting their efficacy later during the infection process ( Saeij et al . , 2007; Peixoto et al . , 2010 ) . While injection of these polymorphic kinases explained some strain variances in virulence , it did not clarify how Toxoplasma induces changes that are more general across isolates . More recently , several new host cell effectors have been identified that appear to be delivered into the host cell via the dense granules—organelles constitutively secreted after invasion during intracellular replication ( Bougdour et al . , 2013; Braun et al . , 2013; Pernas et al . , 2014; Rosowski et al . , 2011 ) . This strongly suggested that Toxoplasma utilizes two export pathways; the rhoptry secretion pathway , which operates early during infection , and the dense granule export pathway , which we characterize here and show is dependent on ASP5 activity . Upon the recent identification of GRA16 , a dense granule effector that translocates into the host cell nucleus and affects p53 turnover , we noticed a PEXEL-like sequence at the approximate location that the PEXEL motif is located in P . falciparum proteins ( Bougdour et al . , 2013; Hiller , 2004; Marti , 2004 ) . Our work described here shows that this TEXEL motif is involved in the export of GRA16 into the host cell and is processed by the Golgi-resident ASP5 , consistent with a recent publication characterizing ASP5 ( Curt-Varesano et al . , 2015 ) . While we identified this system based on its similarity to the Plasmodium export pathway , and indeed it has now come to light that several other Apicomplexan species utilize this ‘PEXEL-like motif’ for protein export ( Pellé et al . , 2015 ) , our work has uncovered important differences between the Toxoplasma and Plasmodium systems and therefore sheds new light on protein export by Apicomplexan parasites . In this study , the consensus substrate sequence for ASP5 was determined to be RRLxx , demonstrating that this enzyme has different substrate specificity to PMV , which requires RxLxE/Q/D for activity ( Boddey et al . , 2009; 2010; Hodder et al . , 2015; Sleebs et al . , 2014b ) . A small molecule compound that mimics RRL and contains the non-cleavable amino acid statine ( WEHI-586 ) , thus a likely competitive inhibitor , inhibited ASP5 activity , whereas WEHI-916 , a potent PEXEL-mimetic inhibitor of PMV that contains valine at P2 ( RVLstatine ) ( Sleebs et al . , 2014b ) was a very poor ASP5 inhibitor ( IC50 >20 μM; data not shown ) . Our structural model of ASP5 in complex with the TEXEL of GRA16 provides a clear explanation for the requirement of RRL within the substrate . Interestingly , we observed that mutation of the P2’ residue increased ASP5 activity . This likely reflects a reduced entropic penalty associated with forming the salt bridge between the otherwise flexible side-chains . The ASP5 model also revealed differences between it and PMV , including the absence of a helix-turn-helix motif in PMV that is thought to participate in protein–protein interactions in the ER , consistent with ASP5’s location in the Golgi . The PEXEL in P . falciparum is usually found in close proximity to the N-terminal signal peptide; however , we have shown here that this positional constraint does not occur in the Toxoplasma TEXEL . We demonstrate that MYR1 , a novel protein essential secreted into the PV , has an ASP5-cleaved TEXEL motif approximately 558 amino acids from the predicated signal peptidase cleavage site . Plasmepsin V of Plasmodium spp . is an ER-resident enzyme that cleaves the PEXEL co-translationally ( Sleebs et al . , 2014b ) , potentially residing in complex with signal peptidase , thus immediately licensing proteins for export upon ER entry . This cannot occur for ASP5 , as it is located in the Golgi , which would require TEXEL-cargo proteins to be trafficked via vesicles to this compartment prior to its activity . It is interesting to note that this localization for ASP5 is not unique as other aspartyl proteases involved in protein trafficking , such as furin and the beta-site amyloid precursor protein cleaving enzyme ( BACE ) , are located in the Golgi ( Thomas , 2002; Evin et al . , 2010 ) . It is therefore possible that the positional constraint of the Plasmodium PEXEL within cargo proteins is the consequence of PMV’s ER localization , whereas ASP5’s location in the Golgi may permit cleavage of a TEXEL motif at any position within a substrate protein . It is possible , however , that PMV may also cleave PEXEL motifs found anywhere within Plasmodium proteins containing a signal peptide , as this is yet to be investigated . An important difference between the Plasmodium and Toxoplasma export pathways is that , while PEXEL cleavage appears to be solely involved in export , the apparent lack of export of cleaved MYR1 suggests that TEXEL processing may also be necessary for correct localization and/or function in the PV . We also found that Δasp5 parasites displayed a profound defect in the biogenesis of the NTN within the PV , which is known to require several PV proteins of dense granule origin . The NTN resides between replicating parasites and the PVM , where it potentially functions in the exchange of solutes by increasing surface area ( Mercier , 2002 ) . It is presently unknown whether the genesis of the NTN requires proteins that are exported into the host cell but it is interesting to note that GRA14 , a protein known to be involved in NTN formation , has a putative RRLxx motif ( Rome et al . , 2008 ) . We also show that trafficking of proteins that lack discernable TEXEL motifs are affected by deletion of ASP5 . We show that mitochondrial recruitment , which depends on the dense granule protein MAF1 , is reduced in parasites lacking ASP5 . We also show that ASP5 is essential for the export of GRA24 , an effector that promotes sustained MAPK signaling within the host cell ( Braun et al . , 2013 ) . Some Plasmodium exported proteins do not have a PEXEL motif and instead rely on a transmembrane domain and other unknown factors for translocation through the PTEX translocon . These PEXEL-negative exported proteins ( PNEPs ) include the major virulence protein PfEMP1 and several proteins required for the transport of this large molecule to its final destination on the erythrocyte surface for cytoadherence ( Maier et al . , 2008; Rug et al . , 2014; Sleebs et al . , 2014b ) . The export of PfEMP1 is also dependent on several PEXEL-containing proteins ( Maier et al . , 2008 , Rug et al . , 2014 ) as well as PMV ( Sleebs et al . , 2014b ) . Thus , our current hypothesis is that trafficking of GRA24 , MAF1 and potentially other TEXEL-negative proteins rely on one or more TEXEL-containing proteins . To understand the importance of ASP5-dependent export pathways on inducing transcriptional changes within the host cell we performed RNA-seq experiments and analyzed differences in up- and down-regulated genes induced by infection with wild type or ∆asp5 parasites . We found that loss of ASP5 results in a global reduction in the magnitude of host cell transcriptional changes in response to parasite infection . By interrogating the biological processes and molecular functions of genes that are influenced by ASP5 , it is evident that this protease plays an important role in influencing the expression of genes involved in cell cycle , nucleic acid metabolism , autophagy , peroxisome fusion , vacuole organization , cell differentiation , signaling processes and proteins that bind DNA and chromatin ( Table 1 ) . While GRA16 has been implicated in cell cycle progression and GRA24 influences transcription factor expression , there are as yet no known effectors that influence the other characterized biological processes . It is clear that understanding how Toxoplasma influences these in the infected cell is an important step . Furthermore , profiling the transcriptional changes that occur in other cell types that Toxoplasma is known to infect , such as macrophages , dendritic cells , myocytes and neurons , and the ability to determine the influence of ASP5-dependent pathways on these changes is now imminently achievable . It is noteworthy that ASP5 could be deleted from the genome of Toxoplasma , demonstrating that this enzyme in not essential , unlike PMV , which cannot be genetically deleted using conventional approaches ( Boddey et al . , 2010; Klemba and Goldberg , 2005; Russo et al . , 2010; Sleebs et al . , 2014b ) . This may be due to the different target host cells of these parasites , with Plasmodium infecting terminally differentiated erythrocytes that require extensive remodeling by exported proteins to sustain parasite development , in contrast to Toxoplasma , which infects nucleated , dynamic host cells . However , Δasp5 parasites displayed a growth defect , demonstrating that this enzyme is important for the lytic cycle of Toxoplasma , in some unknown capacity , at least within HFFs and MEFs . Whilst ASP5-deficient lines appeared to replicate at a similar rate to wild type tachyzoites , our mouse studies suggest that this enzyme is an important virulence factor . Indeed , we show significant attenuation in mice infected with ~50 live ASP5-deficient tachyzoites , with one mouse surviving beyond 15 days post infection . Furthermore , we show that injection of ~15 live ASP5-deficient tachyzoites is non-lethal to mice and confers protective immunity to lethal challenge . This is in contrast to wild-type RH , which typically show an LD100 of 1 parasite . Our data show that ASP5 is important for many cellular and transcriptional changes to the infected host cell and , therefore , this strongly suggests that collectively these changes , even in the highly virulent RH background , are important for Toxoplasma virulence in vivo . The identification of the TEXEL motif and its cleavage by ASP5 provides valuable new insights into the mechanism of host cell modification by Toxoplasma . We demonstrate similarities and important differences between this process and the analogous pathway in Plasmodium spp . Our work therefore poses new questions as to the functions and mechanisms of protein export between these two parasites as well as other Apicomplexan species of agricultural and medical significance .
All Toxoplasma parasites used in this study are of the ‘type I’ RH background , either RHΔhxgprt ( Δhx ) , or RHΔku80 ( Δku80 ) . These parasites , and all subsequently derived lines , were cultured in primary HFFs ( American Type Culture Collection , ATCC ) in Dulbecco’s Modified Eagle medium ( DME ) supplemented with 1% v/v fetal calf serum ( FCS ) ( Invitrogen , Australia ) and 1% v/v Glutamax ( Invitrogen ) ( D1 ) . Prior to infection HFFs were grown to confluency in DME supplemented with 10% v/v cosmic calf serum ( GE Healthcare , New Zealand ) ( D10 ) . Transfection of Toxoplasma tachyzoites was performed as previously described ( Soldati and Boothroyd , 1993 ) . Briefly , parasites were resuspended at 1×107 in 400 μL cytomix and transfected using 15 μg of linear DNA or 50 μg of circular DNA . Linearized DNA was used to tag or modify endogenous loci , while circular DNA was used for transient expression or random integration of ectopic constructs . Electroporation conditions were 1 . 5 kV , 25 uF and 50 Ω using a Bio-Rad Gene Pulser II ( Bio-Rad ) . Electroporated parasites were transferred to HFFs in D1 immediately after transfection . Parasites expressing the HXGPRT cassette were selected through addition of mycophenolic acid ( 25 μg/ml ) and xanthine ( 50 μg/ml ) , the CAT cassette: chloramphenicol ( 20 μM ) , the phleomycin cassette: phleomycin ( 50 μg/ml ) , the DHFR cassette: 1 μM pyrimethamine and FUDR ( 5 μM ) was used for disruption of uprt . All primers used in this study are listed in Supplementary file 1 . Endogenous epitope tagging of ASP5 was achieved through PCR amplification the 3’ end of the gene ( TGME49_242720 ) , which was cloned into pPR2-HA3 ( Sheiner et al . , 2011 ) upstream of a triple HA epitope tag with the -DHFR M2M3 selectable marker to confer pyrimethamine selection . The ectopic expression constructs ASP5WT and ASP5D431A , D682A were synthesized ( Epoch Life Science ) based on the gene model listed above at toxodb . org ( Gajria et al . , 2008 ) and cloned into the pHTU-HA3 vector , which contains the HXGPRT selectable marker , uprt disruption fragment and the Toxoplasma tubulin promoter ( McCoy et al . , 2012 ) . The GRA16 allelic swap plasmid was made by Gibson cloning . Flank 1 ( F1 ) was amplified using primers 1 and 2 , Flank 2 ( F2 ) amplified using primers 3 and 4 . WT GRA16 sequence was synthesized by IDT and amplified using primers 5 and 6 . The HXGPRT selectable marker cassette was amplified using primers 7 and 8 . pBS plasmid backbone was digested out of pHTG ( McCoy et al . , 2012 ) using BamHI/HindIII . Fragments were combined in equimolar concentrations and reactions undertaken as per manufacturer’s instructions . Primers 9 and 10 were used for the mutagenesis of pTKOII-GRA16WT-HA to pTKOII-GRA16AAAAE-HA as per the manufacturer’s protocol ( PfuTurbo DNA Polymerase [Agilent Technologies] ) . The pTKO-Δasp5-CAT vector was made using primers 11 and 12 to amplify F1 of asp5 , which was then digested with FseI and NsiI and ligated into the pTKO vector 5’ of the HXGPRT selectable cassette . Primers 13 and 14 were used the amplify F2 of asp5 , digested with BglII and XmaI , then ligated 3’ of the HXGPRT selectable marker . The HXGPRT cassette was swapped with the CAT cassette using BamHI/HindIII . Plasmid was digested with NsiI and NotI and co-transfected with pSAG1::Cas9-U6::sgASP5-1 as described below . PCR of the genomic DNA ( gDNA ) of the WT asp5 locus ( Figure 4A , PCR1 ) was completed using primers 19 and 20 . The PCRs of the resulting Δasp5 mutants ( PCR2 and PCR3 ) were performed using primers 21 and 22 , and 22 and 23 , respectively ( the GRA1 promoter drives both GFP and the CAT expression ) . Two asp5-targeting Cas9 plasmids were generated for this study , one to facilitate integration of double crossover plasmid pTKO-Δasp5-CAT ( Δku80Δasp5 , Figure 4A ) and one for direct disruption ( Δasp5CRISPR , Figure 4B ) . Briefly , both protospacers were directed towards the first exon and were chosen from toxodb . org if they were specific to the coding region of asp5 and absent from the rest of the genome ( for criteria , see [Cong et al . , 2013; Sidik et al . , 2014] ) . The sequences used for these guides are; gtccgtccccgtctcctcaac and gggtcctgttctgggcagat , respectively . pSAG1::Cas9-U6::sgASP5-1 was generated in the pSAG1::Cas9-U6::sgUPRT ( Shen et al . , 2014 ) plasmid using Q5 mutagenesis ( Stratagene ) , using primers 15 and 16 as applied by Shen et al . ( 2014 ) ( pSAG1::Cas9-U6::sgASP5-1 ) . pU6-Universal:sgASP5-2 , used to generate the Δasp5CRISPR tachyzoites , was generated in the pU6-Universal plasmid ( Sidik et al . , 2014 ) . This was done by first fusing Cas9 with the 2A skip peptide and mCherry , then subsequent FACS sorting and cloning . The guide was introduced using Q5 mutagenesis using primers 17 and 18 . The GRA24-Myc3-expressing plasmid was generated by codon optimizing gra24 based on the current gene model ( toxodb . org: TGME49_230180 ) by IDT , then cloned into the pHTU vector described above . Transfection proceeded by linearization within the uprt flank and selection for ectopic expression at the uprt locus by FUDR selection . Parasites were fixed in 4% v/v paraformaldehyde in PBS for 10 min; permeabilized in 0 . 1% v/v Triton X-100 in PBS and blocked in 3% w/v BSA ( Sigma ) in PBS for 1 hr . The following antibodies were used in this study: αGAP45 ( Gaskins , 2004 ) , αSAG1 DG52 ( Burg et al . , 1988 ) , αHA 3F10 ( Roche ) , αc-Myc Y69 ( Abcam ) , αMAF1 ( Pernas et al . , 2014 ) , αCatalase ( Ding et al . , 2000 ) , αGAPDH ( Santa Cruz ) , αMYR1 ( In press ) and αMyc 9E10 ( Sigma ) . Primary antibodies were diluted in the bovine serum albumin ( BSA ) /PBS solution for 1 hr , washed , and then incubated with Alexa Fluor-conjugated secondary antibodies ( Invitrogen ) for 1 hr . 5 μg/ml DAPI was added in the penultimate wash for 5 min and samples were mounted onto microscope slides with Vectashield ( Vector Labs ) . Parasites were imaged using an Allied Precision DeltaVision Elite wide field microscope at 100× magnification ( 1024 × 1024 pixels ) with a CoolSnap2 CCD detector and deconvolved using Softworx V5 . 0 . Protease cleavage assays were performed using HA-tagged Toxoplasma aspartyl protease 5 ( ASP5WT-HA3 ) or P . falciparum plasmepsin V ( PMV-HA ) immunopurified from parasite lysates , as described previously ( Boddey et al . , 2010; Sleebs et al . , 2014b ) . Briefly , protease bound to agarose was prepared by incubating αHA-agarose ( Sapphire Bioscience ) in parasite lysates , prepared by sonication in 1% Triton X-100/PBS pH 7 . 4 , for 1 hr before extensive washing in 1% Triton X-100/PBS , followed by storage in PBS . ASP5 cleavage assays comprised of 0 . 4 μL ASP5WT-HA3-agarose in digest buffer ( 25 mM Tris . HCl , 25 mM MES , pH 5 . 5; different pH ranges were tested and pH 5 . 5 was optimal ) , 0 . 005% Tween-20 , 5 μM TEXEL peptide substrate ( GRA16: DABCYL-R-VSRRLAEEP-E-EDANS , GRA19: DABCYL-R-VARRLSDRE-E-EDANS , GRA21: DABCYL-R-PVRELLDLE-E-EDANS , MYR1: DABCYL-R-DVRRLSEQA-EDANS , GRA24: DABCYL-R-STRGYHGGS-E-EDANS , DABCYL-R-APRGGLQTP-E-EDANS , DABCYL-R-DYRSLGMLG-E-EDANS ) where residues in bold correspond to the different residues shown in Figure 3B ( GRA16 ) , Figure 3C ( GRA19 and 21 ) , Figure 5D ( MYR1 ) and Figure 7C ( GRA24 ) , in 20 μL total volumes . For PMV , digests comprised of 0 . 2 μL PMV-HA-agarose in digest buffer ( 25 mM Tris . HCl , 25 mM MES , pH 6 . 4 ) , 0 . 005% Tween-20 , 5 μM PEXEL peptide substrate ( DABCYL-R-NKRTLAQKQ-E-EDANS ) where residues in bold correspond to the different residues shown in Figure 3A ( KAHRP ) , in 20 μL total volumes . Samples were incubated at 37°C for 4 hr and processing measured as fluorescence using an Envision plate reader ( PerkinElmer ) excited at 340 nm and reading emissions at 490 nm . Samples were gently shaken during incubation to disperse protease-agarose . All peptides were synthesized by ChinaPeptides to >85% purity . Products of the incubation of ASP5WT-HA3 with DABCYL-R-VSRRLAEEP-E-EDANS were detected by a molecular formula algorithm using an Agilent 6200 TOF/6500 series mass spectrometer , as described previously ( Boddey et al . , 2010 ) . Percentage activity of ASP5 and PMV proteases was determined by measuring the maximum fluorescence of cleaved substrate after 4 hr and setting this to 100% , as performed previously ( Boddey et al . , 2013; Sleebs et al . , 2014a; 2014b; Hodder et al . , 2015 ) . Inhibition of ASP5 by a compound that directly mimics the native GRA16 TEXEL substrate ( RRLStatine ) was performed as described previously ( Sleebs et al . , 2014b ) . Compounds WEHI-916 ( not shown ) ( Sleebs et al . , 2014b ) and WEHI-586 ( synthesis outlined below ) were evaluated using the fluorogenic TEXEL cleavage assay described above in a nine-point 1:2 serial dilution of compounds solubilized in dimethyl sulfoxide ( DMSO ) ( 1% final concentration ) . All assay end-points were set within the linear range of activity ( approximately 2 hr ) . IC50 values were determined using a nonlinear regression four-parameter fit analysis , where two of the parameters were constrained to 0 and 100% . Analytical thin-layer chromatography was performed on Merck silica gel 60F254 aluminum-backed plates and were visualized by fluorescence quenching under ultraviolet light or by KMnO4 staining . Flash chromatography was performed with silica gel 60 ( particle size 0 . 040–0 . 063 μm ) . Nuclear magnetic resonance ( NMR ) spectra were recorded on a Bruker Avance DRX 300 with the solvents indicated ( 1H NMR at 300 MHz ) . Chemical shifts are reported in ppm on the δ scale and referenced to the appropriate solvent peak . MeOD contains H2O . High-resolution electrospray ionization mass spectroscopies ( HRESMS ) were acquired by Jason Dang at the Monash Institute of Pharmaceutical Sciences Spectrometry Facility using an Agilent 1290 infinity 6224 TOF LCMS . Column used was RRHT 2 . 1 x 50 mm 1 . 8 µm C18 . Gradient was applied over the 5 min with the flow rate of 0 . 5 mL/min . For MS: Gas temperature was 325oC; drying gas 11 L/min; nebulizer 45 psig and the fragmentor 125 V . LCMS were recorded on a Waters ZQ 3100 using a 2996 Diode Array Detector . LCMS conditions used to assess purity of compounds were as follows , column: XBridge TM C18 5 µm 4 . 6 x 100 mm , injection volume 10 µL , gradient: 10–100% B over 10 min ( solvent A: water 0 . 1% formic acid; solvent B: AcCN 0 . 1% formic acid ) , flow rate: 1 . 5 mL/min , detection: 100–600 nm . All final compounds were analyzed using ultrahigh-performance LC/ultraviolet/evaporative light scattering detection coupled to MS . Unless otherwise noted , all compounds were found to be >95% pure by this method . The following starting materials were purchased commercially and used without further purification , Cbz-Orn ( N-Boc ) -OH and HCl . NH2-Orn ( N-Boc ) -OMe . HCI , NH2-Sta-NH2 ( CH2 ) 2 Ph 5 was prepared as previously described . WEHI-916 was prepared as previously described ( Sleebs et al . , 2014a; 2014b ) . A mixture of 1 ( 0 . 6 g , 1 . 01 mmol ) and Pd/C ( cat . ) in MeOH ( 20 ml ) under a hydrogen atmosphere was allowed to stir for 18 hr . The mixture was filtered through Celite and concentrated to dryness in vacuo . To the crude oil dissolved in DCM ( 10 ml ) , benzylsulfonyl chloride ( 210 mg , 1 . 1 mmol and Et3N ( 153 μL , 1 . 1 mmol ) was added . The mixture was then allowed to stir for 18 hr at 20oC . The reaction mixture was concentrated to dryness in vacuo . The residue obtained was subjected to silica chromatography gradient eluting with 100% DCM to 5% MeOH/DCM to obtain 2 as a white solid ( 330 mg , 53% ) . 1H NMR ( CDCl3 ) : δ 7 . 64–7 . 37 ( m , 5H ) , 7 . 26 ( m , 1H ) , 5 . 36 ( br s , 1H ) , 4 . 56–4 . 49 ( m , 1H ) , 4 . 28 ( s , 2H ) , 4 . 05 ( br s , 1H ) , 3 . 73 ( s , 3H ) , 3 . 30–3 . 00 ( m , 4H ) , 2 . 00–1 . 50 ( m , 8H ) , 1 . 45 ( s , 18H ) . MS , m/z = 615 [M+H]+ . A mixture of 2 ( 300 mg , 0 . 49 mmol ) , and LiOH hydrate ( 21 mg , 0 . 98 mmol ) in a mixture of water ( 3 mL ) and THF ( 5 mL ) was allowed to stir for 2 hr at 20oC . 10% citric acid solution was added to the reaction mixture . The solution was extracted with EtOAc ( 2 × 20 mL ) . The organic layer was then washed with brine ( 20 mL ) . The organic layer was dried ( MgSO4 ) and the organic layer was concentrated in vacuo to obtain 3 as a white solid ( 220 mg , 75% ) . 1H NMR ( CDCl3 ) : δ 7 . 45–7 . 36 ( m , 5H ) , 5 . 93 ( br s , 1H ) , 5 . 77–5 . 60 ( m , 1H ) , 4 . 90 ( br s , 1H ) , 4 . 52 ( br s , 1H ) , 4 . 28 ( s , 2H ) , 4 . 01–3 . 90 ( m , 2H ) , 3 . 20–3 . 00 ( m , 4H ) , 2 . 05–1 . 48 ( m , 8H ) , 1 . 44 ( s , 18H ) . MS , m/z = 601 [M+H]+ . General Procedure A was followed using 3 ( 100 mg , 0 . 166 mmol ) , to obtain 6 as a white solid ( 45 mg , 32% ) . 1H NMR ( CDCl3 ) ( rotamers ) : δ 7 . 41–7 . 15 ( m , 10H ) , 5 . 10–5 . 80 ( m , 2H ) , 4 . 28–4 . 25 ( m , 2H ) , 4 . 00–3 . 80 ( m , 3H ) , 3 . 53–3 . 41 ( m , 1H ) , 3 . 15–2 . 70 ( m , 8H ) , 2 . 35–2 . 20 ( m , 2H ) , 1 . 80–1 . 20 ( m , 29H ) , 0 . 93–0 . 86 ( m , 6H ) . MS , m/z = 862 [M+H]+ . A mixture of 6 ( 40 mg , 0 . 046 mmol ) , in 4 N HCl in dioxane ( 5 mL ) was allowed to stir for 30 min at 20oC . The reaction mixture was concentrated to dryness in vacuo . The residue was dissolved in DCM ( 10 ml ) and Et3N ( 38 μL , 0 . 276 mmol ) was added . The solution was stirred vigorously for 5 min . N , N'-bis-Boc-1-guanylpyrazole ( 31 mg , 0 . 101 mmol ) was added and the solution was left to stir for 12 hr . 10% citric acid solution was added to the reaction mixture . The solution was extracted with DCM ( 2 × 15 mL ) . The organic layer was then washed with 10% NaHCO3 solution ( 20 mL ) . The organic layer was dried ( MgSO4 ) and the organic layer was concentrated in vacuo to obtain an oil . The oil was subjected to silica chromatography gradient eluting with 100% DCM to 10% MeOH/DCM to obtain 7 as a white solid ( 35 mg , 66% ) . 1H NMR ( CDCl3 ) ( rotamers ) : δ 8 . 60–8 . 30 ( m , 3H ) , 7 . 38–7 . 21 ( m , 11H ) , 6 . 68 ( br s , 1 . 5H ) , 6 . 40 ( br s , 1H ) , 4 . 40–4 . 26 ( m , 4H ) , 4 . 00–3 . 30 ( m , 10H ) , 2 . 85–2 . 75 ( m , 2H ) , 1 . 85–1 . 10 ( m , 47H ) , 0 . 89–0 . 87 ( m , 6H ) . MS , m/z = 1146 [M+H]+ . A mixture of 7 ( 35 mg , 0 . 03 mmol ) in TFA ( 0 . 5 mL ) and DCM ( 1 mL ) was allowed to sit for 18 hr at 20oC . The reaction mixture was concentrated to dryness in vacuo . The oil was triturated with Et2O and filtered off , washing with Et2O , to obtain to obtain WEHI-586 as a white solid ( 26 mg , 87% ) . 1H NMR ( MeOD ) : δ 7 . 47–7 . 17 ( m , 10H ) , 4 . 40–4 . 29 ( m , 4H ) , 3 . 98–3 . 77 ( m , 2H ) , 3 . 45–3 . 19 ( m , 8H ) , 2 . 83–2 . 73 ( m , 2H ) , 1 . 74–1 . 61 ( m , 11H ) , 1 . 00–0 . 85 ( m , 6H ) . HRESMS found: ( M+H ) 745 . 4197; C35H56N10O6S requires ( M+H ) , 745 . 4183 . Homology models for the complex of Toxoplasma ASP5 with GRA16 ( residues S-R3R2L1A1’E2’-E ) were generated using the MODELLER program ( version 9 . 14 ) ( Eswar et al . , 2006 ) using structures of P . vivax PMV in complex with WEHI-842 and P . vivax plasmepsin IV in complex with Pepstatin A as templates ( PDB codes 4ZL4 [Hodder et al . , 2015] and 1QS8 [Bernstein et al . , 2003] , respectively ) . Restraints were included to ensure the carbonyl oxygen of the L3 residue of the substrate was within hydrogen bonding distance of the PvPMV catalytic aspartic acid ( D531 ) . Mutations were introduced using the YASARA program ( http://www . yasara . org ) ; hydrogen atoms were added to complete atomic valencies and the geometries minimized using the AMBER force field ( Duan et al . , 2003 ) . An evaluation of the binding free energy was carried out using the AutoDock potential ( Morris et al . , 1998 ) . In its original formulation , the AutoDock potential includes terms that represent the entropic penalty for restriction of conformational freedom and desolvation of the ligand only . Here , we have included these two components for both ligand ( wild-type GRA16 and mutations ) and receptor ( ASP5 ) and consequently reduced the contribution to the total free energy of interaction of each by half . AMBER all-atom partial atomic charges were used to calculate the electrostatic interaction energy . Ionizable residues were assumed to be in their standard state at neutral pH except for the catalytic aspartic acid , which was neutral . Toxoplasma tachyzoites were prepared for TEM analysis as described ( Breinich et al . , 2009 ) . Briefly , HFFs were infected at a multiplicity of infection ( MOI ) of 5:1 for 16 hr , washed twice with PBS , dislodged with trypsin- ethylenediaminetetraacetic acid ( Gibco ) , quenched with cold PBS and pelleted at 1200 g for 5 min . PBS-trypsin was replaced with 2 . 5% glutaraldehyde ( Electron Microscopy Sciences ) in 0 . 1 M sodium phosphate buffer . Samples were fixed in osmium tetroxide , dehydrated in ethanol , treated with propylene oxide and embedded in epoxy resin . Sections were stained with uranyl acetate and lead citrate and examined on a Jeol 1200 EX electron microscope . SV40-immortalized MEFs derived from C57BL/6 E14 . 5 embryos were retrovirally-infected with the MTS of Smac/DIABLO ( Verhagen et al . , 2000 ) fused to the C-terminus of GFP in an internal ribosome entry site-hygromycin expression vector . HFFs were passaged and grown in D10 media until they reached confluency . Following this , HFFs were transferred into D1 media and left as uninfected ( no parasites ) or infected at a MOI of 5 with either RHΔku80 ( WT ) or Δku80Δasp5 ( Δasp5 ) tachyzoites for 18 hr . HFFs were washed with PBS to remove uninvaded parasites and dislodged with trypsin . Total host and parasite RNA was extracted using the RNeasy kit ( Qiagen ) . Three independent biological replicates of each condition were obtained . DNA libraries were prepared using the Illumina TruSeq v2 protocol and sequenced on an Illumina HiSeq 2000 at the Australian Genome Research Facility ( AGRF ) , Melbourne . On average , 25 . 5 million 100 bp single-end reads were obtained for each sample . The reads were aligned to the human genome ( hg19 ) using the Rsubread aligner ( Liao et al . , 2013 ) . The number of fragments overlapping each Entrez gene were counted using featureCounts ( Liao et al . , 2014 ) and NCBI RefSeq annotation , build 38 . 1 . Differential expression analyses were performed using the Bioconductor packages edgeR ( Robinson et al . , 2010 ) and limma ( Ritchie et al . , 2015 ) . All genes that did not achieve a count per million of 0 . 4 in at least 3 samples were deemed to be unexpressed and subsequently filtered from the analysis . Additionally , genes with no official symbol in the NCBI gene information file were removed . Following filtering , 15 , 018 genes remained for the downstream analysis . Compositional differences between samples were normalized using the trimmed mean of M-values method ( Robinson et al . , 2010 ) . All counts were then transformed to log2-counts per million ( logCPM ) with associated precision weights using voom ( Law et al . , 2014 ) . Differential expression for the three comparisons , WT infected versus uninfected , Δasp5 infected versus uninfected , and Δasp5 infected versus WT infected , was assessed using empirical Bayes moderated t-statistics ( Smyth , 2004 ) . Genes were considered to be differentially expressed if they attained a false discovery rate of 0 . 05 . Gene ontology analysis used the goana function . Shrunk log2-fold-changes for plotting were computed using edgeR’s predFC function with prior count set to 3 . The barcode plot was drawn with limma’s barcodeplot function and the correlation of the Δgra16 gene sets with the RNA-seq data was evaluated using a directional roast gene set test with 10 , 000 rotations ( Wu et al . , 2010 ) . This data has been deposited in NCBI’s Gene Expression Omnibus ( GEO ) under accession number GSE73986 . All animal experiments were performed in accordance with regulations outlined by The Walter and Eliza Hall Institute’s Animal Ethics Committee . Wild-type ( RHΔhxgprt ) , Δasp5CRISPR and Δasp5CRISPR:ASP5WT-HA3 parasites were grown in HFFs , harvested , and counted . Doses were either 100 tachyzoites in 200 μL of PBS ( determined by plaque assay to be 15 ± 3 tachyzoites ) or 200 parasites in 200 μL PBS ( ~50 live parasites ) . All tachyzoites were intraperitoneally injected into 6 × 6–8-week -old C57BL/6 mice . Mice were monitored daily and sacrificed when determined moribund . Sero-conversion was monitored by using serum collected from mice at 14 days post infection and used in Western blot ( 1:500 dilution ) against purified tachyzoites and uninfected HFF lysates . | Toxoplasma gondii is a parasite that is thought to infect over two billion people worldwide . Often these infections cause no noticeable symptoms , but can cause serious illness in people with weakened immune systems . Toxoplasma parasites must enter human cells in order to survive . To dramatically increase their chances of survival , the parasites then deliver specialized proteins into the host cell that disarm the host’s immune defenses . Understanding how these specialized proteins are transported from inside the parasite into the host cell , and how this process can be blocked , may lead to new treatments for these and related parasitic infections . By genetically modifying Toxoplasma parasites to lack a parasite enzyme , Coffey et al . have now discovered that this molecule is required for correctly transporting parasite proteins . This enzyme is called aspartyl protease 5 ( ASP5 ) and is found in the parasite in a structure called the Golgi apparatus , which acts as a main hub for protein transport . ASP5 cuts proteins at a ‘barcode’ that is found in many different types of proteins , priming them for transport out of the parasite and for export into the host cell in some cases . Coffey et al . show that in parasites that lack ASP5 , these proteins are no longer cleaved and are not transported correctly , blocking the activities that parasites normally perform to ensure their survival . Therefore , ASP5 plays an important role in transporting a wide range of proteins associated with disease , including transporting certain proteins directly into the host cell . Future studies that compare parasites that lack ASP5 to normal parasites will aim to identify new proteins used by the parasites to defeat the host’s immune defenses . | [
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] | 2015 | An aspartyl protease defines a novel pathway for export of Toxoplasma proteins into the host cell |
Recovery from serious neurological injury requires substantial rewiring of neural circuits . Precisely-timed electrical stimulation could be used to restore corrective feedback mechanisms and promote adaptive plasticity after neurological insult , such as spinal cord injury ( SCI ) or stroke . This study provides the first evidence that closed-loop vagus nerve stimulation ( CLV ) based on the synaptic eligibility trace leads to dramatic recovery from the most common forms of SCI . The addition of CLV to rehabilitation promoted substantially more recovery of forelimb function compared to rehabilitation alone following chronic unilateral or bilateral cervical SCI in a rat model . Triggering stimulation on the most successful movements is critical to maximize recovery . CLV enhances recovery by strengthening synaptic connectivity from remaining motor networks to the grasping muscles in the forelimb . The benefits of CLV persist long after the end of stimulation because connectivity in critical neural circuits has been restored .
Recovery from serious neurological injury requires substantial rewiring of neural circuits . Many methods have been developed to enhance synaptic plasticity in hopes of enhancing recovery . Unfortunately , these methods have largely failed in the clinic likely due to the challenge of precisely targeting specific synapses and absence of testing in clinically-relevant models ( Gladstone et al . , 2006; Levy et al . , 2016 ) . Real-time control of neural activity provides a new avenue to promote synaptic plasticity in specific networks and restore function after injury ( Engineer et al . , 2011; Nishimura et al . , 2013; McPherson et al . , 2015; Guggenmos et al . , 2013 ) . Human and animal studies demonstrate that precisely timed vagus nerve stimulation ( VNS ) can improve recovery of sensory and motor function . VNS engages neuromodulatory networks and triggers release of pro-plasticity factors including norepinephrine , acetylcholine , serotonin , brain-derived neurotrophic factor , and fibroblast growth factor ( Hays , 2016; Hulsey et al . , 2017; Hulsey et al . , 2016 ) . This in turn influences expression and phosphorylation of proteins associated with structural and synaptic plasticity , including Arc , CaMKII , TrkB , and glutamate receptors ( Alvarez-Dieppa et al . , 2016; Furmaga et al . , 2012 ) . Engagement of neuromodulatory networks activates a transient synaptic eligibility trace to support spike-timing-dependent plasticity ( STDP ) ( He et al . , 2015 ) , thus raising the prospect that closed-loop neuromodulatory strategies may provide a means to direct specific , long-lasting plasticity to enhance recovery after neurological injury . Indeed , in the absence of neurological damage , repeatedly pairing sensory or motor events with brief bursts of VNS yields robust plasticity in sensory or motor cortex that is specific to the paired experience ( Engineer et al . , 2011; Hulsey et al . , 2016 ) . Moreover , the addition of VNS to rehabilitative training improves recovery in rodent models of unilateral brain injury and in chronic stroke patients , highlighting the clinical potential of closed-loop neuromodulatory strategies ( Hays , 2016; Khodaparast et al . , 2016; Pruitt et al . , 2016; Hays et al . , 2014a; Dawson et al . , 2016 ) . We tested the hypothesis that closed-loop VNS ( CLV ) could be harnessed to enhance recovery after spinal cord injury ( SCI ) . To do so , we developed a real-time closed-loop neuromodulation paradigm based on the synaptic eligibility trace to deliver VNS immediately after the most successful forelimb movements during motor rehabilitation . The strategy uses a control algorithm that adaptively scales stimulation threshold to trigger a brief 0 . 5 s train of VNS on trials in which pull forces fall within the top quintile of previous trials ( Top 20% CLV; Figure 1a , b , and Figure 1—figure supplement 1a ) . To test the hypothesis that temporal precision is required for VNS-dependent effects , we employed a similar algorithm in which stimulation was delivered on the weakest quintile of trials ( Bottom 20% CLV; Figure 1b and Figure 1—figure supplement 1g ) . Both algorithms deliver the same amount of VNS during rehabilitative training , but Bottom 20% CLV results in a significant delay between VNS and the most successful trials .
To test whether CLV could improve recovery of motor function after SCI , rats were trained to perform an automated reach-and-grasp task measuring volitional forelimb strength ( Figure 1b , Video 1 ) ( Hays et al . , 2013 ) . Once proficient , rats received a right unilateral impact at spinal level C6 to impair function of the trained forelimb and underwent implantation of a bipolar cuff electrode on the left cervical vagus nerve ( Ganzer et al . , 2016a ) . SCI resulted in a 77% reduction in volitional forelimb strength , consistent with paresis observed in many cervical SCI patients ( Figure 1c , PRE v . Wk 8 , Paired t-test , t ( 29 ) = 37 . 34 , p=4 . 4×10−26 , Video 2 ) . Top 20% CLV substantially boosted recovery of volitional forelimb strength compared to equivalent rehabilitative training without CLV ( Rehab alone ) , demonstrating that CLV enhances recovery of motor function after SCI ( Figure 1c; Two-way repeated measures ANOVA , Interaction; F[6 , 120]=3 . 88 , p=1 . 43×10−3; Videos 3–4 ) . CLV resulted in lasting recovery after the cessation of stimulation after week 11 , consistent with the notion that CLV restores function in critical motor networks ( Top 20% CLV; Wk 11 v . Wk 12; Paired t-test , t ( 12 ) = −0 . 89 , p=0 . 38 ) . Despite equivalent rehabilitation and a comparable number of stimulations delivered during task performance ( Figure 1d , e ) , Bottom 20% CLV resulted in substantially diminished recovery compared to Top 20% CLV ( Figure 1c , Two-way repeated measures ANOVA , Interaction; F[6 , 114]=2 . 40 , p=0 . 03 , Video 5 ) and failed to improve forelimb strength compared to Rehab alone . Together , these findings demonstrate that closed-loop neuromodulation paired with the most successful movements during rehabilitation improves recovery of motor function after cervical SCI . The synaptic eligibility trace theory posits that neuromodulatory reinforcement must occur within seconds after neural activity to drive plasticity ( He et al . , 2015 ) . To clarify how temporally precise CLV must be , a subset of rats received VNS delayed approximately 1 . 5 s after the top 50% most successful trials ( Delayed Top 20% CLV , Figure 1—figure supplement 1d ) . This short delay resulted in comparable recovery to stimulation delivered immediately after a successful trial in the Top 20% CLV group ( Figure 1f ) . Stimulation in the Bottom 20% CLV group was separated by 25 ± 5 s from the most successful trials and failed to drive substantial benefits ( Figure 1f , Figure 1—figure supplement 1g ) . This absence of enhanced recovery despite delivery of CLV may be attributed to either the long delay or greater variance in the timing between stimulation and the most successful trials . These findings support a temporal precision limit for CLV near 10 s , consistent with the synaptic eligibility trace hypothesis ( He et al . , 2015 ) . To determine whether more pairings of VNS with successful trials would improve recovery , we utilized an adaptive algorithm in which VNS was delivered on at least the top 50% most successful trials , resulting in 2 . 5 times more stimulation pairings ( Top 50% CLV , Figure 1—figure supplement 1j ) . Top 50% CLV substantially improved recovery of forelimb function compared to Rehab alone , which provides an independent confirmation that CLV enhances recovery after SCI ( Figure 2a , Two-way repeated measures ANOVA , Interaction; F[6 , 174]=3 . 56 , p=2 . 38×10−3 ) . The rate and degree of recovery were comparable in the Top 50% CLV and the Top 20% CLV groups ( Figure 2—figure supplement 1 ) , suggesting that timing is more important than quantity of stimulation . Plasticity in remaining networks could be harnessed to support recovery after SCI ( Fink and Cafferty , 2016; Manohar et al . , 2017 ) . Unilateral SCI resulted in extensive damage to gray matter , rubrospinal pathways , and propriospinal pathways in the right hemicord while largely sparing the right dorsal corticospinal tract ( CST ) ( Figure 2b and Figure 2—figure supplement 2 ) . Thus , we used intracortical microstimulation to test the hypothesis that CLV enhances output from the corticospinal circuits to the impaired forelimb . CLV resulted in eight times more motor cortex sites that generated grasp movements in the impaired forelimb compared to Rehab alone ( Figure 2c and Figure 2—figure supplement 4 , Unpaired t-test , t ( 10 ) = 2 . 28 , p=0 . 04 ) , providing the first evidence that CLV induces large-scale plasticity in corticospinal networks after neurological injury . We next tested the hypothesis that CLV improves recovery by increasing synaptic connections within the motor network controlling grasping muscles of the forelimb . We injected the retrograde transsynaptic tracer pseudorabies virus ( PRV-152 ) into flexor digitorum profundus and palmaris longus and counted labeled neurons six days later . CLV resulted in a five-fold increase in labeled neurons in motor cortex compared to Rehab alone ( Figure 2d and Figure 2—figure supplement 5 , Unpaired t-test , t ( 9 ) = 7 . 63 , p=3 . 2×10−5 ) . The magnitude of this increase in synaptic connectivity is comparable to the seven-fold increase in the number of motor cortex sites that produce grasp . CLV failed to increase neuronal labeling of spinal motor neurons , red nucleus neurons or propriospinal neurons ( Figure 2d ) . Additionally , CLV did not influence lesion extent ( Figure 2e , Figure 2—figure supplement 2 ) . Together , these results are consistent with anatomical plasticity in the spared corticospinal network contributing to enhanced recovery when CLV is added to rehabilitative training after SCI ( Figure 3 ) . The observation that CLV improves recovery and enhances functional and anatomical plasticity in corticospinal networks suggests that CLV may prove ineffective if the CST is destroyed . Given the severity and anatomical heterogeneity of damage observed in SCI patients ( Sekhon and Fehlings , 2001 ) , such a finding would limit the clinical utility of CLV . We therefore evaluated motor recovery in a bilateral injury model that virtually eliminates the CST on both sides of the cord ( Figure 4b ) . Despite profound damage , CLV more than doubled the degree of forelimb motor recovery compared to Rehab alone ( Figure 4a , Two-way repeated measures ANOVA , Interaction; F[6 , 144]=5 . 29 , p=7 . 62×10−5 ) . The observation that CLV can improve recovery following bilateral SCI suggests CLV could be clinically useful . We hypothesized that CLV enhances recovery by promoting plasticity in the rubrospinal and propriospinal pathways , which were damaged , but not eliminated , by this injury ( Figure 4b and Figure 4—figure supplement 1 ) . Indeed , CLV doubled the number of labeled red nucleus neurons and C3/4 propriospinal neurons compared to Rehab alone ( Figure 3d and Figure 4—figure supplement 3 , Unpaired t-test , Red Nucleus: t ( 4 ) = 3 . 89 , p=0 . 018; Propriospinal: t ( 4 ) = 2 . 77 , p=0 . 05 ) . Consistent with the extensive damage to the corticospinal pathway , CLV had no effect on reorganization of motor cortex ( Figure 4c and Figure 4—figure supplement 4 , Unpaired t-test , t ( 12 ) = −0 . 13 , p=0 . 90 ) and failed to increase the number of labeled neurons in the motor cortex ( Figure 3d , Unpaired t-test , t ( 4 ) = 0 . 83 , p=0 . 45 ) . These results suggest that CLV is capable of supporting recovery following SCI by strengthening anatomical connectivity within remaining pathways ( Figure 4—figure supplement 5 ) .
In this study , we developed a novel closed-loop neuromodulation strategy to make use of the high temporal precision of the synaptic eligibility trace . We demonstrate that activation of the vagus nerve improves recovery when reliably delivered within seconds of a successful movement , and we provide the first evidence that CLV enhances reorganization of synaptic connectivity in remaining networks in two non-overlapping models of SCI . The flexibility to promote reorganization in a range of pathways is a critical benefit of CLV , given the great heterogeneity in the etiology , location , and extent of damage present in SCI patients . Classical studies by Skinner demonstrate that adaptive reinforcement of successive approximations , or shaping , drives behavior toward a desired response ( Skinner , 1953 ) . This principle has been adopted for use in rehabilitation , with the intention to reinforce successively better movements ( Wood , 1990 ) . We made use of this concept by applying an adaptively-scaled stimulation threshold to deliver CLV with the most successful forelimb movements during rehabilitation . Enhanced recovery was observed only when CLV was paired with trials that approximated the desired outcome , highlight the importance of timing for closed-loop stimulation to shape behavioral outcomes and maximize recovery . The magnitude of neuromodulatory activation elicited by an event is directly proportional to the surprise , or unpredictability , of the event ( Hangya et al . , 2015; Hollerman and Schultz , 1998; Sara and Segal , 1991 ) . This phenomenon is ascribed to reward prediction error ( Schultz , 2002 ) . Unsurprising events fail to activate neuromodulatory systems , and even rewarding events fail to trigger neuromodulator release if they are expected . We posit the predictability and accompanying tedium of long , frustrating rehabilitation and the minimal reinforcement of practicing a previously simple motor task blunts plasticity and limits recovery after SCI . The closed-loop neuromodulation strategy developed here circumvents this by artificially engaging neuromodulatory networks and providing a repeated , non-adapting reinforcing signal typically associated with surprising consequences ( Hays , 2016; Hulsey et al . , 2017; Hulsey et al . , 2016 ) . CLV drives temporally-precise neuromodulatory release to convert the synaptic eligibility trace in neuronal networks that generate optimal motor control to long-lasting plasticity ( He et al . , 2015 ) . CLV is a minimally-invasive , safe strategy to provide precisely-timed engagement of multiple neuromodulatory networks to boost plasticity during rehabilitation ( Hays , 2016 ) . Preliminary results in chronic stroke and tinnitus patients highlight the clinical potential of CLV , while delivering less than 1% of the total FDA-approved amount of stimulation ( Dawson et al . , 2016; De Ridder et al . , 2014; Ben-Menachem , 2001 ) . Moreover , the flexibility to deliver stimulation with a variety of rehabilitative exercises raises the possibility to design CLV-based to target motor dysfunction of the lower limbs , somatosensory loss , and bowel and bladder issues , all of which are prevalent in SCI patients . Delineation of the timing requirements and documentation of neuronal changes driven by CLV in this study provide a framework for development of this strategy for a range of neurological conditions , including stroke , peripheral nerve injury , and post-traumatic stress disorder ( Hays , 2016; Lozano , 2011 ) .
All procedures performed in the study were approved by the University of Texas at Dallas Institutional Animal Care and Use Committee ( Protocols: 14–10 and 99–06 ) . Adult female Sprague Dawley rats ( N = 181 ) used in this study were housed one per cage ( 12 hr light/dark cycle ) . Twelve experimentally naïve rats were used for control experiments . One hundred and sixty-nine rats were trained to proficiency on the isometric pull task as in our previous studies ( Khodaparast et al . , 2016; Pruitt et al . , 2016; Hays et al . , 2013; Sloan et al . , 2015; Hays et al . , 2014b; Hays et al . , 2016; Khodaparast et al . , 2013; Pruitt et al . , 2014; Meyers et al . , 2017 ) . Sample sizes were based estimated effect size determined in our initial pilot studies and are consistent with comparable previous studies . Trained rats were food restricted Monday-Friday to provide task motivation ( ad libitum access to water ) . Because of the cage geometry , only the right forelimb can be used to reach the pull handle to trigger a food reward . After reaching task proficiency ( 85% success rate on ten consecutive sessions ) , rats received a unilateral contusive injury ( N = 128 ) or bilateral contusive injury ( N = 41 ) of the cervical spinal cord . After recovery , rats received a vagus nerve cuff electrode and resumed training on the isometric pull task . In addition to the food reward , rats were dynamically allocated to balanced groups to receive a brief burst of vagus nerve stimulation ( VNS ) on appropriate trials . Rehabilitative training , consisting of freely performing the task , continued for six weeks . No VNS was delivered in any group on the final week of rehabilitative training , to allow assessment of lasting effects of stimulation . Terminal motor cortex mapping or transsynaptic tracing experiments occurred the week following the end of therapy in a subset of unilateral ( N = 23 ) and bilateral SCI rats ( N = 20 ) . Eighty-seven rats were excluded from the study due to mortality ( N = 20 ) , inability to perform the task after injury ( N = 25 ) , or VNS device failure ( N = 42 ) . Device failure included mechanical failure of the headmount or loss of stimulation efficacy , determined by a cuff impedance >25 kΩ or by the absence of a reduction in blood oxygenation in response to a train of VNS while under anesthesia ( described below ) . This is a standard method to evaluate VNS efficacy ( Loerwald et al . , 2017; Borland et al . , 2018 ) . Animals that failed to demonstrate a reliable drop in oxygen saturation at the end of therapy were excluded . Bilateral SCI rats were given two additional weeks of recovery time due to their larger spinal lesion and slower return to recumbency ( Figure 4—figure supplement 6 ) . Other than therapy start time ( 6 vs . 8 weeks post-SCI ) , all training and assessment was identical for unilateral and bilateral SCI rats . All source data indexed across animals can be found in Supplementary file 1–4 . The isometric pull task is a fully automated and quantitative assay to measure multiple parameters of forelimb force generation and was performed similar to previous descriptions ( Khodaparast et al . , 2016; Pruitt et al . , 2016; Hays et al . , 2013; Sloan et al . , 2015; Hays et al . , 2014b , 2016; Khodaparast et al . , 2013; Pruitt et al . , 2014; Meyers et al . , 2017 ) . Isometric pull training sessions consisted of two 30 min sessions ( separated by at least 2 hr ) five days per week . Experimenters were blind to treatment group at all times throughout behavioral testing . Early in training , rats were encouraged to interact with the pull handle by dispensing pellets ( 45 mg chocolate-flavored pellets , Bio-Serv; Flemington , NJ ) when they approached or touched the lever . The pull handle was initially located inside the test chamber and then slowly retracted outside of the behavioral chamber to encourage reaching with the right paw . A trial was initiated when the rats exerted at least 10 g of force on the pull handle . A trial window of 2 s started after trial initiation where the animal could receive a reward by pulling with a force exceeding a reward threshold . The reward threshold was scaled adaptively based on the median peak force of the 10 preceding trials , with a fixed bounded minimum of 10 grams and maximum of 120 g based on previous studies ( Figure 1—figure supplement 1 ) ( Ganzer et al . , 2016a; Meyers et al . , 2017 ) . Thus , rats received rewards on trials that exceeded either the median peak force from the previous 10 trials or 120 g . The reward threshold was set to 10 g for the first 10 trials of a training session and adaptive scaled for the remaining trials ( Figure 1—figure supplement 1 ) . This reward threshold paradigm was used for all groups at all timepoints during the study . Rats were trained until they reached proficiency , defined as a 10 consecutive sessions in which greater than 85% of trials exceeded 120 g . After reaching isometric pull task proficiency , rats were given a cervical unilateral or bilateral SCI at spinal level C6 . Post-injury baseline force generation assessment occurred on week six for unilateral SCI and week eight for bilateral contusion SCI and consisted of 2 × 30 min sessions per day across two consecutive days ( POST; Figures 1C and 2A , and 3A ) . Random group assignment was used to determine which rats received VNS for the first 75% of group assignment decisions . To ensure well-balanced treatment groups , the final 25% of rats were assigned to groups based on their post-injury performance . Rehabilitative training continued for 6 weeks with VNS delivered when appropriate . MotoTrak Software ( Vulintus , Inc . ) was used to record and display experimental data during the performance of the isometric pull task similar to previous studies ( Hays et al . , 2013; Ganzer et al . , 2016a; Sloan et al . , 2015; Pruitt et al . , 2014; Meyers et al . , 2016 ) . A microcontroller board ( Vulintus , Inc . ; Dallas Texas USA ) sampled the force transducer every 10 ms and relayed information to the MotoTrak software for offline analysis . For rats receiving VNS , stimulation was triggered by the behavioral software on appropriate trails during rehabilitative training . Peak pull force ( maximum force generated in a trial , g ) was calculated for every rat for every week of behavior . A Two-way repeated measures ANOVA was used to compared peak pull forces in each treatment condition across time , followed by post hoc Bonferroni-corrected unpaired t-tests where appropriate ( Figures 1C , 2A and 4A ) . Percent benefit over Rehab alone was calculated as the recovery of peak pull force after therapy normalized to the average recovery observed in the Rehab alone group ( Figure 1J ) . The distribution of pull forces after injury is shown in Figure 1—figure supplement 2 . Behavioral data for each week for all individual subjects is available in Supplementary file 1 . Experimenters were blind to the group of the rat during surgery . All surgeries were performed using aseptic technique under general anesthesia . Rats were anesthetized with ketamine ( 50 mg/kg ) , xylazine ( 20 mg/kg ) , and acepromazine ( 5 mg/kg ) for all procedures ( i . p . ) . Heart rate and blood oxygenation was monitored during surgery . After achieving isometric pull task proficiency , rats received either a right side ( unilateral ) or midline ( bilateral ) C6 spinal cord contusive impact using surgical technique from previous studies ( Ganzer et al . , 2016a ) . A right side or bilateral dorsal C5 laminectomy was performed for rats receiving a unilateral or bilateral SCI , respectively . The vertebral column was stabilized using spinal microforceps . For unilateral SCI , the right spinal hemicord was contused using the Infinite Horizon Impact Device with a force of 200 kilodynes and zero dwell time as previously reported ( Precision Systems and Instrumentation , Lexington , KY; impactor tip diameter = 1 . 25 mm ) ( Ganzer et al . , 2016a ) . For bilateral SCI rats , the midline of the spinal cord was contused with a force of 225 kilodynes and zero dwell time ( impactor tip diameter = 2 . 5 mm ) . The skin overlying the exposed vertebrae was then closed in layers and the incised skin closed using surgical staples . All rats received buprenorphine ( s . c . , 0 . 03 mg/kg , 1 day post-op ) , enrofloxacin ( s . c . , 10 mg/kg , 3 days post-op ) and Ringer’s solution ( s . c . , 10 mL , 3 days post-op ) immediately after surgery and continuing post-operatively . All rats were monitored daily for at least 1 week post-injury . We documented time to return to recumbency , defined as the return of the righting reflex and ability to self-feed , and plantar placement following SCI . After bilateral SCI , rats took significantly longer to return to recumbency and forepaw plantar placement compared to unilateral SCI rats ( Figure 4—figure supplement 6 ) . Therefore , bilateral SCI rats started therapy 2 weeks later . After injury , rats were hand fed twice daily and given Ringer’s solution ( s . c . , 10 mL ) for up to 1 week post-injury to maintain a healthy diet . A two-channel connector headmount and vagus nerve stimulating cuff were implanted on post-injury week six for unilateral and week eight for bilateral SCI rats similar to previous studies ( Engineer et al . , 2011; Khodaparast et al . , 2016; Pruitt et al . , 2016; Hays et al . , 2014a; Hays et al . , 2014b; Hays et al . , 2016; Khodaparast et al . , 2013; Khodaparast et al . , 2014; Borland et al . , 2016 ) . Regardless of group assignment , all rats underwent implantation of the headmount and cuff . Stimulation of the left cervical branch of the vagus nerve was performed using low current levels to avoid cardiac effects ( Engineer et al . , 2011 ) . Incised skin was closed using suture . All rats received enrofloxacin ( s . c . , 10 mg/kg ) following surgery and as needed at the sign of infection . To confirm cuff functionality and proper placement , heart rate , respiration , and blood oxygenation saturation during VNS ( 0 . 8 mA , 30 Hz , 100 µs pulse width , 1–5 s train duration ) were monitored under anesthesia via pulse oximetry after cuff implant and at the end of therapy . Animals that failed to demonstrate a reliable drop in oxygen saturation at the end of therapy were excluded . Stimulation under anesthesia briefly suppressed cardiopulmonary function and was not more severe or lower threshold in SCI rats compared to intact rats . VNS was triggered by the behavioral software during rehabilitative training based on the stimulation threshold for each group , similar to previous studies ( Khodaparast et al . , 2016; Pruitt et al . , 2016; Hays et al . , 2014b , 2016; Khodaparast et al . , 2013 ) . Each stimulation train consisted of 16 × 100 µsec 0 . 8 mA biphasic pulses delivered at 30 Hz . An adaptive stimulation threshold specific to each CLV group was used to determine stimulation delivery during rehabilitative training ( Figure 1—figure supplement 1 ) . The stimulation threshold was adaptively scaled based on the 10 antecedent trials , with each group receiving VNS triggered on trials which fall into the appropriate range . Rats in the Top 20% CLV group ( N = 13 ) received VNS on trials in which pull force exceeded the top quintile of the previous ten trials , with no minimum or maximum . In the majority of these subjects ( N = 9 ) , VNS was delivered immediately ( ~50 msec ) after pull force exceeded the stimulation threshold ( Figure 1—figure supplement 1A ) . No stimulation was delivered on the first 10 trials during a training session . In a different subset of subjects ( Delayed Top 20% CLV , N = 4 ) , VNS was delivered at the end of the 2 s trial window on trials in which exceeded the stimulation threshold , independent of the when the threshold was crossed ( Figure 1—figure supplement 1D ) . These groups displayed comparable performance ( Top 20% CLV v . Top 20% CLV Delay , Week 12 , Unpaired t-test , p=0 . 78 ) and were thus combined for analysis in Figure 1C–E . Rats in the Bottom 20% CLV group ( N = 8 ) received VNS on trials in which pull force failed to exceed the bottom quintile of the previous ten trials , with no minimum or maximum . VNS was delivered at the end of the 2 s trial window if pull force was below the threshold ( Figure 1—figure supplement 1G ) . Rats in the Top 50% CLV group received VNS on trials that exceeded the median pull force of the previous 10 trials or exceeded 120 g . VNS was delivered immediately ( ~50 msec ) after pull force exceeded the stimulation threshold Figure 1—figure supplement 1J ) . No groups received VNS on the final week of rehabilitative training ( Week 12 for unilateral and Week 14 for bilateral SCI ) to assess effects lasting after the cessation of stimulation . These parameters do not cause discomfort and do not alter reaching behavior ( Hulsey et al . , 2016; Porter et al . , 2012 ) . Terminal intracortical microstimulation mapping ( ICMS ) of motor cortex was performed in a subset of unilateral SCI ( Rehab alone , N = 6; Top 50% CLV , N = 6 ) and bilateral SCI ( Rehab alone , N = 7; Top 50% CLV , N = 7 ) rats at the end of therapy . A group of uninjured rats were used for control ( Naïve , N = 7 ) . Rats were anesthetized with and injection ( i . p . ) of ketamine ( 50 mg/kg ) , xylazine ( 20 mg/kg ) , and acepromazine ( 5 mg/kg ) . A cisternal drain was performed to reduce ventricular pressure and cortical edema during mapping ( Hulsey et al . , 2016; Porter et al . , 2012 ) . A craniotomy was then performed to expose left motor cortex . Intracortical microstimulation ( ICMS ) was delivered in motor cortex at a depth of 1 . 75 mm using a low impedance tungsten microelectrode with an interpenetration resolution of 500 µm ( 100 kΩ – 1 MΩ electrode impedance; FHC Inc . , Bowdin , MD; biphasic ICMS at 333 Hz , 50 ms train duration , 200 µsec pulse width , 0–200 µA current ) . Mapping experiments were performed blinded with two experimenters similar to previous studies ( Hulsey et al . , 2016; Porter et al . , 2012 ) . The first experimenter positioned the electrode for ICMS and recorded movement data . The second experimenter , blind to the experimental group of the animal and electrode position , delivered ICMS and classified movements . Movement threshold was first defined . ICMS current was then increased by no more than 50% to facilitate movement classification using visual inspection . Movements were classified into the following categories similar to previous studies: vibrissae , neck , jaw , digit , wrist , elbow , shoulder , hindlimb and trunk ( Brown and Teskey , 2014; Ganzer et al . , 2016b ) . The cortical area ( mm [Levy et al . , 2016] ) and movement threshold ( µA ) for each movement category was calculated for each group ( Figure 2—figure supplement 4 and Figure 4—figure supplement 4 ) . Based on the 500 µm inter-electrode spacing , each stimulation site eliciting a movement was counted as 0 . 25 mm2 . Movement area and threshold was assessed using One-way ANOVA and unpaired t-tests . Data for all movement classifications for each subject is available in Supplementary file 2 . Transsynaptic tracer injections using pseudorabies virus 152 ( PRV-152 ) were performed in a subset of unilateral SCI ( Rehab , n = 6; VNS + Rehab , n = 5 ) and bilateral SCI ( Rehab , n = 3; VNS + Rehab , n = 3 ) rats following the respective end of therapy . A group of uninjured rats were used for control ( Naïve , n = 5 ) . PRV-152 was a generous gift from the lab of Dr . Lynn Enquist and colleagues at Princeton University and was grown using standard procedures ( Card and Enquist , 2014 ) . An incision was made over the medial face of the radius and ulna of the trained limb to expose the forelimb grasping muscles flexor digitorum profundus and palmaris longus . 15 µL of PRV-152 ( ~8 . 06 ± 0 . 49 x 108 plaque-forming units ) was injected into the belly of each muscle across three separate sites . The incision was then closed with non-absorbable suture . We conducted detailed pilot studies to determine the optimal time of viral infection to allow for layer five cortical labeling . At 5–5 . 5 days post-infection we observed little to no cortical labeling for injured or uninjured animals . At 6–6 . 5 days post-infection we observed consistent layer five cortical labeling across injured or uninjured animals . Therefore , 6–6 . 5 days was used as our PRV-152 infection duration for our transsynaptic tracing studies . Rats were anesthetized with sodium pentobarbital ( 50 mg/kg , i . p . ) and transcardially perfused with 4% paraformaldehyde in 0 . 1 M PBS ( pH 7 . 5 ) at 6–6 . 5 days after injection . The brain and spinal cord were removed . Spinal roots were kept for anatomical reference . Tissue was then post-fixed overnight and cryoprotected in 30% sucrose . Quantification was limited to the spinal motor neurons , C3/4 cervical propriospinal neurons , red nucleus neurons , and cortical layer five neurons because these regions exhibited consistent labeling and were specifically related to our hypotheses . The whole neuraxis from the rostral tip of the forebrain to spinal level T3 was blocked and frozen at −80 C in Shandon M1 embedding matrix ( Thermo Fisher Scientific; Waltham , MA ) . Coronal forebrain and midbrain sections were sliced and slide-mounted at 35 µm using a cryostat ( from the rostral tip of forebrain to 13 mm caudal ) . Coronal spinal cord sections were sliced and slide mounted at 50 µm ( from C4 – T3 ) . After coverslipping , slides were scanned and digitized using the NanoZoomer 2 . 0-HT Whole Slide Scanner ( Hamamatsu Photonics; Japan ) . Tissue images were exported to a custom software program for cell counting ( https://github . com/davepruitt/PRV-Cell-Counting; copy archived at https://github . com/elifesciences-publications/PRV-Cell-Counting ) . PRV-152 infected neurons expressed enhanced green fluorescent protein . PRV-152 neuron counts were made on every other forebrain and midbrain section ( 35 µm inter-slice interval ) and every third spinal cord section ( 100 µm inter-slice interval ) . Experimenters performing analysis were blind to the group of each rat . Cortical neuron counts were restricted to layer 5 of sensorimotor cortex ( Bareyre et al . , 2004 ) . We defined motor cortex using standard anatomical reference ( Paxinos and Watson , 2007 ) . Our ICMS mapping studies confirm that these regions contain the cortical forelimb sensorimotor circuitry . Red nucleus neuron counts were made using standard anatomical reference ( Paxinos and Watson , 2007 ) . Propriospinal neuron counts were made from spinal level C3 – C4 in Rexed lamina VI , VII , VIII and IX using standard anatomical reference similar to previous studies ( Watson et al . , 2009; Gonzalez-Rothi et al . , 2015 ) . Back-labeled putative spinal motor neurons were located in Rexed lamina IX and counted identical to previous studies ( Watson et al . , 2009; Gonzalez-Rothi et al . , 2015 ) . Sensorimotor cortex , red nucleus and cervical propriospinal neuron counts were normalized within rats to the number of putative spinal motor neurons in the lower cervical and upper thoracic spinal cord to control for any differences in injection efficacy . No differences in spinal neuron labeling were observed between CLV and Rehab alone ( Figures 2E and 4E ) . Sensorimotor cortex , red nucleus and putative spinal motor neuron counts were analyzed separately using unpaired t-tests . Data representing raw neuron counts in each ROI is available in Figure 2—figure supplement 5 , Figure 4—figure supplement 3 , and Supplementary file 3 . At the completion of experimental testing , rats were anesthetized with sodium pentobarbital ( 50 mg/kg , i . p . ) and transcardially perfused with 4% paraformaldehyde in 0 . 1 M PBS ( pH 7 . 5 ) . The spinal cord was removed and spinal roots were kept for anatomical reference . Spinal tissue was then post-fixed overnight , cryoprotected in 30% sucrose for 48 hr , blocked and frozen at −80 C in Shandon M1 embedding matrix ( Thermo Fisher Scientific; Waltham , MA ) . Spinal tissue was sliced at 50 µm using a cryostat , slide mounted and stained for Nissl ( gray matter ) and myelin ( white matter ) substance similar to previous studies ( Ganzer et al . , 2016a; Ganzer et al . , 2016b ) . Photomicrographs were taken at 600 µm intervals to quantify gray and white matter lesion metrics using Image J . For unilateral SCI , the rostral and caudal extent of spinal gray and white matter damage was expressed as the percentage of spared gray and white matter of the right hemicord with respect to the left hemicord ( Figure 2—figure supplement 2 and Figure 4—figure supplement 1 ) . For bilateral SCI rats , the rostral and caudal extent of spinal damage was expressed as the percentage of spared gray and white matter for each hemicord with respect to an unlesioned rostral and caudal tissue reference within animals ( Anderson et al . , 2009 ) . Smallest and largest lesion areas were fitted to a schematic of spinal level C6 ( Figures 2E and 3E ) . To calculate damage to fiber tracts , two experimenters blind to group assignment evaluated the percentage of lesioned tissue to the dorsal corticospinal tract , dorsolateral corticospinal tract , ventral corticospinal tract , rubrospinal tract , propriospinal tract , and gray matter at the lesion epicenter . The dorsal , dorsolateral , and ventral corticospinal tracts were combined to calculate total CST damage based on the proportion of fibers in each tract ( Bareyre et al . , 2005 ) . Data representing the damage estimates is available in Supplementary file 4 . All source data supporting the findings of this study are available in the online version of the paper . | The spine houses a network of neurons that relays electric signals from the brain cells to the muscles . When the spine is injured , some of these neurons may be damaged and their connections to the muscles broken . As a result , the muscles they command become weak , and movement is impaired . It is possible to strengthen the remaining neural connections with physical rehabilitation , but the results are limited . Vagus nerve stimulation , VNS for short , is a new technique that could help people recuperate better after their spine is injured . The vagus nerve controls the heart , lungs and guts , and it reports the state of the body to the brain . When this nerve is electrically stimulated , it releases chemicals that can strengthen the neural connections between brain , spine and muscles , and even create new ones . This rewiring process is essential to repair or bypass the broken neural connections caused by a spine injury . However , it is still not clear how best to use VNS to optimize recovery . Here , Ganzer et al . study how VNS helps rats whose forelimbs are weakened after a spine injury . Three groups of rats go through physical rehabilitation , using their affected front paws to pull a handle and feed themselves . Two of these groups also receive VNS: their vagus nerve is stimulated either after the best trials ( strongest pulls ) or worst trials ( weakest pulls ) . Compared to the rehab-only and the worst trials-VNS animals , the rats that receive VNS on the best trials while using their affected paw have many more neuronal connections between their brain and the muscles in this limb . These muscles also become much stronger . VNS during the movement improves recovery whether the rodents have one or two front limbs injured , and the benefits are long lasting . As the rats pull the handle , the neurons involved in the movement get activated: they then carry a molecular ‘signature’ that lasts for a short time . When VNS is applied during that window , it appears to help these neurons form new connections with each other , as well as strengthen existing ones . These improved connections mean the brain can communicate better with the muscles: movement is enhanced , which results in greater functional recovery compared to rehabilitation alone . VNS is already trialed in stroke patients , who have weakened muscles because their brain neurons are damaged . The work by Ganzer et al . provides crucial information on how VNS could ultimately improve the recovery and quality of life of people with spine injuries . | [
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] | 2018 | Closed-loop neuromodulation restores network connectivity and motor control after spinal cord injury |
Autoinducers are small signaling molecules that mediate intercellular communication in microbial populations and trigger coordinated gene expression via ‘quorum sensing’ . Elucidating the mechanisms that control autoinducer production is , thus , pertinent to understanding collective microbial behavior , such as virulence and bioluminescence . Recent experiments have shown a heterogeneous promoter activity of autoinducer synthase genes , suggesting that some of the isogenic cells in a population might produce autoinducers , whereas others might not . However , the mechanism underlying this phenotypic heterogeneity in quorum-sensing microbial populations has remained elusive . In our theoretical model , cells synthesize and secrete autoinducers into the environment , up-regulate their production in this self-shaped environment , and non-producers replicate faster than producers . We show that the coupling between ecological and population dynamics through quorum sensing can induce phenotypic heterogeneity in microbial populations , suggesting an alternative mechanism to stochastic gene expression in bistable gene regulatory circuits .
Autoinducers are small molecules that are produced by microbes , secreted into the environment , and sensed by the cells in the population ( Keller and Surette , 2006; Hense and Schuster , 2015 ) . Autoinducers can trigger a collective behavior of all cells in a population , which is called quorum sensing . For example , quorum sensing regulates the transcription of virulence genes in the Gram-positive bacterium Listeria monocytogenes ( Gray et al . , 2006; Garmyn et al . , 2011; da Silva and De Martinis , 2013 ) and the transcription of bioluminescence genes in the Gram-negative bacterium Vibrio harveyi ( Xavier and Bassler , 2003; Anetzberger et al . , 2009 ) , and it may also autoregulate the transcription of autoinducer synthase genes ( Fuqua and Greenberg , 2002; Waters and Bassler , 2005 ) . When the concentration of autoinducers reaches a threshold value , a coordinated and homogeneous expression of target genes may be initiated in all cells of the population ( Waters and Bassler , 2005; Hense and Schuster , 2015; Papenfort and Bassler , 2016 ) , or a heterogeneous gene expression in the population may be triggered at low concentrations ( Anetzberger et al . , 2009; Williams et al . , 2008; Boedicker et al . , 2009; Garmyn et al . , 2011; Pérez and Hagen , 2010; Ackermann , 2015; Grote et al . , 2014 , Grote et al . , 2015; Papenfort and Bassler , 2016; Pradhan and Chatterjee , 2014 ) . To implement all of these functions and behaviors , a microbial population needs to dynamically self-regulate the average autoinducer production . Within a given population , the promoter activity of autoinducer synthase genes may vary between genetically identical cells ( Garmyn et al . , 2011; Grote et al . , 2014; Anetzberger et al . , 2012; Plener et al . , 2015; Cárcamo-Oyarce et al . , 2015; Grote et al . , 2015 ) . For example , during the growth of L . monocytogenes under well-mixed conditions two subpopulations were observed , one of which expressed autoinducer synthase genes , while the other did not ( Garmyn et al . , 2011 ) . Such a phenotypic heterogeneity was associated with biofilm formation ( Garmyn et al . , 2011; da Silva and De Martinis , 2013; Hense and Schuster , 2015; Cárcamo-Oyarce et al . , 2015 ) . The stable coexistence of different phenotypes in one population may serve the division of labor or act as a bet-hedging strategy and , thus , may be beneficial for the survival and resilience of a microbial species on long time scales ( Ackermann , 2015 ) . The mechanism by which a heterogeneous expression of autoinducer synthase genes is established when their expression is autoregulated by quorum sensing has remained elusive . For example , expression of the above mentioned autoinducer synthase genes in L . monocytogenes is up-regulated through quorum-sensing in single cells ( Garmyn et al . , 2011 , Garmyn et al . , 2009; Waters and Bassler , 2005 ) . From an experimental point of view it is often not known , however , whether autoinducer synthesis is up-regulated for all autoinducer levels or only above a threshold level . To explain phenotypic heterogeneity of autoinducer production , currently favored threshold models of quorum sensing typically assume a bistable gene regulation function ( Fujimoto and Sawai , 2013; Pérez-Velázquez et al . , 2016; Goryachev et al . , 2005; Dockery and Keener , 2001 ) . For bistable regulation , cellular autoinducer synthesis is up-regulated above a threshold value of the autoinducer concentration in the population , whereas it is down-regulated below the threshold ( 'all-or-none' expression ) ; see Figure 1B . Stochastic gene expression at the cellular level then explains the coexistence of different phenotypes in one population . If , however , cellular autoinducer synthesis is up-regulated for all autoinducer concentrations ( monostable up-regulation ) , the mechanism by which phenotypic heterogeneity can arise and is controlled has not been explained . 10 . 7554/eLife . 25773 . 003Figure 1 . The quorum-sensing model for the production of autoinducers in microbial populations . ( A ) Sketch of a typical update step . Individuals are depicted as disks and the degree of autoinducer production ( pi∈[0 , 1] ) is indicated by the size of the green fraction . Non-producers ( orange disks ) reproduce fastest , full producers ( green disks ) slowest . Individual i with pi=1/6 divides into two offspring individuals , one of which replaces another individual j . Both offspring individuals sense the average production level in the population ( ⟨p⟩=1/3 ) , and may either respond to this environment , with probability λ , by adopting the value R ( ⟨p⟩ ) of the response function ( =2/3 here , see ( B ) ) or , with probability 1-λ , retain the production degree from the ancestor ( =1/6 ) . Here , offspring individual i responds to the environment while j does not ( denoted by gray shading ) . ( B ) Quorum sensing is characterized by the response function . Perception of the average production level in the population ( ⟨p⟩ ) enables individuals to change their production degree to the value R ( ⟨p⟩ ) ∈[0 , 1] . Sketched are a monostable response function ( stable fixed point at 1 , unstable fixed point at 0 ) , and a bistable response function ( stable fixed points at 0 and 1 , unstable fixed point at an intermediate threshold value ) . Stable fixed points of the response function are depicted as black circles while unstable fixed points are colored in white . For the sketched bistable response function , autoinducer production is down-regulated with respect to the sensed production level in the population below the threshold value , and up-regulated above this threshold . For the monostable response function , autoinducer production is up-regulated at all sensed production levels . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 003 Here we show that the coupling between ecological and population dynamics through quorum sensing can control a heterogeneous production of autoinducers in quorum-sensing microbial populations . At the same time , the overall autoinducer level in the environment is robustly self-regulated , so that further quorum-sensing functions such as virulence or bioluminescence can be triggered . We studied the collective behavior of a stochastic many-particle model of quorum sensing , in which cells produce autoinducers to different degrees and secrete them into the well-mixed environment . Production of large autoinducer molecules ( for example oligopeptides ) and accompanied gene expression are assumed to reduce fitness such that non-producers reproduce faster than producing cells . Moreover , it is assumed that quorum sensing enables up-regulation of autoinducer production , that is , individuals can increase their production in response to the sensed average production level in the population ( Figure 1 ) . As a central result , we found that the population may split into two subpopulations: one with a low , and a second with a high production rate of autoinducers . This phenotypic heterogeneity in the autoinducer production is stable for many generations and the autoinducer concentration in the population is tightly controlled by how production is up-regulated . If cellular response to the environment is absent or too frequent , phase transitions occur from heterogeneous to homogeneous populations in which all individuals produce autoinducers to the same degree . To capture these emergent dynamics , we derived the macroscopic mean-field equation ( 1 ) from the microscopic stochastic many-particle process in the spirit of the kinetic theory in statistical physics , which we refer to as the autoinducer equation . The analysis of the autoinducer equation explains both phenotypic heterogeneity through quorum sensing and the phase transitions to homogeneity . The key aspect of our work is how the composition of a population changes in time when its constituents respond to an environment that is being shaped by their own activities ( see Box 1 ) . This ecological feedback is mediated by quorum sensing and creates an effective global coupling between the individuals in the population . Such a global coupling is reminiscent of long-range interactions in models of statistical mechanics , such as in the classical XY spin model with infinite range interactions ( Antoni and Ruffo , 1995; Yamaguchi et al . , 2004; Barré et al . , 2002; Choi and Choi , 2003; de Buyl et al . , 2010; Campa et al . , 2009; Pakter and Levin , 2013 ) . Our analysis suggests that quorum sensing in microbial populations can induce and control phenotypic heterogeneity as a collective behavior through such a global coupling and , notably , does not rely on a bistable gene regulatory circuit ( see Discussion ) .
We now introduce the quorum-sensing model for a well-mixed population of N individuals ( Figure 1 ) . The phenotype of each individual i=1 , … , N is characterized by its production degree pi∈[0 , 1] , that is , the extent to which it produces and secretes autoinducers . In an experiment with microbes , the promoter activity of autoinducer synthase genes or their enzymatic activity could be a proxy for the production degree . The limiting case pi=0 denotes a non-producer , and pi=1 denotes a full producer . The state of the population p= ( p1 , … , pN ) changes stochastically ( Figure 1A ) : An individual i reproduces with rate ϕi , which we refer to as the individual’s fitness . We assume that fitness decreases with incurring metabolic costs of induction and synthesis of autoinducers , and with other metabolic burdens in the cell’s phenotypic state ( Ruparell et al . , 2016; Diggle et al . , 2007; He et al . , 2003 ) . For simplicity , we choose ϕi=ϕ ( pi ) =1−spi . The selection strength 0≤s<1 scales the fitness difference with respect to the non-producing phenotype ( ϕ ( 0 ) =1 ) . Thus , the larger an individual’s production , the smaller its reproduction rate . This assumption is discussed in detail further below ( see Discussion ) . Whenever an individual divides into two offspring individuals in the stochastic process , another individual from the population is selected at random to die such that the population size N remains constant . Qualitative results of our model remain valid if only the average population size is constant , which may be assumed , for example , for the stationary phase of microbial growth in batch culture . One recovers the mathematical set-up of frequency-dependent Moran models for Darwinian selection ( Moran , 1958; Ewens , 2004; Blythe and McKane , 2007; Nowak et al . , 2004 ) if one restricts the production degrees to a discrete set , for example , to full producers or non-producers only , pi∈{0 , 1} . The mathematical set-up of the well-known Prisoner’s dilemma in evolutionary game theory is recovered if , in addition , the secreted molecules would confer a fitness benefit on the population ( Nowak et al . , 2004; Traulsen et al . , 2005; Melbinger et al . , 2010; Assaf et al . , 2013 ) . Since we are interested in the mechanism by which heterogeneous production of autoinducers might be induced and do not study the context under which it might have evolved , we do not include any fitness benefits through signaling , for example at the population level , into the modeling here ( see Discussion ) . A central feature of our model is the fact that individuals may adjust their production degree via a sense-and-response mechanism through quorum sensing , which is implemented as follows . After reproduction , both offspring individuals sense the average production level of autoinducers ⟨p⟩=1/N∑ipi in the well-mixed population . With probability λ , they independently adopt the value R ( ⟨p⟩ ) ∈[0 , 1] as their production degree in response to the sensed environmental cue ⟨p⟩ , whereas they retain the ancestor’s production degree with probability 1-λ through non-genetic inheritance . In an experimental setting , the response probability λ relates to the rate with which cells respond to the environment ( Kussell and Leibler , 2005; Acar et al . , 2008; Axelrod et al . , 2015 ) and regulate their production through quorum sensing . We refer to the function R ( ⟨p⟩ ) as the response function , which is the same for all individuals . The response function encapsulates all biochemical steps involved in the autoinducer production between perception of the average production level ⟨p⟩ and adjustment of the individual production degree to R ( ⟨p⟩ ) in response ( He et al . , 2003; Williams et al . , 2008; Drees et al . , 2014; Hense and Schuster , 2015; Maire and Youk , 2015 ) ; see Figure 1B . For example , it may be a bistable step or bistable Hill function , which is often effectively assumed in threshold models of phenotypic heterogeneity ( Fujimoto and Sawai , 2013; Pérez-Velázquez et al . , 2016; Goryachev et al . , 2005; Dockery and Keener , 2001 ) . For a bistable response function , cellular production is up-regulated above a threshold value of ⟨p⟩ , whereas it is down-regulated below the threshold . For the bistable response function sketched in Figure 1B , both values ⟨p⟩=0 and ⟨p⟩=1 are stable fixed points . In this work , however , we particularly focus on monostable response functions R ( ⟨p⟩ ) to model microbial quorum-sensing systems in which autoinducer synthesis is up-regulated at all autoinducer production levels in the population ( Garmyn et al . , 2009; Waters and Bassler , 2005 ) . In other words , cellular production always increases with respect to the sensed production level in the population ( stable fixed point at ⟨p⟩=1 and unstable fixed point at ⟨p⟩=0 ) . The sense-and-response mechanism is further discussed in the Discussion section . From a mathematical point of view , the introduced sense-and-response mechanism through quorum sensing constitutes a source of innovation in the space of production degrees because an individual may adopt a production degree that was not previously present in the population . Thus , a continuous production space with pi∈[0 , 1] as opposed to a discrete production space is a technical necessity for the implementation of the quorum-sensing model . The coupling of ecological dynamics ( given by the average production level of autoinducers ⟨p⟩ ) with population dynamics ( determined by fitness differences between the phenotypes ) through quorum sensing results in interesting collective behavior , as we show next . We emphasize that , as long as this coupling is present , the effects of the quorum-sensing model that we found and report next are qualitatively robust against noise at all steps; see below .
The quorum-sensing model was numerically simulated by employing Gillespie’s stochastic simulation algorithm ( Gillespie , 1976 , 1977 ) for a population size of N=104 individuals and an exemplary selection strength s=0 . 2 , such that sN≫1 . In this regime , demographic fluctuations are subordinate ( Nowak et al . , 2004; Wild and Traulsen , 2007; Blythe and McKane , 2007 ) . Within the scope of our quorum-sensing model , the precise value of the selection strength s that scales the fitness differences is not important for the reported mechanism by which phenotypic heterogeneity can be induced , see below . We tracked the state of the population 𝐩 over time , and depict the histogram of production degrees and the population average in Figure 2 . 10 . 7554/eLife . 25773 . 006Figure 2 . Homogeneous and heterogeneous production of autoinducers in the quorum-sensing model . Temporal evolution of autoinducer production in the quorum-sensing model depicted as histograms of production degrees ( normalized values ) , ( A–C ) ; and average production level of autoinducers in the population ( D–F ) ; see also Videos 1–3 . ( A ) In the absence of sense-and-response ( λ=0 ) , only non-producers proliferate . The approach to stationarity is asymptotically algebraically slow for a quasi-continuous initial distribution of production degrees ( D ) . The black line ⟨p⟩∼t-1 serves as a guide for the eye . ( B ) Sense-and-response through quorum sensing ( λ=0 . 2 here ) promotes autoinducer production , and the population becomes homogeneous ( ultimately , fixation at a single production degree , data not shown ) . The response function used here , R ( ⟨p⟩ ) =⟨p⟩+0 . 2⋅sin ( π⟨p⟩ ) , was chosen such that an individual’s production degree is always up-regulated through quorum sensing ( see Figure 1B ) . Approach to stationarity is exponentially fast ( E ) , but timescales may diverge at bifurcations of the response function ( see Appendix 1—figure 3 ) . The dashed line in ( E ) shows fit to an exponential decay . ( C ) When λ is small ( λ=0 . 05 here ) , the population becomes heterogeneous: quasi-stationary states arise in which the population splits into two subpopulations , one of which does not produce autoinducers , while the other does . The same monostable response function was chosen as in ( B ) . Therefore , heterogeneity may arise without bistable response . For very long times , one of the two absorbing states ( A , B ) is reached , data not shown ( see Figure 3A ) . Heterogeneous , quasi-stationary states arise for a broad class of initial distributions ( see Appendix 1—figure 1 and our mathematical analysis ) . At the same time , the average production level of autoinducers in the population is adjusted by the response probability λ if s is fixed ( F ) or vice versa ( data not shown ) . Bimodal , quasi-stationary states also arise when noisy inheritance , noisy perception , and noisy response are included in the model set-up ( see Appendix 1—figure 2 ) . Mean-field theory agrees with all observations ( autoinducer equation ( 1 ) ) . The time unit Δt=1 means that in a population consisting solely of non-producers , each individual will have reproduced once on average . Ensemble size M=100 , s=0 . 2 , N=104 . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 00610 . 7554/eLife . 25773 . 007Figure 2—source data 1 . Source data accompanying Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 00710 . 7554/eLife . 25773 . 008Video 1 . Video accompanying Figure 2A – Homogeneous production of autoinducers in the population if sense-and-response is absent in the quorum-sensing model ( λ=0 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 00810 . 7554/eLife . 25773 . 009Video 2 . Video accompanying Figure 2B – Homogeneous production of autoinducers in the population if sense-and-response is frequent in the quorum-sensing model ( λ=0 . 2 here ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 00910 . 7554/eLife . 25773 . 010Video 3 . Video accompanying Figure 2C – Heterogeneous production of autoinducers in the population if sense-and-response is rare in the quorum-sensing model ( λ=0 . 05 here ) . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 010 First , we studied the stochastic many-particle process without sense-and-response ( λ=0 ) ; see Figure 2A , D and Video 1 . In this case , non-producers always proliferate because they reproduce at the highest rate in the population , which is well-studied in evolutionary game theory ( Taylor and Jonker , 1978; Maynard Smith , 1982; Hofbauer and Sigmund , 1998 ) . Thus , the initially uniform distribution in the population shifts to a peaked distribution at low production degrees . Ultimately , a homogeneous ( unimodal ) stationary state is reached in which all individuals produce autoinducers to the same low degree plow≃0 . Such a stationary state is absorbing ( Hinrichsen , 2000 ) , that is , the stochastic process offers no possibility of escape from this state of the population . With quorum sensing ( λ>0 ) , absorbing states are reached if , again , all individuals produce to the same degree p* and , in addition , the value of this production degree is a fixed point of the response function ( R ( p* ) =p* ) ; see Figure 2B , E and Video 2 . In such a homogeneous absorbing state with ⟨p⟩∞=p* , an offspring individual can no longer alter its production degree . It either takes over the production degree p* from its ancestor or it adopts that same degree R ( ⟨p⟩∞ ) =⟨p⟩∞=p* through sense-and-response . Thus , all individuals continue to produce with degree p* and the state of the population remains homogeneous ( unimodal ) . Surprisingly , for small response probabilities λ , we found that the population may get trapped in heterogeneous ( bimodal ) states for long times before a homogeneous absorbing state is reached . The temporal evolution of such a heterogeneous state is shown in Figure 2C , F and Video 3 for λ=0 . 05 . A monostable response function was chosen with R ( ⟨p⟩ ) >⟨p⟩ for all ⟨p⟩∈ ( 0 , 1 ) ( unstable fixed point at 0 , and stable fixed point at 1 ) such that the production degree is always up-regulated through quorum sensing; see sketch in Figure 1B . After some time has elapsed , the population is composed of two subpopulations: one in which individuals produce autoinducers to a low degree plow , and a second in which individuals produce to a higher degree phigh that is separated from plow by a gap in the space of production degrees . Only through strong demographic fluctuations can the population reach one of the homogeneous absorbing states ( ⟨p⟩∞=0 or 1 for the response function chosen above ) . The time taken to reach a homogeneous absorbing state grows exponentially with N ( Figure 3A ) . Therefore , states of phenotypic heterogeneity are quasi-stationary and long-lived . These heterogeneous states arise for a broad class of response functions and initial distributions ( Appendix 1—figure 1 ) , and they are robust against demographic noise that is always present in populations of finite size ( Figure 3A ) ; see our mathematical analysis below . We demonstrated that states of phenotypic heterogeneity are also robust against changes of the model set-up , which might account for more biological details ( see , for example , Papenfort and Bassler , 2016 and references therein ) . Upon including , for example , noisy inheritance of the production degree , noisy perception of the environment , and noisy response to the environment into the quorum-sensing model , heterogeneous states still arise; see Appendix 1—figure 2 . Furthermore , the average production in the heterogeneous state is finely adjusted by the interplay between the response probability λ and the selection strength s ( Figure 2F ) . 10 . 7554/eLife . 25773 . 011Figure 3 . Characterization of phenotypic heterogeneity in the quorum-sensing model . ( A ) For small response probability λ , populations get stuck in heterogeneous quasi-stationary states . The time taken to reach a homogeneous absorbing state , Tabs , increases exponentially with the population size N ( filled circles denote the mean , gray bars denote the range within which 95% of the data points lie closest to the mean; dashed lines show fit to Tabs∼eγN ) . ( B ) Heterogeneous states are long-lived only if λ is small and the response function is nonlinear ( in particular , up-regulation is required for some average production level such that R ( ⟨p⟩ ) >⟨p⟩ ) . Here , the monostable response function R ( ⟨p⟩ ) =⟨p⟩+κsin ( π⟨p⟩ ) was chosen such that κ∈[0 , 1/π] scales the magnitude of up-regulation . As κ increases , the gap between the low-productive and high-productive peaks of the heterogeneous state becomes larger such that it takes longer to reach the absorbing state . Mean-field theory ( 1 ) predicts the existence and local stability of heterogeneous stationary distributions for 0<λ<λup=s/2 ( regime below the black line ) . Deviations between the stochastic process and mean-field theory are due to demographic fluctuations that vanish as N→∞ . ( C ) The variance of production degrees in the population reveals whether the population is in a homogeneous ( Var ( p ) =0 ) or heterogeneous state ( Var ( p ) >0 ) . The variance was averaged over long times in the quasi-stationary state . Mean-field theory ( 1 ) ( black line ) agrees with our numerical observations ( red filled circles ) ; see Methods and materials . Ensemble size M=100 , s=0 . 2 , in ( B ) N=103 and in ( C ) N=104 and N=5⋅104 close to λup , in ( A , C ) κ=0 . 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 01110 . 7554/eLife . 25773 . 012Figure 3—source data 2 . Source data accompanying Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 25773 . 012 The establishment of long-lived , heterogeneous states induced by quorum sensing is one central finding of our study . We interpret this phenotypic heterogeneity as the result of the robust balance between population and ecological dynamics coupled through quorum sensing ( see Box 1 ) . On the one hand , fitness differences due to costly production favor non-producers . On the other hand , sensing the population average and accordingly up-regulating individual production enables producers to persist . Remarkably , fitness differences and sense-and-response balance such that separated production degrees may stably coexist in one population; the population does not become homogeneous at an intermediate production degree as one might naively expect . Heterogeneity of the autoinducer production is a robust outcome of the dynamics ( and not a fine-tuned effect ) , and the average production level in the population is adjusted by the interplay of the response probability λ and the selection strength s . Phenotypic heterogeneity does not rely on a bistable response function , but arises due to the global intercellular coupling of ecological and population dynamics through quorum sensing , as we show next . The relevance of quorum sensing for phenotypic heterogeneity in microbial populations is further explored below ( see Discussion ) .
In the following , the observed long-lived states of phenotypic heterogeneity in the quorum-sensing model are explained . First , we derived the macroscopic mean-field equation ( the autoinducer equation ( 1 ) ) from the microscopic dynamics of the quorum-sensing model . Second , we analyzed this mean-field equation and characterized phenotypic heterogeneity of autoinducer production . The microscopic dynamics of the quorum-sensing model are captured by a memoryless stochastic birth-death process as sketched in Figure 1 . Starting from the microscopic many-particle stochastic process , we derived a mean-field equation for the probability distribution of finding any individual at a specified production degree p at time t in the spirit of the kinetic theory in statistical physics ( Kadar , 2007 ) . We call this one-particle probability distribution the production distribution ρ; Figure 2 shows the corresponding histogram numerically obtained from the stochastic many-particle process . The mean-field equation for ρ , which we refer to as the autoinducer equation , is obtained as: ( 1 ) ∂tρ ( p , t ) =2λϕ¯t ( δ ( p−R ( p¯t ) ) −ρ ( p , t ) ) + ( 1−2λ ) ( ϕ ( p ) −ϕ¯t ) ρ ( p , t ) , where ⋅¯t denotes averaging with respect to ρ at time t . The details of the derivation of the autoinducer equation from the microscopic dynamics are given in the Methods and materials section and in Appendix 2 . The autoinducer equation ( 1 ) involves two contributions: the sense-and-response term with prefactor 2λ , and the replicator term with prefactor 1-2λ . Through the replicator term , probability weight at production degree p changes if the fitness ϕ ( p ) is different from the mean fitness in the population ϕ¯t ( here ϕ ( p ) -ϕ¯t=-s ( p-p¯t ) ) . Without quorum sensing ( λ=0 ) , Equation ( 1 ) reduces to the well-known replicator equation of the continuous Prisoner’s dilemma ( Bomze , 1990; Oechssler and Riedel , 2001; Hofbauer and Sigmund , 2003; Cressman , 2005; McGill and Brown , 2007 ) . The sense-and-response term , on the other hand , encodes the global feedback by which individuals adopt the production degree R ( p¯t ) upon sensing the average p¯t through quorum sensing at rate 2λ . The difference between the current state ρ and the state in which all individuals have this production degree R ( p¯t ) determines the change in ρ at every production degree . Through the replicator term and the sense-and-response term , the ecological dynamics ( average production level p¯t ) are coupled with the dynamics of ρ . We now present our results for the long-time behavior of the autoinducer equation ( 1 ) . First , the autoinducer equation ( 1 ) admits homogeneous stationary distributions . Without quorum sensing ( λ=0 ) , the initially lowest production degree in the population , plow , constitutes the homogeneous stationary distribution ρ∞ ( p ) =δ ( p−plow ) , which is attractive for generic initial conditions . With quorum sensing ( λ>0 ) , fixed points of the response function p*=R ( p* ) yield homogeneous stationary distributions as ρ∞ ( p ) =δ ( p-p* ) , which are attractors of the quorum-sensing dynamics ( 1 ) for all initial distributions if λ>s/2; see analysis below . These homogeneous stationary distributions confirm our observations of homogeneous absorbing states in the quorum-sensing model , in which all individuals produce to the same degree; see Figure 2A , B . Time scales at which stationarity is approached are discussed in the Methods and materials section . Second , to analytically characterize long-lived heterogeneous states of the population , we decomposed ρ into a distribution at low production degrees and a remainder distribution at higher degrees . We found that such a decomposition yields the bimodal , heterogeneous , stationary distribution of the autoinducer equation ( 1 ) : ( 2 ) ρ∞ ( p ) =yδ ( p ) + ( 1−y ) δ ( p−phigh ) , with phigh=R ( β ) and y=1−β/R ( β ) , if the conditions 0<phigh≤1 and 0<y<1 are fulfilled; see Box 1 for an illustration and Appendix 3 for the derivation . The parameter β=2λ/s quantifies the balance between fitness differences and sense-and-response mechanism through quorum sensing . Heterogeneous stationary distributions ( 2 ) are constituted of a probability mass y at the low-producing degree plow=0 and a coexisting δ-peak with stationary value 1-y at a high-producing degree phigh separated from plow by a gap . Such heterogeneous stationary distributions have mean p¯∞=β and variance Var ( p ) ∞=β ( R ( β ) -β ) . Therefore , the interplay between selection strength s and response probability λ adjusts the average production of autoinducers in the population ( Figure 2F ) . For simplicity , we assumed in Equation ( 2 ) that the initially lowest production degree in the population is plow=0; generalized bimodal distributions for arbitrary initial distributions ρ0 are given in Appendix 3 . From the conditions on phigh and y below Equation ( 2 ) , one can derive the following conditions on the response function and the value of the response probability λ ( for given selection strength s ) for the existence of heterogeneous stationary distributions: ( i ) The response function needs to be nonlinear with R ( p¯∞ ) =phigh>p¯∞; that is , quorum sensing needs to up-regulate the cellular production in some regime of the average production level . Therefore , both monostable and bistable response functions depicted in Figure 1B may induce heterogeneous stationary distributions through the ecological feedback . ( ii ) The response probability needs to be small with λ<λup=s/2; that is , to induce phenotypic heterogeneity , cells must respond only rarely to the environmental cue p¯ . This estimate of an upper bound on λ is confirmed by our numerical results of the stochastic process ( Figure 3A–C ) . Vice versa , for a given response probability , the selection strength needs to be big enough to induce heterogeneous stationary distributions . As we show in the Methods and materials section , phase transitions in the space of stationary probability distributions govern the long-time dynamics of the autoinducer equation ( 1 ) from heterogeneity to homogeneity as the response probability changes ( λ→0 and λ→λup ) ; see Figure 3C . For small λ , the coexistence of the low-producing and the high-producing peaks in solution ( 2 ) is stable due to the balance of fitness differences and sense-and-response through quorum sensing . In Appendix 3 we show that the heterogeneous stationary distributions ( 2 ) are stable up to linear order in perturbations around stationarity . As our numerical simulations show , these bimodal distributions are the attractor of the mean-field dynamics ( 1 ) for a broad range of initial distributions when λ is small; see Appendix 1—figure 1 for some examples . They are also robust against noisy inheritance , noisy perception , and noisy response as demonstrated in Appendix 1—figure 2 . We interpret the stability of the bimodal stationary distributions ( 2 ) as follows ( see also Box 1 ) . Fitness differences quantified by the selection strength s increase probability mass at production degree plow , whereas nonlinear response to the environment with probability λ pushes probability mass towards the up-regulated production degree phigh=R ( p¯∞ ) . The gap phigh−plow>0 ensures that the exponential time scales of selection and sense-and-response stably balance the coexistence of both peaks; see Methods and materials . Because heterogeneous stationary distributions ( 2 ) are attractive and stable , heterogeneous states of the stochastic many-particle process arise and are quasi-stationary . Consequently , the time to reach a homogeneous absorbing state in the stochastic process through demographic fluctuations scales exponentially with the population size N ( Elgart and Kamenev , 2004; Kessler and Shnerb , 2007; Assaf and Meerson , 2010; Frey , 2010; Hanggi , 1986 ) ; see Figure 3A . Thus , phenotypic heterogeneity is long-lived . In summary , our mathematical analysis explains how phenotypic heterogeneity in the autoinducer production arises when quorum sensing up-regulates the autoinducer production in microbial populations ( Box 1 ) . As an emergent phenomenon , the population may split into two subpopulations: one in which cells do not produce autoinducers ( ‘off’ state , plow=0 ) and a second in which cells produce autoinducers ( ‘on’ state , phigh=R ( 2λ/s ) >0 ) , but grow slower . The fraction of individuals in the ‘off’ state is given by the value of y in Equation ( 2 ) . If quorum sensing is absent ( λ=0 ) , the whole population is in the ‘off’ state ( y=1 ) , whereas all individuals are in the ‘on’ state ( y=0 ) if quorum sensing is frequent ( λ≥λup ) . Only when response to the environment is rare ( 0<λ<λup ) can the two phenotypic states , plow and phigh , coexist in the population ( 0<y<1 ) . The transitions from heterogeneous to homogeneous populations are governed by nonequilibrium phase transitions when the response probability changes ( λ→0 and λ→λup ) . Our mathematical analysis shows that phenotypic heterogeneity arises dynamically , is robust against perturbations of the autoinducer production in the population , and is robust against noise at the level of inheritance , sense , and response .
In this work , we studied a conceptual model for the heterogeneous production of autoinducers in quorum-sensing microbial populations . The two key assumptions of our quorum-sensing model are as follows . First , production of large autoinducer molecules and accompanied gene expression in the cell’s phenotypic state are negatively correlated with fitness such that non-producers reproduce faster than producers . Second , cells sense the average production level of autoinducers in the population and may accordingly up-regulate their production through quorum sensing . As a result , not only does the interplay between fitness differences and sense-and-response give rise to homogeneously producing populations , but it can also induce a heterogeneous production of autoinducers in the population as a stable collective phenomenon . In these heterogeneous states , the average production level of autoinducers in the population is adjusted within narrow limits by the balance between fitness differences ( selection strength s in the model ) , and the rate with which cells respond to the environment and up-regulate their production through quorum sensing ( response probability λ and response function R ( ⟨p⟩ ) in the model ) . Due to this robust adjustment of the production level in the population , the expression of other genes ( for example , bioluminescence and virulence genes ) can be regulated by quorum sensing even when the production of autoinducers is heterogeneous in the population . In the following , we discuss the assumptions of our model in the light of the empirical reality for both quorum sensing and phenotypic heterogeneity . Furthermore , we indicate possible directions to experimentally test the ecological feedback that is suggested by the results of our theoretical work . In our quorum-sensing model , it is assumed that the individual's production degree of autoinducers is negatively correlated with its growth rate ( ϕi=1−spi ) . Is this assumption of growth impairment for producing phenotypes justified ( Parsek and Greenberg , 2005 ) ? This would be the case if cellular production of autoinducers directly causes a reduction of the cell's growth rate . For example , in L . monocytogenes populations , heterogeneous production was observed for an autoinducer oligopeptide that is synthesized via the agr operon ( Garmyn et al . , 2011 , Garmyn et al . , 2009 ) . This signaling oligopeptide incurs high metabolic costs through the generation of a larger pre-protein . For the oligopeptide signal synthesized via the agr operon in Staphylococcus aureus , the metabolic costs were conservatively estimated by Keller and Surette to be 184 ATP per molecule ( metabolic costs for precursors were disregarded in this estimate ) ; see Keller and Surette , 2006 for details . In contrast , basically no costs ( 0–1 ATP ) incur for the different signaling molecule Autoinducer-2 ( AI-2 ) that is considered as a metabolic by-product . As to what extent the production of oligopeptides for signaling reduces an individual's growth rate has , to our knowledge , not been studied quantitatively . For quorum-sensing systems that involve N-acyl homoserine lactones ( AHLs ) as signaling molecules , however , a reduced fitness of producers has been reported for microbial growth in batch culture ( Ruparell et al . , 2016; Diggle et al . , 2007; He et al . , 2003 ) . Even though metabolic costs for the synthesis of C4-HSL ( one of the simplest AHL signaling molecules that is synthesized via the rhl operon ) were conservatively estimated with only 8 ATP per molecule ( Keller and Surette , 2006 ) , a growth impairment was experimentally reported only recently for a C4-HSL-producing strain ( Ruparell et al . , 2016 ) . Furthermore , a strain producing a long-chain AHL ( OC12-HSL , synthesized via the las operon ) showed a reduced fitness in both mono and mixed culture compared with a non-producing strain . The reduced fitness of AHL-producers was attributed to ( i ) metabolic costs of autoinducer production , in particular also to metabolic costs of precursors that were disregarded in the estimates by Keller and Surette , 2006 , and ( ii ) accumulation of toxic side products accompanying the synthesis of autoinducers ( Ruparell et al . , 2016 ) . As another example , the strain Sinorhizobium fredii NGR234 synthesizes AHLs via both the ngr and the tra operon ( Schmeisser et al . , 2009 ) , and it was shown that gene expression related to autoinducer production reduces the strain’s growth rate in mono culture ( He et al . , 2003 ) . On the other hand , a heterogeneous expression of the corresponding autoinducer synthase genes was observed during growth of NGR234 only recently ( Grote et al . , 2014 ) . As to what extent the production of AHLs reduces fitness of NGR234 in mixed culture and , thus , whether the phenotypic heterogeneity observed in Grote et al . , 2014 could be explained through the ecological feedback proposed by our quorum-sensing model , remains to be explored experimentally . In the quorum-sensing model , even small growth rate differences between producer and non-producer , which are quantified by the ratio ( growth rate of producer ) / ( growth rate of non-producer ) =1-s , may give rise to a bimodal production of autoinducers in the population . Furthermore , it would be interesting to track the expression level of autoinducer synthase genes of a microbial strain during growth for which growth differences between the producing and the non-producing phenotype are known such as in the study of Ruparell et al . , 2016 . We emphasize that it would be desirable to report the full distribution of expression levels in the population in order to detect whether a population splits into several subpopulations; note that variance or percentiles are not suitable measures to characterize and compare the bimodality of distributions . A bimodal expression of autoinducer synthase genes in the population together with a tightly controlled average expression level could be a signature of the feedback between ecological and population dynamics underlying the observation of phenotypic heterogeneity as suggested by our results . Autoinducers are secreted into the environment where they get dispersed and are degraded . For simplicity and to facilitate our mathematical analysis , we assumed in the quorum-sensing model that individuals respond to the current average production level of autoinducers in the whole population . Temporal availability and spatial dispersal of autoinducers determine whether this assumption is valid or not . On the one hand , temporal availability of autoinducers in the environment for signaling depends on many factors . For example , pH and temperature influence the stability of autoinducers ( Yates et al . , 2002; Byers et al . , 2002; Decho et al . , 2009; Grandclément et al . , 2016; Hmelo , 2017 ) . Biochemical mechanisms that inhibit or disrupt the functioning of signaling molecules ( commonly referred to as 'quorum quenching' ) further determine the time scales at which autoinducers are degraded in the environment ( LaSarre and Federle , 2013; Grandclément et al . , 2016; Hmelo , 2017 ) . On the other hand , spatial dispersal of autoinducers in the population depends , for example , upon cellular mechanisms that import and export autoinducers into the cell from the environment and vice versa , and upon the spatial structure of the microbial population ( Platt and Fuqua , 2010; Hense and Schuster , 2015 ) . The degree of dispersal determines whether autoinducers remain spatially privatized to a single cell , diffuse to neighboring cells , or are spread evenly between all cells of the population . Consequently , the spatio-temporal organization of the microbial population determines as to what extent microbes sense rather the current average production level or a time-integrated production of autoinducers , and to what extent they sense rather the global or a local average production level . Our quorum-sensing model assumes that autoinducers are uniformly degraded in a well-mixed environment . These assumptions do not hold true for a spatially structured microbial biofilm , but should be fulfilled during the stationary phase of microbial growth in a well-mixed batch culture ( Yates et al . , 2002; Byers et al . , 2002 ) . Some of the questions raised above may be addressed most effectively with single-cell experiments . For example , it would be desirable to simultaneously monitor , at the single-cell level , the correlations between autoinducer levels in the environment , the expression of autoinducer synthase genes , and the transcriptional regulators that mediate response to quorum sensing . Upon adjusting the level of autoinducers in a controlled manner , for example in a microfluidic device , one could characterize how cells respond to autoinducers in the environment . This way , it might be possible to answer questions of ( i ) how the cellular production of autoinducers is regulated ( monostable or bistable regulation , or a different form of regulation ) , ( ii ) whether response times to environmental changes are stochastic and whether response rates can be identified , ( iii ) as to what extent cellular response in the production of autoinducers depends on both the level of autoinducers in the environment and on the cell's present production degree , and ( iv ) how production of autoinducers is correlated with single-cell growth rate . In the context of the quorum-sensing model , the results of such single-cell experiments would help to identify the form of the fitness function ϕ and the response function R , to quantify the selection strength s and response probability λ , and to refine the model set-up . Different mechanisms at the cellular ( microscopic ) level may yield the same behavior at the population ( macroscopic ) level . Therefore , observations at the population level might not discriminate between different mechanisms at the cellular level . Is phenotypic heterogeneity in the production of autoinducers an example of such a case ? In this work , we discussed that phenotypic heterogeneity in the autoinducer production could be the result of stochastic gene expression in bistable gene regulation or , as suggested by our model , the result of the feedback between ecological and population dynamics . We believe that the above-mentioned single-cell experiments could elucidate the mechanisms that allow for phenotypic heterogeneity in quorum-sensing microbial populations , and help to understand how population dynamics and ecological dynamics influence each other . The purpose of the quorum-sensing model presented here is to explain how phenotypic heterogeneity in the autoinducer production arises and how it is controlled in quorum-sensing microbial populations . With the current model set-up , however , we did not address its function . Why might this phenotypic heterogeneity in the autoinducer production be beneficial for a microbial species on long times ? From an experimental point of view , the evolutionary contexts and ecological scenarios under which this phenotypic heterogeneity may have arisen are still under investigation ( Garmyn et al . , 2011; Grote et al . , 2014 , Grote et al . , 2015 ) . From a modeling perspective , one could extend , for example , our chosen fitness function with a term that explicitly accounts for the benefit of signaling either at the cellular or population level , and study suitable evolutionary contexts and possible ecological scenarios ( Pollak et al . , 2016; Dandekar et al . , 2012; Czárán and Hoekstra , 2009; Carnes et al . , 2010; Hense and Schuster , 2015 ) . Such theoretical models together with further experiments might help to clarify whether heterogeneous production of autoinducers can be regarded as a bet-hedging strategy of the population or rather serves the division of labor in the population ( Ackermann , 2015 ) . Overall , our analyses suggest that feedbacks between ecological and population dynamics through signaling might generate phenotypic heterogeneity in the production of signaling molecules itself , providing an alternative mechanism to stochastic gene expression in bistable gene-regulatory circuits . Spatio-temporal scales are important for the identified ecological feedback to be of relevance for microbial population dynamics: growth rate differences between producers and non-producers need to balance the rate at which cells respond to the environment , degradation of signaling molecules should be faster than time scales at which growth rate differences affect the population composition significantly , and signaling molecules should get dispersed in the whole population faster than they are degraded . In total , if microbes sense and respond to their self-shaped environment under these conditions , the population may not only respond as a homogeneous collective as is typically associated with quorum sensing , but may also become a robustly controlled heterogeneous collective . Further experimental and theoretical studies are needed to clarify the relevance of the different mechanisms that might control phenotypic heterogeneity , in particular for quorum-sensing microbial populations .
The microscopic dynamics are captured by a memoryless stochastic birth-death process ( a continuous-time Markov process ) as sketched in Figure 1 . The state of the population 𝐩 is updated by non-genetic inheritance and sense-and-response through quorum sensing such that at most two individuals i and j≠i change their production degree at one time . The temporal evolution of the corresponding joint N-particle probability distribution P ( 𝐩 , t ) is governed by a master equation for the stochastic many-particle process ( Gardiner , 2009; Van Kampen , 2007; Weber and Frey , 2017 ) , whose explicit form is derived from Figure 1 and given in Appendix 2 . This master equation tracks the correlated microscopic dynamics of the production degrees of all N individuals . To make analytical progress , we focused on the reduced one-particle probability distribution ρ ( 1 ) ( p , t ) =1/N⟨∑iδ ( p−pi ) ⟩P in the spirit of a kinetic theory ( Kadar , 2007 ) starting from the microscopic stochastic dynamics . ρ ( 1 ) denotes the probability distribution of finding any individual at a specified production degree p at time t; the numerically obtained histogram of ρ ( 1 ) was plotted in Figure 2 . The temporal evolution of ρ ( 1 ) is derived from the master equation , and couples to the reduced two-particle probability distribution and to the full probability distribution P through quorum sensing . By assuming that correlations are negligible , one may approximate ρ ( 1 ) by the mean-field distribution ρ , which we refer to as the production distribution . The mean-field equation ( 1 ) for ρ is derived in Appendix 2 and referred to as the autoinducer equation . Note that Equation ( 1 ) conserves normalization of ρ , that is , ∫01dp ∂tρ ( p , t ) =0 . We also proved that ρ ( 1 ) converges in probability to ρ as N→∞ for any finite time if initial correlations are not too strong . In other words , the autoinducer equation ( 1 ) captures exactly the collective dynamics of the stochastic many-particle process for large N . To show this convergence , we introduced the bounded Lipschitz distance d between ρ and ρ ( 1 ) , applied Grönwall’s inequality to the temporal evolution of d , and used the law of large numbers; see ( Frey et al . , 2017 ) for details . Similar distance measures and estimates have been used , for example , to prove that the Vlasov equation governs the macroscopic dynamics of the above-mentioned classical XY spin model with infinite range interactions ( Braun and Hepp , 1977; Dobrushin , 1979; Spohn , 1991; Yamaguchi et al . , 2004 ) . Without quorum sensing ( λ=0 ) , one finds the analytical solution for ρ by applying the method of characteristics to Equation ( 1 ) in the space of moment and cumulant generating functions as: ρ ( p , t ) =ρ0 ( p ) e-stp/∫01dpe-stpρ0 ( p ) ; see Appendix 3 for details . Thus , the initially lowest production degree in the population , plow , constitutes the homogeneous stationary distribution ρ∞ ( p ) =δ ( p−plow ) , which is attractive for generic initial conditions . Only δ-peaks at production degrees greater than plow are stationary as well , but they are neither attractive nor stable . The temporal approach to the homogeneous stationary distribution is algebraically slow for continuous initial distributions ρ0 , and exponentially fast if plow is separated from all greater degrees by a gap in production space; see Appendix 3 and Figure 2D . With quorum sensing ( λ>0 ) , fixed points of the response function p*=R ( p* ) yield homogeneous stationary distributions of the autoinducer equation ( 1 ) as ρ∞ ( p ) =δ ( p-p* ) . In particular , stable fixed points of the response function ( R′ ( p∗ ) <1 ) constitute homogeneous stationary distributions that are stable up to linear order in perturbations around stationarity . For λ>s/2 , these distributions are also attractors of the mean-field dynamics ( 1 ) for all initial distributions; see Appendix 3 . The temporal approach towards homogeneous stationary distributions with quorum sensing is generically exponentially fast ( Figure 2E ) . This exponentially fast approach is illustrated for the special case of a linear response function and λ=1/2 , for which one finds the analytical solution as: ρ ( p , t ) =y ( t ) ρ0 ( p ) + ( 1-y ( t ) ) δ ( p-p¯0 ) with y ( t ) =e-ϕ¯0t . However , time scales at which stationarity is approached may diverge at bifurcations of the response function . Such can be seen , for example , if one chooses a supercritical pitchfork bifurcation of a polynomial response function and λ=1/2; see Appendix 1—figure 3 and Appendix 3 . Here we discuss how the long-time behavior of the quorum-sensing model changes from heterogeneous to homogeneous populations as the response probability λ vanishes or reaches the upper threshold λup while the selection strength s is kept fixed . For small response probabilities , 0<λ<λup , the heterogeneous stationary distributions of the autoinducer equation ( 1 ) explain the long-lived , heterogeneous states of the stochastic quorum-sensing process . The coexisting δ-peaks at the low-producing and high-producing degree in the heterogeneous stationary distribution are separated by a gap in production space , which gives rise to the non-vanishing variance Var ( p ) ∞ in the phase of heterogeneity ( Figure 3C ) . As λ→λup , the gap closes , phigh→R ( phigh ) , and y→0 , such that a homogeneous stationary distribution with Var ( p ) ∞=0 is recovered in a continuous transition . This nonequilibrium phase transition from heterogeneity to homogeneity proceeds without any critical behavior . As λ→0 , and under the assumption that 0 is an unstable fixed point of the response function ( R ( 0 ) =0 and 1<R′ ( 0 ) ; we further assume R′ ( 0 ) <∞ ) , the gap between the low-producing and the high-producing peak closes as well because phigh→0 . However , y does not approach 1 , but the value 1−1/R′ ( 0 ) <1 . The probability weight at the low-producing mode jumps by the value 1/R′ ( 0 ) and the homogeneous stationary distribution with Var ( p ) ∞=0 is recovered in a discontinuous transition . Therefore , a discontinuous phase transition in the space of stationary probability distributions governs the long-time dynamics of the autoinducer equation ( 1 ) from heterogeneity to homogeneity as the response probability λ vanishes ( for fixed selection strength s ) . | Bacteria and other microbes can communicate with each other using chemical languages . They release small signaling molecules called autoinducers into their surroundings and sense the levels of the autoinducers in the environment . The response to these autoinducers – known as quorum sensing – can regulate how whole communities of microbes grow and behave; for example , autoinducers can alter the ability of microbes to infect humans or enable the microbes to collectively switch on light production . Recent experiments suggest that , in a population of genetically identical microbes , some individuals may produce autoinducers while others do not . The coexistence of these different “phenotypes” in one population may enable different individuals to perform different roles , or act as a “bet-hedging” strategy that helps the population to survive if it is later exposed to a stressful situation . It is not clear how microbes regulate autoinducer production so that only some individuals produce these molecules . Bauer , Knebel et al . developed a theoretical model to address this question . In the model , the microbes shape their environment by producing autoinducers and can respond to this self-shaped environment by changing their level of autoinducer production . Bauer , Knebel et al . found that this establishes a feedback loop that can result in autoinducers being produced by some individuals but not others . The next step following on from this work is to carry out experiments to test the assumptions and predictions made by the theoretical model . These findings may help to understand how the coexistence of different phenotypes affects collective behaviors , and vice versa , in populations of microbes that use quorum-sensing . | [
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Cell junctions are scaffolds that integrate mechanical and chemical signaling . We previously showed that a desmosomal cadherin promotes keratinocyte differentiation in an adhesion-independent manner by dampening Epidermal Growth Factor Receptor ( EGFR ) activity . Here we identify a potential mechanism by which desmosomes assist the de-neddylating COP9 signalosome ( CSN ) in attenuating EGFR through an association between the Cops3 subunit of the CSN and desmosomal components , Desmoglein1 ( Dsg1 ) and Desmoplakin ( Dp ) , to promote epidermal differentiation . Silencing CSN or desmosome components shifts the balance of EGFR modifications from ubiquitination to neddylation , inhibiting EGFR dynamics in response to an acute ligand stimulus . A reciprocal relationship between loss of Dsg1 and neddylated EGFR was observed in a carcinoma model , consistent with a role in sustaining EGFR activity during tumor progression . Identification of this previously unrecognized function of the CSN in regulating EGFR neddylation has broad-reaching implications for understanding how homeostasis is achieved in regenerating epithelia .
Tissue morphogenesis and homeostasis are critically dependent on the proper spatial control of receptor tyrosine kinase activity ( Casaletto and McClatchey , 2012 ) . This is particularly so in complex regenerating tissues , such as the epidermis . The epidermis is a multi-layered epithelium that continuously renews itself through a highly choreographed program of morphological and biochemical changes , which are necessary to form a barrier against environmental insults and water loss ( Koster and Roop , 2007 ) . This process requires proliferating basal cells to stop dividing and undergo a commitment to differentiate as they transit toward the outer surface of the skin . Chemical cues governed by Epidermal Growth Factor Receptor ( EGFR ) and other signaling receptors must be tightly regulated to maintain a proper balance of proliferation and differentiation and to drive critical steps at each stage of epidermal stratification ( Fuchs and Nowak , 2008; Fuchs and Raghavan , 2002; Koster and Roop , 2007; Lopez-Pajares et al . , 2013 ) . While the mechanisms that dictate EGFR activity and dynamics during epidermal differentiation are not well understood , properly positioning the receptor in proximity to effector and regulatory machinery is likely to play a role . Candidate cytoarchitectural components that could serve in this manner are cell-cell junctions . In the epidermis , the most prominent of these junctions are desmosomes , which confer mechanical strength to tissues by linking the intermediate filament ( IF ) cytoskeleton through the IF anchoring protein Desmoplakin ( Dp ) to sites of desmosomal cadherin-mediated adhesion ( Kowalczyk and Green , 2013 ) . The importance of the desmosome-IF complex is underscored by the existence of mutations in genes encoding desmosome components that lead to skin , heart , and hair defects ( Lai-Cheong et al . , 2007; Petrof et al . , 2012 ) . While the desmosome has classically been regarded as an adhesion complex , recent studies have demonstrated that it can act as a signaling scaffold to ensure the spatial and temporal execution of the epidermal differentiation program ( Broussard et al . , 2015; Harmon and Green , 2013; Schmidt and Koch , 2007; Sumigray and Lechler , 2015 ) . In particular , we showed that the desmosomal cadherin , Desmoglein 1 ( Dsg1 ) , is not only required for maintaining epidermal tissue integrity in superficial layers , but also promotes keratinocyte differentiation as cells transit out of the basal layer ( Getsios et al . , 2009 ) . It performs this function at least in part by attenuating the activity of ErB receptors ( EGFR and ErbB2 ) and downstream MAPK signaling ( Getsios et al . , 2009; Harmon et al . , 2013 ) . Dsg1 is optimally positioned to exert this sort of spatial control on the EGFR pathway , as it is first expressed as cells commit to differentiation and becomes progressively concentrated in the superficial epidermal layers . Thus , it is a good candidate to serve as a scaffold for machinery necessary to down-regulate EGFR family activity , which is required for this commitment to differentiation . The unusually long cytoplasmic tail of Dsg1 , but not the adhesive ectodomain , was required to suppress EGFR and Extra Cellular Signal-regulated Kinase 1/2 ( Erk1/2 ) signaling , indicating a previously unrecognized function for Dsg1 that transcends its canonical roles in adhesion . However , the molecular machinery that integrates the desmosome-IF scaffold with the biochemical differentiation program is not well-defined . To identify mediators of Dsg1 signaling functions we carried out a yeast-two-hybrid ( Y2H ) screen using the cytoplasmic domain of Dsg1 as bait ( Harmon et al . , 2013 ) . Among the putative binding partners identified was the third subunit ( Cops3 ) of the eight-subunit complex called the COP9 signalosome ( CSN ) . Discovered in 1994 in Arabidopsis , the Constitutively Photomorphogenic ( COP ) mutants , cops1-8 , exhibit a photomorphogenic phenotype in complete darkness ( Chory et al . , 1989; Deng et al . , 2000; Denti et al . , 2006; Miséra et al . , 1994; Wei et al . , 1994; Wei and Deng , 1996; Wei and Deng , 1999 ) . The CSN functions as a Nedd8 isopeptidase to remove Nedd8 moieties from its substrates , an activity depending on the Cops5 subunit . This hydrolysis has been classically studied in the context of de-neddylation of cullin-RING ( Really Interesting New Gene ) E3 ligases ( Cul1 , 2 , 3 , 4a , 4b , 5 , 7 ) ( Cope et al . , 2002; Duda et al . , 2008; Saha and Deshaies , 2008 ) , which regulate fundamental processes important for development and tissue homeostasis , including cell cycle , signal transduction , transcription and DNA replication ( Petroski and Deshaies , 2005 ) . To promote the activity of cullin-RING E3 ligases , the CSN de-neddylates and partners with de-ubiquitinase enzymes to ‘de-ubiquitinate’ and rescue the cullin from spurious self-ubiquitination that occurs when the Nedd8 modification is present ( Hetfeld et al . , 2005; Wee et al . , 2005; Zhou et al . , 2003 ) . These functions create activation cycles allowing cullin-RING E3 ligases to focus their activity on specific substrates . Cells deficient for the CSN are unable to remove Nedd8 , and as a consequence cullin-RING E3 ligase subunits are autoubiquitinated resulting in reduced function ( Bosu and Kipreos , 2008; Hetfeld et al . , 2005; Hotton and Callis , 2008; Schwechheimer et al . , 2001; Wee et al . , 2005; Wu et al . , 2005; Zhou et al . , 2003 ) . It has yet to be demonstrated whether the CSN is able to de-neddylate other substrates modified by Nedd8 . In this report , we show that the Cops3 subunit of the CSN interacts with desmosomal components , Dsg1 and Dp . Loss of Cops3 results in an increase in phosphorylated EGFR ( pEGFR ) , which is associated with compromised keratinocyte differentiation , suggesting that Cops3 , and consequently the CSN , inhibits EGFR signaling to promote differentiation . Further , Cops3 deficiency compromises the ability of ectopic Dsg1 to promote expression of keratinocyte differentiation markers , consistent with functional cooperation between desmosomes and the CSN . We go on to show that EGFR is neddylated in human keratinocytes and genetically interfering with desmosomal components or Cops3 results in elevated EGFR receptor neddylation , suggesting that EGFR is a non-cullin substrate for CSN-dependent de-neddylation . Importantly , this increase in neddylation was accompanied by a decrease in ubiquitination and altered EGFR dynamics in response to an acute ligand stimulus . A reciprocal relationship between Dsg1 and neddylated EGFR was observed in a 3D carcinoma model , raising the possibility that loss of desmosomes during cancer progression unscaffolds membrane-associated CSN complexes , resulting in hyper-neddylated EGFR . These data support a model whereby epidermal differentiation is assisted by desmosome-dependent scaffolding of the CSN complex to down-regulate EGFR through removal of Nedd8 modifications .
We recently identified a role for the desmosomal cadherin , Dsg1 , in promoting epidermal differentiation through attenuation of EGFR/MAPK signaling ( Getsios et al . , 2009 ) . This function did not require the Dsg1 adhesive ectodomain , but did require the unique , extended cytoplasmic tail . While a Dsg1 binding partner , Erbin , was shown to interfere with Ras-Raf coupling downstream of EGFR ( Harmon et al . , 2013 ) , the upstream regulation of EGFR is still poorly understood . A Y2H screen using the Dsg1 tail as bait revealed the CSN subunit , Cops3 , as a new Dsg1 binding partner . Cops3 could interact with the constructs comprising the full cytoplasmic tail of Dsg1 or a construct lacking only the membrane proximal intracellular anchor ( IA ) in yeast ( Figure 1a ) . The interaction with Cops3 was selective , as the fourth subunit of the CSN was unable to interact with the Dsg1 tail in yeast ( Figure 1b ) . To validate the interaction , we tested whether recombinant Dsg1 ( GST-Dsg1 ) can associate with endogenous Cops3 in normal human keratinocyte ( NHEK ) lysates in vitro . Indeed , purified GST-Dsg1 bound to sepharose beads specifically precipitated endogenous Cops3 from NHEK lysates ( Figure 1c ) . To address whether Cops3 engages more broadly with the desmosome , we carried out immunoprecipitations of Dp , an IF-anchoring protein present in all desmosomes . Cops3 was also present in immunoprecipitates of endogenous Dp ( Figure 1d ) . To narrow down the domains in Dp that were necessary for Cops3 interaction we performed a Y2H analysis utilizing Dp truncation constructs . While the N-terminal portion of Dp harboring the amino acid residues 1–584 ( DP-NTP ) was not sufficient for Dp’s interaction with Cops3 ( Figure 1—figure supplement 1 ) , a 32 kDa truncated version of the C-terminal tail of Dp co-precipitated with endogenous Cops3 in the SCC9 cell line , which was used to overcome the limitations of ectopic expression of the Dp S-tag fusion protein ( DP-S-tag ) in primary cells . This suggested the site of interaction on Dp resides within the C-terminus ( Figure 1e ) . To further examine the relative disposition of Cops3 , Dsg1 and Dp we carried out structured illumination microscopy ( SIM ) , which produces two times the resolution of conventional optical microscopes , resulting in the minimal overlap of associated proteins in co-localization . SIM images revealed Cops3 to be distributed throughout the cell , impinging upon Dsg1 or Dp concentrated at cell-cell junctions . Cops3 was also closely associated with cytoplasmic Dsg1 and Dp particles , which could represent vesicles or cytoplasmic precursors , previously described ( Godsel et al . , 2005 ) ( Figure 1f ) . To further address the interaction between desmosomes and the CSN we took advantage of the proximity ligation assay ( PLA ) , a powerful technique that provides an indication of protein proximity from 40 to 100 nm to examine the spatial relationship of endogenous Dsg1 and Cops3 in cells ( Weibrecht et al . , 2010 ) . A positive PLA signal was observed for the Dsg1 and Cops3 antibody pair , which was abrogated by treatment with either DSG1 or COPS3 siRNA ( Figure 1g and Figure 1—figure supplement 2 ) , consistent with an association between Dsg1 and Cops3 in NHEKs . In addition , PLA analysis revealed a positive signal for Dp and Cops3 in NHEKs treated with high calcium media to induce epidermal differentiation , which was likewise inhibited through knockdown of Dp and Cops3 ( Figure 1h and Figure 1—figure supplement 3 ) . To investigate further the possibility that the entire desmosome serves as a docking site for the recruitment of the CSN , we also assessed whether Dp knockdown diminished Cops3’s ability to co-localize with Dsg1 ( Figure 1g ) and vice versa ( Figure 1h ) . In both cases , PLA signals were significantly reduced , supporting the idea that the integrity of the entire desmosome is required to scaffold the COP9 signalosome . To address the specificity of Cops3 interactions with desmosomes we paired the Cops3 antibody with the adherens junction protein E-cadherin ( E-cad ) . As a positive control , E-cad and Beta-catenin ( B-cat ) were paired as well as Alpha-catenin ( A-cat ) and B-cat ( Figure 1i and Figure 1—figure supplement 4 ) . These experiments clearly show that while Cops3 interacts with desmosomal molecules Dsg1 and Dp , it does not interact with adherens junction components . In addition , we used the cell-cell junction protein Plakoglobin ( Pg ) as a fluorescence marker for cell-cell junctions to determine the extent to which PLA signals co-localize with cell-cell interfaces . These data show that while not all desmosome-Cops3 interactions are in close proximity to the Pg signal , a large percentage of PLA dots do appear to be localized at cell-cell interfaces ( ~60% for Dsg1/Cops3;~67% for Dp/Cops3 ) , although to a somewhat lesser extent than the positive control PLA pairings Alpha-Catenin/Beta-Catenin ( ~82% ) and Ecad/Beta-catenin ( ~88% ) ( Figure 1i and Figure 1—figure supplement 4c ) . Cytoplasmic Cops3 interactions with desmosome molecules may occur between non-membrane bound Dp precursors and endocytosed membrane associated Dsg1 vesicles that we have previously identified in these cells , which could account for cytoplasmic PLA signal ( Godsel et al . , 2005; Harmon et al . , 2013; Nekrasova et al . , 2011; Patel et al . , 2014 ) . We previously showed that Dsg1 promotes epidermal differentiation and that the adhesive ectodomain is dispensable for this function ( Getsios et al . , 2009 ) . Towards addressing whether the interaction between Cops3 and the desmosome contribute to this function of the Dsg1 cytoplasmic domain , we silenced COPS3 in NHEKs through electroporation of siRNAs targeted specifically against this CSN subunit . Biochemical analysis revealed a reduction in differentiation related gene products including Dsg1 , Desmocollin1 ( Dsc1 ) , Keratin1 ( K1 ) , and Keratin 10 ( K10 ) ( Figure 2a ) . Similar impairment of these features of the keratinocyte differentiation program were observed using five oligos each with independent targets within the COPS3 sequence ( siCOPS3-465 , siCOPS3-Dharma3 , siCOPS3-IDT pool including 3 oligos ) , supporting the idea that the observed changes were not due to off target effects ( Figure 2—figure supplement 1 ) . To further address whether Cops3 contributes to proper epidermal differentiation and morphogenesis , we generated Cops3 deficient 3D organotypic cultures lifted to an air-medium interface ( epidermal raft cultures ) and harvested 3 days later ( Figure 2b ) . Biochemical analysis of raft lysates revealed similar differentiation defects when compared with monolayer cultures reflected by a decrease in Dsg1 , Dsc1 , the suprabasal keratins K1 and K10 and the cell envelope protein Loricrin ( Lor ) ( Figure 2c ) . Immunofluorescence of siCOPS3 Day 3 rafts also revealed defects in the differentiation marker K10 and Dsg1 ( Figure 2d ) . Interestingly , we noted a decrease in Dsg1 protein expression ( Figure 2a and c ) , which could contribute to the differentiation defect ( Getsios et al . , 2009 ) . To test whether aberrant differentiation was due to the loss of Dsg1 protein expression , we introduced ectopic Dsg1-FLAG in the background of NHEKs deficient for Cops3 . As we previously reported , ectopic Dsg1 promotes expression of differentiation specific markers in control cultures ( Getsios et al . , 2009 ) . Biochemical analysis of cells retrovirally transduced with a Dsg1-Flag vector revealed that Dsg1-Flag was unable to drive expression of Dsc1 , K1 , and K10 in Cops3-silenced cultures after inducing differentiation for 2 days to the same extent as control cells ( Figure 2e ) . To directly capture the impact of siCOPS3 on early differentiation in a specific population of cells expressing Dsg1 , we scored NHEKs transiently expressing a GFP-tagged version of Dsg1 for an early differentiation marker , K10 , in control and COPS3-silenced cultures after 1 day of differentiation . We observed that Dsg1-GFP expressing cells were unable to express K10 upon silencing of COPS3 ( Figure 2f and g ) . Thus , Dsg1 requires Cops3 for efficient Dsg1-stimulated keratinocyte differentiation . Since the loss of Cops3 interferes with differentiation , we addressed the extent to which EGFR and downstream effectors , Erk and Mek , are affected upon the silencing of COPS3 . Increases in pEGFR , phosphorylated Erk ( pErk ) , and phosphorylated Mitogen Activated Protein Kinase ( pMek ) were observed ( Figure 3a ) . These increases in MAPK signaling corresponded to a decrease in differentiation markers Dsg1 , Dsc1 , K10 and Lor ( Figure 3a right panel and Figure 2a ) , consistent with our previous demonstration that the canonical Ras-Raf-Erk pathway is functionally linked to Dsg1’s ability to promote differentiation ( Getsios et al . , 2009; Harmon et al . , 2013 ) . An increase in pErk was also observed in 3D organotypic raft cultures treated with siRNA targeting COPS3 ( Figure 3b ) . To test whether inhibition of EGFR is capable of restoring differentiation in Cops3-deficient cultures , we treated NHEK siRNA-targeted COPS3 cells with the specific EGFR inhibitor AG1478 . Pharmacological inhibition of EGFR restored Dsg1 , Dsc1 , and K10 expression without increasing Cops3 protein levels ( Figure 3c ) . These data are consistent with the hypothesis that the CSN , recruited to the desmosome through Cops3 , is required to properly modulate EGFR activity . While cullins are the best-known substrates for the Nedd8 modification , EGFR neddylation was previously reported to occur in CHO ( chinese hamster ovary ) cells ( Oved et al . , 2006 ) . More recently Transforming Growth Factor Beta Receptor Type II ( TGFβRII ) was shown to be regulated by neddylation ( Zuo et al . , 2013 ) , raising the possibility of receptor neddylation as a means of modulating EGFR’s activity . To address whether EGFR neddylation occurs in NHEKs and whether the desmosome-CSN complex regulates EGFR neddylation status , we performed RNAi-mediated silencing of DSG1 , DP , and COPS3 in NHEKs differentiated for 1 day and assessed the association of the Nedd8 moiety with EGFR using PLA . A significant increase in PLA positive signals between EGFR and Nedd8 was observed when DP , DSG1 or the CSN component COPS3 was silenced ( Figure 4a and Figure 4—figure supplement 1 ) , supporting a model whereby both the desmosome and the CSN are necessary for regulating EGFR de-neddylation . To examine the levels of EGFR neddylation biochemically , we performed EGFR immunoprecipitations in SCC9 cells transiently expressing HA-tagged Nedd8 , to facilitate the detection of the modification . In this case , the use of this cell line helped overcome limitations of ectopic expression in primary cells . SCC9 cells are devoid of Dsg1 when grown in a monolayer , however they do express other desmosomal cadherins , including desmoglein 2 , and assemble robust desmosomes that include Dp ( Chen et al . , 2012; Godsel et al . , 2005; Nekrasova et al . , 2011 ) . Loss of either Dp or Cops3 via RNAi-mediated silencing resulted in elevated EGFR neddylation ( Figure 4b ) . The fact that DP knockdown in desmosome-containing SCC9 cells impacts EGFR neddylation status is consistent with the observation that loss of either Dsg1 or Dp interferes with recruitment of Cops3 to desmosomes in NHEK cells ( Figure 1g and h ) . Since neddylation and ubiquitination occur on lysines , we questioned whether neddylation competes with ubiquitination on EGFR . To assess endogenous ubiquitination of EGFR we treated the cells with EGF ( 50 ng/ml ) for 5 min , then performed EGFR immunoprecipitations in SCC9 cells . Upon loss of Cops3 and Dp we observed reduced ubiquitination of EGFR ( Figure 4c ) . We also found a decrease in a PLA signal between EGFR and Ubiquitin when Dsg1 , Cops3 , or Dp was silenced ( Figure 4d ) . A previous report suggested that neddylation of ectopically expressed EGFR in CHO cells is required for subsequent ubiquitination and turnover of the receptor ( Oved et al . , 2006 ) , whereas our observations are consistent with a reciprocal relationship between neddylation and ubiquitination . In order to directly address whether ubiquitination of endogenous EGFR depends on the Nedd8 modification human epithelial cells , EGFR ubiquitination was assayed in SCC9 cells silenced for NEDD8 . Upon immunoprecipitation of EGFR , it was found that EGFR is ubiquitinated in Nedd8 depleted cells , on average about 1 . 4-fold higher than control RNAi-treated cells ( Figure 4e ) . Additionally , PLA analysis revealed an association between EGFR and Ubiquitin upon the loss of Nedd8 ( Figure 4—figure supplement 2 ) . Overall , this suggests that both desmosome and CSN components support the generation and/or maintenance of ubiquitinated EGFR , at the expense of EGFR neddylation . Ubiquitination is important for the internalization and post-internalization sorting of EGFR , where the ubiquitin ligase , Cbl , coordinates the transfer of the Ubiquitin from an E2 enzyme to the receptor’s cytoplasmic domain ( Conte and Sigismund , 2016; Joazeiro et al . , 1999; Levkowitz et al . , 1999; Sorkin and Goh , 2008; Waterman et al . , 1999; Yokouchi et al . , 1999 ) . When EGFR fails to acquire Ubiquitin , it is recycled back to the membrane ( Levkowitz et al . , 1998 ) . Further , neddylation has previously been shown to stabilize substrates including other membrane receptors such as TGFβRII ( Zuo et al . , 2013 ) . Together with the results described above , this raises the possibility that by scaffolding the CSN near EGFR , desmosomes might promote EGFR turnover by shifting the balance of neddylation and ubiquitination modifications on the receptor . A prediction of this model is that EGFR would fail to re-localize in response to an acute ligand stimulus in the absence of the desmosome-CSN complex . To address this , we performed immunofluorescence on NHEKs treated with 50 ng/ml of EGF ligand for 5 min to induce internalization of EGFR in the absence of Dsg1 , Dp , or Cops3 . As shown in Figure 5a ( and Figure 4—figure supplement 1 ) , EGFR border intensity in cells silenced for COPS3 , DSG1 , and DP remained elevated compared with control treated cultures after EGFR internalization was induced by treatment with EGF ligand . To more directly determine how neddylation regulates EGFR dynamics , we treated NEDD8 ( siNEDD8 ) or COPS3 ( siCOPS3 ) silenced NHEKs with EGF ligand to induce EGFR internalization and measured EGFR border intensity at 15 min . EGFR intensity was dramatically reduced at cell-cell interfaces in both the siCONT and siNEDD8 cultures , whereas EGFR was stable at borders in siCOPS3 cells ( Figure 5b and Figure 4—figure supplement 2 ) . These data are consistent with the idea that the desmosome-CSN complex destabilizes EGFR by de-neddylating the receptor . To address the long-term impact of NEDD8 knockdown on EGFR stability we treated NHEKs with cycloheximide ( 0 . 02 mg/mL ) to inhibit protein translation and assessed EGFR receptor levels over time . Our results indicate that upon the loss of Nedd8 , the stability of EGFR significantly decreases over a 24 hr time period ( Figure 5c ) . These data are consistent with the idea that the neddylated state of EGFR promotes its stabilization . Additionally , we investigated the effect of NEDD8 silencing on EGFR signaling . While the simple prediction of our model would be a decrease in EGFR activity , we did not detect reproducible reductions in pEGFR or the activity of downstream effectors at a population level in differentiating cultures . It seems plausible that feedback mechanisms are stimulated by a complete loss of Nedd8 within these heterogeneous differentiating cultures , which could mask changes occurring at a local level ( Figure 5—figure supplement 1 ) . To address whether regulation of EGFR’s neddylation is affected under conditions where EGFR expression and activity is expected to be elevated , we created 3D organotypic raft cultures using SCC9 and 1483 squamous cell cancer lines ( Figure 6a and b ) . In both cases , EGFR was distributed more broadly throughout the culture and extended further into suprabasal layers than in control cultures or human epidermis . Additionally , 1483 and SCC9 3D cultures , when compared to controls , exhibited elevated levels of EGFR and Cops3 ( Figure 6a ) . While we are unable to detect Dsg1 protein levels in monolayer SCC9 cultures , we have found Dsg1 to be expressed in 3D organotypic cultures that have been harvested 10 days after lifting to an air-liquid interface ( Figure 6a ) . However , the level of Dsg1 , and to a lesser extent Dp , is reduced in the SCC9 and 1483 cancer 3D rafts when compared to control , primary NHEK 3D rafts ( Figure 6a ) . PLA analysis of organotypic cancer rafts revealed an increase in EGFR-Nedd8 , which is consistent with the known elevation of EGFR activity in HNSCC ( Figure 6b top and middle panels ) . Further , we observed a reciprocal staining pattern of EGFR and Dsg1 ( Figure 6b bottom panel ) , consistent with the idea that loss of Dsg1 in progressing tumors is associated with elevated EGFR expression ( Wong et al . , 2008 ) . While the HNSCC cultures displayed an increase in Cops3 protein expression , this potentially compensatory effect does not suffice to override the desmosome defect in a 3D context . To show that proper CSN functioning is required for EGFR-neddylation maintenance in a 3D model , we carried out PLA analysis of rafts silenced for COPS3 and harvested at Day 3 ( Figure 2c ) , and found that upon the loss of Cops3 there is an increase in Nedd8 associated with EGFR ( Figure 6d ) .
Here we demonstrate a novel form of EGFR regulation and turn over by a newly described desmosome-CSN cell junctional complex . We propose a model by which the desmosome scaffolds the CSN in proximity to EGFR , to control the balance of neddylation-ubiquitination modifications , thus affecting receptor dynamics . Our data is consistent with a model whereby the CSN de-neddylation function contributes to EGFR turnover to promote differentiation in a regenerating tissue , in this case , the epidermis . Previous work has shown that the loss of one CSN component affects the formation and function of the entire CSN ( Denti et al . , 2006; Wei and Deng , 1999 ) . Thus , silencing of the COPS3 subunit , shown here to interact with the desmosome , is predicted to compromise the entire CSN . Similarly , silencing either DP or DSG1 disrupts the association of Cops3 with the other junction component , consistent with the idea that entire desmosome is involved in scaffolding the CSN . This idea is further supported by a mass spectrometry study that identified multiple desmosomal proteins ( Plakophilin2 ( Pkp2 ) , Pg , Dsg1 , and Dp ) , and not adherens junction proteins , as putative interactors with the first subunit ( Cops1 ) of the COP9 signalosome ( Fang et al . , 2012 ) . The fact that desmosomal control over the neddylation-ubiquitination balance occurs in SCC9s and keratinocytes in the absence of Dsg1 supports the idea that desmosomes of varying composition broadly regulate the CSN . Our data provide further support for a functional role of Nedd8 modifications on less commonly reported non-cullin substrates , in this case EGFR . Further , our findings are consistent with the idea that neddylation of EGFR stabilizes the receptor ( Figure 5c ) and prevents EGFR from ligand-induced internalization ( Figure 5a and b ) . While one study reported a correlation between EGFR neddylation and degradation in Chinese hamster ovary ( CHO ) cells , the majority of studies have linked neddylation with an increase in protein stability ( e . g . MDM2 ( Watson et al . , 2010 ) , L11 ( Sundqvist et al . , 2009 ) , HIF1α ( Ryu et al . , 2011 ) , and TβRII ( Zuo et al . , 2013 ) . It is unknown whether specific neddylated residues on EGFR are important for driving EGFR towards certain outcomes or whether the number of Nedd8 modifications on EGFR is important . Poly-Nedd8 chains have been demonstrated , but it is still unclear whether these chains have a function in vivo ( Jones et al . , 2008; Xirodimas et al . , 2008 ) . Nevertheless , our data showing a reciprocal relationship between neddylated and ubiquitinated EGFR suggest that Nedd8 and Ubiquitin compete for lysine residues on EGFR to regulate receptor dynamics . Furthermore , our data demonstrate that EGFR ubiquitination can occur independent of neddylation ( Figure 4e and Figure 4—figure supplement 2 ) , suggesting a new level of regulation that could have broad-reaching implications for receptor-mediated signaling in multiple contexts . While we show changes in the neddylation state of EGFR , we have not ruled out the possibility that the desmosome-CSN super complex also regulates other proteins at the membrane , such as better-known substrates for the CSN , the cullin proteins , which could promote local Ubiquitin activity at the membrane . It is possible that a uniquely targeted protein degradation program could work in concert with transcriptional epidermal differentiation programs to remove proteins that inhibit , or are no longer required for , epidermal differentiation . Such a process could contribute to the re-sculpting and polarization of cell membrane proteins known to occur during differentiation ( Niessen et al . , 2012; Wollner et al . , 1992 ) . In addition to a role for desmosomal scaffolding of the CSN in regulating normal epidermal differentiation and homeostasis , our data from a 3D organotypic cancer model suggests that EGFR neddylation may contribute to elevated EGFR/MAPK signaling in human diseases including inherited keratodermas ( Harmon et al . , 2013 ) and cancers such as head and neck squamous cell carcinoma ( HNSCC ) ( Figure 6a–c ) . EGFR has been shown to be a strong prognostic indicator in epithelial cancers , and increased expression has been associated with reduced survival rates in 70% of studies ( Nicholson et al . , 2001 ) . This suggests the possibility that elevation of EGFR in different types of cancer may be partly due to the stabilization of the receptor through increased neddylation . Additionally , Cops3 has been implicated in keratinocyte interleukin signaling ( Banda et al . , 2005 ) , which is interesting in light of the recent observation that loss of Dsg1 leads to an increase in inflammatory mediators in SAM syndrome , featuring severe dermatitis , multiple allergies , and metabolic wasting ( Samuelov et al . , 2013 ) . A desmosome-CSN super complex could thus serve as a general mechanism by which desmosomes regulate multiple downstream functions that guide normal skin homeostasis and paracrine signaling pathways in which keratinocytes participate . Further , components of this super complex could provide new targets for treating diseases associated with inappropriate epidermal differentiation , inflammation , and cancer .
Primary normal human epidermal keratinocytes ( NHEKs ) were cultured in M154 media ( Invitrogen ) adjusted to 0 . 07 mM calcium ( termed as low calcium ) and supplemented with human keratinocyte growth supplement ( HKGS ) and gentamicin/amphotericin B . To induce differentiation , NHEKs were switched to media with 1 . 2 mM calcium ( termed as high calcium ) . High calcium media did not contain HKGS except where noted in text . Human-derived oral squamous cell carcinoma SCC9 cells ( RRID: CVCL_1685 ) and 1483 ( RRID: CVCL_6980 ) were maintained in DMEM/F-12 medium ( Mediatech ) supplemented with 10% FBS and 1% penicillin/streptomycin . EGFR inhibitor , AG1478 ( Selleck Chemicals ) was used at a final concentration of 2 μM . Cycloheximide ( CHX ) was used at a final concentration of 0 . 02 mg/ml . Primary normal human epidermal keratinocyte isolates ( NHEKs ) are obtained through the Northwestern University Skin Disease and Research Core , where mycoplasma , HIV-1 , hepatitis B and C testing is routinely performed . Primary keratinocyte purity is assessed by immunostaining for epidermal keratinocyte specific markers , such as keratins K1/K10 and K5/K14 . Cell lines are routinely confirmed to be mycoplasma negative using the Lonza MycoAlert mycoplasma detection Kit and/or by real-time PCR ( IDEXX BioResearch ( Columbia , MO ) . The SCC9 and 1483 lines were analyzed by short tandem repeat ( STR ) profiling to detect both contamination and misidentification , including intra- and inter-species contamination by IDEXX BioResearch ( Columbia , MO ) . The SCC9 cell line ( RRID: CVCL_1685 , gift from J . Rheinwald , Harvard Medical School , Boston , MA ) scored above 80% indicating the sample is consistent with the cell line of origin . The 1483 cell line ( RRID: CVCL_6980 , a human HNSCC line derived from the oropharynx obtained from Jennifer Grandis ) is not available from ATCC . STR analysis ruled out inter-species contamination . While the line scored less than an 80% match compared with the IDEXX standard for STR analysis , this standard was shown to be contaminated with the UM-SCC-1 line ( Zhao et al . , 2011 ) . Thus , key features of this line critical for the present work were validated independently through analysis of EGFR expression , response to inhibitors , and assessment of HNSCC keratins and junctional proteins , including Dsg1 whose expression is lost in many of the available HNSCC lines ( Thomas et al . , 2008; Desai BD , Todorovic V , and Green KJ , unpublished ) . The following primary antibodies were used in this study: Cell Signaling: rabbit anti-HA ( RRID:AB_1549585 ) , rabbit anti-EGFR D38B1 ( RRID:AB_2246311 ) , rabbit anti-pEGFRY1045 ( RRID:AB_331710 ) , anti-pEGFRY1068 ( RRID:AB_2096270 ) , anti-pEGFRY1086 ( RRID:AB_823485 ) , rabbit anti-pErk42/44 ( RRID:AB_2315112 ) , rabbit anti-pMek ( RRID:AB_2138017 ) , rabbit anti-Mek ( RRID:AB_823567 ) rabbit anti-ErbB2 ( RRID:AB_10692490 ) , rabbit anti-pErbB2 ( RRID:AB_490899 ) . Abcam: rabbit anti-Cops3 ( RRID:AB_1603748 ) , rabbit anti-Nedd8 ( RRID:AB_1267251 ) . Sigma-Aldrich: M2 mouse anti-FLAG ( RRID:AB_259529 ) , rabbit anti-FLAG ( RRID:AB_439687 ) , rabbit anti-GAPDH ( RRID: AB_796208 ) , mouse anti-HA ( RRID:AB_262051 ) , C2206 rabbit anti-Beta-catenin ( RRID:AB_476831 ) , mouse anti-Nedd8 ( RRID:AB_260757 ) . Progen: U100 mouse anti-Dsc1 ( Cat#65192 ) , p124 mouse anti-Dsg1 ( Cat # 651111 ) . Invitrogen: 27B2 mouse anti-Dsg1 ( RRID:AB_2533088 ) . NeoMarkers: AB-12 mouse anti-EGFR ( Cat # MS-400-P1 ) . GE Healthcare Biosciences: goat anti-GST ( RRID:AB_771432 ) . Enzo Life Sciences: rabbit anti-Nedd8 ( RRID:AB_2051982 ) . Millipore: FK2 mouse anti-Ub ( RRID:AB_612093 ) . Aves Laboratories: 1407 chicken anti-Pg . R&D Systems: goat anti-Dsg1 ( RRID:AB_2277393 ) . NW161 and NW6 rabbit anti-desmoplakin ( Bornslaeger et al . , 1996 ) . Promega: Anti-Erk 1/2 ( Cat #V114a ) . Santa Cruz: mouse anti-Cops3 ( RRID:AB_2081616 ) . Gift antibodies: 1G4 mouse anti-Dp ( gift from J . Wahl III , University of Nebraska , Omaha , NE , USA ) , rabbit anti-K1 , rabbit anti-K10 , rabbit-anti-Lor ( gifts from J . Segre National Human Genome Research Institute , Bethesda , Maryland , USA ) , 1G5 mouse anti-Alpha-catenin ( gift from Margaret Wheelock , University of Nebraska , Omaha , NE , USA , in memoriam ) , HecD-1 mouse anti-Ecad ( gift from M . Takeichi and O . Abe , Riken Center for Developmental Biology , Kobe , Japan ) . Western blot analysis included use of peroxidase-conjugated anti- mouse , -rabbit , and -chicken secondary antibodies purchased from SeraCare Life Sciences , Baltimore , MD ( formerly Kirkegaard and Perry Laboratories ) . AlexaFluor 488/568/647-conjugated goat anti- mouse , -rabbit , and –chicken secondary antibodies ( Invitrogen ) were used in immunofluorescence studies . The Dsg1 cytyoplasmic tail was cloned into a bait construct ( pSos-Dsg1 ) ( constructs provided by Invitrogen ) . The Dsg1-FLAG and Dsg1-GFP construct was generated as previously described ( Getsios et al . , 2009; Harmon et al . , 2013 ) . The HA-Nedd8 construct was obtained from Addgene ( plasmid #18711 ) and transiently transfected using Lipofectamine 2000 ( Invitrogen ) 2–15 μg/μl DNA . The phoenix packaging cell line ( provided by G . Nolan , Stanford University , Stanford California , USA ) , maintained in DMEM ( Mediatech ) supplemented with 10% FBS and 1% penicillin/streptomycin , was transfected with LZRS constructs ( 0 . 5–2 μg/ml ) . The day after transfection , cells were re-seeded into selection media with 1 μg/ml puromycin . Once cells reached 80% confluency , they were switched to 32°C for 24 hr to collect viral supernatant . In some cases viral supernatant was used immediately to infect primary keratinocytes , and in other cases viral supernatant was concentrated using Amicon Ultra-15 Centrifugal Filter Units ( Millipore ) . NHEKs were infected with virus and 8 μg/ml polybrene for 90–180 min at 32°C , after which cells were washed and returned to growth at 37°C in fresh media . NHEKs and SCC9s were electroporated with siRNA oligonucleotides at a final concentration of 10 nM via AMAXA nucleoporation ( Lonza ) using the Ingenio Electroporation Solution ( Mirus ) or solution V ( Lonza ) and program X-001 . siRNA directed towards DSG1 ( 5' CCA UUA GAG AGU GGC AAU AGG AUG A ) , DP ( Invitrogen Pool: oligonucleotides: 5’-GAA GAG AGG UGC AGG CGU A , 5’ GAC CGU CAC UGA GCU AGU A , and 5’ AAA CAG AAC GCU CCC GAU A ) , and COPS3 ( Invitrogen Stealth siRNA_siCOPS3-465: 5’ AUC AAU GUU AUG AAG CAU GGC UGG G or Integrated DNA Technologies Pool_IDT Pool: 5’-GGA UAU CUG UAA AGA GAA UGG AGC C , 5’-GGA UGUACA AGA ACA CUC CUU GGG C , 5’-UCC AUC CUG AGC UAA ACA AGA GAA A , and Dharmacon ( D-011494–03 ) _siCOPS3 Dharma3: 5’- UCC GAA ACC UGG UGA AUA A ) , NEDD8 ( Dharmacon siGENOME SMARTpool ( M-020081–01 ) : 5’-GAA AGG AGA UUG AGA UUG A , 5’-AGA UUG AGA UUG ACA UUG A , 5’-CAG ACA AGG UGG AGC GAA U , 5’-GGA GAU UGA GAU UGA CAU U ) , and scramble/non-targeting siRNA ( Dharmacon D-001206-14-20 ) . For analysis of protein expression levels , cells were washed in phosphate buffered saline ( PBS ) and lysed in urea sample buffer ( 8 M deionized urea , 1% sodium dodecyl sulfate , 10% Glycerol , 60 mM Tris pH 6 . 8 , and 5% β-mercaptoethanol ) . After equalizing total protein concentrations , samples were run on 7 . 5–15% SDS-PAGE gels and transferred to polyvinylidene fluoride ( PVDF ) or nitrocellulose membranes to be probed with primary and secondary antibodies against proteins of interest . For analysis of protein localization in cultured cells , NHEK coverslips were fixed and permeabilized by submerging in anhydrous methanol for 2–3 min at −20°C when visualizing Dp . All other antibodies were applied to NHEKs fixed and permeablized in paraformaldehyde followed with a 10 min 0 . 2% Triton X100 wash at 4°C . Cells were incubated in solutions with primary antibodies at 4°C overnight , and secondary antibodies at 37°C for 30–60 min , with multiple PBS washes after each step . For analysis of protein localization in tissue sections , paraffin sections of organotypic raft cultures were rehydrated and then heated to 95°C in 0 . 01 M citrate buffer for antigen retrieval . Sections were incubated in solution with primary antibody at 4°C overnight and in solution with secondary antibody at 37°C for 60 min . Coverslips and tissue sections were mounted on polyvinyl alcohol ( Sigma-Aldrich ) . Cells and tissues were visualized using a Leica microscope ( model DMR , Melville NY ) fitted with 40X ( PL Fluotar , NA 1 . 0 ) and 63X ( PL APO , NA 1 . 32 ) objectives . Images on the DMR microscope were acquired using an Orca 100 CCD camera ( model C4742-95; Hamamatsu , Bridgewater , NJ ) and analyzed using ImageJ software ( NIH version 2 . 0 . 0 ) or Metamorph . For N-SIM analysis , the samples were illuminated with spatially high-frequency patterned excitation light ( 100X objective lens , NA 1 . 49; TiE N-SIM microscope [Nikon] and iXON X3 897 camera [Andor Technology] ) . Images were reconstructed and analytically processed to reconstruct subresolution structure of the samples using Elements version 4 software ( Nikon ) . For siNEDD8 and siCOPS3 EGFR internalization , the Axioimager and the Apotome2 was used with the 40x objective and a MIP of the Z sections was used . 3D raft cultures were grown following protocols previously described ( Simpson et al . , 2010 ) . Collagen plugs were formed using rat-tail collagen type I ( BD ) and seeded with J2 fibroblasts ( NIH 3T3 ) . J2 fibroblasts were cultured in DMEM ( Mediatech ) supplemented with 10% FBS and 1% penicillin/streptomycin ( Mediatech ) . E-media was made as a 3:1 mix of DMEM/F-12 and DMEM media with 5% FBS , 10 μg/ml gentamicin ( Life Technologies ) , 0 . 4 μg/ml hydrocortisone ( Sigma ) , 10 ng/ml cholera toxin ( Sigma ) , and 0 . 25 μg/ml amphotericinB ( Life Technologies ) , mixed with a cocktail of 180 μM adenine ( Sigma ) , 5 μg/ml human recombinant insulin ( Sigma ) , 5 μg/ml human apo-transferrin ( Sigma ) , and 5 μg/ml triiodothyronine ( Sigma ) . After seeding with NHEKs , cancer cell lines ( SCC9 , 1483 ) , or NHEKs that had been electroporated with control or Cops3 siRNA ( Amaxa ) , rafts were grown in E-media with 5 ng/ml epidermal growth factor ( EGF ) ( EMD Millipore ) . Two days later , rafts were lifted to an air liquid interface and grown in E-media without EGF to induce epidermal differentiation , and harvested on the indicated number of days . Y2H screening was performed following the CytoTrap vector kit ( Stratagene ) as described ( Harmon et al . , 2013 ) . The CytoTrap screen ( Stratagene ) utilizes the temperature-sensitive cdc25H strain of S . cerevisiae , which is deficient in Ras signaling and unable to grow at 37°C . While yeast grow at the permissive temperature of 25°C , only yeast that are co-transformed with a bait ( pSos ) construct that binds a membrane-associated myristoylated target ( pMyr ) protein activate Ras signaling to allow for growth at 37°C . pMyr constructs are expressed under galactose promoters; thus , growth at 37°C on galactose plates without growth at 37°C on glucose plates indicates target-bait interaction ( growth on glucose plates at 37°C indicates temperature sensitive revertants ) . A library of HeLa cell cDNAs expressed in the pMyr vector was co-transformed with pSos-DP-NTP or pSos-Dsg1 into cdc25H yeast . After obtaining putative positives by identifying clones that were capable of growing on galactose plates at 37°C for interactions with Dsg1 , pMyr constructs were purified and individually co-transformed with pSos-Dsg1 to confirm specific interactions . pMyr-Pg was used as a positive binding control for Dsg1 , and pMyr and pMyr-SB ( Stratagene ) were respectively used as negative and positive controls for the screen . Dsg1-GST and GST constructs were bacterially expressed by adding isopropyl β-D-1-thiogalactopyranoside ( IPTG ) to BL21A1 bacteria ( Invitrogen ) . Bacteria were lysed and proteins were purified using glutathione agarose ( GE Healthcare ) . Beads were then incubated with NHEK cell lysates at 4°C and washed with buffer ( 500 mM NaCl , 50 mM Tris pH 7 . 6 , 10 mM MgCl2 , 1% Tx-100 , 0 . 1% SDS , 0 . 5% deoxycholate ) and eluted in urea sample buffer or Laemmli buffer with 5% β-mercaptoethanol . S-tag immunoprecipitations were performed as previously described ( Albrecht et al . , 2015 ) . Cells used for co-immunoprecipitation studies were rinsed twice in PBS on ice and lysed in ice-cold 1 . 0 ml RIPA buffer ( 500 mM NaCl , 50 mM Tris pH 7 . 6 , 10 mM MgCl2 , 1% Tx-100 , 0 . 1% SDS , 0 . 5% deoxycholate ) with complete protease inhibitor cocktail ( Roche ) . Cells were incubated with 0 . 5–1 . 5 μg of antibody against the protein of interest overnight at 4°C . The immunoprecipitate was then conjugated to Protein A/G beads ( Santa Cruz sc-2003 ) and eluted in urea sample buffer or Laemmli/Sample buffer ( 200 mM Tris pH6 . 8 , 20% glycerol , 4% SDS , 0 . 3% bromophenol blue ) with 5% β -mercaptoethanol . SCC9s and NHEKs used in EGFR immunoprecipitations were rinsed twice with PBS on ice and lysed in ice-cold 1 . 0 mL RIPA buffer ( 50 mM Tris H-Cl pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 1% sodium deoxycholate , 0 . 1% SDS ) with phosphatase inhibitors ( 1:100 Millipore Phosphatase Inhibitor Cocktail Set II ( 524625 ) and 1:50 Millipore Phosphatase Inhibitor Cocktail Set IV ( 524628 ) ) and Complete EDTA-free protease inhibitor cocktail ( Roche 05056489001 ) . Cells were incubated with EGFR antibody ( 1 μg/mL ) from Cell Signaling ( D38B1 ) for an overnight rotation at 4°C . 15 μL of Protein A/G PLUS-agarose beads ( Santa Cruz sc-2003 ) were added the next day for 45 min at 4°C . Beads were then washed in the RIPA buffer used for lysis , and proteins were eluted off beads using 30 μl of Laemmli lysis buffer . The entire lysate was loaded onto a SDS-PAGE gel for western blot analysis . To assay whether ubiquitination required neddylation for EGF-bound receptors , we performed EGFR IPs in SCC9s silenced for NEDD8 and serum starved , and treated these cells with EGF ligand ( 50 ng/mL ) for 5 min . Reagents used to conduct PLA were purchased from DUOLink Biosciences and used as described ( Harmon et al . , 2013 ) . Cells were rinsed and fixed as described above . After incubation with primary antibody overnight at 4°C , samples were incubated with PLA secondary antibodies conjugated to DNA oligonucleotides for 60 min at 37°C . Samples were then subjected to a 30 min incubation at 37°C for ligation of nucleotides , followed by a 100 min incubation at 37°C for rolling circle polymerization , resulting in the production of fluorescent dots ( shown in yellow or red ) if the antigens targeted by secondary antibodies were in close proximity ( 40–100 nm ) . ImageJ software or Metamorph was used to quantify the number of PLA and DAPI signals per image field . All experiments were performed independently >3 times ( i . e . biological replicates performed on different days , not technical replicates performed in parallel at the same time ) . For each independent/biological replicate , multiple experimental/control arms were processed and analyzed in parallel . Representative experiments are displayed throughout the figures and where indicated in the figure legends , quantification of experiments is reported as mean ±standard error mean ( SEM ) . For biochemical analyses , cell lysates were derived from a population of cells grown in 10 cm dishes ( approximately 4 million cells ) or 3 cm dishes ( approximately 1 million cells ) . For immunofluorescence or PLA analyses , cells were plated onto a coverslip in a 3 cm dish and quantified as follows: PLA particles were counted for 5–10 randomly selected fields , each containing 20–100 cells ( depending on whether image was obtained using a 40X or 63X objective and/or whether the image was from cultured cells or 3D organotypic rafts ) . Parallel analyses of protein specific antibodies with IgG ( mouse or rabbit ) were performed to control for false positives . Fluorescence pixel intensity at randomly selected cell borders was determined by multiplying the mean pixel intensity by the area of the defined border divided by the border length . Background intensity was randomly selected from an area on the image and subtracted from the border intensity . Two group comparisons were performed using two-tailed , two-sample equal variance Student’s t test using Excel ( Microsoft , Redmond , WA , USA ) . p-values<0 . 05 were considered statistically significant . Densitometric analyses were performed on scanned films of immunoblots with the lightest possible exposure , and numbers derived from the densitometric analyses were displayed below immunoblots as indicated in the figures . All densitometric quantifications were normalized to a protein standard , as indicated in figures . All immunofluorescence and densitometric calculations were performed using FIJI ( FIJI Is Just ImageJ ) ( Schindelin et al . , 2012 ) , ImageJ ( Schneider et al . , 2012 ) or Metamorph . | The outer layer of skin – the epidermis – forms a critical barrier against a range of stresses from the environment . The epidermis itself consists of multiple layers of cells that are constantly being renewed . New cells are made in the deepest layer and move upwards until they eventually reach the skin’s surface . During this journey , the cells change the molecules they make in a process called epidermal differentiation . To maintain an effective barrier , the epidermis must balance the division of cells in the deepest layer with the differentiation of cells in the layers above . When activated , a protein called the Epidermal Growth Factor Receptor ( or EGFR for short ) encourages cells in the deepest layer to divide . However , it remains poorly understood how the balance between cells dividing and cells differentiating is achieved . The desmosome is a structure that can link together cells within the epidermis . Najor et al . now report a new interaction between the desmosome and a very large protein complex called the COP9- signalosome known to remove protein-based tags from other proteins . The experiments show that the COP9-signalosome results in the removal of these tags from EGFR . The status of the tags on EGFR regulates whether or not it is found at the cell surface . Najor et al . propose that that the desmosome acts as a scaffold and holds the COP9 signalosome close to EGFR . The enzyme in the COP9 signalosome then removes protein-based tags from EGFR , which triggers a series of events that remove EGFR from the cell surface . This dampens down the signals EGFR would normally send to make cells divide , and allows differentiation to proceed . The balance between cell division and differentiation is a fundamental process that is affected in many skin conditions , including psoriasis and atopic dermatitis . EGFR is also commonly overactive in cancers . As such , understanding how epidermal differentiation and cell division are controlled will shed light on a variety of disorders , allowing for the potential development of new treatments for these diseases . | [
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] | 2017 | Epidermal Growth Factor Receptor neddylation is regulated by a desmosomal-COP9 (Constitutive Photomorphogenesis 9) signalosome complex |
Humans and other animals routinely identify and attend to sensory stimuli so as to rapidly acquire rewards or avoid aversive experiences . Emotional arousal , a process mediated by the amygdala , can enhance attention to stimuli in a non-spatial manner . However , amygdala neural activity was recently shown to encode spatial information about reward-predictive stimuli , and to correlate with spatial attention allocation . If representing the motivational significance of sensory stimuli within a spatial framework reflects a general principle of amygdala function , then spatially selective neural responses should also be elicited by sensory stimuli threatening aversive events . Recordings from amygdala neurons were therefore obtained while monkeys directed spatial attention towards stimuli promising reward or threatening punishment . Neural responses encoded spatial information similarly for stimuli associated with both valences of reinforcement , and responses reflected spatial attention allocation . The amygdala therefore may act to enhance spatial attention to sensory stimuli associated with rewarding or aversive experiences .
Inherently emotional stimuli and stimuli that have acquired emotional meaning through learning can enhance spatial attention ( Armony and Dolan , 2002; Maunsell , 2004; Anderson , 2005; Phelps et al . , 2006; Peck et al . , 2009 ) . Enhanced spatial attention improves sensory processing of behaviorally relevant stimuli ( Anderson et al . , 2011 ) and quickens purposeful actions based on these stimuli ( Posner , 1980 ) . Subjects must not only register the emotional significance of a stimulus to enhance spatial attention , but they also must locate the stimulus . For example , in the wild , carnivores must identify potential predators and prey , register their emotional or motivational significance , and then locate the predator or prey if they want to mount an attack or retreat . The brain must therefore combine information about the motivational significance and location of stimuli to enhance spatial attention and facilitate actions . The amygdala , a brain area traditionally linked to emotional processes ( LeDoux , 2000; Phelps and LeDoux , 2005; Murray , 2007; Pessoa and Adolphs , 2010 ) , may play an important role in enhancing spatial attention to emotional stimuli . A substantial body of literature has documented the amygdala's importance in processing both aversive ( Campeau and Davis , 1995; Quirk et al . , 1995 ) and appetitive stimuli ( Sanghera et al . , 1979; Nishijo et al . , 1988; Holland and Gallagher , 1993; Schoenbaum et al . , 1998; Baxter and Murray , 2002; Carelli et al . , 2003; Sugase-Miyamoto and Richmond , 2005; Ambroggi et al . , 2008; Tye et al . , 2008; Shabel and Janak , 2009; Bermudez and Schultz , 2010; Jenison et al . , 2011 ) . Recent studies have compared amygdala neural responses to the same conditioned stimulus ( CS ) when paired with a rewarding or aversive unconditioned stimulus ( US ) ; neurons often responded differentially to the CS depending on whether it predicted a positive or negative outcome ( Paton et al . , 2006; Belova et al . , 2007 , 2008; Morrison et al . , 2011 ) suggestive of a valence-specific coding scheme where neurons respond to stimuli on a ‘good-to-bad’ scale . Further , different populations of neurons fired more for either reward- or punishment-predicting stimuli , raising the possibility that the amygdala contains distinct networks for processing stimuli possessing appetitive and aversive associations ( Zhang et al . , 2013 ) . Neural signals encoding valence are likely critical for a range of cognitive and behavioral functions where adaptive responses differ fundamentally depending upon valence , such as approach and defensive or avoidance behaviors , economic choice behavior , and many psychophysiological responses that are known to be valence-specific ( e . g . the startle response ) ( Lang and Davis , 2006 ) . Indeed , a recent study has now documented that distinct appetitive and aversive circuits in the amygdala are causally related to valence-specific behavior ( Redondo et al . , 2014 ) . Valence alone cannot describe all emotions , as both positive and negative emotional experiences can also vary in intensity . Emotional intensity may be related to processes like arousal , which can be triggered by stimuli of both valences and be characterized quantitatively by psychophysiological measures ( Lang et al . , 1993 ) . Prior studies indicate that the firing rates of amygdala neurons are correlated in some circumstances with valence-nonspecific aspects of conditioned ( Shabel and Janak , 2009 ) and unconditioned stimuli ( Belova et al . , 2007 ) , suggesting that the amygdala could modulate arousal or related processes . Nearly all previous studies have assumed that if the amygdala modulates valence-nonspecific processes , it does so in a non-spatial manner ( Holland and Gallagher , 1999; Maddux et al . , 2007 ) . Recent work , however , has provided a new conceptual framework for understanding how the amygdala might modulate valence-nonspecific processes , as neural activity in the amygdala has been linked to spatial attention . Amygdala neurons encode information about both the spatial location and reward association of visual stimuli ( Peck et al . , 2013; Peck and Salzman , 2014 ) , and the maintenance of coordinated amygdala signals representing space and reward is task dependent ( Peck et al . , In press ) . The encoding of space and reward in the primate amygdala has now also been confirmed by human neuroimaging data ( Ousdal et al . , 2014 ) . Furthermore , amygdala neural activity is correlated with a behavioral measure of spatial attention , saccadic reaction times to a barely perceptible target ( Peck et al . , 2013; Peck and Salzman , 2014 ) . Correlations between reaction time and amygdala activity have a different sign depending upon the location of the target , with increased activity predicting shorter reaction times to some locations and longer reaction times to other locations . A framework in which the amygdala merely represents the motivational significance of a stimulus in a valence-nonspecific and spatial-nonspecific manner cannot explain these data due to the spatial dependence of these correlations . If an amygdala neuron merely represents motivational significance then correlations between amygdala activity and reaction times would have the same sign regardless of the location of the saccade target . Increased activity , for example , would predict shorter reaction times for all target locations , which is not consistent with the recent findings . The reports just described indicate that the amygdala encodes information about space and reward and that neural activity is correlated with spatial attention allocation to stimuli associated with reward . Of course , subjects often exhibit enhanced attention to stimuli threatening aversive events , a behavior that may be mediated by the amygdala as well . In humans , an intact amygdala is vital for guiding gaze towards emotionally-relevant features of fearful face stimuli ( Adolphs et al . , 2005 ) and for augmenting BOLD responses to these stimuli in the ventral visual areas ( Vuilleumier et al . , 2004 ) that receive amygdalar input ( Amaral and Price , 1984 ) and play an important role in attentional processing ( Reynolds and Chelazzi , 2004 ) . Moreover , neuroimaging data has revealed that unilateral amygdala lesions are associated with decreased selectivity for negatively valenced stimuli in ipsilateral visual cortices ( Vuilleumier et al . , 2004 ) , a finding consistent with amygdalar projections to visual cortex being primarily ipsilateral ( Iwai and Yukie , 1987 ) . Thus , the pathway from the amygdala to ventral visual areas may be important in guiding spatial attention towards stimuli associated with both rewarding and aversive outcomes . If neural activity in the amygdala has a consistent role in modulating spatial attention allocation to both rewarding and threatening stimuli , then amygdala neurons should represent threatening stimuli within a spatial framework , just like they do for rewarding stimuli . Furthermore , neural activity should reflect the allocation of attention to stimuli threatening aversive events . We therefore determined if neural activity in the amygdala reflects the motivational significance of sensory stimuli of both valences in a spatial framework . We recorded the activity of individual amygdala neurons while monkeys performed a task in which stimuli associated with aversive or appetitive outcomes attracted spatial attention . Amygdala neurons represented the spatial location of both reward- and punishment-predicting stimuli , and modulation occurred in the same direction for both types of stimuli . These results suggest that the amygdala provides a means for modulating the neuronal networks responsible for spatial attention allocation to emotionally significant stimuli of both valences .
We trained two monkeys on a detection task in which conditioned stimuli associated with either appetitive or aversive outcomes biased attention ( Figure 1A ) . While monkeys maintained fixation , two visual cues appeared briefly for 300 ms on either side of the fixation point . Following cue offset , a variable-length delay period ensued before the 50 ms presentation of a barely-perceptible target in the same location as one of the two cues . The monkeys completed the trial correctly ( a ‘hit’ ) by making a saccadic eye movement to the location of the target within 600 ms . Generally , ‘miss’ trials occurred when the monkey failed to make a saccade at all ( 61% of miss trials ) since the timing of target onset was variable and the target itself was difficult to detect; however , miss trials also included those where a saccade was directed towards the location opposite the target ( 28% of incorrect trials ) or elsewhere ( 11% of incorrect trials ) . All trials where monkeys' gaze left the fixation window before target onset were repeated such that they could not avoid a particular trial type . 10 . 7554/eLife . 04478 . 003Figure 1 . Detection task design . ( A ) Task schematic . After fixating , a pair of cues appeared at either side of the fixation point . Following a variable delay , a target appeared at one of the two locations; trials were scored a ‘hit’ if monkeys made a saccade to the target's location and a ‘miss’ if they failed to do so . ( B ) Association between cues and outcomes . The table illustrates the outcomes associated with each cue type ( given that the target appeared at that cue's location ) on hit and miss trials . ( C ) Trial types . On a given trial , monkeys viewed a reward and punishment cue ( R/P ) , a reward and neutral cue ( R/N ) , or a punishment and neutral cue ( P/N ) . Each trial type had two possible spatial configurations ( only one configuration is shown ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 003 We used three types of cues in our experiments: ( 1 ) a reward ( R ) cue which indicated an opportunity to obtain a drop of juice , ( 2 ) a punishment ( P ) cue which threatened delivery of an air puff if the monkey missed the target , and ( 3 ) a neutral ( N ) cue which predicted no outcome for either hit or miss trials ( Figure 1B ) . The location of the target was selected randomly , and the reinforcement contingencies enforced on the trial were dictated by the visual cue that had appeared at that location earlier in the trial . Two different cues were randomly chosen to appear on each trial , resulting in 3 randomly interleaved trial types ( Figure 1C; R/P , R/N , & P/N ) . The spatial configuration of the cues was also chosen at random on each trial . In addition , two distinct cue sets were interleaved to control for any neural or behavioral preferences specific to a given cue's appearance; the same set of 6 cues was used throughout data collection . Behavioral metrics indicate that monkeys understood the reinforcement contingencies dictated by the cues . Monkeys paid more attention to cues that predicted an opportunity to obtain a reward or that threatened a punishment as compared to those that predicted a performance-independent neutral outcome . When the target appeared at the reward cue location relative to either the punishment cue or neutral cue location ( R/P & R/N trials ) , or when the target appeared at the punishment cue location relative to the neutral cue location ( P/N trials ) , hit rate was greater ( Figure 2A; χ2-test , P << 10−4 ) , reaction time was shorter ( Figure 2B; Wilcoxon , p < 10−23 ) , and false alarm frequency was greater ( i . e . frequency of saccades to a cue location before the target appeared; Figure 2C; χ2-test , P << 10−4 ) . These results were true for each monkey ( p < 0 . 05 ) with the exception that reaction times did not differ on P/N trials for monkey L ( p = 0 . 45 ) . Thus , obtaining a reward was of greatest importance for the monkeys , but they also preferred to avoid an air puff rather than responding to a target that resulted in no reinforcement outcome . 10 . 7554/eLife . 04478 . 004Figure 2 . Monkeys allocate attention according to stimulus–outcome associations in a detection task . ( A ) Hit rate varied according to the cue–outcome associations . Hit rate is plotted for R/P trials ( mean ± standard error; R cue: 0 . 879 ± 0 . 004; P cue: 0 . 362 ± 0 . 007 ) , R/N trials ( R cue: 0 . 896 ± 0 . 004; N cue: 0 . 288 ± 0 . 006 ) and P/N trials ( P cue: 0 . 744 ± 0 . 006; N cue: 0 . 597 ± 0 . 007 ) ; green asterisks indicate a significant difference between each pair of target conditions ( χ2-test , p < 10−4 ) . ( B ) Reaction times for R/P trials ( R cue: 0 . 189 ± 0 . 001 s; P cue: 0 . 266 ± 0 . 002 s ) , R/N trials ( R cue: 0 . 187 ± 0 . 001 s; N cue: 0 . 277 ± 0 . 002 s ) and P/N trials ( P cue: 0 . 222 ± 0 . 002 s; N cue: 0 . 247 ± 0 . 002 s; green astericks: Wilcoxon , p < 10−4 ) . ( C ) False alarm frequency for R/P trials ( R cue: 0 . 333 ± 0 . 003; P cue: 0 . 034 ± 0 . 001 ) , R/N trials ( R cue: 0 . 353 ± 0 . 003; N cue: 0 . 023 ± 0 . 001 ) and P/N trials ( P cue: 0 . 147 ± 0 . 002; N cue: 0 . 097 ± 0 . 002; green asterisks: χ2-test , p < 10−4 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 004 During task performance , we recorded the extracellular action potentials of 186 single units ( SUA ) and 159 multi-unit sites ( MUA ) from the left amygdala of two monkeys ( monkey L: 46 SUA , 45 MUA; monkey O: 140 SUA , 114 MUA ) . We analyzed firing rates in three time windows: 100–400 , 400–700 , and 700–1000 ms after cue onset; given that the earliest time of target onset was 700 ms after cue onset , these windows encompassed the cue–driven activity , the delay activity before a target could appear , and the delay activity during which a target could appear , respectively . Firing rates were not analyzed if target onset was before the end of that window . For all forms of selectivity , we used a receiver-operator characteristic analysis ( ROC ) to compare firing rate distributions across trial conditions , and a Wilcoxon test to assess the significance of firing rate differences ( p < 0 . 05 ) . We combined MUA and SUA for all our analyses since the results were similar for each; we address this similarity below with respect to the specific analyses . Spatial selectivity for the reward cue was characterized by comparing activity from when the reward cue appeared contralateral to the recording site ( R-contra trials ) to when it appeared ipsilaterally ( R-ipsi trials ) . For this analysis , we combined data from R/P and R/N trials ( Figure 3A ) ; as we discuss later , neural discrimination between R/P & R/N trials ( given a particular spatial configuration ) was relatively weak compared to the discrimination between R-contra and R-ipsi trials . The location of the reward cue had a strong influence on firing rates such that many sites in each time window responded differentially on R-contra and R-ipsi trials ( Table 1; Figure 3B ) ; this population included sites that had significantly greater ( spatial-reward selectivity index >0 . 5 ) or lesser ( index <0 . 5 ) firing rates when the reward cue appeared contralaterally . The overall reward predicted by the cues influenced firing rates as well ( Table 1; Figure 3B ) ; firing rates were often significantly higher ( reward selectivity index >0 . 5 ) or lower ( index <0 . 5 ) when the reward cue was presented ( R-present trials ) than when it was absent ( R-absent trials ) . 10 . 7554/eLife . 04478 . 005Figure 3 . The spatial location of cues predicting rewarding and aversive outcomes modulates amygdala neural activity . ( A ) Grouping of trial types for the purpose of neural analyses . ( B ) Example neuron firing rates as a function of time relative to cue onset . Firing rates are plotted for the four trial types ( illustrated in A ) and shading indicates the standard error of firing rates across trials . For each neuron , spatial-reward selectivity was significant in all time windows ( Wilcoxon , p < 0 . 05 ) ; for the neuron on the left , spatial-punishment selectivity was significant in the first two time windows ( 100–400 ms , 400–700 ms ) , and the for the neuron on the right , spatial-punishment selectivity was significant in the last two time windows ( 400–700 ms , 700–1000 ms ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 00510 . 7554/eLife . 04478 . 006Table 1 . Counts of sites with significant selectivityDOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 006Reward selectivitySpatial-reward selectivitySpatial-punishment selectivityR present > R absentR contra > R ipsiP contra > P ipsiR present < R absentR contra < R ipsiP contra < P ipsi100–400 ms95 ( 27 . 5% ) 101 ( 29 . 3% ) 26 ( 7 . 7% ) 85 ( 24 . 6% ) 51 ( 14 . 8% ) 22 ( 6 . 5% ) n = 345 , p < 10−4n = 345 , p < 10−4n = 345 , p < 10−4400–700 ms85 ( 24 . 6% ) 67 ( 19 . 4% ) 12 ( 3 . 6% ) 91 ( 26 . 4% ) 64 ( 18 . 6% ) 16 ( 4 . 8% ) n = 345 , p < 10−4n = 345 , p < 10−4n = 334 , p = 0 . 0065700–1000 ms77 ( 22 . 5% ) 62 ( 18 . 3% ) 8 ( 2 . 8% ) 77 ( 22 . 5% ) 54 ( 15 . 9% ) 9 ( 3 . 1% ) n = 342 , p < 10−4n = 339 , p < 10−4n = 288 , p < 0 . 4969For each cell within the table , the counts of sites with ‘positive’ selectivity ( Wilcoxon , p < 0 . 05; selectivity index >0 . 5 ) and ‘negative’ selectivity ( selectivity index <0 . 5 ) are displayed . Percentages were calculated relative to the number of sites for each type of selectivity and time window; this number varied due to the decreasing number of trials available for analysis in later time windows . The frequency of significantly selective neurons , including those with positive and negative selectivity , was tested against chance frequency ( Binomial-test , α = 0 . 05 ) . We quantified spatial selectivity for the punishment predicting cues by comparing firing rates on P/N trials according to whether the punishment cue appeared contralaterally ( P-contra ) or ipsilaterally ( P-ipsi ) . We expected that the spatial influence of the punishment cue would be more apparent when it was the primary locus of attention ( i . e . when the R cue was absent ) and therefore focused our analysis on these trials . Sites often fired differentially between P-contra and P-ipsi trial ( Table 1; Figure 3B ) , with sites firing significantly more ( spatial-punishment selectivity index >0 . 5 ) or less ( index <0 . 5 ) on P-contra trials . In agreement with the relatively small effect the punishment cue had on behavior as compared to the reward cue ( Figure 2 ) , spatial-punishment selectivity was generally weaker than spatial-reward selectivity . The population of spatial-punishment selective sites was significantly smaller than the population of spatial-reward selective sites ( χ2-test , p < 10−4 for each time window ) , and the magnitude of spatial-reward selectivity was significantly greater than that of spatial-punishment selectivity ( compare |ROC—0 . 5|; Paired Wilcoxon , p < 10−5 in each time epoch ) . These observations do not necessarily imply that the behavioral significance of reward is inherently greater than that of punishment , only that the punishment was made relatively mild in our task in order to keep the monkeys engaged in the task . The relative frequency of sites with positive- and negative-selectivity , on the other hand , was similar for spatial-reward and spatial-punishment selectivity ( χ2-test , p > 0 . 12 for each time window ) . Finally , while the frequency of neurons demonstrating significant spatial-punishment selectivity was not greater than chance in the time window where the monkeys could be required to detect the target ( Table 1; 700–1000 ms ) , this signal was still apparent for the neural population , which we discuss below . We next examined the relationship between the types of selectivity that we have described . Consistent with our previous results ( Peck et al . , 2013 ) , we found a strong , positive relationship between reward selectivity and spatial-reward selectivity that was significant in each time epoch ( Figure 4A; linear regression , p < 10−32 ) , indicating that those sites that fired more when the reward cue was present tended to fire more when that reward cue appeared contralaterally . Time epoch did not have a significant effect on the slope of these regressions ( ANCOVA , p = 0 . 0940 ) . 10 . 7554/eLife . 04478 . 007Figure 4 . Amygdala neurons exhibit consistency between reward selectivity , spatial-reward selectivity , and spatial-punishment selectivity . ( A ) Relationship between reward selectivity and spatial-reward selectivity indices in each time epoch . ( B ) Relationship between spatial-reward selectivity and spatial-punishment selectivity indices . For both ( A ) and ( B ) , plot style indicates the significance selectivity for each recording site ( see legends ) and regressions lines are plotted ( significant in each case , p < 0 . 001 ) . ( C ) 3D reconstruction of the whole brain and the amygdala for Monkey O ( top ) , and the 3D reconstruction of the amygdala overlaid on a single coronal MRI slice for that monkey ( bottom ) . ( D and E ) Recording sites . Each coronal slice has been tilted to enable visualization of all electrode tracks . Arrows provide the orientation of the slice after tilting . Each data point represents the location of one site recorded during the task for each monkey ( Monkey L: left; Monkey O: right ) and the significance of selectivity for that site ( D , as in A; p < 0 . 05 in at least one time epoch ) or the degree of sign-matching/magnitude of the spatial-reward and spatial-punishment selectivity indices ( E ) . In ( E ) positive values ( green dots ) indicate those neurons with matching signs of selectivity , while negative values ( red dots ) indicate non-matching selectivity; the brightness of the data points indicates the magnitude of the selectivity . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 007 Crucially , we next asked whether the spatial selectivity for the reward cue matched the spatial selectivity for punishment cues by examining the linear relationship between spatial-reward and spatial-punishment selectivity indices . Since these two sets of selectivity indices were computed from firing rates on non-overlapping sets of trials ( either R-present or R-absent ) , there was no inherent relationship between the indices and any observed correlation would indicate systematic correspondence of spatial selectivity for reward-predictive and punishment-predictive cues . We also note that an analysis parallel to that in Figure 4A with a spatially non-specific form of punishment selectivity ( on the x-axis ) was not possible since reward had a considerably more profound influence on firing rates . We observed a clear positive relationship between spatial selectivity for reward and punishment cues in each time epoch ( Figure 4B; linear regression , p = 1 . 6*10−22 , 0 . 0001 , 0 . 0007 in the 100–400 ms , 400–700 ms , and 700–1000 ms epochs , respectively ) ; these regression slopes were statistically indistinguishable between monkeys ( ANCOVA , p = 0 . 10 , 0 . 59 , 0 . 74 for each time window ) and between SUA/MUA ( p = 0 . 51 , 0 . 59 , 0 . 65 ) . This relationship ( Figure 4B ) was not a byproduct of the correlation between reward and spatial-reward selectivity; using a multiple linear regression , we found that spatial-reward selectivity ( β = 0 . 22 , 0 . 12 , 0 . 10 in each time epoch ) , more so than reward selectivity ( β = 0 . 08 , −0 . 00 , −0 . 02 ) , was predictive of spatial-punishment selectivity . There was a significant effect of time epoch on the slope of the regressions lines ( ANCOVA , p < 10−6 ) , which were greatest in the 100–400 ms epoch ( see Figure 4B ) . Despite the difference in the relationship across time epochs , reward , spatial-reward , and spatial-punishment selectivity indices themselves were all positively correlated across time windows ( 100–400 -> 400–700 ms & 400–700 -> 700–1000 ms; p < 0 . 0001 except p = 0 . 14 for spatial-punishment , 400–700 -> 700–1000 ms ) . The correspondence between spatial-reward and spatial-punishment selectivity was also apparent for individual recording sites . Of those responses ( i . e . for each site and time window ) with significant spatial-reward and spatial punishment selectivity ( n = 64 ) , the sign of selectivity was the same for 54 ( 84%; Binomial-test , p < 10−7 ) . Since attention was biased contralaterally when either a reward ( on R/P or R/N trials ) or punishment cue appeared contralaterally ( on P/N trials ) , the positive relationship and sign-agreement between these selectivity indices suggest that the spatial signals in the amygdala may influence spatial attention in a similar manner for stimuli promising rewards or threatening punishments . MRI reconstruction of the recording sites ( Figure 4C–E ) indicated that neurons were not anatomically clustered according to their response selectivity . Sites exhibiting a significant preference for either R-present or R-absent trials were intermingled anatomically , and significant spatial selectivity was widespread ( Figure 4D ) . Sites with sign-agreement between spatial selectivity indices ( spatial-reward and spatial-punishment indices both >0 . 5 , or both <0 . 5 ) were also intermingled with those whose selectivity disagreed in sign ( Figure 4E ) . In the previous analyses , firing rates were modulated according to the primary focal point of attention , which was either the location of the reward cue on R-present trials ( i . e . spatial-reward selectivity ) or the punishment cue on R-absent trials ( i . e . spatial-punishment selectivity ) . We next asked if the cue secondary in terms of attentional priority modulated neural activity when it was presented simultaneously with the reward cue . Monkeys' behavior indicated that the cue appearing along with the reward cue ( either the P or N cue ) modulated spatial attention . We compared R/P and R/N trials and found that hit rate was higher when the target appeared at the P cue location on R/P trials than when it appeared at the N cue location on R/N trials ( Figure 5A; χ2-test , p < 10−4 ) ; this effect was similar for both monkeys ( monkey O: p < 10−4; monkey L: p = 0 . 0696 ) . 10 . 7554/eLife . 04478 . 008Figure 5 . Amygdala neurons reflect subtle changes in attention on R-present trials driven by punishment cues . ( A ) Hit rates for secondary cues compared across R/P and R/N trials ( bottom ) . Hit rates were greater for the P cue relative to the N cue; green stars indicate significance ( p < 0 . 05 ) . ( B ) Trial types used to compute punishment-contra and punishment-ipsi selectivity indices and corresponding behavior; dashed rectangles indicate the contralateral hemifield in each comparison . ( C ) Firing rates of an example neuron on R/P & R/N trials . For this neuron , both punishment-contra and punishment ipsi selectivity were significant ( p < 0 . 05 ) in the early time window ( 100–400 ms ) . ( D and E ) Relationship between spatial-reward selectivity indices and ( D ) punishment-contra or ( E ) punishment-ipsi selectivity indices for each time epoch . Spatial-reward selectivity indices on the x-axis are the same as those on the x-axis of Figure 4B . Plot style indicates the significance of selectivity indices ( see legend ) ; solid and dashed regression lines indicate significant ( p < 0 . 05 ) and non-significant relationships , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 008 The data presented so far suggests that even when the highly salient R cue is present , monkeys allocate slightly more attention to the ‘non-rewarded’ field when a P cue appeared there ( R/P trials ) compared to an N cue ( R/N trials ) . We hypothesized that neural activity would reflect the bias in attention elicited by the P cue . To compare neural activity when the P cue appeared on R/P trials to when the N cue appeared on R/N trials , we again computed selectivity indices ( ROC ) to assess the difference in firing rates on R/P and R/N trials given a particular spatial configuration . We analyzed the effect of including the P cue contralaterally when the reward cue was ipsilateral ( punishment-contra selectivity; Figure 5B , top ) as well as the effect of the P cue appearing ipsilaterally while the R cue was contralateral ( punishment-ipsi selectivity; Figure 5B , bottom ) . On an individual site basis , the P cue had a relatively small influence on firing rate when appearing along with the reward cue . The number of sites that exhibited significant ( Wilcoxon , p < 0 . 05 ) punishment-contra selectivity was greater than chance only in the 100–400 time window ( Binomial-test , p = 0 . 0011; p > 0 . 22 otherwise ) and was not greater than chance in any window for punishment-ipsi selectivity ( p > 0 . 21 ) . The strong bias in attention towards the reward cue on these trials , made clear from behavioral metrics ( Figure 2 ) , likely attenuated neural discrimination between the P cue and N cue . Despite the relatively weak selectivity of individual sites for these comparisons , modulation of firing rates due to the primary ( R ) cue predicted the modulation induced by the secondary ( P vs N ) cue . Consider the example neuron in Figure 5C . For this neuron , positive spatial-reward selectivity ( selectivity index >0 . 5 ) indicated that the neuron fired more when attention was directed contralaterally by the R cue . The punishment-contra selectivity index for this neuron was significantly greater than 0 . 5 ( 100–400 ms; Wilcoxon , p < 0 . 05 ) , indicating that this cell increased firing when more attention was pulled contralaterally by the P cue ( relative to the N cue ) . Moreover , the punishment-ipsi selectivity index was significantly less than 0 . 5 ( 100–400 ms; p < 0 . 05 ) , indicating that the neuron fired less when attention was pulled ipsilaterally . Across recordings , spatial-reward and punishment-contra selectivity indices were positively correlated ( Figure 5D; linear regression , p = 0 . 0012 , 0 . 0082 , 0 . 0439 in each time epoch ) indicating that those sites firing more when an R cue appeared contralaterally tended to fire more when a contralateral P cue ( as opposed to a contralateral N cue ) appeared with an ipsilateral R cue; time epoch did not have a significant effect on the slope of these regressions ( ANCOVA , p = 0 . 83 ) . Strikingly , spatial-reward and punishment-ipsi selectivity indices were negatively correlated ( as opposed to the positive correlation observed between spatial-reward and punishment-contra indices ) in the 700–100 ms epoch ( Figure 5E; p = 0 . 0009; p = 0 . 59 , 0 . 59 in the 100–400 and 400–700 ms epochs , respectively ) . The difference in this relationship across time epochs was verified by a significant effect of epoch for the spatial-reward/punishment-ipsi relationship ( ANCOVA , p = 0 . 0136 ) . Both punishment-contra and punishment-ipsi selectivity indices were correlated across time epoch ( 100–400 -> 400–700 ms & 400–700 -> 700–1000 ms; linear regression , p < 0 . 001 in each case ) suggesting consistent coding across time even when its strength differed . Both relationships ( Figure 5D , E ) did not differ significantly between SUA/MUA ( ANCOVA , p > 0 . 15 in each time epoch ) . Across monkeys , relationships were generally statistically indistinguishable ( ANCOVA , p > 0 . 30 ) except that the slope between spatial-reward and punishment-contra selectivity indices was significant greater ( p = 0 . 0068 ) for Monkey L ( linear regression , β = 0 . 23 , p = 0 . 0071 ) than for Monkey O ( β = 0 . 04 , p = 0 . 12 ) in the 400–700 ms epoch ( Figure 5D , center ) . This discrepancy may be related to the fact that the relationship was more apparent for Monkey O in the 700–1000 epoch ( Monkey O: β = 0 . 06 , p = 0 . 0384; Monkey L: β = 0 . 03 , p = 0 . 68; Figure 5D , right ) . Since both of these relationships were present late in the trial during times when the target could appear , these signals could have influenced perceptual detection . Overall , amygdala neurons reflect changes in attention driven by stimuli threatening aversive outcomes even when attention is primarily directed at the location of a reward-predicting cue . While not a trial-to-trial measure , the neural tracking of these small biases in spatial attention may be related to the correlation between small trial-to-trial fluctuations in attention and amygdala firing rates that we have described previously ( Peck et al . , 2013; Peck and Salzman , 2014 ) . The results presented so far demonstrate that stimuli predicting rewards and stimuli threatening delivery of an aversive outcome can enhance attention . Furthermore , amygdala neurons encode spatial information in a manner appropriate for balancing attention between the two hemifields upon viewing stimuli that predict rewarding and aversive events . We next considered the possibility that in our task P cues may attract more attention than N cues because monkeys find the act of avoiding an aversive outcome inherently rewarding , in which case the P cue may not be viewed as being aversive . If this were the case , then both the R cue and P cue could be viewed as more ‘rewarding’ than the N cue given the pleasurable possibilities of obtaining reward and avoiding punishment , respectively . Below , we report behavioral measures that confirm that monkeys in fact viewed P cues as aversive stimuli , indicating that the potentially rewarding aspect of punishment avoidance did not confound our neural results . Two lines of evidence suggest that the P cue retained aversive meaning to monkeys despite its offering the possibility of avoiding the air puff . First , monkeys still experienced the P cue in association with punishment quite frequently during experiments . Overall , monkeys failed to detect 45% of targets appearing at the P cue location ( across R/P and P/N trial types ) ; all of these ‘miss’ trials resulted in air puff delivery . Second , monkeys were more likely to abort trials that included the P cue , indicated that the trials were associated with a less desirable outcome . When the monkey's gaze left the fixation window before the target appeared , some saccades were directed at one of the two cue locations ( false alarms ) and others were directed elsewhere ( aborts ) . False alarms likely reflect a monkey's desire to detect a target at a given location ( Figure 2C ) , but aborts likely indicating that a monkey preferred not to complete a particular trial type . By this logic , if the P cue was associated with aversive meaning to monkeys , we would observe the highest abort rate on the least valuable trials ( P/N ) , and the lowest abort rate on the most valuable trials ( R/N ) . For each monkey , the inclusion of the R cue tended to decrease the frequency of aborts ( Figure 6 , compare R/P vs P/N trials; Bonferroni-corrected χ2-test , p < 10−4 each for monkey ) . In contrast , the inclusion of the P cue tended to increase the frequency of aborts ( compare R/P vs R/N trials; p < 0 . 05 for each monkey ) . We observed this behavior during distinct portions of the trial for each monkey: monkey O exhibited this pattern for aborts around the time that the cue was on ( 0–300 ms after cue onset ) , and monkey L exhibited this pattern during the subsequent delay ( 300–1000 ms after cue onset ) . The results in the other time window for each monkey ( Monkey L , 0–300 ms; Monkey O , 300–1000 ms ) did not contradict these results; neither monkey showed a significant difference in abort frequency between R/P and R/N trials ( p > 0 . 62 ) . Abort frequency therefore correlated with the overall reinforcement value of the cues where R/N trials are the most valuable and P/N trials are the least valuable . Overall , these behavioral results suggest that monkeys find the punishment cue aversive , and modulations in attention are therefore unlikely to be due to the rewarding aspect of anticipating punishment avoidance . 10 . 7554/eLife . 04478 . 009Figure 6 . Monkeys break fixation in proportion to the reinforcement value of the cues . Fixation break frequency is plotted for each trial type for monkey L ( left; mean ± standard error; R/P: 0 . 082 ± 0 . 004; R/N: 0 . 069 ± 0 . 004; P/N: 0 . 127 ± 0 . 005 ) and monkey O ( right; R/P: 0 . 012 ± 0 . 001; R/N: 0 . 009 ± 0 . 001; P/N: 0 . 028 ± 0 . 002 ) . Time windows are relative to cue onset , and green asterisks indicate the significance of comparisons ( p < 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 009 In our task , monkeys have two ways of responding adaptively to the threat of an air-puff: they can detect the target and make an eye movement to avoid air-puff , or they can simply abort the trial by breaking fixation ( although the trial would be repeated ) . We wondered whether neural activity would reflect the threat of the air-puff , and we used the monkeys abort behavior as a behavioral assay of threat . Recall that abort frequency was highest on P/N trials , when reward was not possible but the threat of an air-puff loomed . For each trial type ( R-contra , R-ipsi , P-contra , and P-ipsi ) , we compared firing rates on abort and non-abort trials ( Wilcoxon , p < 0 . 05 ) . Significant selectivity for aborts was more frequent than expected by chance on P-contra trials ( 7 . 9% , 7 . 6% , 10 . 3% of neurons in each time epoch; Binomial-test , p < 0 . 0414 , 0 . 0707 , 0 . 0050; Figure 7A , B ) ; the proportion of significantly selective neurons was greater than chance in the 100–400 ms epoch for R-contra trials as well ( 8 . 2% , p = 0 . 0469 ) . 10 . 7554/eLife . 04478 . 010Figure 7 . Amygdala firing rate selectivity for aborted trials . ( A and B ) Example neuron cue-aligned firing rates for aborted ( dashed lines ) and non-aborted trials ( solid lines ) on R-present trials ( left; R-contra , R-ipsi ) and R-absent trials ( right; P-contra , P-ipsi ) . Shading indicates standard error across trials . Note that firing rates for abort trials are not plotted in cases where there were fewer than 6 aborts . ( A ) Neuron with reward selectivity and spatial-reward selectivity indices <0 . 5 in all time epochs ( p < 0 . 05 ) . Abort selectivity was significant only on P-contra trials in the 700–1000 ms epoch ( index = 0 . 70 , p < 0 . 05 ) . ( B ) Neuron with reward selectivity and spatial-reward selectivity indices >0 . 5 in all time epochs ( p < 0 . 05 ) . Abort selectivity was significant only on P-contra trials in the 700–1000 ms epoch ( index = 0 . 39 , p < 0 . 05 ) . Different y-axis ranges are used for R-present and R-absent trials to illustrate abort selectivity . ( C ) Relationship between reward selectivity and abort selectivity indices on punishment-contra trials for each time epoch . Plot style indicates the significance of selectivity indices ( see legend ) ; solid lines indicate significant regressions ( p < 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04478 . 010 We next hypothesized that the neural selectivity for aborts was related to neural signals reflecting the overall value of a trial . We used an ROC analysis to compute ‘abort selectivity’ indices; indices greater than 0 . 5 indicated higher firing on abort trials and indices less 0 . 5 indicated higher firing on non-abort trials . We examined the relationship between abort selectivity indices and reward selectivity indices ( as in Figure 4A , x-axis ) . We found a statistically significant negative correlation between reward selectivity indices and abort selectivity indices on P-contra trials in all 3 time epochs ( Figure 7C , linear regression , p < 0 . 0006 , 0 . 0066 , 0 . 0030 for each time epoch ) ; these relationships were statistically indistinguishable across monkeys ( ANCOVA , p > 0 . 26 ) . Relationships for other trial types and epochs did not approach significance ( linear regression , p > 0 . 11 in each case ) . Overall , neurons in the amygdala exhibit firing rate selectivity that is predictive of monkeys' assessment of threat , as measured by abort behavior .
A long tradition of work has implicated the amygdala in mediating valence-specific emotional behavior . In rodents , experimental lesions and pharmacological or optogenetic manipulations of neural activity in the amygdala affect approach or defensive behaviors ( Ciocchi et al . , 2010; Haubensak et al . , 2010; Stuber et al . , 2011; Redondo et al . , 2014 ) . Physiological data from rodents and monkeys have linked amygdala neural activity to either appetitive or aversive valence ( Sanghera et al . , 1979; Nishijo et al . , 1988; Quirk et al . , 1995; Schoenbaum et al . , 1998; Sugase-Miyamoto and Richmond , 2005; Paton et al . , 2006; Belova et al . , 2007; Shabel and Janak , 2009; Bermudez and Schultz , 2010 ) . Prior work has also implicated the amygdala in mediating valence-nonspecific processes such as autonomic and metabolic arousal ( Davis and Whalen , 2001 ) . Furthermore , we have previously shown that expectation can modulate amygdala neural responses to rewarding or aversive unconditioned stimuli in a similar manner ( Belova et al . , 2007 ) , reminiscent of unsigned prediction errors in reinforcement learning algorithms ( Pearce and Hall , 1980; Schultz and Dickinson , 2000 ) . These results were suggestive of a role for the amygdala in generating arousal , which can be produced by motivationally significant stimuli of both valences . However , the results did not pertain to visuo-spatial attention since the observed response characteristics were tied to the unconditioned stimulus , not to conditioned stimuli . Furthermore , in those experiments , conditioned stimuli were not presented at peripheral locations . Recent work has suggested that the amygdala participates in the modulation of spatial attention induced by emotional stimuli ( Vuilleumier et al . , 2004; Adolphs et al . , 2005; Peck et al . , 2013; Ousdal et al . , 2014; Peck and Salzman , 2014; Peck et al . , In press ) , but these studies have not examined whether amygdala neurons encode spatial information about stimuli of both valences , and , if so , whether that activity correlates with spatial attention deployment . Scientists have often struggled to disambiguate neural signals related to valence ( or value ) from those related to attention . Attention can be modulated by motivationally significant stimuli of both valences , and stimuli with high motivational significance may therefore be considered to be more salient . In general , as value increases , motivational significance and attention can increase , presenting an interpretive conundrum ( Maunsell , 2004 ) . One approach for disentangling these quantities involves testing whether neural responses are modulated similarly for stimuli predicting rewarding and aversive outcomes . Even this approach has caveats , because a neuron might encode both valence and motivational significance . Titration of outcome intensity across valences might be helpful in characterizing the relative contribution of valence and intensity on neuronal firing . This approach , however , presents experimental challenges because the subjective assessment of stimulus intensity and valence can change within experiments due to satiation to rewards and/or habituation to punishments . Our prior results indicate that amygdala neurons encode valence when monkeys perform a trace-conditioning task in which behavioral measures of attention were not obtained ( Paton et al . , 2006; Belova et al . , 2008; Zhang et al . , 2013 ) . In those studies , neurons preferring positive valence tended to increase firing to a fixation point , which was a mildly positive over-trained visual conditioned stimulus ( Belova et al . , 2008 ) . These positive neurons would then tend to increase firing further if a rewarded conditioned stimulus appeared , or decrease firing if a stimulus associated with an aversive stimulus appeared ( Belova et al . , 2008 ) , suggesting that even though both visual stimuli likely attracted attention , firing rate changed in opposite directions for stimuli associated with reinforcement of different valences . Neurons that preferred negative valence had the opposite response profile . In the present study , the intensity of rewards and aversive stimuli were likely not equivalent , as rewards had a greater influence on performance . Nonetheless , for a given spatial configuration of stimuli , the presence of a cue threatening an aversive event modulated neural activity in the same direction as a cue promising reward . This could occur if the influence of intensity , or motivational significance , on neural firing was greater than the influence of valence . We did not measure neural responses during a conditioning procedure in these experiments , but the observed differential responses between R-present and R-absent trials bear similarity to valence-related responses observed during trace-conditioning tasks when stimuli were presented over the fovea ( Paton et al . , 2006; Belova et al . , 2008; Morrison et al . , 2011 ) . We observed that amygdala neural activity is similarly modulated by the spatial location of reward- and punishment-predicting stimuli , but monkeys could avoid aversive stimuli by performing the task correctly . Conceivably , this could mean that the modulation of neural activity was related to the rewarding aspects of avoiding an aversive air-puff and not to the threat of an aversive air-puff . For several reasons , we consider it unlikely that the rewarding aspects of punishment avoidance could explain our results . First , air puff delivery was still associated with the P cue on ∼45% of trials in which the target appeared at the P location . Second , monkeys aborted R/P trials at a higher rate than R/N trials , suggesting that the punishment cue was more aversive than the neutral cue . Third , prior studies have shown that air puffs have aversive value to monkeys ( Amemori and Graybiel , 2012 ) , and our experience is that monkeys' propensity to quit performing a task relates to the number of air puffs they have received in that session . Finally , even if monkeys find successful avoidance to be rewarding , this would not be apparent until later in the trial after the monkey successfully acquires the target . The monkeys' tendency to abort trials more often when an air-puff could occur indicates that the threat of an aversive outcome impacted behavior . Neural activity also reflected the threat of an aversive air-puff , as indexed by whether a monkey aborted a trial . When a P cue appeared on the contralateral side , neural activity in the amygdala predicted whether or not the monkey would later abort the trial in a manner dependent upon the reward selectivity of neurons . Neurons that fire more strongly in a more rewarding situation ( e . g . R/N trials ) tend to fire less on trials when a monkey breaks fixation . By contrast , neurons that fire more strongly when a monkey is in a less rewarding situation ( e . g . P/N trials ) , fire more on trials when a monkey aborts . The relationship between the reward selectivity and abort selectivity of amygdala neurons was only apparent on trials in which the P cue appeared contralaterally . It is possible that monkeys exhibit two different kinds of abort behaviors in our task , one in which the monkey's concentration simply lapsed and another where the monkey aborted specifically in response to the value of the trial , that is to the threat of air-puff . The former behavior might be equally likely to occur on all trial types , would not be related to an assessment of threat , and also may have occurred more rarely . The second type of trial abort would occur preferentially on trials in which the R cue did not appear . The fact that amygdala neurons are correlated with trial aborts only when the punishment cue appeared contralaterally , but not ipsilaterally , suggests that there is a spatial component to this response property as well . This is reminiscent of our previous observation that correlations between reaction times and firing rates are apparent only when motivationally significant stimuli appear in the contralateral hemifield ( Peck et al . , 2013 ) . Overall , the relationship between neural activity and the tendency to abort trials does suggest that the amygdala represents threats as well as rewards within a spatial framework . The current results add an important component to our understanding of neural encoding in the amygdala . Previous studies that described signals in relation to motivational significance or arousal in the amygdala did not determine whether these properties were represented in a spatial framework ( Belova et al . , 2007; Shabel and Janak , 2009 ) . The present study indicates that for many amygdala neurons , the representation of motivational significance is linked to a representation of space . These results highlight the possibility that the amygdala's influence on attention may not be limited to non-spatial processes like emotional arousal . Instead , the amygdala may provide signals that contribute directly to spatial attention . This implies that for a neuron that encodes both space and motivational significance , increases in firing rates may pull attention more towards the contralateral visual hemifield , and less to the ipsilateral field . Since the amygdala also contains neurons with opposite response preferences for space and motivational significance , these other amygdala neurons could have the opposite relationship with spatial attention . Of note , the neural representation provided by the amygdala differs fundamentally from representations that encode motivational significance independent of space , or that encode space and motivational significance but in a non-coordinated manner . Despite a large body of research characterizing the response properties of individual neurons in the primate brain , relatively few experiments have strived to disentangle valence from motivational significance by comparing responses to stimuli predicting either rewards or punishment with those predicting less salient outcomes . In one example , Kobayashi et al . ( 2006 ) identified a neural population in the lateral prefrontal cortex that responded similarly to stimuli predicting either appetitive or aversive outcomes . Additionally , a population of neurons in the dopamine-producing ventral tegmental area ( VTA ) and substantia nigra pars compacta ( SNc ) respond in proportion to the motivational significance of predicted outcomes ( Matsumoto and Hikosaka , 2009 ) . Finally , Leathers and Olson ( 2012 ) found that lateral intraparietal area ( LIP ) neurons fired in proportion to the intensity of predicted outcomes , regardless of valence . Although these neurons may be distinct from those typically recorded in LIP given differences in their fundamental response properties ( Newsome et al . , 2013 ) , they do seem to exhibit responses reflecting motivational significance . The population of neurons described in LIP , however , does not include neurons that prefer less salient stimuli . As a result , LIP does not appear to provide a systematic , coordinated representation of space and motivational significance , as the amygdala does . Examples of neural responses consistent with the encoding of motivational significance have also been found in the rodent brain . Aside from the report in the amygdala described above ( Shabel and Janak , 2009 ) , basal forebrain neurons respond according to the motivational significance of outcome-predicting stimuli ( Lin and Nicolelis , 2008 ) , although the responses described in these studies may have been influenced by the sensory characteristics of the conditioned stimuli themselves . The physiological studies in rodents , however , have not reported a systematic relationship between the encoding of motivational significance and space . In the present paper , some neurons fire more strongly for more motivationally significant ( or salient ) stimuli , especially when they appear contralaterally . Other neurons fire more strongly for less-salient stimuli , especially when they appear ipsilaterally . To our knowledge , this property has only been described in the primate amygdala . At least three sets of projections from the amygdala to target structures might influence visual processing and attention . First , the amygdala projects directly to neurons in primate ventral visual areas ( Amaral and Price , 1984; Iwai and Yukie , 1987 ) whose firing rates modulate depending upon where attention is allocated ( Chelazzi et al . , 1993; Desimone and Duncan , 1995 ) . Stimuli predicting aversive events have not been employed while investigating modulation of individual neurons' visual responses in the ventral stream . One prediction of the current work is that if the amygdala directly modulates visual representations , then attention-attracting stimuli associated with aversive outcomes should modulate responses in the same manner as reward-predicting stimuli . Supporting this notion , unilateral amygdala lesions attenuate preferential BOLD response for negative-valence stimuli ( Vuilleumier et al . , 2004 ) . The amygdala might also influence attention through projections to the basal forebrain or to dopamine neurons . Selective visual attention in rodents appears to involve a projection from the amygdala central nucleus to the basal forebrain ( Holland , 2007 ) , which may be important for influencing attention-related cortical processing given the basal forebrain's widespread cortical projections ( Mesulam et al . , 1983 ) and attention-like influences over cortical activity ( Goard and Dan , 2009 ) . Recent data indicate that the basal forebrain also encodes spatial and reward information ( Peck and Salzman , 2014 ) , but the output of basal forebrain neurons does not appear to be correlated with spatial attention on a trial-by-trial basis . Enhanced attention to conditioned stimuli may also involve projections between the amygdala central nucleus to dopamine neurons ( El-Amamy and Holland , 2007 ) . Amygdala and dopamine neurons are reciprocally connected ( Price and Amaral , 1981; Amaral et al . , 1982 ) , and dopamine neurons signal quantities related to motivation ( Matsumoto and Hikosaka , 2009 ) . It remains unclear whether these pathways might regulate spatial or non-spatial aspects of attention . The circuit-level mechanisms by which the amygdala might influence attention remain unclear . Distinct populations of amygdala neurons decrease or increase firing rate with respect to spatial attention , unlike modulation in brain areas such as V4 ( Mitchell et al . , 2007 ) and LIP ( Sugrue et al . , 2004; Peck et al . , 2009 ) where firing rates typically only increase when attention is directed towards the contralateral hemifield . The sign of modulation for amygdala neurons does not predict whether spiking statistics are characteristic of excitatory or inhibitory neurons ( Peck et al . , 2013; Peck and Salzman , 2014 ) . Amygdala projections to visual cortices are primarily ipsilateral ( Iwai and Yukie , 1987 ) and excitatory in nature ( Freese and Amaral , 2006 ) . Future studies must therefore discern whether the sign of amygdala neurons' spatial selectivity predicts whether projections target excitatory or inhibitory neurons in visual cortex . In addition , much work remains to characterize the spatial specificity of signals provided by the amygdala . The present results suggest that the amygdala could play a role in influencing attention at the level of the hemifield , but the topographical specificity of these signals remains to be characterized . Human and non-human primates possess a remarkable capacity for dedicating spatial attention to emotionally important visual stimuli ranging from evocative paintings , to wine bottles associated with past pleasures , to frightened faces that reveal looming threats . The physiological data presented in this paper provide a unifying view of the role of the amygdala in representing these emotionally significant stimuli in space so as to modulate attention . Amygdala neurons not only register the emotional significance of stimuli promising reward and threatening aversive events , but they also represent information about the spatial location of those stimuli . Given that the motivational significance and location of stimuli together bias spatial attention , the spatial representation in the amygdala may serve to link our emotional world to cognitive actions—the enhancement of spatial attention to relevant stimuli—that promote our survival .
Two male rhesus monkeys ( Macaca mulatta , 8–10 kg ) were used in these experiments . All Materials and methods complied with the National Institutes of Health guidelines and were approved by the Institutional Animal Care and Use Committees at the New York State Psychiatric Institute and Columbia University . General methods for these experiments have been described previously ( Peck et al . , 2013 ) . Recordings from single neurons ( SUA ) and multi-unit sites ( MUA ) in the amygdala were made through a surgically implanted plastic cylinder affixed to the skull . Four to eight electrodes were individually lowered into the left ( monkeys O and L ) amygdala using a multiple electrode microdrive ( NaN Instruments , Nazareth , Israel ) . Extracellular activity was recorded using tungsten electrodes ( 2 MΩ impedance at 1000 Hz; FHC Inc . , Bowdoinham , ME ) . Analog signals were amplified , bandpass filtered ( 250–7500 Hz ) , and digitized ( 30 , 000 Hz ) for unit isolation ( Blackrock Microsystems , Salt Lake City , Utah ) . We initially defined SUA online as units whose waveforms were clearly distinguishably from noise and/or other units; this classification was made online using either a time-amplitude window or manual clustering in principal component space . After experiments , we reanalyzed the waveforms ( Plexon Offline Sorter , Plexon , Dallas , TX ) and manually clustered the waveforms of SUA in principal component space; waveform groups were defined as SUA only if they formed a distinct , non-overlapping cluster in principal component space . Because neurons occasionally drift over the course of an experiment such that their waveforms either emerge or descend into the noise cluster , we defined the time interval during which the SUA was clearly distinguishable from the noise and excluded all other data within that session from our analyses . Mutli-unit activity consisted of waveforms that were not sorted as single-units . We re-thresholded the data offline to correct any major deviations in MUA baseline firing rate due to threshold changes over the course of an experimental session . When MUA and SUA ( s ) were recorded on the same channel , we removed MUA timestamps within 2 ms of any SUA timestamp to ensure that threshold ‘double-crossings’ by the single-unit did not contaminate the multi-unit signal . Monkeys performed a detection task designed to assess how reinforcement expectations influenced attention . Each trial began with the presentation of a central fixation point ( 0 . 25° × 0 . 25° ) ; the monkey was required to moved its gaze to a window within 2° of the fixation point . After a fixation period of 500–1500 ms ( exponential distribution , λ = 170 ms ) , two cues appeared at either side of the fixation point along the horizontal axis ( 7° eccentricity ) for 300 ms . Following the offset of the cues , the monkeys continued to fixate during a delay period where no peripheral stimuli were present . At a randomly chosen time 400–4000 ms ( exponential distribution , λ = 390 ms ) later , a target appeared ( 50 ms ) at one of the two locations at which the cues had been presented . Monkeys were required to make a direct saccade to within 3° of the target between 100–600 ms after its onset . Eye movements were classified based on where they were directed and when they occurred in the trial . For trials in which the monkeys' eye position left the fixation window before the appearance of the target , eye movements were classified as either a false alarm if the saccade was directed within 3° of one of the two cue locations ( Figure 2C ) or as an abort if they were directed elsewhere ( Figure 6 ) . Since the target appeared at a random time , the time range within which false alarms and aborts could occur varied from trial-to-trial . Additionally , saccades within 100 ms of the target were considered to be false alarms or aborts since it is unlikely that the monkey could have reacted to the target's appearance within this short time window . False alarm and abort trials were repeated so that monkeys weren't able to avoid selected trial types; cue configuration , target position , and delay length were re-randomized on repeated trials . For trials where the monkey maintained fixation until target onset , a ‘hit’ occurred when the monkey successfully made a saccade to the target's location , and a ‘miss’ occurred when monkeys ( 1 ) failed to make a saccade , ( 2 ) made a saccade to the opposite cue location , or ( 3 ) made a saccade elsewhere . Miss trials that involved a saccade to the opposite cue location or elsewhere were fundamentally distinct from false alarms/aborts since the monkey could receive a punishment in these instances whereas a false alarm or abort simply resulted in a repeated trial . Both hit and miss trials were considered to be ‘completed’ trials , and outcomes were delivered 1000 ms ( monkey L ) or 400 ms ( monkey O ) after trials were completed . Reward consisted of ∼1 ml of water controlled by a solenoid and delivered to the monkey through a lick tube; punishments came in the form of a 70 millisecond long 20-40 PSI puff of compressed air aimed at the cheek . Monkeys learned to associate abstract visual cues with three possible outcomes: reward , punishment , or no outcome . Reward delivery occurred only on hit trials where the target had appeared at the same location as the reward cue . Punishments occurred only on miss trials where the target appeared at the same location as a punishment cue had appeared . These contingencies meant that monkeys never received both a reward and a punishment on the same trial . All completed trials resulted either in reward ( ‘hit’ to the target when an R cue had appeared at that location ) , punishment ( ‘miss’ when the target appeared at the location where the P cue had appeared ) , or no reinforcement ( ‘miss’ when the target had appeared at the R location , or ‘hit’ when the target appeared at the P location ) . Cues were colored rectangles ( 2 . 25 deg . 2 at 7° eccentricity ) equated for luminance , and we randomly interleaved two distinct sets of cues associated with the same outcomes ( 6 cues total ) . Targets were Gabor patches; we adjusted the contrast and size of the Gabors online to maintain an overall performance level of ∼70% correct . Because the interval during which the target could appear was long and the reaction time window was relatively short , theoretical chance performance was about 23% . Maximal chance performance levels were determined by assuming that all saccades were made at the specific time in the trial at which a hit was most likely to ‘accidently’ occur . Given the reaction time window of 100–600 ms , the optimal time to saccade was 100 ms after the 500 ms within which targets were most frequent , in which case ∼46% of saccades would occur within the reaction time window . Finally , since these saccades would only be directed at the correct location 50% of the time , chance performance was determined to be 23% . In practice , false alarms were distributed throughout the delay period and occurred most frequently at 790 ms after cue onset , 60 ms before the most frequent time of target onset ( 850 ms ) , whereas the optimal time to saccade would have been at 1080 ms . Thus , monkeys did not follow this strategy , and effective chance levels were lower than 23% . We used two-tailed statistical tests in all instances . Non-parametric Wilcoxon tests were performed on unpaired data ( rank-sum test ) unless specified otherwise ( sign-rank test ) . Behavioral and neural data was similar across cue sets , so the data were combined except where noted . For selectivity indices , we used a receiver-operator characteristic ( ROC ) analysis to compare firing rate distributions between conditions; selectivity indices were computed only if at least 15 trials were available for each distribution . We used a standard linear regression to assess the relationship between selectivity indices . Firing rates were analyzed in the time windows 100–400 , 400–700 , and 700–1000 ms after cue onset . We chose the first window ( 100–400 ms after cue onset ) to capture the visually-driven activity due to the cue; factoring in the ∼100 ms visual onset latency of amygdala neurons ( Paton et al . , 2006 ) , this window captures the presentation of the cue , which appeared for 300 ms . The other two time windows ( 400–700 ms and 700–1000 ms after cue onset ) were chosen to surround the earliest time that the target could possibly appear , which was ∼700 ms after cue onset . Since monkeys could not predict exactly when the target would appear , it was in their best interest to be prepared for the target's appearance at all times after 700 ms . Since we excluded responses where the target appeared during that time window ( 46% of targets appeared in the 700–1000 ms window ) , a decreasing number of trials were available for analysis in later time windows which occasionally results in a site being included in the analysis of early , but not late , time windows . For the neural analysis of abort trials ( Figure 7 ) , we truncated firing rates at the time that the abort occurred ( truncated at the time of the abort , included only if the abort occurred at least 100 ms after the start of the epoch ) , again resulting in a decreasing sample size for later time windows . We logged the inferior/superior , anterior/posterior , and medial/lateral position of each recorded neuron to generate a 3D reconstruction using Brainsight software ( Figure 4C–E ) . To determine the degree of sign-matching ( Figure 4E ) for each site's spatial selectivity , we took the product of the spatial-reward and spatial-punishment selectivity magnitude ( |ROC–0 . 5| ) ; ‘sign-agreement’ values were averaged across the three time windows , but similar results are observed if considering any given time window alone . We also estimated the number of recording sites in the basolateral nucleus , the central nucleus , and the anterior amygdala area by comparing our MRI reconstructions with an anatomical atlas ( Paxinos , Huang and Toga , 1999 brain atlas ) . As with previous work ( Peck et al . , 2013 ) , we estimated that the vast majority of our recordings were in the basolateral nucleus of the amygdala ( n = 283 ) as opposed to the central nucleus ( n = 51 ) or anterior amygdaloid area ( n = 11 ) . Further , those recordings in the basolateral nuclei excluded the most lateral extent of the amygdala suggesting that these neurons were mainly in the basal and accessory basal nuclei . We targeted these amygdala nuclei because they are the densest source of the projections to the ventral visual stream and/or the basal forebrain , which we believe may have the most direct role in influencing spatial attention . | In our everyday lives , we are surrounded by stimuli that compete for our attention . However , the brain pays more attention to some stimuli—such as those that signal rewards or warn of potential threats—than to others . These stimuli receive extra attention because they activate a structure deep within the brain called the amygdala . The amygdala , which is named after the Greek word for ‘almond’ owing to its shape , receives input from the sensory areas of the brain , and sends output to the hypothalamus , which controls the body's stress response , and other structures . While the role of the amygdala in signaling the presence of threats or rewards has been recognized for many years , recent studies have suggested that the amygdala also signals the location of potential rewards . Using electrodes to record electrical signals from the amygdala of awake monkeys , Peck and Salzman now show that it also alerts animals to the location of potential threats . In response to cues appearing on a screen , the monkeys moved their eyes in a way that either earned them a reward ( fruit juice ) , or enabled them to avoid an annoying puff of air . As expected , amygdala neurons responded to cues that predicted the reward and also to cues that signaled the air puff . Surprisingly , however , the neurons also varied their responses according to the location of the cues . This dual function of the amygdala—signaling both the presence and the location of stimuli that predict rewards and threats—helps animals to plan whether or not they will approach or avoid a stimulus . Moreover , given that some of the most salient reward and punishment cues that we encounter today are facial expressions—which elicit strong amygdala responses—it could also provide clues to disorders of social functioning such as autism . | [
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] | 2014 | Amygdala neural activity reflects spatial attention towards stimuli promising reward or threatening punishment |
Alternative splicing ( AS ) creates proteomic diversity from a limited size genome by generating numerous transcripts from a single protein-coding gene . Tissue-specific regulators of AS are essential components of the gene regulatory network , required for normal cellular function , tissue patterning , and embryonic development . However , their cell-autonomous function in neural crest development has not been explored . Here , we demonstrate that splicing factor Rbfox2 is expressed in the neural crest cells ( NCCs ) , and deletion of Rbfox2 in NCCs leads to cleft palate and defects in craniofacial bone development . RNA-Seq analysis revealed that Rbfox2 regulates splicing and expression of numerous genes essential for neural crest/craniofacial development . We demonstrate that Rbfox2-TGF-β-Tak1 signaling axis is deregulated by Rbfox2 deletion . Furthermore , restoration of TGF-β signaling by Tak1 overexpression can rescue the proliferation defect seen in Rbfox2 mutants . We also identified a positive feedback loop in which TGF-β signaling promotes expression of Rbfox2 in NCCs .
Approximately 3–4% of infants are born with congenital diseases . Collectively , craniofacial and cardiovascular abnormalities are the most common defects , contributing to more than one-third of the congenital diseases . Proper formation of these structures involves intricate processes such as proliferation , migration , and differentiation of NCCs , as well as their interaction with neighboring cells ( Martik and Bronner , 2017; Plein et al . , 2015 ) . NCCs are a transient population of migratory progenitor cells that reside in the dorsal side of the neural tube . The NCCs separate from their neighboring cells through a delamination process and migrate via different pathways throughout the body to diverse locations , differentiating into multiple cell types at their respective destinations ( Martik and Bronner , 2017 ) . NCCs can be subdivided into five axial populations: cranial , cardiac , vagal , trunk and sacral NC cells . Lineage-tracing experiments in mouse embryos have demonstrated that NCCs differentiate into a diverse array of cell types including the craniofacial skeletal elements , autonomous nervous system of the heart and GI-tract , as well as smooth muscle cells of the cardiac OFT ( Chai et al . , 2000; Jiang et al . , 2002; Jiang et al . , 2000; Lee et al . , 2004; Martik and Bronner , 2017; Plein et al . , 2015; Waldo et al . , 1999; Yoshida et al . , 2008 ) . Cranial NCCs migrate from anterior portions of the folded neural tube and contribute to the formation of the skull , cartilage , and connective tissue , where they populate the first and second pharyngeal arches that give rise to cranial ganglia , the maxilla , the mandible , palates and other structures of the developing head ( Chai et al . , 2000; Jiang et al . , 2002; Martik and Bronner , 2017 ) . In mammals , both the primary and secondary palates are morphologically visible as early as E11 . 5 as demonstrated by an outgrowth from the oral side of the medial nasal and maxillary processes , respectively . The primary palate is formed by fusion of the maxillary prominence with the frontonasal prominence ( Bush and Jiang , 2012; Dixon et al . , 2011 ) . Secondary palates are formed from the outgrowths of neural crest-derived mesenchyme that lie on either side of the developing tongue ( Bush and Jiang , 2012 ) . Initially , palatal shelves grow vertically flanking the developing tongue , but they elevate between E13 . 5 and E14 . 5 to a horizontal position above the tongue , grow toward the midline and fuse with each other to form an intact palate ( Bush and Jiang , 2012 ) . Functional defects in NCCs result in craniofacial malformations including cleft lip and/or cleft palate . Many transcription factors , chromatin remodeling factors , non-coding RNA and signaling molecules have been implicated in impaired neural crest development that result in cardio-craniofacial syndromes ( Bélanger et al . , 2018; Martik and Bronner , 2017; Plein et al . , 2015; Singh et al . , 2013; Strobl-Mazzulla et al . , 2012 ) . However , the cell-autonomous role of splicing regulators in neural crest biology remains unclear and warrants further investigation . AS of pre-messenger RNAs is essential for regulating gene expression and creating proteomic diversity . Changes in exon inclusion or exclusion can produce multiple mRNAs and protein isoforms with related or distinct functions . The RNA-binding fox-1 ( Rbfox ) homolog proteins: Rbfox1 , Rbfox2 , and Rbfox3 are evolutionarily conserved splicing factors that have been implicated in diverse cellular processes such as cell proliferation , cell death and epithelial-mesenchymal transition ( Kuroyanagi , 2009 ) . A conserved RNA-Recognition Motif that recognizes UGCAUG element in the introns , flanking target exons , characterizes Rbfox proteins ( Jin et al . , 2003 ) . Rbfox proteins regulate splicing either positively or negatively , depending on their binding sites . They promote exon skipping or inclusion when they bind to either the upstream or downstream of the alternative exon ( Jin et al . , 2003; Ponthier et al . , 2006 ) . In addition to splicing , Rbfox proteins also regulate transcriptional gene networks ( Fogel et al . , 2012 ) . Although all Rbfox proteins bind to the same recognition sequence , their in vivo functions are only partially redundant due to their markedly distinct expression pattern . Both Rbfox1 ( A2BP1 ) and Rbfox2 ( RBM9 ) are expressed in the neurons , heart and skeletal muscle ( Gehman et al . , 2012; Gehman et al . , 2011; Singh et al . , 2014; Wei et al . , 2015 ) . Unlike the aforementioned , Rbfox3 ( NeuN or HRNBP3 ) expression is more restricted to neuronal tissues ( Kim et al . , 2014 ) . Several diseases have been associated with dysregulation of splicing ( Garcia-Blanco et al . , 2004 ) . For example , genetic deletion of Rbfox1 and Rbfox2 in the nervous system increased susceptibility to seizures and impaired cerebellum development , respectively ( Gehman et al . , 2012; Gehman et al . , 2011 ) . Morpholino-mediated knockdown of Rbfox1 and Rbfox2 in zebrafish embryos impaired cardiac and skeletal muscle functions ( Gallagher et al . , 2011 ) . During skeletal muscle development , Rbfox2-mediated splicing is required for myoblast fusion and differentiation ( Runfola et al . , 2015; Singh et al . , 2014 ) . Decreased Rbfox2 expression has been reported in response to transverse aortic constriction in the mouse heart , and cardiac-specific deletion leads to pressure overload-induced heart failure ( Wei et al . , 2015 ) . Abnormal expression of Rbfox2 has also been associated with hypoplastic left heart syndrome ( Verma et al . , 2016 ) . Recently , Nutter et al . ( 2016 ) demonstrated that elevated Rbfox2 protein expression in diabetic hearts affects diabetes-induced AS of cardiac-specific genes . Despite extensive studies of Rbfox2 regulation on neuronal , cardiac and skeletal muscle cells , the cell-autonomous role of Rbfox2 in the functioning of neural crest cells has not been explored . In the present study , we demonstrate that Rbfox2 is expressed in the neural crest cells ( NCCs ) , neural crest-derived palate shelves , dorsal root ganglia , and somites . To determine the role of Rbfox2 during embryonic development , we deleted Rbfox2 using floxed Rbfox2 ( Rbfox2flox/flox ) and Pax3Cre/+ knock in mice ( Engleka et al . , 2005; Gehman et al . , 2012 ) . We observed that Pax3Cre/+-mediated deletion of Rbfox2 results in neonatal lethality . Rbfox2 mutant ( Rbfox2Pax3-CKO ) embryos develop a cleft palate and defects in the development of craniofacial skeleton , suggesting defective cranial neural crest development . To determine whether cleft palate and craniofacial defects were due to deletion of Rbfox2 in the neural crest cells , we generated a neural crest-specific Rbfox2 mutant ( Rbfox2Wnt1-CKO ) using Wnt1Cre/+ allele ( Gehman et al . , 2012; Lewis et al . , 2013 ) . Similar to Rbfox2Pax3-CKO embryos , Rbfox2Wnt1-CKO mutants developed a cleft palate and craniofacial skeleton defects and died postnatally at day 1 . RNA-Seq analysis revealed an essential role of Rbfox2 in regulating splicing and expression of genes required for the development of cranial neural crest-derived structures . We demonstrate that Rbfox2-TGF-β-Tak1 signaling axis is impaired in the neural crest-derived cells of the Rbfox2 mutant embryos and restoration of Tak1 expression in cultured palatal mesenchymal cells can rescue the proliferation defect observed in Rbfox2 mutants . We also identified a positive feedback loop by which TGF-β signaling components , Smad2/3/4 , bind directly to the Rbfox2 promoter and regulate its expression in the neural crest cells . Together , these results reveal a highly regulated Rbfox2-dependent splicing and transcriptional program that modulates cranial neural crest development .
To determine the expression pattern of Rbfox2 , we performed Rbfox2 immunostaining on transverse sections from E9 . 5 to E11 . 5 embryos at different rostrocaudal axis . At E9 . 5 , Rbfox2 is expressed in the premigratory NCCs at the dorsal neural tube , as well as in the migratory NCCs ( Figure 1A–C , F–H , K–M ) throughout the rostrocaudal axis , with strong expression of Rbfox2 observed in the somites . Rbfox2 expression is gradually reduced in migratory NCCs ( Figure 1K–M ) . Rbfox2 expression was detected in the neural crest-derived craniofacial tissues including the palate shelves but not in the cardiac tissues such as OFT ( Figure 1D–E , I–J , N–O ) . To compare Rbfox2 expression with a known neural crest cell marker , we performed Rbfox2 and Pax3 immunostaining on transverse sections from E9 . 5 , E10 . 5 and E11 . 5 embryos ( Figure 1P–U ) . At E9 . 5 , Rbfox2 expression is identical to Pax3 in NCCs , dorsal neural tube , and somites ( Figure 1P and S ) . At E10 . 5 , in addition to premigratory NCCs , Rbfox2 is also expressed in the dorsal root ganglia ( Figure 1Q ) . In contrast to Pax3 expression in the dorsal neural tube , the Rbfox2 expression is more restricted to the ventral neural tube ( Figure 1Q and T ) . At E11 . 5 , Rbfox2 is expressed in the ventral neural tube and dorsal root ganglia ( Figure 1R ) . However , Pax3 is more restricted to the dorsal neural tube ( Figure 1U ) . At an early stage , Rbfox2 expression was similar to that of Pax3 , which is transiently expressed in all premigratory , migratory NCCs and somites . In contrast to Pax3 expression in the dorsal neural tube , Rbfox2 expression was restricted to the ventral neural tube at later stages . These results indicate that Rbfox2 is expressed in NCCs , dorsal neural tube , palate shelves , dorsal root ganglia , and somites . To establish the role of Rbfox2 during embryonic development , we conditionally deleted Rbfox2 using Pax3Cre/+ knock in allele and floxed Rbfox2 ( Rbfox2flox/flox ) mice ( Engleka et al . , 2005; Gehman et al . , 2012 ) . Pax3Cre/+ knock in allele was used to delete Rbfox2 not only in the premigratory and migratory NCCs , but also in paraxial mesoderm and somite derivatives where Rbfox2 is expressed . Pax3Cre/+;Rbfox2flox/+ mice were fertile , born at the expected Mendelian ratio , and exhibited no gross abnormalities . However , we did not recover any mutant ( Rbfox2Pax3-CKO ) pups at postnatal day 10 from breeding Pax3Cre/+;Rbfox2flox/+ and Rbfox2flox/flox mice , demonstrating that Pax3Cre/+-mediated inactivation of Rbfox2 leads to embryonic or neonatal lethality ( Figure 2—figure supplement 1A ) . Genotyping of embryos from series of timed point matings demonstrated that Rbfox2Pax3-CKO embryos are present at the expected Mendelian ratios at all embryonic time points analyzed ( E10 . 5-E18 . 5 ) ( Figure 2—figure supplement 1A ) . However , pups monitoring after birth revealed that mutant pups that are characterized by a shortened body axis and abnormal craniofacial features die at postnatal day one ( P1 ) ( Figure 2—figure supplement 1B and G ) . Our morphological and histological analyses demonstrate that Rbfox2Pax3-CKO embryos develop cleft palate and severe subcutaneous edema , which are present in mid- and late-gestation ( Figure 2—figure supplement 1C–F , H–K ) . Irregular breathing and hardly inflated lungs in Rbfox2Pax3-CKO pups support the notion that the neonatal lethality is caused by respiratory failure ( Figure 2—figure supplement 1L–O ) . In Rbfox2Pax3-CKO embryos , development of primary palates is not affected . However , all of the Rbfox2Pax3-CKO pups died neonatally , exhibiting a secondary cleft palate defect . To determine when the cleft palate defects were first evident , we examined Rbfox2Pax3-CKO embryos at progressively earlier stages by analyzing their morphological and histological data ( Figure 2A–L ) . At E12 . 5 , the palatal shelves in both control and Rbfox2Pax3-CKO embryos were initiated normally , growing vertically flanking the developing tongue ( Figure 2A–B , G–H ) . No obvious morphological or histological differences were observed at this embryonic stage . At E15 . 5 and E18 . 5 , the control palatal shelves were elevated above the tongue to a horizontal position and met each other at the midline along the anterior-posterior axis ( Figure 2C–F ) . However , in Rbfox2Pax3-CKO embryos , palatal shelves were elevated above the tongue to a horizontal position , but they completely failed to fuse at the midline throughout the anterior-posterior axis ( Figure 2I–L ) . Since Pax3Cre is expressed in both the neural crest and mesodermal derivatives , it was not clear if the cleft palate defects were due to Rbfox2 deletion in the neural crest cells . To determine the neural crest-specific requirement of Rbfox2 , we generated neural crest-specific Rbfox2 mutant ( Rbfox2Wnt1-CKO ) embryos using Wnt1Cre/+ mice ( Gehman et al . , 2012; Lewis et al . , 2013 ) . Similar to Rbfox2Pax3-CKO , Rbfox2Wnt1-CKO mice were born at the expected Mendelian ratio but died at P1 ( Figure 2—figure supplement 1P–R ) . To determine the craniofacial defects , we analyzed Rbfox2Wnt1-CKO palates at different time points . Similar to Rbfox2Pax3-CKO embryos , palatal shelves in Rbfox2Wnt1-CKO embryos were elevated above the tongue to a horizontal position , but completely failed to fuse at the midline throughout the anterior-posterior axis ( Figure 2M–T ) . To determine the cellular mechanisms responsible for the impaired palatal growth in Rbfox2 mutant embryos , we performed cell proliferation , and apoptosis assays on transverse palatal sections from control and Rbfox2 mutant embryos . Ki67 immunohistochemistry revealed a significant reduction in cell proliferation in Rbfox2 mutants when compared with control embryos at E12 . 5 , in which the morphological or histological changes were not evident ( Figure 3A–C ) . A significant difference in cell proliferation was more evident at E15 . 5 ( Figure 3D–F and G–I ) . TUNEL assay demonstrated no differences in cell death between the control and Rbfox2 mutants at E15 . 5 ( Figure 3J–K ) . E-cadherin expression is observed in the nasal , palatal and tongue epithelium and not affected by Rbfox2 deletion ( Figure 3L–M ) . To determine whether cleft palate defect was due to impaired neural crest cell migration , we performed lineage-tracing analysis at E15 . 5 in both control ( Wnt1Cre/+:Rbfox2flox/+:R26mTmG/+ ) and mutant ( Wnt1Cre/+:Rbfox2flox/flox:R26mTmG/+ ) embryos . Labeled neural crest cells marked by GFP immunostaining were abundantly present in the palate shelves of mutant embryos ( Figure 3N–O ) . To determine any gross abnormality in NCCs migration , we performed lineage-tracing analysis at E12 . 5 and E14 . 5 in both control ( Pax3Cre/+:Rbfox2flox/+:R26mTmG/+ ) and mutant ( Pax3Cre/+:Rbfox2flox/flox:R26mTmG/+ ) embryos . No obvious NCCs migration defect was observed ( Figure 3—figure supplement 1 ) . To examine the nature and severity of the skeleton defects , we performed Alizarin Red S and Alcian Blue staining on Rbfox2Pax3-CKO and Rbfox2Wnt1-CKO embryos ( Figure 4 ) . In both Rbfox2Pax3-CKO and Rbfox2Wnt1-CKO embryos , neural crest-derived bones such as frontal bones of the calvaria were hypoplastic and widely separated leaving a wide dorsal opening ( Figure 4C and M ) . Decreased ossification of nasal bone was observed in Rbfox2Pax3-CKO mutant embryos compared to the controls ( Figure 4B–C and L–M ) . Reduction in the lower jaw or mandible size was observed in Rbfox2 mutant embryos ( Figure 4E–H and N–Q ) . Further analysis revealed that both the shape and size of most neural crest-derived bones including alisphenoid , premaxilla , palatal process of premaxilla , palatal process of maxilla and palatine are affected in Rbfox2 mutant embryos ( Figure 4J–K and R–S ) . The palatal process of palatine bone is also missing in Rbfox2 mutant embryos ( Figure 4K and S ) . The whole embryo skeletal preparations displayed severe defects in the axial skeleton of Rbfox2Pax3-CKO embryos ( Figure 4—figure supplement 1A ) . Mutants are characterized by their shortened axial skeleton and smaller thoracic cavity . The vertebral column of control embryos showed a clear S-bend in the cervical and thoracic region ( Figure 4—figure supplement 1A ) . However , in the Rbfox2Pax3-CKO embryos , the vertebral column was rather straight , positioning the skull and vertebral column perpendicular to each other ( Figure 4—figure supplement 1A ) . Ectopic bone formation and fusion of vertebral bodies were observed in Rbfox2Pax3-CKO embryos ( Figure 4—figure supplement 1A ) . No defects in the axial skeletons were observed in Rbfox2Wnt1-CKO embryos ( Figure 4—figure supplement 1B ) . Von Kossa staining of the calvaria from E17 . 5 Rbfox2Pax3-CKO mutant embryos revealed the impaired development of mesenchymal condensations that become ossified bone ( Figure 4—figure supplement 1C ) . Reduced thickness in the ossified calvaria bone was observed in Rbfox2Pax3-CKO embryos . As shown by normal septation and alignment of the aorta and pulmonary trunk , the development of cardiac OFT was not affected in Rbfox2 mutants ( Figure 4—figure supplement 2A–J ) . No change in smooth muscle actin ( SMA ) staining was observed ( Figure 4—figure supplement 2C , F ) . Fate-mapping analysis demonstrated that cardiac NCCs migration was grossly intact in the Rbfox2 mutant embryos ( Figure 4—figure supplement 2K–P ) . To determine the effect of Rbfox2 deletion on neurons that populate the dorsal root , sympathetic and enteric ganglia , we performed whole mount neurofilament ( 2H3 ) staining to mark the differentiated neurons . The neurofilament 2H3 staining in Rbfox2 mutant embryos revealed abnormalities in the oculomotor ( III ) , trochlear ( IV ) and hypoglossal nerve ( XII cranial nerve ) ( Figure 4—figure supplement 3A ) . Oculomotor and trochlear nerves appear to be deformed . Analysis of cranial ganglia at higher magnification revealed that the roots of the hypoglossal cranial nerve are not fully developed . The hypoglossal cranial nerve is disorganized and shorter in Rbfox2Pax3-CKO embryos ( Figure 4—figure supplement 3A ) . Hypoglossal nerve defects could be secondary to defects in the hypoglossal cord which is derived from the occipital somites , and where Pax3 is expressed ( Bajard et al . , 2006 ) . No significant difference was observed in the size of the dorsal root ganglion ( Figure 4—figure supplement 3B ) . Enteric nervous system development was not affected in the absence of Rbfox2 ( Figure 4—figure supplement 3C–D ) . Similarly , other neural crest-derived organs such as thymus and adrenal gland ( chromaffin cells ) were not affected in Rbfox2 mutant embryos ( Figure 4—figure supplement 4 , and Figure 4—figure supplement 5 ) . Since Pax3Cre/+ transgene is active in non-neural crest-derived tissues such as limb and diaphragm muscles , we also analyzed these tissues and found no significant changes ( Figure 4—figure supplement 6 ) . To determine the splicing program and transcriptional network regulated by Rbfox2 in vivo , we performed RNA-Seq profiling using poly ( A ) + RNA isolated from microdissected craniofacial tissues from E12 . 5 control and Rbfox2 mutant embryos ( Figure 5A ) . We performed RNA-Seq analysis at E12 . 5 because of the minimal morphological and structural changes observed in Rbfox2 mutant embryos at this stage . Multiplexed libraries were prepared for all replicates and sequenced together using Illumina HiSeq 4000 platform to produce 65–80 million , 151-nucleotide paired-end reads per sample ( see Materials and methods for detail ) . Paired-end fastq sequence reads from each sample were aligned to mouse reference genome using ultrafast RNA-Seq aligner STAR with 82% average mapping rate and negligible ribosomal RNA contamination ( <1% ) . Differential expression of genes and transcript isoforms between controls and Rbfox2 mutant samples were determined using two tools: MISO ( Mixture of Isoforms ) , which quantitates the expression level of alternatively spliced genes and identifies differentially regulated isoforms or exons across samples and Cuffdiff , which finds significant changes in transcript expression , splicing , and promoter use . Using MISO analysis , we identified 81 differentially expressed transcripts from 59 genes in Rbfox2 mutant samples as compared with control ( Figure 5B and Figure 5—source data 1 ) . However , Cuffdiff analysis identified 33 alternatively spliced transcripts from 30 genes in Rbfox2 mutant samples as compared with controls ( Figure 5C and Figure 5—source data 2 ) . Pathway enrichment analysis identified significant enrichment of genes that control cellular and anatomical structure morphogenesis ( Figure 5D ) . We analyzed the location of UGCAUG sequences in different gene features ( 5'-UTR , promoter , exons , introns , 3-UTR ) of Rbfox2 target genes identified through Cuffdiff and MISO analysis . We performed an analysis of motif enrichment ( AME ) through the MEME suite ( version 5 . 0 . 5 ) and identified significant UGCAUG motif enrichment only in the introns of the Rbfox2 target genes ( adjusted p-value=1 . 4E-08 ) ( Figure 5E ) . We also performed AME on 75 randomly selected genes whose expression levels were comparable to the Rbfox2-target genes , but did not show any transcript-level differential expression in response to Rbfox2 deletion . However , no significant motif enrichment was observed ( adjusted p-value=1 . 0E-01 ) . Next , we used the FIMO tool in MEME to analyze the location of intronic UGCAUG sequences in the Rbfox2 target genes . Most UGCAUG sequences were located within 1000 bases of the exon on either flanking introns ( Figure 5F ) . Venn analysis was performed to identify a small set of high-confidence alternatively spliced transcripts that could be validated in downstream experiments . Venn analysis identified 11 transcripts that were identified by both programs and constituted a high confidence set of differentially expressed and AS isoforms in Rbfox2 mutants ( Figure 5G–H and Figure 5—figure supplement 1 ) . Specific genes identified by Venn analysis were selected for validation by RT-PCR based on their established function in the neural crest or craniofacial development . Using RNA isolated from control and Rbfox2 knockout craniofacial tissues , we validated the splicing changes identified by RNA-Seq by performing reverse transcriptase PCR ( RT-PCR ) in a selected group of genes such as mitogen-activated protein kinase kinase kinase 7 ( Map3k7 ) , fibronectin 1 ( Fn1 ) , periostin ( Postn ) and UAP1 UDP-N-acetylglucosamine pyrophosphorylase 1 ( Uap1 ) . Rbfox2 deletion significantly impacted splicing and expression of these candidate genes ( Figure 5I–L ) . For example , Rbfox2 deletion reduced the expression of the predominantly expressed Map3k7 transcript . The short Fn1 transcript was not present in Rbfox2 mutants . To determine that Rbfox2 directly bind to these target genes in vivo , we performed RNA immunoprecipitation ( RIP ) assays using anti-Rbfox2 antibody on palatal mesenchymal cells , followed by quantitative RT-PCR . We observed significant enrichment suggesting that Rbfox2 can directly bind to the RNA of these target genes and modulate splicing ( Figure 5J ) . To determine the transcriptional changes associated with the craniofacial defects , we analyzed genes that are differentially expressed between control and Rbfox2 knockouts . We identified 56 differentially expressed genes ( Figure 5K ) . We further validated the expression of candidate genes that were either differentially expressed or spliced by quantitative PCR in both control and Rbfox2 knockout tissues ( Figure 5L ) . We observed a significant reduction in Map3k7 , Fn1 , Myl1 , Sfrs18 and Smarca2 in Rbfox2 knockout tissues ( Figure 5L ) . Together , we identified over 100 AS transcripts and 56 genes that are differentially expressed between control and Rbfox2 mutant embryos . By analyzing alternatively spliced and differentially expressed genes from the RNA-Seq data , we observed that a number of genes known to affect TGF-β signaling pathway , such as Map3k7 , Fn1 , Meg3 etc . were significantly reduced in Rbfox2 mutants . This led us to hypothesize that TGF-β signaling pathway may be affected by Rbfox2 deletion . TGF-β signaling pathway involves both canonical and non-canonical signaling cascades . Recent work has shown that Tak1 , encoded by Map3k7 is required for activation of both canonical and non-canonical signaling . To identify if TGF-β signaling pathway is affected by Rbfox2 deletion in vivo , we harvested palatal shelves from control and Rbfox2 mutant embryos and performed western blot analysis for Tak1 and phosphorylated Tak1 . We found that both Tak1 and phosphorylated Tak1 levels were significantly reduced in Rbfox2 mutant embryos , demonstrating impaired TGF-β signaling pathway in neural crest-derived palate shelves ( Figure 6A ) . We further investigated the downstream signaling targets of Tak1 such as p38 Mapk , phosphorylated p38 Mapk , Smad2 , and phosphorylated Smad2 . The level of C-terminal Smad2 phosphorylation was significantly reduced; however , no change in total Smad2 was observed . Similarly , the level of phosphorylated p38 Mapk was significantly reduced , although there was no change in total p38 Mapk ( Figure 6A and Figure 6—figure supplement 1 ) . Consistent with the reduction in its mRNA levels , Fn1 protein levels were also significantly reduced in Rbfox2 mutant neural crest as compared with controls ( Figure 6B and Figure 6—figure supplement 1 ) . Next , we tested how neural crest-derived palatal mesenchymal cells respond to TGF-β stimulation . We established culture conditions to grow palatal mesenchymal cells from control and Rbfox2 mutant embryos . The neural crest cell’s origin and purity were confirmed by growing cultures of Pax3Cre/+;Rbfox2flox/+;Rosa26mTmG/+ embryos . Majority of the cultured cells were GFP positive confirming their neural crest origin ( Figure 6C ) . Consistent with the in vivo data , palatal mesenchymal cells isolated from Rbfox2 mutant embryos showed reduced levels of both Tak1 and phosphorylated Tak1 when compared with control following TGF-β stimulation ( Figure 6D ) . In addition , no significant change in pTak1/Tak1 ratio was observed , suggesting that Tak1 activation was not affected by Rbfox2 deletion ( Figure 6E ) . Similarly , TGF-β-induced C-terminal Smad2 and p38 Mapk phosphorylation levels were reduced in Rbfox2 mutant cells when compared with controls , while no significant change was observed in total Smad2 or p38 Mapk ( Figure 6D–E and Figure 6—figure supplement 1 ) . Quantification of these proteins showed a significant reduction in the ratio of pSmad2-C/Smad2-C and p-p38 Mapk/p38 Mapk , suggesting that Rbfox2-TGF-β-Tak1 signaling axis was impaired in Rbfox2 mutant cells ( Figure 6D–E and Figure 6—figure supplement 1 ) . To determine whether Tak1 overexpression can restore TGF-β signaling pathway and rescue the proliferation defects seen in Rbfox2 mutant embryos in vivo , we cultured palatal mesenchymal cells from control and Rbfox2 mutant embryos . We then transfected Rbfox2 mutant cells with either empty plasmid ( pcDNA3 ) or plasmid expressing Tak1 ( pcDNA3-Tak1 ) , and performed Ki67 immunostaining ( Figure 6F–H ) . We confirmed that both Tak1 and phosphorylated Tak1 levels were significantly increased after Tak1 transfection in Rbfox2 mutant cells ( Figure 6G ) . Consistent with the in vivo data , Rbfox2 mutant palatal cells proliferate slower than control cells and Tak1 overexpression can rescue the proliferation defects ( Figure 6H ) . To determine the effect of Tak1 overexpression on the expression of Rbfox2-dependent genes , we performed qRT-PCR for Map3k7 , Fn1 , Myl1 , Sfrs18 and Smarca2 on control and Rbfox2 mutant cells transfected with either empty plasmid vector or plasmid expressing Tak1 ( Figure 6—figure supplement 2 ) . Compared with empty vector transfected controls , we observed significant increase in the expression of Map3k7 and Fn1 in Tak1 overexpressing Rbfox2 mutant cells . No change in Myl1 , Sfrs18 and Smarca2 expression was observed ( Figure 6—figure supplement 2 ) . Together , these results demonstrate that Rbfox2 deficiency leads to impaired Rbfox2-TGF-β-Tak1 signaling axis , resulting in reduced palatal cell proliferation . In addition , Tak1 overexpression restores TGF-β signaling pathway and rescues the proliferation defects in Rbfox2 mutant cells . To determine whether TGF-β signaling pathway regulate Rbfox2 expression in a positive feedback loop , we analyzed Rbfox2 expression in wildtype palatal mesenchymal cells after TGF-β stimulation . We observed a significant increase in Rbfox2 expression after TGF-β treatment ( Figure 7A and Figure 7—figure supplement 1 ) . To determine the mechanism by which TGF-β regulates Rbfox2 expression , we stimulated palatal mesenchymal cells with TGF-β in the presence or absence of chemical inhibitors blocking either canonical ( Smad3 inhibitor ) or non-canonical ( Tak1 and p38 inhibitor ) TGF-β signaling pathway ( Figure 7B–D and Figure 7—figure supplement 1 ) . We observed that Smad3 inhibitor abolished TGF-β-induced increase in Rbfox2 expression ( Figure 7B and Figure 7—figure supplement 1 ) . A similar trend was observed with Tak1 inhibitor ( Figure 7C and Figure 7—figure supplement 1 ) . However , p38 inhibitor did not impact TGF-β-induced Rbfox2 expression ( Figure 7D and Figure 7—figure supplement 1 ) . Together , these results suggest that TGF-β induces Rbfox2 expression either through Tak1-dependent or -independent canonical pathways . To further investigate how Smad-dependent canonical pathway regulates Rbfox2 expression , we analyzed 2 . 5 kb Rbfox2 promoter and identified two sites with multiple Smad binding elements ( SBEs ) . Rbfox2 promoter fragment ( 1 . 6 kb ) with multiple SBEs was PCR-amplified , cloned into a luciferase reporter plasmid , and tested in luciferase reporter assays ( Figure 7—figure supplement 2 ) . Smad2 , Smad3 or Smad4 significantly activated the Rbfox2 luciferase reporter in the presence/absence of recombinant TGF-β . However , in the presence of recombinant TGF-β , the fold activation was much higher when compared with no TGF-β stimulation ( Figure 7E–G ) . Using the wild-type palatal mesenchymal cells in the presence or absence of TGF-β , we next tested Smad2/3 binding activity to SBEs identified in the Rbfox2 promoter in vivo by chromatin immunoprecipitation ( ChIP ) assays . Our data indicate that Smad2/3 binds directly to these sites in vivo . Moreover , we observed enrichment in Smad2/3 chromatin binding after TGF-β treatment ( Figure 7H ) . Together , these results demonstrate that Rbfox2 expression in neural crest-derived palatal mesenchymal cells is tightly regulated by TGF-β signaling pathway ( Figure 7I ) .
During embryonic development , various organ formations require precise spatial and temporal regulation of gene expression . Traditionally , developmental studies were more focused on the role of transcription factors and signaling pathways . It is only in recent years that the importance of splicing factors has been demonstrated in regulating various developmental processes . The gene regulatory network required to orchestrate neural crest development is very well defined ( Gammill and Bronner-Fraser , 2003; Hutson and Kirby , 2003; Sauka-Spengler and Bronner-Fraser , 2008; Simões-Costa et al . , 2014 ) . However , the cell-autonomous role of splicing factors in neural crest development is poorly investigated . In the present study , we demonstrate a critical role for splicing regulator Rbfox2 in NCCs . We show that Rbfox2 is expressed in pre-migratory and migratory NCCs , neural crest-derived palate shelves , dorsal root ganglia , and somites . Genetic deletion of Rbfox2 using Pax3Cre/+ or Wnt1Cre/+ mouse strains affected neural crest cells and their derivatives , causing severe craniofacial defects including cleft palate . We show that cleft palate defect was due to impaired palate cell proliferation and not due to cell death or impaired NCCs migration . To examine the effect of Rbfox2 deletion on cranial NCCs , we examined the cranial NC-derived craniofacial skeletons and found that majority of the NC-derived bones were affected in Rbfox2 mutants . NCCs contribute to the formation and septation of the cardiac OFT , as well as patterning and remodeling of aortic arch arteries . In contrast to a fully penetrant cleft palate and craniofacial bone defects , no cardiac defects were observed in Rbfox2 mutants . This surprising finding led us to investigate if Rbfox2 is expressed during OFT development . We found Rbfox2 expression was not detected in the NC-derived cardiac tissues; thereby explaining the lack of OFT defects . NCCs also contribute to the peripheral tissues such as nervous systems , thymus , adrenal gland and others ( Dupin and Sommer , 2012 ) . With the exception of cranial nerves , all other neural crest-derived tissues analyzed develop grossly normal , suggesting that Rbfox2 may not be or only transiently expressed in these NC-derived tissues . These findings indicate that Rbfox2 is required for the development of a narrow subset of the NCCs . Since Pax3Cre is also active in non-neural crest-derived tissues such as somites and limb and diaphragm muscles ( Bajard et al . , 2006; Engleka et al . , 2005 ) , we included these tissues in our analysis . In contrast to the controls , ectopic bone formation and fusion of vertebral bodies was observed in Rbfox2Pax3-CKO embryos , most likely the reason for the straight vertebral column . As the metameric organization of the axial skeleton is derived from the somites , these results demonstrate that Rbfox2 in the somites is necessary for proper development of the vertebral column . No obvious defects in the limb and diaphragm muscles were observed . Since both Rbfox1 and Rbfox2 are co-expressed in skeletal muscle , it is possible that Rbfox1 is able to compensate for the loss of Rbfox2 , thus preventing any developmental defects . Recently , Singh et al . ( 2018 ) generated skeletal muscle-specific Rbfox1/2 double knockout and observed a severe reduction in muscle mass , suggesting functional redundancy . Splicing is a tightly regulated process required for increasing the transcriptome complexity using a finite set of genes , ( Baralle and Giudice , 2017; Revil et al . , 2010 ) . It enhances proteomic diversity by increasing the number of distinct mRNAs transcribed from a single gene . Depending upon the type of tissues and organs , subtle changes in mRNA by splicing may alter RNA stability and/or function , protein interaction networks by either removing or inserting protein domains , subcellular localization and gene expression ( Baralle and Giudice , 2017; Garcia-Blanco et al . , 2004; Revil et al . , 2010 ) . Genetic mutations in splicing regulators have been reported in various human diseases ( Cieply and Carstens , 2015; Garcia-Blanco et al . , 2004 ) . Here , we not only demonstrate that Rbfox2 is essential for the development of tissues derived from NCCs , but also uncover over 100 Rbfox2-dependent splicing events that occur during neural crest development . RNA sequencing analysis revealed that Rbfox2 deletion altered splicing and expression of genes involved in the neural crest or craniofacial development . For example , Map3k7 encodes transforming growth factor β ( TGF-β ) -activated kinase 1 ( Tak1 ) required for the proliferation of palatal mesenchymal cells . Neural crest-specific deletion of Tak1 results in the cleft palate ( Song et al . , 2013; Yumoto et al . , 2013 ) . Fibronectin 1 ( Fn1 ) , which is a component of the extracellular matrix , has various alternatively spliced variants . Wang et al . recently demonstrated that neural crest-specific deletion of Fn1 leads to the cleft palate development ( Wang and Astrof , 2016 ) . Reduced expression of both Map3k7 and Fn1 was observed in Rbfox2 mutant tissues . We also identified a number of candidate genes that are novel and their role in neural crest/craniofacial development has not been established . In the present study , in addition to changes in splicing , we also identified 56 genes that are differentially expressed between control and Rbfox2 mutant embryos . A number of genes implicated in craniofacial bone development such as Igf1 , Wnt5a , Fn1 , and Aldh1a2 etc . were downregulated in Rbfox2 mutants ( Halilagic et al . , 2007; Lohnes et al . , 1994; Yamaguchi et al . , 1999; Zhang et al . , 2002 ) . We found that only five ( Meg3 , Ccnl2 , Smarca2 , Tpm1 , and Fn1 ) of 56 differentially expressed genes were also alternatively spliced , suggesting that Rbfox2 regulates transcriptional gene networks apart from alternative splicing . This is not surprising considering Rbfox2 has been reported to regulate gene expression patterns by different mechanisms ( Damianov et al . , 2016; Fogel et al . , 2012; Lee et al . , 2016; Wei et al . , 2016 ) . For example , Rbfox2 can affect transcript stability by directly binding to the 3’UTR of target genes ( Damianov et al . , 2016; Lee et al . , 2016 ) . Rbfox2 may affect gene expression by recruiting polycomb complexes to the DNA ( Wei et al . , 2016 ) . Future work in this direction will help to determine the mechanism by which Rbfox2 regulates expression of neural crest genes . Recent studies have defined the transcriptional network required for regulating many aspects of cranial neural crest biology ( Simões-Costa et al . , 2014 ) . However , the mechanisms by which splicing factors could be integrated into these gene regulatory networks need to be further explored . The importance of both canonical and non-canonical TGF-β signaling pathways in craniofacial development , including secondary palate formation , has been studied extensively ( Iwata et al . , 2012; Massagué , 2012; Shim et al . , 2009; Song et al . , 2013; Yumoto et al . , 2013 ) . Canonical TGF-β signaling occurs via activation of receptor-regulated Smads ( R-Smads ) 2/3 . However , non-canonical TGF-β signaling occurs via activation of the mitogen-activated protein kinase ( MAPK ) pathway , including TGF-β-activated kinase 1 ( Tak1 ) ( Massagué , 2012 ) . TGF-β signaling is required in the NCCs to regulate cell proliferation during palatogenesis ( Iwata et al . , 2012; Song et al . , 2013 ) . In humans , altered TGF-β signaling pathway has been associated with both syndromic and non-syndromic cleft palates . For instance , mutations in either TGF-β receptor type I ( TGFBR1 ) or type II ( TGFBR2 ) are associated with Loeys-Dietz syndrome ( Loeys et al . , 2005; Mizuguchi et al . , 2004 ) . Patients with Loeys-Dietz syndrome have craniofacial malformations , including cleft palate , craniosynostosis and hypertelorism ( Loeys et al . , 2005; Mizuguchi et al . , 2004 ) . Similarly , patients with Marfan or DiGeorge syndrome develop craniofacial malformations from altered TGF-β signaling ( Brooke et al . , 2008; Kalluri and Han , 2008; Lindsay , 2001; Wurdak et al . , 2005 ) . In mice , neural crest-specific genetic inactivation of several TGF-β receptors in mice , including BmprIa , Tgfbr1 , and Tgfbr2 causes craniofacial deformities , including cleft palate ( Dudas et al . , 2006; Ito et al . , 2003; Li et al . , 2013 ) . In the present study , we observed that Rbfox2 deletion in NCCs affected the expression and splicing of a number of genes implicated in the TGF-β signaling pathway , leading to deregulated Rbfox2-TGF-β-Tak1 signaling axis . For example , Map3k7 encoding Tak1 is differentially spliced and downregulated at both mRNA and protein levels in Rbfox2 mutant cells . Tak1 expression , but not its activity , was significantly reduced in the palatal mesenchyme of Rbfox2 mutant embryos . Recent studies in mice demonstrated that Tak1 is required in the NCCs to activate both TGF-β-induced canonical ( R-Smads ) and non-canonical ( p38 Mapk ) pathways ( Yumoto et al . , 2013 ) . Fn1 , a positive regulator of TGF-β signaling , was downregulated at both mRNA and protein levels . Fn1 and its integrin receptor positively regulate TGF-β signaling by promoting the receptor complex formation on the cell surface ( Tian et al . , 2012 ) . Fibronectin also regulates TGF-β by controlling the matrix assembly of latent TGF-β-binding protein-1 ( Dallas et al . , 2005 ) . In a reciprocal manner , TGF-β1 induces Fn1 expression via a Smad independent pathway ( Hocevar et al . , 1999 ) . Meg3 modulates the expression of TGF-β pathway genes by binding to the distal regulatory elements ( Mondal et al . , 2015 ) . Similar to Rbfox2 mutants , neural crest-specific deletion of Map3k7 or Fn1 leads to craniofacial defects including cleft palate ( Song et al . , 2013; Wang and Astrof , 2016; Yumoto et al . , 2013 ) . Chondrocyte-specific deletion of Tak1 results in severe chondrodysplasia with impaired ossification and joint abnormalities including tarsal fusion ( Shim et al . , 2009 ) . Osteoblast-specific deletion of Tak1 results in clavicular hypoplasia and delayed fontanelle fusion ( Greenblatt et al . , 2010 ) . Interestingly , loss of Rbfox2 results in similar defects such as fused cervical bones , hypoplastic craniofacial bone , delayed ossification and fusion of cranial bones . Consistent with these findings , heterozygous mutations in MAP3K7 cause cardiospondylocarpofacial syndrome , as characterized by craniofacial and cardiac defects including dysmorphic facial bones and extensive posterior cervical vertebral synostosis ( Le Goff et al . , 2016; Wade et al . , 2016 ) . Altogether , the striking similarities in craniofacial and skeletal phenotypes between Tak1 and Rbfox2 mutants suggest that Tak1 is a downstream target of Rbfox2 and it may significantly contribute to the phenotype observed in Rbfox2 mutant embryos . Furthermore , restoration of TGF-β signaling by Tak1 overexpression can rescue the proliferation defects seen in Rbfox2 mutant embryos . This indicates that Tak1 regulates neural crest-derived tissues downstream of Rbfox2 . It is possible that other Rbfox2 target genes identified in our RNA-Seq screen may be responsible or contribute to the craniofacial phenotype present in Rbfox2 mutant embryos . Thus , further functional characterization and investigation of their expression and splicing patterns are clearly warranted . Since a single splicing factor affects the expression/splicing of numerous genes and displays profound downstream effects , changes in the expression levels of splicing factors must be tightly regulated during embryonic development ( Baralle and Giudice , 2017; Revil et al . , 2010 ) . We found that expression of Rbfox2 in cranial NCCs is dependent on TGF-β signaling . Furthermore , Rbfox2 is required for TGF-β-Tak1 signaling axis in embryonic neural crest development . Given that altered TGF-β signaling is well studied in multiple human congenital malformations and syndromes , our observation may be relevant in human disease studies . In summary , we have provided evidence that Rbfox2 modulates neural crest development .
Pax3Cre/+ , Wnt1Cre2 , Rbfox2flox/flox , and R26mTmG/+ mice were maintained on a mixed genetic backgrounds ( Engleka et al . , 2005; Gehman et al . , 2012; Lewis et al . , 2013; Muzumdar et al . , 2007 ) . Rbfox2 mutant mice were generated by crossing the Pax3Cre/+ mice with Rbfox2flox/flox mice ( Engleka et al . , 2005; Gehman et al . , 2012 ) . Resulting Pax3Cre/+;Rbfox2flox/+ offspring were then back-crossed to Rbfox2flox/flox mice to obtain Pax3Cre/+;Rbfox2flox/flox ( presented as Rbfox2Pax3-CKO throughout the manuscript ) mice . Similarly , Wnt1Cre2-mediated neural crest-specific Rbfox2 mutant mice were generated by crossing the Wnt1Cre2 mice ( Jackson Laboratory , 022137 ) with Rbfox2flox/flox mice ( Gehman et al . , 2012; Lewis et al . , 2013 ) . Resulting Wnt1Cre2;Rbfox2flox/+ offspring were then back-crossed to Rbfox2flox/flox mice to obtain Wnt1Cre2;Rbfox2flox/flox ( presented as Rbfox2Wnt1-CKO throughout the manuscript ) mice . Control ( Rbfox2flox/+ or Rbfox2flox/flox or Pax3Cre/+;Rbfox2flox/+ or Wnt1Cre2;Rbfox2flox/+ ) and mutant ( Pax3Cre/+;Rbfox2flox/flox or Wnt1Cre2;Rbfox2flox/flox ) embryos were harvested from timed pregnancies counting the afternoon of the plug date as E0 . 5 . Embryos were dissected in PBS and fixed in 4% paraformaldehyde ( PFA ) solution in PBS . Genotyping was performed on DNA isolated from either yolk sacs or tail biopsies using following primers: 5’-ATTCTCCCACCGTCAGTACG-3’ and 5’-CGTTTTCTGAGCATACCTGGA-3’ for Pax3Cre/+; 5’-CAG CGC CGC AAC TAT AAG AG-3’ and 5’-CAT CGA CCG GTA ATG CAG-3’ for Wnt1Cre2 and , 5’-AACAAGAAAGGCCTCACTTCAG-3’ and 5’-GGTGTTCTCTGACTTATACATGCAC-3’ for Rbfox2flox/flox . R26mTmG/+ embryos were genotyped based on RFP expression ( Muzumdar et al . , 2007 ) . Littermate embryos were analyzed in all experiments unless otherwise noted . The Institutional Animal Care and Use Committee ( IACUC ) at SingHealth and Duke-NUS Medical School approved all the animal experiments . Whole embryos and isolated tissues were dissected in PBS , fixed in 4% paraformaldehyde ( PFA ) overnight at 4°C , followed by PBS washes and transferred to different gradients of ethanol for processing and paraffin embedding for sectioning . H and E staining was performed for gross histological analysis using standard procedures ( Katz et al . , 2012; Singh et al . , 2010 ) . Immunohistochemical analysis was performed on paraffin sections of PFA-fixed embryos . Primary antibodies used for whole mount or section immunohistochemistry were: anti-Rbfox2 ( Fox2/RBM9 ) mouse monoclonal ( Abcam ab57154 ) , anti-Ki67 rabbit monoclonal antibody ( Abcam , Cat . no . ab16667 ) , anti-α-Smooth Muscle actin mouse monoclonal antibody ( Sigma , Cat . No . A2547 ) , and anti-2H3 mouse polyclonal ( Iowa Hybridoma Bank , developed by T . M . Jessell and J . Dodd ) . Whole-mount immunostaining for neurofilament ( 2H3 ) was carried out as described previously ( Meadows et al . , 2013; Singh et al . , 2005a; Singh et al . , 2011 ) . Briefly , endogenous peroxidase activity was blocked with 5% H2O2/methanol for 2 hr at room temperature . The anti-2H3 mouse polyclonal primary antibody ( Iowa Hybridoma Bank , developed by T . M . Jessell and J . Dodd ) was applied overnight at 4°C at a dilution of 1:200 . The goat anti-mouse IgG-HRP secondary antibody ( Santa Cruz , Cat . no . sc-2005 ) was applied overnight at 4°C at a dilution of 1:500 . Detection of HRP activity was performed using a DAB kit ( Vector Laboratories , SK-4100 ) . Cell proliferation was evaluated by Ki67 immunohistochemistry ( Abcam , Cat . no . ab16667 ) on E12 . 5 and E15 . 5 control and knockout palate sections . DAPI ( Vector Laboratories ) was used to stain the nuclei . For each genotype , images of 4–6 different sections of 3–4 independent embryos were used . Apoptosis was detected using In Situ Cell Death Detection Kit , Fluorescein ( Roche , Cat no . 11684795910 ) following the manufacturer’s instructions . Alcian Blue/Alizarin Red staining of bone and cartilage was performed as described previously ( Singh et al . , 2005b ) . Briefly , euthanized embryos were placed in tap water for 1–2 hr at 4°C . Embryos were placed in 65°C water for 30 s allowing easy removal of skin . Visceral organs were removed under the microscope and the embryos were placed in 100% ethanol for 2–3 days at room temperature . Cartilage staining was performed using Alcian blue solution ( 150 mg/L Alcian blue 8GX in 80% ethanol/20% acetic acid ) for 2–3 days . Embryos were rinsed and post-fixed overnight in 100% ethanol . Bone staining was performed using Alizarin red solution ( 50 mg/L Alizarin red S in 0 . 5% KOH ) for 1–2 days at room temperature . Embryos were incubated in 0 . 5% KOH until most of the soft tissues were digested . The 0 . 5% KOH solution was replaced with 20% glycerol in water and incubated at room temperature until tissues cleared completely . Imaging was done using an inverted Olympus dissecting microscope . Craniofacial tissue was microdissected from E12 . 5 control and knockout embryos in cold PBS . Three independent biological replicates were used for each genotype group . Tissues were homogenized and RNA was isolated using a PureLink RNA Mini kit from Thermo Fisher ( Cat . no . 12183018A ) . Sequencing libraries of poly ( A ) +RNA from 3 control and three mutant samples were prepared using the TruSeq Stranded mRNA Library Prep Kit ( Illumina ) according to manufacturer’s instructions . Biological replicates were individually barcoded and pooled for paired-end sequencing using Illumina HiSeq4000 platform at the Genome Institute of Singapore . For each sample , approximately ~60–80 million paired-end reads of 151 bp were used for genome-guided alignment . Paired-end fastq sequence reads from each sample were aligned to mouse reference genome ( GRCm38 ) using ultrafast RNA-seq aligner STAR ( Dobin et al . , 2013 ) with 82% average mapping rate and negligible ribosomal RNA contamination ( <1% ) . Differential expression of genes and transcripts between controls and knockout samples were determined using two tools: MISO ( Mixture of Isoforms ) ( Katz et al . , 2010 ) and Cuffdiff ( Trapnell et al . , 2013 ) . For Cuffdiff analysis , transcripts with FPKM >5 either in all control or all mutant samples were retained for further analysis . A similar filtering was employed for MISO where transcripts with assigned count >10 either in all control or all mutant samples were retained . Transcripts with log2 ( fold change ) >1 or <-1 and nominal p<0 . 05 were considered differentially expressed in Cuffdiff . Transcripts with delta PSI ( percent spliced in ) >0 . 2 or<−0 . 2 and Bayes factor >1 were similarly considered differentially expressed in MISO . The overlap of differentially expressed transcripts representing alternative splicing events between Cuffdiff and MISO was visualized via Venn diagrams ( http://bioinfogp . cnb . csic . es/tools/venny/ ) . For selected genes , Sashimi plots ( https://software . broadinstitute . org/software/igv/Sashimi ) were generated in MISO , depicting the distribution of raw RNA-Seq densities mapped to the exons and splice junctions of gene isoforms across control and mutant samples . A number of alternatively spliced genes were validated by RT-PCR as described previously ( Singh et al . , 2016 ) . Briefly , for cDNA synthesis 1 ug of total RNA was used from craniofacial tissue samples . RNA was reverse-transcribed using random hexamer primed M-MLV reverse transcriptase ( Promega , Madison , WI ) . Primers used for RT-PCR analysis to detect splicing changes in Rbfox2 target genes are listed below . Map3k7 Exon11-F: GAGCTTGGGAGCCTCTCGTG Map3k7 Exon13-R: GGTTCTGTCCCAGTAACAGTC Fn1 Exon21-F: GAGGTGACAGAGACCACAATTG Fn1 Exon23-R: GTAAGCACTCCAGTGTCAGG Uap1 Exon2-F: CGCACGAATGGAGCCTGTG Uap1 Exon4-R: AACTCCTTCGTTGATTCCATTG Postn Exon16-F: GTTCGTGGCAGCACCTTCAAAG Postn Exon18-R: CCGTGGATCACTTCTGTCACCG Primers used for RT-PCR analysis are listed below . Map3k7 F: GTTCAAACCGAAATCGCATTG Map3k7 R: CTTGTCGTTTCTGCTGTTGGC Fn1 F: GAAGCAACGTGCTATGACGATG Fn1 R: GTCTCTGTCAGCTTGCACATC Smarca2 F: CTCCTGGACCAATTCTGGGG Smarca2 R: CATCGTTGACAGAGGATGTGAG Myl1 F: AAGATCGAGTTCTCTAAGGAGCA Myl1 R: TCATGGGCAGAAACTGTTCAAA Sfrs18 F: GGAGCAGTTCCGAATCCCC Sfrs18 R: GCCTTCTTACCAGACCTTTGAG Palate shelves were dissected from E14 . 5 control and knockout embryos in cold PBS . Palate shelves were homogenized and plated on gelatin-coated culture plates . After 6–8 days , the cells were stimulated with 10 ng/ml recombinant TGFβ growth factors ( PeproTech Cat no . #100–21 ) at different time points and harvested for western blot analysis . The neural crest origin and purity of cultures were confirmed by establishing cultures of embryos that carried a Pax3Cre/+ knock in and a Rosa26mTmG/+ reporter ( Pax3Cre/+; Rbfox2flox/+; Rosa26mTmG/+ ) . Majority of cultured cells are GFP positive demonstrating their neural crest origin . For inhibitor experiments , primary palatal mesenchymal cells were seeded with a density of 2 × 106 cells/ml onto a 6-well plate and cultured in DMEM supplemented with 1% penicillin/streptomycin and 10% FBS . After 48 hr , cells were washed with PBS and starved overnight in basal medium supplemented with 1% FBS . Cells were then stimulated with/without recombinant TGFβ ( 10 ng/ml ) in the presence/absence of SB203580 ( 5 , 10 and 20 μM ) ( Selleckchem Cat no . #S1076 ) , SIS3 ( 1 , 5 and 10 μM ) ( Selleckchem Cat no . #S7959 ) or 5Z-7-Oxozeaenol ( 0 . 1 , 1 . 0 and 5 μM ) ( R and D Systems Cat no . #3604 ) for desired time period ( 6 hr and 24 hr ) and harvested for western blot analysis . For Tak1 rescue experiment , primary palatal mesenchymal cells were isolated from E14 . 5 control and knockout embryos and upon reached ~80% of confluence , knockout cells were transfected with control vector ( pcDNA3 ) or pcDNA3-TAK1/FLAG ( Addgene , Plasmid #44161 ) using Lipofectamine 2000 reagent ( Thermo Scientific , catalog no . 11668–027 ) , according to manufacturer’s protocol . Seventy-two hours after the transfection , cells were fixed with 4% paraformaldehyde and processed for Ki67 immunostaining . In a separate experiment , cell lysate was collected to detect the TAK1 and pTAK1 by western blot analysis . Micro-dissected palate shelves or cultured palate mesenchymal cells were washed with DPBS and lysed with RIPA buffer ( Thermo Scientific , catalog no . 89901 ) containing 1:100 diluted protease and phosphatase inhibitor cocktail ( Sigma ) . The cell lysates were centrifuged at 13 , 000 rpm for 10 min at 4°C and the supernatants were collected for immunoblot analyses . Total protein concentration was determined by using the Pierce BCA protein assay kit ( Thermo Scientific , catalog no . 23225 ) . Western blots were performed as described previously ( Singh et al . , 2019; Singh et al . , 2016 ) . Briefly , for western blotting , 20–25 μg of total protein samples were separated by SDS-PAGE and transferred to nitrocellulose membrane using a Trans-Blot Turbo system ( Bio-Rad ) . Membranes were then blocked with 2–5% BSA in TBS containing 0 . 1% Tween ( TBST ) and subsequently incubated with primary antibodies diluted in TBST containing 2–5% BSA for overnight at 4°C . Blots were then washed in TBST and incubated for 1 . 5 hr at room temperature with the appropriate horseradish peroxidase-linked secondary antibodies ( Santa Cruz ) . Immunoreactive bands were detected by chemiluminescence ( Hiss GmbH , catalog no . 16026 ) using Gel Doc XR + System ( Bio-Rad ) . Primary antibodies used were as follows: anti-Tak1 ( 1:300; Santa Cruz sc-166562 ) , anti-pTak1 ( 1:500; Cell signaling 9339S ) , anti-Smad2-C ( 1:500; Cell signaling 5339 ) , anti-pSmad2-C ( 1:500; Cell signaling 3108 ) , anti-p38 Mapk ( 1:500; Cell signaling 9212 ) , anti-p-p38 Mapk ( 1:500; Cell signaling 4631 ) , anti-Fibronectin ( 1:300; Santa Cruz sc-8422 ) , anti-Rbfox2 ( 1:500; Abcam ab57154 ) and anti-β-actin ( 1:1000; Santa Cruz sc-47778 ) . Mouse Rbfox2 promoter ( ~1 . 7 kb ) was amplified and cloned into pGL4 . 27 vector ( Promega ) using In-Fusion HD Cloning Kit ( Clontech Cat no . 639645 ) for the luciferase assays . Expression vectors pCMV5B-HA-Smad2 ( Addgene plasmid # 11734 ) ( Eppert et al . , 1996 ) , pCMV5B-Flag-Smad3 ( Addgene plasmid # 11742 ) ( Labbé et al . , 1998 ) and pCMV5B-Smad4 ( Addgene plasmid # 11743 ) ( Macías-Silva et al . , 1996 ) were a gift from Jeff Wrana . Expression vector pcDNA3-TAK1/F was a gift from Xin Lin ( Addgene plasmid # 44161 ) ( Blonska et al . , 2005 ) . Luciferase assay was performed as previously described ( Singh et al . , 2016; Singh and Epstein , 2012 ) . HEK293T cells are the most commonly used cell line for monitoring the activity of the TGF/SMAD signaling pathway . Briefly , HEK293T cells were seeded in 12-well plates for 24 hr before transfection . The Rbfox2 luciferase reporter plasmid along with other indicated plasmids ( Smad2 , Smad3 or Smad4 ) was co-transfected using FuGENE6 reagent ( Promega , catalog no . E2691 ) . To normalize transfection , 50 ng of lacZ expression plasmid was also transfected together with other indicated plasmids . Cell extracts were prepared 60 hr post-transfection using lysis buffer ( Promega , catalog No . E3971 ) . Luciferase activities were assayed using Luciferase Reporter Assay System kit ( Promega , catalog no . E1500 ) . Lysates were also assayed for b-galactosidase activity using the b-Galactosidase Enzyme Assay System ( Promega , Cat . no . E2000 ) . Luciferase reporter activity was normalized to b-galactosidase activity . The luciferase assay results were reproduced in at least three independent experiments . All experiments were performed in duplicate , and the representative data are shown in the bar graphs . ChIP experiments were performed as previously described ( Singh et al . , 2016 ) . ChIP assay was performed on either unstimulated or TGFb-stimulated palate mesenchymal cells using Smad2/3 antibody ( Abcam , Cat . no . ab207447 ) , according to Millipore Chip Assay Kit protocol with minor modifications ( Catalog no #17–295 ) . RNA-IP experiments were performed as previously described with minor modifications ( Niranjanakumari et al . , 2002 ) . Statistical analyses were performed using the two-tailed Student's t-test . Data are expressed as mean ± SD . Differences were considered significant when the p-value was <0 . 05 . One-way analysis of variance ( ANOVA ) was used to assess statistical differences between groups . Significant ANOVA results were further analyzed by Bonferroni's multiple comparisons test ( * , p<0 . 05; ** , p<0 . 01; *** , p<0 . 001; NS , not significant ) . | Abnormalities affecting the head and face – such as a cleft lip or palate – are among the most common of all birth defects . These tissues normally develop from cells in the embryo known as the neural crest cells , and specifically a subset of these cells called the cranial neural crest cells . Most cases of cleft lip or palate are linked back to genes that affect the biology of this group of cells . The list of genes implicated in the impaired development of cranial neural crest cells code for proteins with a wide range of different activities . Some encode transcription factors – proteins that switch genes on or off . Others code for chromatin remodeling factors , which control how the DNA is packed inside cells . However , the role of another group of proteins – the splicing factors – remains unclear and warrants further investigation . When a gene is switched on its genetic code is first copied into a short-lived molecule called a transcript . These transcripts are then edited to form templates to build proteins . Splicing is one way that a transcript can be edited , which involves different pieces of the transcript being cut out and the remaining pieces being pasted together to form alternative versions of the final template . Splicing factors control this process . Cibi et al . now show that neural crest cells from mice make a splicing factor called Rbfox2 and that deleting this gene for this protein from only these cells leads to mice with a cleft palate and defects in the bones of their head and face . Further analysis helped to identify the transcripts that are spliced by Rbfox2 , and the effects that these splicing events have on gene activity in mouse tissues that develop from cranial neural crest cells . Cibi et al . went on to find a signaling pathway that was impaired in the mutant cells that lacked Rbfox2 . Forcing the mutant cells to over-produce one of the proteins involved in this signaling pathway ( a protein named Tak1 ) was enough to compensate for the some of the defects caused by a lack of Rbfox2 , suggesting it acts downstream of the splicing regulator . Lastly , Cibi et al . showed that another protein in this signaling pathway , called TGF-β , acted to increase how much Rbfox2 was made by neural crest cells . Together these findings may be relevant in human disease studies , given that altered TGF-β signaling is a common feature in many birth defects seen in humans . | [
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] | [
"developmental",
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] | 2019 | Neural crest-specific deletion of Rbfox2 in mice leads to craniofacial abnormalities including cleft palate |
Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade , we investigated the source and the mechanistic triggers of influenza epidemics . We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions . The strongest predictor groups are as follows , ranked by importance: ( 1 ) the host population’s socio- and ethno-demographic properties; ( 2 ) weather variables pertaining to specific humidity , temperature , and solar radiation; ( 3 ) the virus’ antigenic drift over time; ( 4 ) the host population’€™s land-based travel habits , and; ( 5 ) recent spatio-temporal dynamics , as reflected in the influenza wave auto-correlation . The models we infer are demonstrably predictive ( area under the Receiver Operating Characteristic curve 80% ) when tested with out-of-sample data , opening the door to the potential formulation of new population-level intervention and mitigation policies .
Computational study of the dynamics and factors influencing infectious disease spread began with compartmental models , such as the Susceptible-Infected-Resistant ( SIR ) model , which traces its origins to the beginning of the last century ( Kermack and McKendrick , 1927 ) . Initially a purely theoretical tool , these SIR-style models were subsequently enhanced with population and geographic data , allowing their application to specific cities and the distances between them ( Keeling and Rohani , 2002 ) . For instance , one approach , termed ‘gravity wave’ modeling , used geographic , short- and long-range , work-related human movement and demographic data to formulate gravity potentials between US counties in order to infer the dynamics of infection spread ( Viboud et al . , 2006 ) . Some studies have focused on one specific factor affecting infection , such as air travel , to simulate the spread of influenza ( Colizza et al . , 2006 ) ; other studies used SIR models , generalized for a collection of interconnected geographic areas , spatial-network or patch models , to model a number of common infections , including influenza , measles , and foot-and-mouth disease ( Riley , 2007 ) . More ambitious network model approaches have simulated the global transmission of infectious disease using high-resolution , worldwide population data and the locations of International Air Transport Association ( IATA ) -indexed airports ( Balcan et al . , 2009 ) . Similar to ( Viboud et al . , 2006 ) , the authors of the study computed the global infection-pre-disposing ‘gravity field’ over the network of international airports . This network-based approach was subsequently developed further ( Balcan and Vespignani , 2011 ) through the modeling of ‘phase transition’–that is the chain-reaction switch of geographic infection status–in complex networks , utilizing approaches introduced in theoretical physics . Another layer of sophistication was achieved by incorporating rich historical records . For example , Eggo et al . ( Eggo et al . , 2011 ) modeled the Spanish influenza epidemic of 1918–1919 , using mortality documents from both the UK and the US , explicitly accounting for the size and distances between cities . In the same spirit , Brockmann and Helbing ( Brockmann and Helbing , 2013 ) represented infection as diffusion on a complex network , estimating arrival times for infection across the globe . Following the formulation of the hypothesis that absolute humidity modulates influenza survival and transmission ( Shaman and Kohn , 2009 ) , researchers began incorporating climate variables into SIR-like models ( Chowell et al . , 2012 ) . More recent dynamic models have incorporated a probabilistic description of influenza infection’s spatial transitions in space and time , accounting for selected demographic confounders ( Gog et al . , 2014 ) and ( Charu et al . , 2017 ) . In this study , rather than following the SIR-style modeling tradition , we used statistical epidemiology- and econometric-like approaches , in addition to a causality-network method presented here for the first time . There are some prior studies that are close to ours in spirit ( but not in details ) . For example , ( Barreca and Shimshack , 2012 ) used historical US influenza mortality data ( 1973–2002 ) in conjunction with collinear humidity and temperature records to establish county-level statistical associations between variables in the datasets . They concluded that absolute humidity was ‘an especially critical determinant of observed human influenza mortality , even after controlling for temperature . ’ Another study , focusing on historical influenza records in the Netherlands , ( te Beest et al . , 2013 ) used the number of weekly influenza-like patient visits ( transformed into an estimated rate of infection ) as a response variable in a regression analysis of climate data . They concluded that the bulk of explained variation ( 57% ) was attributed to the depletion of susceptible hosts during the disease season and non-weather-related ‘between-season effects , ’ with only 3% explained by absolute humidity , represented as a continuous predictor variable . Additionally , this study observed that school holidays did not have a statistically significant effect on influenza transmission . As all causality detection methods come with dissimilar limitations and are imperfect in unique ways , we designed our study intentionally to attack the same target problem using three different statistical approaches: Approach 1: A non-parametric Granger analysis ( Granger , 1980 ) focusing on infection flows’ directionalities across the US and whether influenza propagates via long- vs . short-distance travel ( we run analysis across all pairs of air- and land-travel county neighbors , respectively ) . Approach 2: A mixed-effect Poisson regression ( Hedeker and Gibbons , 2006 ) explicitly accounting for the auto-correlation of infection waves in time and space , along with the full set of socioeconomic , climate , and geographic predictors . Approach 3: A county-matching , non-parametric analysis to identify the minimum predictive set of factors that distinguish those counties associated with the onset of the influenza season ( Morgan and Winship , 2015 ) . Our study became possible through access to several , very large longitudinal datasets: ( 1 ) a nine-year collection of insurance records capturing the dynamics of influenza-like illnesses ( ILIs ) in the United States ( Truven MarketScan database , see Materials and methods ) ; ( 2 ) temporally collinear , high-resolution weather measurements over every US county; ( 3 ) detailed air travel ( The United States Bureau of Transportation Statistics , 2010 ) and geographic proximity data ( The United States Census , 2016 ) showing connectivity between US counties; ( 4 ) billions of geo-located Twitter messages reflecting long- and short-distance human movement patterns , and; ( 5 ) US census data accounting for US county and county-equivalent population distribution , demographic , and socioeconomic properties ( HRSA , 2016 ) . An explicit comparison of the ILI data in the insurance claims to the influenza records provided by the Center for Disease Control and Prevention ( CDC , 2016 ) showed that the two sources agree well ( ρ=0 . 91 , p=3 . 5×10-201 ) , with insurance claims providing higher data resolution , see Figure 1—figure supplement 1 . Curiously , the relationship between the two sources of ILI observations is not linear: We attribute this to the lower resolution of the CDC data . These three types of analysis produce congruent–albeit not identical–results .
Our analysis of health insurance claims covers nine years of influenza cycles ( 2003 to 2011 , inclusively ) , see Figure 2 . We visualized weekly , county-level prevalence as a movie ( see Supplement ) ; Figure 2A–H show a few relevant weekly snapshots from different years . The plates in Figure 2A–H , and especially the movie , clearly show that seasonal influenza cycles initiate in the South/Southeastern US and sweep the country from south to north . This pattern is repeated , with some variation , each season . Figure 3G shows the country-wide propagation dynamics as represented by our computed causality streamlines . The alignment of causality flow vectors into long , continuous streamlines suggests a stable propagation mechanism across the country; the probability of a long sequence of summary movement vectors accidentally matching in the direction by mere chance is vanishingly small ( p<10-16 for longer streamlines ) . Do epidemics originate from the same counties season after season ? To answer this question , we follow ‘causality streamlines’ back to their source county . Informally , influenza onset in these source counties has little or no causal dependency on their neighbors . That is , their epidemic states are seemingly caused by factors outside of disease prevalence in other counties . Figure 2K presents the county-specific likelihood of streamline initiation across our nine years of data . To verify the near-shores position of these source counties is not a mere manifestation of a boundary effect of shore counties ( no neighbors at the side of large water body ) , we carried out identical causality analyses with two different infections , specifically choosing diseases less likely to share etiologies with influenza: HIV and Escherichia coli . The results for both HIV and Escherichia coli infections are shown in Figure 3J and K , which exhibit flow patterns very different from those obtained for influenza . These streamlines almost never originate from the coasts , thus reducing the likelihood that the pattern observed for influenza is a geo-spatial boundary effect . Combined with the exceedingly low probability ( ∼10-185 ) of chance inference for the streamlines , this strongly supports our conclusion that the epidemics are of coastal origin . We directly validated our conclusion that influenza waves tend to start in the South by identifying counties which seem to trigger the epidemic . We computed a ‘trigger period’ of five to six weeks for each season , defined as the period immediately preceding an exponential increase in influenza dispersion . To calculate this weekly dispersion , we treated each county as a node in an undirected graph , each with an edge connecting two geographically adjacent counties–only if they had both reported at least one influenza case in the specified week . We defined dispersion as the size of the largest , connected component in this undirected graph . Thus , a trigger period describes the period in which the size of the giant component of the infection graph rises above 250 counties from being under 100 as shown in Figure 2I , and then proceeds to the seasonal peak . Figure 2J presents the likelihood of a county being part of this largest , connected component during the trigger period . In the second approach , we followed causality streamlines back to their source county . Figure 2K presents the county-specific likelihood of streamline initiation across nine years . These approaches produced qualitatively similar results ( Figure 2J and K ) . While epidemics seem to start in many places around the country ( see the origins of streamlines in Figure 3J and I ) , they successfully gain traction near large bodies of water ( as evidenced by the most likely places of epidemic onset , see Figure 2J and K ) ) . Otherwise , they fizzle out before triggering an actual epidemic cycle ( see Figure 2J ) . Seasonal initiation is neither spatially uniform nor simply a reflection of county-specific population density . Our analysis of the Twitter movement matrix indicates that people most frequently travel between neighboring counties , preferentially towards higher-population-density areas , which shows that the maximum-probability movement patterns follow the local gradient of increasing population density ( see Figure 4—figure supplement 1 ) . In contrast , the geo-spatially-averaged movement vectors for each county reveal global flows in the movement patterns ( see Figure 3H , along with Methods for the calculation of spatial averages ) . Figure 3H–I suggest that average movement patterns largely agree with the influenza streamlines: Both patterns , especially in the South/Southeast of the country , are associated with flow pointing away from large bodies of water . In addition to looking at the direction of short-range travel , we used our non-parametric Granger analysis to investigate the comparative strength of short- vs . long-range influenza propagation . In the first case , we considered the neighborhood map shown in Figure 4A ( for a detailed definition of ”neighbors , ’ see Materials and methods ) , and the in the second case , we considered associations between major , airport-bearing counties ( see Figure 4B ) . We then plotted the distribution of the maximum pairwise coefficient of causality , where the maximization is carried out by fixing the source and the target and varying the delay in weeks , after which we attempt to predict the target stream . Conclusions associated with Approach 1: The inferred causality streamlines computed from the infection time series in all counties ( Figure 3 ) show that epidemics are mostly triggered near large water bodies and flow inland and away . They also illustrate that the US continental Southern states act as ‘sinks’ to a large proportion of these streamlines . ( ‘Sinks , ’ in our definition here , are geographic areas that multiple streamlines converge towards; sinks are especially obvious when we look at the vector representation of causality direction . The opposite of a ‘sink’ is a ‘source , ’ defined as an area at which at least one streamline starts . ) This might explain the increased prevalence in the designated region . Additionally , the analysis shows that human travel is a very important driver of emergent epidemiological patterns , and that short-range , land-based travel is more important than air-travel . This result is cross-corroborated by our Poisson regression analysis ( described next in Approach 2 ) . Approaches 2 and 3 are motivated by the ‘why’ questions: ( 1 ) Why do epidemics initiate where and when they do ? and; ( 2 ) Why do some disease initiations become epidemics while others do not ? We focused on a subset of weeks associated with the initial rise of influenza waves ( indicated by the gray bars in Figure 2I , and calculated as discussed earlier ) . The results from our best-fit model are illustrated in Figure 1A . We selected this particular model out of a total of 126 compared in the Bayesian analysis , a few of which we list in Table 5 , ranked by their decreasing goodness-of-fit , measured with the Deviance Information Criterion , DIC ( see Supplement ) . From the values of the inferred coefficients corresponding to the different factors , and taking into account their significance levels and credible intervals , we concluded that the roles played by weather variables , particularly humidity , appear to be substantially more complicated compared to what has been suggested in the literature . In Approach 3 , we investigated combinations of factors presented as ‘treatment’ via a non-parametric , exact-matching analysis of US counties during the weeks of epidemic onset on a season-by-season basis . First , we collected the list of all counties with a drop in maximum specific humidity during the weeks leading up to an influenza season in a particular year . This is the ‘treated set’: the set of counties that may be thought of as subjected to the positive ‘treatment’ of a drop in specific humidity . We split this set into two , considering counties that also experience increased influenza prevalence during the epidemic onset , and ones that do not ( counties with two different values of the outcome variable ) . The number of counties in these two sets define the first row of a 2×2 contingency table . In the second row ( the ‘control set’ ) , we focused on counties that do not experience a drop in the maximum specific humidity . However , we only considered counties that have a matching counterpart in the treated set in the following sense: For each county in the control set , we found at least one in the treated set such that the rest of the significant variables ( other than specific humidity ) had similar variation patterns in both counties . Once we defined the control set , we split it in the manner described for the treated set: We counted the number of control counties that experienced an increased influenza prevalence during epidemic onset , and those which did not . This defined the second row of the contingency table . Finally , we used Fisher’s exact test to compute an odds ratio ( the odds of realizing these numbers by chance ) , along with the test-derived significance of the association between the ‘treatment’ and epidemic wave initiation ( p-value ) . Furthermore , we defined our treatment to consist of multiple factors simultaneously , e . g . specific humidity and its change in the preceding week , along with average temperature and degree of urbanity , see Figure 6 . Note that geographic clustering of ‘treated’ and ‘untreated’ counties arose automatically as a result of similar weather patterns being imposed via the constraint of multiple climate variables . Unlike the mixed-effect regression approach ( Approach 2 ) , this matching analysis is non-parametric , and intended to reveal whether multiple factors are , indeed , simultaneously necessary . The results of Approach 3’s analysis are presented in Figure 6 and Tables 2 and 3 . We found that no single variable was able to consistently yield a statistically significant odds ratio greater than one; multiple factors interacted to shape an epidemic trigger ( see Table 3 for a few examples ) . With a total of 47 significant variables in our best mixed-effect model , an exhaustive search for all combinations was not feasible . Instead , we performed a standard evolutionary search , looking for combinations that yielded a significant odds ratio for individual seasons . Additionally , we considered all seasons together ( by simply adding the contingency tables , element-wise ) in order to increase the test’s statistical power . We isolated ten variables ( as shown in Figure 6 , Plate B ) in this manner which included maximum specific humidity and average temperature along with their variations , the degree of urbanity , antigenic variation , and vaccination coverage . The factors that appeared most often in our analysis are illustrated in Plate A: It appears that maximum specific humidity and average temperature , along with their variations , and the degree of urbanity have the most frequent contribution , followed by antigenic variation and vaccination coverage . We did see some new factors here that failed to be of significance in the regression analysis ( Approach 2 ) , e . g . , degree of urbanity and vaccination coverage . While vaccination coverage was included as a factor in our best performing model in Approach 2 , it failed to achieve significance , perhaps due to its strong dependence on antigenic variation ( see Figure 1J–M ) . Degree of urbanity was indeed significant for some of the regression models we considered ( see Supplementary Information ) , but failed to be so for the model with the least DIC . Thus , Approach three corroborates and strengthens key claims of Approach 2 . The exact set of factors varied somewhat over the seasons; nevertheless , together , they yield significant results when all seasons are considered together . The matching analysis corroborates our results from both the mixed-effect regression and the geographic streamline analyses: The sets of counties initiating the wave are near coasts on the Southern region of the continental US ( see Plates A - I in Figure 6 ) . Our conclusion that local travel is predominantly responsible for disease wave propagation is supported by several lines of analysis . First , continuous land-movement infection waves are visible in the weekly influenza rate movie; we computed this movie from insurance claim data and made it available with results of this study . Second , because our all-weeks-included dataset was too large for the R MCMCglmm package to handle , we performed mixed-effect Poisson regression calculations using a 50 percent random sample of all the weeks for which data were available . In this computation , the airport-proximity , fixed-effect coefficient turned out to be statistically significantly negative ( see Supplement A , as well as the editable output file ‘flu-50-percent-weeks . txt’ ) . Third , the results from our Granger-causality inference showed that:
The following aspects make our study of influenza triggers new in the influenza literature: ( 1 ) Instead of simulating the plausibility of one particular epidemic trigger model with a dynamic disease transmission model , we used formal model selection tools to compare the goodness of fit of hundreds of plausible models; ( 2 ) We explicitly attempted to systematically cross-compare the importance of numerous individual factors typically hypothesized to contribute to epidemic onset; ( 3 ) To accomplish this , we collected an unprecedented volume of temporal and spatial data on disease dynamics and the dynamics of putative predisposing factors; ( 4 ) We used several orthogonal , computational causality-inference techniques ( one of which was developed specifically for this study ) to probe associations between disease onset and putative epidemic triggers; ( 5 ) We tested our best models for their predictive potential and demonstrated that they are , indeed , suitable for forecasting disease waves , and; ( 6 ) We combined , for the first time , numerous candidate factors in a single , integrative study . Convergent conclusions , culled from these radically different techniques , strengthen our claims and make it statistically unlikely that we are observing analysis artifacts . First , the Granger causality analysis results ( Approach 1 ) provide insights into the details of influenza’s epidemiological dynamics . Figure 3G traces out the paths most likely followed by the infection , on average , across the continental US . We note that ∼75% of the streamlines sink in counties belonging to the Southern states , which matches up well with the streamline-encoded dynamics of weekly disease incidence over nine years ( see Figure 3I ) . What drives this particular causality field’s geometry ? While we cannot definitively answer this question , a comparison of the global patterns emerges from the local mobility data culled from the aforementioned Twitter database and offers a tentative explanation ( see Figure 3H ) . Second , contrary to reported human travel pattern influence on seasonal epidemics ( Viboud et al . , 2006 ) ( but consistent with [Gog et al . , 2014] ) , we find that short-distance travel contributes more significantly to disease spread ( see Figure 4 ) . In particular , we find that long-range air travel is important as an epidemic trigger , but once infection waves are triggered , air travel patterns ( or proximity to major airports ) become less important . Short-range mobility , on the other hand , is apparently important for sustaining infection transmission over each season . Thus , we find short-range travel to be more important for defining the emergent spatio-temporal geometry of infection waves , while proximity to airports is more important for actually triggering an influenza season; the latter loses positive influence once an infection is under way . This conclusion is justified as follows: ( 1 ) When we performed regression calculations using all weeks for which data were available ( as opposed to wave initiation weeks only ) the airport proximity predictor coefficient turned out to be statistically significantly negative ( see Supplement ) . ( 2 ) Results from our Granger-causal inference indicate that , on average , the local , putatively causal connections are far stronger compared to the putatively causal connections between counties within which the major airports are located ( see Figure 4C and F ) . Additionally , from our best mixed-effect regression model ( Figure 1A ) , we find that land connectivity effects are significantly stronger than air connectivity effects . The predictive value of Twitter connectivity , which intuitively captures both local and long-distance travel , lies in-between land and air connectivity coefficients . Note that Twitter connectivity is represented as a directed graph , where for each pair of counties , i and j , the ( i , j ) edge weight represents the conditional probability of ending at county j , given that a traveler/Twitter user started her journey in county i . Transition probabilities from i to j sum to one over all j . Therefore , intuitively , the Twitter connectivity graph should have the features of both a land-connectivity and an air travel graph; which indeed appears to be close to reality . 3 ) While airport diffusion is a significant factor in our best Poisson model ( using data from the initiation period ) , the causal streamlines ( constructed with the complete , all-year incidence data ) do not seem to originate from airport-bearing counties . The role of short-distance travel is particularly crucial in explaining influenza’s time-averaged , geo-spatial prevalence . While the mixed-effect regression analysis explains seasonal initiation in the vicinity of the continental US Southern shores , it might not , by itself , adequately explain its average prevalence patterns across the country . Also not explained solely by our regression models is the occurrence of relatively high infection prevalence in the central parts of the country . These differences cannot be attributed to long-distance air travel , as discussed before . However , the routes taken by the causality streamlines ( as computed by the non-parametric Granger analysis ) , interpreted as paths followed by an infection on average , suggest an explanation: The close match between the Granger-causal flow and the short-range mobility patterns ( derived from Twitter analysis ) strongly suggest that average disease prevalence is modulated by short-range mobility . The traditional empirical approach of testing a causal link between a factor and an outcome of an experiment was to vary one factor at a time , while keeping the other factors ( experimental conditions ) constant . This ‘all the rest of the conditions are equal’ assumption is often referred to by its Latin form as ceteris paribus . R . A . Fisher ( [Fisher , 1935] , p . 18 ) noted that , in real-life experiments , perfect ceteris paribus is not achievable ‘because uncontrollable causes which may influence the results are always … innumerable . ’ Fisher’s proposed solution to this problem is to design experiments to involve random assignment of treatment ( the putative causal factor’s states ) to individual trials and then use regression analysis to estimate the value and significance of the putative causal effect . Likewise , hypothesis-driven science , wherein investigators formulate a single , testable hypothesis and design specific experiments to test it , is a core element of the scientific method , and works well in most scientific fields . However , a new challenge emerges in data-rich scientific fields , such as genomics , epidemiology , economics , climate modeling , and astronomy: How do we choose the most promising hypotheses among millions of eligible candidates that potentially fit data ? One solution to this challenge is the many-hypotheses approach , a method of automated hypothesis generation in which many hypotheses are systematically produced and simultaneously tested against all available data . This approach is currently used , for example , in whole-genome association or genetic linkage studies , and often enables truly unexpected discoveries . In contrast to the single-hypothesis approach , the many-hypotheses approach explicitly accounts for the large universe of possible hypotheses through calibrated statistical tests , effectively reducing the likelihood of accidentally accepting a mediocre hypothesis as a proxy for the truth ( Nuzzo , 2014 ) . The many-hypotheses approach provides a complement to carefully controlled and highly focused wet laboratory experiments . Running controlled experiments to test a single hypothesis necessarily ignores many of the complexities of a real-world phenomenon; these complexities are necessarily present in large , longitudinal datasets . Of course , the data-driven ‘many-hypotheses’ approach is only one aspect of the broader scientific process progressing toward the development of verifiable general theories . Intuitively , we expected that all three approaches would produce similar , if not identical results . In practice , while the three approaches agreed in most cases , this agreement was not perfect . For example , the highlighted areas ( greater incidence ) in the first influenza season snapshot for Figure 2A–H each should match relatively well to the maps in Figure 6B ( or at least some unspecified subset of ‘high incidence treatment counties’ ) . While the county-matching results point to initiation at coasts , in Figure 2 , 2006-2007 initiation seems to spread from the West Coast and , in , 2010 has a scattered pattern across the middle of the US . The intuitive explanation of perceived discrepancy is that the matching method agrees with other analysis types predominantly , but not in all cases . Each analysis has limitations . In the case of the matching analysis , we have less statistical power than in , for example , Poisson regression; matching by numerous parameters reduces the initial set of thousands of counties to a handful of matching ‘treated’ counties ( which meet a particular combination of weather and sociodemographic conditions ) and ‘untreated’ counties ( very similar to ‘treated’ ones in all respects but treatment ) . The difficult-to-match , ‘weeded out’ counties may happen to be in the coastal areas indicated as the most likely places of influenza wave origin by other analyses . In the case of the 2007 and 2010 results , the matching analyses pick patterns that are different from those produced by the causality streamline analysis and mixed-effect Poisson regression models . Figure 6’s Plate B shows the distribution of the treatment counties and matched-non-treatment counties . Note that here , we are not directly predicting initiation , so while the patterns in Figure 2 and Figure 6 should indeed show some similarity , they are not required to match up perfectly . The most similar treated counties do indeed show up in the Southern shores . Our analysis uses no prior knowledge specific to influenza epidemiology . As such , these methods are not limited by either the pathogen under consideration ( influenza ) , or the geospatial context ( United States ) . The tools developed here are expected to be equally applicable to analyzing general epidemiological dynamics for pathogens other than influenza , unfolding in arbitrary geographical regions . The specific conclusions we draw about the initiation and propagation of the seasonal influenza in US might not hold true for influenza epidemiology in a different geographical context . However , the analysis tools are still applicable . More broadly , our tools delineate a general approach to modeling complex spatio-temporal dynamics , with applications beyond solely disease epidemiology . We conclude by highlighting the structure of overlapping conclusions delivered by our three approaches . Approach 1: Granger-causality analysis suggests that an epidemic tends to begin in the South , near water bodies and that short-range , land-based travel is more influential compared to air-travel for infection propagation , providing a map of mean infection flow across the continental US . Approach 2: Poisson regression identifies significant predictive factors , ranks these factors by importance , suggests that Southern shores are where the epidemic begins , and corroborates Approach 1’s result on short-range vs . long-range travel . Approach 3 ( county-matching ) : This approach drills down further to the epidemic onset source to the Southeastern shores of continental US , and identifies a smaller validated subset of predictive factors .
To investigate county-specific variability , we grouped candidate factors into several categories: demographic , relation to human movement , infection state of county neighbors , and county’s own recent state , and climatic . Major hypotheses regarding putative causal factors affecting infection dynamics can be traced to a handful of earlier publications: | Influenza – or ‘the flu’ – is a contagious disease which sweeps across the globe like clockwork , claiming tens of thousands of lives . This is known as ‘seasonal flu’ . Many scientists have tried to identify the factors that spark these yearly outbreaks . Some past studies have found that seasonal flu occurs when air that is normally humid turns dry , suggesting weather patterns play an important part . Other research has shown that air travel contributes to the flu spreading across the world . However , these studies typically focus on just one or two factors on their own . It is still not clear how exactly these factors combine to drive outbreaks , and then sustain the wave of infection . To address this , Chattopadhyay et al . analyze the medical histories of 150 million American people over a decade , combining this information with large datasets about the different factors that trigger flu outbreaks . This includes detailed data about air travel and weather patterns , as well as census data that describe features of the population . Patterns of movement are also examined , for example by processing billions of Twitter messages “tagged” with a location . Chattopadhyay et al . used all of these datasets to model outbreaks of the flu in the United States , and see which factors play the biggest role . It turns out that yearly outbreaks of seasonal flu are a result of a combination of elements . Some factors interact to help trigger the start of the wave , like humid weather in a highly populated area with nearby airports . Other factors , such how people move , encourage the spread of the infection . Finally , certain features of the population , for example how closely knitted a community is , make specific areas of the country more susceptible to the arrival of the disease . Overall , some of the most important elements of the model relate to the characteristics of the populations , the weather , the type of virus , and the number of short-distance journeys ( rather than air travel ) . Understanding how and why outbreaks occur can help policy-makers design strategies that reduce the spread and impact of seasonal flu , which could potentially save thousands of lives . Ultimately , the model developed by Chattopadhyay et al . could be used to test whether these policies would work before they are implemented in the real world . | [
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] | 2018 | Conjunction of factors triggering waves of seasonal influenza |
Passive mechanisms of mate guarding are used by males to promote sperm precedence with little cost , but these tactics can be disadvantageous for their mates and other males . Mated females of the plant bug Lygus hesperus are rendered temporarily unattractive by seminal fluids containing myristyl acetate and geranylgeranyl acetate . These antiaphrodisiac pheromones are gradually released from the female’s gonopore , declining until they no longer suppress male courtship . Because starting quantities of these compounds can vary widely , the repellant signal becomes less reliable over time . Evidence was found of a complimentary mechanism that more accurately conveys female mating status . Once inside the female , geranylgeranyl acetate is progressively converted to geranylgeraniol then externalized . Geranylgeraniol counteracts the antiaphrodisiac effect despite having no inherent attractant properties of its own . This is the first evidence for such an anti-antiaphrodisiac pheromone , adding a new element to the communication mechanisms regulating reproductive behaviors .
Chemical signaling is an essential part of the regulation of mating in many insects , with a combination of pheromonal attractants and repellents indicating the suitability of prospective mates ( Gillott , 2003 ) . Several species have been shown to rely upon the transfer of an antiaphrodisiac from male to female during mating , the effect of which is to reduce the sexual attractiveness of females concurrent with a post-copulatory ovipositional period ( Happ , 1969; Gilbert , 1976; Kukuk , 1985; Tompkins and Hall , 1981a , 1981b; Jallon et al . , 1981; Scott , 1986; Andersson et al . , 2000 , 2003; Schulz et al . , 2008; Yew et al . , 2009 ) . The mating male benefits from a reduced risk of sperm competition , while potential successor suitors avoid sperm competition as well as reduce the energetic costs and predation risks associated with courting a female that is unlikely to be receptive ( Gillott , 2003; Malouines , 2016 ) . Females benefit from this change in their chemical signature by a reduction in male harassment , which might otherwise negatively impact longevity , ovipositional opportunities , and predation avoidance ( Forsberg and Wiklund , 1989; Magnhagen , 1991; Cook et al . , 1994; Clutton-Brock and Langley , 1997; Bateman et al . , 2006; den Hollander and Gwynne , 2009 ) . This system is particularly useful for species in which females mate only once and for whom a protracted or permanent loss of attractiveness has no negative consequences ( Gillott , 2003 ) . Similarly , mated females can also cease releasing their attractant pheromones ( Raina , 1989; Kingan et al . , 1993; Ayasse et al . , 1999; Eliyahu et al . , 2003; Fukuyama et al . , 2007; Oku and Yasuda , 2010 ) , potentially adding to the impact of an antiaphrodisiac . However , in species with females that can or need to mate multiple times over their lives , the antiaphrodisiac might actually fail to accurately convey a female’s reproductive state to conspecific males ( Malouines , 2016 ) . Often the antiaphrodisiac consists of just one or at most a few chemicals that are repellant to males ( Jallon et al . , 1981; Andersson et al . , 2000; Schulz et al . , 2008; Yew et al . , 2009; Zawistowski and Richmond , 1986; Krueger et al . , 2016 ) or that mask a female’s attractants ( Andersson et al . , 2003; Zhang and Aldrich , 2003; Zhang et al . , 2007 ) . These pheromones are emitted from the female over days or weeks until fully discharged or degraded , at which point the female can attract a new mate . One disadvantage of such a simple signaling system is that the amount of antiaphrodisiac being emitted by a female may not coincide with her readiness to mate again . A male’s maturity , health , or the interval between insemination events can all influence the amount of antiaphrodisiac that he can transfer along with his sperm . There is even evidence that males can intentionally bias the size of their spermatophore in response to female mating history and the local level of intrasexual competition ( Larsdotter-Mellström et al . , 2016 ) . Such variability in the starting amount can result in a female being ready to mate well before a large load of antiaphrodisiac is sufficiently depleted for her to be attractive again , or being prematurely courted if the male transfers too little . Such signaling uncertainty is potentially costly to the fitness of both females and males , and should create selective pressure to produce a more accurate signaling system that incorporates information beyond the amount of remaining antiaphrodisiac ( Estrada et al . , 2011 ) . To date , the only mechanism shown to allow females to counteract the effect of an antiaphrodisiac is in Drosophila , in which females actively eject mating-transferred cis-vaccenyl acetate from their reproductive tract ( Laturney and Billeter , 2016 ) . Females of the western tarnished plant bug , Lygus hesperus Knight , are polyandrous , mating repeatedly throughout their lives to ensure a steady supply of sperm and to maintain an elevated rate of oviposition ( Strong et al . , 1970; Brent , 2010a; Brent and Spurgeon , 2011; Brent et al . , 2011 ) . Mating also causes these females to become less attractive than virgins to males ( Strong et al . , 1970; Brent , 2010a ) . A sex-pheromone has been identified for this species ( Byers et al . , 2013 ) , but there is no evidence that the release rate of this chemical blend is affected by the mating status of a female . All evidence suggests that L . hesperus female attractiveness is instead modulated by myristyl acetate ( MA ) , a volatile compound transferred during insemination that is released via the female’s vaginal pore , where it is detected by antennating males and acts as an antiaphrodisiac ( Brent and Byers , 2011 ) . Mated females remain unattractive to males for a 4–5 day post-copulatory period during which they also become sexually unreceptive and increase their rate of oviposition ( Brent , 2010b ) . MA is produced in the male accessory glands and transferred along with other components in the spermatophore during mating ( Brent and Byers , 2011 ) . Some of these other seminal components may also play a role in male assessment of female mating history during courtship . Although females regain their attractiveness to prospective mates over time ( Strong et al . , 1970; Brent , 2010b ) , the mechanism by which this is accomplished , either by passive degradation or active countermeasure , had not been determined . Here , we undertook a detailed analysis of the composition of L . hesperus spermatophores and the compounds emitted by mated females to determine what other volatiles might shape male reproductive behavior and how the compounds change over time . We used gas-chromatography-mass spectrometry coupled with behavioral assays to identify compounds , in addition to MA , that are involved in modulating male responses to potential mates and to monitor their changing concentrations . We obtained the first evidence of a complex sexual communication system that actively counters the male chemical mate-guarding through use of an anti-antiaphrodisiac , to produce an honest indicator of female readiness to mate .
Mated females are less likely to be courted by a male than virgin females on the first day after mating ( χ2 = 19 . 309 , df = 1 , p<0 . 001 ) . However , this effect only persists for a few days and does not appear to influence the behavior of all potential mates ( Figure 1 ) . Over time the rate of courtship increases so that by five days after mating those females are as likely to be courted as virgins ( χ2 = 0 . 004 , df = 1 , p=0 . 953 ) . The variability and gradual increase in courtship observed may be the effect of changing levels of antiaphrodisiac being emitted by the female . 10 . 7554/eLife . 24063 . 003Figure 1 . Proportion ( ±95% binomial confidence limits ) of recently mated L . hesperus females courted by virgin males at different intervals after insemination relative to courtship rates for similarly aged virgin females . Numbers above bars indicate sample sizes . Rates of courtship differs significantly between mated and virgin females for each of the initial four days ( 2 × 2 χ2-tests , p<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 003 GC-MS was used to identify and quantify three compounds that are found in the male accessory glands and in spermatophores dissected from females at various dates after mating ( an example chromatogram of a spermatophore measured two days after mating is provided in Figure 2 ) . We confirmed the presence of the previously identified antiaphrodisiac ( Brent and Byers , 2011 ) myristyl acetate ( MA ) , and also found two diterpenes , geranylgeranyl acetate ( GGA ) and geranylgeraniol ( GGOH ) . The first two are found in high concentrations ( tens of ng ) in the male accessory glands , but GGOH is observed there only in trace quantities ( Figure 3 ) . When the spermatophore is sampled shortly after mating , the concentration of GGOH begins to increase , peaking two days later and subsequently declining . MA and GGA decline over this same period but the decrease in GGA is much more precipitous , likely as a result of the combined effects of externalization and conversion to GGOH . GC-MS analysis of the headspace of mated females over the same post-mating span indicated that these compounds were externalized in concentrations proportional to the amount found in the spermatophores , although by four days after mating the concentrations were too low to be accurately quantified ( Figure 4 ) . 10 . 7554/eLife . 24063 . 004Figure 2 . MSD ion chromatograms on GC column of ( A ) the three standards , and ( B ) a hexane extract of five pooled spermatophores excised from females two days after mating . Highlighted by arrows are peaks with matching retention times for myristyl acetate ( MA ) , geranylgeranyl acetate ( GGA ) and geranylgeraniol ( GGOH ) . Also shown are the mass spectra and chemical structures for each of the three compounds: ( C ) MA , ( D ) GGA , and ( E ) GGOH . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 00410 . 7554/eLife . 24063 . 005Figure 3 . Mean concentration ( ± SD ) of myristyl acetate ( MA ) , geranylgeranyl acetate ( GGA ) and geranylgeraniol ( GGOH ) per individual sets of accessory glands ( AG ) of virgin males and in individual spermatophores from females sampled at daily intervals after mating . GC-MS was used to quantify ten samples consisting of five pooled spermatophores for each interval . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 00510 . 7554/eLife . 24063 . 006Figure 3—source data 1 . Contents of virgin male accessory glands and spermatophore taken from mated females either shortly after insemination or after a 1–5 day long interval . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 00610 . 7554/eLife . 24063 . 007Figure 4 . Mean concentration ( ± SD ) of externalized myristyl acetate ( MA ) , geranylgeranyl acetate ( GGA ) and geranylgeraniol ( GGOH ) found in the headspace of four groups each comprised of 20 females sampled at daily intervals after mating . Data is shown for calculated individual female emittance . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 00710 . 7554/eLife . 24063 . 008Figure 4—source data 1 . Headspace measures of four groups of 20 mated females sampled by SPME for 2 hr at 24 hr intervals , starting on the day of mating . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 008 Male responses to the three compounds were tested using synthetic versions ( 500 ng in 1 µl of ethanol ) applied topically to virgin females . The results varied significantly across the treatments ( χ2 = 25 . 993 , df = 7 , p<0 . 001 ) . Males exhibited less interest in courting MA-treated females relative to controls ( Figure 5; 2 × 2 χ2 = 5 . 762 , df = 1 , p=0 . 016 ) . The male response to MA appears to be dosage dependent ( Figure 6; χ2 = 37 . 466 , df = 4 , p<0 . 001 ) . Males courted treated females as often as controls when the topical application concentration was 5 × 10−10 g µl−1 ( χ2 = 0 . 004 , df = 1 , p=0 . 953 ) , but any amount of MA above that elicited a similarly significant suppression of courtship ( p<0 . 01 ) suggesting an activation threshold rather than a gradient response ( Figure 6 ) . Unlike MA , applications of either GGA ( χ2 = 0 . 686 , df = 1 , p=0 . 407 ) or GGOH ( χ2 = 0 . 041 , df = 1 , p=0 . 839 ) at the same dosage did not significantly change courtship behavior compared to ethanol ( Figure 5 ) . Courtship rates did not change when GGA was combined with MA ( χ2 = 0 . 018 , df = 1 , p=0 . 894 ) or GGOH ( χ2 = 0 . 041 , df = 1 , p=0 . 840 ) relative to individual applications of these compounds . In contrast , combining GGOH with MA counteracted the antiaphrodisiac effect of the latter so that courtship rates were as high as the control ( χ2 = 0 . 168 , df = 1 , p=0 . 682 ) . Topical application of 1 or 10 ng GGOH to newly mated females was also able to overcome the natural antiaphrodisiac effect of the full suite of seminal constituents ( Figure 7; χ2 = 16 . 506 , df = 3 , p<0 . 001 ) . However , applying GGOH in combination with both MA and GGA did not prevent females from becoming as unattractive to males as females treated with just MA ( Figure 5; χ2 = 6 . 994 , df = 1 , p=0 . 008 ) , potentially indicating a synergistic effect of GGA with MA to reduce female attractiveness . 10 . 7554/eLife . 24063 . 009Figure 5 . Proportion ( ±95% binomial confidence limits ) of virgin females courted by virgin males after the females were topically treated with myristyl acetate ( MA ) , geranylgeranyl acetate ( GGA ) , geranylgeraniol ( GGOH ) , or a combination of the compounds ( 5 ng compound in 1 µl of ethanol ) , and with ethanol as control . Treatments with an asterisk over them differed significantly in frequency from the EtOH control ( 2 × 2 χ2-tests , p<0 . 05 ) . For all treatments , n = 50 . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 00910 . 7554/eLife . 24063 . 010Figure 6 . Proportion ( ±95% binomial confidence limits ) of virgin females courted by males after the females were topically treated with one of four concentrations of myristyl acetate ( MA ) in ethanol or ethanol alone ( n = 111 each ) . An asterisk indicates a treatment that evoked significantly fewer courtships than the ethanol control ( 2 × 2 χ2-tests , p<0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 01010 . 7554/eLife . 24063 . 011Figure 7 . Proportion ( ±95% binomial confidence limits ) of similarly aged females courted by virgin males . The females were virgins or newly mated and treated topically with ethanol , or mated and treated with 1 or 10 ng GGOH in ethanol ( n = 100 each ) . Only ethanol treated mated females were courted less often than control virgins ( χ2 =12 . 654 , df = 1 , p<0 . 001 ) , indicating that GGOH can counter the full antiaphrodisiac blend of a mated female at biologically relevant doses . DOI: http://dx . doi . org/10 . 7554/eLife . 24063 . 011
Mating causes a precipitous decline in the attractiveness of an L . hesperus female ( Strong et al . , 1970 ) , but over the course of several days she becomes increasingly likely to be courted by males ( Brent , 2010a ) . Our previous investigation of this phenomenon led us to conclude that when a male inseminates a female , he transfers an antiaphrodisiac that is slowly released through the female’s gonopore and dissuades other males from courting her , and that the gradual dissipation of this antiaphrodisiac restores the female’s attractiveness ( Brent and Byers , 2011 ) . This simple picture was consistent with the conclusions of previous antiaphrodisiac studies ( Andersson et al . , 2000 , Andersson et al . , 2003; Schulz et al . , 2008; Forsberg and Wiklund , 1989; Carlson and Langley , 1986; Schlechter-Helas et al . , 2011 ) , however the results of our current investigation indicate a more complicated signaling system regulating L . hesperus reproductive behavior . Three compounds appear to be involved: myristyl acetate , the previously identified antiaphrodisiac ( Brent and Byers , 2011 ) , geranylgeranyl acetate and geranylgeraniol . The final compound , rather than being transferred directly from the male like the first two , is primarily produced within the female via chemical conversion of the GGA . While MA’s role has been reconfirmed here , GGOH appears to play a counteracting role , diminishing or negating the effect of MA . In no instance did application of GGOH result in courtship rates being elevated above that for solvent controls , indicating that the compound does not act as an attractant or excitatory agent . The only observable effect was to counteract the antiaphrodisiac effect , thus GGOH appears to serve as an anti-antiaphrodisiac . The signaling function of GGA is not as easily categorized . When applied by itself , GGA did not significantly affect male courtship rates , although some depression is visible . GGA also did not make females less likely to be courted when combined with MA . Of course the dose of MA used in the mixture may have been sufficient to achieve the maximal level of repellency to males by itself , leaving little room for an additive effect to be observed . However , when added to a mix of MA and GGOH , GGA was able to counteract the anti-antiaphrodisiac effect of GGOH . The resulting reduction in female attractiveness suggests that GGA contributes towards the antiaphrodisiac effect , but only in a context-specific manner . The depletion of GGA over time will diminish this repellant effect , and when combined with the commensurate increase in GGOH , will accelerate the process of the female again becoming attractive . Compared to our previously hypothesized single-compound signal communication system of L . hesperus , the greater complexity we have found could facilitate much better coordination between male courtship and the cessation of the female refractory period . During this three to seven day span subsequent to insemination , a female refrains from interaction with courting males ( Brent , 2010a ) , a behavioral transition largely driven by male seminal products ( Brent , 2010a; Brent et al . , 2016 ) . Because the female may copulate several times throughout her life ( Strong et al . , 1970; Brent et al . , 2011 ) , she cannot afford to have prospective mates repelled by an overly persistent antiaphrodisiac . For a certain period after mating an antiaphrodisiac would be an honest indicator of a female’s receptivity and would help prevent males from engaging in unnecessary sperm competition ( Estrada et al . , 2011; Malouines , 2016 ) . However , given that the amount of MA that a male transfers can be highly variable , the signal would not be a reliable indicator of a female’s suitability and readiness to mate over the natural refractory period . Like females of Pieris napi ( Andersson et al . , 2004 ) , there is no indication that L . hesperus females can modulate the rate of antiaphrodisiac release , so they are unlikely to have control over how quickly the supply can be depleted . Thus , if the initial MA concentration in a spermatophore is quite high , such as when contributed by a male that has not mated for a week or more , the female would be rendered unattractive well past when she again becomes reproductively receptive such that both sexes lose out on potential mating opportunities . If the initial MA is quite low , such as when contributed by a male that had already mated within the past day , a still refractory female would be harassed by unwanted suitors . By utilizing additional signaling elements in this reproductive communication system , males can more accurately estimate the likelihood that a female is suitable to mate again , an added capability that would have been selectively favored ( Arak and Enquist , 1995; Estrada et al . , 2011 ) . During the first couple of days after mating when male courtship is inhibited ( Brent , 2010b ) , the combined emissions of MA and GGA are greater than GGOH . Once there is sufficient GGOH produced to offset the antiaphrodisiac effect , subsequent males will try to court a mated female . The starting concentration of MA and GGA can vary , so long as they are above activation thresholds needed to repel other males . The relative quantities of MA and GGA transferred from a male are consistently similar although absolute amounts can vary between males . The amount of GGOH ultimately produced within the female is intrinsically tied to the starting quantity of GGA . Thus the signaling system is not affected by the variability between males in the quantities of pheromones transferred because only the time-dependent ratios are what matter . A high starting concentration of GGA will eventually produce a commensurately high concentration of GGOH to offset a high starting level of MA . So long as the conversion of GGA to GGOH occurs at a predictable rate , males will be able to accurately determine when a female is ending her refractory period . It is likely that a combination of absolute quantities and relative proportions of these chemicals determine female attractiveness . This is suggested by some apparent inconsistency in the data . By the second day after mating , the concentration of GGOH appears comparable to that of MA , and GGA is substantially reduced . Despite these early shifts in proportions , the courtship rates remain suppressed until the fifth day after mating . GGA at this late stage may have fallen to such a low level that it no longer has a synergistic effect with MA to counteract the GGOH . We show here that MA has an activation threshold to be effective , but after that relative amounts of GGA and GGOH may be key modulators . Relative amounts of other , as yet unidentified , minor seminal constituents might also influence female attractiveness . Another consideration is that the threshold concentration identified for MA , and activation concentrations of the other compounds , may be substantially lower than our tests indicate . The external application of the chemical compounds does not fully mimic their normal release from the female gonopore . Applied chemicals can rapidly degrade , volatilize , or bind with chemicals on the cuticle . This would quickly diminish the perceivable amounts of the regulatory pheromones during the course of the bioassay , so that females avoided by males at the start were attractive again by the end . With each compound having different chemical properties , the effects of external application will not necessarily be consistent in these tests . Additional assays are needed to sort out the contributions toward female attractiveness of concentrations and proportions . Another noteworthy finding was the variability in the responsiveness of males to these compounds . Even under the highest topical dosages of MA some males did not stop courting . This may be the result of diminished odorant receptor expression in males that cannot detect the antiaphrodisiac . Alternatively , a subgroup of males may employ a different mating strategy from the norm and are willing to tolerate sperm competition by indiscriminately courting every female encountered regardless of indicators of recent mating . This can be periodically rewarding as they are likely to be the first to access a female coming out of her refractory period earlier than her release of MA would otherwise indicate . We have also found a small but consistent subset of females that fail to enter into a refractory period , mating multiple times within a short span . Males willing to mate with one of these promiscuous females may benefit by fertilizing at least a portion of her eggs . Although this is the first recorded instance of a pheromone being utilized as an anti-antiaphrodisiac , such compounds may not be rare . Just as it is likely that the occurrence of antiaphrodisiacs is far more widespread than has been reported due to a lack of thorough investigations ( Malouines , 2016 ) , anti-antiaphrodisiacs may have been overlooked until now because no one knew to search for them . Given the advantages of a two-dimensional chemical signaling system , it is quite likely that this is not the only instance . In fact , the evolution of a controlled antiaphrodisiac expulsion mechanism in Drosophila females ( Laturney and Billeter , 2016 ) , supports the likely occurrence of other such active counter measures to chemical mate guarding .
L . hesperus were obtained from an established colony at the USDA-ARS Arid Land Agricultural Research Center ( Maricopa , AZ , USA ) . Colony health and genetic diversity were maintained by periodic outbreeding with locally-caught conspecifics . Insects were reared at 27°C , 20% relative humidity , under a 14:10 hr ( L:D ) photoperiod . Adults were produced from groups of mixed-sex nymphs reared in waxed paper containers covered with nylon mesh to ensure adequate ventilation and light exposure . Each container was provisioned with approximately 20 g of fresh green bean pods ( Phaseolus vulgaris L . ) and 12 g of artificial diet ( Debolt , 1982 ) packaged in Parafilm M ( Pechiney Plastic Packaging , Chicago , IL , USA ) ( Patana , 1982 ) . Provisions were replaced every 48 hr . Daily monitoring allowed adults to be collected within 24 hr of emergence . Cohorts of same-aged adults were separated by gender and reared under conditions similar to those for nymphs , with the exception that the artificial diet was replaced with raw sunflower seeds ( Helianthus annuus L . ) . A no-choice behavioral assay was used to test male courtship response to females after different time intervals subsequent to their being mated . All males were aged 7–9 days post-eclosion to ensure they were sexually mature and willing to copulate ( Brent , 2010a ) . They were isolated from the opposite gender throughout adulthood to prevent their behavior towards prospective mates from being influenced by post-mating refractoriness ( Strong et al . , 1970; Brent , 2010b ) , and to guarantee the males were naïve with regard to the odor associated with mated and unreceptive females ( Brent , 2010b ) . A large cohort of 7d old virgin females were housed with similarly aged virgin males and allowed to mate for six hours . A group of 300 mated females were selected from this pool based on the presence of a visible spermatophore just below the surface of the cuticle ( Cooper , 2012 ) . The attractiveness of these mated females to males was determined at 1 , 2 , 3 , 4 or 5 days after mating . Due to mortality over the test period , daily sample sizes ranged from 47 to 58 . Females and males were only used once . During the interval between mating and testing , females were held in isolation in clean 1 . 5 × 5 . 0 cm covered glass Petri dishes with a 1 inch section of green bean and two sunflower seeds , which were changed out every other day . To determine post-mating attractiveness , a male was introduced to a female’s dish and the pair was observed for 1 hr during which all instances of courtship were recorded . Courtships were distinguished from incidental approaches by characteristic male behaviors indicating the intent to mate ( Strong et al . , 1970 ) . Females were dissected after each trial to ensure they had previously mated , as indicated by the presence of a spermatophore ( Brent , 2010b ) . The identification of myristyl acetate in the spermatophore using GC-MS was previously described ( Brent and Byers , 2011 ) . The same approach was used to identify geranylgeraniol and geranylgeranyl acetate as components of the spermatophore . GGA and GGOH identities were confirmed by additional comparisons of retention times and mass spectra of male Lygus accessory gland extracts and authentic standards . To determine if there are temporal changes to male-derived pheromonal components after delivery into the female , spermatophore composition was analyzed every 24 hr for 5 days after mating . To produce the spermatophores , adult female and male L . hesperus , both aged 7 days , were mated together . Females were then housed in individual Petri dishes , as described above , then dissected at different intervals to remove the seminal depository and the spermatophore . The initial spermatophore sampling occurred within three hours of mating . Subsequent samples were taken every 22–25 hr thereafter . For each sample , five spermatophores were pooled . For each time tested , there were a total of 10 samples . Accessory glands , the source of the pheromones ( Brent and Byers , 2011 ) , were also sampled from virgin 7 day old males , again pooling five per sample and analyzing 15 samples . Tissues were homogenized twice in 200 µl of hexane ( Sigma Aldrich , St . Louis , MO , USA ) in a conical glass vial ( Wheaton Scientific Products , Millville , NJ , USA ) using a glass rod , then centrifuged at 1200 rcf . The resultant supernatant was stored in a clean glass vial with a teflon lined cap at −80°C until analysis . Just prior to analysis the supernatant was dried down under nitrogen to an injection volume of 3 µl . Samples were analyzed using a 7890A Series GC ( Agilent Technologies , Sanata Clara , CA , USA ) equipped with a 30 m x 0 . 25 mm Zebron ZB-WAX column ( Phenomenex , Torrance , CA , USA ) coupled to a 5975 C inert mass selective detector ( Agilent Technologies ) . The helium carrier gas was programmed for constant flow of 1 . 2 ml min−1 . Samples were manually injected into the GC port at 250°C using the splitless mode . The oven/column temperature was initially held at 60°C for 1 min , and then increased at a rate of 20 °C min−1 to 240°C , where it was held for 35 min . Samples were analyzed using the MS SIM mode using set m/z values for MA ( 55 , 69 , 83 , 97 ) , GGA and GGOH ( 69 , 81 , 93 , 107 ) . Total abundance was quantified against standard curves for each compound . The detection limit of the assay is approximately 0 . 5 pg . Hydroxyl groups of 1-tetradecanol ( Acros Organics , Geel , Belgium ) , and geranylgeraniol ( Toronto Research Chemicals , Toronto , Canada ) were acetylated by acetic anhydride and pyridine to obtain the corresponding acetates ( Zada et al . , 2004 ) . Myristyl acetate was purified by vacuum distillation using glass oven Kugelrohr ( Buchi , Flawil , Switzerland ) at 115°C/1 mm , 90% yield , >98% purity . Geranylgeranyl acetate was purified by column chromatography ( SiO2/ether:hexane 95:5 ) 80% yield , 96% purity . Purity was determined by GC ( Agilent 6890 ) with FID detector . Head space samples were collected from four groups of mated females . For each group , thirty females , aged 8–10 days , were mated to virgin males of the same age over a three hour period . Female mating status was confirmed by observation of the spermatophore through the abdominal cuticle ( Cooper , 2012 ) . For each test , 20 females were randomly selected from those mated and were still alive on the day of data collection . Females were placed in a 50 mL volumetric flask stoppered with a rubber septum through which was inserted a solid-phase microextraction fiber ( SPME ) with an 85 μm Polyacrylate coating ( Sigma ) . Prior to collection , the SPME fibers were preconditioned by heating within the GC injector ( 250°C ) for one hour . Sample collection occurred over a two hour period , either within three hours of mating ( Day 0 ) or every 24 hr thereafter through four days . Samples were immediately analyzed as described above by direct insertion of the SPME into the injection port of the GC-MS . Fibers were kept in the injector port throughout the run . Mating status of the sampled females was subsequently confirmed by dissection . A total of four samples were run for each time period using the same groups of mated females . The same behavioral assay as described for testing the attractiveness of mated females was used to test for a dose-dependent effect of myristyl acetate on male courtship . Virgin 7 day old females were topically treated with 1 µl of ethanol ( 95% ) containing myristyl acetate diluted at each of four concentrations ( 0 . 5 , 5 , 50 and 500 ng µl−1 ) , or 1 µl of an ethanol control . Males were introduced to the dishes for one hour and instances of courtship recorded . A total of 111 trials were conducted during which all five treatments were run concurrently in separate dishes . Dishes were cleaned between trials . The same approach was used to determine male courtship responses to virgin female treated topically with the pheromones either individually or in combination . Treatments had 500 ng of each compound in 1 µl of ethanol in the following mixtures: MA , GGA , and GGOH alone , MA with GGA , MA with GGOH , GGA with GGOH , all three combined , and ethanol as control . Fifty trials were run in which the eight treatments were run concurrently using the same cohort of animals . The efficacy of GGOH as an anti-antiaphrodisiac was also demonstrated by topical treatments and recording male responses . As above , males were exposed to virgin females treated with ethanol , or to newly mated females treated with ethanol or GGOH at concentration of 1 or 10 ng µl−1 . A total of 100 trials using females of the same cohort were conducted with all four treatments run concurrently in separate dishes . The effect of the topical applications on the attractiveness of females to males was determined by n x 2 χ2-tests comparing the proportion of males exhibiting courtship behavior across all treatments and when significance was indicated individual comparisons between treatments were conducted using 2 × 2 χ2-tests . To reduce the incidence of false negatives , comparisons were made only between treatments and the virgin female control group , not between all treatments . The Dunn-Šidák correction was used to adjust the significance level for multiple comparisons . Analyses were conducted using Sigmaplot 13 . 0 ( Systat Software , Point Richmond , CA , USA ) . Overall , no data points were excluded as outliers . Biological but not technical replicates were used . Appropriate minimum sample sizes were determined by power analysis for multiple pairwise comparisons , using π = 0 . 80 with both α and β set to 0 . 05 . Preliminary experiments provided anticipated rates of courtship for mated and virgin females at 15% and 45% respectively . | In many animal species , males guard females to prevent rivals from mating so that they can be sure that they fathered the female’s offspring . Some guarding methods work even when the male is not present . For example , the semen of some male insects contains chemicals known as antiaphrodisiacs that repel other males from females who have recently mated . Over the course of several days or weeks , the females expel or degrade the antiaphrodisiacs , making themselves attractive to other mates again . How long it takes to eliminate the antiaphrodisiacs depends on how much of the chemicals were deposited in the first place . Therefore , males could gain an advantage in fertilizing more eggs by depositing excess antiaphrodisiac to make the females unattractive to other mates for a long time . The Western tarnished plant bug ( Lygus hesperus ) is an agricultural pest that targets cotton , strawberries and other crops . One antiaphrodisac had already been identified in the semen of male Lygus bugs . To investigate whether the males produced any others , Brent et al . tested the molecules emitted by recently mated females . This search identified another potential antiaphrodisiac . However , females are able to convert this second chemical into a third one that neither attracts nor repels males . This “anti-antiaphrodisiac” acts against the males’ two antiaphrodisiacs , and allows the females to more accurately signal when they are ready to mate again , giving them more control over their reproduction . Anti-antiaphrodisiacs were not previously known to exist , but now that scientists know where to look , more are likely to be found in other species . A better understanding of how different chemicals interact to influence the mating behavior of insects could also lead to new methods of targeting pests of crops , which are safer for the environment than existing chemical pesticides . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] | [
"ecology"
] | 2017 | An insect anti-antiaphrodisiac |
Axon injury triggers dramatic changes in gene expression . While transcriptional regulation of injury-induced gene expression is widely studied , less is known about the roles of RNA binding proteins ( RBPs ) in post-transcriptional regulation during axon regeneration . In C . elegans the CELF ( CUGBP and Etr-3 Like Factor ) family RBP UNC-75 is required for axon regeneration . Using crosslinking immunoprecipitation coupled with deep sequencing ( CLIP-seq ) we identify a set of genes involved in synaptic transmission as mRNA targets of UNC-75 . In particular , we show that UNC-75 regulates alternative splicing of two mRNA isoforms of the SNARE Syntaxin/unc-64 . In C . elegans mutants lacking unc-75 or its targets , regenerating axons form growth cones , yet are deficient in extension . Extending these findings to mammalian axon regeneration , we show that mouse Celf2 expression is upregulated after peripheral nerve injury and that Celf2 mutant mice are defective in axon regeneration . Further , mRNAs for several Syntaxins show CELF2 dependent regulation . Our data delineate a post-transcriptional regulatory pathway with a conserved role in regenerative axon extension .
Axon regeneration requires coordinated gene expression at many levels ( Benowitz et al . , 1981; Gervasi et al . , 2003; Glasgow et al . , 1992; Skene and Willard , 1981 ) . While much work has focused on injury-regulated gene transcription , increasing evidence points to roles for post-transcriptional regulation of mRNAs by RNA binding proteins ( RBPs ) . In rodents , the Zipcode Binding Protein ZBP1 can bind axonal mRNAs and affect peripheral nerve regeneration via mRNA transport and decay ( Donnelly et al . , 2011 ) . In Xenopus , hnRNP K binds mRNAs of growth-associated proteins such as GAP43 and NF-M and promotes protein synthesis in optic nerve regeneration ( Liu et al . , 2012 ) . Recently , the conserved RNA 3’-terminal phosphate cyclase has been identified as an inhibitor of axon regeneration in C . elegans , Drosophila and mouse , acting through RNA repair and splicing ( Kosmaczewski et al . , 2015; Song et al . , 2015 ) . Despite these advances , mechanistic understanding of the roles of RBPs in axon regeneration remains limited . CELF ( CUG-BP and ETR-3-like Factor ) family RNA binding proteins are highly conserved throughout animals ( Dasgupta and Ladd , 2012 ) . All six mammalian CELF proteins are expressed in the nervous system and several have been implicated in neuronal alternative splicing ( Ladd , 2013 ) . Analysis of Celf mutant mice has begun to reveal their roles in neuronal development and behavior ( Dev et al . , 2007; Dougherty et al . , 2013; Kress et al . , 2007; Wagnon et al . , 2012; Yang et al . , 2007 ) . Celf4 deficient mice exhibit a seizure disorder ( Wagnon et al . , 2012; Yang et al . , 2007 ) , whereas Celf6 mutant mice display abnormal behaviors and reduced brain serotonin ( Dougherty et al . , 2013 ) . However , CELF proteins have not previously been examined in the context of axon regeneration . Here , we addressed the roles of CELF proteins in axon regeneration , focusing on C . elegans UNC-75 and mouse CELF2 , both of which are localized to the nucleus ( Loria et al . , 2003; Otsuka et al . , 2009 ) . To identify direct targets of UNC-75 in C . elegans neurons we performed neuronal CLIP-seq to locate UNC-75 binding sites . Many UNC-75 target sites are in genes involved in synaptic transmission . We show that UNC-75 binding to an intronic site of UNC-64/Syntaxin promotes expression of neuronal UNC-64/Syntaxin isoforms . Loss of UNC-75 or of UNC-64 causes distinctive phenotypes in which regenerative growth cones are formed but are unable to extend . Overexpression of UNC-64 in unc-75 null mutants can rescue axon regeneration defects , indicating that UNC-64 is a major target of UNC-75 in regenerating neurons . Extending these findings to mammals , we find that mouse Celf2 expression is induced by axon injury and that CELF2 is required for effective peripheral axon regeneration . Furthermore , we identify multiple Syntaxin genes as CELF2 targets . Together , our data reveal a conserved pro-regeneration pathway operating at the level of alternative splicing .
PLM axon development was normal in all unc-75 mutants tested except that around 5% of PLMs lacked ventral synaptic branches . PLM axon regeneration in unc-75 mutants was reduced to 30–40% of wild type levels ( Figure 1A , B ) . Interestingly , unc-75 ( md1344 ) , a small deletion affecting the last exon encoding part of RRM3 and a nuclear localization signal ( Figure 1—figure supplement 1A ) , displayed impairment in regrowth equivalent to the null mutants ( Figure 1A , B ) . Transgenic animals expressing Pmec-4-GFP::UNC-75ΔNLS ( aa 1–472 ) showed diffuse fluorescence throughout the cell , as compared to full-length GFP::UNC-75 , which localizes to neuronal nuclei ( Figure 1—figure supplement 1B ) ( Loria et al . , 2003 ) , suggesting that nuclear localization may be critical for UNC-75 function in axon regeneration . We further tested whether unc-75 affected regeneration of GABAergic motor neurons . In wild type , around 35% of DD2 commissures and 50% of VD4 commissures regrow to the dorsal cord by 24 hr after axotomy , whereas in unc-75 mutants fewer than 10% of commissures regrew to the dorsal cord ( Figure 1E ) . Thus UNC-75 is critical for regenerative regrowth of multiple neuron types . 10 . 7554/eLife . 16072 . 003Figure 1 . UNC-75 is required cell autonomously for axon regeneration . ( A ) Images of regenerating PLM axons at 24 hr post axotomy; anterior is to the left and dorsal up . Red asterisk: PLM cell body; red arrow: injury site . ( B ) Quantitation of PLM axon regrowth 24 hr post laser axotomy , normalized to wild type . The unc-75 alleles md1309 and md1344 display defects in axon regrowth similar to e950 . The unc-75 ( e950 ) PLM regrowth defect is rescued by multicopy and single copy unc-75 ( + ) transgenes . Statistics: One-way ANOVA with Bonferroni post test . ( C ) unc-75 ( e950 ) animals showed reduced axon regrowth at all time points examined , and reduced growth rate 0–24 hr post axotomy . ( D ) Representative image series from time-lapse movies of the tip of a regenerating PLM axon from wild-type and unc-75 ( e950 ) animals starting at 14 hr post axotomy; see Video 1 and 2 . Red and orange arrows point to the ends of regenerating axons at 14 and 15h post axotomy respectively . ( E ) unc-75 ( e950 ) is defective in regeneration of GABAergic motor neurons [marked by Punc-25-GFP ( juIs76 ) ]; this was rescued by Prgef-1-UNC-75 ( juSi76 ) . Images of GABAergic motor neuron commissures at 24 hr post axotomy . DD2 and VD4 were axotomized , and VD3 was uncut . Red arrowheads indicate the ends of regenerating or non-regenerating axons . Scale: 10 μm . Bar charts showing reduced regrowth of DD2 and VD4 neurons; N = 30–52 . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 00310 . 7554/eLife . 16072 . 004Figure 1—figure supplement 1 . unc-75 alleles and role of nuclear localization . ( A ) Schematic of unc-75 deletion mutations . The deletion e950 removes exons 1–5 as well as 5’ upstream sequences; the breakpoint of the deletion is not known . md1309 is a deletion affecting exons 4–7 but the exact boundary is not known . More information can be found in Loria et al . , 2013 . md1344 is a 780 bp deletion that deletes most of intron 8 and the first 15 bp of exon 9 . By determining the sequences of transcripts from unc-75 ( md1344 ) we found that splicing of intron 8 is altered , resulting in the inclusion of 26 bp of intron 8 before exon 9 and deletion of 15 bp of exon 9 . md1344 causes a frame shift in the resulting transcript , leading to expression of a truncated UNC-75 protein containing aa 1–472 . ( B ) Images of PLM cell body showing GFP::UNC-75 protein localization . Wild type UNC-75 is predominantly localized to the nucleus . A truncated protein containing 472 amino acids corresponding to that encoded by md1344 allele localizes to cytoplasm . Scale bar: 5 µm . ( C ) Normalized PLM regrowth 24 hr post axotomy . Wild type UNC-75 cDNA expressed using the unc-75 promoter was able to rescue the regrowth defect in unc-75 mutant , but a mutant cDNA lacking the NLS ( nuclear localization signal ) failed to rescue . ( D ) Representative images of unc-75 ( e950 ) and unc-75 ( e950 ) ; Prgef-1-FLAG::UNC-75 ( juIs369 ) ( CZ14662 , as used for neuronal UNC-75 CLIP-seq ) animals . The Unc phenotype in unc-75 ( e950 ) was rescued by juIs369 . Scale: 500 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 00410 . 7554/eLife . 16072 . 005Figure 1—figure supplement 2 . Regenerative growth cones in unc-75 and other mutants . ( A ) Representative PLM axon regrowth images of indicated genotypes . Asterisk: PLM cell body; red arrow: injury site . Scale: 10 μm . ( B ) Percentage of regenerating axons with a growth cone-like structure at 24 hr post axotomy . Regenerative growth cones were observed at significantly higher frequency in unc-75 and synaptic transmission mutants compared to WT . Statistics: One-way ANOVA with Bonferroni post test . N ≥ 3 experiments , each experiment involved 10 or more animals . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 00510 . 7554/eLife . 16072 . 006Figure 1—figure supplement 3 . unc-75 acts in parallel to other axon regeneration pathways . ( A ) exc-7 is not required for axon regrowth and does not enhance unc-75 ( 0 ) . N ≥ 10 . Statistics: One-way ANOVA with Bonferroni post test . ( B ) Normalized PLM regeneration 24 hr post axotomy . The dlk-1 ( 0 ) ; unc-75 ( 0 ) double mutant resembles dlk-1 ( 0 ) single mutant ( no growth cone and no axon extension ) . ( C ) Overexpression of DLK-1 or loss of EFA-6 can partially rescue the defects of unc-75 ( e950 ) mutant , suggesting that UNC-75 functions in a parallel pathway . ( D ) RT-qPCR data showing no change in dlk-1 or efa-6 transcripts in unc-75 ( e950 ) mutant . N = 4 . Two sets of efa-6 primers ( one set at the 3’ end , the other at the middle of mRNA ) were used . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 006 We rescued unc-75 PLM and motor axon regrowth defects with transgenes expressing UNC-75 under its own promoter , or with a single copy transgene expressing UNC-75 under the control of the pan-neuronal rgef-1 promoter ( Figure 1A , B ) . Moreover , expression of UNC-75 using a touch neuron specific promoter ( Pmec-4 ) fully rescued PLM regrowth , indicating that UNC-75 acts cell-autonomously ( Figure 1A , B ) . Expression of UNC-75 in wild type animals did not enhance regrowth , indicating that UNC-75 is necessary but not sufficient to promote axon regeneration ( Figure 1B ) . A mutant unc-75 cDNA lacking the NLS , expressed under its own promoter , failed to rescue behavioral defects of unc-75 mutant ( Loria et al . , 2003 ) and did not significantly rescue the regeneration defects ( Figure 1—figure supplement 1C ) , supporting its function in the nucleus . In unc-75 mutant neurons the rate of axon extension was reduced at multiple time points after injury ( Figure 1C ) , despite the presence of growth cones at the tip of the regrowing axons ( Figure 1A ) . Using time-lapse imaging we found that wild-type PLM regenerative growth cones tended to be small and transient , and converted rapidly to elongating axons . In contrast , unc-75 mutants frequently displayed regenerative growth cones with dynamic filopodial protrusions , but these did not drive long-range axon extension ( Figure 1—figure supplement 1D and Video 1 , 2 ) . Indeed , we observed growth cones more frequently in non-regenerating PLM axons in unc-75 mutants than in the wild type ( Figure 1—figure supplement 2 ) , consistent with previous observations that presence of a growth cone does not correlate highly with axon extension ( Chen et al . , 2011; Edwards and Hammarlund , 2014 ) . Injured motor axons in unc-75 mutants also formed regenerative growth cones that failed to extend ( Figure 1E ) . Thus UNC-75 is required for regenerative axon extension but is either dispensable or a negative regulator of regenerative growth cone formation . 10 . 7554/eLife . 16072 . 007Video 1 . Time-lapse movie of the tip of a regenerating PLM axon from a wild type animal , starting at 14 hr post axotomy , ending at 15 hr post axotomy . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 00710 . 7554/eLife . 16072 . 008Video 2 . Time-lapse movie of the tip of a regenerating PLM axon from an unc-75 ( e950 ) mutant , starting at 14 hr post axotomy , ending at 15 hr post axotomy . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 008 The C . elegans Elav-like RNA binding protein EXC-7 functions partially redundantly with UNC-75 in synaptic transmission ( Loria et al . , 2003 ) , and co-regulates overlapping mRNA splicing events ( Norris et al . , 2014 ) . exc-7 mutants displayed normal PLM axon regeneration , and unc-75 exc-7 double mutants resembled unc-75 single mutants in regeneration ( Figure 1—figure supplement 3 ) . fox-1/RBFOX , another known interactor of unc-75 in neuronal alternative splicing ( Kuroyanagi et al . , 2013a ) , is also not required for PLM regrowth ( Chen et al . , 2011 ) . These observations suggest UNC-75 functions in axon regeneration non-redundantly with these known neuronal splicing regulators . We next addressed how unc-75 interacted with the regrowth promoting MAP kinase kinase kinase DLK-1 and the regrowth inhibiting factor EFA-6 . In C . elegans , PLM and motor neuron axon regeneration is completely blocked in null mutants of dlk-1 and is increased in animals overexpressing active DLK-1 ( Yan and Jin , 2012 ) . Double mutants unc-75 ( 0 ) ; dlk-1 ( 0 ) did not further reduce PLM axon regrowth compared to dlk-1 ( 0 ) , and the regrowing axons had no regenerative growth cones , resembling dlk-1 ( 0 ) ( Figure 1—figure supplement 2A , 3B ) . Overexpression of DLK-1 in unc-75 ( 0 ) partially improved regrowth compared to unc-75 ( 0 ) single mutants ( Figure 1—figure supplement 3C ) , suggesting that unc-75 and dlk-1 likely function in parallel . The conserved protein EFA-6 regulates axonal microtubule dynamics , and loss of EFA-6 strongly enhances PLM axon regrowth ( Chen et al . , 2011; 2015 ) . efa-6 ( 0 ) partially rescued unc-75 ( 0 ) PLM regrowth defects ( Figure 1—figure supplement 3C ) , but not the growth cone phenotype ( Figure 1—figure supplement 2 ) , suggesting that EFA-6 and UNC-75 function in parallel . Moreover , dlk-1 and efa-6 transcript levels were normal in unc-75 mutants ( Figure 1—figure supplement 3D ) . Thus , these analyses suggest the UNC-75 axon extension pathway acts partly independently of the DLK-1 and EFA-6 axon regeneration regulators . To dissect how UNC-75 regulates axon regeneration , we isolated RNAs bound by UNC-75 in neurons ( Figure 2A ) . We expressed functional FLAG-tagged UNC-75 in neurons in unc-75 ( 0 ) mutants , in which the locomotion defect was rescued by the FLAG::UNC-75 transgene ( Figure 1—figure supplement 1D ) . We then performed crosslinking immunoprecipitation coupled with deep sequencing ( CLIP-seq ) ( Figure 2—figure supplement 1A ) ( see Materials and methods ) . We used two methods to map the unique reads onto C . elegans genome ( Figure 2A ) , and also manually inspected the genomic loci containing UNC-75 CLIP peak positions . We identified 533 functionally annotated protein-coding genes as UNC-75 targets ( Supplementary file 1 ) . 79% of the peaks in protein-coding genes were in intronic regions , and 21% in exons or UTRs , consistent with previous results implicating UNC-75 in alternative splicing ( Kuroyanagi et al . , 2013a; 2013b; Norris et al . , 2014 ) . We determined overrepresented motifs for UNC-75 binding ( Figure 2B ) . The most enriched motif was UGUGUGUG , as exemplified by the binding site on unc-75 mRNA ( Figure 2C ) , consistent with the UNC-75 binding site ( G/U ) UGUUGUG previously inferred from RNA-seq ( Kuroyanagi et al . , 2013b ) and the U ( G/A ) UUGUG consensus motif defined by RNAcompete ( Norris et al . , 2014 ) . The second most enriched motif G/CAAAAAA is not previously known , and is exemplified by nrx-1 , a known UNC-75 target ( Kuroyanagi et al . , 2013b; Norris et al . , 2014 ) ( Figure 2C ) . The list of putative UNC-75 targets identified in our CLIP-seq analysis showed significant overlap with those identified in comparisons of whole-organism transcriptomes of wild type and unc-75 mutants ( Kuroyanagi et al . , 2013b; Norris et al . , 2014 ) ( Supplementary file 2 ) . Such partial overlap is anticipated given the different techniques used ( CLIP-seq of neuronal transcripts vs RNA-seq of the entire organism; see Discussion ) . 10 . 7554/eLife . 16072 . 009Figure 2 . CLIP-seq of UNC-75 in C . elegans neurons . ( A ) Flow chart of CLIP-seq analysis that identified 533 potential mRNA targets bound by UNC-75 . ( B ) The top two motifs enriched in UNC-75 CLIP-seq peaks , based on MEME analysis of the sequence of bound mRNAs . ( C ) UNC-75 CLIP-seq peaks in unc-75 , nrx-1 , and unc-41 , displayed using the UCSC Genome Browser . Splicing variants are shown under each genomic locus . Sequence of the annotated CIMS peak is shown next to the peak . 's' in peak number stands for 'substitution' . Red and blue tags indicate the two different gene orientation on chromosomes . Scale , 5 kb . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 00910 . 7554/eLife . 16072 . 010Figure 2—figure supplement 1 . Purification of UNC-75 and CELF2 bound RNA using CLIP . ( A ) Autoradiogram showing size-separated crosslinked UNC-75-RNA complexes following complete digestion with high , or partial digestion with low amounts of micrococcal nuclease , immunopurification with an anti-FLAG antibody ( or IgG for control ) and 5’ end radiolabeling . The red box depicts the areas on the nitrocellulose membrane from which crosslinked RNAs were purified for reverse transcription and deep sequencing . ( B ) Autoradiogram showing size-separated crosslinked CELF2-RNA complexes following digestion with micrococcal nuclease at different dilution . The red box depicts the areas on the nitrocellulose membrane from which crosslinked RNAs were purified for reverse transcription and deep sequencing . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 010 When manually checking the genomic loci of UNC-75 targets , we observed that UNC-75 binding sites in introns often overlapped with regions that express snoRNAs . For example , peak s5644 ( s stands for substitution of CIMS ) , located in an intron of unc-41 , overlaps with the snoRNA C27H6 . 5 ( Figure 2C ) . Although from the CLIP-seq data we cannot determine whether UNC-75 binds both mRNA and snoRNA , it is likely that UNC-75 at least binds to the mRNA , as the mapped reads included nucleotides outside the snoRNA . We performed Gene Ontology ( GO ) analysis on the 533 potential protein coding targets of UNC-75 using DAVID ( Huang da et al . , 2008 ) . The three most enriched functional annotation clusters are alternative splicing ( 49 genes , P = 3 . 9E-20 , Fisher’s exact test ) , nucleotide-binding ( 60 genes , P = 4 . 5E-17 ) and transmembrane ( 66 genes , P = 2 . 2E-12 ) , consistent with previously reported functions of UNC-75 in alternative splicing , and validating the quality of our CLIP-seq . The most significantly enriched signaling network is MAPK pathway ( 9 genes , P = 4 . 7E-3 ) . Having identified many mRNA targets bound by UNC-75 , we sought targets involved in axon regeneration by taking advantage of our previous genetic screen ( Chen et al . , 2011 ) . Among the 533 protein-coding target genes of UNC-75 , we found 17 genes previously identified as required for axon regeneration ( Supplementary file 2 ) . GO analysis on these 17 genes resulted in the most significantly enriched cluster as cholinergic synaptic transmission ( P = 1 . 6E-6 , 4 genes including unc-32 , unc-41 , unc-64 , unc-75 and unc-104 ) . Mutants of these genes have normal PLM axon outgrowth in development and display significantly reduced PLM regrowth ( Chen et al . , 2011 ) , yet their injured PLM axons were able to form regenerative growth cone-like structures more often than wild type axons ( Figure 1—figure supplement 2 ) . Thus , these synaptic transmission genes , like unc-75 , appear to be specifically required for efficient regenerative axon extension . To understand how UNC-75 regulates its targets , we chose unc-64/Syntaxin for further study , as a prominent UNC-75 CLIP-seq peak mapped to the last intron ( intron 7 ) of unc-64 ( Figure 3A ) . Alternative splicing of unc-64 exons 8a and 8b , which flank intron 7b , generates transcripts encoding UNC-64A and UNC-64B isoforms , which differ in their C-terminal hydrophobic membrane anchors ( Ogawa et al . , 1998; Saifee et al . , 1998 ) . UNC-64A is expressed predominantly in neurons and in some non-neuronal tissues , whereas UNC-64B is only expressed in non-neuronal tissues ( Saifee et al . , 1998 ) ( Figure 3B , C ) . RT-qPCR analyses showed that unc-64A mRNAs were significantly reduced in unc-75 mutants , whereas unc-64B mRNAs were increased ( Figure 3—figure supplement 1A ) . Total unc-64 mRNA levels were reduced in unc-75 mutants compared to wild type , and expression of UNC-64 proteins in neurons was strongly reduced as determined by immunostaining using antibodies that recognize both isoforms ( Figure 3—figure supplement 1B ) . Thus , UNC-75 is required for neuronal expression of UNC-64/Syntaxin isoforms . 10 . 7554/eLife . 16072 . 011Figure 3 . UNC-75 regulates alternative splicing of unc-64/Syntaxin in neurons . ( A ) UNC-75 CLIP-seq peaks in the unc-64 locus; genomic track display from UCSC Genome Browser . ( B ) In wild-type animals , UNC-64A::GFP is strongly expressed in most neurons . In unc-75 ( 0 ) mutants UNC-64A::GFP is expressed at lower levels in most neurons . Images of nerve ring and head neurons . ( C ) UNC-64B::GFP is not expressed in the nervous system in wild-type background but is ectopically expressed in head neurons in unc-75 ( 0 ) . ( D ) Deletion of the 38 bp UNC-75 binding site in intron 7 results in neuronal expression of UNC-64B::GFP in wild-type background . ( E ) Images of anterior portion of animals expressing the UNC-64A/B splicing reporter in wild-type and unc-75 ( 0 ) mutant backgrounds . unc-75 ( 0 ) is e950 . The splicing reporter contains the 3’ part of unc-64 genomic sequence ( boxes are exon7 , 8a and 8b , lines are intron 7a and 7b ) ; RFP is inserted at the end of exon 8a and GFP inserted at the end of exon 8b . For B-E , scale bar 20 μm . Bar charts show quantitation of fluorescence intensity in the nerve ring region ( ROIs shown in dashed boxes ) . Statistics: Student’s t-test . N=5–10 . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01110 . 7554/eLife . 16072 . 012Figure 3—figure supplement 1 . UNC-64 expression is regulated by UNC-75 . ( A ) Positions of unc-64 exons and PCR primers used in RT-qPCR . Primers for common exons ( black arrow ) and isoform specific exons ( red arrow for a isoform and green arrow for b isoform ) used in RT-qPCR were indicated . Relative mRNA expression levels of unc-64 detected by RT-qPCR are shown in the bar graph . Statistics: Student’s t-test . N = 4–8 . ( B ) Immunostaining using an antibody recognizing the UNC-64 N terminus . Quantification of expression in the nerve ring ( ROI enclosed by dashed line ) is shown in the bar graph . Statistics: Student’s t-test . N=5–10 . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01210 . 7554/eLife . 16072 . 013Figure 3—figure supplement 2 . unc-64 RNA splicing in wild type and unc-75 mutants . 3’ RNAseq data are displayed using the UCSC Genome Browser . The two alternatively spliced isoforms of unc-64 ( a and b ) are shown at the bottom . Solid blue boxes represent exons and lines with arrowheads represent introns . For reads from RNA-seq , each block represents one tag/read . Two blocks linked by a line represent one tag mapped to two adjacent exons separated by an intron ( showed as the line linking two blocks ) . There are reads mapped to intron 7a and 7b in both wild type and unc-75 ( e950 ) animals . In unc-75 mutants more reads mapped to intron 7a and fewer mapped to intron 7b , consistent with reduced expression of unc-64a isoform and increased unc-64b isoform . The inserted table shows the number of RNA-seq reads mapped to intron 7a and intron 7b from four biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01310 . 7554/eLife . 16072 . 014Figure 3—figure supplement 3 . UNC-64A and UNC-64B have distinct roles in neuronal function . Representative images and quantification of locomotion velocity of animals with indicated genotypes . Locomotion defects in unc-64 ( md130 ) and unc-64 ( e246 ) mutants were rescued by expression of UNC-64A cDNA driven by pan neuronal promoter ( Prgef-1 ) , but not by expression of UNC-64B using the same pan-neuronal promoter . Statistics: One way ANOVA with Bonferroni post test . Scale: 500 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 014 To dissect how UNC-75 differentially regulates the unc-64A and unc-64B isoforms we next examined unc-64 isoform-specific reporters , in which GFP was fused to exon 8a or exon 8b ( Ogawa et al . , 1998; Saifee et al . , 1998 ) . In unc-75 mutants expression of an UNC-64A::GFP reporter was strongly reduced ( Figure 3B ) , whereas an UNC-64B::GFP reporter was ectopically expressed in the nervous system ( Figure 3C ) . To define the roles of UNC-75 binding sites in this differential regulation , we then generated a neuronal splicing reporter for UNC-64A and UNC-64B , using unc-64 genomic DNA from exon 7 to exon 8b , with RFP inserted 3’ to exon 8a and GFP inserted 3’ to exon 8b ( Figure 3E ) . In a wild type background , these transgenic animals expressed RFP strongly in the nervous system ( UNC-64A-like expression ) , whereas GFP ( reflecting the UNC-64B isoform ) was undetectable . In unc-75 mutants , neuronal RFP expression was greatly reduced and GFP expression was increased in neurons ( Figure 3E ) . Furthermore , deletion of the UNC-75 CLIP site in intron 7 of the splicing reporter increased UNC-64B::GFP expression in neurons and in non-neuronal tissues ( Figure 3D ) . In addition , by RNA-seq , we detected significantly more reads mapping to intron 7a of unc-64 in unc-75 mutants , and fewer reads mapping to intron 7b compared to wild type ( Figure 3—figure supplement 2 ) . These analyses suggest UNC-75 binding in intron 7 promotes alternative splicing of exon 8a and represses inclusion of exon 8b through intron retention , resulting in neuronal expression of UNC-64A and repression of UNC-64B . The two isoforms of UNC-64 are both localized to the plasma membrane via their transmembrane domains at the C terminus . A premature stop codon mutation ( js116 ) in the UNC-64A transmembrane domain causes complete loss of function , indicating the importance of this domain ( Saifee et al . , 1998 ) . Consistent with this , expression of UNC-64 lacking a transmembrane domain ( UNC-64ΔTM ) was unable to rescue the movement defect of unc-64 ( md130 ) ( Figure 3—figure supplement 3 ) , a partial loss of function ( plf ) allele affecting both isoforms ( van Swinderen et al . , 1999 ) . We further expressed specific isoforms using cDNAs , and found that pan-neuronal expression of UNC-64A , but not of UNC-64B , was able to rescue the locomotion defects of unc-64 ( plf ) ( Figure 3—figure supplement 3 ) . Thus UNC-64A appears to be the major functional Syntaxin isoform in neurons; even when ectopically expressed in neurons , as in unc-75 mutants , UNC-64B is unable to substitute for UNC-64A . Partial loss of function in unc-64 ( md130 or e246 ) results in a partial block in PLM axon regeneration ( Figure 4A ) . As unc-64 ( js115 ) null mutants arrest in the first larval stage ( Saifee et al . , 1998 ) , we used two approaches to examine the null phenotype of unc-64 in regeneration . We first tested unc-64 ( js115 ) null mutants in which lethality , but not movement , was rescued by expression of UNC-64 under the combined control of acr-2 , unc-17 , and glr-1 promoters ( Hammarlund et al . , 2007 ) . In such animals PLM developed normally and axon regeneration was inhibited to a similar extent as in unc-64 ( plf ) ( Figure 4A ) . To address caveats due to possible misexpression of this transgene in touch neurons , we also generated a single-copy transgene containing LoxP-flanked unc-64A ( cDNA ) driven by the pan-neural rgef-1 promoter , which fully rescued unc-64 ( 0 ) lethality and locomotor defects ( Figure 4B , C ) . We then excised the floxed copy of unc-64A ( + ) in touch neurons using Pmec-7-nCre ( Chen et al . , 2015 ) . The unc-64 ( 0 ) PLM axons developed normally ( Figure 4—figure supplement 1 ) and displayed reduced regrowth after axotomy , comparable to unc-64 ( plf ) ( Figure 4A ) . We conclude that UNC-64 is specifically required for axon regeneration but not development , and that the null phenotype of unc-64 is a partial block in regeneration . Regenerative growth cones in unc-64 ( md130 ) mutants had dynamic filopodia but did not effectively elongate , resembling those of unc-75 ( 0 ) ( Video 3 ) . Moreover , regrowth defects in unc-64 ( md130 ) were rescued by pan-neuronal expression of either UNC-64A or UNC-64B ( Figure 4A ) , suggesting either isoform is sufficient for function in regeneration when overexpressed . 10 . 7554/eLife . 16072 . 015Figure 4 . unc-64 is required cell autonomously for PLM axon regeneration . ( A ) Normalized PLM regrowth 24 hr post axotomy . PLM axon regeneration is reduced in mutants with unc-64 partial loss of function alleles md130 and e246 , as well as null allele js115 . These alleles affect both isoforms ( Saifee et al . , 1998 ) . unc-64 ( md130 ) regeneration phenotypes are rescued by pan-neural expression of UNC-64A or UNC-64B , but not by UNC-64∆TM . Expression of UNC-64A or B in a wild type background does not affect PLM regeneration . Statistics , One-way ANOVA followed by Bonferroni's Multiple Comparison Post Test . N ≥ 10 . ( B ) Schematic illustration of two strategies to generate unc-64 mutation in touch neurons . The lethality of unc-64 ( js115 ) is rescued by oxEx705 or juSi316 . juSi316 was crossed to Pmec-7-nCre to delete transgenic UNC-64 in touch neurons . ( C ) Representative images of animals with indicated genotypes . js115; oxEx705 animals were viable but severely Unc , while js115; juSi316 animals were viable and slightly Unc . ( D ) Representative PLM regrowth images 24 hr post axotomy . Asterisks: PLM cell body; red arrow , injury site . Scale bar: 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01510 . 7554/eLife . 16072 . 016Figure 4—figure supplement 1 . UNC-64 is not required for PLM development . PLM neurons labeled by muIs32 ( Pmec-7-GFP ) in young adult animals of indicated genotypes . Cre-induced deletion of the floxed unc-64 allele in juSi316 did not affect PLM development . Red asterisks: PLM cell body . White arrow points to the terminus of PLM axon . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01610 . 7554/eLife . 16072 . 017Video 3 . Time-lapse movie of the tip of a regenerating PLM axon from an unc-64 ( md130 ) mutant , starting at 14 hr post axotomy , ending at 15 hr post axotomy . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 017 To test the hypothesis that altered UNC-64 expression underlies the regeneration defects in unc-75 mutants , we overexpressed UNC-64 in unc-75 mutants and examined axon regrowth . Strikingly , transgenes containing unc-64 genomic DNA , which produce both UNC-64A and UNC-64B , significantly suppressed the PLM regeneration defects of unc-75 mutants ( Figure 5A , B ) . Furthermore , pan-neuronal overexpression of UNC-64A ( using cDNA ) , but not of UNC-64B , rescued unc-75 regeneration defects to a similar degree as unc-64 genomic DNA , whereas UNC-64ΔTM did not rescue ( Figure 5A , B ) . Moreover , transgenes expressing unc-64 genomic DNA or UNC-64A strongly suppressed unc-75 locomotor phenotypes , while overexpression of UNC-64B or UNC-64ΔTM did not rescue ( Figure 5C , D ) . We infer that decreased neuronal expression of the UNC-64A isoform is a major contributor to unc-75 mutant phenotypes in regeneration and behavior . 10 . 7554/eLife . 16072 . 018Figure 5 . Overexpression of UNC-64A suppresses unc-75 neuronal phenotypes . ( A-B ) Quantitation ( n ≥ 10 per genotype ) and images of PLM axon regeneration 24 hr post axotomy; scale , 10 μm . Asterisks: PLM cell body; red arrow , injury site . Transgenic UNC-64 expression using 9 kb genomic DNA encoding both UNC-64A and UNC-64B was able to partially rescue the regeneration defect of unc-75 ( 0 ) . Pan-neuronal expression of UNC-64A cDNA , but not UNC-64B , significantly increased axon regrowth in unc-75 ( 0 ) mutants . ( C-D ) Quantitation of locomotion velocity and images of animals with indicated genotypes; scale , 0 . 5 mm . N ≥ 10 per genotype; statistics: One way ANOVA with Bonferroni post test . Genomic DNA or UNC-64A cDNA driven by Pan-neuronal promoter partially rescues the unc-75 locomotor phenotype; expression of UNC-64B or UNC-64ΔTM cDNA does not rescue . e950 is used in unc-75 ( 0 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 018 The CELF protein family is highly conserved from C . elegans to humans . By sequence comparison , UNC-75 is more closely related to the CELF3/4/5/6 subfamily ( Figure 6—figure supplement 1A ) , and a previous study showed that expression of human CELF4 partly rescues the locomotion defects of unc-75 mutants ( Loria et al . , 2003 ) . Here , we found that either mouse CELF2 or CELF4 , when expressed in C . elegans touch neurons , could significantly rescue the unc-75 PLM regrowth defect , with CELF2 being slightly more effective than CELF4 ( Figure 6A ) . This may be because CELF2 , like UNC-75 , is generally localized in the nucleus in neurons ( Otsuka et al . , 2009 ) whereas CELF4 is predominantly cytoplasmic ( Wagnon et al . , 2012 ) . Nonetheless , this result suggests that the CELF proteins may also play a conserved role in axon regeneration . 10 . 7554/eLife . 16072 . 019Figure 6 . CELF proteins play conserved roles in axon regeneration . ( A ) Expression of full length cDNAs of mouse CELF2 or CELF4 with mCherry tag in C . elegans touch neurons significantly rescues the unc-75 axon regrowth defect . Normalized PLM axon regrowth 24 hr post axotomy , N = 14–28 . Based on mCherry fluorescence the CELF2 and CELF4 transgenes are expressed at similar levels ( not shown ) . ( B ) Celf2 transcript levels in DRG neurons decrease during postnatal development , whereas Celf4 levels increase . Expression was normalized to P1 , and mouse β-Actin was used as internal reference . Statistics , One-way ANOVA followed by Bonferroni's Multiple Comparison Post Test . N = 4–6 . ( C ) Expression of Celf2 transcripts in DRG of 2 month old mice is significantly enhanced 3 days after sciatic nerve injury . Ratio of the crushed side to the uncrushed side within the same animal is plotted . Statistics , Student’s t-Test . ( D ) Mutation of Celf2 impairs axon regeneration in DRG PV+ neurons . Confocal images of longitudinal sciatic nerve sections distal to the lesion , stained with anti-SCG10 ( green ) at 3 days post crush . tdTomato expression is from Rosa26-lox-STOP-lox-tdTomato and marks neurons in which Cre was active . Enlarged images of the boxed areas are shown on the right; white dashed line marks the lesion site . Scale bar: 0 . 5 mm . ( E ) Quantitation of SCG10 intensity in tdTomato positive axons at different distances from the lesion site , normalized to SCG10 intensity proximal to the lesion . 6 control and 5 mutant animals were analyzed . Statistics , One-way ANOVA followed by Bonferroni's multiple comparison post test . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 01910 . 7554/eLife . 16072 . 020Figure 6—figure supplement 1 . Celf2 is required for neurite growth in mouse DRG neurons . ( A ) Dendrogram created using Clustal Omega showing mouse CELF1-6 , C . elegans UNC-75 and ETR-1 , as well as Drosophila Bruno . ( B ) Schematic illustration of mouse gene targeting vector and the Celf2flox allele . Black boxes represent exons and solid lines represent introns . Exon 3 is flanked by two loxP sites . ( C ) Loss of CELF2 impairs neurite growth of E13 . 5 dorsal root ganglia ( DRG ) explants . '-' indicates null allele derived from crossing to ZP3-Cre . ( D ) Quantification of the average neurite length of each DRG explant . N= 5 animals for control and 5 for mutant . DRGs from each animal were cultured in two wells . Statistics , Student’s t-test . Scale bar , 300 µm . ( E ) Nestin-cre driven tissue-specific knockout of Celf2 causes reduced animal size . ( F ) Cultured adult DRG neurons from Celf2 mutant show significantly reduced neurite growth , compared to DRG neurons from littermate controls . In this in vitro axon regeneration paradigm , primary cultured neurons were resuspended and re-plated then fixed after 24 hr . ( G ) Quantitation of average DRG neurite length in each well . N= 5 animals for control and 5 for mutant . DRGs from each animal were cultured in two wells . Statistics , Student’s t-test . Scale bar , 200 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 020 Among mouse Celf genes , Celf2 and Celf4 are most highly expressed in the postnatal CNS ( Supplementary file 3 ) . We further examined Celf2 and Celf4 expression in dorsal root ganglion ( DRG ) neurons using RT-qPCR . Celf4 transcripts increased during postnatal development ( Figure 6B ) , consistent with reported expression ( Supplementary file 3 ) . In contrast , Celf2 mRNA levels declined from perinatal stages to adult ( Figure 6B ) . Notably , Celf2 transcript levels significantly increased in DRGs after sciatic nerve crush in 2-month old animals ( Figure 6C ) . As the peripheral processes of DRG neurons are capable of regeneration , and such capacity declines with age ( Mar et al . , 2014 ) , the developmental decline in Celf2 expression and its upregulation after injury suggest Celf2 expression correlates with axon regeneration capacity . To test the function of CELF2 in mouse axon regeneration , we generated a conditional allele of Celf2 in which exon 3 was flanked by loxP sites ( Figure 6—figure supplement 1B ) . Exon 3 encodes part of RRM1 , a domain present in most CELF2 isoforms and required for protein function ( Singh et al . , 2004 ) . Cre-mediated deletion of exon 3 would alter splicing , resulting in Celf2 mRNA encoding non-functional CELF2 proteins due to frameshift followed by premature stop ( Figure 6—figure supplement 1B ) . We expressed Cre recombinase ubiquitously in the Celf2flox background ( see Materials and methods ) to generate a Celf2- allele , and verified the genomic deletion of exon 3 . These constitutive Celf2-/- mice died neonatally , with 5% escapers surviving up to 3 weeks . No obvious morphological defects were detected in newborn Celf2-/- animals by histological analysis ( not shown ) . To test the role of Celf2 in axon growth , we assayed cultured primary neurons . Explants of DRG neurons derived from E13 . 5 Celf2-/- embryos displayed significantly reduced axon extension ( Figure 6—figure supplement 1C , D ) . Thus although Celf2 does not seem to play a major role in developmental axon outgrowth , it is required for efficient regrowth of axons from explants . To study CELF2 function in adult axon regeneration , we next generated a nervous system conditional knockout by crossing the Celf2 ( flox ) allele to a Nestin-Cre line ( Tronche et al . , 1999 ) . Nestin-Cre induced Celf2 knockout animals were smaller than littermates ( Figure 6—figure supplement 1E ) and usually survived 1–2 months . Using an in vitro regeneration assay ( Saijilafu and Zhou , 2012 ) , we found Celf2 knockout neurons showed defects in adult DRG regeneration after re-suspension and re-plating of DRG cells ( Figure 6—figure supplement 1F , G ) . To examine in vivo regeneration , we crossed Celf2flox allele to a Parvalbumin-Cre driver to delete Celf2 in parvalbumin-expressing large diameter DRG neurons , which make up ~30% of DRG axons ( Hippenmeyer et al . , 2005 ) . Celf2flox/-; parvalbumin-cre+/- mice were superficially wild-type , allowing us to examine axon regeneration in adult stages . We used R26/CAGtdTomato ( Madisen et al . , 2010 ) to label cells with Cre induced Celf2 deletion . We performed sciatic nerve crush on 2 months old animals and evaluated DRG axon regeneration 3 days post injury by staining for the regeneration marker SCG10 ( Shin et al . , 2014 ) . SCG10 positive DRG axons distal to the crush site were significantly reduced in the Celf2 mutant compared to littermate controls ( Figure 6C , D ) , indicating that CELF2 is required for DRG axon regeneration . Together , these data support a conclusion that CELF proteins play a conserved role in axon regeneration from C . elegans to mouse . CELF2 is known as a splicing regulator , but its role in the nervous system has not been explored . To test whether CELF2 regulates similar sets of target mRNAs as UNC-75 , we performed CLIP-seq of mouse CELF2 using a neuroblastoma N2A cell line ( http://www . atcc . org/products/all/CCL-131 . aspx#generalinformation ) that stably expresses BLRP ( biotin ligase recognition peptide ) tagged CELF2 ( Figure 2—figure supplement 1B ) . We identified 2919 protein coding genes as CELF2 targets ( Supplementary file 4 ) and 'UGUGUGUG' as the most significant binding motif , which is conserved to UNC-75 , suggesting the function of CELF genes in target regulation is highly conserved . As C . elegans UNC-75/CELF regulates UNC-64/Syntaxin alternative splicing , we asked whether CELF-Syntaxin regulation might be conserved in mammals . From CELF2 CLIP-seq we identified CELF2 binding sites in multiple mouse Syntaxin genes ( Figure 7A and Supplementary file 4 ) . We examined expression of candidate Syntaxin genes in Celf2 knockout mouse brain by RT-qPCR , and observed that mRNA levels of splicing variants of Syntaxin2 and Syntaxin16 were significantly altered in Celf2-/- mouse brain ( Figure 7B ) . Syntaxin2 is ubiquitously expressed ( Bennett et al . , 1993 ) , but its role in the nervous system is not known . Reminiscent of unc-64 , Syntaxin2 is alternatively spliced , generating two isoforms differing in the C-terminal membrane anchor . From CLIP-seq we found a CELF2 binding site in the intron 5’ to the alternatively spliced exons ( Figure 7A ) . Syntaxin16 expression is enriched in neurons in the brain and has been implicated in neurite growth ( Chua and Tang , 2008 ) . Two splicing variants of Syntaxin16 differ in exons encoding the N terminus , which is known to interact with Vps45 ( Dulubova et al . , 2003 ) . We detected CELF2 binding on the introns in the immediate vicinity to the alternatively spliced exons , as well as on the 3’ UTR ( Figure 7A ) . Taken together , these data support a hypothesis that CELF-mediated regulation of alternative splicing of Syntaxin genes is likely a conserved mechanism in axon regeneration . 10 . 7554/eLife . 16072 . 021Figure 7 . CELF2 regulates expression of specific neuronal Syntaxin isoforms . ( A ) Genome browser tracks displaying CELF2 CLIP-seq peaks on the stx2 and stx16 gene loci . Red and blue tags indicate the two different gene orientation on chromosomes . CELF2 binding peaks are mapped to introns near alternatively spliced exons . Isoform labeling is consistent to gene annotation on Ensembl . Red arrows under the exons implicate primers used for RT-qPCR in panel b . ( B ) Transcript levels of Syntaxin genes were measured by RT-qPCR in E15 . 5 control and Celf2-/- brains . Statistics , Student’s t-test , N = 5–6 . Expression of the alternatively spliced isoform 002 of stx2 and isoform 001 of stx16 was significantly decreased in Celf2-/- constitutive mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 16072 . 021
CELF proteins are long known as regulators of RNA splicing and stability , but their roles in neuronal RNA regulation are only recently being explored . Our findings , together with previous RNA-seq analyses , identify contexts in which UNC-75/CELF regulates alternative splicing for inclusion of neuron-specific exons in C . elegans . The production of neuronal mRNA encoding unc-32/ATPase depends on alternative splicing of two sets of mutually exclusive exons , which involves UNC-75 binding to their flanking introns , partly in collaboration with the RBPs ASD-1 and FOX-1 ( Kuroyanagi et al . , 2013a ) . Motor neuron subtype alternative splicing of unc-16/JIP3 mRNA also involves UNC-75 binding to intron sequences , and the outcome of exon inclusion or exclusion partly depends on the Elav-like RBP EXC-7 in cholinergic neurons ( Norris et al . , 2014 ) . These previous studies were based on comparisons of whole-organism RNA levels , which reflect both direct and indirect regulation by UNC-75 . As neurons contribute a small fraction of the overall C . elegans transcriptome , many neuronal targets of UNC-75 may have been overlooked in such studies . Our use of neuronal CLIP-seq validates some previous targets as direct interactors of UNC-75 and has expanded the number of candidate targets by an order of magnitude . Many of UNC-75’s neuronal targets are involved in synaptic transmission , and were also identified as required for axon regeneration ( Chen et al . , 2011 ) . The close similarity between unc-75 and unc-64 regeneration phenotypes , and the ability of UNC-64 overexpression to rescue unc-75 defects suggest that they act in a common pathway for regenerative axon extension . We therefore focused on how UNC-75 mechanistically promotes UNC-64 expression . Our analysis shows that UNC-75 binding to intron 7b promotes inclusion of the upstream exon 8a , leading to production of UNC-64A at the expense of UNC-64B . These results support the general model that UNC-75 tends to promote inclusion of upstream exons by binding downstream intronic sites ( Kuroyanagi et al . , 2013b; Norris et al . , 2014 ) . Loss of UNC-75 caused increased pre-mRNA containing unc-64 intron 7a and decreased pre-mRNA containing intron 7b , suggesting that alternative splicing of unc-64 is regulated via selective intron retention . In unc-75 mutants , we detected reduced UNC-64A::GFP expression from our reporter line ( Punc-64-unc-64A::gfp ) , although the reporter lacked intron 7b ( Figure 3B ) . The reduction of UNC-64A expression from this reporter was not as dramatic as that from the dual reporter containing intron 7b ( Figure 3E ) . We speculate that if the primary binding site is not available ( i . e . intron 7b ) , UNC-75 can bind to secondary sites ( e . g . intron 7a ) to regulate UNC-64A expression . Consistent with this idea , we detected UNC-75 binding tags from our CLIP-seq in intron 7a ( Figure 3A ) . CELF2 was identified almost 20 years ago , in multiple studies , first as a CUG repeat binding protein CUG-BP2 ( Timchenko et al . , 1996 ) ; in screens for homologs of the Elav family ( ETR-3 ) ( Lu et al . , 1999 ) ; and as a gene induced in apoptotic neuroblastoma cells ( NAPOR ) ( Choi et al . , 1998 ) . However until now , in vivo functions of CELF2 have not been explored using genetics . We find that CELF2 is essential for viability . The cellular basis of the lethal phenotype remains to be determined , but may reflect CELF2 functions in non-neuronal cells , as animals with a neuronal deletion of CELF2 were viable , albeit smaller and short-lived compared to wild type . Alternative splicing ( AS ) is pervasive in neurons and has been implicated in axon guidance , synaptogenesis and synaptic transmission ( Raj and Blencowe , 2015 ) . The function of CELF family members in AS was first explored in non-neuronal cells ( e . g . cardiomyocytes ) , but given their widespread expression in the brain increasing attention has been given to the neuronal roles of CELF proteins ( Ladd , 2013 ) . CELF4 appears to be predominantly expressed in the cytoplasm of neurons and is implicated in neuronal excitability ( Wagnon et al . , 2012; Yang et al . , 2007 ) . A CELF4 CLIP-seq analysis in mouse brain identified numerous targets , including many genes implicated in synaptic transmission , although CELF4 is thought to primarily regulate mRNA translation ( Wagnon et al . , 2012 ) . CELF2 is known to regulate the AS of specific neuronal genes , including NMDAR1 , MAPT/tau , and NF1 ( Barron et al . , 2010; Han and Cooper , 2005; Leroy et al . , 2006 ) . Screens for CELF2 targets have been performed using SELEX ( Faustino and Cooper , 2005 ) and by CELF2 overexpression in T-cells ( Mallory et al . , 2015 ) . Here we report a genome-wide CLIP-seq approach to find CELF2 targets in neuronal cells . Consistent with our analysis of UNC-75 neuronal targets , in neuroblastoma cells , CELF2 binding sites were found in several Syntaxin genes . Like UNC-64 , several mammalian Syntaxins are known to undergo alternative splicing , and we find that expression of two Syntaxin isoforms ( stx2-002 and stx16-001 ) is significantly reduced in Celf2 mutant brains . We find that CELF2 expression is upregulated after nerve injury . Expression of CELF2 or other CELF genes is altered by cellular stress or damage in several contexts , although the mechanisms of induction remain poorly understood . In epithelial cells CELF2 expression is induced by UV or gamma irradiation injury ( Mukhopadhyay et al . , 2003 ) . In the nervous system CELF1 has been reported to be upregulated after spinal cord injury ( Yang et al . , 2015 ) . CELF2 expression is also downregulated in some models of ischemic brain injury and fetal alcohol syndrome , although it is unclear whether this is cause or consequence ( Otsuka et al . , 2009 ) . Acute induction of CELF2 expression has been most extensively studied during T cell activation , and occurs partly at the level of transcription and partly by stabilization of Celf2 transcripts ( Mallory et al . , 2011; 2015 ) . Recently CELF2 has been found to regulate alternative splicing of the MAPKK MKK7 , facilitating a JNK-dependent positive feedback loop during T cell activation ( Martinez et al . , 2015 ) . JNK is required for the increased stability of CELF2 messages after T cell activation , by as-yet unknown mechanisms . Many targets of JNK-regulated AS are also dependent on CELF2 ( Martinez et al . , 2015 ) , suggesting the CELF2/JNK positive feedback loop might function widely to regulate inducible alternative splicing . UNC-75 dependent splicing is critical for UNC-64A expression in neurons , at the expense of UNC-64B . The roles of the two UNC-64 isoforms and the mechanistic basis for their functional difference have remained a topic of debate . As truncation of UNC-64A causes apparent complete loss of function ( Saifee et al . , 1998 ) , UNC-64A appears to be the predominant functional isoform in neurons . Overexpression of either A or B isoform rescued the axon regrowth defect of unc-64 hypomorphs , but only the A isoform was sufficient to rescue unc-64 locomotor defects when overexpressed . The level of UNC-64 function required in regeneration may be lower than in behavior . A recent study also reported unc-64 splicing is regulated by unc-75 ( Norris et al . , 2014 ) . However , using fosmid transgenes , this study found that while either A or B isoform could rescue unc-64 Ric phenotypes , only UNC-64B could rescue unc-64 locomotor phenotypes . The basis for this discrepancy might reflect differences in the promoters and nature of expression ( pan-neural and cDNA in our study , endogenous regulatory sequences including splicing of 5’ exons in Norris et al . ) . We find that transgenic expression of UNC-64A ( cDNA bypassing splicing ) also significantly suppressed unc-75 locomotor and regeneration phenotypes whereas overexpression of UNC-64B was unable to rescue . The simplest interpretation of these data is that UNC-64B is unable to fully substitute for reduced levels of UNC-64A . Possibly , UNC-64B but not UNC-64A requires some additional cofactor lacking in unc-75 . The C-terminal membrane anchors of UNC-64A and B are both 25 aa residues long and differ only in the last 14 aa , suggesting that the precise composition of the anchor is critical for Syntaxin function . Both have the same number of hydrophobic residues in their C-termini , with the only discernible difference being that these residues are more clustered in the B isoform . Nevertheless seemingly small differences in hydrophobicity can cause Syntaxins to sort into distinct membrane domains ( Milovanovic et al . , 2015; Milovanovic and Jahn , 2015 ) , and may underlie the nonequivalence of the UNC-64A and B isoforms . UNC-64 is known to localize along the axonal plasma membrane , and may provide a SNARE function involved in membrane addition during axon extension . Like unc-64 , Syntaxin2 encodes two alternatively spliced isoforms that differ in the membrane anchor . Syntaxin2 ( also called epimorphin ) was first identified as an extracellular morphogen ( Hirai et al . , 1992 ) , but later was also found to function in the cytoplasm as t-SNARE regulating vesicle fusion ( Bennett et al . , 1993 ) . The localization of this protein on either the cytoplasmic face or the extracellular surface of the plasma membrane is determined by its distinct conformation ( Chen et al . , 2009 ) . Little is known of the neuronal functions of Syntaxin2 . In mammals Syntaxin16 has been implicated in neurite outgrowth , and appears to localize to the Golgi ( Chua and Tang , 2008 ) . Mammalian Syntaxin12 ( previously called Syntaxin13 ) has been shown to promote axon regeneration under the control of the mTOR pathway ( Cho et al . , 2014 ) . Whether other Syntaxins also play a role in axon regeneration remains to be tested . The mechanism by which Syntaxin contributes to regenerative axonal elongation remains to be elucidated . Because unc-64 mutants are defective in axon extension rather than growth cone reformation , we believe it is less likely that Syntaxin is required for membrane resealing immediately after injury . Instead , Syntaxin may contribute to the rapid plasma membrane expansion required during axon regrowth ( Bloom and Morgan , 2011 ) . In mammalian neurons Syntaxin-3 has been implicated in fatty acid stimulated axon plasma membrane expansion resulting from fusion of transport organelles ( Darios and Davletov , 2006 ) . Such organelles might be related to the Syntaxin-containing transport packets involved in presynaptic assembly ( Ahmari et al . , 2000 ) . The recycling endosome component Syntaxin13 is upregulated by injury and is thought to promote membrane recycling required for regrowth ( Cho et al . , 2014 ) . Finally , Syntaxin might contribute to membrane expansion via a non-fusogenic mechanism as shown for the SNARE Sec22b ( Petkovic et al . , 2014 ) . In summary , we have revealed a novel regulatory pathway important for axon extension in regenerative regrowth , involving CELF-dependent alternative splicing of neuronal Syntaxins . As well as being relevant to axon regeneration , our finding may shed light on the roles of CELFs in neurological disease . CELF2 is misregulated in a mouse model of spinal muscular atrophy ( Anderson et al . , 2004 ) and CELF2 copy-number variation has been linked to schizophrenia ( Xu et al . , 2011 ) . Our results add to the conceptual framework for dissecting these complex diseases .
C . elegans were cultured at 15–25°C using standard procedures . Transgenes were introduced into mutants by crossing or injection; homozygosity for all mutations was confirmed by PCR or sequencing . We followed standard procedures to generate new clones and transgenes ( All new strains and transgenes are listed in Supplementary file 5 ) . Fluorescence images were collected using Zeiss LSM710 or LSM510 confocal microscopes . Laser axotomy was performed as previously described ( Chen et al . , 2011 ) . Raw ( non normalized ) axon regrowth measurements are show in Supplementary file 7 . Timelapse movies of PLM axon regrowth were taken with Zeiss LSM710 using agarose beads to immobilize worms . Immunostaining was performed as described ( Saifee et al . , 1998 ) . Briefly , worms were fixed in Bouin’s fixative and washed in fresh BTB buffer ( 1 x Borate Buffer , 0 . 5% Triton X-100 , 2% BME ) , then stained with UNC-64 antiserum at 1:50 dilution . We measured locomotion velocity using WormTracker 2 . 0 as previously described ( Chen et al . , 2015 ) . Briefly , individual young adults were transferred to a fresh tracking plate with thin OP50 bacteria lawn . 1 min later , the plate was placed on the tracker platform and locomotion recorded for 1 min at 10 frames per second for each animal . CLIP-seq was performed as previously described ( Zisoulis et al . , 2010 ) . CZ14662 [unc-75 ( e950 ) ; Prgef-1-UNC-75S ( juIs369 ) ] worms were crosslinked in a 150 mm plate ( with no food ) with an energy output of 6 kJ/m2 . Worms were lysed by sonication in Homogenization Buffer ( 100 mM NaCl , 25 mM HEPES , 250 μM EDTA , 2 mM DTT , 0 . 1% NP-40 , 25 units/ml RNasin and Protease Inhibitors ) . Lysates were centrifuged at 16 , 000g for 15 min at 4°C and supernatants collected and incubated with M2 magnetic beads ( Sigma ) overnight on a rotator . Beads were collected and washed twice with Wash Buffer ( 1X PBS with 0 . 1% SDS , 0 . 5% sodium deoxycholate , and 0 . 5% NP-40 ) , twice with High Salt Wash Buffer ( 5X PBS , 0 . 1% SDS , 0 . 5% sodium deoxycholate , and 0 . 5% NP-40 ) and twice with Polynucleotide Kinase Buffer ( PNK Buffer ) ( 50 mM Tris-Cl pH 7 . 4 , 10 mM MgCl2 and 0 . 5% NP-40 ) . Beads were incubated with 500 μl of Micrococcal Nuclease Reaction Buffer ( 50 mM Tris-Cl pH 7 . 9 , 5 mM CaCl2 ) containing 1 ng of Micrococcal Nuclease ( NEB ) for a total of 10 min at 4°C with intermittent shaking on a Thermomixer ( Eppendorf ) ( 1200 rpm for 1 min and then 1200 rpm for 15 sec every 3 min ) . Beads were then washed twice with PNK+EGTA Buffer ( 50 mM Tris-Cl pH 7 . 4 , 20 mM EGTA and 0 . 5% NP-40 ) , twice with Wash Buffer and twice with PNK Buffer . The beads were incubated for 10 min at 37°C in the Thermomixer with intermittent shaking ( 1200 rpm for 15 sec every 3 min ) in 80 μl NEB Buffer 3 containing 30 units of Calf Intestinal Phosphatase ( NEB ) . The beads were then washed twice with PNK+EGTA Buffer , twice with PNK Buffer and twice with 0 . 1 mg/ml BSA . An RNA linker ( 5'- CUCGUAUGCCGUCUUCUGCUUG-3’ 3’ Puromycin , 5’P ) with a puromycin modification at the 3′ end to avoid self-circularization was linked to the mRNA present in the complexes by T4 RNA Ligase and incubated overnight at 16°C with gentle shaking ( 1300 rpm every 5 min for 15 s in the Thermomixer ) . The beads were washed three times with PNK Buffer and incubated in 80 μl PNK Buffer ( NEB ) with 40 units of T4 PNK enzyme ( NEB ) in the presence of 32P-γ-ATP ( 1 mCi ) . Samples were incubated for 10 min at 37°C with intermittent shaking ( 1000 rpm every 4 min for 15 s ) . Cold ATP was added to the reaction at a final concentration of 1 . 25 mM and incubated for 5 min . The reaction was terminated with three washes of PNK+EGTA Buffer . Complexes were eluted from the beads by incubation for 10 min at 70°C in Nupage LDS Buffer . Samples were loaded onto a native 10% Bis-Tris gel with MOPS SDS Running Buffer . Next , the samples were transferred to a nitrocellulose membrane . The band corresponding to UNC-75/RNA complexes ( 65–100 kDa ) was cut out of the membrane and proteins degraded by Proteinase K . The samples were then subjected to phenol/chloroform extraction followed by ethanol precipitation . RNA was resuspended and ligated to 5′ RNA Linker ( 5'-GCUGAUGCUACGACCACAGGNNNU-3' 3'OH , 5'OH ) with T4 RNA ligase at 16°C overnight . The RNA samples were then treated with DNase I followed by phenol/chloroform extraction and ethanol precipitation . The ligated RNA was reverse transcribed with P3 primer ( 5’-CAAGCAGAAGACGGCATACGAG-3’ ) followed by PCR . PCR primers 5’-CAAGCAGAAGACGGCATACGAG and 5’-GCTGATGCTACGACCACAGG-3’ were used . PCR product was analyzed on PAGE gel , bands corresponding to 75–150 nt were isolated and the DNA was extracted for sequencing . 52 , 415 , 296 total reads were obtained , among which 22 , 399 , 451 were mapped to the C . elegans genome ( ce10 ) . After removing PCR duplicates and non-unique reads , 688 , 276 unique reads were obtained . From these unique reads , overlapping CLIP tags were grouped to define CLIP peaks . We identified 1219 peaks distributed over 820 protein-coding genes , among which 410 genes are annotated with gene names ( e . g . scpl-1 ) , the remaining 410 genes being listed by sequence names ( e . g . Y106G6D . 8 ) ( Supplementary file 1 ) . Crosslinking induced mutation sites ( CIMS ) introduced by reverse transcriptase when bypassing the crosslink sites have been frequently detected in CLIP-seq data ( Ule et al . , 2005 ) . Therefore we also used CIMS analysis method ( Zhang and Darnell , 2011 ) when defining the peaks . We identified 1060 peaks located on 885 protein-coding genes ( 513 of which are functionally annotated ) ( Supplementary file 1 ) . As expected , the two different peak calling methods ( with or without CIMS ) showed partial overlap , with 212 genes in common . We manually inspected all genomic loci of the functionally annotated genes on the two lists and identified 533 potential targets ( Supplementary file 1 ) . The Neuro2A cell line was obtained from ATCC ( http://www . atcc . org/products/all/CCL-131 . aspx#generalinformation ) and cultured in DMEM ( high glucose ) containing 10% FBS with 5% CO2 . To generate an inducible Neuro2A stable cell line expressing biotin-tagged Celf2 ( cDNA ) that can be controlled to express at endogenous levels , we modified the original in vivo biotin-tagging system using a Tet-On retroviral inducible system from Clontech . Briefly , Celf2 was fused in-frame to the C terminus of the peptide MAGGLNDIFEAQKIEWHEDTGGGGSGGGGSGENLYFQSDYKDDDDK in the BLRP expression construct . Amino acids 1–20 represent a biotin ligase recognition peptide ( BLRP ) , in which the lysine residue at position 13 is a substrate for the bacterial biotin ligase ( BirA ) upon co-expression in mammalian cells ( Liu et al . , 2014 ) . The glycine-rich stretch following the BLRP sequence provides a spacer region , and the ENLYFQS sequence ( in bold and underlined ) provides a specific cleavage site for TEV protease ( Life Technologies , Carlsbad , CA ) . Following the TEV site , there is a FLAG tag . The BLRP-Celf2 cassette was ligated into a retrovirus-based Tet-On vector then co-transfected with pCL-Ampho packaging plasmid into 293T cell line to prepare retrovirus . Retrovirus was then transduced into a parental Neuro2A stable line that was engineered to stably express BirA and a Tet Repressor using a retroviral vector . G418 ( 150 μg/ml ) , hygromycin ( 200 μg/ml ) , and puromycin ( 0 . 7 μg/ml ) were used for stable selection . BLRP-Celf2 cells were induced to express low level BLRP-Celf2 by doxycycline , then rinsed once with PBS , placed in HL-2000 Hybrilinker and irradiated at 150 mJ/cm2 on ice for 1–1 . 5 min . Cells were washed twice with cold PBS and collected in PBS by scraping from culture flasks . Cells were gently resuspended in 500 μl 1x Hypotonic Buffer ( 500 μl per 10 cm dish or 107 cells , scaled up as necessary ) by pipetting up and down several times , followed by incubation on ice for 15 min . 25 μl detergent ( 10% NP40 ) was added per 500 μl 1x Hypotonic Buffer followed by vortexing for 10 s at highest speed . The homogenate ws centrifuged for 10 min at 3 , 000 rpm at 4°C to get the nuclear fraction in the pellet . The pellet was then washed twice in 1x Hypotonic Buffer and resuspended in 1 ml CLIP lysis buffer ( 50 mM Tris–HCl , pH 7 . 4; 100 mM NaCl; 1 mM MgCl2; 0 . 1 mM CaCl2; 1% NP-40; 0 . 5% sodium deoxycholate; 0 . 1% SDS; Protease inhibitor and anti-RNase ) per 100 μl pellet followed by sonication on ice . 5 ul Turbo DNase was added per 1 ml of lysates followed by incubation at 37°Cfor 3–5 min . Lysates were centrifuged at 16 , 000 g for 10 min at 4°C and supernatants collected and incubated with streptavidin magnetic beads ( Sigma ) overnight on a rotator . Beads were then treated as for the CLIP-seq in C . elegans ( see above ) with some modification . Complexes were eluted from the beads and run on a native 10% Bis-Tris gel followed by transferring to a nitrocellulose membrane . The band corresponding to the CELF2/RNA complexes ( 65–100 kDa ) was cut out of the membrane . After RNA extraction , RNA was resuspended and ligated to 5′ RNA Linker with T4 RNA ligase at 16°C overnight . The ligated RNA was reverse transcribed with P3 primer ( 5’-CAAGCAGAAGACGGCATACGAG-3’ ) followed by two rounds of PCR . PCR primers 5’-CAAGCAGAAGACGGCATACGAG and 5’-GCTGATGCTACGACCACAGG-3’ were used for the first round of PCR for 12 cycles . Primer 5’-CAAGCAGAAGACGGCATACGAG-3’ and barcoded PCR primers 5’- AATGATACGGCGACCACCGAGATNNNNGCTGATGCTACGACCACAGG-3’ were used for the second round of PCR for 4 cycles . PCR product was analyzed on PAGE gel , bands corresponding to 75–150 nt were isolated and the DNA was extracted for deep sequencing . We obtained 19 , 563 , 928 reads , among which 13 , 505 , 933 were mapped to mouse genome . After filtering PCR duplicates and non-unique tags , we obtained 4 , 587 , 451 unique tags . We used an algorithm in peak calling to include peaks with and without CIMS . The peaks with top 15% significance score were distributed over 2919 protein coding genes ( Supplementary file 4 ) . The CLIP-seq data are available at the Gene Expression Omnibus under the accession number GSE78111 . 3’-RNA-seq was performed as previously described ( Fox-Walsh et al . , 2011 ) . Briefly , total RNA was extracted from synchronized worms using Trizol ( Invitrogen ) . cDNA synthesis was performed using SuperScript III ( Invitrogen ) and 3 µg of total RNA , together with 1 µl of 50 µM Biotin labeled oligodT and Adaptor-Random primers . cDNA was then purified using PCR purification kit ( Macherey-Nagel ) followed by terminal transferase treatment to add ddNTP to protect 3’-end . cDNA was then captured using streptavidin coated magnetic beads and primer extension was done on beads with Adaptor-Random primers . DNA was then eluted from beads by heating to 95°C before PCR amplification using barcoded primers . PCR products were then size selected and used for deep sequencing . Mouse husbandry and surgeries were performed under the supervision of the University of California San Diego Institutional Animal Care and Use Committee ( IACUC ) . We used homologous recombination to create a 'floxed' Celf2 allele consisting of loxP sites flanking Celf2 exon 3 ( Figure 6—figure supplement 1 ) . A 1 . 1 kb genomic DNA fragment containing Celf2 exon 3 was cloned into the targeting vector , flanked by two loxP sites . 2 . 2 kb genomic DNA upstream and a 4 . 8 kb genomic DNA fragment downstream to the 1 . 1 kb fragment were cloned into the targeting vector to generate 5’ and 3’ homology arms . The targeting vector was linearized by Not I digestion and transfected to CB6F1 ES cells . Homologous recombination was analyzed by Southern blotting using probes generated by PCR using the following primers: ( 5’ probe: For-GGGACAGCAAGAAAGACAGT; Rev- CATAGATGCAGCATTTAGTAGG . 3’ probe: For-ACTCATTTCATTAAGGTTGTA; Rev-TAGTTTATCAGGACCATTTG ) . Cells heterozygous for the targeted mutation were microinjected into blastocysts to obtain germ-line transmission following standard procedures . Mice were genotyped using PCR ( primers: For-GAGGTGTCTGCCGAACT; Rev-CACTCAGTCCCTGTTTGTAA; Wt 470 bp , mutant 370 bp ) . ZP3-Cre ( Lewandoski et al . , 1997 ) was used to generate Celf2 null allele Celf2- . A Parvalbumin-Cre transgene ( Hippenmeyer et al . , 2005 ) was used to delete Celf2 and Rosa26-tdTomato ( Madisen et al . , 2010 ) was used to label the Cre+ cells . Parvalbumin-Cre; Rosa26-tdTomato mice were crossed to Celf +/- mice to generate PV-Cre; Rosa26-tdTomato; Celf2null/+ progeny , which were then crossed to Celf2fx/fx mice to generate PV-cre; Rosa26-tdTomato; Celf2fx/- animals for in vivo axon regeneration experiments . E13 . 5 mouse embryos were dissected from the uterus and put into cold F12 medium . The embryo was opened to expose the entire spinal cord which was then lifted to allow the dorsal root ganglia to be removed and transferred to culture medium ( NB + 2% B27 + 10% FBS + L-glutamine ) then to cover slips pre-coated with poly-ornithine and 5 μg/ml laminin . The DRGs were allowed to adhere for 4 hr before flooding the wells with culture medium . 24 hr later , DRGs were fixed with 4% PFA and stained with anti-Tuj1 ( Covance , MMS-435P ) . Adult ( 8 weeks ) DRGs were dissected in cold F12 medium and then digested with 0 . 5 mg/ml collagenase ( Roche , 10103578001 ) and 1 mg/ml dispase ( Roche , 04942078001 ) for 40 min at 37°C followed by 0 . 125% trypsin digestion for 30 min at 37°C . Tissues were triturated in culture medium ( NBA with 2% of B27 and 10% of FBS ) with 1 ml tips and passed through a 0 . 45 μm cell strainer . Cells passing through the strainer were spun down and re-suspended in culture medium and plated to 12-well plates pre-coated with poly-ornithine and 5 μg/ml laminin . For in vitro axon regrowth analysis , 24 hr after plating , cells were re-suspended and re-plated to pre-coated cover slips . 24 hr later , re-plated cells were fixed and stained with anti-Tuj1 . 2 month old mice were anesthetized with isoflurane and the sciatic nerve exposed by a small incision on the skin . The nerve was crushed with a pair of fine ( #55 ) forceps for 20 s and the crush site marked using activated carbon powder ( Bauder and Ferguson , 2012 ) . 3 days later the mice were euthanized by CO2 and sciatic nerves obtained for analysis . Sciatic nerves were fixed in 4% paraformaldehyde for 3 hr , then washed with PBS , immersed in 30% sucrose in PBS , cryopreserved in OCT compound ( TissueTek ) and cryosectioned at 10 μm thickness . Samples were immunostained with anti-SCG10 ( 1:3000 ) ( Novus Biologicals STMN2 NBP1-49461 ) . After staining , multiple images along the nerve were taken using a 10X objective ( Zeiss LSM710 ) and montaged using Photoshop ( Adobe ) . Representative images are shown in Figure 6D . We used Metamorph software to measure SCG10 staining fluorescence intensity in tdTomato positive axons ( Cre-expressing neurons ) . At each distance point , at least 10–20 regions with tdTomato signal were randomly selected in the red channel and regions of interest ( ROI ) defined . These regions were then transferred to the green channel and the average intensity of green fluorescence measured . Average intensity from all the regions at each distance point was then normalized to the average intensity immediately proximal to the crush site . Total RNA from worms or mouse tissues ( E15 . 5 embryonic brains or dorsal root ganglia at different stages ) were extracted using Trizol ( Invitrogen ) or RNeasy kit ( Qiagen ) following the manufacturers’ protocols . First strand cDNA was reverse-transcribed using SuperScript III ( Invitrogen ) . qPCR was run on Bio-Rad CFX96 Touch Real-Time PCR Detection System with iQ SYBR Supermix ( Bio-Rad ) . Data were analyzed using CFX manager ( Bio-Rad ) . PCR primers are listed in ( Supplementary file 6 ) . A two-tailed Student’s test was used for comparisons of two groups . One-way ANOVA with Bonferroni post test was used to compare multiple groups in Prism ( GraphPad , La Jolla , CA ) . | Nerve cells or neurons carry information around the body along projections known as axons . An injury or trauma , such as a stroke , can damage the axons and lead to permanent disability because the damaged axons fail to regenerate over long distances . Axon damage triggers large changes in the activity of many genes that promote regeneration . When a gene is active , its DNA is copied to make molecules of messenger RNA ( mRNA ) , which are then used as templates to make proteins . Many mRNAs undergo a process called alternative splicing , in which different combinations of mRNA sections may be removed from the final molecule . This enables a single gene to produce more than one type of protein . Recent studies point to an important role for so-called RNA binding proteins in regulating the alternative splicing process . An RNA binding protein called UNC-75 in a worm known as Caenorhabditis elegans has previously been shown to be involved in axon regeneration , but it was not clear how UNC-75 acts on neurons . Here , Chen et al . combined a technique called CLIP-seq ( Cross-linking ImmunoPrecipitation-deep sequencing ) with genetic testing to identify the mRNAs that UNC-75 regulates during axon regeneration . The experiments found a set of C . elegans genes required for information to pass between neurons whose mRNAs are also targeted by UNC-75 . Many of these genes are also required for axon regeneration . Chen et al . studied one of the mRNA targets – which encodes a protein called syntaxin – in more detail and found that the syntaxin mRNA is required for regenerating axons over long distances . UNC-75 alternatively splices this mRNA to produce a particular form of syntaxin that is mainly found in neurons . Mutant worms that lack either UNC-75 or syntaxin are unable to properly regenerate axons over long distances . Further experiments show that a mouse protein known as CELF2 that is equivalent to worm UNC-75 plays a similar role in regenerating axons . Moreover , mouse CELF2 restores the ability of worm neurons that lack UNC-75 to regenerate . Like worm UNC-75 , the mouse protein is also involved in alternative splicing of syntaxin . The next step is to examine the other mRNA targets of UNC-75 to find out what role they play in axon regeneration and other processes in neurons . | [
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] | 2016 | CELF RNA binding proteins promote axon regeneration in C. elegans and mammals through alternative splicing of Syntaxins |
Metabolic pathways in eubacteria and archaea often are encoded by operons and/or gene clusters ( genome neighborhoods ) that provide important clues for assignment of both enzyme functions and metabolic pathways . We describe a bioinformatic approach ( genome neighborhood network; GNN ) that enables large scale prediction of the in vitro enzymatic activities and in vivo physiological functions ( metabolic pathways ) of uncharacterized enzymes in protein families . We demonstrate the utility of the GNN approach by predicting in vitro activities and in vivo functions in the proline racemase superfamily ( PRS; InterPro IPR008794 ) . The predictions were verified by measuring in vitro activities for 51 proteins in 12 families in the PRS that represent ~85% of the sequences; in vitro activities of pathway enzymes , carbon/nitrogen source phenotypes , and/or transcriptomic studies confirmed the predicted pathways . The synergistic use of sequence similarity networks3 and GNNs will facilitate the discovery of the components of novel , uncharacterized metabolic pathways in sequenced genomes .
The explosion in the number of sequenced eubacterial and archaeal genomes provides a challenge for the biological community: >50% of the proteins/enzymes so identified have uncertain or unknown in vitro activities and in vivo physiological functions . Genome context can provide important clues for assignment of functions to individual enzymes and , also , guide the discovery of novel metabolic pathways: pathways often are encoded by operons and/or gene clusters . However , large-scale approaches are required to efficiently mine this information for entire protein/enzyme families ( Dehal et al . , 2010; Caspi et al . , 2012; Markowitz et al . , 2012; Franceschini et al . , 2013; Overbeek et al . , 2014 ) . In this manuscript , we describe the use of a new bioinformatic strategy , genome neighborhood networks ( GNNs ) , to discover the enzymes , transport systems , and transcriptional regulators that constitute metabolic pathways , thereby facilitating prediction of their individual in vitro activities and combined in vivo metabolic functions . As the first demonstration of its use , we applied this approach to the functionally diverse proline racemase superfamily ( PRS ) and predicted functions for >85% of its members . The predictions were verified using high-throughput protein expression and purification , in vitro enzyme activity measurements , microbiology ( phenotypes and transcriptomics ) , and X-ray crystallography . Three enzymatic activities have been described for the PRS: proline racemase ( ProR; eubacteria [Stadtman et al . , 1957] and eukaryotes [Reina-San-Martín et al . , 2000] , 4R-hydroxyproline 2-epimerase ( 4HypE; eubacteria [Adams and Frank , 1980; Goytia et al . , 2007; Gavina et al . , 2010] ) , and trans 3-hydroxy-L-proline dehydratase ( t3HypD; eukaryotes [Visser et al . , 2012] and eubacteria [Watanabe et al . , 2014] ) ; these reactions and the pathways in which they participate are shown in Figure 1 . The previously characterized ProRs and 4HypEs catalyze racemization/epimerization of the a-carbon in a 1 , 1-proton transfer mechanism that , in the structurally characterized enzymes , uses two general acidic/basic Cys residues located on opposite faces of the active site ( Buschiazzo et al . , 2006; Rubinstein and Major , 2009 ) . The syn-dehydration reaction catalyzed by t3HypD requires a general basic catalyst to abstract the proton from the a-carbon; its conjugate acid likely functions as the general acidic catalyst to facilitate departure of the 3-hydroxyl group . Sequence alignment of the functionally characterized t3HypDs and ProRs suggests the presence of a single active site Cys residue in the active sites of the t3HypDs ( the second Cys in ProR is replaced by a Thr residue ) . 10 . 7554/eLife . 03275 . 003Figure 1 . The reactions catalyzed by proline racemase ( ProR ) , 4R-hydroxyproline 2-epimerase ( 4HypE ) , and trans-3-hydroxy-L-proline dehydratase ( t3HypD ) and the metabolic pathways in which they participate . cHyp oxidase , Pyr4H2C deaminase , a-KGSA dehydrogenase , and ? 1-Pyr2C reductase belong to the D-amino acid oxidase ( DAAO ) , dihydrodipicolinate synthase ( DHDPS ) , aldehyde dehydrogenase , and ornithine cyclodeaminase ( OCD ) ( or malate/L-lactate dehydrogenase 2 [MLD2] ) superfamilies , respectively . Abbreviations: L-Pro: L-proline; D-Pro: D-proline; 5-AV: 5-aminovalerate; t4Hyp: trans-4-hydroxy-L-proline; c4Hyp: cis-4-hydroxy-D-proline; Pyr4H2C: ? 1-pyrroline 4-hydroxy 2-carboxylate; a-KGSA: a-ketoglutarate semialdehyde; a-KG: a-ketoglutarate; t3Hyp: trans-3-hyroxy-L-proline; ? 2-Pyr2C: ? 2-pyrroline 2-carboxylate; ? 1-Pyr2C: ? 1-pyrroline 2-carboxylate . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 003
A sequence similarity network ( SSN ) ( Atkinson et al . , 2009 ) for 2333 unique sequences in the PRS ( InterPro family IPR008794; release 43 . 0 ) was constructed and displayed at various e-value thresholds ( Figure 2 ) . When the network is displayed with an e-value threshold of 10-55 ( > ~35% sequence identity is required to draw an edge [line] between nodes [proteins] ) , the majority of the members of the PRS are located in a single functionally heterogeneous cluster ( Figure 2A ) . As the e-value threshold stringency is increased to 10-110 ( sequence identity required to draw an edge is increased to > ~60% ) , the PRS separates into 28 clusters and 49 singletons ( Figure 2B ) . For analyses of the genome neighborhoods ( vide infra ) , each cluster in the 10-110 network was assigned a unique color and number as shown in Figure 2B ( the node colors in Figure 2A depict their association with the clusters in Figure 2B ) . 10 . 7554/eLife . 03275 . 004Figure 2 . Sequence similarity networks ( SSNs ) for the PRS . ( A ) The SSN displayed with an e-value threshold of 10-55 ( ~35% sequence identity ) . ( B ) The SSN displayed with an e-value threshold of 10-110 ( ~60% sequence identity ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 004 At the e-value threshold of 10-110 ( Figure 2B ) the nodes for the experimentally characterized functions—ProR ( magenta; cluster 7 ) , 4HypE ( blue and red; clusters 1 and 2 , respectively ) , and t3HypD ( brown; cluster 8 ) —are located in separate clusters that account for ~30% of the sequences in the PRS . When the e-value threshold is relaxed to 10-55 , most of the clusters merge , although the nodes associated with the two previously characterized 4HypE clusters in the 10-110 network remain separated . Sequence alignments predict that the active sites of both characterized 4HypE clusters contain two active site Cys residues . We conclude that these two families of 4HypEs evolved from divergent , but homologous , ancestors . At the e-value threshold of 10-110 ( Figure 2B ) , the separated clusters are expected to be isofunctional because , from sequence alignments , their active sites are formed from conserved amino acid residues ( acid/base catalysts and specificity determining residues ) . Although many of the clusters are predicted to have the two active site Cys residues found in the structurally characterized ProR ( PDB: 1W61 ) and 4HypE ( PDB: 2AZP [Liu et al . ] ) , others are missing one or both of the Cys residues . The previously uncharacterized enzymes with differing residues could either represent new functions or additional examples of evolution of the ProR , 4HypE , and t3HypD functions from divergent , but homologous , ancestors . We predicted functions for ~80% of the remaining members of the PRS by analyzing the SSN for the proteins ( including enzymes , transport systems , and transcriptional regulators ) encoded by the genome neighborhoods for ‘all’ members of the PRS ( specifically , ± 10 genes relative to the gene encoding each PRS member , the query ) . A protein in this genome neighborhood SSN , designated the ‘genome neighborhood network’ ( GNN ) , is expected to be functionally related to a query in the PRS if they are located in an operon and/or gene cluster that encodes a metabolic pathway that includes the query . By analyzing many genome neighborhoods simultaneously , e . g . , for all members of the PRS , the signals associated with functionally related proteins will be amplified; the signals associated with functionally unrelated genome proximal proteins that occur ‘randomly’ across many species will contribute to the background ‘noise’ . We propose that this large-scale approach is more efficient in identifying ‘all’ of the enzymes/transport systems/transcriptional regulators in a conserved metabolic pathway than by a one-genome-at-a-time analysis . Our approach for visualizing a GNN first assigns a unique query color and number to the members of each cluster in the input SSN that separates the members of the PRS into clusters that are likely to be isofunctional ( e-110 in this work ) . After collecting the genome neighbors , we assign each of them the same color as the color of the query; with this strategy , proteins that are encoded by the same genome neighborhood as the query are easily identified in the GNN because they share the same color as the query . We then perform an all-by-all BLAST on the sequences of the genome neighbors and display the results as an SSN using an e-value threshold of 10-20; this SSN is the GNN . Using this e-value threshold , most of the clusters in the GNN contain the members of distinct protein families and superfamilies ( e . g . , Pfam families ) ; however , in some cases , divergent families in functionally diverse superfamilies may be found in separate clusters . Genome neighborhood proteins that occur randomly across divergent species and are functionally unrelated to the queries are expected to be located in small clusters with multiple colors , so these can be quickly identified visually and discarded from further analysis . The PRS queries from the input SSN ( ‘zero sequences’ in collecting the ±10 neighbors ) are not displayed in the GNN , except when multiple members of the PRS are proximal on the genome , that is , when one PRS member is in the genome neighborhood of another ( vide infra ) . The GNN for the PRS ( Figure 3A ) contains many clusters ( protein families ) . In some clusters , all of the nodes have the same color , that is , they are identified by a single query cluster in the SSN ( e . g . , the clusters in Figure 3B , C ) . However , in most clusters the nodes have multiple colors , that is , they are identified by several query clusters in the SSN ( e . g . , the clusters in Figure 3D–H ) ; this suggests that different query clusters in the SSN have the same in vitro activity and in vivo metabolic function . The clusters in the GNN ( Figure 3A ) are labeled with their Pfam annotations . The ligand/substrate specificities and/or reaction mechanisms that characterize these families are then used to predict the individual in vitro activities and the shared metabolic pathway identified by a query cluster . 10 . 7554/eLife . 03275 . 005Figure 3 . The genome neighborhood network ( GGN ) for the PRS . ( A ) The GNN displayed with an e-value threshold of 10-20 . The nodes are colored by the color of query nodes in the SSN ( Figure 2A ) . The clusters are labeled with the UniProtKB/TrEMBL annotations . ( B–I ) Selected superfamily clusters from the GNN showing node colors . ( B ) D-proline reductase PrdA . ( C ) D-proline reductase , PrdB . ( D ) D-amino acid oxidase ( DAAO ) . ( E ) Dihydrodipicolinate synthase ( DHDPS ) . ( F ) Aldehyde dehydrogenase . ( G ) Ornithine cyclodeaminase ( OCD ) . ( H ) Malate/L-lactate dehydrogenase 2 ( MLD2 ) . ( I ) Proline racemase . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 005 As a retrospective use of the GNN , the ProR function is encoded by anaerobic eubacteria that ferment L-proline and is represented by the magenta cluster ( cluster 7 ) in the SSN ( Figure 2B ) . The first step in the catabolism of L-proline is racemization to D-proline ( by ProR ) that is reduced to 2-keto-5-aminopentanoate by D-proline reductase ( Kabisch et al . , 1999 ) ( by PrdAB; Figure 1 ) . In the GNN , the clusters for the PrdA and PrdB polypeptides in D-proline reductase are uniformly magenta , as expected if the genes encoding ProR and PrdAB are colocalized with the gene encoding ProR ( Figure 3B , C ) . The lack of other colors in the PrdAB clusters in the GNN implies that no other clusters in the SSN have the ProR function . As a second retrospective example , the 4HypE function has been assigned to members of the blue ( cluster 1 ) and red ( cluster 2 ) clusters in the SSN ( Figure 2B ) . In the GNN , clusters identified by the blue and red clusters include the D-amino acid oxidase ( DAAO; Figure 3D ) ( Watanabe et al . , 2012 ) , dihydrodipicolinate synthase ( DHDPS; Figure 3E ) ( Singh and Adams , 1965; Watanabe et al . , 2012 ) , and aldehyde dehydrogenase ( Figure 3F ) ( Koo and Adams , 1974; Watanabe et al . , 2007 ) superfamilies as well as components of several types of transport systems . As we and others recently established for organisms that use trans-4-hydroxy-L-proline betaine as sole carbon and nitrogen source ( Zhao et al . , 2013; Kumar et al . , 2014 ) , the catabolic pathway for trans-4-hydroxy-L-proline ( t4Hyp ) ( Figure 1 ) can be initiated by the epimerization of t4Hyp to cis-4-hydroxy-D-proline ( c4Hyp ) by 4HypE , followed by reactions catalyzed by c4Hyp oxidase ( a member of the DAAO superfamily ) , c4Hyp imino acid dehydratase/deaminase ( a member of the DHDPS superfamily ) , and a-ketoglutarate semialdehyde dehydrogenase ( a member of the aldehyde dehydrogenase superfamily ) . Thus , the occurrence of blue and red nodes in these three clusters in the GNN is expected . The DAAO ( Figure 3D ) , DHDPS ( Figure 3E ) , and aldehyde dehydrogenase ( Figure 3F ) clusters also contain nodes with other colors from the SSN ( Figure 2B ) , including orange ( cluster 9 ) , pale green ( cluster 11 ) , and teal ( cluster 4 ) . Proteins from the orange and pale green clusters were purified and assayed using a library of proline derivatives ( Figure 4 ) . As expected , members of the orange and pale green clusters catalyze the 4HypE reaction ( Tables 1 and 2 ) . We were unable to purify proteins from the teal cluster ( insolubility ) , so we used the growth phenotypes of the encoding organisms and transcriptomics to identify their in vitro enzymatic activities and in vivo metabolic functions . As predicted from the GNN , Bacillus cereus ATCC14579 ( cluster 4 , teal ) and Streptomyces lividans TK24 ( cluster 11 , pale green ) both utilize t4Hyp as sole carbon source ( Table 3 ) ; also , the genes encoding the predicted 4HypEs ( Table 4 ) and the proximal genes encoding the predicted c4Hyp oxidases , c4Hyp imino acid dehydratase/deaminases , and a-ketoglutarate semialdehyde dehydrogenases ( Table 5 ) are up-regulated when the encoding organism is grown on t4Hyp as carbon source ( Table 4 ) . The purified proteins from the orange groups are promiscuous for the 3HypE reaction ( Tables 1 and 2 ) , but their genome neighborhood context identifies their physiological functions as 4HypE . 10 . 7554/eLife . 03275 . 006Figure 4 . Library of proline and proline betaine derivatives tested for ESI-MS screening . These substrates were divided into four groups to avoid mass duplication . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 00610 . 7554/eLife . 03275 . 007Table 1 . Mass spectroscopy screening results in D2O . Hits were observed by mass shift for racemization/epimerization ( +1 ) and dehydration ( -17 ) for reactions performedDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 007Locus tagUniProtL-ProD-Prot4Hypc4Hypt3Hypcis-3-OH-L-ProCluster 1: bluePden_4859A3QFI100+1+100Shew_2363A9AQW900+1+100Bmul_5265A6WXX700+1+1+1+1Oant_1111D2QN4400+1+1+1+1Slin_1478B9JHU600+1+1+1+1Arad_8151Q8FYS000+1+100BR1792A1BBM500+ 1+1+1+1Cluster 2: redA1S_1325A3M4A900+1+1+1+1Bamb_3550Q0B9R900+1+1+1+1BceJ2315_47180B4EHE600+1+1+1+1BMULJ_04062B3D6W200+1+1+1+1BTH_II2067Q2T3J400+1+1-170CV_2826Q7NU7700+1+1+1+1Csal_2705Q1QU0600+1+100PFL_1412A5VZY600+1+1+1+1Pput_1285Q1QBF3+1+1+1+1+1+1Pcryo_1219A3M4A900+1+1+1+1XCC2415Q8P83300+1+1+1+1Bmul_4447A9AL5200+1+1+1+1ABAYE2385B0VB4400+1+1+1+1BURPS1106B_1521C5ZMD2+1+1+1+1+1+1BURPS1710b_A1887Q3JHA900+1+1+1+1PA1268Q9I47600+1+100Cluster 3: ligthskybluePden_1184A1B1950000-170SIAM614_28502A0NXQ90000-170Atu4684A9CH0100+1+1-170Avi_7022B9K4G40000-170Oant_0439A6WW1600+1+100SM_b20270Q92WR900+1+1-170BMEI1586Q8YFD600+1+1+1+1BR0337Q8G2I30000-170Cluster 5: navyBC_0905Q81HB100+1+1-170BCE_0994Q73CS000+1+1-170BT9727_0799Q6HMS900+1+1-170Cluster 9: orangeAvi_0518B9JQV300+1+1+1+1Atu0398A9CKB400+1+1+1+1RHE_CH00452Q2KD1300+1+1+1+1Arad_0731B9J8G800+1+1+1+1Cluster 11: palegreenSros_6004D2AV8700+1+100Cluster 12: oliveBamb_3769Q0B9500000-170Bmul_4260A9AKG800+1+1+1+1Cluster 16: salmonCsal_2339Q1QV1900+1+100Maqu_2141A1U2K1000000Cluster 17: limeRsph17029_3164A3PPJ800+1+100RSP_3519Q3IWG200+1+100Cluster 18: cyanSIAM614_28492A0NXQ700+1+100SADFL11_2813B9R4E300+1+100SPOA0266Q5LKW300+1+1+1+1Cluster 22: steelblueSpea_1705A8H3920000-170Swoo_2821B1KJ7600+1+1-170Cluster 61:Plim_2713D5SQS400+1+1+1+110 . 7554/eLife . 03275 . 008Table 2 . Kinetic constants for 3/4HypE and t3HypD activities of the screened PRS targetsDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 008ClusterLocus tagUniProtFunctionkcat [s-1]Km [mM]kcat/KM[M-1s-1]1Pden_4859A1BBM54HypE16 ± 225 ± 5630Shew_2363A3QFI14HypE50 ± 612 ± 34000Bmul_5265A9AQW93HypE0 . 34 ± 0 . 03- a- a4HypE5 . 6 ± 0 . 511 ± 2530Oant_1111A6WXX73HypE2 . 4 ± 0 . 231 ± 7774HypE89 ± 27 . 1 ± 0 . 6130002BTH_II2067Q2T3J4t3HypD17 ± 326 ± 96604HypE40 ± 41 . 4 ± 0 . 428000CV_2826Q7NU773HypE30 ± 0 . 657 ± 45204HypE70 ± 76 . 8 ± 310000Pput_1285A5VZY63HypE4 . 8 ± 0 . 619 ± 52504HypE26 ± 0 . 70 . 54 ± 0 . 0848000ProR2 . 8 ± 0 . 1200 ± 2014XCC2415Q8P8334HypE28 ± 0 . 40 . 67 ± 0 . 05420003HypE1 . 3 ± 0 . 0715 ± 3863Pden_1184A1B195t3HypDnd bnd bnd bSIAM614_28502A0NXQ9t3HypD15 ± 0 . 97 . 8 ± 11900Atu4684A9CH01t3HypD27 ± 14 . 2 ± 0 . 863004HypE0 . 40 ± 0 . 022 . 0 ± 0 . 3200Avi_7022B9K4G4t3HypD4 . 3 ± 0 . 415 ± 3280Oant_0439A6WW164HypE0 . 064 ± 0 . 0021 . 3 ± 0 . 249SM_b20270Q92WR9t3HypD7 . 9 ± 0 . 23 . 8 ± 0 . 421004HypE0 . 089 ± 0 . 016 . 3 ± 214BMEI1586D0B5563HypE0 . 085 ± 0 . 0032 . 6 ± 0 . 4334HypE0 . 082 ± 0 . 0054 . 5 ± 118BR0337Q8G2I3t3HypD17 ± 25 . 1 ± 233005BCE_0994Q73CS0t3HypDnd bnd bnd b4HypE1 . 2 ± 0 . 033 . 2 ± 0 . 3370BT9727_0799Q6HMS9t3HypD23 ± 57 . 5 ± 331004HypE0 . 16- a- a9Avi_0518B9JQV33HypE0 . 75 ± 0 . 044 . 8 ± 0 . 91604HypE1 . 3 ± 0 . 075 . 6 ± 0 . 5230Atu0398A9CKB43HypE4 . 0 ± 0 . 625 ± 71604HypE0 . 86 ± 0 . 14 . 6 ± 2190RHE_CH00452Q2KD133HypE0 . 94 ± 0 . 062 . 1 ± 0 . 74504HypE1 . 9 ± 0 . 082 . 1 ± 0 . 388011Sros_6004D2AV874HypE14 ± 0 . 87 . 8 ± 1180012Bamb_3769Q0B950t3HypD43 ± 413 ± 33400Bmul_4260A9AKG83HypE30 ± 118 ± 217004HypE1 . 3 ± 0 . 042 . 7 ± 0 . 347016Csal_2339Q1QV194HypE0 . 070 ± 0 . 0052 . 5 ± 0 . 72817RSP_3519Q3IWG24HypEnd bnd bnd bRsph17029_3164A3PPJ84HypEnd bnd bnd b18SIAM614_28492A0NXQ74HypE55 ± 33 . 2 ± 0 . 517000SADFL11_2813B9R4E34HypE67 ± 54 . 1 ± 0 . 81600022Spea_1705A8H392t3HypD0 . 15 ± 0 . 03- b- bSwoo_2821B1KJ76t3HypD4 . 1 ± 0 . 46 . 7 ± 2600aThe reaction is to slow to measure Km . bThe reaction is slow to measure kinetic parameters . 10 . 7554/eLife . 03275 . 009Table 3 . Growth phenotypes of bacterial strains when grown on the indicated carbon sourcesDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 009Organismt4Hypc4Hypt3Hypcis-3-OH-L-prolineL-ProD-glucoseAgrobacterium tumefaciens C58+++++-++++++Sinorhizobium meliloti 1021+++++-++++++Labrenzia aggregate IAM12614++++++++++Pseudomonas aeruginosa PAO1+++++-++++++Paracoccus denitrificans PD1222++++++++++++++Rhodobacter sphaeroides 2 . 4 . 1++--++++++Rhodobacter sphaeroides 2 . 4 . 1 ? RSP3519-+--++++++Bacillus cereus ATCC14579++++++++++++Roseovarius nubinhibens ISM+++++-++++++Escherichia coli MG1655----++++++Streptomyces lividans TK24++++++ND++++++‘+++’ represents robust growth ( like growth on D-glucose ) ; ++/+ represents slow growth phenotype; ‘--’ represents growth-deficient phenotype; ‘ND’ , not determined10 . 7554/eLife . 03275 . 010Table 4 . Transcriptional analysis of PRS membersDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 010Organism/Locus Tagt4Hypt3HypAgrobacterium tumefaciens C58A9CKB412 ± 211 ± 1 . 5A9CFV03 ± 1NCA9CH0164 ± 532 ± 4Sinorhizobium meliloti 1021Q92WS15 ± 13 ± 1Q92WR95 . 5 ± 1 . 53 . 5 ± 1Labrenzia aggregate IAM12614A0NXQ722 ± 25 ± 1A0NXQ912 ± 26 ± 2Pseudomonas aeruginosa PAO1Q9I4898 ± 25 ± 1Q9I47635 ± 37 ± 2Paracoccus denitrificans PD1222A1B0W22 . 0 ± 0 . 5NCA1B195NCNCA1B7P4NCNCA1BBM54 . 5 ± 0 . 5NCRhodobacter sphaeroides 2 . 4 . 1Q3IWG210 ± 1NCBacillus cereus ATCC14579Q81HB14 ± 14 . 5 ± 1Q81CD722 ± 218 ± 3Roseovarius nubinhibens ISMA3SLP212 ± 24+1 . 5Fold change in expression for each gene when grown on the indicated carbon source , relative to growth on D-glucose . The identities of the bacterial species and the protein encoded by each gene are indicated . Fold-changes are the averages of five biological replicates with standard deviation ( p value < 0 . 005 ) . NC , no change . 10 . 7554/eLife . 03275 . 011Table 5 . Transcriptional analysis of genome neighborhoodsDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 011Organism/Locus tagUniProtEnzymeClustert4Hypt3HypL-ProBacillus cereus ATCC 14579Bc_0905Q81HB1ProRnavy121 ± 1187 ± 10NCBc_0906Q81HB0OCD20 ± 314 ± 2NCBc_2832Q81CE0ALDH630 ± 39625 ± 5713 ± 2Bc_2833Q81CD9DHDPS644 ± 61498 ± 376 ± 0 . 7Bc_2834Q81CD8ProRhot pink594 ± 27485 ± 298 ± 1Bc_2835Q81CD7ProRteal408 ± 15567 ± 335 ± 0 . 5Bc_2836Q81CD6oxidase623 ± 37633 ± 4210 ± 0 . 6Streptomyces lividans TK24SSPG_01342D6EJL0DAAO81 ± 520 ± 5NCSSPG_01341D6EJK9oxidase65 ± 96 ± 0 . 2NCSSPG_01340D6EJK8oxidase225 ± 2230 ± 33 ± 0 . 4SSPG_01339D6EJK7DHDPS136 ± 516 ± 0 . 2NCSSPG_01338D6EJK6ProRpalegreen171 ± 823 ± 13 ± 0 . 2Agrobacterium tumefaciens C58Atu_0398A9CKB4ProRorange14 ± 0 . 416 ± 0 . 6NCAtu_3947Q7CTP1DAAONC4 ± 0 . 2NCAtu_3948Q7CTP2AlaRNCNCNCAtu_3949Q7CTP3OCDNCNCNCAtu_3950Q7CTP4ALDHNCNCNCAtu_3951A9CFU8LysRNCNCNCAtu_3952A9CFU9DAAONCNCNCAtu_3953Q7CFV0ProRblueNCNCNCAtu_3958Q7CTQ2DAAONCNCNCAtu_3959Q7CTQ3ALDHNCNCNCAtu_3960A9CFV4DHDPSNCNCNCAtu_3961Q7CTQ5GntRNCNCNCAtu_3985A9CFW8ProCNCNCNCAtu_4675A9CGZ4DHDPS148 ± 287 ± 7NCAtu_4676Q7CVK1MLD230 ± 540 ± 7NCAtu_4678A9CGZ5SBP198 ± 1879 ± 8NCAtu_4682A9CGZ9DAAO294 ± 1514 ± 3NCAtu_4684A9CH01ProRlight sky blue116 ± 148 ± 1NCAtu_4691A9CH042-Hacid_dhNCNCNCFold changes in expression for the indicated gene when grown on the indicated carbon source , relative to growth on Dglucose . Fold changes are the averages of three biological replicates with standard deviation . NC , no change . The X-ray structure of one of the previously functionally assigned 4HypEs ( Uniprot: Q4KGU2; locus tag: PFL_1412; red , cluster 2 ) was determined in the presence of the substrate , t4Hyp and , also , pyrrole-2-carboxylate ( PYC ) , a stable analogue of the enolate anion intermediate ( Figure 5A , B; Table 6 ) . These are the first liganded structures of a 4HypE and the first structure of a PRS with an authentic substrate . These structures corroborate the positioning of the active site Cys/Cys pair ( Cys 88 , Cys 236 ) to facilitate substrate epimerization , highlight residues specific to the coordination of the 4-hydroxyl group , and validate the hypothesis that PYC and substrate bind in a similar fashion . In addition , the X-ray structure of one of the newly functionally assigned 4HypEs ( Uniprot: B9K4G4; locus tag: Avi_7022; orange , cluster 8 ) was determined in the presence of its substrate , t4Hyp . The active site contains Ser 93 on one face and Cys 255 on the opposite face ( Figure 5C ) . Thus , despite the conserved ability of this enzyme to catalyze the 4HypE reaction ( a two-base 1 , 1-proton transfer reaction ) , the Cys–Cys general acid/base pair observed in the structure of Q4KGU2 from the red cluster is not conserved . This observation highlights the structural diversity associated with evolution of function in the PRS . Without the information provided by the GNNs , the 4HypE function would not have been expected . 10 . 7554/eLife . 03275 . 012Figure 5 . Structures of members of the PRS . ( A ) Structure of Q4KGU2 ( locus tag: PFL_1412; cluster 2 ) with PYC illustrating the utilization of the carboxyl group to bridge the N-terminal amide backbone groups of two opposing a-helices . While In B9K4G4 ( D ) and B9JQV3 ( C ) the relative positions of residues that coordinate the prolyl nitrogen ( Asp 232 , His 90 ) are conserved His 90 is replaced by a Ser . ( B ) Structure of Q4KGU2 with t4Hyp illustrating the interactions Q4KGU2 with the 4-hydroxyl group and the relative positions of the two catalytic cysteine residues . ( C ) Structure of B9JQV3 ( locus tag: Avi_0518 , cluster 9 ) with t4Hyp illustrating the interactions of B9JQV3 with the 4-hydroxyl group of t4Hyp and the relative positions of the catalytic Ser ( Ser 93 , trans ? cis ) and Cys ( Cys 236 , cis ? trans ) . ( D ) Structure of B9K4G4 ( Avi_7022 , cluster 3 ) with PYC illustrating the position of the catalytic Ser ( Ser 90 , dehydration ) , and the non-catalytic orientation of Thr 256 which replaces the Cys observed in Cys/Cys containing PRS members . In addition , the catalytic Ser ( Ser 90 ) is positioned by hydrogen bonding interactions between the side chain of Asn 93 ( shown ) and the backbone nitrogen of Asn 93 ( not shown ) . Based on this work , all ProR family members with a catalytic Ser at this position ( including B9JQV3 , determined here ) are proposed to have this motif . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 01210 . 7554/eLife . 03275 . 013Table 6 . Data Collection and Refinement StatisticsaDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 013UNIPROT / CLUSTER / PROTEINA5VZY6 / 2 / Pput_1285A5VZY6 / 2 / Pput_1285Q1QU06 / 2 / Csal_2705Q8P833 / 2 / XCC_2415B3D6W2 / 2 / BMULJ_04062Q4KGU2 / 2 / PFL_1412Q4KGU2 / 2 / PFL_1412A6WW16 / 3 / Oant_0439B9K4G4 / 3 / Avi_7022B9JQV3 / 9 / Avi_0518OrganismPseudomonas putida F1Pseudomonas putida F1Chromohalobacter salexigens DSM 3043Xanthomonas campestrisBurkholderia multivoransPseudomonas fluorescens Pf-5Pseudomonas fluorescens Pf-5Ochrobacterium anthropiAgrobacterium vitis S4Agrobacterium vitis S4PDBID4JBD4JD74JCI4JUU4K7X4J9W4J9X4K8L4K7G4LB0DIFFRACTION DATA STATISTICSSpace GroupI2P212121P212121P212121I4122P21P212121I222P43212P42212Unit Cell ( Å , ° ) a=45 . 2 b=54 . 2 c=142 . 7a=64 . 8 b=96 . 8 c=109 . 2a=48 . 1 b=54 . 4 c=253 . 0a=54 . 9 b=108 . 8 c=116 . 2a=114 . 9 b=114 . 9 c=173 . 7a=56 . 2 b=74 . 6 c=87 . 1 β=105 . 5a=64 . 8 b=96 . 8 c=109 . 2a=77 . 3 b=78 . 3 c=114 . 4a=54 . 9 b=108 . 8 c=116 . 2a=178 . 0 b=178 . 0 c=49 . 7Resolution ( Å ) 1 . 3 ( 1 . 3-1 . 32 ) 1 . 5 ( 1 . 5-1 . 58 ) 1 . 7 ( 1 . 7-1 . 79 ) 1 . 75 ( 1 . 75-1 . 84 ) 1 . 75 ( 1 . 75-1 . 84 ) 1 . 6 ( . 6-1 . 69 ) 1 . 7 ( 1 . 7-1 . 79 ) 1 . 9 ( 1 . 9-2 . 0 ) 2 . 0 ( 2 . 0-2 . 1 ) 1 . 7 ( 1 . 7-1 . 79 ) Completeness ( % ) 99 . 8 ( 99 . 6 ) 99 . 5 ( 98 . 9 ) 97 . 0 ( 94 . 0 ) 99 . 7 ( 99 . 4 ) 100 . 0 ( 100 . 0 ) 99 . 3 ( 99 . 5 ) 99 . 5 ( 99 . 0 ) 99 . 8 ( 100 . 0 ) 100 ( 100 ) 99 . 9 ( 99 . 9 ) Redundancy3 . 6 ( 3 . 5 ) 7 . 3 ( 7 . 1 ) 9 . 3 ( 7 . 8 ) 7 . 3 ( 7 . 1 ) 14 . 3 ( 13 . 5 ) 3 . 6 ( 3 . 5 ) 6 . 7 ( 6 . 0 ) 7 . 2 ( 7 . 3 ) 14 . 1 ( 13 . 2 ) 10 . 4 ( 7 . 9 ) Mean ( I ) /sd ( I ) 7 . 9 ( 1 . 4 ) 18 . 0 ( 1 . 1 ) 17 . 5 ( 3 . 3 ) 18 . 0 ( 1 . 1 ) 14 . 1 ( 1 . 1 ) 6 . 9 ( 1 . 7 ) 11 . 6 ( 1 . 5 ) 6 . 0 ( 1 . 3 ) 11 . 6 ( 3 . 3 ) 18 . 3 ( 2 . 7 ) Rsym0 . 062 ( 0 . 735 ) 0 . 067 ( 0 . 707 ) 0 . 073 ( 0 . 644 ) 0 . 074 ( 0 . 725 ) 0 . 130 ( 0 . 699 ) 0 . 093 ( 0 . 434 ) 0 . 088 ( 0 . 531 ) 0 . 09 ( 0 . 594 ) 0 . 17 ( 0 . 836 ) 0 . 078 ( 0 . 745 ) REFINEMENT STATISTICSResolution ( Å ) 1 . 3 ( 1 . 3-1 . 31 ) 1 . 5 ( 1 . 5-1 . 52 ) 1 . 7 ( 1 . 7-1 . 72 ) 1 . 75 ( 1 . 75-1 . 77 ) 1 . 75 ( 1 . 75-1 . 78 ) 1 . 6 ( 1 . 6-1 . 62 ) 1 . 7 ( 1 . 7-1 . 72 ) 1 . 9 ( 1 . 9-1 . 97 ) 2 . 0 ( 2 . 0-2 . 02 ) 1 . 7 ( 1 . 72-1 . 70 ) Unique reflections827491098887212870700585749074077405276748662887548Rcryst ( % ) 15 . 8 ( 30 . 4 ) 15 . 9 ( 22 . 6 ) 17 . 1 ( 23 . 7 ) 15 . 2 ( 21 . 5 ) 13 . 8 ( 19 . 7 ) 19 . 7 ( 28 . 8 ) 19 . 4 ( 23 . 5 ) 16 . 8 ( 17 . 6 ) 13 . 6 ( 19 . 5 ) 15 . 8 ( 22 . 9 ) Rfree ( % , 5% of data ) 18 . 4 ( 31 . 1 ) 17 . 5 ( 25 . 4 ) 20 . 5 ( 26 . 2 ) 18 . 4 ( 26 . 4 ) 15 . 6 ( 18 . 5 ) 23 . 2 ( 33 . 8 ) 22 . 5 ( 27 . 5 ) 20 . 7 ( 21 . 7 ) 16 . 6 ( 22 . 9 ) 19 . 2 ( 27 . 3 ) Residues In Model [Expected]A1-A308 [1-308]A ( -5 ) -A308 , D ( -3 ) -D308 [1-308]A ( -3 ) -A169 , A171-A309 [1-311]A ( -2 ) -A312 , B ( -2 ) -B312 [1-312]A ( -3 ) -A310 [1-311]A1-A310 , B1-B310 [1-310]A1-310 , B1-310 [1-310]A0-A157 , A161-A184 , A193-A245 , A255-280 , A289-A332 [1-343]B5-B342 , D ( -9 ) -D342 [1-342]A1-A323 , A326-A344 , B0-B346 [1-347]Residues / Waters / Atoms total308 / 453 / 3142626 / 752 / 6225620 / 494 / 5780626 / 596 / 5841314 / 463 / 3223620 / 537 / 5301620 / 630 / 5378305 / 191 / 2824690 / 780 / 6761689 / 701 / 6633Bfactor Protein/Waters/Ligand17 . 3 / 31 . 2 / 21 . 719 . 3 / 30 . 5 / 27 . 924 . 8 / 33 . 6 / -23 . 9 / 35 . 2 / 37 . 315 . 6 / 34 . 0 / 30 . 621 . 1 / 32 . 2 / 12 . 922 . 9 / 34 . 0 / 16 . 331 . 3 / 37 . 7 / -24 . 1 / 37 . 5 / 15 . 225 . 1 / 36 . 2 / 17 . 9LigandCitrateSulfate-Phosphate / UNLPhosphate ( PYC ) Pyrrole 2-carboxylate ( t4Hyp ) Trans- 4OH-L-Proline- ( PYC ) Pyrrole 2-carboxylate ( t4Hyp ) Trans- 4OH-L-Proline / AcetateRMSD Bond Lengths ( Å ) / Angles ( ° ) 0 . 008 / 1 . 2830 . 009 / 1 . 3250 . 011 / 1 . 3320 . 010 / 1 . 260 . 009 / 1 . 2680 . 006 / 1 . 0790 . 006 / 1 . 0930 . 011 / 1 . 3490 . 011 / 1 . 3110 . 010 / 1 . 320Ramachandran Favored / Outliers ( % ) 98 . 7 / 0 . 096 . 8 / 0 . 0098 . 2 / 0 . 0099 . 0 / 0 . 097 . 7 / 0 . 098 . 7 / 0 . 098 . 5 / 0 . 098 . 3 / 0 . 098 . 0 / 0 . 398 . 4 / 0 . 3Clashscore b2 . 32 ( 99th pctl ) 3 . 02 ( 98th pctl ) 3 . 74 ( 97th pctl ) 4 . 14 ( 97th pctl ) 3 . 12 ( 97th pctl ) 1 . 59 ( 99th pctl ) 1 . 82 ( 99th pctl ) 6 . 6 ( 93rd pctl ) 2 . 8 ( 99th pctl ) 2 . 2 ( 99th pctl ) Overall scoreb1 . 01 ( 99th pctl ) 1 . 29 ( 95th pctl ) 1 . 16 ( 99th pctl ) 1 . 22 ( 99th pctl ) 1 . 16 ( 99th pctl ) 0 . 97 ( 100th pctl ) 0 . 94 ( 100th pctl ) 1 . 36 ( 98th pctl ) 1 . 08 ( 100th pctl ) 1 . 0 ( 100th pctl ) aData in parenthesis is for the highest resolution binbScores are ranked according to structures of similar resolution as formulated in MOLPROBITY The t3HypD function previously was assigned to eukaryotic members of the PRS ( Visser et al . , 2012 ) , so their genome neighbors are not represented in the GNN . However , the members of the navy cluster ( cluster 5; species of Bacilli ) identify several clusters in the GNN , including families of the components of TRAP and ABC transport systems , families of peptidases , and a family in the ornithine cyclodeaminase superfamily ( OCDS ) ; several members of the olive cluster ( cluster 12 ) also identify the same OCDS cluster ( Figure 3G ) . Members of the OCDS catalyze NAD ( P ) +/NAD ( P ) H-dependent reactions that involve the ketimines obtained by oxidation of a-amino acids ( Goodman et al . , 2004; Schröder et al . , 2004; Gatto et al . , 2006 ) ; some have been reported to catalyze the reduction of the ketimine of proline ( Hallen et al . , 2011 ) ( and oxidation of L-proline; Figure 6A ) . Using purified proteins , we determined that members of both the navy ( cluster 5 ) and olive ( cluster 12 ) clusters in the SSN catalyze the t3HypD reaction ( Tables 1 and 2 ) . We also determined that members of the OCDS cluster catalyze the NADPH-dependent reduction of the ketimine of proline to form L-proline ( Figure 6A , B ) . The catabolic pathway for trans-3-hydroxy-L-proline is known to proceed by dehydration , nonenzymatic tautomerization of the dehydration product to the ketimine of proline and , finally , reduction of the ketimine to form L-proline ( Figure 1 ) . In the OCDS SSN ( Figure 6A ) , the previously characterized proline ketimine reductases are located in clusters/families distinct from the members of the OCDS identified in our GNN . Thus , assignment of the t3HypD function to the members of navy and olive clusters in the SSN would not have been possible without the synergistic information contained in the GNN . 10 . 7554/eLife . 03275 . 014Figure 6 . Sequence divergent members of the ornithine cyclodeaminase superfamily ( OCDS ) have been the assigned novel pyrroline-2-carboxylate reductase ( Pyr2C reductase ) function in this work . ( A ) The OCDS SSN displayed at the e-value cutoff 10-45 ( ~35% sequence identity ) . The Pyr2C reductase function is located in four clusters; these proteins are shown in large colored circles , labeled from 1 to 16 , and color-coded by the colors of the PRS query sequences shown in Figure 2B . Proteins representing several previously characterized functions in the OCDS are shown by large diamonds , with borders in hotpink ( L-alanine dehydrogenase [Schröder et al . , 2004] ) , brown ( ornithine cyclodeaminase [Goodman et al . , 2004] ) , magenta ( lysine cyclodeaminase [Gatto et al . , 2006] ) , red ( ketamine reductase [Hallen et al . , 2011] ) , green ( L-arginine dehydrogenase [Li and Lu , 2009] ) and palegreen ( tauropine dehydrogenase [Kan-No et al . , 2005; Plese et al . , 2008] ) , respectively . Their annotations are shown in italics . The diamonds with blue and olive borders are Pyr2C reductases recently characterized by Watanabe et al . ( 2014 ) . ( B ) Kinetics data for the Pyr2C reductase activity for the 16 members of the OCDS shown in panel A using NADPH as the cosubstrate . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 014 We determined the structure of a t3HypD ( B9K4G4 ) from the light sky blue cluster ( cluster 3 ) in the presence of PYC ( Table 6 ) . Instead of the typical PRS Cys/Cys pair , B9K4G4 contains Ser 90 in a similar conformation as was determined for B9JQV3 from the orange cluster ( 4HypE activity ) and Thr 256 on the opposing face ( Figure 5D ) . Thr 256 mimics the conformation of the typical PRS Cys residue but with the side-chain methylene positioned against the anomeric carbon . Again , the assignment of function enabled by the GNNs identifies convergent evolution of function within the PRS . Members of the light sky blue ( cluster 3 ) cluster in the SSN identify the same ( super ) families identified by both the 4HypE and t3HypD clusters ( transport systems , transcriptional regulators , DAAO [Figure 3D] , DHDPS [Figure 3E] , aldehyde dehydrogenase [Figure 3F] , and OCD [Figure 3G] ) ; however , several members of the light sky blue cluster identify a GNN cluster annotated as the malate/L-lactate dehydrogenase 2 superfamily ( MLD2; NADH-dependent oxidoreductases ) ( Muramatsu et al . , 2005 ) ( Figure 3H ) . Using purified members of the PRS , we determined that the light sky blue cluster is functionally heterogeneous ( and some members are promiscuous ) for the 4HypE and t3HypD functions ( Tables 1 and 2 ) . We also determined that members of the MLD2 superfamily in the GNN catalyze the reduction of proline ketimine ( Table 7 ) . Thus , the GNN provided essential information for predicting/assigning functions to the members of the light sky blue cluster in the PRS SSN . 10 . 7554/eLife . 03275 . 015Table 7 . Kinetic constants for the proline ketimine reductases ( members of the malate/Llactate dehydrogenase 2 [MLD2] and ornithine cyclodeaminase [OCD] superfamilies ) that are in the genome neighborhoods of members of the PRSDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 015ClusterUniProtLocus tagCofactorkcat [s-1]Km [mM]kcat/KM[M-1s-1]MLD2_PRS_light skyblue ( 3 ) Q7CVK1Atu4676NADPH32 ± 10 . 33 ± 0 . 0499000Q9I492PA1252NADPH1 . 6 ± 0 . 050 . 41 ± 0 . 063900MLD2_PRS_Red ( 2 ) Q4KGT8PFL_1416NADPH20 ± 0 . 81 . 1 ± 0 . 218000Q0B9S2Bamb_3547NADPH54 ± 139 . 4 ± 45700A9ALD3Bmul_4451NADPH33 ± 27 . 4 ± 14400MLD2_PRS_indigo ( 13 ) Q4KAT3PFL_3547aNADPH--2300bOCD_PRS_light skyblue ( 3 ) A1B196Pden_1185NADPH260 ± 203 . 1 ± 0 . 785000NADH81 ± 2016 ± 65100A3S939EE36_06353aNADPH6 . 8 ± 0 . 71 . 0 ± 0 . 36700A3SU01NAS141_11281aNADPH39 ± 41 . 2 ± 0 . 432000NADH8 . 2 ± 473 ± 50110Q16D96RD1_0323aNADPH15 ± 10 . 27 ± 0 . 0756000NADH3 . 7 ± 0 . 411 ± 3320Q5LLV0SPO3821aNADPH130 ± 203 . 0 ± 0 . 943000NADH--840bQ3IZJ8RSP_0854aNADPH66 ± 40 . 43 ± 0 . 09150000NADH12c--OCD_PRS_navy ( 5 ) Q81HB0BC_0906NADPH15 ± 10 . 47 ± 0 . 131000NADH19 ± 111 ± 21800Q73CR9BCE_0995NADPH15 ± 11 . 1 ± 0 . 313000NADH2 . 1 ± 0 . 37 . 6 ± 3270Q6HMS8BT9727_0800NADPH11 ± 13 . 4 ± 0 . 93100NADH2 . 1 ± 0 . 418 ± 6120Q63FA5BCE33L0803NADPH5 . 8c--NADH0 . 87 ± 0 . 14 . 9 ± 2180OCD_PRS_olive ( 12 ) Q0B953Bamb_3766NADPH106 ± 41 . 6 ± 0 . 264000NADH41 ± 67 . 3 ± 35700Q2T596BTH_II1457aNADPH73 ± 20 . 39 ± 0 . 05190000NADH203 ± 2332 ± 76400Q3JFG0BURPS1710b_A2543aNADPH7 . 8 ± 0 . 50 . 64 ± 0 . 112000NADH6 . 0 ± 131 ± 13190A9AKH1Bmul_4263NADPH25 ± 64 ± 26400OCD_PRS_blue ( 1 ) Q485R8CPS_1455NADPH35 ± 0 . 81 . 8 ± 0 . 220000NADH--170bA3QH73Shew_2955aNADPH6 . 7 ± 0 . 71 . 6 ± 0 . 64300NADH0 . 37 ± 0 . 126 ± 1014aHighly homologous to MLD2 or OCD which are in the gene context of proline racemase . bThe enzyme didn’t saturate . cKM is too small ( < 0 . 03mM ) .
Although in most cases interpretations of the functional relationships of the clusters in the GNN with those in the query SSN are straightforward , complications can arise . For example , in several species , two members of the PRS are encoded by proximal genes , that is , a 4HypE and a t3HypD; these species can utilize both t4Hyp and trans-3-hydroxy-L-proline as carbon and nitrogen sources . Thus , the GNN contains a cluster for the PRS ( right-hand cluster in the top row [when used as query , each PRS finds the adjacent PRS; Figure 3I] ) . For these species , clusters in the GNN are a composite of two genome contexts , that is , the proteins/enzymes that participate in both catabolic pathways . These situations can be deconvoluted by coloring the nodes identified by two queries with the colors for both query clusters in the GNN . With the genome contexts/metabolic pathways identified for ‘genome-isolated’ 4HypEs and t3HypDs , this complication is easy to identify and understand . The GNN also is useful to assess the physiological importance of in vitro promiscuity . Several of the purified proteins catalyze both the 4HypE and t3HypD reactions ( Tables 1 and 2 ) . Some of these promiscuous proteins identify both the OCD or MLD2 superfamilies ( predicting the t3HypD pathway ) and the DAAO , DHDPS , and aldehyde dehydrogenase superfamilies ( predicting the 4HypE pathway ) in their genome neighborhoods ( Figure 7 ) . In these cases , we conclude that the in vitro promiscuity is not an ‘artifact’ but is physiologically significant . 10 . 7554/eLife . 03275 . 016Figure 7 . Mapping members of GNN clusters back to the SSN for the PRS . ( A ) SSN for the PRS with cluster numbers . ( B ) D-amino acid oxidase ( DAAO ) . ( C ) Dihydrodipicolinate synthase ( DHDPS ) . ( D ) Aldehyde dehydrogenase . ( E ) Ornithine cyclodeaminase ( OCD ) . ( F ) Malate/L-lactate dehydrogenase 2 ( MLD2 ) . ( G ) The color scheme for B–F . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 016 As established in this study , the majority of the members of the PRS catalyze only the three previously characterized ( known ) reactions ( Figure 1 ) . As a result , we were able to use the GNN without any additional information to correctly predict functions for all of the highly populated clusters/families ( >85% of the members; Figure 8 ) . Because of this simplicity , the PRS provides a lucid illustration of the strategy by which a query SSN and its GNN can be used to predict and assign enzymatic functions . 10 . 7554/eLife . 03275 . 017Figure 8 . Experimentally characterized enzymes reported by Swiss-Prot ( small colored circles ) and newly characterized in this work ( large colored circles ) . Colors match the color scheme in Figure 2B . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 017 However , large-scale prediction and assignment of function to members of many functionally diverse ( super ) families will be more complicated than that described for the PRS and require information from complementary experimental and computational approaches . The use of GNNs is restricted to those enzymes that are encoded by proximal operons and/or gene clusters in eubacteria and archaea . For Escherichia coli K-12 , 60% of the genes are located in polycistronic transcriptional units that may provide linked functional information that can be used to identify pathways; 40% are located in monocistronic transcriptional units ( http://regulondb . ccg . unam . mx/menu/tools/regulondb_overviews/chart_form . jsp ) . Thus , genome neighborhood context is not a general solution to infer functions for many proteins/enzymes of unknown function encoded eubacterial and archaeal genomes . Even for those proteins encoded by polycistronic transcriptional units , complete metabolic pathways may be encoded by multiple transcriptional units ( mono- and/or polycistronic ) that are not genome proximal; these pathways and their component enzymes and ligand binding proteins ( solute binding proteins for transport systems and transcriptional regulators ) may be recognized by regulon analyses that identify conserved binding sites for transcriptional regulators ( Ravcheev et al . , 2013; Rodionov et al . , 2013 ) . To the extent that genome neighborhoods and/or regulons allow the identification of the components of unknown/novel metabolic pathways , the locations of these proteins/enzymes in the SSNs for their ( super ) families will provide restrictions on their ligand/substrate specificities and/or reaction mechanisms ( Atkinson et al . , 2009 ) . Also , as we recently demonstrated ( Zhao et al . , 2013 ) , in silico ( virtual ) docking of ligand libraries to multiple binding proteins and enzymes in an unknown metabolic pathway ( pathway docking ) is a powerful approach to enhance the reliability of docking to predict novel ligand/substrate specificities and identify novel metabolic pathways Irrespective of the many complications associated with assignment of function to unknown proteins/enzymes , we conclude that GNNs provide a novel approach for large-scale analysis and visualization of genome neighborhood context in enzyme ( super ) families . We are continuing to improve the use of GNNs as well as regulon analyses and pathway docking to facilitate the discovery of novel enzymes and the metabolic pathways in which they function .
The SSNs for the PRS ( Figure 2 ) and the OCDS ( Figure 5A ) were created using Pythoscape v1 . 0 ( Barber and Babbit , 2012 ) that is available for download from http://www . rbvi . ucsf . edu/trac/Pythoscape The input sequences were downloaded from the InterPro webpages of PRS and OCDS: http://www . ebi . ac . uk/interpro/entry/IPR008794 , http://www . ebi . ac . uk/interpro/entry/IPR003462 , respectively . Cytoscape v2 . 8 ( Smoot et al . , 2011 ) is used for visualization and analysis of the SSN . The GNN for the PRS ( Figure 3 ) was also created using Pythoscape v1 . 0 ( Barber and Babbit , 2012 ) . At an e-value cutoff 10-110 , each cluster in the SSN was assigned a unique cluster number and color , which are used for labeling and coloring genome context sequences . Genome context sequences were collected from the ±10 gene range of each PRS member and used as the input sequences for making the GNN using the procedure for generating a SSN . Genes for members of the PRS that are encoded by the genomic DNAs in the Macromolecular Therapeutics Development Facility at the Albert Einstein College of Medicine were cloned into pNIC28-BSA4-based expression vectors as previously described ( Sauder et al . , 2008 ) . The pNIC28-BSA4-based expression plasmids were transformed into Escherichia coli BL21 ( DE3 ) containing the pRIL plasmid ( Stratagene , Agilent Technologies , Inc . , Wilmington , DE ) and used to inoculate 20 ml 2xYT cultures containing 50 µg/ml kanamycin and 34 µg/ml chloramphenicol . Cultures were allowed to grow overnight at 37°C in a shaking incubator; these were used to inoculate 2 L of PASM-5052 auto-induction medium ( Studier ) . The cultures were placed in a LEX48 airlift fermenter and incubated at 37°C for 5 hr and then at 22°C overnight ( 16–20 hr ) . The cells were collected by centrifugation at 6000×g for 10 min and stored at -80°C . Cells were resuspended in Lysis Buffer ( 20 mM HEPES , pH 7 . 5 , containing 20 mM imidazole , 500 mM NaCl , and 5% glycerol ) and lysed by sonication . Lysates were clarified by centrifugation at 35 , 000×g for 45 min . The clarified lysates were loaded on a 1-ml His60 Ni-NTA column ( Clontech ) using an AKTAxpress FPLC ( GE Healthcare ) . The columns were washed with 10 column volumes of Lysis Buffer and eluted with buffer containing 20 mM HEPES , pH 7 . 5 , containing 500 mM NaCl , 500 mM imidazole , and 5% glycerol . The purified proteins were loaded onto a HiLoad S200 16/60 PR gel filtration column equilibrated with a buffer containing 20 mM HEPES , pH 7 . 5 , 150 mM NaCl , 5% glycerol , and 5 mM DTT . The purities of the proteins were analyzed by SDS-PAGE . The proteins were snap frozen in liquid N2 and stored at -80°C . Proteins were screened for crystallization conditions using commercially available screens ( MCSG 1 , 2 , and 4 [Microlytic , Woburn MA] and MIDAS [Molecular Dimensions , Altamonte Springs FL] ) using sitting drop vapor diffusion 96-well INTELLIPLATES ( Art Robbins Instruments , Sunnyvale CA ) , a PHOENIX crystallization robot ( Art Robbins Instruments ) , and stored and monitored in a Rock Imager 1000 ( Formulatrix , Waltham MA ) plate hotel . Protein ( 1 µl ) was combined with an equivalent volume of precipitant and equilibrated against a 70 µl reservoir of the same precipitant at room temperature ( ~292 K ) . A5VZY6 , ( 27 . 9 mg/mL , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) was crystallized in 0 . 1 M sodium acetate , pH 4 . 6 , containing 1 . 5 M LiSO4; the crystals grew as rectangular bricks over a 1-week period ( SPG-P212121 ) . For the cryoprotectant , the LiSO4 concentration was increased to 1 . 8M . A5VZY6 was also crystallized ( 27 . 9 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) in 0 . 2 M diammonium hydrogen citrate pH 5 . 0 , containing 20% ( wt/vol ) PEG 3350; the crystals grew as wedges over a 1-week period . The cryoprotectant contained 20% glycerol . Q1QU06 ( 21 . 1 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) was crystallized in 0 . 2 M di-ammonium hydrogen citrate , pH 5 . 0 , containing 20% ( wt/vol ) PEG 3350; the crystals grew as plates over 2–3 days . The cryoprotectant contained 20% glycerol . XCC2415 ( 29 . 3 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) was crystallized in 0 . 1 M HEPES , pH 7 . 5 , containing 0 . 8 M sodium phosphate and 0 . 8 M potassium phosphate and grew as thin rods over 2–3 days . The cryoprotectant contained 20% glycerol . B3D6W2 ( 21 . 8 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) was crystallized in 0 . 1 M phosphate-citrate , pH 4 . 2 , containing 1 . 6 M NaH2PO4 , and 0 . 4 M K2HPO4 and grew as large rods over 2 weeks . The cryoprotectant contained 20% glycerol . Q4KGU2 ( 25 . 7 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 150 mM NaCl , and 5 mM DTT ) was crystallized in 0 . 2 M ammonium acetate , 0 . 1 M trisodium citrate , pH 5 . 6 , containing 14% PEG4000 , 5% glycerol , and either 20 mM PYC or 50 mM t4Hyp and grew as thick plates over 2–3 days . The cryoprotectant contained 20% glycerol . For A6WW16 , B9K4G4 , and B9JQV3 , TEV protease ( Tropea et al . , 2009 ) was added at a 1/80 ratio prior to crystallization setup . The samples were incubated on ice for 2 hr , and the buffer was exchanged with 15 mM HEPES , pH 7 . 5 , containing 5 mM DTT by dilution and centrifugal filtration . The extent of TEV cleavage was not measured . A6WW16 ( 17 . 3 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 5 mM DTT ) was crystallized in 0 . 2 M sodium nitrate and 20% PEG3350 and grew as leaf petals over 2 to 3 weeks . The cryoprotectant contained 20% glycerol . B9K4G4 , ( 17 . 1 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 5 mM DTT ) was crystallized in 0 . 1 M sodium acetate , pH 4 . 6 , containing 1 M ammonium citrate and 25 mM pyrrole 2-carboxylate . Crystals grew from an initial precipitate as multifaceted crystals over a month . The cryoprotectant contained 20% glycerol . B9JQV3 ( 30 . 0 mg/ml , 15 mM HEPES , pH 7 . 5 , containing 5 mM DTT ) was crystallized in 0 . 1 M sodium acetate , containing 25% Peg4000 , 8% 2-propanol , and 200 mM t4Hyp and grew as tetragonal rods over 2–3 days . The cryoprotectant contained 20% 2-propanol . Diffraction data were collected on beamline 31-ID ( LRL-CAT , Advanced Photon Source , Argonne National Laboratory , IL ) from single crystals at 100 K and a wavelength of 0 . 9793 Å . Data were integrated using MOSFLM ( Battye et al . , 2011 ) and scaled in SCALA ( Evans , 2006 ) . Suitable molecular replacement models existed for all of the protein targets of this study . These included , 2AZP , a putative 4HypE ( from cluster 2 ) determined unliganded by the Midwest Center for Structural Genomics , and 1TM0 ( Forouhar et al . , 2007 ) , a putative t3HypD ( cluster 3 , also similar to cluster 9 ) with an unliganded and disordered active site , determined by the Northeast Structural Genomics Consortium . Molecular replacement computations were performed in AMORE ( Navaza , 1994 ) utilizing the structure that exhibited the greatest homology to the target . If this was unsuccessful , either due to the particular issues with the space group , asymmetric unit composition , or a different orientation of the two domains , molecular replacement was performed with each of the domains separately within PHENIX ( Adams et al . , 2004; Zwart et al . , 2008 ) . Iterative cycles of manual rebuilding within COOT ( Emsley and Cowtan , 2004 ) and refinement within PHENIX were performed until the entire sequence was modeled . Inclusion of ligands , TLS ( translation/libration/screw ) refinement ( domains chosen automatically within PHENIX ) ( Winn et al . , 2001; Painter and Merritt , 2006 ) and editing of the solvent structure were performed in the final refinement cycles . With one exception , the entire sequences of all of the targets could be modeled , except for a small number of residues at the N- or C-termini . The one outlier was A6WW16 that had several disordered regions around the active site similar to the previously determined structure from this cluster ( 1TM0 , cluster 3 , light sky blue ) . Due to the relatively weak binding of the proline racemase family members for their substrates , inhibitors and substrates were included at high concentrations ( 25–200 mM ) . Even at these concentrations , several structures were determined from cluster 2 that bound anionic ligands ( phosphate , citrate , etc ) from the crystallization medium rather than the co-crystallized ligand , and the degree of domain closure about that ligand varied . For all of the structures liganded with either PYC or t4Hyp , the structures are determined in a closed state with Ca–Ca distances of 7–8 Å for the opposing active site catalytic Cys–Cys ( cluster 2 , red ) , Ser–Thr ( cluster 3 , light sky blue ) or Ser–Cys dyad ( cluster 9 , orange ) . In the case of Q4KGU2 , the ligand was t4Hyp state based on the electron density . In contrast , for B9JQV3 , the density for the ligand had significant planer character , suggesting a mixture of t4Hyp and c4Hyp . Enzyme activity was screened by the mass change resulting from racemization /epimerization ( +1 peak shift ) and/or dehydration ( -17 peak shift ) for reactions in D2O . Each enzyme ( 1 µM ) was incubated with substrate libraries ( Table 1 ) containing proline and proline betaine derivatives ( 0 . 1 mM each ) along with 20 mM ammonium bicarbonate in D2O at a final volume of 200 µl at 30°C for 16 hr . 50 µl of the reaction mixture was aliquoted and dried with an Eppendorf vacufuge concentrator . The residue was suspended in 10 µl of H2O , and 5 µl of the solution was mixed with the 5 µl of 50% methanol containing 0 . 4% ( vol/vol ) formic acid . A 10 µl sample was analyzed for ESI-MS . If a change in mass was observed in the ESI-MS screening assays , a 1H NMR assay was performed to determine the product . Each reaction mixture contained 1 µM enzyme , 10 mM substrate , and 25 mM sodium phosphate buffer , pD 8 , in a total volume of 800 µl D2O . The mixture was incubated at 30°C for 16 hr before acquisition of the 500 MHz ( Hunter et al . , 2012 ) H NMR spectrum ( Figure 9 ) . 10 . 7554/eLife . 03275 . 018Figure 9 . Demonstration of the 4HypE , 3HypE , and t3HypD reactions by 1H NMR . ( A ) 1H NMR spectra of the 4Hyp substrate mixture in 25 mM Na+-phosphate buffer , pD 8 , in D2O ( top ) and 4Hyp mixture with A3QFI1 ( cluster 1 , blue ) showing 4Hyp epimerization ( bottom ) . The red arrow indicates the proton at C2 for epimerization . The enzyme was stored in glycerol , so the spectra show resonances for glycerol between 3 . 4 and 3 . 7 ppm . ( B ) 1H NMR spectra of the t3Hyp substrate mixture in 25 mM Na+-phosphate buffer , pD 8 , in D2O ( top ) , t3Hyp mixture with D0B556 ( cluster 3 , light sky blue ) showing 3Hyp epimerization ( middle ) , and t3Hyp mixture with B9K4G4 ( cluster 3 , light sky blue ) showing t3Hyp dehydration ( bottom ) . The red arrow indicates the proton at C2 for epimerization; the green arrow indicates the proton at C3 for dehydration . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 018 The enzyme activity was measured in a Jasco P-1010 polarimeter with a Hg 405-nm filter at 25°C by quantitating the change in optical rotation . The assay mixture contained 1 mM dithiothreitol ( DTT ) and 50 mM Na+-phosphate buffer , pH 8 . 0 . ? 1-Pyr2C reductase assays were performed by measuring the decrease in the absorbance of NAD ( P ) H at 340 nm at 25°C with a Cary 300 Bio UV-Visible spectrophotometer ( Varian ) . The reaction mixture ( 300 µl ) contained variable concentrations of Pyr2C , 50 mM Tris–HCl buffer , pH 7 . 6 , 0 . 16 mM NAD ( P ) H , and enzyme . The reaction mixture contained 10 mM ? 1-Pyr2C , 1 µM enzyme , 0 . 16 mM NADPH , 25 mM phosphate-Na buffer , pD 8 . 0 , 1 U/ml alcohol dehydrogenase ( NADP+-dependent from Thermoanaerobium brockii , Sigma ) and 80 µl isopropanol in a total volume of 800 µl of D2O; the reaction was incubated at 30°C for 16 hr . The solvent was removed by lyophilization , 800 µl of D2O was added , and the 1H NMR spectrum was recorded . Representative spectra are shown in Figure 10 . 10 . 7554/eLife . 03275 . 019Figure 10 . Representative 1H NMR spectra for ? 1-pyrroline-2-carboxylate ( ? 1-Pyr2C ) reductase activity . ( A ) 1H NMR spectrum of ? 1-Pyr2C substrate in sodium phosphate , pD 8 . 0 , in D2O . ( B ) 1H NMR spectrum of Q7CVK1 ( locus tag: Atu4676 ) incubated with ? 1-Pyr2C , NADPH , and the cofactor regeneration system of alcohol dehydrogenase ( NADP+-dependent ) and isopropanol in sodium phosphate , pD 8 . 0 in D2O . ( C ) 1H NMR spectrum of L-proline in 25 mM sodium phosphate , pD 8 . 0 , in D2O . DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 019 Bacterial strains are listed in Table 8 . All strains were grown at 30°C with shaking at 225 rpm and were routinely cultured in Tryptic Soy Broth ( Difco ) , supplemented with 30 g L-1 sea salts ( Sigma-Aldrich ) for Labrenzia aggregata IAM12614 and Roseovarius nubinhibens ISM . 10 . 7554/eLife . 03275 . 020Table 8 . Strains used in this studyDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 020OrganismAgrobacterium tumefaciens C58Sinorhizobium meliloti 1021Labrenzia aggregata IAM12614Pseudomonas aeruginosa PAO1Paracoccus denitrificans PD1222Rhodobacter sphaeroides 2 . 4 . 1Rhodobacter sphaeroides 2 . 4 . 1 ? RSP3519Bacillus cereus ATCC14579Roseovarius nubinhibens ISMEscherichia coli MG1655Streptomyces lividans TK24 For gene expression analyses and carbon utilization studies , strains were cultured in the following defined media: Agrobacterium tumefaciens C58 was cultured in M9 minimal medium ( per liter: 12 . 8 g Na2HPO4 . 7H2O , 3 . 0 g KH2PO4 , 0 . 5 g NaCl , 1 . 0 g NH4Cl ) ; B . cereus ATCC 14579 was cultured in a modified Spizizen's minimal medium ( Spizizen . , 1958 ) ( per liter: 2 . 0 g ( NH4 ) 2SO4 , 11 . 0 g K2HPO4 , 6 . 0 g KH2PO4 , 1 . 0 g sodium citrate . 2H2O ) . Streptomyces lividans TK24 was cultured in a modified minimal medium of Hopwood ( Hopwood . , 1967 ) ( per liter: 1 . 0 g ( NH4 ) 2SO4 , 0 . 5 g K2HPO4 , 0 . 005 g FeSO4 . 7H2O ) . M9 minimal medium , and Spizizen's minimal medium were supplemented with the following trace metals ( per liter: 0 . 003 mg CuSO4 . 5H2O , 0 . 025 mg H3BO3 , 0 . 007 mg CoCl2 . 6H2O , 0 . 016 mg MnCl2 . 4H2O , 0 . 003 mg ZnSO4 . 7H2O , 0 . 3 mg FeSO4 . 7H2O ) . The minimal medium of Hopwood was supplemented with the following trace metals ( per liter: 0 . 08 mg ZnCl2 , 0 . 4 mg FeCl3 . 6H2O , 0 . 02 mg CuCl2 . 2H2O , 0 . 02 mg MnCl2 . 4H2O , 0 . 02 mg Na2B4O7 . 10H2O , 0 . 02 mg ( NH4 ) 6Mo7O24 . 4H2O ) . All other strains were grown in the following defined medium ( per liter: 17 . 0 g K2HPO4 , 2 . 5 g ( NH4 ) 2SO4 , 2 . 0 g NaCl ) supplemented with the following trace metals ( 0 . 3 mg FeSO4 . 7H2O , 0 . 003 mg ZnSO4 . 7H2O , 0 . 003 mg CuSO4 . 5H2O , 0 . 025 mg H3BO3 ) , supplemented with 30 g L-1 sea salts ( Sigma-Aldrich ) for L . aggregata IAM12614 and R . nubinhibens ISM . All of the above defined media were additionally supplemented with 1 mM MgSO4 , 100 µM CaCl2 , and vitamins ( 33 µM thiamine , 41 µM biotin , 10 nM nicotinic acid ) . 20 mM of one of the following served as the sole source of carbon: D-glucose ( Thermo Fisher ) , t3Hyp ( BOC Sciences ) , c3Hyp ( Chem Impex Int’l ) , t4Hyp ( Bachem ) , c4Hyp ( Sigma-Aldrich ) , or L-proline ( CalBiochem ) . RSP3519 was amplified from Rhodobacter sphaeroides 2 . 4 . 1 genomic DNA using Pfu DNA polymerase ( Thermo ) with primers RSP3519F and RSP3519R ( Table 9 ) . The resulting PCR product was inserted into the pGEM T Easy vector ( Promega ) to generate plasmid pRK_RSP3519-1 . pRK_RSP3519-1 was digested with SmaI and ligated to a 900 bp blunt-ended chloramphenicol resistance cassette to generate pRK_RSP3519-2 . pRK_RSP3519-2 was then used as the template in a PCR with primers RSP3519F and RSP3519R . The resulting product was digested with EcoRI and ligated into pSUP202 to give the plasmid used for gene disruption: pRK_RSP3519-3 . To disrupt RSP3519 , pRK_RSP3519-3 was electroporated into R . sphaeroides 2 . 4 . 1 , and double crossover chromosomal gene disruptions were selected by resistance to chloramphenicol and sensitivity to ampicillin ( Matsson et al . , 1998 ) . 10 . 7554/eLife . 03275 . 021Table 9 . Oligonucleotide primers used for construction of the RS3519 knock-out in Rhodobacter sphaeroides 2 . 4 . 1DOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 021OligoSequence ( 5'–3' ) RS3519F . KOCATATGATGCGCGTTCAGGACGTGTATAACGRS3519R . KOGCTGAGCTCAGAGGACGAGGAAGCCCGCGTCC Starter cultures were initiated from a single colony and grown in the appropriate rich medium overnight . This culture was used to inoculate the appropriate minimal medium ( 1% inoculum ) supplemented with 20 mM D-glucose; the cultures were grown until OD600 0 . 3–0 . 5 . The cells were pelleted by centrifugation ( 4750×g for 5 min at 4°C ) , washed once , and resuspended in minimal medium with no carbon source . For gene expression analysis of individual PRS genes , cultures were divided into two equal volumes , 20 mM D-glucose was added to one volume and 20 mM trans-4-hydroxy-L-proline or trans-3-L-hydroxy proline was added to the other , and cultures were grown for three additional hr prior to cell harvest . For evaluation of whole genome neighbourhoods of select PRS targets ( orange , navy , hotpink , pale green , blue , and sky blue clusters ) in A . tumefaciens C58 , B . cereus ATCC 14579 , and S . lividans TK24 , cultures were divided into four equal volumes , supplemented with D-glucose , trans-4-hydroxy-L-proline , trans-3-hydroxy-L-proline , or L-proline to a final concentration of 20 mM , and grown until OD600 0 . 8–1 . 0 . At the time of cell harvest , one volume of RNAprotect Bacteria Reagent ( Qiagen ) was added to two volumes of each culture . Samples were mixed by vortexing for 10 s and then incubated for 5 min at room temperature . Cells were pelleted by centrifugation ( 4750×g for 5 min at 4°C ) , the supernatant was decanted , and cell pellets were stored at -80°C until further use . RNA isolation was performed in an RNAse-free environment at room temperature using the RNeasy Mini Kit ( Qiagen ) per the manufacturer's instructions . For B . cereus ATCC 14579 and S . lividans TK24 , cells were initially disrupted using a modified bead-beating procedure: cells were resuspended in 400 µl Soil Pro Lysis Buffer ( MP Bio ) , transferred to Lysis Matrix E tubes ( MP Bio ) , and agitated horizontally on a Vortex Mixer ( Fisher ) with Vortex Adapter ( Ambion ) for 10 min at speed 10 . Beads and cellular debris were pelleted by centrifugation at 16 , 000 × g for 5 min . 200 µl of the supernatant was used for subsequent RNA isolation . Cell pellets for all other organisms were disrupted according to the ‘Enzymatic Lysis Protocol’ in the RNAprotect Bacteria Reagent Handbook ( Qiagen ) ; lysozyme ( Thermo-Pierce ) was used at 15 mg ml-1 . RNA concentrations were determined by absorption at 260 nm using the Nanodrop 2000 ( Thermo ) and absorption ratios A260/A280 and A260/A230 were used to assess sample integrity and purity . Isolated RNA was stored at -80°C until further use . Reverse transcription ( RT ) PCRs for A . tumefaciens C58 and B . cereus ATCC 14579 were performed with 300 ng of total isolated RNA using the ProtoScript First Strand cDNA Synthesis Kit ( NEB ) as per the manufacturer's instructions . For S . lividans TK24 RT-PCRs were performed with 300 ng of total RNA using the Transcriptor First Strand cDNA Synthesis Kit ( Roche ) , with 2 . 5% DMSO added to relieve secondary structures . All other RT-PCRs were performed with 1 µg of total RNA using the RevertAid H Minus First Strand cDNA Synthesis Kit ( Fermentas ) . Primers for quantitative real-time ( qRT ) PCR for A . tumefaciens C58 and B . cereus ATCC 14579 gene targets were designed using the Primer3 primer tool; amplicons were 150–200 bps in length; primers for all other qRT-PCRS were designed using the Universal ProbeLibrary System ( Roche ) ; amplicons were 66–110 bps in length Primer sequences are provided in Tables 10 and 11 . Primers were 18–27 nucleotides in length and had a theoretical Tm of 55–60°C . Primer efficiency was determined to be at least 90% for each primer pair . 10 . 7554/eLife . 03275 . 022Table 10 . qRT-PCR primers for transcriptional analysis of individual proline racemase superfamily membersDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 022OligoSequence ( 5'–3' ) Atu16s-FGACACGGCCCAAACTCCTACAtu16s-RGGGCTTCTTCTCCGACTACCAtu0398-FTCACCATTGAGAAGGCCAATAtu0398-RGGTTGACGAGGTCCTTCAGAAtu3953-FCAGCTTCAGTGGCATCAGGAtu3953-RGTGTTGTGCCCAATGATCCAtu4684-FGAAGAGGCGCATGAGATTGAtu4684-RCGAAACCCAAAGCCTTGTTBc16s-FCTCGTGTCGTGAGATGTTGGBc16s-RTGTGTAGCCCAGGTCATAAGGBc0905-FCTTCGCTGACGGACAAGTAGABc0905-RTGTACCGCTGTTACGGACAABc2835-FAACAGACCCGTGTCATCCTGBc2835-RACTAAGCCAGCCGGTGTATCTLa16s-FTGGTGGGGTAAAGGCCTACLa16s-RTGGCTGATCATCCTCTCAGACLa28492-FTGTTGAAGACGAGGCCAAGLa28492-RAAAAGCCGAGCTGTTCGTTLa28502-FCGCGTAATCGACAGCCATALa28502-RGGCACAGAAATCGAGATGCTRs16s-FACACTGGGACTGAGACACGGRs16s-RTACACTCGGAATTCCACTCARs3519-FAGGACATCGCCTTCGAACTRs3519-RCGATGATGCCGAAATAGTTGPa16s-FTCACACTGGAACTGAGACACGPa16s-RATCAGGCTTTCGCCCATTPa1255-FCCACCCTCTGGGAACAGTCPa1255-RTCGTTGAGGACGAAGTTGCPa1268-FAACAGTGGCTACCTCGGCAPa1268-RTCGCCGACCGGTGTCTCGATRn16s-FATCTGTGTGGGCGCGATTRn16s-RGTGAGCGCATTGGTGGTCTRn08250-FTATGGCGGCGACAGTTTCRn08250-RGACGGCTCGAGCGTAAACPd16s-FGACTGAGACACGGCCCAGAPd16s-RTCACCTCTACACTCGGAATPd1045-FTCGGACTACTATGTGCCGATGPd1045-RCCTGATCGAGGCCAAAGACPd1184-FGCAATTTCGTGTTGAACGAGPd1184-RCATGATGATCCAGCCCATCTPd3467-FCTTCGCAGCCCTGTTCATPd3467-RGACCAGCCCTTCCTCGATPd4859-FGGCAAGGTGGACATCGAATAPd4859-RCCTCGGGGTAAAGGAAGCSm16s-FCGTGGGGAGCAAACAGGATTSm16s-RCTAAGGGCGAGGGTTGCGCTCSm20268-FCTGGCAAGGTGGACATCACSm20268-RGTAAGGCGCACTTCCTCAASm20270-FCGCCATGTCAATCTCCTGGTSm20270-RGGCAGCATCCACGATCACGA10 . 7554/eLife . 03275 . 023Table 11 . qRT-PCR primers for transcriptional analysis of genome neighborhoodsDOI: http://dx . doi . org/10 . 7554/eLife . 03275 . 023PrimerSequence ( 5'–3' ) Sliv-Sco16srRNA-FCCGTACAATGAGCTGCGATASliv-Sco16srRNA-RGAACTGAGACCGGCTTTTTGSliv-Sco6289-FGACCCTGAAGGTCGTCGTCSliv-Sco6289-RGGTGACCGTGACGTCCATSliv-Sco6290-FGTCTTCTGCGGCATCGGSliv-Sco6290-RAGTCATCGTCGTCCTCCASliv-Sco6291-FGCCGACCTCGACGAAGASliv-Sco6291-RTTGTCGGTTTCACTGCTGTCSliv-Sco6292-FCATCGACACCAAGGTGGACSliv-Sco6292-RTGACCCCGACGATGTACCSliv-Sco6293-FGACTACGGCGTGCTCTTCATSliv-Sco6293-RCTCGGTGACCTCGACCATBc0905-FCTTCGCTGACGGACAAGTAGABc0905-RTGTACCGCTGTTACGGACAABc0906-FACTACGAACGCAACCACACCBc0906-RCGGAACTTGAAGGTCTCCTGTBc2832-FTACCAGGCTTTGGTCCTGAABc2832-RATTTGCCGCCAAGCTCTAACBc2833-FGGATGGGTTTCAGTAGCAGGABc2833-RCCTAGTCTTGGATAGCGAGAAGGBc2834-FAGGTGCGTATTCGCCAGAAABc2834-RCCTGGCGAACGTACGATAAABc2835-FAACAGACCCGTGTCATCCTGBc2835-RACTAAGCCAGCCGGTGTATCTBc2836-FCCTTGCATTCTCGCTTCTGTBc2836-RAATCTTAGGAGCCCACACACCAtu3947-FTCCGGCCAAGTATGTGAAAGAtu3947-RCTATAGCCGTTCGCAGCAAGAtu3948-FATTTCGCCCGTGATCTGTCAtu3948-RCGGCATCCACAATAATCCAGAtu3949-FGCGAACAGGCTGAAGAGATGAtu3949-RCGGCGGTAATTCCTGTTTGAtu3950-FGCTGCCGAACATATCAAGGTAtu3950-RGACCTTCGCGGTTATCTGGTAtu3951-FTGACGGACTCCAGCCTTATCAtu3951-RATGTAACATCGGCGTGGTCTAtu3952-FGATATCGTCAAGGGCGGTTTAtu3952-RACGCAGAGCCTTCATGTGTTAtu3953-FCAACGTCGCCAGTTACCTTCAtu3953-RGGCTGAGATCAACGACATCCAtu3958-FGGCGGCTGATACACATCTTCAtu3958-RAAAGTTGGTGCTTCGTCAGGAtu3959-FCATTCCTGACACGATCCACAAtu3959-RCAGCATCAGCAAAGGGAAGTAtu3960-FGAATGTCGTCGCCATCAAGAtu3960-RTCGTAGAGTGCCACATGCTCAtu3961-FTTCGGCACTTCTTTCTGGTCAtu3961-RGCTCGCCTGCAGATAAACAAtu4675-FTTCCTGTTATCGTCGGCACTAtu4675-RGCCTTGAAGTGAGCCTTCTGAtu4676-FACGGCTATCGTGAAGGTCAAAtu4676-RGAATAGCTCGGGCACATCACAtu4682-FTCCTCAGAAAGACCGACACCAtu4682-RGTGAATGTGCCGCAGGTAAAtu4684-FCCTCGGCAAACTCAAGGTCAtu4684-RGCGAAGAGGCAGAAGGAAAAtu4691-FAAGGGCGATATGGGTCTTTCAtu4691-RGAGCTCTTCGATGCTGTCGT qRT-PCRs were carried out in 96-well plates using the Roche LightCycler 480 II instrument with the LightCycler 480 SYBR Green I Master Mix ( Roche ) per the manufacturer's instructions . Each 10-µl reaction contained 1 µM of each primer , 5 µl of SYBR Green I Master Mix , and an appropriate dilution of cDNA . Reactions were run as follows: one cycle at 95°C for 5 min , 45 cycles at 95°C for 10 s , 50°C for 10 s , 72°C for 10 s , and a final dissociation program at 95°C for 15 s , 60°C for 1 min , and 95°C for 15 s . Minus-RT controls were performed to verify the absence of genomic DNA in each RNA sample for each gene target analyzed . Gene expression data were expressed as crossing threshold ( CT ) values . Data were analyzed by the 2- ? ? CT ( Livak ) method ( Livak and Schmittgen , 2001 ) , using the 16S rRNA gene as a reference . Each qRT-PCR was performed in triplicate , and fold-changes are the averages of at least three biological replicates . The atomic coordinates and structure factors for ‘4R-hydroxyproline 2-epimerases’ ( 4HypE ) from Pseudomonas putida F1 ( citrate-liganded , PDBID:4JBD; sulfate-liganded , PDBID:4JD7 ) , Chromohalobacter salexigens DSM 3043 ( apo , PDBID:4JCI ) , Xanthomonas campestris ( phosphate-liganded , PDBID:4JUU ) , Burkholderia multivorans ( phosphate-liganded , PDBID:4K7X ) , Pseudomonas fluorescens Pf-5 ( pyrrole 2-carboxylate-liganded , PDBID:4J9W; trans-4-hydroxy-L-proline-liganded , PDBID:4J9X ) , Ochrobacterrium anthropic ( apo , PDBID:4K8L ) , and Agrobacterium vitis S4 ( trans-4-hydroxy-L-proline-liganded , PDBID:4LB0 ) and ‘trans-3-hydroxy-L-proline dehydratase’ ( t3HypD ) from Agrobacterium vitis S4 ( pyrrole 2-carboxylate-liganded , PDBID:4K7G ) have been deposited in the Protein Data Bank , www . pdb . org . This manuscript describes functional characterization of proteins with the following UniProt accession IDs: A0NXQ7 , A0NXQ9 , A1B0W2 , A1B195 , A1B196 , A1B7P4 , A1BBM5 , A1U2K1 , A3M4A9 , A3PPJ8 , A3QFI1 , A3QH73 , A3S939 , A3SU01 , A5VZY6 , A6WW16 , A6WXX7 , A8H392 , A9AKG8 , A9AKH1 , A9AL52 , A9ALD3 , A9AQW9 , A9CFU8 , A9CFU9 , A9CFV0 , A9CFV4 , A9CFW8 , A9CGZ4 , A9CGZ5 , A9CGZ9 , A9CH01 , A9CH04 , A9CKB4 , B0VB44 , B1KJ76 , B3D6W2 , B4EHE6 , B9J8G8 , B9JHU6 , B9JQV3 , B9K4G4 , B9R4E3 , C5ZMD2 , D2AV87 , D2QN44 , D5SQS4 , D6EJK6 , D6EJK7 , D6EJK8 , D6EJK9 , D6EJL0 , Q0B950 , Q0B953 , Q0B9R9 , Q0B9S2 , Q16D96 , Q1QBF3 , Q1QU06 , Q1QV19 , Q2KD13 , Q2T3J4 , Q2T596 , Q3IWG2 , Q3IZJ8 , Q3JFG0 , Q3JHA9 , Q485R8 , Q4KAT3 , Q4KGT8 , Q4KGU2 , Q5LKW3 , Q5LLV0 , Q63FA5 , Q6HMS8 , Q6HMS9 , Q73CR9 , Q73CS0 , Q7CFV0 , Q7CTP1 , Q7CTP2 , Q7CTP3 , Q7CTP4 , Q7CTQ2 , Q7CTQ3 , Q7CTQ5 , Q7CVK1 , Q7NU77 , Q81CD6 , Q81CD7 , Q81CD8 , Q81CD9 , Q81CE0 , Q81HB0 , Q81HB1 , Q8FYS0 , Q8P833 , Q8YFD6 , Q92WR9 , Q92WS1 , Q9I476 , Q9I489 , and Q9I492 . | DNA molecules are polymers in which four nucleotides—guanine , adenine , thymine , and cytosine—are arranged along a sugar backbone . The sequence of these four nucleotides along the DNA strand determines the genetic code of the organism , and can be deciphered using various genome sequencing techniques . Microbial genomes are particularly easy to sequence as they contain fewer than several million nucleotides , compared with the 3 billion or so nucleotides that are present in the human genome . Reading a genome sequence is straight forward , but predicting the physiological functions of the proteins encoded by the genes in the sequence can be challenging . In a process called genome annotation , the function of protein is predicted by comparing the relevant gene to the genes of proteins with known functions . However , microbial genomes and proteins are hugely diverse and over 50% of the microbial genomes that have been sequenced have not yet been related to any physiological function . With thousands of microbial genomes waiting to be deciphered , large scale approaches are needed . Zhao et al . take advantage of a particular characteristic of microbial genomes . DNA sequences that code for two proteins required for the same task tend to be closer to each other in the genome than two sequences that code for unrelated functions . Operons are an extreme example; an operon is a unit of DNA that contains several genes that are expressed as proteins at the same time . Zhao et al . have developed a bioinformatic method called the genome neighbourhood network approach to work out the function of proteins based on their position relative to other proteins in the genome . When applied to the proline racemase superfamily ( PRS ) , which contains enzymes with similar sequences that can catalyze three distinct chemical reactions , the new approach was able to assign a function to the majority of proteins in a public database of PRS enzymes , and also revealed new members of the PRS family . Experiments confirmed that the proteins behaved as predicted . The next challenge is to develop the genome neighbourhood network approach so that it can be applied to more complex systems . | [
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In theory , sensory perception should be more accurate when more neurons contribute to the representation of a stimulus . However , psychophysical experiments that use larger stimuli to activate larger pools of neurons sometimes report impoverished perceptual performance . To determine the neural mechanisms underlying these paradoxical findings , we trained monkeys to discriminate the direction of motion of visual stimuli that varied in size across trials , while simultaneously recording from populations of motion-sensitive neurons in cortical area MT . We used the resulting data to constrain a computational model that explained the behavioral data as an interaction of three main mechanisms: noise correlations , which prevented stimulus information from growing with stimulus size; neural surround suppression , which decreased sensitivity for large stimuli; and a read-out strategy that emphasized neurons with receptive fields near the stimulus center . These results suggest that paradoxical percepts reflect tradeoffs between sensitivity and noise in neuronal populations .
Perception relies on the spiking responses of sensory neurons . Indeed , individual neurons can exhibit exquisite selectivity for specific stimulus features . However , this single-neuron selectivity is of limited utility for stimulus encoding for two reasons . One is that neuronal responses are modulated by multiple stimulus dimensions , so that identical responses can be associated with very different stimuli . Another reason is that single-neuron responses can be quite variable , so that the response to the same stimulus can differ from one presentation to the next . Some of this variability can be reduced by combining the responses of multiple neurons . If the variability is independent across neurons , it can be eliminated by simply averaging the responses of many neurons . In this case , the available information about the stimulus theoretically increases with neuronal population size ( Zohary et al . , 1994; Shadlen et al . , 1996 ) . However , in reality neuronal noise is usually correlated across nearby neurons , and such noise correlations are thought to greatly influence on the fidelity of a population code ( Abbott and Dayan , 1999; Sompolinsky et al . , 2001; Panzeri et al . , 1999; Averbeck et al . , 2006; Ecker et al . , 2011 ) . Still , current theories predict the stimulus information will increase or saturate as the size of the corresponding neuronal pool increases . One simple way to manipulate the neuronal pool size is to change the physical size of a visual stimulus . Because neurons in early visual structures have small receptive fields , large stimuli recruit more neurons , potentially leading to more effective coding of stimulus properties and correspondingly better behavioral performance . It is therefore surprising that behavioral studies in humans have sometimes found that larger stimuli are associated with diminished perceptual performance ( Tadin et al . , 2003 ) . Moreover , this psychophysical suppression of behavioral performance in human subjects is strongly correlated with various markers of mental function , including schizophrenia , major depression , and even I . Q ( Tadin et al . , 2006; Golomb et al . , 2009; Melnick et al . , 2013 ) . These results have previously been hypothesized to reflect the strength of neuronal surround suppression in individual cortical neurons ( Tadin et al . , 2003; Churan et al . , 2008 ) , but it is unclear how such suppression affects neuronal populations , particularly in the presence of noise correlations . To address this issue , we recorded from small populations of neurons in visual cortical area MT , in macaque monkeys trained to report the perceived direction of a moving stimulus . We varied stimulus size randomly from trial to trial , and found , as reported in human studies ( Tadin et al . , 2003 ) , that increased stimulus size led to a drastic deterioration of behavioral performance . Our neurophysiological recordings revealed that the magnitude of the neuronal surround suppression of individual neurons is too small to account for psychophysical suppression . However , analysis of multi-electrode recordings revealed a novel aspect of neuronal noise correlations that further suppressed population coding for large stimuli: those neurons with the smallest surround suppression , and hence the ones most sensitive to large stimuli individually , also had noise correlations most closely aligned with signal correlations; such correlations are damaging to the total information carried by the population ( Abbott and Dayan , 1999; Averbeck et al . , 2006 ) . These mechanisms , combined with conservative assumptions about the animals’ behavioral strategies ( Pelli , 1985; Burr et al . , 2009; Beck et al . , 2012 ) , provided a full account of the observed psychophysical suppression . These results further our understanding of the relationship between neural activity and perception , in normal and pathological states .
We examined neuronal responses and behavioral performance during a task in which the visual stimulus size was varied across trials ( Figure 1A , C ) ( Tadin et al . , 2003 ) . During the task , monkeys viewed drifting Gabor grating stimuli and reported their percepts of visual motion direction ( Britten et al . , 1992; 1996 ) ( Figure 1C ) . As in most human studies , we used a very brief stimulus duration ( 50 ms ) ( Tadin et al . , 2003 ) in order to increase the difficulty of the task . In preliminary behavioral experiments we also compared psychophysical performance using Gabor patches of low ( 4% ) and high ( 100% ) contrast . Based on the dependency of the density of receptive fields on eccentricity in early visual structures ( Van Essen et al . , 1981; Erickson et al . , 1989 ) , we calculated that the number of visual cortical neurons activated by our stimulus should increase with stimulus size ( Figure 1B ) . Consistent with previous findings in humans ( Tadin et al . , 2003 ) , we found that increasing the size of a low-contrast stimulus improved behavioral performance ( Figure 1D , dashed lines ) , while under high-contrast conditions , behavioral performance worsened at larger sizes ( Figure 1D , solid lines ) . Thus , paradoxically , psychophysical performance was best for stimuli of medium intensity , with performance declining as contrast and size were increased ( Figure 1D , Wilcoxon rank sum test , p<0 . 001 ) . To quantify this effect , we computed a psychophysical suppression index ( SIpsy ) ( Figure 1D and Material and methods ) , which captures the decrease in performance ( on a scale from 0 to 1 , with 0 corresponding to no suppression , and 1 to complete suppression ) for large stimuli relative to the best performance across all stimuli . At 100% contrast , the SIpsy of the psychometric function ( mean ± s . d . ) was 0 . 42 ± 0 . 25 for monkey C and 0 . 54 ± 0 . 19 for monkey Y , indicating that monkeys were approximately half as likely to accurately perceive the motion of a large stimulus , compared to a small one . 10 . 7554/eLife . 16167 . 003Figure 1 . Stimuli , sequence of events , and behavioral performance in the task . ( A ) Receptive fields from an example recording session , shown as black ovals , relative to lines of different visual eccentricity ( gray circles ) commensurate with the stimulus sizes used in the experiments . ( B ) The estimated neuron pool size as a function of stimulus size , for the eccentricities and stimulus sizes used in the experiments ( top ) . Cortical mapping of visual space from ( A ) , showing that larger stimuli projected onto larger extents of cortical space ( bottom ) . The sizes of the shaded areas correspond to the estimated cortical footprint ( see Materials and methods ) . ( C ) Behavioral task . The animals were required to maintain fixation in a 2° window for 300 ms , after which a drifting Gabor appeared briefly . Animals were then required to fixate for another 300 ms until the fixation spot disappeared . The animals then indicated their motion percept with an eye movement to one of two targets within 700 ms . ( D ) Examples of the animals’ psychometric functions for high contrast ( solid line , circles ) and low contrast ( dashed line , triangles ) stimuli . Error bars represent 95% binomial proportion confidence interval . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 003 We recorded from small populations of neurons in MT using linear electrode arrays , while monkeys performed the high-contrast motion discrimination task described above . Area MT is thought to be causally involved in behavioral decisions for motion direction ( Born and Bradley , 2005 ) , and it contains many neurons with responses that are modulated by stimulus size and contrast ( Allman et al . , 1985; Pack et al . , 2005 ) . To maximize the number of stimulus repetitions per recording session , we fixed the stimulus contrast at 100% and varied stimulus size across trials . We analyzed data from 165 single units , with 2–8 cells being available on any given day . The responses of an example MT neuron to stimuli centered on the receptive field are shown in Figure 2A . Here the orange and violet dots show the responses to the preferred and null direction stimuli , and these responses decrease slightly with increasing stimulus size . The distributions of these responses across trials can be converted into a single measure of neuronal selectivity , d’ , which is plotted as a function of stimulus size in Figure 2B . Based on this neurometric function , we can compute a neural measure of suppression , SIneu , which is defined analogously to SIpsy . The value of SIneu for this neuron was 0 . 18 , which indicates a modest suppression of motion signaling by large stimuli . The decrease in neuronal selectivity with stimulus size resembles the psychophysical performance of the monkey ( Figure 2B ) . However , the strength of neuronal surround suppression is substantially less than that of the simultaneously measured psychophysical suppression ( 0 . 54 ) . This was often the case in our data: For the MT population , the mean neuronal d’ ( SIneu = 0 . 27 ) was much less suppressed than the mean psychometric d’ ( SIpsy = 0 . 48 , Figure 2D ) . Moreover , many neurons exhibited no surround suppression at all , even for stimuli extending beyond their receptive fields ( Born and Tootell , 1992 ) , and the selectivity of these neurons to large stimuli routinely exceeded that of the monkeys ( Figure 2B , C ) . This was especially clear in neurons with receptive fields near the edges of the larger stimuli ( Figure 2—figure supplement 1B ) ; in these neurons responses increased with stimulus size ( Figure 2—figure supplement 1C ) . Together these results suggest that the psychophysical performance is not solely driven by typical single-neuron selectivity , as only a small fraction of neurons showed suppression comparable to that of the behavior . One caveat to this conclusion is that subjects might have relied more heavily on a subpopulation of MT neurons to form their perceptual decisions . Indeed , if neurons with strong surround suppression exerted a greater influence on perception , perhaps by virtue of anatomical connectivity ( Born et al . , 2000; Berezovskii and Born , 2000 ) , then psychophysical suppression would presumably increase accordingly . However , using choice probability analysis ( Britten et al . , 1996; Nienborg et al . , 2012; Haefner et al . , 2013 ) , we found no evidence that neurons with strong surround suppression were more correlated with the animals’ behavior choices; indeed the correlation between SIneu and choice probability was modestly negative ( Figure 2E; r = -0 . 14 , P = 0 . 04 ) . 10 . 7554/eLife . 16167 . 004Figure 2 . Quantification of single neuron selectivity for an example MT neuron , and the summary for the population . ( A ) Size tuning curves , plotting the firing rate ( mean ± s . e . m . ) for the preferred ( orange ) and null ( violet ) direction stimuli as a function of Gabor patch size . The lines indicate difference of error functions fits . ( B ) Neurometric function ( filled symbols ) for the example neuron plotting the d’ value as a function of stimulus size . The corresponding psychometric function is superimposed ( open symbols ) . Solid and dashed lines indicate difference of error functions fits . The psychophysical performance differs from Figure 1D since the stimulus was tailored to the neural population measured on any one day . ( C ) Scatter plot of the psychophysical d’ against the neuronal d’ at the largest stimulus size . Filled circles represent monkey C ( n = 105 ) , and open squares represent monkey Y ( n = 60 ) . Red represents neurons with weak surround suppression , and blue represents neurons with strong surround suppression . The distribution of d’neu-d’psy is shown at the diagonal . ( D ) The mean d’psy as a function of size from all sessions ( monkey C: n = 28 , monkey Y: n = 11 ) superimposed with the mean single neuron d’neu from all MT neurons ( 165 single neurons ) . Error bars denote s . e . m . ( E ) Population summary of choice probability ( CP ) . Scatter plot of CP against the suppression index of the neurometric function . Filled symbols represent CP values that are significantly different from 0 . 5 ( p<0 . 05 , permutation test ) . Solid line indicates linear fit ( r = −0 . 14 , P = 0 . 04 ) . The marginal distributions of SIneu and CP are shown on the top and the right . Filled and open bars indicate neurons with significant and non-significant choice probabilities , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 00410 . 7554/eLife . 16167 . 005Figure 2—figure supplement 1 . ( A ) RF positions of the neurons recorded . The dots are RF centers for the MT neurons from each animal . The circles indicate the average placement of the stimulus centers . Maximal stimulus radius is 15° . The dots represent the centers of the RFs of off-stimulus center neurons simultaneously recorded using the foveal stimulus for the motion direction discrimination task ( black circle ) , and when the stimulus was centered on the RFs ( blue circle ) . ( B , C ) Single neuron selectivity of peripheral neurons when the foveal stimulus was not centered on the RFs ( black ) , and when the stimulus was centered on the RFs ( blue ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 00510 . 7554/eLife . 16167 . 006Figure 2—figure supplement 2 . Quantification of choice probability ( CP ) of single neurons and the time course of CP . ( A ) Distributions of firing rates of an MT neuron , grouped according to the monkey’s choice of preferred or null direction motion . ( B ) The ROC curve of the distributions yielded a significant choice probability of 0 . 63 ( P = 0 . 010 , permutation test ) . ( C ) Mean CP across the population was calculated in 20 ms bins . CP was significantly above 0 . 5 from approximately 100 to 200 ms after stimulus onset ( p<0 . 01 , permutation test ) . ( D ) Scatter plot of CP against eccentricity of the neurons . Solid line indicates linear fit ( r = -0 . 48 , P = 0 . 05 ) . ( E ) Mean d’ across the population was calculated in 20 ms bins . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 006 The mean levels of noise correlations were typically on the order of 0 . 1 ( 0 . 099 ± 0 . 007 ) , compatible with previous reports ( Zohary et al . , 1994; Bair et al . , 2001; Huang and Lisberger , 2009 ) . Their strength was independent of motion direction or stimulus size ( Wilcoxon rank sum test for direction , 94% of experiments with p>0 . 05; for smaller and larger sizes , P = 0 . 55 Figure 4—figure supplement 1A ) . Next , we considered the relationship between noise correlations and tuning curve similarity; these have been found to correlate in previous studies ( Bair et al . , 2001; Huang and Lisberger , 2009 ) . Figure 3A shows the responses of two example neurons that were recorded simultaneously; each dot represents the mean response to a preferred ( red ) or null ( blue ) direction stimulus , with different dots corresponding to responses to different stimulus sizes . The responses of these neurons exhibit a clear signal correlation ( rsignal = 0 . 61 ) . Figure 3B shows trial-by-trial data from the same pair of neurons; here the responses have been z-scored to remove changes in the mean due to different stimulus sizes or directions ( Zohary et al . , 1994 ) . The remaining dependency reflects noise correlations in the responses of the two neurons ( rnoise = 0 . 21 ) . The relationship illustrated by this example pair is characteristic of the population ( Figure 4A ) , across which noise correlations and signal correlation are significantly correlated ( r = 0 . 32 , p<0 . 001 ) . 10 . 7554/eLife . 16167 . 007Figure 3 . Quantification of noise correlation ( rnoise ) and signal correlation ( rsignal ) between neuron pairs . ( A ) The mean responses of the two simultaneously recorded neurons across both directions and sizes . rsignal ( 0 . 61 ) is the Pearson correlation coefficient of the mean responses for the conditions . ( B ) The responses for each stimulus condition were z scored across the repetitions , and each point represents a response from one trial . rnoise ( 0 . 21 ) is the Pearson correlation coefficient of the entire dataset . The dashed lines represent linear fits . ( C , D ) Response correlations for an NS-NS pair and an SS-SS pair for one example stimulus size ( 1° ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 007 Interestingly , we find that this correlation structure appears to be different for pairs of neurons with different levels of surround suppression . This is apparent in the examples shown in Figure 3 . To study this relationship across the population ( N = 370 pairs ) , we classified neurons as surround suppressed ( SS ) or not ( NS ) , based on a simple median split of the SIneu distribution ( Glasser et al . , 2011 ) ( Figure 2C ) . This yielded three types of neuron pairs: both suppressed ( SS-SS ) , both non-suppressed ( NS-NS ) , and mixed ( SS-NS ) . Across the population , the magnitudes of rnoise were not significantly different across types of neuron pairs ( Wilcoxon rank sum tests , p>0 . 86 ) . However , the correlation structure differed substantially for different cell classes: For the NS-NS pairs , rnoise and rsignal tended to be correlated ( Figure 4A , red dots ) . By contrast pairs of SS neurons showed less of a dependency of noise correlation on signal correlation ( Figure 4A , blue dots ) . The difference in the slopes of the lines relating signal and noise correlations was significantly lower for the SS pairs than for the NS pairs ( ANCOVA , P = 0 . 03 , multiple comparison test ) ( Figure 4A , red and blue lines ) . For NS-SS pairs , this dependency was intermediate ( Figure 4A , black line ) . We performed several control analyses to verify that these results reflected a genuine difference in correlation structure across cell types . First , we recalculated rsignal using direction tuning curves that were measured for a fixed stimulus size . This controlled for any variation in rsignal that arose from differences in the size-tuning functions of NS and SS neurons . The results ( Figure 4—figure supplement 1D ) were similar to those in Figure 4A ( ANCOVA , P = 0 . 04 , multiple comparison test ) . Second , we verified that these results were not due to changes in firing rate across the different cell types , as the mean firing rates of NS-NS pairs ( median = 39 . 1 ) and SS-SS pairs ( median = 36 . 4 ) were not significantly different ( Wilcoxon rank sum test , P = 0 . 45 ) . Also , sampling from rate-matched sub-distributions of SS-SS and NS-NS pairs ( Materials and methods ) yielded significantly higher rnoise vs . rsignal slopes for the NS-NS sub-distributions ( Figure 4B; Wilcoxon rank sum test , p<0 . 001 ) . Finally , the reduction of this rnoise dependency did not depend on the categorical classification of SS and NS neurons , as we obtained similar results using continuous values of the joint SIneu for pairs of neurons ( Figure 4—figure supplement 1C; linear correlation: ( r = -0 . 232 , p<0 . 0001 ) . This finding suggests that the correlated variability between two neurons with similar stimulus preferences may largely arise from the same inputs that are responsible for surround suppression in those neurons . Differential correlations ( Moreno-Bote et al . , 2014 ) between neurons i and j are those that are proportional to fi’fj’ , where fi denotes the tuning function of neuron i , and the prime denotes the derivative with respect to the task-relevant direction in stimulus space; such correlations will limit the information carried even for arbitrarily large neural populations ( Moreno-Bote et al . , 2014 ) . We calculated differential correlations for all neuronal pairs , and found that there is indeed a positive relationship between noise correlations and f’f’ ( Figure 4—figure supplement 1E ) . Furthermore , we find the same difference between SS-SS and NS-NS pairs as reported above ( Figure 4A ) : the magnitude of the information-limiting correlations is greater between NS-NS pairs than between SS-SS pairs ( Figure 4—figure supplement 1E , rNS-NS = 0 . 48 , rSS-SS = 0 . 23 , P = 0 . 08 ) . In brief , while NS neurons are individually more informative for large stimuli than SS neurons , as a population they are more limited by their correlation structure than SS neurons . 10 . 7554/eLife . 16167 . 008Figure 4 . Relationship between noise correlation ( rnoise ) and signal correlation ( rsignal ) . ( A ) Scatter plot of rnoise versus rsignal for pairs of SS and SS ( blue ) , SS and NS ( black ) , and NS and NS ( red ) neurons . Lines represent linear regression fits . Marginal distributions of rnoise are also shown ( right panel ) . Lines and numbers mark the mean values of rnoise for each combination of neuron pairs . ( B ) Sampling from rate matched sub-distributions of SS-SS and NS-NS pairs gives similar differences in rnoise vs . rsignal slope . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 00810 . 7554/eLife . 16167 . 009Figure 4—figure supplement 1 . Effects of stimulus conditions , firing rate , and tuning similarity on the rnoise on rsignal dependency . ( A ) Box-whiskers plots of the value of rnoise across stimulus conditions . The value of rnoise is not significantly different between the preferred and null directions ( Wilcoxon rank sum test , P = 0 . 07 ) , and smaller and larger sizes ( Wilcoxon rank sum test , P = 0 . 55 ) . ( B ) Population selectivity as a function of number of neurons included in the simulation . Only the single neuron selectivity at the smallest stimulus size is considered with the mean noise correlation structure observed . ( C ) The joint SI for each neuron pair ( n = 370 ) , determined as the sum of the individual SIs , plotted against the product of rnoise and rsignal for the pair . For the latter , we first subtracted off the mean rnoise and rsignal to isolate the covariance of the two measures . Large positive values correspond to neuron pairs in which rnoise and rsignal have the same sign , as expected for NS-NS pairs ( Figure 4A ) . Small values indicate no consistent relationship between rnoise and rsignal , as expected for the SS-SS pairs ( Figure 4A ) . A linear regression ( solid line ) confirms a negative relationship between joint SI and the dependency of noise correlations on signal correlations ( r = -0 . 232 , p<0 . 001 ) . ( D ) A similar rnoise vs . rsignal relationship was observed when rsignal was calculated using separately measured direction tuning curves . ( E ) A similar rnoise vs . f’f’ relationship was observed when f’ was calculated using the difference between the preferred and null direction responses . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 009 Based on our empirical measurements described above , we devised a model to investigate to which degree each aspect of the neural data contributed to the observed psychophysical behavior . Such modeling is naturally limited by the impossibility of measuring the relevant properties of all the sensory neurons involved in processing the stimuli . Thus we accounted for this uncertainty explicitly by examining a large number of models from a joint probability distribution over parameters corresponding to the properties of the MT population response ( e . g , firing rates , noise correlations , direction tuning bandwidth , etc . ) . A detailed description of the modeling approach is given in the methods . In brief , we generated populations of synthetic neurons by sampling neural properties from a joint truncated Normal distribution over tuning curve parameters inferred from our measurements . In that way we could simulate neural populations that not only matched the observed marginal statistics but also the correlations between the measured parameters ( Figure 5—figure supplement 1 ) . Since we only measured neurons with RFs covering approximately the central 5° of the stimulus , we extrapolated from these neurons to those at larger eccentricities by shifting the size tuning curves of our measured neurons according to the distance between the simulated RF and the center of the stimulus . Furthermore , we scaled the number of model neurons according to the observed dependency of the magnification factor on eccentricity ( Van Essen et al . , 1981; Erickson et al . , 1989 ) . We sampled the noise correlation structure from a Wishart distribution around the empirical means as a function of the signal correlation between neuron pairs ( Figure 4A ) . By generating many such populations for each model , we extracted a range of predictions of behavioral performance for different stimulus sizes ( represented by the error bars in Figure 5—figure supplement 3A ) , so that for each model we could compute its range of predicted psychophysical suppression ( Figure 5—figure supplement 3B ) . The predicted model suppression is the key metric that we are interested in , and its dependency on the key model parameters are explored in Figure 5— figure supplement 4 and Figure 5D . In order to relate our simulated neural responses to behavioral performance ( Figure 5A ) we used a standard linear read-out in which a weighted average of the responses is compared to a decision-threshold ( Shadlen et al . , 1996; Gold and Shadlen , 2007; Haefner et al . , 2013; Smolyanskaya et al . , 2015; Pitkow et al . , 2015 ) . We made the assumption of a factorial decoder ( Figure 5B ) , in which the read-out weight for each neuron only depends on the properties of that neuron itself , for two primary reasons: First , such a set of read-out weights can be learned easily since each weight only depends on the properties of the individual neuron itself ( Law and Gold , 2009 ) , and second , it has recently received empirical support ( Pitkow et al . , 2015 ) . ( We also performed our analysis using an optimal linear read-out , as well as one in which each neuron’s weight depended only on its sensitivity to the stimulus and not its variability , and obtained qualitatively similar results – see Supplementary Information , Figure 5—figure supplement 2 ) . Since the stimulus size in our experiment is randomized , and since the duration is extremely brief ( 50 ms ) , we furthermore assume that the read-out is fixed and does not adjust dynamically to the stimulus size . We initially limited the read-out to neurons with receptive fields within 5° of the stimulus center; we examine the impact of this choice below . Figure 5C shows the average performance over 100 runs of this model . As in the behavioral data , we find that performance decreases for larger stimuli . The suppression shown by the model is of the same magnitude as the empirical behaviour ( Figure 5E , black ) , with the model SI being 0 . 48 ( Figure 5C , E cyan ) . To understand the source of this suppression , we performed additional analyses in which key components of the model were removed: Specifically , we considered models in which ( Zohary et al . , 1994 ) noise correlations were absent; ( Shadlen et al . , 1996 ) correlations were as measured , but surround suppression was absent; and ( Abbott and Dayan , 1999 ) correlations and surround suppression were on average as measured , but the observed relationship between them ( Figure 4A ) was missing . We found that both the noise correlation structure and surround suppression were necessary to account for the decreased performance as a function of size , since models ( Zohary et al . , 1994 ) and ( Shadlen et al . , 1996 ) did not show any psychophysical suppression at all ( SI = 0; data not shown ) . However , these components together were not sufficient to account for the observed behavioral results , since model ( Abbott and Dayan , 1999 ) exhibited only modest psychophysical suppression ( SI = 0 . 28; Figure 5C , E magenta ) . Thus the relationship between surround suppression and correlation structure appears to have important consequences for motion perception . From Figure 5C ( cyan ) it is apparent that the surround-suppression-dependent correlation structure has two separate effects on performance: One is a suppression of motion signal for large sizes . Perhaps more surprising is an increase in performance seen for small stimuli ( Figure 5C , E ) . This suggests that the combined effect of correlation structure and surround suppression is an increase in the capacity of the MT population to discriminate the direction of very small stimuli , at the expense of large stimuli ( see Discussion ) . The preceding analyses suggests that psychophysical suppression is due to a combination of two known aspects of neural coding , surround suppression and noise correlations . Equally important is novel interaction between these two factors , wherein the correlation between neurons depends on their respective surround suppression ( Figure 4A ) . To arrive at these conclusions , we assumed that the animals used a fixed read-out , focusing on neurons with receptive fields near the center of the stimuli . To determine the importance of this assumption , we ran model simulations in which the integration radius was varied ( Figure 5D ) . Unsurprisingly the SI decreased with increasing integration radius , dropping to 0 . 26 when the radius was 15° , which is significantly less than that exhibited psychophysically by the monkeys ( Figure 5E green , Wilcoxon rank sum test , p<0 . 001 ) . The overall model performance , obtained by summing the performance across all sizes , was , however , unaffected by this parameter ( ANOVA , P = 0 . 25 ) . This is due to the fact that a larger integration radius increases the performance at large sizes , while decreasing the performance at small sizes ( Figure 5C ) . This suggests that behavioral SI could vary substantially according to the internal strategies used by the observer . 10 . 7554/eLife . 16167 . 010Figure 5 . Simulation of population selectivity and model comparisons . ( A ) Schematic of the population selectivity simulation . The preferred and null responses were sampled from the distribution of parameters recorded . We tested combinations of different correlation structures and readout weights . ( B ) Calculation of the population selectivity . Each point represents the response from a trial from n neurons ( here , n = 2 ) . The one-dimensional distributions for the preferred and null direction responses were generated by projecting the points onto the normalized axis that connects the mean responses in n-dimensional space ( factorial read-out ) . The calculation of population d’ then follows the equation in the Materials and methods . ( C ) The predicted population neuronal selectivity plotted as a function of the stimulus size for each model . Data points represent averages across 100 iterations of the simulation , with each iteration based on a different re-sampling of the parameter set from the original data sets . Results are shown for the full model based on all empirical measurements in which surround suppression modulates noise correlation structure ( cyan ) ; a model where correlations and surround suppression were on average as measured , but the observed relationship between them was missing ( magenta ) , and finally the full model again , but with integration radius of 15° ( green ) . ( D ) SI of population d’ for simulations where increasing number of neurons with peripheral receptive fields are included . The x-axis indicates the integration window for including neurons’ receptive fields relative to the stimulus center . Error bars denote standard deviation . ( E ) Comparison of the SI of different simulations: colors as in C . Error bar for the psychophysical data denotes s . e . m . , and error bars for the model predictions denote standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 01010 . 7554/eLife . 16167 . 011Figure 5—figure supplement 1 . Distributions of the parameters for the Difference of Error functions fits . The histograms at the top row are the distribution of the parameters . The scatter plots shows the correlation of the parameters . The numbers at the top of each scatter plot are the Pearson correlation coefficients . Together these are used to create a multivariate Gaussian copula that is subsequently truncated to have only positve parameter values . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 01110 . 7554/eLife . 16167 . 012Figure 5—figure supplement 2 . Comparison of the SI of different decoders . Surround suppression modulation of noise correlation structure ( cyan ) , noise correlation structure with no surround suppression modulation ( magenta ) from Figure 5E using the factorial versus optimal decoders . Error bars denote standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 01210 . 7554/eLife . 16167 . 013Figure 5—figure supplement 3 . Distributions of measured and simulated behavioral d’ and SI . ( A ) The d’ values plotted as a function of the stimulus size . Data points represent averages across 39 psychophysical measurements ( black ) and 100 simulations ( cyan ) . Error bars denote standard deviation . ( B ) Distributions of SIs for the same psychophysical measurements ( black ) and simulations ( cyan ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 01310 . 7554/eLife . 16167 . 014Figure 5—figure supplement 4 . Predicted model SI as other model parameters were varied . ( A ) The predicted model SI when single-neuron SI is adjusted . The x-axis indicates the average SI of single neurons . ( B ) The predicted model SI when the slope of the relationship between correlation and surround suppression is varied . The offset was fixed at 2 . ( C ) The predicted model SI when the offset of this relationship is varied . The slope was fixed at 1 . 3 . Error bars denote standard deviations across 100 simulations in all plots . DOI: http://dx . doi . org/10 . 7554/eLife . 16167 . 014
Surround suppression has often been hypothesized to reduce correlations in natural inputs ( Snyder et al . , 2014; Vinje and Gallant , 2002 ) . We find that neurons with strong surround suppression can exhibit larger or smaller noise correlations , depending on the strength of their signal correlations . This relationship holds for all stimuli , even those that do not engage the receptive field surrounds strongly . Previous studies have shown that the magnitude of rnoise is not fixed , but can be reduced by adaptation ( Gutnisky and Dragoi , 2008 ) , learning ( Gu et al . , 2011 ) , and attention ( Cohen and Maunsell , 2009; Mitchell et al . , 2009 ) . The latter is particularly relevant , because attention increases the effective contrast of the stimulus ( Treue and Trujillo , 1999 ) , which also increases surround suppression ( Sundberg et al . , 2009 ) and decreases correlations ( Kohn and Smith , 2005 ) . Thus a single mechanism ( Reynolds and Heeger , 2009 ) may account for the effects of attention and surround suppression on noise correlations , as implemented with divisive normalization ( Tripp , 2012; Wiechert et al . , 2010 ) . Attention is also of interest because , like surround suppression , it can increase or decrease the strength of noise correlations , depending on the stimulus encoding of the neuron pairs ( Ruff and Cohen , 2014 ) . These differential effects on positive and negative noise correlations are particularly important in MT , where negative correlations are quite common ( Zohary et al . , 1994; Huang and Lisberger , 2009 ) . Negative correlations likely arise from motion-opponent mechanisms , in which the outputs of neurons with opposite direction tuning are subtracted . Such effects are stronger in MT than in V1 ( Qian and Andersen , 1994 ) , and they play an important role in decision-making models ( Shadlen et al . , 1996; Cohen and Newsome , 2009 ) . The results shown in Figure 5D suggest that incorporating the responses of a limited number of the MT neurons also contributed to psychophysical suppression . In a technical sense such a strategy is suboptimal ( Beck et al . , 2012 ) , as subjects could probably have performed better by making use of the neurons with receptive fields near the edges of the stimulus . Although we have no direct measure of the actual readout strategy used by the subjects , we suggest that the limited sampling used here is a more realistic model of the neural decision process , for several reasons . First , recall ( Figure 1C ) that stimuli sizes were randomly interleaved , so that motion information was always present in central locations , but for peripheral locations it was only present for large stimuli . Previous work suggests that subjects allocate resources according to the uncertainty associated with individual stimulus positions ( Pelli , 1985 ) , so that monkeys in our task likely made greater use of neurons with receptive fields positioned near the center of the stimulus . In addition , although the subjects could have used neurons with receptive fields positioned near the edge of the stimulus to extract additional information about the motion of large stimuli ( Tsui et al . , 2010 ) , we found instead that choice probability decreased with receptive field eccentricity ( Figure 2—figure supplement 2D; r = -0 . 48 , P = 0 . 05 ) . This suggests that the monkeys likely based their decisions on neurons with receptive fields closer to the center of the stimulus , where motion information was present reliably on every trial . It would therefore be interesting to study psychophysical suppression in a paradigm in which the stimulus location was unpredictable from trial to trial . We predict that psychophysical suppression would be reduced in this case , as would overall performance across sizes ( Herrmann et al . , 2010 ) . A related possibility is that the subjects made use of a suboptimal decoding strategy ( Moreno-Bote et al . , 2014; Pitkow et al . , 2015 ) . Indeed our analyses were based on a standard factorial decoder ( Abbott and Dayan , 1999; Sompolinsky et al . , 2001; Ecker et al . , 2011 ) , which ignores correlation structure and hence loses information . We have reanalyzed our results using an optimal linear decoder ( Moreno-Bote et al . , 2014; Pitkow et al . , 2015; Salinas and Abbott , 1994 ) , and found that this approach does improve performance in general . However , the main conclusions with respect to correlation structure and its dependence on surround suppression are unchanged ( Figure 5—figure supplement 2 ) . The paradoxical decline in motion perception with increasing stimulus size , first observed in human psychophysics ( Tadin et al . , 2003 ) , has often been attributed to neuronal surround suppression at the level of MT . Indeed , transcranial magnetic stimulation ( TMS ) that targets MT reduces the spatial suppression effect ( Tadin et al . , 2011 ) . However , the TMS protocols used to modulate spatial suppression are inhibitory , and so one might just as easily interpret these results as an effect on noise correlations ( Waterston and Pack , 2010 ) . This interpretation is consistent with our results , assuming that inhibitory connectivity plays a role both in generating surround suppression and in regulating noise correlations ( Tripp , 2012; Carandini and Heeger , 2012; Renart et al . , 2010 ) . The distinction is important in interpreting a large body of data showing reduced spatial suppression in certain human populations . Examples include people with schizophrenia ( Tadin et al . , 2006 ) , and older individuals ( Betts et al . , 2005 ) . Although these subjects may have deficits in GABAergic efficacy ( Tadin et al . , 2006; Betts et al . , 2005 ) , our results suggest that the connection to psychophysical spatial suppression could also be through noise correlations , as these are necessary to produce any effect of neural surround suppression at the population level . Our simulation results suggest that surround suppression can increase the selectivity of the neuronal population to the smallest stimulus size in this task , while worsening the selectivity at larger sizes ( Figure 5C; note performance for the 1° stimulus ) . Therefore , one benefit of surround suppression might be in the tracking of small moving stimuli . Indeed , activity in clusters of surround-suppressed neurons has been found to be causally linked to the tracking of small targets in smooth pursuit ( Born et al . , 2000 ) . The link between MT activity and smooth pursuit initiation has been further strengthened by the finding that neuronal variability in MT can account for the majority of motor variation in smooth pursuit ( Osborne et al . , 2005; Hohl et al . , 2013 ) . These observations have led to the suggestion that correlation structure in MT might limit the precision of pursuit initiation ( Huang and Lisberger , 2009 ) . Our results suggest that such comparisons should take into account the center-surround properties of individual MT neurons , as the neurons that seem to contribute most directly to pursuit initiation ( Born et al . , 2000 ) exhibit more advantageous correlation structure ( Figure 4A ) . As a result , the pursuit initiation system might benefit from averaging the activity of many surround-supressed MT neurons . This would explain both the weak correlation between single-neuron MT activity and pursuit and the relatively low choice probability of surround suppressed neurons in our perception task ( Figure 2E ) . It is interesting in this regard that some models of smooth pursuit initiation ( Hohl et al . , 2013 ) involve both a motion opponency step and a normalization operation . Normalization in these models serves the function of computing a vector average of the MT population response , and it also affects the levels of noise correlations in a manner that accounts for trial-to-trial fluctuations in behavior . Our results suggest the additional function of reshaping the selectivity of the MT population response in such a way as to favor the motion of small stimuli , precisely as would be expected for a system that initiates orienting responses to moving objects in a natural environment ( Lettvin et al . , 1959 ) .
Two adult female rhesus monkeys ( Macaca mulatta , both 7 kg ) were used for electrophysiological recordings in this study . Before training , under general anesthesia , an MRI-compatible titanium head post was attached to each monkey’s skull . The head posts served to stabilize their heads during subsequent training and experimental sessions . For both monkeys , eye movements were monitored with an EyeLink1000 infrared eye tracking system ( SR Research ) with a sampling rate of 1000 Hz . Visual motion stimuli were displayed at 60 Hz at 1280 by 800 pixels resolution; the viewing area subtended 60° × 40° at a viewing distance of 50 cm . The sizes of the Gabor patches were defined by 2 standard deviations of the Gaussian envelope and ranged from 1° to 15° in steps of 2° . All procedures conformed to the regulations established by the Canadian Council on Animal Care and were approved by the Institutional Animal Care Committee of the Montreal Neurological Institute . Area MT was identified based on an anatomical MRI scan , as well as depth , prevalence of direction-selective neurons , receptive field size to eccentricity relationship , and white matter to grey matter transition from a dorsal-posterior approach . We recorded single-units using linear microelectrode arrays ( V-Probe , Plexon ) with 16 contacts . Neural signals were thresholded online , and spikes were assigned to single units by a template-matching algorithm ( Plexon MAP System ) . Offline , spikes were manually sorted using a combination of automated template matching , visual inspection of waveform , clustering in the space defined by the principle components , and absolute refractory period ( 1 ms ) violations ( Plexon Offline Sorter ) . Animals were trained to perform coarse motion direction discrimination tasks with Gabor patches . The structure of an individual trial is illustrated in Figure 1C . Each trial began with the onset of a fixation point . The monkey was required to establish and maintain fixation within a 2° × 2° window for 300 ms , after which a drifting Gabor patch appeared on the receptive field centers . The parameters of the Gabor patch were matched to the multi-unit preferences for spatial position , preferred direction , and spatiotemporal frequency ( Figure 1A and Figure 2—figure supplement 1A ) . We included all units that exhibited significantly different responses ( t-test; p<0 . 05 ) to their preferred and null directions at the smallest stimulus size , and a preferred direction within ± 42° of one of the directions of the stimulus used for behavioral testing . The range of stimulus sizes ( 0–15° radius at 2 . 3 ± 0 . 5° eccentricity ) was chosen to straddle the receptive field sizes ( 2 . 2 ± 1 . 1° radius at 3 . 2 ± 1 . 3° eccentricity ) of the recorded neurons ( Figure 1A and Figure 2—figure supplement 1A ) . The motion stimulus was presented for a brief period ( typically 50 ms ) , after which the monkey was required to maintain fixation for another 300 ms . The fixation point then disappeared , and two choice targets appeared , after which the monkey made a saccade to the corresponding target to report its perceived motion direction ( preferred or null relative to the neuron isolated ) . The monkey was required to indicate its decision within 700 ms following the onset of the choice targets . Correct choices were rewarded with a drop of liquid . If fixation was broken at any time during the stimulus , the trial was aborted . In a typical session , the monkeys performed 20–40 repetitions of each distinct stimulus . The psychophysical d’ was calculated asdpsy′=zhit rate−zfalse alarm rate where the hit and false alarm rates were z-transformed with zero mean and unit variance . The neuronal d’ was calculated asdneu′=μpref−μnullσpref2+σnull22 where μpref and μunlll are the means of the preferred and null direction responses , and σpref2 and σnull2 are the variances ( Green and Swets , 1966 ) . To quantify the neuronal selectivity of both the single neurons and the population , we used the firing rate during the 100200 ms interval after stimulus onset to calculate the d’ . This interval was chosen because the firing rates in response to the preferred and null directions were significantly different ( Figure 2—figure supplement 2E; p<0 . 05 , t-test ) , and spikes during this time window were significantly correlated with the animals’ behavioral choices ( Figure 2—figure supplement 2C ) ; other time windows between 60–300 ms did not result in differences in the results reported here . To quantify surround suppression in both psychophysics and neural responses , we first calculated d’ for each stimulus size . The resulting size-tuning curves were fitted with the DoE function ( DeAngelis and Uka , 2003 ) ( Figure 2B ) :Aeerf ( xcse ) −Aierf ( xcse+si ) +m where Ae and Ai scale the height of the excitatory center and inhibitory surround , respectively , se and siare the excitatory and inhibitory sizes , and m is the baseline firing rate of the cell , which is set to 0 for the psychophysical and neural selectivity functions . The suppression index ( SIneu ) for each neuronal size tuning curve was then calculated as SIneu = ( d’m – d’L ) /d’m , where d’m is the maximum selectivity across responses to different stimulus sizes , and d’L is the selectivity observed at the largest size . The psychophysical suppression index SIpsy was calculated analogously , using psychophysical selectivity rather than neuronal selectivity . Since using the raw responses is sensitive to noise at both the maximum response and the response at the largest size , we used the values from the DoE fits for SI calculations . Choice probabilities ( CP ) were used to quantify the relationship between behavioral choice and response variability ( Britten et al . , 1996 ) . For an identical stimulus , the responses can be grouped into two distributions based on whether the monkeys made the choice that corresponds to the neuron’s preferred direction , or the null direction ( Figure 2—figure supplement 2A ) . As long as the monkeys made at least five choices for each direction , ROC values were calculated from these response distributions , and the area underneath the ROC curve was taken as the CP value ( Figure 2—figure supplement 2B ) . The single CP for each neuron was computed by averaging the CP across all stimulus conditions . The alternative method of z-scoring the data for each stimulus conditions and then combining them into a single pair of distributions for preferred and null choices can underestimate the CP when the number of choices for preferred and null directions differs across stimulus conditions ( Kang and Maunsell , 2012 ) . Noise correlation ( rnoise ) was computed as the Pearson correlation coefficient ( ranging from -1 to 1 ) of the trial-by-trial responses of two simultaneously recorded neurons ( Zohary et al . , 1994 ) . For each size and direction combination , responses were z-scored by subtracting the mean response and dividing by the s . d . across stimulus repetitions . This operation removed the effect of size and direction on the mean response , such that rnoise measured only correlated trial-to-trial fluctuations around the mean response . To prevent correlations driven by outliers , we only considered trials on which the responses were within ±3 s . d . of the mean ( Zohary et al . , 1994 ) . We also normalized for slow changes in the responses in blocks of 20 trials ( Zohary et al . , 1994 ) . Signal correlation ( rsignal ) was computed as the Pearson correlation coefficient ( ranging from -1 to 1 ) between size tuning curves of preferred and null directions for two simultaneously recorded neurons . Size tuning curves were constructed by plotting mean firing rates as a function of size for preferred and null directions . In addition , we calculated an alternative measure of rsignal based on the similarity in direction tuning between the two neurons , and found similar trends between the neuron pairs ( Figure 4—figure supplement 1D ) . As the measure of rnoise can depend on the firing rates of the neuron pairs ( Cohen and Kohn , 2011 ) , we created matched rate distributions of SS-SS and NS-NS pairs by subsampling from the original distributions in Figure 4A . We first created distributions of the geometric means of SS-SS and NS-NS pairs and then resampled randomly to create sub-distributions with equal amounts of data in each bin ( Ruff and Cohen , 2014 ) . We resampled 10 , 000 times and calculated the slope of the rnoise vs . rsignal fit of each sub-distribution . The distribution of SS-SS and NS-NS slopes are shown in Figure 4B . The data and Matlab code to generate Figure 5C , D and E are available at http://packlab . mcgill . ca/suppression data and code . zip . For all simulations , we considered a population of MT neurons with different receptive field positions and different preferences for stimulus size . The RF locations were determined by fitting a spatial Gaussian to the neuronal response over a 5 x 5 grid . For neurons with RFs within 5° radius of the stimulus center , the responses to different sizes were taken from the size-tuning curves of the actual MT neurons . For neurons with RFs that were not within 5° radius of the stimulus center , we shifted the size-tuning curves by the same proportion as the RF offset , so that a larger stimulus was required to generate the equivalent level of activation . We estimated that the shift in the size-tuning curve is roughly proportional to the shift of the stimulus from the RF center . This was determined by measuring the size-tuning with the stimulus placed at different spatial locations ( Figure 2—figure supplement 1B , C ) . The number of neurons activated by each stimulus was determined using the previously measured cortical magnification in MT , Magnification factor=6 ∗ eccentricity−0 . 9 ( Van Essen et al . , 1981; Erickson et al . , 1989 ) . This maps visual space in degrees into cortical space in millimeters . The integral of cortical space activation yields the cortical footprint ( in square millimeters ) as a function of stimulus size . The absolute number of neurons can then be obtained by multiplying the cortical footprint by a factor that indicates the number of neurons per millimeter . We set this factor to 20 neurons/mm2 , which yielded a range of pool sizes comparable to those used in other studies ( Shadlen et al . , 1996 ) ( Figure 1B ) . The range ofpool sizes is in the regime where population sensitivity is saturated ( Figure 4—figure supplement 1B ) . We verified that our results are robust with respect to this parameter re-running the simulations with a value of 40 neurons/mm2; the results were qualitatively similar to those reported here . All simulations involved extrapolations from the statistics of our neural recordings . To generate the size tuning curves for the preferred and null directions , Si ( s , θ ) , for each simulated neuron , we first used the distributions of DoE parameters from all neurons recorded during the discrimination task to estimate the parameters of a multivariate Gaussian distribution . We then randomly sampled from this distribution to obtain DoE parameters that were subsequently converted to tuning curves . The variance , Vi ( s , θ ) , for each simulated neuron , was generated by multiplying the Si ( s , θ ) with Fano factors randomly sampled from a Gaussian distribution estimated from the measured Fano factors . For each combination of size and direction in a simulated trial , the response of the ith neuron was generated by randomly drawing a value from a Gaussian distribution having the same mean , Si ( s , θ ) , and variance , Vi ( s , θ ) , as the generated tuning curveRi ( s , θ ) =Si ( s , θ ) +xiVi ( s , θ ) where x is a vector of independent random deviates with zero mean and unit variance . This procedure generated a set of responses in which each neuron’s noise was independent . To reproduce the relationship between rnoise , rsignal and surround suppression , the covariance matrix , rnoise between neurons i and j was assigned according tornoise ( i , j ) =SI dependency ( i , j ) ×m×rsignal ( i , j ) +b where rsignal represents the signal correlation between size tuning curves of preferred and null directions for a pair of neurons . The slope m and intercept b were acquired from a linear regression fit to the measured relationship between rnoise and rsignal for all pairs of neurons . The SI dependency term was set to 1 in the no SI modulation condition ( Figure 5C , E , magenta ) . In the SI dependency condition ( Figure 5C , E , cyan ) we estimated the dependency empirically from the data as , SI dependency ( i , j ) =1 . 3 ( maxi , j ( SIi+SIj ) −SIi−SIj−2 ) where SIi and SIj were the suppression indices for neurons i and j , respectively . When the joint SI of the neuron pairs is high , the value of SI dependency will be low , and vice versa , capturing the modulation of the rnoise on rsignal slope by surround suppression . The constants , 1 . 3 and 2 , were determined using a least squares method to obtain the closest slopes for the 3 groups of neuron pairs in Figure 4A . In each iteration of the simulation , we sampled the noise correlation structure from a Wishart distribution with maximum variance around the empirical means . After assuming the covariance matrix , the response simulation becomesRi ( s , θ ) =Si ( s , θ ) +yiVi ( s , θ ) where y represents the product of the matrix square root of the covariance matrix with the vector of independent deviates , x ( Shadlen et al . , 1996; Cohen and Newsome , 2009; Liu et al . , 2013 ) . For each simulation , we generated 1000 trials of responses for each neuron , each size , and each direction . After generating the responses for 1000 trials for a fixed number of neurons , n , at each stimulus size , the 1000 responses in n-dimensional space were projected onto the axis that connects the mean responses . This subsequently generated 1D distributions for the preferred and null direction responses . The 1D distribution of preferred and null direction responses was normalized by their variance and the population d’ was then computed while the decoder was blinded to their correlations ( Figure 5B ) . This is commonly referred to as a factorial decoder; the readout weights the responses depending on the neuronal sensitivity functions and not on their correlations ( Pitkow et al . , 2015 ) . In addition to this correlation-blind decoder ( Figure 5 ) , we also explored the performance of an optimal linear estimator that considers not only the responses of neurons , but also the covariance matrix ( Salinas and Abbott , 1994 ) . The impact of the dependency between surround suppression and correlation structure is smaller , but still present ( Figure 5—figure supplement 2 ) . | People usually find it easier to see things when they are big and bright , but there are occasionally exceptions . One example concerns moving objects: when they are small , we can identify their direction of motion easily , but this becomes much more difficult for larger objects . This decreased perceptual sensitivity appears to be linked to other mental processes . For example , studies have suggested that people with high IQs have more difficulty perceiving the motion of large objects , whereas people with various psychiatric disorders , such as schizophrenia , are better able to see such movement . Although several theories have been proposed , there is currently no good explanation for these findings . Liu et al . set out to determine why the part of the brain that is responsible for vision ( the visual cortex ) fails to register the direction of large moving objects and how this failure might relate to mental function in general . To do this , Liu et al . trained monkeys to report which direction different sized stimuli were moving on a screen . The electrical activity of nerve cells in the part of the visual cortex that deals with movement was recorded while the monkeys performed this task . The results of the experiments revealed that , on average , these cells responded strongly to large moving stimuli , even though the monkeys had trouble seeing their motion . However , nerve cells are “noisy” – they respond a bit differently every time they are presented with the same stimulus – and this noise was stronger for larger stimuli . By studying the mathematical relationship between the noise and what the animals perceived , Liu et al . found that the visual cortex attempts to suppress the noise and in the process often shuts off the responses to large stimuli entirely . This suppression is likely to cause the movement of large stimuli to be poorly perceived . If suppressing this kind of noise is really responsible for failures in perceiving motion , then this mechanism could also explain the connection between motion perception and other mental processes . Liu et al . are currently testing this idea . | [
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"neuroscience"
] | 2016 | A neural basis for the spatial suppression of visual motion perception |
The adult frog retina retains a reservoir of active neural stem cells that contribute to continuous eye growth throughout life . We found that Yap , a downstream effector of the Hippo pathway , is specifically expressed in these stem cells . Yap knock-down leads to an accelerated S-phase and an abnormal progression of DNA replication , a phenotype likely mediated by upregulation of c-Myc . This is associated with an increased occurrence of DNA damage and eventually p53-p21 pathway-mediated cell death . Finally , we identified PKNOX1 , a transcription factor involved in the maintenance of genomic stability , as a functional and physical interactant of YAP . Altogether , we propose that YAP is required in adult retinal stem cells to regulate the temporal firing of replication origins and quality control of replicated DNA . Our data reinforce the view that specific mechanisms dedicated to S-phase control are at work in stem cells to protect them from genomic instability .
Adult stem cell maintenance is required to sustain long-term preservation of tissue homeostasis . In the fish or amphibian retina , a continuously proliferating peripheral domain called ciliary marginal zone ( CMZ ) ( Wetts et al . , 1989; Perron et al . , 1998 ) was recently formally demonstrated to contain genuine multipotent and self-renewing neural stem cells ( Centanin et al . , 2011 ) . The CMZ not only ensures cell replacement , but also contributes to life-long growth of the eye through the permanent generation of all retinal cell types . The CMZ thus represents an ideal model for dissecting molecular cues underlying retinal stem cell properties in vivo . Such knowledge is essential for the development of innovative therapeutic strategies based on the mobilization and targeted activation of endogenous neural stem cells for tissue repair . The Hippo pathway effector yes-associated protein ( YAP ) was identified as a major regulator of organ growth through its actions on embryonic precursor cells ( Lian et al . , 2010; Ramos and Camargo , 2012 ) . YAP function in adult stem cells , however , remains unclear . For instance , Yap overexpression increases self-renewal of airway basal stem cells ( Zhao et al . , 2014 ) . In contrast , it surprisingly leads to a loss of intestinal stem cells ( Barry et al . , 2013 ) , while being seemingly neutral regarding the quantity and function of hematopoietic stem cells ( Jansson and Larsson , 2012 ) . Inactivation studies further suggested that YAP is largely dispensable in a physiological context for the homeostasis of several adult organs ( Cai et al . , 2010; Azzolin et al . , 2014; Chen et al . , 2014; Zhang et al . , 2014 ) , although this might reflect in some cases functional redundancy with the other Hippo effector TAZ ( Imajo et al . , 2015 ) . YAP is implicated in tissue regeneration but its effects are controversial ( Cai et al . , 2010; Barry et al . , 2013 ) . Thus , the role of YAP in vertebrate adult stem cells may likely be context-dependent and clearly deserves further investigation . Since its function in adult neural stem cells is presently unknown , we took advantage of the Xenopus CMZ model system and investigated whether Yap is involved in the maintenance of an active pool of retinal stem cells in the continuously growing post-embryonic frog eye . Although YAP gain of function led quite expectedly to CMZ cell overproliferation , the loss of function analysis revealed a more complex phenotype . Indeed , we found that stem cells were still present but exhibited aberrant cell cycle progression . In particular , DNA replication timing was found to be altered leading to a dramatic S-phase shortening . This correlates with increased DNA damage and eventually cell death . We also found that YAP functionally and physically interacts with PKNOX1 , a transcription factor required to maintain genomic stability ( Iotti et al . , 2011 ) .
In situ hybridization at the optic vesicle stage revealed prominent Yap expression in the presumptive retinal pigmented epithelium ( RPE ) and in the neural retina/RPE border ( Figure 1—figure supplement 1A ) , a region we previously proposed to be the presumptive adult stem cell niche ( El Yakoubi et al . , 2012 ) . In line with this , we found that in the post-embryonic retina , Yap is expressed in the most peripheral stem cell-containing region of the CMZ ( Figure 1A , B ) . We also performed immunostaining using an antibody whose specificity was assessed in a loss of function context , that is , in tadpoles injected with Yap Morpholinos ( Yap-MO; Figure 1—figure supplement 2; see also Figure 2—figure supplement 1 for efficiency and specificity evaluation of Yap-MO ) . YAP protein was detected in stem cells located at the tip of the CMZ , mainly in the cytoplasm , although some signal could be observed as well in the nuclei of these cells . Of note , we also found YAP labeling in Müller glial cells ( Figure 1C ) . To delineate more precisely the Yap expression domain , we co-labeled Yap and proliferative cells ( Figure 1D ) . A short EdU pulse was performed allowing slow dividing stem cells to be distinguished from fast proliferating transit amplifying progenitors in the CMZ ( Xue and Harris , 2011 ) . Yap staining was found to be prominent in EdU-negative stem cells and in the most peripheral EdU-positive cells ( young progenitors ) . The staining then waned in more central older progenitor cells . Of note , in contrast to Yap , Taz is faintly expressed in the post-embryonic retina and only a weak and diffuse signal could be detected in the CMZ ( Figure 1—figure supplement 1B ) . 10 . 7554/eLife . 08488 . 003Figure 1 . Yap overexpression expands the proliferating cell population in the post-embryonic retina . ( A ) Schematic transversal section of a Xenopus tadpole retina ( RPE: retinal pigment epithelium; NR: neural retina; ON: optic nerve ) . Within the CMZ ( right panel ) , retinal stem cells ( RSC ) reside in the most peripheral margin while actively dividing progenitors ( P1 ) and their post-mitotic progeny ( P2 ) are localized more centrally . ( B ) In situ hybridization analysis of Yap expression on stage 40 retinal sections . The image on the right is a higher magnification of the CMZ ( dashed lines represent the different zones as in a ) . ( C ) Immunostaining with anti-YAP antibody on stage 42 retinal sections . YAP labeling is detected in the CMZ as well as in Müller glial cells ( arrows ) . Images on the right are higher magnifications of the CMZ . ( D ) EdU labeling ( 3-hr pulse ) following in situ hybridization with a Yap probe ( dotted line ) on stage 40 retinal sections . ( E ) Lateral views ( left panels ) , head dorsal views ( middle panels ) and dissected eyes ( right panels ) of stage 40 tadpoles following two-cell stage microinjection of GFP mRNA as a lineage tracer with either ß-gal ( control ) or Yap mRNA . The asterisk indicates the injected side . ( F ) Quantification of dissected eye area . ( G–J ) TUNEL ( G , H; stage 33/34 ) or EdU incorporation ( I , J; 3-hr pulse at stage 40 ) assays analyzed on retinal sections from tadpoles injected as in ( E ) . Arrows point to TUNEL-positive cells in higher magnifications of the area delineated with dotted line ( G ) . The number of analyzed retinas is indicated in each bar . Data are represented as mean ± SEM . Scale bar = 1 mm in ( E ) and 40 µm in other panels . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 00310 . 7554/eLife . 08488 . 004Figure 1—figure supplement 1 . Yap , Taz and Tead expression . ( A ) In situ hybridization analysis of Yap expression on stage 24–25 ( left ) , 26–27 ( middle ) and stage 35 ( right ) retinal sections . Yap is expressed in the presumptive neural retina ( NR ) of the optic vesicle but is more strongly detected in the presumptive retinal pigmented epithelium ( RPE ) , consistently with previous data in fish ( Miesfeld and Link , 2014 ) . It also labels the RPE/NR border region ( delineated with dotted lines ) , which is believed to give rise to the CMZ ( El Yakoubi et al . , 2012 ) . At latter stages , Yap gets restricted to the ciliary margin ( black arrows ) . ( B , C ) In situ hybridization analysis of Taz ( B ) , Tead1 and Tead2 ( C ) expression on stage 40 retinal sections . Images on the right are higher magnifications of the ciliary margin . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 00410 . 7554/eLife . 08488 . 005Figure 1—figure supplement 2 . Validation of YAP antibody specificity . Immunostaining with anti-YAP antibody on retinal sections from stage 42 tadpoles following microinjection of either Yap-5-mismatch-MO ( control ) or Yap-MO . YAP is undetectable in Yap morphants . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 00510 . 7554/eLife . 08488 . 006Figure 1—figure supplement 3 . YapΔTBS does not promote CMZ cell proliferation . EdU incorporation assays ( 3-hr pulse at stage 40 ) analyzed on retinal sections from tadpoles injected as in Figure 1E . The number of analyzed retinas is indicated in each bar . Data are represented as mean ± SEM . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 006 Finally , as YAP acts as a co-transcriptional activator , we wondered whether its classical partners of the TEAD family were also expressed in the CMZ . We found consistent labeling of both Tead1 and Tead2 in the periphery of the CMZ where Yap is expressed ( Figure 1—figure supplement 1C ) . To investigate YAP function in the post-embryonic retina , we first undertook a gain of function approach by the means of mRNA injection at the two-cell stage . Yap-overexpressing tadpoles displayed eye overgrowth on the injected side ( Figure 1E , F ) . This phenotype prompted us to analyze the impact of YAP on both cell death and proliferation . We found that Yap overexpression results in both a decreased number of TUNEL-positive cells ( Figure 1G , H ) and a dramatic expansion of the EdU-positive cell population ( Figure 1I , J ) . The overproliferative phenotype was strongly exacerbated upon overexpression of a Yap mutant construct where Ser-98 was replaced by an alanine ( YapS98A ) ( Figure 1I ) . This residue ( Ser-127 in mammalian YAP ) is a conserved Lats phosphorylation site that has been shown to mediate the growth-suppressive output of the Hippo signaling cascade in vivo ( Zhao et al . , 2007 ) . In contrast , overexpression of a truncated construct lacking the TEAD binding site ( YapΔTBS ) was unable to trigger enhanced proliferation in the CMZ , suggesting that the overproliferative phenotype requires interaction with a TEAD protein ( Figure 1—figure supplement 3 ) . Together , these data reveal that Yap-dependent retinal overgrowth is likely caused by enhanced cell survival and cell proliferation . We next sought to determine whether Yap is essential for post-embryonic retinal growth by knocking it down using Yap-MO . The Morpholino concentration was chosen to efficiently decrease YAP quantity ( as inferred from Western-blot analysis; Figure 2—figure supplement 1A ) , while avoiding previously described early embryonic defects ( Gee et al . , 2011 ) . In these conditions , morphant tadpoles developed but exhibited significantly reduced eye size compared to controls ( Figure 2A , B ) . Importantly , this phenotype was restored upon co-injection of Yap-MO with non-targetable Yap mRNAs , demonstrating specificity ( Figure 2—figure supplement 1B , C ) . To exclude potential growth impairment at the level of the whole organism and assess the tissue autonomy of eye size defects , we performed optic vesicle isotopic and isochronic graft experiments ( Figure 2C ) . When the optic vesicle of a control tailbud was transplanted into an enucleated morphant embryo , it nevertheless reached a normal size . In contrast , Yap-MO optic vesicles grafted in a control host generated smaller eyes , which is in accordance with Yap knockdown effects being eye autonomous . 10 . 7554/eLife . 08488 . 007Figure 2 . Yap knockdown decreases eye size and EdU incorporation in the post-embryonic retina . ( A ) Lateral views ( left panels ) , head dorsal views ( middle panels ) and dissected eyes ( right panels ) of stage 40 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) or Yap-MO . The asterisk indicates the injected side . ( B ) Quantification of dissected eye area . ( C ) Eyes of stage 40 tadpoles following optic vesicle transplantation at stage 25 as shown in the schematic . Dotted lines delineate the eye circumference . ( D ) EdU incorporation assay ( 6-hr pulse ) analyzed on retinal sections from stage 40 tadpoles injected as in ( A ) . Higher magnifications of the CMZ ( delineated by dotted lines ) are shown for each condition . ( E ) Quantification of EdU-positive cells within the whole CMZ or within the most peripheral stem cell compartment . ( F ) In situ hybridization analysis of Hes4 ( retinal stem cell marker; El Yakoubi et al . , 2012 ) and Atoh7 ( progenitor cell marker; Kanekar et al . , 1997 ) expression on stage 40 retinal sections from tadpoles injected as in ( A ) . ( G–J ) Hoechst staining and PCNA immunolabeling on stage 40 retinal sections from tadpoles injected as in ( A ) . The CMZ is delineated with dotted lines . The number of analyzed retinas per condition is indicated in each bar . Data are represented as mean ± SEM . Scale bar = 1 mm in ( A ) and 40 µm in other panels . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 00710 . 7554/eLife . 08488 . 008Figure 2—figure supplement 1 . Validation of Yap-MO efficiency and specificity . ( A ) Western blot analysis showing YAP expression decrease at different stages following microinjection of either Yap-5-mismatch-MO ( control ) or Yap-MO . α-tubulin is used as a loading control . ( B ) Lateral views and dissected eyes of stage 40 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO + ß-gal mRNA ( control ) , Yap-MO + ß-gal mRNA ( Yap-MO ) , Yap-5-mismatch-MO + Yap mRNA ( Yap ) , Yap-MO + Yap mRNA ( Yap-MO + Yap ) . ( C ) Quantification of dissected eye area . The Yap-MO-induced small eye phenotype is rescued by co-injection of Yap mRNA . Of note a suboptimal dose of Yap mRNA was used for the rescue experiment so that it does not alone give any eye phenotype . The number of analyzed tadpoles is indicated for each bar . Scale bar = 1 mm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 008 Finally , to address whether the reduced eye size was due to abnormal embryonic morphogenesis or to post-embryonic growth defects , we adapted in Xenopus the use of photo-cleavable Morpholinos ( photo-MO ) . This technology , previously set up in zebrafish ( Tallafuss et al . , 2012 ) , allows for an inducible or reversible gene knockdown through UV-induced cleavage of either sense or antisense photo-MOs ( Figure 3A ) . We found that restoring YAP function at late embryogenesis , following knockdown during development , leads to tadpoles with normal sized eyes ( Figure 3B–D ) . This supports the hypothesis that the Yap-MO phenotype is not the indirect consequence of developmental morphogenetic defects . In line with this , conditional Yap knockdown starting at late retinogenesis was found to be sufficient to trigger a small eye phenotype ( Figure 3B , E , F ) . Together , these data point to a specific role for YAP in the homeostatic control of post-embryonic retinal growth . 10 . 7554/eLife . 08488 . 009Figure 3 . Conditional Yap knockdown in the retina . ( A ) Principle of reversible and inducible gene knockdown using photo-Morpholinos ( photo-MO ) . Photo-MO contains a photo-sensitive subunit cleaved by 365 nm light . Yap-AS-photo-MO is degraded upon UV light exposure and its translation blocking activity is thus interrupted . Unmodified Yap-MO is rendered inactive by binding to Yap-S-photo-MO . It therefore cannot bind its mRNA target until light-induced cleavage of the sense MO . ( B ) Diagram of the experimental design . Embryos are microinjected with MO at the two-cell stage , subjected to UV exposure at different developmental stages as indicated ( black flashes ) and then sacrificed for analyses . ( C–F ) Analysis of reversible ( C , D ) and inducible ( E , F ) Yap knockdown . ( C , E ) Lateral views and dissected eyes of stage 41 tadpoles following two-cell stage microinjection of the indicated MO ( see table in B ) . ( D , F ) Quantification of dissected eye area . The stage at which UV exposure was performed is indicated above each bar . The Yap-AS-photo-MO ( without any UV exposure ) shows the same efficiency as the Yap-MO in reducing eye size . It is efficiently cleaved by UV light since exposure right after injection ( stage 4 ) leads to a wild type phenotype . Restoring Yap function from stage 33/34 or even from stage 37/38 onwards leads to normal eye sized embryos , demonstrating that restricting Yap knockdown to embryogenesis is not sufficient to affect tadpole eye growth . The Yap-S-photo-MO efficiently blocks Yap-MO since their co-injection does not affect eye size . It is efficiently cleaved by UV light since exposure right after co-injection ( stage 4 ) leads to a small eye phenotype , as observed in Yap-MO-injected embryos . Conditional Yap knockdown by light exposure from stage 33/34 or even from stage 37/38 onwards is sufficient to reduce eye size , suggesting that Yap is required at post-embryonic stages to maintain CMZ-dependent eye growth . Of note , in our experimental conditions , UV light exposure does not generate any significant effects on eye size ( data not shown ) . The number of analyzed tadpoles is indicated within each bar . Scale bar = 1 mm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 00910 . 7554/eLife . 08488 . 010Figure 3—figure supplement 1 . Inducible Yap knockdown at post-embryonic stages reduces EdU incorporation in the CMZ . ( A ) EdU incorporation assay ( 3-hr pulse ) analyzed on retinal sections from stage 41 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) , Yap-MO and/or Yap-S-photo-MO , as indicated in the table . ( B ) Quantification of EdU-positive cells within the CMZ . Conditional Yap knockdown by light exposure from stage 37/38 onwards is sufficient to reduce EdU incorporation . The total number of analyzed retinas per condition is indicated in each bar . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 010 To investigate the cause of eye size reduction in Yap morphant tadpoles , we determined the level of proliferation within the CMZ . Yap-MO-injected tadpoles harbored a significantly decreased number of EdU+ cells compared to a control situation ( Figure 2D , E ) . The same was true when Yap knockdown was conditionally induced from late embryogenesis ( Figure 3—figure supplement 1 ) . The difference in EdU-labeling between control and Yap morphant tadpoles was even more pronounced at the tip of the CMZ where stem cells reside and Yap expression is the strongest ( Figure 2E ) . We thus reasoned that Yap knockdown might decrease the number of proliferative cells in the CMZ as a consequence of stem cell depletion . Using in situ hybridization , we examined the expression of several retinal stem and progenitor cell markers ( Figure 2F and data not shown ) . Surprisingly , stainings observed in Yap-MO-injected tadpoles were similar to control ones , indicating that both stem and progenitor cell populations were still present . Accordingly , neither the total number of cells within the CMZ nor the size of the proliferative cell population ( PCNA labeled ) was significantly affected ( Figure 2G–J ) . Together , these data indicate that Yap knockdown does not induce stem/progenitor cell exhaustion but rather alters the relative proportion of time these cells spend in S-phase of the cell cycle . The observed phenotype suggests that cell cycle kinetics of retinal stem/progenitor cells is perturbed in morphant tadpoles . As a first global approach to test this hypothesis , we evaluated the mitotic index in the whole post-embryonic CMZ using the mitotic marker phospho-histone H3 ( PH3; Figure 4A , B ) . We found that compared to the control , Yap knockdown results in a significantly lower percentage of mitotic cells per section , which is suggestive of a longer total cell cycle length . To further investigate cell cycle progression defects at the level of the whole CMZ , we then measured G2 length using the percentage of labeled mitosis ( PLM ) paradigm ( Quastler and Sherman , 1959; Cai et al . , 1997; Locker et al . , 2006 ) ( Figure 4C–E ) . As expected from the asynchrony among retinal cells in the CMZ , the percentage of PH3/EdU-labeled cells increased sigmoidally with increasing EdU exposure times , before reaching a plateau ( Figure 4E ) . Noticeably , the PLM was consistently lower at each time point in Yap morphant retinas compared to control ones , indicating a delayed S- to M-phase progression . We estimated that the mean G2 length ( TG2; see ‘Materials and methods’ for details ) doubled in Yap morphant retinas compared to the control ( from approximately 2 . 5 to 4 . 5 hr ) . It thus appears that Yap knockdown results in perturbed cell cycle kinetics within the CMZ . 10 . 7554/eLife . 08488 . 011Figure 4 . Yap loss of function slows down cell cycle kinetics of retinal stem cells . ( A ) PH3 immunolabeling on retinal sections from stage 40 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) or Yap-MO . The CMZ is delineated with dotted lines . ( B ) Quantification of the mitotic index within the CMZ . The number of analyzed retinas per condition is indicated in each bar . ( C ) Outline of the PLM and EdU cumulative labeling experiments: tadpoles injected as in ( A ) were fixed at different time points following EdU injection at stage 39 . EdU and PH3 labeling was then analyzed on retinal sections . ( D ) Retinal sections stained for both PH3 and EdU . The CMZ is delineated with dotted lines . Arrows and arrowheads point to PH3+/EdU+ and PH3+/EdU− cells respectively . ( E ) Quantification of the PLM within the whole CMZ . TG2: G2-phase duration . ( F ) Quantification of the EdU labeling index within retinal stem cells along with increasing EdU exposure times . GF: growth fraction; TC: total cell cycle; TS: S-phase . ( G ) Estimation of TC and TS . Data are represented as mean ± SEM . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 011 In order to specifically measure total cycle length ( TC ) of retinal stem cells , we next turned to a cumulative labeling assay ( Nowakowski et al . , 1989 ) , a well-established technique that also allows evaluating S-phase ( TS ) length ( see ‘Materials and methods’ for details ) . As shown in Figure 4F , the labeling index in the linear part of the curve was consistently lower in Yap morphant retinal stem cells compared to control ones . Calculation of TC confirmed the hypothesis of extended cell cycle duration following Yap-Mo injection . Surprisingly , it also revealed a dramatic reduction of S-phase length in morphant cells ( Figure 4G ) . Such unexpected S-phase shortening prompted us to further investigate the underlying molecular mechanisms . In eukaryotes , origins of replication are activated throughout the S-phase in a temporally controlled manner such that some origins fire early and others fire late . The c-Myc proto-oncogene has been shown to accelerate S-phase by inducing premature origin firing initiation and increasing origin density ( Robinson et al . , 2009; Srinivasan et al . , 2013 ) . In situ hybridization and qPCR analyses revealed an upregulation and expansion of c-Myc expression in the CMZ of the Yap morphant retina ( Figure 5A–C ) . This may account , at least in part , for the S-phase length shortening . To strengthen this hypothesis , we examined EdU-labeled replication foci at the tip of the CMZ . Their abundance and distribution , as classically observed in synchronized cultured cells , are indeed known to differ between early ( numerous small foci located throughout the nucleus ) and mid/late S-phase ( limited number of large foci; Figure 5D ) ( van Dierendonck et al . , 1989; Koberna et al . , 2005 ) . In the control situation , we found both early and late patterns within the stem cell population of the CMZ . In contrast , the proportion of cells in mid/late S-phase was dramatically reduced in Yap morphant cells ( Figure 5E , F ) . Altogether , these data highlight that loss of YAP function in retinal stem cells alters their temporal program of DNA replication and points to c-Myc as a potential actor in this process . Interestingly , a similar phenotype ( decrease in late replication patterns and c-Myc up-regulation ) was also observed in the ventricular zone of the neural tube where Yap is expressed , suggesting that YAP function in S-phase progression may also hold true in other neural precursor cells ( Figure 6A–D ) . 10 . 7554/eLife . 08488 . 012Figure 5 . Yap loss of function affects the temporal program of retinal stem cell DNA replication . ( A ) In situ hybridization analysis of c-Myc expression on stage 40 retinal sections following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) or Yap-MO . Images on the right show higher magnifications of the CMZ ( dotted lines ) . Note the strong expansion of c-Myc expression area ( bracket ) . ( B ) Quantification of the staining in the CMZ . The number of analyzed retinal sections per condition is indicated in each bar . ( C ) qPCR analysis of c-Myc expression in the retina of tadpoles injected as in ( A ) . ( D ) Schematic representation of replication foci observed during S-phase progression , as inferred from EdU labeling . Pictures illustrate two examples of EdU-labeled foci observed in control CMZ cells , one homogenous ( early S-phase ) and one with large dots ( mid/late S-phase ) . ( E ) Analysis of EdU-labeled replication foci ( 1 hr-pulse ) in the CMZ of stage 40 tadpoles injected as in ( A ) . Enlargements of the CMZ tip ( dotted lines ) are shown on the right . Early ( red arrows ) and mid/late profiles ( white arrows ) were distinguished . ( F ) Corresponding quantification . The number of analyzed retinas per condition is indicated in each bar . Data are represented as mean ± SEM . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 01210 . 7554/eLife . 08488 . 013Figure 6 . Effects of Yap knockdown in the neural tube . ( A ) Immunostaining with anti-YAP antibody on stage 42 sections . The left side of the neural tube is delineated with yellow dotted line . A higher magnification of the ventricular zone ( white dotted line ) is provided in the right panel . YAP labeling is most strongly detected in this region where progenitor cells reside ( arrows ) . ( B ) Analysis of EdU-labeled replication foci ( 1 hr-pulse ) in the neural tube of stage 40 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) or Yap-MO . Enlargements ( dotted lines ) are shown on the right . Early ( red arrows ) and mid/late profiles ( white arrows ) were distinguished . ( C ) Corresponding quantification . The number of analyzed tadpoles per condition is indicated in each bar . Data are represented as mean ± SEM . ( D , E ) In situ hybridization analysis of c-Myc or p53 expression in the neural tube of stage 40 tadpoles injected as in ( B ) . Note the strong upregulation in the ventricular zone of the neural tube ( black arrows ) . Scale bars = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 013 DNA replication stress results in DNA damage and consequent genomic instability ( Zeman and Cimprich , 2014 ) . We thus examined the expression of phosphorylated histone H2AX ( γ-H2AX ) , the most sensitive marker for DNA double-strand breaks ( Rogakou et al . , 1998 ) . The number of γ-H2AX-positive cells was significantly increased in Yap-MO-injected CMZ compared to controls ( Figure 7A , B ) . Since extensive DNA damage may trigger apoptosis , we next turned to a TUNEL assay and found that cell death was indeed severely increased in morphant retinas ( Figure 7C , D ) . Surprisingly , the majority of apoptotic cells was found at ‘the exit’ of the CMZ close to the neural retina , and not in its most peripheral part where Yap is expressed . This strongly suggests that apoptosis occurs as a secondary consequence in late progenitor cells generated from stem cells that experienced YAP function inhibition . 10 . 7554/eLife . 08488 . 014Figure 7 . Yap loss of function induces DNA damage . ( A ) γ-H2AX immunolabeling in the CMZ of retinal sections from stage 40 tadpoles following two-cell stage microinjection of Yap-5-mismatch-MO ( control ) or Yap-MO . Arrows point to γ-H2AX-positive cells . ( B ) Corresponding quantification . ( C ) TUNEL assay on retinal sections from stage 40 tadpoles injected as in ( A ) . Images on the right show higher magnifications of the CMZ delineated with dotted lines . ( D ) Quantification of TUNEL-positive cells in the different compartments of the CMZ as illustrated on the schematic . ( E ) 2 days-chase of EdU-labeled cells in the CMZ of stage 42 tadpoles injected as in ( A ) . EdU-positive cells inside the zone encircled with a red dotted line have exited the CMZ ( white dotted lines ) and integrated the different retinal layers . GCL: ganglion cell layer; INL: inner nuclear layer; PR: photoreceptor layer . ( F ) Quantification of EdU-positive cells in the neural retina layers . ( G ) mRNA expression levels of cell cycle genes as measured with the NanoString nCounter system in heads from stage 40 tadpoles following two-cell stage microinjection of Standard MO ( control ) or Yap-MO . Data are the mean of four independent experiments . ( H ) In situ hybridization analyses of p53 and p21 expression on retinal sections from stage 40 tadpoles injected as in ( A ) . The number of analyzed retinas per condition is indicated in each bar ( B , F ) or on the graph ( n in D ) . Data are represented as mean ± SEM . Scale bar = 40 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 014 As stated above , the number of CMZ cells is not significantly changed in Yap morphant tadpoles and therefore retinal growth impairment cannot be simply explained by a depletion of the stem/progenitor pool . To foresee whether the increased cell death at the ‘exit’ of the CMZ could contribute to the reduced eye size , proliferating CMZ cells were pulse-labeled and their progeny chased and counted in the three retinal layers ( Figure 7E ) . As expected , we found that fewer neurons were generated in a 2-day period in Yap morphant retinas compared to control ones ( Figure 7F ) . In order to gain additional insight into the molecular mechanisms underlying the Yap knockdown phenotype , we analyzed the expression of 15 genes encoding cell cycle regulators using the NanoString technology . Among them , only p53 and p21Cip1/WAF1 ( previously named Xic2 in Xenopus and p21 hereafter ) were significantly affected , with much higher levels present in Yap morphant retinas compared to control ones ( Figure 7G ) . The tumor suppressor protein p53 is activated in response to a variety of cellular stresses ( including DNA damage ) and triggers cell cycle arrest or apoptosis . In situ hybridization analysis revealed that its expression in the wild type retina is restricted to the CMZ . In addition and consistent with the NanoString data , p53 staining was strongly enhanced in the CMZ of Yap-MO-injected tadpoles ( Figure 7H ) . Of note , an up-regulation was observed in the neural tube as well ( Figure 6E ) . p21 is a member of the CIP/KIP family of cyclin-dependent kinase inhibitors that blocks the G1-S transition and has emerged as a key player in the p53 pathway ( Attardi and DePinho , 2004 ) . Since it was previously described as a lens-specific marker in the Xenopus eye ( Daniels et al . , 2004 ) , we asked if it could be linked with the observed CMZ phenotype . We thus performed in situ hybridization and observed a strong ectopic p21 labeling within the CMZ of Yap morphant tadpoles ( Figure 7H ) . As a whole , these results suggest that increased apoptosis and probably delayed cell cycle progression resulting from Yap knockdown might be driven by the p53-p21 pathway . As stated above , YAP is a co-transcriptional activator that functions in association with transcription factors such as its classical partners of the TEAD family . Among other interacting factors described so far is Drosophila Homothorax ( Peng et al . , 2009; Zhang et al . , 2011 ) . Interestingly , PKNOX1 ( also named PREP1 ) , a mammalian Homothorax ortholog belonging to the Meis/Prep homeodomain factor family , has recently been involved in the maintenance of genomic stability ( Iotti et al . , 2011 ) . However , its physical interaction with YAP has not yet been reported in vertebrates . We found that the two proteins indeed interact in vitro using a two-hybrid assay ( data not shown ) . In order to address whether they might do so in vivo as well , we performed a bimolecular fluorescence complementation ( BiFC ) experiment ( Figure 8A ) ( Ohashi and Mizuno , 2014 ) . In this purpose , constructs encoding YAP , PKNOX1 or TEAD1 ( as a positive control for interaction with YAP ) fused to either the amino or carboxyl-terminal fragment of the VENUS fluorescent protein were transfected in HEK293T cells ( Figure 8B and data not shown for inverse VN/VC fusion combinations ) . As expected , co-transfection of Yap and Tead1 constructs resulted in a significant nuclear BiFC signal . This was lost using a Yap∆TBS mutant devoid of TEAD binding site , validating the specificity of the BiFC staining . Co-transfection of both Yap and pknox1 constructs led to a positive BiFC signal that mainly localized to the cytoplasm . YAP/PKNOX1 interaction was further confirmed by co-immunoprecipitation analyses following expression of tagged proteins ( Figure 8C ) . 10 . 7554/eLife . 08488 . 015Figure 8 . Physical interaction between YAP and PKNOX1 . ( A ) Schematic representation of BiFC principle . ( B ) Immunolabeling/BiFC analysis on HEK293T cells transfected with VN and VC chimeric constructs , as indicated . ( C ) Co-immunoprecipitation assay of 293T cells transfected with tagged constructs as indicated . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 015 Since we found that pknox1 is expressed in the CMZ ( Figure 9A ) , we next sought for potential functional interaction with YAP in retinal stem cells . A loss of function approach was first undertaken to compare Yap and pknox1 knockdown phenotypes ( Figure 9B–G ) . The injected dose of pknox1-MO was adapted to avoid broad developmental defects ( see Figure 9—figure supplement 1 for validation of pknox1-MO specificity and efficiency ) . Although the eye phenotype appeared more dramatic than that observed upon Yap knockdown ( layering defects of the retina ) , it similarly led to a significant reduction in total eye size ( Figure 9B , C ) , associated with decreased EdU incorporation in the CMZ compared to controls ( Figure 9G , H ) . In addition , analysis of EdU-labeled replication foci revealed a decreased proportion of stem cells in mid/late S-phase ( Figure 9D–E ) in pknox1 morphant , as observed following Yap knockdown . These embryos also displayed upregulation of c-Myc expression in the CMZ , thus recapitulating main features of the Yap knockdown phenotype ( Figure 9F ) . We then asked whether YAP and PKNOX1 might synergize in a co-overexpression assay . Of note , pknox1 overexpression alone does not significantly affect CMZ cell proliferation . The injected dose of Yap mRNA was lowered so that it only leads to a moderate although significant increase in the number of EdU-labeled cells . In these conditions , pknox1 mRNA injection was found to exacerbate Yap gain of function phenotype ( Figure 9I , J ) . Finally , we tested whether pknox1 knockdown might rescue the overproliferative effects of Yap misexpression . We found indeed that EdU incorporation was restored to a basal level in a pknox1 morphant context ( Figure 9K , L ) . Together , these data support the idea that PKNOX1 physically and functionally interacts with YAP in the CMZ . 10 . 7554/eLife . 08488 . 016Figure 9 . Functional interaction between YAP and PKNOX1 . ( A ) In situ hybridization analysis of pknox1 expression on stage 40 retinal sections . The right panels shows an enlargement of the CMZ region delineated with dotted lines . ( B ) Lateral views ( left panels ) , head dorsal views ( middle panels ) and dissected eyes ( right panels ) of stage 40 tadpoles following two-cell stage microinjection of pknox1-5-mismatch-MO ( control ) or pknox1-MO . The asterisk indicates the injected side . ( C ) Quantification of dissected eye area . ( D ) Analysis of EdU-labeled replication foci ( 45 min-pulse ) in the CMZ of tadpoles injected as in ( B ) . Enlargements of the CMZ tip ( dotted lines ) are shown on the right . Early ( red arrows ) and mid/late profiles ( white arrows ) were distinguished . ( E ) Corresponding quantification . ( F ) In situ hybridization analysis of c-Myc expression on stage 40 retinal sections from tadpoles injected as in ( B ) . ( G–L ) EdU incorporation assays ( 3-hr pulse ) analyzed on retinal sections from stage 40 tadpoles . ( G , H ) shows the effect of pknox1 knockdown ( injection of pknox1-5-mismatch-MO ( control ) or pknox1-MO ) . ( I , J ) shows the synergistic effects of pknox1 and Yap ( injection of GFP mRNA and either ß-gal mRNA ( control ) , Yap + ß-gal mRNA ( Yap ) , pknox1 + ß-gal mRNA ( pknox1 ) , Yap + pknox1 mRNA ( Yap + pknox1 ) ) . ( K , L ) shows the rescue of Yap overexpression by pknox1 knockdown ( injection of either pknox1-5-mismatch-MO + ß-gal mRNA ( control ) , pknox1-MO + ß-gal mRNA ( pknox1-MO ) , pknox1-5-mismatch-MO + Yap mRNA ( Yap ) , pknox1-MO + Yap mRNA ( Yap + pknox-MO ) ) . Of note , a suboptimal dose of pknox1-MO was used for the rescue experiment so that it does not alone give any eye phenotype . The total number of analyzed retinas per condition is indicated in each bar . Scale bar = 1 mm in ( B ) and 40 µm for all other panels . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 01610 . 7554/eLife . 08488 . 017Figure 9—figure supplement 1 . Validation of pknox1-MO efficiency and specificity . ( A ) Schematic representation of the chimeric construct containing GFP downstream of pknox1-MO complementary sequence ( pknox1 ( 5′ ) -GFP ) . ( B ) GFP fluorescence analysis at stage 18 following two-cell stage microinjection of GFP mRNA + pknox1-5-mismatch MO ( control ) , GFP mRNA + pknox1-MO ( pknox1-MO ) , pknox1 ( 5′ ) -GFP + pknox1-5-mismatch-MO ( pknox1 ( 5′ ) -GFP ) or pknox1 ( 5′ ) -GFP + pknox1-MO . Fluorescence imaging at 594 nm detects the MO-bound lissamine tag . pknox1-MO efficiently inhibits GFP translation of pknox1 ( 5′ ) -GFP mRNA . ( C ) Lateral views and dissected eyes of stage 40 tadpoles following two-cell stage microinjection of pknox1-5-mismatch-MO + ß-gal mRNA ( control ) , pknox1-MO + ß-gal mRNA ( pknox1-MO ) , pknox1-5-mismatch-MO + pknox1 mRNA ( pknox1 ) , pknox1-MO + pknox1 mRNA ( pknox1-MO + pknox1 ) . ( D ) Quantification of dissected eye area . The pknox1-induced small eye phenotype is rescued by co-injection with pknox1 mRNA . The number of analyzed tadpoles is indicated for each bar . Scale bar = 1 mm . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 017
Long-term maintenance of tissue homeostasis relies on the fine-tuning of adult stem cell activity . Our knowledge regarding the molecular basis sustaining somatic stemness features is still very limited but may have important implications for regenerative medicine and cancer therapy . In the present study , we identified YAP , a downstream effector of the Hippo pathway , as a stem cell specific marker required for homeostatic growth of the frog post-embryonic retina . Our in vivo loss of function approach unexpectedly revealed a novel role for YAP in governing DNA replication timing of retinal stem cells . We propose a model in which this function would contribute to the maintenance of their genomic stability ( Figure 10 ) . Importantly , based on our findings in the neural tube , we propose that such function might be generalizable to other neural precursor populations . Whether this involves YAP cytoplasmic and/or nuclear activity remains an open question . 10 . 7554/eLife . 08488 . 018Figure 10 . Model illustrating YAP function in retinal stem cells . We found that YAP is expressed in CMZ retinal stem cells ( left panel ) . The middle panel shows the cell cycle of wild type retinal stem cells and the putative role of the YAP/PKNOX1 complex in the control of S-phase temporal progression ( represented by the distinct patterns of DNA replication foci ) . YAP knock-down ( right panel ) leads to a dramatic reduction of S-phase length likely due to c-Myc-dependent premature firing of late replication origins . This results in increased occurrence of DNA damage , enhanced p21 and p53 expression and eventually cell death . DOI: http://dx . doi . org/10 . 7554/eLife . 08488 . 018 The ability of nuclear YAP to expand stem/progenitor cell populations has been established in numerous model systems ( Barry and Camargo , 2013; Piccolo et al . , 2014 ) . However , the mechanisms underlying altered cell proliferation ( changes in cell cycle length and/or re-entry ) are rarely investigated in detail . In the developing neural tube for instance , YAP-driven increase in neural progenitor cell number has been proposed to result from accelerated cell cycle progression but whether its loss of function alters cell cycle kinetics as well remains an open question ( Cao et al . , 2008; Zhang et al . , 2012 ) . As observed in other adult organs in mammals ( Azzolin et al . , 2014; Chen et al . , 2014; Zhang et al . , 2014 ) , our results suggest that YAP is dispensable for the maintenance of the stem cell pool . We instead uncovered that its depletion lengthens retinal stem cell divisions . However , to our surprise , this is associated with a dramatic shortening of their S-phase . It should be emphasized that such phenotype likely corresponds to a hypomorphic one due to the use of Morpholinos that allows partial loss of function . DNA replication is a tightly regulated process that follows a strict temporal program . Our observation that Yap knockdown results in a mark reduction of late S-phase labeling patterns can suggest that some firing origins have advanced their activation timing , which may account for the reduced S-phase duration . The genetic control of DNA replication temporal progression has not been elucidated yet and there are thus very few examples in the literature where gene perturbation leads to a deregulation of S-phase duration ( Aparicio , 2013; Yamazaki et al . , 2013 ) . Beyond its well-characterized transcriptional activity , it was reported that c-Myc exerts a non-transcriptional control on DNA replication . Its overexpression indeed causes increased replication origin activity and consequent S-phase shortening ( Dominguez-Sola et al . , 2007; Robinson et al . , 2009; Srinivasan et al . , 2013 ) . Intriguingly , in contrast to Drosophila or cancer cells , where c-Myc has been reported to be positively regulated by Yap ( Neto-Silva et al . , 2010; Stocker , 2011; Xiao et al . , 2013 ) , we found that its expression is enhanced in the CMZ of Yap morphant tadpoles . Although the underlying mechanism deserves further investigation , this raises the hypothesis that c-Myc may contribute to the S-phase defects caused by Yap knockdown . Besides , in addition to be involved in replication progression , we do not exclude that YAP may also regulate replication origin licencing in G1 , as recently reported in human umbilical vein endothelial cells ( Shen and Stanger , 2015 ) . This might explain the observed lengthening of the cell cycle as a result of impaired G1/S transition . Alternatively , delayed G-phase progression might occur as a secondary consequence of S-phase defects . Replication stress is a source of DNA damage , which may ultimately trigger activation of the p53-p21 pathway ( Bartek and Lukas , 2001 ) . As observed following c-Myc overexpression in vitro ( Dominguez-Sola et al . , 2007; Robinson et al . , 2009; Srinivasan et al . , 2013 ) , we found an increased occurrence of double-strand breaks in Yap morphant retina , associated with an upregulation of both p53 and p21 . Since p21 is known to inhibit G1/S and G2/M transitions and p53 to induce programmed cell death ( Vogelstein et al . , 2000 ) , this could contribute to both the lengthening in G phases and the increased number of apoptotic cells . These findings raise key questions regarding specific features of stem cell biology . In addition to unique properties ( such as the ability to self renew ) , emerging evidence suggests that somatic stem cells also differ from progenitor cells in the way they regulate basic cellular processes including their metabolic state ( Burgess et al . , 2014 ) or DNA-damage responses ( Insinga et al . , 2014 ) . Regarding cell cycle progression , it has been shown during development that mammalian cortical neural stem cells exhibit a substantially longer S-phase than progenitors committed to neuron production ( Arai et al . , 2011; Turrero García et al . , 2015 ) . It was thus proposed that neural stem cells may need to invest more time during S-phase into quality control of replicated DNA . In agreement with this , we also found that CMZ retinal stem cells exhibit a longer S-phase compared to fast amplifying progenitors ( data not shown ) . In addition , our work points for the first time towards a factor , YAP , that may be critically involved in this stem cell specific regulation of S-phase . Although its precise function in this process remains to be investigated , it indeed appears to be required for proper choreography of the DNA replication program and may as such be necessary to maintain genomic integrity of retinal stem cells . Our findings also have important medical implications since aberrant DNA replication timing has been proposed to be a causative factor in diseases such as cancer and neuronal disorders ( Aladjem , 2007; Watanabe and Maekawa , 2010; Donley and Thayer , 2013 ) . Interestingly , another component of the Hippo pathway , LATS1 , has very recently been involved in ATR-mediated response to replication stress in lung cancer cells ( Pefani et al . , 2014 ) . Several Hippo pathway components may thus regulate ( independently or in concert ) S-phase progression and quality control and thereby safeguard genomic integrity . Although Homothorax is known to partner the Drosophila Yap homologue Yorkie in some developmental contexts ( Peng et al . , 2009; Zhang et al . , 2011 ) , this has not been reported yet for its vertebrate orthologs . Here , we provide biochemical and functional evidences supporting an interaction between PKNOX1 and YAP in the retina . PKNOX1 belongs to the TALE ( three amino acids loop extension ) class of homeodomain proteins and is involved in many developmental processes ( Berthelsen et al . , 1998; Ferretti et al . , 2006 ) . Down-regulation of pknox1 in both zebrafish and mouse embryos leads to a small eye phenotype ( Deflorian et al . , 2004; Ferretti et al . , 2006 ) , reminiscent of what we found in Xenopus . Interestingly , pknox1 inactivation was reported to trigger cell death in the zebrafish CNS ( Deflorian et al . , 2004 ) . Furthermore , pknox1 deficiency leads to increased DNA damage and apoptosis both in embryonic fibroblasts and in the mouse epiblast ( Micali et al . , 2009; Fernandez-Diaz et al . , 2010; Iotti et al . , 2011 ) . On the basis of these different reports and our findings , we thus propose that PKNOX1 and YAP interact together to maintain genomic stability in retinal stem cells . Whether PKNOX1 functions in competition with TEAD for YAP interaction or whether they all associate in a tripartite complex are important questions to be addressed in the future . | In animals , stem cells divide to produce the new cells needed to grow and renew tissues and organs . Understanding the biology of these cells is of the utmost importance for developing new treatments for a wide range of human diseases , including neurodegenerative diseases and cancer . Before a stem cell divides , it copies its DNA and the two sets of genetic instructions are then separated so that the two daughter cells both have a complete set . This process needs to be as accurate as possible because any errors would result in incorrect genetic information being passed on to the daughter cells . Stem cells in the light-sensitive part of the eye—called the retina—divide to produce the cells that detect light and relay visual information to the brain . In many animals , these stem cells stop dividing soon after birth and the retina stops growing . However , the stem cells in frogs and fish continue to divide throughout the life of the animal , which enables the eye to keep on growing . A protein called YAP regulates the growth of organs in animal embryos , but it is not clear what role this protein plays in stem cells , particularly after birth . To address this question , Cabochette et al . studied YAP in the retina of frog tadpoles . The experiments show that YAP is produced in the stem cells of the retina after birth and is required for the retina to continue to grow . Cabochette et al . used tools called ‘photo-cleavable Morpholinos’ to alter the production of YAP in adult stem cells . The cells that produced less YAP copied their DNA more quickly and more of their DNA became damaged , which eventually led to the death of these cells . Further experiments revealed that YAP interacts with a protein called PKNOX1 , which is involved in maintaining the integrity of DNA . Cabochette et al . 's findings provide the first insights into how YAP works in the stem cells of the retina and demonstrate that it plays a crucial role in regulating when DNA is copied . A future challenge is to find out whether YAP plays a similar role in the stem cells of other organs in adult animals . | [
"Abstract",
"Introduction",
"Results",
"Discussion"
] | [
"developmental",
"biology"
] | 2015 | YAP controls retinal stem cell DNA replication timing and genomic stability |
Desmoplasia , a fibrotic mass including cancer-associated fibroblasts ( CAFs ) and self-sustaining extracellular matrix ( D-ECM ) , is a puzzling feature of pancreatic ductal adenocarcinoma ( PDACs ) . Conflicting studies have identified tumor-restricting and tumor-promoting roles of PDAC-associated desmoplasia , suggesting that individual CAF/D-ECM protein constituents have distinguishable tumorigenic and tumor-repressive functions . Using 3D culture of normal pancreatic versus PDAC-associated human fibroblasts , we identified a CAF/D-ECM phenotype that correlates with improved patient outcomes , and that includes CAFs enriched in plasma membrane-localized , active α5β1-integrin . Mechanistically , we established that TGFβ is required for D-ECM production but dispensable for D-ECM-induced naïve fibroblast-to-CAF activation , which depends on αvβ5-integrin redistribution of pFAK-independent active α5β1-integrin to assorted endosomes . Importantly , the development of a simultaneous multi-channel immunofluorescence approach and new algorithms for computational batch-analysis and their application to a human PDAC panel , indicated that stromal localization and levels of active SMAD2/3 and α5β1-integrin distinguish patient-protective from patient-detrimental desmoplasia and foretell tumor recurrences , suggesting a useful new prognostic tool .
The mesenchymal stroma typically found in normal tissues , which is composed mainly of naïve quiescent fibroblastic cells and their secreted interstitial extracellular matrix ( ECM ) , constitutes a natural tumor suppressive microenvironment that enforces cellular homeostasis ( Mintz and Illmensee , 1975; Soto and Sonnenschein , 2011; Xu et al . , 2009; Bissell and Hines , 2011; Dolberg and Bissell , 1984; Petersen et al . , 1992; Wong et al . , 1992; Anderson et al . , 2006; Wall et al . , 2005 ) . By contrast , the emergence of a desmoplastic ( activated ) stroma , encompassing fibrotic-like modifications of local and recruited fibroblasts based on tumor interactions with the local microenvironment , has been proposed to play a major role in the development and progression of solid tumors such as pancreatic ductal adenocarcinoma ( PDAC ) and others ( Bijlsma and van Laarhoven , 2015; Jonasch et al . , 2012; Erkan et al . , 2007; Xu et al . , 2015; Gupta et al . , 2011 ) . Many studies suggest that a fibrotic reaction , such as that seen in pancreatitis , drives a pro-tumorigenic wound-healing response that often precedes tumor development ( Whitcomb and Pogue-Geile , 2002; Binkley et al . , 2004 ) . Tumor fibrosis , also known as desmoplasia , has been reported to promote tumorigenesis , providing chemoresistance and shielding tumors from therapeutic agents ( Olive et al . , 2009; Ireland et al . , 2016; Laklai et al . , 2016; Koay et al . , 2014 ) . A mechanism of ‘stromal reciprocation’ , involving mutual signaling between tumor and neighboring cancer-associated fibroblasts ( CAFs ) that promotes tumor growth , has been demonstrated for PDAC ( Tape et al . , 2016 ) . On the basis of these findings , several studies have attempted complete ablation of desmoplastic stroma as a therapeutic approach to limit tumor growth , but paradoxically , this resulted in the evolution of existing tumors to a more aggressive state , and accelerated rates of tumorigenesis ( Özdemir et al . , 2014; Rhim et al . , 2014 ) . By contrast , the idea of chronically ‘normalizing’ activated stroma by reprogramming desmoplasia from a tumor-promoting to a tumor-restrictive state has been suggested to bear greater therapeutic promise ( Bijlsma and van Laarhoven , 2015; Sherman et al . , 2014; Klemm and Joyce , 2015; Stromnes et al . , 2014; Froeling et al . , 2011 ) , and the identification of a clinically applicable means to revert desmoplastic stroma is of considerable interest ( Stromnes et al . , 2014; Froeling et al . , 2011; Alexander and Cukierman , 2016 ) . Activation of local fibroblastic pancreatic stellate cells and recruited naïve fibroblastic cells during desmoplasia involves their transforming growth factor-beta ( TGFβ ) -dependent conversion to a myofibroblastic ( activated ) phenotype ( Desmoulière et al . , 1993; Meng et al . , 2016; Principe et al . , 2016; Xu et al . , 2016 ) . This phenotype is characterized by the induction of stress fibers and the elevated expression of alpha-smooth muscle actin ( αSMA ) , palladin and other actin-binding proteins , and by the production of an aligned and organized ( anisotropic ) ECM , with parallel fibers that are rich in discrete fibronectin splice variants ( e . g . , ED-A ) and in type I collagen ( Mishra et al . , 2007; Serini et al . , 1998; Hinz , 2016; Rönty et al . , 2006; Goetz et al . , 2011 ) . Despite the strong effect of TGFβ on fibroblast activation and ECM remodeling during epithelial cancer-associated desmoplasia , knowledge of the mechanisms and downstream consequences of this activation remain limited ( Hesler et al . , 2016; Oshima et al . , 2015 ) . In previous studies , we demonstrated that culturing naïve fibroblasts within CAF-secreted D-ECM is sufficient to induce myofibroblastic conversion ( Amatangelo et al . , 2005 ) . Compatible with this dynamic ECM-dependent reprogramming , specific cell-matrix receptors , such as integrins αvβ5 and α5β1 , have been identified as regulators of myofibroblastic αSMA ( Asano et al . , 2006; Lygoe et al . , 2004 ) and as participants in the maturation of specific types of cell-matrix adhesions that support anisotropic fiber formation ( Dugina et al . , 2001 ) . In the present work , we first asked whether D-ECM production and the ability of D-ECM to induce myofibroblastic activation can be decoupled and independently regulated . Using a patient-derived human pancreatic model ( Lee et al . , 2011 ) as main focus , with supporting data from human renal cancer ( Gupta et al . , 2011 ) and additional stromal ( Amatangelo et al . , 2005 ) models , we found that although TGFβ is needed for production of D-ECM , it is dispensable for subsequent D-ECM-induction of myofibroblastic activation of naïve fibroblasts . We also found that D-ECM controls αvβ5-integrin signaling , which prompts the accumulation of activated , but FAK-independent , α5β1-integrin pools in specific intracellular vesicles . This D-ECM control of αvβ5-integrin signaling prevents the enrichment of active α5β1-integrin at the plasma membrane ( PM ) , where α5β1 activity opposes myofibroblastic activation . Using a novel integrative approach combining multi-colored immunofluorescence and a new quantitative algorithm , we first validated our in vitro findings and then applied this process to annotated clinical samples . This defined two readily distinguishable desmoplastic phenotypes that were correlated with markedly distinct clinical outcomes . These phenotypes are based on differences in the stromal localization and levels of either activated SMAD2/3 ( indicative of TGFβ signaling ) or active α5β1-integrin and FAK . These signatures help clarify the controversial role of desmoplasia in the progression of cancer . Further , insofar as reversion of D-ECM has been suggested have the potential to confer significant clinical benefit ( Stromnes et al . , 2014; Whatcott et al . , 2015; Neuzillet et al . , 2015 ) , these data suggest potential therapies to stabilize patient-protective or to revert patient-detrimental stroma .
Fibroblasts were isolated from seven PDAC surgical specimens obtained from five different individuals ( with four specimens reflecting two matched tumor-normal pairs , one tumor specimen lacking a matched normal control , and two specimens pathologically designated as non-tumor/normal ) . These fibroblasts were characterized as naïve pancreatic stellate cells or PDAC-associated desmoplastic CAFs on the basis of assessments of the mRNA and protein expression of the myofibroblastic markers palladin and αSMA ( Figure 1A–B ) . All specimens were used in parallel for subsequent analyses . In primary culture , these fibroblasts produced characteristic ECM ( Franco-Barraza et al . , 2016 ) . Desmoplastic CAFs produced anisotropic D-ECM with multi-layered myofibroblastic spindled nuclei and increased levels of stress fiber-localized αSMA reminiscent of myofibroblastic cells in vivo ( Goetz et al . , 2011; Provenzano et al . , 2006; Conklin et al . , 2011; Eyden , 2001; Kalluri and Zeisberg , 2006 ) , whereas fibroblasts derived from normal specimens did not ( Figure 1C ) . Quantification of ECM fiber alignment provided a robust measure of tumor-dependent fibroblast activation . We used an arbitrary quantitative threshold of at least 55% of fibers oriented at a spread of 15° from the mode angle as indicative of D-ECMs that had been produced by activated CAFs ( Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 20600 . 003Figure 1 . Characterization of human fibroblastic cells isolated from PDAC surgical samples . Fibroblastic cells were isolated from normal or tumoral surgical samples from PDAC patients . ( A ) Representative indirect immunofluorescent assessments of vimentin-positive and pan-cytokeratin-negative fibroblasts , isolated from PDAC surgical specimens . Harvested cells were probed for desmoplastic markers αSMA and palladin , while the pancreatic cancer cell line , Panc1 , was used as an epithelial-to-mesenchymal transduced ( EMT ) control that is known to express both epithelial and mesenchymal markers . Assorted markers are shown in white while counterstained Hoechst-identified nuclei are shown in yellow . ( B ) The bar chart shows normal vs . desmoplastic mRNAs levels , corresponding to αSMA and palladin obtained by RT-qPCR from the indicated 3D-cultures following ECM production ( obtained by confluent culturing of fibroblasts in the presence of ascorbic acid for a period lasting 8 days [Franco-Barraza et al . , 2016] ) ( **p=0 . 0286 ) . ( C ) Representative images of normal vs . desmoplastic phenotypes after 3D ECM production; comparison of low vs . high αSMA levels ( white ) , heterogeneous/round vs . elongated/spindled nuclei ( yellow ) and disorganized/isotropic vs . parallel aligned/anisotropic ECMs ( magenta ) are evident in the representative images . Note that the examples shown corresponds to the matching pair of ( naïve vs . desmoplastic ) fibroblastic cells that were harvested from surgical samples corresponding to patient #1 and that this pair of cells was used for all examples provided in figures below , unless otherwise stated . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 00310 . 7554/eLife . 20600 . 004Figure 1—figure supplement 1 . CAFs produce anisotropic D-ECMs . ( A ) Images representative of 3D ECM phenotypes: normal ( produced by naïve stellate cells N-ECM ) and desmoplastic ( produced by CAFs D-ECM ) . The distributions of ECM fiber angles , measured with Image-J’s ‘OrientationJ’ plug , are represented by the various colors; all were normalized using hue values for common , cyan , mode angle visualization as represented on the bar in the right . ( B ) Curves corresponding to the indicated experimental conditions depicting averaged and variations of angle distributions that were normalized to 0˚ modes . Dotted line areas depict a 15˚ spread from the mode . ( C ) Plotted data depicting summarized percentages of fibers distributed at 15˚angles from the mode corresponding to the indicated experimental conditions . Note that comparison between N-ECMs and D-ECMs showed statistically significant differences with p values smaller than 0 . 0001 ( **** ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 00410 . 7554/eLife . 20600 . 005Figure 1—figure supplement 2 . TGFβ inhibition disrupts anisotropy of D-ECM devoid of preventing CAF matrix fibrillogenesis . ( A ) TGFβ protein levels from lysates corresponding to 3D normal ( N-ECM ) and desmoplastic ( D-ECM ) matrices , produced by naïve fibroblastic stellate cells or CAFs , respectively , in the presence of the small molecule TGFβ1-receptor inhibitor SB431542 ( D+TGFβ-i ) or vehicle control ( D+DMSO ) , were measured by ELISA . Data were normalized to total protein concentration in intact D-ECMs; results are expressed as arbitrary units ( one arbitrary unit; a . u . ) . Significance values are ***p=0 . 0014 , ****p<0 . 0001 . ( B ) ECM fiber angle distributions , measured with Image-J’s ‘OrientationJ’ plug , are represented by the various colors; all were normalized using hue values for common , cyan , mode angle visualization as represented on the bar on the right . Samples represent D-ECMs produced in the presence TGFβ1-receptor blockage ( D+TGFβ-i ) or vehicle ( D+DMSO ) . Note how TGFβ inhibition causes disorganization of D-ECM ( e . g . , loss of anisotropic alignment ) . ( C ) Curves corresponding to the indicated experimental conditions depicting averaged and variations of angle distributions that were normalized to 0˚ modes . Dotted lines depict areas of 15˚ spreads . ( D ) Plotted data depicting summarized percentages of fibers distributed at 15˚angles from the mode corresponding to the indicated experimental conditions . TGFβ blockage during D-ECM production established that alignments were reduced to 47% ( ****p<0 . 0001; n = 21 ) , rendering ECMs isotropic or disorganized . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 00510 . 7554/eLife . 20600 . 006Figure 1—figure supplement 3 . TGFβ inhibition disrupts N-ECM production by naïve fibroblastic stellate cells . Representative indirect immunofluorescent images of assorted ‘unextracted’ 3D cultures depicting nuclei ( yellow ) and ECMs ( magenta ) produced by naïve fibroblastic stellate cells or CAFs in the presence or absence of TGFβ1-receptor blockage . Note how TGFβ inhibition causes the disorganization of desmoplastic and the ablation of normal ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 006 To first test if autocrine TGFβ signaling is essential for CAF production of TGFβ that then contributes to the formation of anisotropic D-ECM , we used an ELISA-based approach to measure levels of TGFβ present in D-ECM in comparison to levels found in normal ECM ( N-ECM ) , or in ECM produced by CAFs treated with SB-431542 , a small-molecule inhibitor of the TGFβ1 receptor . Levels of TGFβ in D-ECM were ~2 fold higher than in N-ECM or D-ECM produced in the presence of SB-431542 ( D+TGFβi in Figure 1—figure supplement 2 ) . Importantly , following SB-431542 treatment , CAFs produced isotropic ECMs that were phenotypically indistinguishable from intact N-ECMs ( Figure 1—figure supplement 2B–D ) . This result differed from that produced by the treatment of control naïve pancreatic stellate cells with SB-431542 , which produced interrupted TGFβ signaling that caused complete loss of fibrillogenesis , resulting in the absence of matrix production ( Figure 1—figure supplement 3 ) . Together , these results suggested that the increased TGFβ observed in D-ECM was critical for CAF production of anisotropic matrices . Stripping matrix-producing cells from their secreted ECMs produces a residual ‘extracted’ matrix into which new fibroblasts ( e . g . , naïve fibroblastic stellate cells ) or cancer cells can be seeded ( Franco-Barraza et al . , 2016 ) . Using primary human naïve fibroblastic cells , we have previously shown that the residual three-dimensional ( 3D ) D-ECM produced by CAFs is sufficient to induce a myofibroblastic phenotype ( Amatangelo et al . , 2005 ) . Applying this analysis to pancreatic extracted matrices , we found that D-ECM produced by CAFs in the presence of TGFβ blockade was similar to N-ECM in being incapable of inducing de novo myofibroblastic activation ( as reflected by increased αSMA stress fiber localization and protein levels ) in naïve fibroblastic stellate cells . By contrast , untreated and control treated D-ECM effectively induced such activation ( Figure 2 , Table 1 ) . Similar results were obtained using D-ECM and naïve fibroblastic stellate cells for all five PDAC patients ( Figure 2—figure supplement 1 , and Table 2 ) , suggesting that myofibroblastic activation is a general phenomenon during interactions between naïve pancreatic stellate cells and CAF-produced PDAC-associated D-ECMs . A post-translational effect was implied by the fact that αSMA mRNA levels were comparable in naïve fibroblastic stellate cells cultured in N- vs D-ECM ( Figure 2—figure supplement 2A ) and because use of cycloheximide to inhibit protein translation did not alter αSMA expression levels ( Figure 2—figure supplement 2B ) , whereas αSMA localization differed ( Figure 2 ) . We also asked whether autocrine TGFβ signaling within naïve stellate cells is necessary for their myofibroblastic activation by D-ECM ( Figure 2—figure supplement 3 and Table 3 ) . Growth of these cells in the presence of SB-431542 did not block D-ECM-induced αSMA expression or localization to stress fibers . 10 . 7554/eLife . 20600 . 007Table 1 . αSMA stress fiber localization and expression levels in naïve fibroblasts ( stellate cells ) cultured overnight within assorted ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 007N-ECMD-ECMD+TGFβi D+DMSO αSMAStress fiber localization25% percentile0 . 170 . 820 . 320 . 85Median 0 . 58 1 . 00 0 . 55 1 . 02 75% percentile1 . 071 . 120 . 871 . 40expression25% percentile0 . 050 . 410 . 520 . 48Median 0 . 27 1 . 00 0 . 73 0 . 89 75% percentile0 . 781 . 501 . 141 . 19Values obtained from naïve cells ( pancreatic stellate cells isolated from patient #1 ) cultured overnight within intact D-ECMs ( made from CAFs isolated from patient #1 ) were used for normalization and assigned an arbitrary unit of 1 . 00 . Assorted , patient #1 derived , ECMs were intact N-ECM or intact D-ECM while experimental conditions included D-ECMs made by CAFs treated with SB-431542 ( D+TGFβi ) or DMSO ( D+DMSO ) during ECM production . Note that quantitative immunofluorescent obtained values of αSMA and F-actin were used to calculate stress fiber localization and expression of αSMA . P values , listed below , were calculated using the two-sided and two-tailed Mann Whitney test needed for normalized data . N-ECM vs . D-ECM; p<0 . 0001 stress fiber localization; p<0 . 0001 expressionN-ECM vs . D+TGFβi; p=0 . 8847 stress fiber localization; p=0 . 0002 expressionN-ECM vs . D+DMSO; p=0 . 0036 stress fiber localization; p<0 . 0001 expressionD-ECM vs . D+TGFβi; p<0 . 0001 stress fiber localization; p=0 . 0198 expressionD-ECM vs . D+DMSO; p=0 . 3578 stress fiber localization; p=0 . 1208 expressionD+TGFβi vs . D+DMSO; p<0 . 0001 stress fiber localization; p=0 . 6236 expression10 . 7554/eLife . 20600 . 008Table 2 . αSMA stress fiber localization and expression levels in assorted naïve pancreatic fibroblasts ( stellate cells ) cultured overnight within different D-ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 008 ( #X naïve cell ) / ( #Y D-ECM ) αSMA Stress fiber localizationExpression 25% percentileMedian75% percentile25% percentileMedian75% percentile ( 2 ) / ( 1 ) 0 . 351 . 00 1 . 260 . 651 . 00 1 . 19 ( 2 ) / ( 2 ) 0 . 901 . 15 1 . 380 . 871 . 10 1 . 30 ( 3 ) / ( 2 ) 0 . 921 . 08 1 . 360 . 861 . 20 1 . 72 ( 4 ) / ( 1 ) 0 . 080 . 85 1 . 030 . 792 . 62 3 . 04 ( 4 ) / ( 5 ) 0 . 060 . 83 1 . 360 . 291 . 15 2 . 40 ( 2 ) / ( 5 ) 0 . 521 . 24 1 . 330 . 601 . 44 2 . 31 ( 2 ) / ( 1 +TGFβi ) 0 . 000 . 00 0 . 040 . 040 . 07 0 . 10 ( 2 ) / ( 5 +TGFβi ) 0 . 000 . 06 1 . 200 . 010 . 14 0 . 76 ( 3 ) / ( 1 +TGFβi ) 0 . 010 . 06 0 . 230 . 010 . 15 0 . 28 ( 4 ) / ( 5 +TGFβi ) 0 . 110 . 22 0 . 710 . 250 . 74 1 . 66Values obtained from naïve cells ( e . g . , inactive stellate cells ) isolated from patient number ‘#2’ cultured overnight within D-ECMs made from CAFs isolated from patient #1 were used for normalization and assigned an arbitrary unit of 1 . 00 . Assorted , naïve cells ( patient numbers indicated ) were cultured within D-ECMs derived from the indicated CAFs , while experimental conditions included assorted D-ECMs treated with SB-431542 ( D+TGFβi ) during ECM production . Note that quantitative immunofluorescent obtained values of αSMA and F-actin were used to calculate stress fiber localization and expression of αSMA . P values , listed below , were all calculated using the Mann Whitney test , compared to the normalized ( 2 ) / ( 1 ) experimental condition , to account for non-paired , two-tailed and non-Gaussian distributions of the data . ( 2 ) / ( 1 ) vs . ( 2 ) / ( 2 ) ; p=0 . 0836 stress fiber localization; p=0 . 3825 expression ( 2 ) / ( 1 ) vs . ( 3 ) / ( 2 ) ; p=0 . 1680 stress fiber localization; p=0 . 1736 expression ( 2 ) / ( 1 ) vs . ( 4 ) / ( 1 ) ; p=0 . 4266 stress fiber localization; p=0 . 0755 expression ( 2 ) / ( 1 ) vs . ( 4 ) / ( 5 ) ; p=0 . 8927 stress fiber localization; p=0 . 7509 expression ( 2 ) / ( 1 ) vs . ( 2 ) / ( 5 ) ; p=0 . 1885 stress fiber localization; p=0 . 4192 expression ( 2 ) / ( 1 ) vs . ( 2 ) / ( 1+TGFβi ) ; p<0 . 0001 stress fiber localization; p<0 . 0001 expression ( 2 ) / ( 1 ) vs . ( 2 ) / ( 5+TGFβi ) ; p=0 . 0909 stress fiber localization; p=0 . 0040 expression ( 2 ) / ( 1 ) vs . ( 3 ) / ( 1+TGFβi ) ; p=0 . 0018 stress fiber localization; p<0 . 0001 expression ( 2 ) / ( 1 ) vs . ( 4 ) / ( 5+TGFβi ) ; p=0 . 1018 stress fiber localization; p=0 . 5181 expression10 . 7554/eLife . 20600 . 009Table 3 . αSMA stress fiber localization and expression levels in naïve fibroblasts ( stellate cells ) cultured overnight in the presence or absence of TGFβ inhibitor within intact D-ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 009TGFβ-i DMSO αSMA Stress fiber localization 25% percentile0 . 710 . 72Median 0 . 96 1 . 00 75% percentile1 . 291 . 04expression 25% percentile0 . 440 . 49Median 0 . 62 0 . 94 75% percentile1 . 301 . 41Values obtained from naïve cells cultured overnight within intact D-ECMs were used for normalization ( as shown in Table 1 ) and assigned an arbitrary unit of 1 . 00 . TGFβ-i is the experimental condition in which naïve pancreatic stellate cells were cultured overnight in the presence of SB-431542 within intact D-ECM . DMSO treatment corresponds to vehicle control . Note that quantitative immunofluorescent obtained values of αSMA and F-actin were used to calculate stress fiber localization and expression of αSMA . P values , listed below , were calculated using the two-sided and two-tailed Mann Whitney test needed for normalized data . TGFβi vs . DMSO; p=0 . 3508 stress fiber localization; p=0 . 3361 expressionTGFβi vs . N-ECM ( from Table 1 ) ; p=0 . 0110 stress fiber localization; p=0 . 0010 expressionTGFβi vs . D-ECM ( from Table 1 ) ; p=0 . 9132 stress fiber localization; p=0 . 3401 expressionDMSO vs . N-ECM ( from Table 1 ) ; p=0 . 0036 stress fiber localization; p<0 . 0001 expressionDMSO vs . D-ECM ( from Table 1 ) ; p=0 . 2635 stress fiber localization; p=0 . 7408 expression10 . 7554/eLife . 20600 . 010Figure 2 . TGFβ is necessary for functional CAF-produced D-ECM . Naïve PDAC fibroblasts were cultured overnight within normal ( N-ECM ) vs . desmoplastic ( D-ECM ) ECMs that were produced in the presence or absence of TGFβ1-receptor inhibitor ( D + TGFβi ) or vehicle control ( D + DMSO ) . All samples were subjected to indirect immunofluorescent labeling of αSMA and counterstained with fluorescently labeled phalloidin to detect actin stress fibers ( F-actin ) . ( A ) Monochromatic images indicating double-labeled staining for αSMA and F-actin . ( B ) Quantification of the levels of localization of αSMA to actin stress fibers ( F-actin ) from ( A ) . ( C ) Pseudo-colored images representing intensity-maps of αSMA levels , with an intensity color bar scale ( 0–255 intensity tone values ) shown to the right . ( D ) Quantification of αSMA intensity from ( C ) . Untreated D-ECM conditions were included in all experiments summarized in this figure and served as normalization controls ( one arbitrary unit; a . u . ) . Checkmarks indicate conditions that induce myofibroblastic activation phenotypes . X marks indicate conditions that did not induce myofibroblastic activation . All quantifications and p-values can be found in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01010 . 7554/eLife . 20600 . 011Figure 2—figure supplement 1 . Primary CAF-produced D-ECMs induce naïve-to-myofibroblastic activation in normal pancreatic stellate cells . Naïve PDAC fibroblasts or CAFs were isolated from four additional patients and these fibroblasts were cultured overnight within the indicated patient CAF produced matrices , in the presence ( D + TGFβi ) or absence ( D-ECM ) of TGFβ1-receptor inhibitor . ( A ) Quantification of the localization of αSMA to actin stress fibers ( F-actin ) from images of indirect immunofluorescent labeling of αSMA counterstained with fluorescently labeled phalloidin to detect actin stress fibers ( F-actin ) . ( B ) Quantification of images displaying αSMA intensity , from the indicated combinations of fibroblasts and ECMs . Untreated D-ECM conditions from patient #2 naïve fibroblast re-plated on patient-#1-derived D-ECM serve as normalization controls ( one arbitrary unit; a . u . ) . Corresponding quantifications and p-values are listed in Table 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01110 . 7554/eLife . 20600 . 012Figure 2—figure supplement 2 . D-ECM imparts αSMA post-translational effects during naïve-to-myofibroblastic activation . ( A ) αSMA mRNA levels assessed by RT-qPCR of naïve/normal fibroblasts plated in N- vs . D-ECMs were unaltered . ( B ) Semi-quantitative indirect immunofluorescence showing similar pseudocolors depicting comparable αSMA protein intensity levels of naïve fibroblasts re-plated in D-ECMs comparing vehicle ( control ) vs . cycloheximide overnight treatments . Intensity levels correspond to the color bar scale shown on the right . Checkmarks indicate conditions that induce myofibroblastic activation phenotypes . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01210 . 7554/eLife . 20600 . 013Figure 2—figure supplement 3 . TGFβ activity is dispensable for D-ECM-induced naïve-to-myofibroblastic activation . Intact pre-produced D-ECMs were seeded overnight with naïve fibroblasts together with TGFβ1-receptor inhibitor ( D + TGFβi ) or vehicle control ( DMSO ) . Samples were subjected to indirect immunofluorescent labeling of αSMA and counterstained with fluorescently labeled phalloidin to detect actin stress fibers ( F-actin ) . ( A ) Representative images of αSMA and F-actin ( left images ) and the quantification of αSMA at stress fibers ( graph on the right ) . ( B ) Pseudocolored images represent intensity maps of αSMA , with an intensity color bar scale ( 0–255 intensity tone values ) ( left images ) and the quantification of αSMA intensity in the graph ( Right ) . Checkmarks indicate that conditions effectively induced the myofibroblastic phenotype . Corresponding quantifications and p-values are summarized in Table 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 013 Together , these results suggested that TGFβ inhibition during D-ECM production reduces the ability of CAFs to produce ECM that can induce myofibroblastic activation , but that once D-ECM has been deposited by CAFs , TGFβ retained within the D-ECM is subsequently dispensable for D-ECM-induced myofibroblastic activation of naïve stellate cells . Integrins αvβ5 and α5β1 have been reported to participate in myofibroblastic activation ( Asano et al . , 2006; Lygoe et al . , 2004; Dugina et al . , 2001 ) . We first established that both of these integrin heterodimers were highly abundant , compared to other β-integrins , on the PMs of human naïve pancreatic stellate cells and of PDAC-associated CAFs ( Figure 3A ) . To determine whether either or both of these heterodimers is essential for D-ECM induction of myofibroblastic conversion , naïve fibroblastic stellate cells were plated overnight on N-ECM or D-ECM in the presence of integrin-inhibitory or negative control antibodies . ALULA , a highly specific αvβ5-integrin-blocking antibody ( Su et al . , 2007 ) , eliminated the ability of D-ECM to induce αSMA stress fiber localization ( and expression ) beyond levels induced by N-ECM ( Figure 3B–E and Table 4 ) . By contrast , mAb16 , which specifically blocks human α5β1-integrin activity ( Akiyama et al . , 1989 ) , had limited effects on D-ECM induction of αSMA localization ( or expression ) , with naïve cells undergoing robust myofibroblastic conversion ( Figure 3B–E and Table 4 ) . Intriguingly , combined application of ALULA and mAb16 eliminated the effects of αvβ5-integrin inhibition seen with ALULA alone , with naïve cells undergoing robust myofibroblastic transition similar to that in untreated and IgG controls ( Figure 3B–E and Table 4 ) . This raised the possibility that inhibition of α5β1 might act downstream of inhibition of αvβ5 in receiving D-ECM signals . 10 . 7554/eLife . 20600 . 014Table 4 . αSMA stress fiber localization and expression levels in naïve fibroblasts ( stellate cells ) cultured overnight within D-ECMs in the presence of integrin functional antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 014αvβ5-i α5β1-i β5+α5-i α5β1-act IgG αSMA Stress fiber localization 25% percentile0 . 140 . 510 . 830 . 410 . 92Median 0 . 54 0 . 77 1 . 11 0 . 56 1 . 00 75% percentile0 . 711 . 201 . 550 . 981 . 00expression 25% percentile0 . 110 . 310 . 440 . 080 . 31Median 0 . 29 0 . 71 0 . 90 0 . 32 1 . 25 75% percentile0 . 701 . 272 . 090 . 961 . 95Values obtained from naïve cells cultured overnight within intact D-ECMs treated with ALULA ( αvβ5-i ) , mAb16 ( α5β1-i ) , ALULA plus mAb16 ( β5+α5-i ) , SNAKA ( α5β1-act ) or control pre-immune antibody ( IgG ) . Values obtained in untreated D-ECMs ( from Table 1 ) were used for normalization and assigned an arbitrary unit of 1 . 00 . Note that the quantitative values of αSMA and F-actin obtained by immunofluorescence were used to calculate stress fiber localization and expression of αSMA . P values , listed below , were calculated using the two-sided and two-tailed Mann Whitney test needed for normalized data . αvβ5-i vs . IgG; p=0 . 0001 stress fiber localization; p=0 . 0002 expressionα5β1-i vs . IgG; p=0 . 1311 stress fiber localization; p=0 . 1229 expressionβ5+α5-i vs . IgG; p=0 . 0333 stress fiber localization; p=0 . 9171 expressionα5β1-act vs . IgG; p=0 . 0047 stress fiber localization; p=0 . 0020 expression10 . 7554/eLife . 20600 . 015Figure 3 . Integrin αvβ5 regulates α5β1 activity thereby maintaining D-ECM-induced naïve-to-myofibroblastic activation . ( A ) An integrin-dependent cell adhesion array test was used to assess the PM expression of integrin heterodimers in primary fibroblasts isolated from normal ( white bars ) vs . matched tumor tissue ( desmoplastic; dark bars ) . Note that no differences were apparent between the two cell types with regards to levels of αvβ5 and α5β1 integrins . ( B ) Naïve human pancreatic fibroblastic stellate cells were re-plated onto D-ECMs overnight in the presence of functional blocking anti-αvβ5-integrin ( ALULA [Su et al . , 2007]; αvβ5-i ) , functional blocking anti-α5β1-integrin ( mAb16 [Akiyama et al . , 1989]; α5β1-i ) , combinations of both functional blocking antibodies ( β5-i + α5-i ) , functional stabilizing anti-α5β1-integrin ( SNAKA51 [Clark et al . , 2005]; α5β1-act ) , or non-immunized isotypic antibodies ( IgG ) . Representative monochromatic images of αSMA- and F-actin stained fibroblasts are shown . ( C ) Quantification of the experiment performed in ( B ) . ( D ) Pseudocolored images depicting the intensity of αSMA expression including a color bar scale ( 0–255 intensity tone values ) . ( E ) Quantification of ( D ) . Note that corresponding quantifications and p-values , for results shown in ( B–E ) are summarized in Table 4 . ( F ) Naïve murine skin fibroblasts were re-plated onto murine D-ECMs ( mD-ECM ) produced by murine skin squamous cell carcinoma associated CAFs ( Amatangelo et al . , 2005 ) , and subjected to αvβ5-integrin and α5β1-integrin inhibitors alone ( ALULA: αvβ5-i [Su et al . , 2007] and BMA5: α5β1i ) or in combination ( β5+ α5-i ) . The effects on myofibroblastic activation were measured for αSMA as in ( B ) . The red asterisk illustrates the area outlined in red in the magnified insert for the intact ( untreated ) control . The same magnification is shown for the experimental conditions in the additional panels . As a method of quantifying the percentage of cells showing myofibroblastic features , the percentage of cells that have a stress fiber localized ( αSMA ) phenotype is shown ( ****p<0 . 0001 ) . Note that inhibition of α5β1-integrin effectively reinstituted the mD-ECM-induced phenotype that was lost by inhibition of αvβ5-integrin , just as seen above for the human PDAC system . Checkmarks identify conditions that resulted in myofibroblastic activation , while Xs identify conditions that did not result in myofibroblastic activation . ( G ) Model of D-ECM-induced activation of naïve fibroblasts , dependent on the activity of integrins αvβ5 and α5β1 . Inhibition of αvβ5- integrin results in release of active α5β1-integrin , leading to blockade of D-ECM-induced myofibroblastic activation ( 1st arrow , red X ) . The activity of αvβ5- integrin is no longer needed in the absence of α5β1-integrin activity , suggesting that α5β1-integrin activity in not necessary for fibroblasts to undergo D-ECM-induced myofibroblastic activation ( 2nd arrow , green checkmark ) . Double inhibition of αvβ5-integrin and α5β1-integrin results in D-ECM myofibroblastic activation , which proposes that inhibition of α5β1-integrin can overcome or rescue the effects seen under αvβ5-integrin inhibition ( 3rd arrow , green checkmark ) . Stabilization of α5β1-integrin in its active conformation overcomes the inhibitory/regulatory effects imparted by αvβ5-integrin , resulting in ineffective D-ECM-induced myofibroblastic activation ( 4th arrow , red X ) . Overall , the model suggests that D-ECM induces αvβ5- integrin activity , which in turn results in the regulation of active α5β1-integrin , allowing D-ECM-induced myofibroblastic activation ( large arrow to the right , green checkmark ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01510 . 7554/eLife . 20600 . 016Figure 3—figure supplement 1 . Naïve β5-integrin KO fibroblasts display stunted D-ECM-induced myofibroblastic activation . ( A ) The CRISPR/CAS9 system was used to generate naïve fibroblastic stellate cell β5-integrin knock-out ( KO ) ( β5-KO ) or control KO ( cntrl-KO ) lines , whose genoype was confirmed by western blotting . The naïve cntrl-KO and β5-KO1 fibroblastic stellate cells were challenged by overnight culture within intact D-ECM and their myofibroblastic features were assessed . ( B ) Samples were subjected to indirect immunofluorescent labeling of αSMA and counterstained with fluorescently labeled phalloidin to detect actin stress fibers ( F-actin ) and representative monochromatic images are displayed . ( C ) Quantification of αSMA localized at actin stress fibers ( F-Actin ) from the experiment in ( B ) ( ***p=0 . 0006; n = 46 ) . ( D ) Pseudocolored images represent intensity map outputs of αSMA , with an intensity color bar scale ( 0–255 intensity tone values ) shown to the right . ( E ) Measured values from ( D ) are summarized in the graph ( ***p=0 . 0001; n = 46 ) . An experimental condition consisting of naïve cntrl-KO fibroblastic stellate cells cultured in intact D-ECM was included in all experiments; this condition is summarized in this figure and is used for normalization ( one arbitrary unit; a . u . ) . Checkmarks indicate conditions that induce a myofibroblastic activation phenotype , while Xs indicate loss of D-ECM-induced activation . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01610 . 7554/eLife . 20600 . 017Figure 3—figure supplement 2 . Expression of α5- , αv- and β3-integrins in naïve fibroblastic stellate cells is necessary for D-ECM-induced myofibroblastic activation . ( A ) The CRISPR/CAS9 system was used to generate α5-integrin knock-out ( α5-KO ) , αv-integrin knock-out ( αV-KO ) , β3-integrin knock-out ( β3-KO ) and control KO ( cntrl-KO ) naïve fibroblasts , which were confirmed by western blotting . The naïve cntrl-KO and assorted integrin KO fibroblastic stellate cells were challenged by overnight culture within intact D-ECM , and their myofibroblastic features were assessed . ( B ) All samples were subjected to indirect immunofluorescent labeling of αSMA and counterstained with fluorescently labeled phalloidin to detect actin stress fibers ( F-actin ) and representative monochromatic images are displayed . ( C ) Quantification of αSMA localized at actin stress fibers ( F-actin ) from the experiment in ( B ) ( ***p=0 . 0002; ****p<0 . 0001 ) . ( D ) Pseudocolored images represent intensity maps of αSMA , with an intensity color bar scale ( 0–255 intensity tone values ) shown to the right . ( E ) Measured values from ( D ) are summarized in the graph ( ****p<0 . 0001 ) . An experimental condition consisting of naïve cntrl-KO fibroblastic stellate cells cultured in intact D-ECM was included in all experiments summarized in this figure and is used for normalization ( one arbitrary unit; a . u . , indicated by dotted lines in graphs ) . X marks indicate loss of D-ECM-induced activation for all tested mutant cells . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 017 To test whether these signaling interactions were specific to human pancreatic cells or also observed in other systems , we used murine skin squamous cell carcinoma associated fibroblasts , which are known to produce myofibroblastic-activating D-ECMs ( mD-ECM ) ( Amatangelo et al . , 2005 ) . We cultured naïve murine skin fibroblasts within mD-ECM overnight in the presence of ALULA alone , to inhibit αvβ5-integrin , or in combination with BMA5 , to inhibit the activity of murine α5β1-integrin specifically ( Fehlner-Gardiner et al . , 1996 ) . The results of this experiment ( Figure 3F ) paralleled those seen with pancreatic stellate cells , suggesting a general mechanism . To explore the observation that αvβ5-integrin-induced myofibroblastic activation is dispensable upon loss of α5β1-integrin activity , we directly tested the possibility that αvβ5-integrin negatively regulates α5β1-integrin to prevent active α5β1 from blocking D-ECM-induced activation ( see model in Figure 3G ) . For this , we asked whether stabilizing the activity of α5β1-integrin could phenocopy αvβ5-integrin inhibition . We found that SNAKA51 , an activating antibody that stabilizes the active conformation of α5β1-integrin ( Clark et al . , 2005 ) , reduced the ability of D-ECM to induce αSMA stress fiber localization ( and expression ) in naïve stellate cells ( Figure 3 and Table 4 ) . As an independent approach to simulate ALULA-dependent inhibition of αvβ5-integrin , we used Clustered Regularly Interspaced Short Palindromic Repeats ( CRISPR ) -editing to generate a series of naïve stellate cells in which the β5 , α5 , αv or β3 ( a negative control ) integrin subunits were ablated ( Figure 3—figure supplements 1 and 2 ) . Cells lacking the β5 subunit had a greatly diminished response to D-ECM , with very limited induction of αSMA stress fiber localization ( and expression ) ( Figure 3—figure supplement 1 ) . By contrast , loss of α5- , αv- or β3-integrin profoundly reduced growth of naïve fibroblasts , yielding slow-growing cells that were also unable to respond to D-ECM . The slow growth and poor condition of the additional KO cells suggests pleiotropic roles for these integrin subunits in supporting cell viability , and thus makes it difficult to draw conclusions . Nonetheless , the results do not rule out a role for α5β1-integrin activity , regulated by D-ECM control of αvβ5-integrin , that opposes the stimulation of the naïve-to-myofibroblastic transition ( Figure 3G ) . Next , we asked whether manipulation of integrin activities in CAFs , as opposed to in naïve stellate cells , could influence their ability to produce functional D-ECM . In contrast to results with TGFβ blockade , neither αvβ5-integrin inhibition ( with ALULA ) nor stabilization of active α5β1-integrin ( with SNAKA51 ) altered the anisotropic fiber formation in D-ECM deposited by CAFs ( Figure 4 ) , or the ability of these matrices to induce myofibroblastic activation in naïve fibroblastic stellate cells ( Figure 4—figure supplement 1 ) . We also analyzed the ECM produced by CRISPR-edited CAFs lacking specific integrin subunits . In contrast to results with integrin inhibition , CAFs lacking β5-integrin or α5-integrin subunits had decreased αSMA expression and localization of αSMA to stress fibers . Loss of β5 expression also affected levels of D-ECM anisotropy , while CAFs lacking α5 failed to produce substantial matrices ( matching earlier reports [Pankov et al . , 2000; McDonald et al . , 1987; Fogerty et al . , 1990] ) ( Figure 4—figure supplement 2 ) . In addition , loss of αv-integrin caused significant reduction in the ability to produce D-ECM , while loss of the negative control , β3 , had no effect on this phenotype ( Figure 4—figure supplement 3 ) . These results suggested that the ability of CAFs to grow and produce functional D-ECMs was selectively affected by loss of α5 , αv and β5 integrin subunits but not by β3 loss . 10 . 7554/eLife . 20600 . 018Figure 4 . Transient αvβ5-integrin inhibition or α5β1-integrin stabilization failed to alter CAF production of anisotropic D-ECM . ( A ) Representative indirect immunofluorescent images of 3D D-ECM producing CAFs in the presence of functional blocking anti-αvβ5-integrin ( ALULA [Su et al . , 2007]; D + αvβ5-i ) , active conformation stabilizing anti-α5β1-integrin ( SNAKA51 [Clark et al . , 2005]; D + α5β1-act . ) or non-immunized isotypic antibodies ( D + IgG ) . Spinning disk confocal monochromatic images , obtained following indirect immunofluorescence , show nuclei ( Hoechst; yellow ) , αSMA ( white ) and ECM ( fibronectin; magenta ) . ( B ) The corresponding ECM fiber angle distributions , determined by Image-J’s ‘Orientation J’ plugin , were normalized using hue values for a cyan mode angle visualization as shown in the bar on the right . ( C ) Corresponding curves depicting experimental-repetition-averaged variations of angle distributions normalized to 0° modes and summarizing the results . Dotted lines correspond to 15° fiber angle spreads . ( D ) Plotted data depicting summarized percentages of fibers distributed at 15° angles from the mode for each experimental condition . The dotted line denotes 55% alignment . Note how none of the treatments seem to have altered the myofibroblastic features of CAFs or their capability to produce anisotropic D-ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01810 . 7554/eLife . 20600 . 019Figure 4—figure supplement 1 . D-ECMs produced by CAFs under transient αvβ5-integrin inhibition or stabilization of α5β1-integrin activity are functionally intact . Naïve fibroblasts were plated onto D-ECMs that were produced in the presence of functional blocking anti-αvβ5-integrin ( ALULA [Su et al . , 2007]; D + αvβ5-i ) , active conformation stabilizing anti-α5β1-integrin ( SNAKA51 [Clark et al . , 2005]; D + α5β1-act . ) or non-immunized isotypic antibodies ( D + IgG ) , and αSMA and actin stress fibers ( F-actin ) were immunofluorescently labeled . ( A ) Monochromatic images indicative of double-labeled αSMA and F-actin are shown , while levels of total αSMA localized at corresponding stress fibers are plotted in the graph to the right . ( B ) Pseudocolored images represent αSMA intensity values , which are summarized in the graph to the right . Checkmarks indicate conditions that induce a myofibroblastic activation phenotype . Note that D-ECMs that were produced under αvβ5-integrin inhibitory or active α5β1-integrin stabilizing conditions resulted in no apparent alteration of D-ECM function of CAFs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 01910 . 7554/eLife . 20600 . 020Figure 4—figure supplement 2 . Loss of β5-integrin expression in CAFs impairs D-ECM anisotropy , while expression of α5-integrin is imperative for effective ECM fibrillogenesis . Representative western blot of β5-integrin ( A ) and α5-integrin ( C ) expression in desmoplastic fibroblasts ( CAFs ) , illustrating the result of CRISPR/CAS9-mediated KO of integrins ( A — β5-KO1 + β5-KO2 and C — α5-integrin α5-KO1 + α5-KO2 ) compared to non-targeting gRNA control ( cntrl-KO ) . Histone three was used as a loading control . Representative confocal microscopy images of either control CAF-KO ( cntrl-KO ) or CAF-β5-integrin-KO2 ( β5-KO2 ) ( B ) or CAF-α5-integrin-KO2 ( α5-KO2 ) ( D ) , depicting αSMA ( white ) and nuclei ( yellow ) . Inserts show the image-matching ECM fibers ( fibronectin , magenta ) . The images on the right show the corresponding ECM fiber angle distributions as obtained using the Image-J’s ‘OrientationJ’ plug . ( E ) Quantification of the distribution of fiber angles that are within 15° of the mode from ( B ) ( ****p<0 . 0001 ) . Note that α5-KO2 CAFs did not produce matrices that were substantial enough for quantification and were therefore omitted from ( E ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02010 . 7554/eLife . 20600 . 021Figure 4—figure supplement 3 . KO of αV-integrin , but not β3-integrin , disrupts CAF myofibroblastic features and D-ECM production . Representative western blots of αV-integrin ( A ) and β3-integrin ( C ) expression in CAFs , illustrating the result of CRISPR/CAS9 editing of αV-integrin ( αV-KO1 + αV-KO2 ) ( A ) and β3-integrin ( β3-KO1 + β3-KO2 ) ( C ) compared to non-targeting , gRNA , control ( cntrl-KO ) . Histone three was used as a loading control . Representative confocal microscopy images of CAF-αV-integrin-KO1 ( αV-KO1 ) ( B ) and CAF-β3-integrin-KO1 ( β3-KO2 ) ( D ) depicting αSMA ( white ) or nuclei ( yellow ) . Inserts show the image-matching ECM fibers ( fibronectin , magenta ) . The images on the right are the corresponding ECMs , which for αV-KO1 was not substantial enough to pass the quality thickness test and was therefore omitted from further assessment; for β3-KO2 , ECM fiber angle distributions shown by pseudocoloring were obtained using Image-J’s ‘OrientationJ’ plug in . ( E ) Quantification of the distribution of fiber angles that are within 15° of the mode shown in ( D ) . The dotted line corresponds to the percentage of fiber alignment seen in control KO . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 021 Together , these data indicated that the requirement for specific integrins in the response of naïve fibroblastic cells to D-ECMs differs from the requirement for integrins during CAF production of anisotropic D-ECMs . Although integrins often signal through focal adhesion kinase ( FAK ) , an increasing number of FAK-independent integrin signaling activities have been observed ( Cukierman et al . , 2001; Wu et al . , 2008; Zoppi et al . , 2008; Horton et al . , 2016 ) . We investigated the role of FAK in D-ECM-induced myofibroblastic activation , treating naïve fibroblastic stellate cells plated in D-ECM with the small molecule FAK inhibitor PF573 , 228 ( Slack-Davis et al . , 2007 ) . FAK inhibition strongly reduced the ability of these cells to acquire myofibroblastic traits , similar to the result seen with αvβ5-integrin inhibition ( Figure 5 ) . Further , naïve pancreatic stellate cells treated concomitantly with PF573 , 228 and the α5β1-integrin-inhibiting mAb16 ( Akiyama et al . , 1989 ) underwent a myofibroblastic conversion , re-localizing and upregulating αSMA ( Figure 5 ) . 10 . 7554/eLife . 20600 . 022Figure 5 . FAK-independent α5β1-integrin activity negatively regulates PDAC D-ECM-induced naïve-to-myofibroblastic activation . Naïve fibroblasts were re-plated onto D-ECMs and challenged with either control conditions ( DMSO + IgG ) , small molecule FAK inhibitor PF573 , 228 ( Slack-Davis et al . , 2007 ) ( FAK-i ) alone or FAK inhibitor in combination with α5β1-integrin inhibitor ( FAK-i + α5-i , mAb16 [Akiyama et al . , 1989] ) , and activation of fibroblasts was tested . ( A ) Representative monochromatic images of immunofluorescently labeled αSMA and actin stress fibers ( F-actin ) . Colored asterisks in ( A ) represent areas that are magnified in the corresponding panels to the right . ( B ) Quantification of αSMA at actin stress fibers ( F-actin ) from the experiment in ( A ) and normalized to DMSO + IgG control ( one arbitrary unit; a . u . ) ( ****p<0 . 0001 ) . ( C ) Pseudocolored images represent intensity maps of αSMA , with an intensity color bar scale ( 0–255 intensity tone values ) shown to the right . ( D ) Quantification of αSMA intensity from ( C ) ( ***p=0 . 0001 ) . Note that the D-ECM-induced phenotype that was lost under FAK inhibition was rescued under α5β1-integrin co-inhibition ( just as for the two integrin co-inhibitions shown in Figure 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02210 . 7554/eLife . 20600 . 023Figure 5—figure supplement 1 . FAK-independent α5β1-integrin activity negatively regulates mD-ECM-induced murine naïve-to-myofibroblastic activation . Naïve murine skin fibroblasts were re-plated onto murine D-ECMs ( Amatangelo et al . , 2005 ) ( mD-ECM ) and were treated with PF573 , 228 ( FAK inhibitor ) and also subjected to αvβ5-integrin or α5β1-integrin inhibitors ( ALULA — αvβ5-i ( Su et al . , 2007 ) or BMA5 — α5β1i ) or IgG control . The effects on naïve-to-myofibroblastic activation were measured in all treated PF573 , 228 conditions by indirect immunofluorescence of αSMA expression . ( A ) Representative monochromatic images of stress fiber localized αSMA . ( B ) Quantification of the percentage of cells showing myofibroblastic features ( stress fiber localized αSMA phenotype % ) during the experiment illustrated in ( A ) ( ***p=0 . 0001 ) . Note that co-inhibition of FAK and α5β1-integrin effectively reinstituted the mD-ECM-induced phenotype lost as a result of FAK inhibition alone ( cnt ) , just as in the human PDAC stroma system ( Figure 5 ) and as was the case for the two integrin co-inhibitions shown in Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02310 . 7554/eLife . 20600 . 024Figure 5—figure supplement 2 . α5β1-integrin inhibition restores mD-ECM-induced naïve-to-myofibroblastic activation in murine FAK-/- skin fibroblasts . FAK null ( FAK -/- ) naïve murine skin fibroblasts were re-plated onto murine D-ECMs ( mD-ECM ) subjected to αvβ5-integrin and α5β1-integrin inhibitors ( ALULA — αvβ5-i [Su et al . , 2007] or BMA5 — α5β1i ) . The effects on myofibroblastic activation were measured by indirect immunofluorescence of αSMA expression as before . ( A ) Representative monochromatic images of αSMA expression . ( B ) Quantification of the percentage of cells showing myofibroblastic features ( stress fiber localized αSMA phenotype % ) during the experiment illustrated in ( A ) ( ****p<0 . 0001 ) . Note that inhibition of α5β1-integrin effectively reinstituted the mD-ECM-induced phenotype that was lost as a result of FAK deletion . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02410 . 7554/eLife . 20600 . 025Figure 5—figure supplement 3 . α5β1-integrin inhibition restores mD-ECM-induced naïve-to-myofibroblastic in murine FAK-KD skin fibroblasts . Wild-type ( WT ) or FAK kinase dead ( KD ) naïve murine skin fibroblasts were re-plated onto murine D-ECMs ( mD-ECM ) and subjected to α5β1-integrin inhibitor ( BMA5 — WT α5i or KD α5i ) or IgG control ( WT cnt and KD IgG ) ) . The effects on myofibroblastic activation were measured by indirect immunofluorescence of αSMA expression . ( A ) Representative monochromatic images of αSMA expression . ( B ) Quantification of the percentage of cells showing myofibroblastic features ( stress fiber localized αSMA phenotype % ) during the experiment illustrated in ( A ) . Note that inhibition of α5β1-integrin effectively reinstituted the mD-ECM-induced phenotype that was lost as a result of FAK KD mutation . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02510 . 7554/eLife . 20600 . 026Figure 5—figure supplement 4 . Ablation of SRC family kinases negates the ability of naïve fibroblasts to respond to mD-ECM . SRC naïve murine skin fibroblasts lacking SRC , YES and FYN ( SYF -/- ) were re-plated onto murine D-ECMs ( mD-ECM ) and αvβ5-integrin and α5β1-integrin inhibitors ( ALULA — αvβ5-i [Su et al . , 2007] and BMA5 — α5β1i ) or IgG control ( IgG ) . The effects on myofibroblastic activation were measured by indirect immunofluorescence of αSMA expression . ( A ) Representative monochromatic images of αSMA expression . ( B ) Quantification of the percentage of cells showing myofibroblastic features ( stress fiber localized αSMA phenotype % ) during the experiment illustrated in ( A ) . Note that inhibition of α5β1-integrin could not reinstitute the mD-ECM-induced phenotype that was lost in SRC-/- cells . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02610 . 7554/eLife . 20600 . 027Figure 5—figure supplement 5 . FAK inhibition in conjunction with loss of α5-integrin expression in human naïve fibroblasts fails to restore D-ECM-induced myofibroblastic activation . Naïve fibroblastic stellate control KO ( cntrl-KO ) or α5-integrin ( α5-KO ) cells were re-plated within intact D-ECMs and challenged with DMSO as control or with the small molecule FAK inhibitor PF573 , 228 ( Slack-Davis et al . , 2007 ) ( FAK-i ) , and the activation of fibroblasts was tested . Representative monochromatic images of αSMA and actin stress fibers ( F-actin ) were immunofluorescently labeled and are shown . Note that unlike α5β1-integrin inhibition with mAb16 , loss of α5-integrin expression did not rescue the myofibroblastic phenotype . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 027 Emphasizing a general mechanism , treatment of naïve murine fibroblasts plated in mD-ECM with PF573 , 228 blocked acquisition of myofibroblastic traits ( Figure 5—figure supplement 1 ) . In naïve murine fibroblasts , concomitant FAK and α5β1-integrin inhibition with combined PF573 , 228 and BMA5 significantly increased mD-ECM-induced myofibroblastic conversion when compared with PF573 , 228 treatment alone , while αvβ5-integrin co-inhibition with FAK did not ( Figure 5—figure supplement 1 ) . Further , FAK-/- murine naïve fibroblastic cells ( Ilić et al . , 1995 ) grown in mD-ECM had greatly reduced myofibroblastic activation , which was significantly increased if α5β1 but not αvβ5 integrin activation was blocked ( Figure 5—figure supplement 2 ) . As an additional control to exclude off-target or indirect effects of drug inhibition or FAK KO background compensation , we asked whether murine fibroblasts engineered to express a dominant negative FAK Kinase-Dead mutant ( FAK-KD ) ( Lim et al . , 2010 ) behaved similarly to those with deleted or inhibited FAK , and whether they also recovered responsiveness to D-ECM following inhibition of α5β1-integrin activity . Immortalized murine fibroblasts overexpressing FAK-KD displayed a lack of myofibroblastic response to mD-ECM compared to the response seen in isogenic hTert immortalized control cells , and again , an efficient rescue of this phenotype was imparted by α5β1-integrin inhibition ( Figure 5—figure supplement 3 ) . FAK often interacts with SRC family kinases to mediate integrin signaling . To further probe the mechanisms , we evaluated the mD-ECM responsiveness of fibroblasts cells that are genetically null for the SRC family kinases SRC , FYN and YES ( Klinghoffer et al . , 1999 ) . We found that the ablation of SRC family kinases also negated the ability of fibroblasts to respond to mD-ECM . Interestingly , in this context , inhibition of α5β1-integrin did not restore the mD-ECM-induced myofibroblastic phenotype , indicating non-equivalent functions of FAK and SRC ( Figure 5—figure supplement 4 ) . Last , we explored the relationship between FAK and α5-integrin expression ( as opposed to integrin activity ) . The ability of the FAK inhibitor to block D-ECM-induced myofibroblastic transition depended on an intact α5β1 heterodimer , as PF573 , 228 did not rescue the mD-ECM-induced process that was disrupted in α5-KO naïve stellate cells ( Figure 5—figure supplement 5 ) . Together , these results suggested that FAK is essential for D-ECM-induced myofibroblast activation , but dispensable for the α5β1-integrin inhibition of this process . The data further indicate that genetic ablation of α5β1-integrin does not recapitulate the result of inhibition of integrin activity in D-ECM responsiveness , perhaps due to additional functional requirements for this integrin in supporting fundamental cell growth . In vivo , fibroblasts engage N-ECM through 3D matrix adhesion structures ( 3D-adhesions ) that mediate matrix-dependent homeostasis ( Cukierman et al . , 2001 ) . 3D-adhesions are elongated adhesion plaques that depend on α5β1-integrin activity for their formation and are characterized by encompassing active α5β1-integrin concomitant with constitutive , albeit low , levels of auto-phosphorylated , activated FAK ( pFAK-Y397 ) ( Cukierman et al . , 2001 ) . We tested the idea that D-ECM might alter structure , protein composition , or signaling at 3D-adhesions . Naïve stellate cells cultured within D-ECM typically increased the length of 3D-adhesions by ~14% compared to adhesions formed in N-ECM ( Figure 6 ) . While inhibition of α5β1-integrin with mAb16 was previously shown to cause 3D-adhesion loss ( Cukierman et al . , 2001 ) , stabilization of α5β1-integrin activity using SNAKA51 eliminated the ability of D-ECM to induce 3D-adhesion lengthening . However , αvβ5-integrin inhibition with ALULA did not ( Figure 6 ) . These results separate the requirements for D-ECM-induced αvβ5-integrin regulation of αSMA ( Figure 3 ) from the lack of requirement for αvβ5-integrin for adhesion reorganization in naïve fibroblastic cells . 10 . 7554/eLife . 20600 . 028Figure 6 . D-ECM regulates 3D-adhesion structure length , dependent on α5β1-integrin activity . ( A ) Indirect immunofluorescent and spinning disc confocal generated images of 3D-adhesions ( identified using mAb11 ) ( Cukierman et al . , 2001 ) , formed by naïve fibroblastic cells cultured within N-ECM or D-ECM in the absence ( cnt . ) or presence of ALULA ( Su et al . , 2007 ) for αvβ5-integrin inhibition ( αvβ5-i ) or SNAKA52 ( Clark et al . , 2005 ) to stabilize α5β1-integrin activity ( α5β1 act ) or IgG as control . ( A ) The artificially colored structures represent computer-selected internally threshold objects ( ITOs ) of 3D-adhesion structures . ( B ) Quantification of the length of ITO generated objects from ( A ) ( ***p=0 . 0026 . ****p<0 . 0001 ) . Note the significant differences in 3D-adhesion length observed between N-ECM and D-ECM as well as between IgG and SNAKA51 treatments . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02810 . 7554/eLife . 20600 . 029Figure 6—figure supplement 1 . Naïve fibroblastic cells increase overall levels of active α5β1-integrin in response to D-ECM . ( A ) Double-labeled images depicting 3D-adhesion ( red in overlay; 3D-adh . ) and active α5β1-integrin ( green in overlay; α5β1-act ) in naïve fibroblasts cultured overnight in normal-ECMs ( N-ECM ) or desmoplastic-ECMs ( D-ECM ) . The pseudocolored images on the far right represent semi-quantitative images of maximum reconstructions of active α5β1-integrin levels ( α5β1-act inten ) , with a corresponding intensity bar shown on the right . ( B ) Summary of total active α5β1-integrin levels using median levels on D-ECM for normalization ( one arbitrary unit; a . u . ) ( ****p<0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 02910 . 7554/eLife . 20600 . 030Figure 6—figure supplement 2 . D-ECMs induce an increase in pFAK levels at 3D-adhesions and redistribution of increased active α5β1-integrin to locations away from 3D-adhesions . Naïve fibroblasts were cultured within assorted ECMs in the absence ( N-ECM or D-ECM ) or presence of αvβ5-integrin inhibitor ALULA ( Su et al . , 2007 ) ( D-ECM + αvβ5-i ) and were stained for adhesion structures , pFAK , and active α5β1-integrin and the images were quantified . ( A ) Graph depicting active α5β1-integrin levels localized at 3D-adhesions , normalized to D-ECM median intensities , calculated using SMIA-CUKIE publicly available at https://github . com/cukie/SMIA . ( B ) Graph depicting levels of pFAK-Y397 , calculated using SMIA-CUKIE , localized at 3D-adhesions structures ( ****p<0 . 0001 ) . ( C ) Graph showing active α5β1-integrin intensity values , normalized to D-ECM mean intensities and calculated using SMIA-CUKIE , that are localized away from 3D-adhesions ( ****p<0 . 0001 ) . Note that the D-ECM-induced increases in active α5β1-integrin , which are evident at locations away from 3D-adhesions , are concomitant with increased 3D-adhesion localized pFAK . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03010 . 7554/eLife . 20600 . 031Figure 6—figure supplement 3 . D-ECM-induced increase in active α5β1-integrin is reversed by inhibition of αvβ5-integrin . Summary of results from naïve fibroblasts cultured within assorted ECMs in the absence ( N-ECM or D-ECM ) or presence of αvβ5-integrin inhibitor ALULA ( Su et al . , 2007 ) ( D-ECM + αvβ5-i ) and stained for adhesion structures and active α5β1-integrin . The sizes of pie graphs are relative to SMIA-CUKIE output corresponding to total intensity levels , while relative percentage distributions at 3D-structures ( yellow ) and away from these structures ( light green ) are shown . Note that the percentage distributions between locations at and away from 3D-adhesions are relatively unchanged while total intensity levels of active integrin are increased in response to D-ECM . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03110 . 7554/eLife . 20600 . 032Figure 6—figure supplement 4 . D-ECM induces intracellular accumulation of active α5β1-integrin . Naïve cells were plated overnight within D-ECM . ( A ) Indirect immunofluorescent images of active α5β1-integrin ( α5β1-act . ; SNAKA51 in green ) locations relative to 3D-adhesions ( 3D-adh . ; mAb11 in red ) , showing a representative cell under permeable vs . non-permeable conditions . The third image ( on the right ) demonstrates the specificity of active α5β1-integrin detection when samples were treated with functional blocking anti-α5β1-integrin antibody , mAb16 , under permeable conditions ( perm . + α5β1i ) . ( B ) Graph summarizes permeable vs . non-permeable values of active α5β1-integrin from experimental repetitions in which median permeable intensity levels were used for normalization ( one arbitrary unit; a . u . ) ( ***p=0 . 0005 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03210 . 7554/eLife . 20600 . 033Figure 6—figure supplement 5 . D-ECM-induced increase in intracellular α5β1-integrin activity is regulated by αvβ5-integrin . Summary of results from naïve fibroblasts cultured within assorted ECMs in the absence ( N-ECM or D-ECM ) or presence of αvβ5-integrin inhibitor ALULA ( Su et al . , 2007 ) ( D-ECM + αvβ5-i ) and stained , following permeable vs . non-permeable conditions , for adhesion structures and active α5β1-integrin . The pie graphs depict active α5β1-integrin fractions localized exogenously on the PM at ( non-permeable conditions; yellow or away from ( light green ) 3D-adhesions , or intracellularly ( dark green ) . Diminished levels of intracellular active α5β1-integrin are represented by the ‘empty’ pie wedges ( gray ) . Data were normalized to D-ECM-induced levels obtained under permeable conditions as in Figure 6—figure supplements 3–4 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 033 Next , we developed a semi-quantitative indirect immunofluorescence analytic method ( based on SMIA-CUKIE software; see Methods ) to evaluate the total intensity of active α5β1-integrin , versus its relative intensity , and area distributions related to 3D-adhesion sites . Plating of naïve stellate cells in D-ECM induced a 4-fold increase in active α5β1-integrin levels compared to plating in N-ECM ( Figure 6—figure supplement 1 ) , but surprisingly , this did not reflect an increase in α5β1 localized to 3D adhesions ( Figure 6—figure supplement 2A ) but rather a concentration ( ~2 fold ) in membrane-proximal regions areas lacking adhesions . To test the idea that D-ECM might affect the recruitment or activation of FAK in 3D adhesions , potentially through control of α5β1-integrin activation or localization , we also examined the localization of activated FAK ( pFAK-Y397 ) to 3D adhesions . Results normalized to levels obtained in D-ECMs ( a . u . = 1 . 0 ) indicated that D-ECM induced ~2 fold more pFAK-Y397 than N-ECM , and that this activated FAK was localized at 3D-adhesions ( Figure 6—figure supplement 2B ) . By contrast , if fibroblasts are cultured in the presence of the αvβ5-integrin inhibitor ALULA , significantly lower levels of pFAK-Y397 at 3D-adhesions and active α5β1-integrin away from 3D-adhesions were observed in cells cultured within D-ECM ( Figure 6—figure supplement 2B–C ) . A summary of results regarding the levels and locations of active α5β1-integrin is shown in Figure 6—figure supplement 3 . To better refine the location of α5β1-integrin activation , and taking advantage of the fact that the epitope recognized by SNAKA51 is typically extracellular with PM-localized integrin ( Clark et al . , 2005 ) , we performed SMIA-CUKIE analysis comparing SNAKA51 levels in permeabilized versus non-permeabilized cells plated on D-ECM versus N-ECM . Non-permeabilized cells had a 5-fold reduction of detectable D-ECM-induced active ( SNAKA51-positive ) α5β1-integrin relative to permeabilized cells ( Figure 6—figure supplement 4 ) , indicating that the activated integrin may be rapidly internalized . As a control and to demonstrate the specificity of the active integrin conformation detection , we demonstrated that D-ECM treatment in the presence of the mAb16 inhibitory antibody eliminated all detectable active α5β1-integrin . In addition , inhibition of αvβ5-integrin , by treatment with ALULA , effectively eliminated the intracellular pool while it considerably increased the amounts of surface-exposed active α5β1-integrin induced by growth in D-ECM ( Figure 6—figure supplement 5 ) . Generally similar results were obtained in analysis of pancreatic human naïve stellate cells deficient in discrete integrin subunits . Naïve β5-integrin KO fibroblastic stellate cells plated within D-ECM had 2 . 7- and 2 . 5-fold decreases in total α5β1-integrin activity and pFAK-Y397 levels , respectively , when compared to control KO cells ( Figure 7 ) . Experiments were also conducted using naïve α5 , αv and β3-integrin KO stellate cells as controls . As expected , no detectable α5β1-integrin activity was observed in α5-KO , while only modest decreases in the activity of α5β1-integrin was observed in αv and β3-integrin KO cells grown in intact D-ECM . Interestingly , while a modest pFAK-Y397 downregulation was observed in α5-KO , there were no appreciable changes in pFAK-Y397 in αv-KO cells , whereas a small increase in pFAK-Y397 was induced by D-ECM in β3-integrin KO fibroblastic stellate cells ( Figure 7—figure supplement 1 ) . Although results with αv- and β3-integrin KO did not exclude these integrins from any role in response to D-ECM , these experiments indicated that β5-integrin was an effective regulator of both active α5β1-integrin and pFAK-Y397 levels in the response of naïve stellate cells to D-ECM . These results suggest that the observed requirements in naïve fibroblasts for D-ECM-regulated αvβ5-integrin induction of αSMA ( Figure 3 ) are concomitant with αvβ5-integrin control of redistributions and levels of active α5β1-integrin and pFAK-Y397 . 10 . 7554/eLife . 20600 . 034Figure 7 . Loss of β5-integrin expression in naïve fibroblasts effectively reduces D-ECM-induced levels of active α5β1-integrin and pFAK-Y397 . ( A ) Pseudocolored images showing the intensities of representative indirect immunofluorescence images indicating active α5β1-integrin ( α5β1-act . ) or pFAK from control KO ( cntrl KO ) or β5-integrin KO ( β5-KO1 ) naïve fibroblasts . The fibroblasts were cultured overnight within D-ECMs . Intensity scale bars are shown to the right . ( B and C ) Quantification of total active α5β1-integrin ( **p=0 . 0420 ) ( B ) and pFAK-Y397 ( **p=0 . 0246 ) ( C ) levels of cells from ( A ) . Note that both activities that were induced by D-ECM in naïve fibroblasts are lost in β5-integrin KO naïve fibroblastic stellate cells . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03410 . 7554/eLife . 20600 . 035Figure 7—figure supplement 1 . Loss of αV- or β3-integrins does not significantly reduce overall levels of active α5β1-integrin that is induced by D-ECM in naïve fibroblasts . ( A ) Pseudocolored images showing the intensities of representative indirect immunofluorescence images indicating active α5β1-integrin ( α5β1-act . ) or pFAK from α5-integrin KO ( α5-KO1 ) , αV-integrin KO ( αV-KO1 ) or β3-integrin KO ( β3-KO1 ) naïve fibroblasts cultured overnight with D-ECMs . ( B ) Quantification of total active α5β1-integrin levels of cells from ( A ) . ( C ) Quantifiction of pFAK levels of cells from ( A ( **p=0 . 0343 ) . Dotted lines in ( B ) and ( denote control KO normalized levels shown in Figure 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 035 We next examined whether stabilizing α5β1-integrin activity with SNAKA51 altered its D-ECM-regulated intracellular relocation . For this , naïve pancreatic stellate cells were cultured overnight in D-ECM in the presence of Alexa 660 fluorophore pre-labeled SNAKA51 or of Alexa 660 pre-labeled isotype antibody control . Cells were then fixed with or without permeabilization and visualized with additional SNAKA51 pre-labeled with a different fluorophore ( Alexa 488 ) . SNAKA51 stabilization of α5β1-integrin activity reduced intracellular , while increasing PM , pools of active α5β1-integrin when compared to a non-specific IgG treatment control ( Figure 8A ) . As an independent approach to confirm this result , double immunogold labeling of 3D-adhesions with mAb11 and active α5β1-integrin with SNAKA51 was analyzed by transmitted electron microscopy of naïve fibroblastic stellate cells cultured within N-ECM , or of fibroblasts cultured in D-ECM while being treated with the αvβ5-integrin inhibiting antibody ALULA or a control antibody . D-ECM induced the intracellular enrichment of the active integrin in discrete punctate structures , which probably reflected endosomes that did not include recognizable clathrin-coated pits . Conversely , inhibition of αvβ5-integrin reduced the accumulation of active α5β1-integrin intracellular pools , with most signal localized to the PM ( Figure 8B ) . Supporting this mechanism in an independent model , the relocation of active α5β1-integrin from intracellular to PM locations following culture within intact D-ECM was also observed in naïve β5-integrin KO fibroblastic stellate cells that were compared to control naïve fibroblasts ( Figure 8—figure supplement 1 ) . 10 . 7554/eLife . 20600 . 036Figure 8 . D-ECM prompts the internalization or relocation of α5β1-integrin activity in an αvβ5-integrin-dependent manner . ( A ) Representative indirect immunofluorescent images corresponding to overnight ‘chase’ incubations with pre-labeled anti-active-α5β1-integrin antibodies ( SNAKA51 [Clark et al . , 2005] ) or IgG controls ( blue , –not shown ) , followed by de novo detected active α5β1-integrin labeling after fixation ( α5β1-act . in green ) relative to 3D-adhesion structures ( 3D-adh . in red ) under permeable vs . non-permeable conditions . Note how SNAKA51 treatment but not IgG prompts the relocation of integrin activity to the PM while there is practically no change between permeable and non-permeable active α5β1-integrin levels . ( B ) Transmitted electron microscopy images of double immunogold-labeled 3D-adhesions ( –3D-adh . large particles , [Cukierman et al . , 2001] ) vs . active α5β1-integrin ( -α5β1-act . small particles , [Clark et al . , 2005] ) , detected in naïve cells cultured within N–ECM vs . D-ECM in the presence or absence of αvβ5-integrin blockage using ALULA ( Su et al . , 2007 ) ( αvβ5-i ) . Both reduction and relocation of active α5β1-integrin pools are observed . Arrowheads point at random immunogold particles as examples , while the closed arrow indicates the location of a clathrin-coated vesicle . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03610 . 7554/eLife . 20600 . 037Figure 8—figure supplement 1 . KO of β5-integrin in naïve fibroblasts results in a redistribution of D-ECM-induced active α5β1-integrin from intracellular pools back to the plasma membrane . Transmitted electron microscopy images of double immunogold-labeled 3D-adhesions ( –3D-adh . large particles , [Cukierman et al . , 2001] ) vs . active α5β1-integrin ( -α5β1-act . small particles , [Clark et al . , 2005] ) , detected in naïve control KO ( cntrl-KO ) or β5-integrin KO ( β5-KO1 ) fibroblastic stellate cells cultured within D-ECM . The images show a reduction of the total amounts of active α5β1-integrin and relocation of active α5β1-integrin pools to the PM in β5-integrin KO fibroblasts . The magnified insert depicts an example of intracellular pools of active α5β1-integrin at what appears to be multi-vesicular endosomes . Note that small particles , indicative of active α5β1-integrin , seemed to be absent from the clathrin-coated vesicle; these particles are indicated by the closed arrow , while arrowheads point to examples of random immunogold particles . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 037 We directly tested whether the observed intracellular increase in active α5β1-integrin corresponded to enrichment in discrete endosomal vesicles using a double labeling indirect immunofluorescence approach in naïve control and β5-integrin KO pancreatic stellate cells plated into D-ECM . We compared the localization of activated α5β1-integrin to that of proteins restricted to the early ( EEA1 and Rab5 ) and late ( Rab7 and Rab11 ) endosomal compartments , as well as that of a multivesicular endosomal marker ( the tetraspanin CD81 ) . D-ECM induced the partial localization of active α5β1-integrin to Rab7- , Rab11- , and most clearly , to CD81-positive endosomes in control KO fibroblasts ( Figure 9 ) . By contrast , β5-KO naïve fibroblasts did not similarly relocalize active α5β1-integrin to these intracellular locations following plating in D-ECM . 10 . 7554/eLife . 20600 . 038Figure 9 . Loss of β5-integrin expression in naïve fibroblasts effectively reduces D-ECM-induced relocation of active α5β1-integrin to late and multivesicular endosomes . Control KO ( cntrl-KO ) or β5-integrin knock-out ( β5-KO1 ) naïve fibroblastic stellate cells were cultured overnight in desmoplastic-ECMs ( D-ECM ) , and were subjected to indirect immunofluorescence using SNAKA51 to detect active α5β1-integrin ( α5β1-act in green ) in combination with one of the following endosomal markers shown in red: anti-EEA-1 ( for early endosome ) , anti-Rab5 ( for clathrin-mediated endocytosis early endosome ) , anti-Rab7 ( late endosome to be degraded , recycled or rerouted ) , anti-Rab11 ( late endosome to be recycled ) , or anti-CD81 ( multivesicular endosomes ) . Top panels: confocal images were captured for each double-stained condition to identify the localization of active α5β1-integrin in relation to the assorted types of endosomes shown in red . Yellow arrowheads point to assorted endosomes and are identically placed in the slightly zoomed monochromatic inserts shown below , which allow better appreciation of the relative locations of the markers vs . those of active α5β1-integrin . Note that the partial co-localization of active α5β1-integrin with Rab7 , Rab11 and especially with CD81 is lost in the naïve β5-KO compared to control KO naive fibroblasts in response to D-ECM . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 038 We complemented the preceding analysis of naïve pancreatic integrin KO fibroblasts using the SMIA-CUKIE algorithm to evaluate active α5β1-integrin and pFAK-Y397 levels in 3D ECM-producing CAFs lacking specific integrin subunits . β5-integrin KO CAFs , which have deficiencies in myofibroblastic expression of αSMA and in the formation of anisotropic D-ECM ( see Figure 4—figure supplement 2 ) , had no discernible differences in levels of active α5β1-integrin when compared to control KO CAFs . However , β5-integrin KO CAFs cells had notable downregulation of pFAK-Y397 expression , suggesting a disturbance in overall integrin signaling that resulted from the loss of this cell-matrix receptor ( Figure 10 ) . In contrast to the observed concomitant regulation of stress-fiber-localized αSMA and of the increased active levels of α5β1-integrin and FAK in naïve fibroblastic cells responding to D-ECM , a specific mechanistic decoupling between myofibroblastic features , such as αSMA expression and anisotropic D-ECM production , and the regulation of active α5β1-integrin in CAFs was observed in this analysis of β5-integrin KO CAFs . This interpretation was further supported by comparative analysis of 3D ECM production in CAFs lacking other integrin subunits . Loss of αv-integrin in CAFs , which led to failure to produce 3D ECMs and to low αSMA expression , did not affect levels of active α5β1-integrin , whereas loss of the β3-integrin subunit , which is associated with cells competent for induction of myofibroblastic features , significantly lowered active α5β1-integrin levels ( Figure 10—figure supplement 1 ) . 10 . 7554/eLife . 20600 . 039Figure 10 . Loss of β5-integrin in D-ECM-producing CAFs fails to significantly reduce overall levels of active α5β1-integrin . Representative indirect immunofluorescence images showing active α5β1-integrin ( with SNAKA51 [Clark et al . , 2005] [α5β1-act . ] ) or pFAK in control KO ( cntrl-KO ) or β5-integrin KO ( β5-KO1 ) CAFs at the completion of the 3D matrix production process ( see Materials and methods ) . ( B-C ) Graphs depicting levels of active α5β1-integrin ( B ) or pFAK ( C;**p=0 . 0465 ) in control and β5-integrin KO CAFs from ( A ) . Note that active α5β1-integrin levels in CAFs were not significantly changed in response to β5-integrin loss . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 03910 . 7554/eLife . 20600 . 040Figure 10—figure supplement 1 . Loss of β3- but not of αV-integrin subunits in D-ECM-producing CAFs alters levels of active α5β1-integrin . ( A ) Representative indirect immunofluorescence images showing active α5β1-integrin ( with SNAKA51 [Clark et al . , 2005] [α5β1-act . ] ) or pFAK corresponding to α5-integrin KO ( α5-KO2 ) , αV-integrin KO ( αV-KO1 ) or β3-integrin KO ( β3-KO1 ) CAFs at the conclusion of matrix production ( see Materials and methods ) . ( B-C ) Graphs depicting levels of active α5β1-integrin ( B ) or pFAK ( C ) in control and β5-integrin KO CAFs from ( A ) ( ****p<0 . 0001 ) . Dotted lines in ( B–C ) denote control KO CAF normalized levels from Figure 10 . Note that active α5β1-integrin levels in CAFs were altered in response to β3- but not αV-integrin loss , whereas levels detected in α5-integrin KO CAFs served as background control and were therefore marked as ‘not applicable’ ( NA ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 040 The observed differences between the requirement in CAFs for αvβ5 integrin expression during the concomitant regulation of αSMA levels and anisotropic D-ECM production , versus the dispensable nature of αvβ5 for the maintenance of high active α5β1-integrin levels in these cells , suggests a functional decoupling between the processes . To determine whether the relationships between D-ECM , TGFβ and integrins described above pertain to additional human cancers of clinical relevance , we turned to a human renal cell carcinoma ( RCC ) stroma model ( Gupta et al . , 2011; Goetz et al . , 2011 ) . RCC stroma is of particular interest because it encompasses a microenvironment that is highly angiogenic and that physically intercalates with cancer cells ( Lohi et al . , 1998 ) , thus distinguishing it from the desmoplasia seen in PDAC . We first demonstrated that renal ( r ) fibroblasts include an overrepresentation of α5β1 and αvβ5 integrins relative to alternative β-integrin heterodimers at the plasma membrane ( Figure 11A ) . Naïve r-fibroblasts expressed low levels of αSMA ( Figure 11B–C ) and produced isotropic rN-ECM when compared to rCAF-derived matrices , which produced anisotropic rD-ECM ( Figure 11—figure supplement 1 ) . As with PDAC CAFs , treating rCAFs with a TGFβ inhibitor ( SB-431542 ) during ECM production caused cells to produce isotropic matrices ( Figure 11—figure supplement 1 ) . Overnight plating of naïve r-fibroblasts in rD-ECM induced the formation of myofibroblastic features such as an increase in stress-fiber-localized αSMA , as compared to cells plated in rN-ECM . Further , the isotropic 3D matrices produced by TGFβR1-inhibited rCAFs failed to induce naïve-to-myofibroblastic activation , whereas TGFβ signaling activity was dispensable in naïve fibroblasts undergoing myofibroblastic activation in response to intact rD-ECM induction . 10 . 7554/eLife . 20600 . 041Figure 11 . Renal fibroblasts present a similar profile to pancreatic fibroblasts during matrix production . Fibroblasts were isolated from RCC surgical pathologically normal or tumoral samples , and their ECM-producing phenotypes were assessed after seven days of matrix production . ( A ) An integrin-dependent cell adhesion array test was used to assess the PM expression of integrin heterodimers in primary fibroblasts isolated from normal ( white bars ) vs . tumoral ( dark bars ) tissues . Note that no differences were apparent between the two cell types with regards to levels of αvβ5 and α5β1 integrins . ( B ) Normal vs . desmoplastic mRNAs levels , corresponding to αSMA and palladin ( used as an additional myofibroblastic marker as before ) were obtained via RT-qPCR from the indicated 3D-cultures following renal ECM ( rECM ) production , which was achieved via confluent culturing of fibroblasts in the presence of ascorbic acid for a period lasting 8 days ( Franco-Barraza et al . , 2016 ) ( **p=0 . 0286 ) . ( C ) Representative images of normal vs . desmoplastic phenotypes , subsequent to 3D rECM production , are shown; comparison of low vs . high αSMA levels ( white ) , heterogeneous/round vs . elongated/spindled nuclei ( yellow ) and disorganized/isotropic vs . parallel aligned/anisotropic rECMs ( magenta ) are evident in the representative images . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04110 . 7554/eLife . 20600 . 042Figure 11—figure supplement 1 . rD-ECM production by rCAFs is TGFβ-dependent . ( A ) Images representative of the ECM phenotypes of renal normal ( rN-ECM ) and RCC-associated CAF-derived ECMs ( rD-ECM ) , and of cells treated with small molecule TGFβ1-receptor SB431542 ( TGFβ-inhib ) or with vehicle ( DMSO ) during rD-ECM production are shown . ECM fiber angle distributions , measured with Image-J’s ‘OrientationJ’ plug , are represented by the various colors , while all images were normalized using hue values for common , cyan , mode angle visualization as represented in the bar in the right . ( B ) Curves corresponding to the indicated experimental conditions depicting averaged and variations of angle distributions that were normalized to 0˚ modes . Dotted lines indicate 15° spread from the mode . ( C ) Plotted data from ( B ) summarizing percentages of fibers distributed at 15˚angles from the mode in each of the indicated experimental conditions . ( **** p<0 . 0001 , ***p<0 . 0028 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04210 . 7554/eLife . 20600 . 043Figure 11—figure supplement 2 . rD-ECM-induced renal naïve-to-myofibroblastic activation is reminiscent of pancreatic D-ECM fibroblastic stellate cell activation . Naïve renal fibroblasts were cultured overnight within normal ( rN-ECM ) or RCC-associated CAF-derived ECMs ( rD-ECM ) . rD-ECMs were produced in the presence of vehicle control ( D + DMSO ) or TGFβ1-receptor inhibitor ( D + TGFβi ) . Alternatively , naïve cells cultured within rD-ECMs were treated with TGFβ1 inhibitor ( TGFβ-i ) , with vehicle ( DMSO ) , with the function-blocking anti-αvβ5-integrin ALULA ( Su et al . , 2007 ) ( αvβ5-i ) , with the function-blocking anti-α5β1-integrin mAB16 ( Akiyama et al . , 1989 ) ( α5β1-i ) , with combinations of ALULA + mAb16 ( β5-i + α5-i ) , with the function-stabilizing anti-active α5β1-integrin antibody SNAKA51 ( Clark et al . , 2005 ) ( α5β1-act ) , or with corresponding isotype controls ( IgG ) . ( A ) Representative monochromatic images of immunofluorescently labeled αSMA and corresponding actin stress fibers ( F-actin ) . Checkmarks indicate conditions that induce a myofibroblastic activation phenotype in response to rD-ECM . X marks indicate conditions that did not induce myofibroblastic activation . ( B ) Graph depicting measured levels of stress fiber ( F-actin ) localized αSMA ratios . The results and statistical analysis for this set of experiments are summarized in Table 5 . Note that , while TGFβ inhibition rendered matrices produced by CAFs ( D + TGFβi ) nonfunctional , naïve renal fibroblasts are effectively activated by rD-ECM in a TGFβ-independent manner ( TGFβ-i ) that is apparently maintained by the same integrin crosstalk seen in the human PDAC model . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04310 . 7554/eLife . 20600 . 044Figure 11—figure supplement 3 . FAK-independent α5β1-integrin activity negatively regulates rD-ECM-induced naïve-to-myofibroblastic activation . Naïve r-fibroblasts were re-plated onto rD-ECMs and challenged with either control conditions ( DMSO + IgG ) , small molecule FAK inhibitor PF573 , 228 ( Slack-Davis et al . , 2007 ) ( FAK-i ) alone or FAK-i in combination with α5β1-integrin inhibitor ( FAK-i + α5-i , mAb16 [Akiyama et al . , 1989] ) , and the activation of fibroblasts was tested . ( A ) Representative monochromatic images of immunofluorescently labeled αSMA and actin stress fibers ( F-actin ) . Colored asterisks in ( A ) represent areas that are magnified in the corresponding panels to the right . ( B ) Quantification of αSMA at actin stress fibers ( F-actin ) from ( A ) normalized to DMSO + IgG control ( one arbitrary unit; a . u ) . ( IgG/DMSO vs . FAK-i: ****p=<0 . 0001 , FAK-i vs . FAK-i + α5β1-i: ***p=0 . 0051 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04410 . 7554/eLife . 20600 . 045Figure 11—figure supplement 4 . rD-ECM-induced increase in pFAK levels localized at 3D-adhesions are regulated by αvβ5–integrin in naïve renal fibroblasts . Naïve renal fibroblasts were cultured overnight within N-ECM or RCC-associated CAF-derived ECMs ( rD-ECM ) in the presence or absence of ALULA ( Su et al . , 2007 ) ( rD-ECM + αvβ5-i ) and 3D-adhesions using mAb11 and pFAK-Y397 were detected by indirect immunofluorescence . The resulting SMIA-CUKIE-generated integrated intensities of pFAK-Y397 at 3D-adhesions ( ****p<0 . 0001 ) are depicted in this graph . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04510 . 7554/eLife . 20600 . 046Figure 11—figure supplement 5 . rD-ECM regulates 3D-adhesion structure length , dependent on α5β1-integrin activity . ( A ) Indirect immunofluorescent and spinning disc confocal generated images of 3D-adhesions , identified using mAb11 ( Cukierman et al . , 2001 ) , formed by naïve fibroblastic cells cultured within rN-ECM or rD-ECM in the absence ( cnt . ) or in the presence of ALULA ( Su et al . , 2007 ) for αvβ5-integrin inhibition ( αvβ5-i ) or of SNAKA51 ( Clark et al . , 2005 ) to stabilize α5β1-integrin activity ( α5β1 act ) or of IgG as control . Images were processed using the computer-selected internally threshold objects ( ITOs ) function of the MetaMorph 7 . 8 . 0 . 0 software . ( B ) Quantification of the length of ITO-generated objects from ( A ) ( **p=0 . 0492 , *p=0 . 0928 ) . Note the significant differences in 3D-adhesion length observed between rN-ECM and rD-ECM as well as between IgG and SNAKA51 treatments . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04610 . 7554/eLife . 20600 . 047Figure 11—figure supplement 6 . Naïve renal fibroblastic cells increase overall levels of active α5β1-integrin in response to rD-ECM . ( A ) Double-labeled images depicting 3D-adhesion ( red in overlay; 3D-adh . ) and active α5β1-integrin ( green in overlay; α5β1-act ) in naïve r-fibroblasts cultured overnight in renal normal-ECMs ( rN-ECM ) or RCC-associated CAF-derived ECMs ( rD-ECM ) . Pseudocolored images at the far right represent semi-quantitative images , maximum reconstructions of active α5β1-integrin levels ( α5β1-act inten ) , with a corresponding intensity bar shown on the right . Total active α5β1-integrin levels were calculated using SMIA-CUKIE , which is publicly available at https://github . com/cukie/SMIA . ( B–D ) Results are summarized in ( B ) , using median levels on rD-ECM for normalization ( one arbitrary unit; a . u . ) ( ***p=0 . 0001 ) , in ( C ) using integrated levels of active α5β1-integrin localized at 3D-adhesions , and in ( D ) , using active α5β1-integrin integrated intensity levels , measured away from 3D-adhesions , in the absence or presence of αvβ5-integrin inhibition ( ****p<0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 047 In addition , as with the pancreatic model , regulation of active α5β1-integrin by αvβ5-integrin was needed for naïve fibroblasts to undergo rD-ECM-induced myofibroblastic activation ( Figure 11—figure supplement 2 and Table 5 ) . Accordingly , treatment of naïve r-fibroblasts seeded within rD-ECM in the presence of small molecule FAK inhibitor PF573 , 228 ( Slack-Davis et al . , 2007 ) , blocked acquisition of myofibroblastic traits ( Figure 11—figure supplement 3 ) . In naïve r-fibroblasts , concomitant FAK and α5β1-integrin inhibition with combined PF573 , 228 and mAb16 significantly increased rD-ECM-induced myofibroblastic activation compared to PF573 , 228 treatment alone . Again , effects similar to those observed in the PDAC system were seen: αvβ5-integrin co-inhibition with FAK failed to rescue rD-ECM naïve-to-myofibroblastic conversion ( Figure 11—figure supplement 3 ) . Also , pFAK-Y397 levels measured in 3D-adhesions of naïve r-fibroblasts induced by rD-ECM were effectively reduced in the presence of αVβ5-integrin inhibitor ( ALULA [Su et al . , 2007] ) , rendering pFAK-Y397 levels similar to N-ECM levels ( Figure 11—figure supplement 4 ) . Culturing naïve r-fibroblasts in rD-ECM triggered lengthening of 3D-adhesion structures compared to those in rN-ECM , and this was effectively prevented by stabilization of α5β1-integrin activity using SNAKA51 ( Clark et al . , 2005 ) ( Figure 11—figure supplement 5 ) . Last , plating naïve r-fibroblasts in rD-ECM increased levels of active α5β1-integrin that was localized away from 3D-adhesion structures compared to those in rN-ECM . These levels were regulated by αvβ5-integrin , as treatment with ALULA restricted the enrichment of α5β1-integrin activity in areas lacking adhesions ( Figure 11—figure supplement 6 ) . 10 . 7554/eLife . 20600 . 048Table 5 . αSMA stress fiber localization and expression levels in naïve renal fibroblasts cultured overnight within assorted renal ECMs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 048rN-ECM rD-ECM rD+TGFβi rD+DMSO TGFβi DMSO αvβ5-i α5β1-i 5+α5-i α5β1-act IgG αSMA stress fiber localization 25% percentile0 . 050 . 880 . 280 . 6890 . 870 . 910 . 460 . 540 . 630 . 740 . 96Median 0 . 29 1 . 00 0 . 72 0 . 89 1 . 03 1 . 05 0 . 67 0 . 89 1 . 00 0 . 93 1 . 06 75% percentile0 . 671 . 100 . 971 . 031 . 171 . 160 . 961 . 061 . 331 . 542 . 19Values obtained from naïve renal fibroblastic cells cultured overnight within intact rD-ECMs ( made from rCAFs ) were used for normalization and assigned an arbitrary unit of 1 . 00 . Assorted ECMs were intact rN-ECM or intact rD-ECM , while experimental conditions included D-ECMs made by CAFs treated with SB-431542 ( rD+TGFβi ) or DMSO ( rD+DMSO ) during ECM production . Experimental conditions during replating of naïve cells within rD-ECM also included treatment with SB-431542 ( TGFβi ) or vehicle ( DMSO ) as well as treatment with ALULA ( αvβ5-i ) , mAb16 ( α5β1-i ) , ALULA plus mAb16 ( β5+α5-i ) , SNAKA ( α5β1-act ) or control pre-immune antibody ( IgG ) . Note that quantitative values for αSMA and F-actin immunofluorescence were used to calculate stress fiber localization and expression of αSMA . P values , listed below , were calculated using the two-sided and two-tailed Mann Whitney test needed for normalized data . rN-ECM vs . rD-ECM; p<0 . 0001 stress fiber localizationrN-ECM vs . rD+TGFβi; p=0 . 0221 stress fiber localizationrN-ECM vs . rD+DMSO; p<0 . 0001 stress fiber localizationrD-ECM vs . rD+TGFβi; p=0 . 0058 stress fiber localizationrD-ECM vs . rD+DMSO; p=0 . 1657 stress fiber localizationrD+TGFβi vs . rD+DMSO; p=0 . 0630 stress fiber localizationTGFβi vs . DMSO; p=0 . 7996 stress fiber localizationTGFβi vs . rN-ECM; p<0 . 0001 stress fiber localizationTGFβi vs . rD-ECM; p=0 . 6560 stress fiber localizationDMSO vs . rN-ECM; p<0 . 0001 stress fiber localizationDMSO vs . rD-ECM; p=0 . 5042 stress fiber localizationαvβ5-i vs . IgG; p=0 . 0001 stress fiber localizationα5β1-i vs . IgG; p=0 . 0008 stress fiber localizationβ5+α5-i vs . IgG; p=0 . 0880 stress fiber localizationα5β1-act vs . IgG; p=0 . 0665 stress fiber localization Together , these results imply that the mechanisms identified for PDAC D-ECM production and activity were also relevant in RCC-associated fibroblastic stroma . Reciprocal signaling between tumor and stromal cells occurs in vivo ( Tape et al . , 2016 ) . Such bidirectional signaling is not simulated in the in vitro stroma 3D model used for the analysis described above . To extend our findings , we assessed whether the results we established in vitro were reflected in vivo in the original surgical samples used to harvest fibroblasts . We investigated whether the integrin redistribution phenotype that was commonly observed in both naïve fibroblasts in response to D-ECM and CAFs during D-ECM production in vitro , was also evident in the in vivo tumor-associated stroma . First , optimizing conditions in vitro , we used a simultaneous multi-channel immunofluorescence ( SMI ) approach , coupled with SMIA-CUKIE , to perform quantitative parallel analysis of seven relevant biomarkers in CAFs or control normal pancreatic stellate cells at the endpoint of production of D-ECM or N-ECM , respectively . These included a master mix ( see Materials and methods ) of epithelial and tumor cell markers ( cyan; note , absent from in vitro specimens ) , the stromal marker vimentin ( magenta ) , a nucleus-detecting DNA intercalating agent ( Draq5 , yellow ) , a 3D-adhesion marker ( mAb11 [Cukierman et al . , 2001] , red ) and SNAKA51 ( Clark et al . , 2005 ) to detect levels and localizations of active α5β1-integrin ( green ) , anti-pFAK-Y397 ( orange ) and anti-pSMAD2/3 ( indicative of TGFβ pathway activation [Tsukazaki et al . , 1998]; blue ) . This method reiterated our observation that during D-ECM production in vitro , CAFs maintain high levels of active α5β1-integrin , with much of the active pool not localized to 3D-adhesions , as well as high levels of pFAK-Y397 that are localized at 3D-adhesions . TGFβ activation also increased during the production of D-ECM , represented by augmented total and nuclear pSMAD2/3 levels , when compared to naïve/normal cultures in which active α5β1-integrin mostly localizes at 3D-adhesion sites during N-ECM matrix production ( Figure 12 ) . We also conducted a parallel experiment using a classic indirect immunofluorescence approach to include a two-by-two marker comparison between active α5β1-integrin and 3D-adhesions , pFAK or pSMAD2/3 . This experiment assessed the precision of the SMI approach , and evaluated the extent of phenotypic similarities between the pancreatic and renal matrix-producing CAFs and normal fibroblastic systems in vitro . While normal pancreatic and renal 3D cultures included active α5β1-integrin that is mostly localized at 3D-adhesion locations and included relatively low pFAK and pSMAD2/3 levels , PDAC and RCC CAF 3D matrix-producing cultures again exhibited high active α5β1-integrin levels that were evident at locations away from 3D-adhesions , concomitant with increased pFAK-Y397 , and nuclei that were enriched with pSMAD2/3 ( Figure 12—figure supplement 1 ) . 10 . 7554/eLife . 20600 . 049Figure 12 . In vitro characterization of normal and tumor-associated 3D matrix-producing fibroblastic cultures provide a set of prognostic markers to be tested in vivo . ( A ) SMI approach image outputs of in vitro 3D cultures of naïve fibroblastic stellate cells ( normal ) and desmoplastic CAFs ( tumor associated ) during ECM production . Leftmost panels demonstrate the positive staining of vimentin ( magenta ) , and the lack of cytokeratin ( cyan ) , indicating the purity of the fibroblasts isolated; nuclei are marked in yellow . White masks ( SMI approach; SMIA ) in the next panel represent vimentin-positive/cytokeratin-negative ( in this case vimentin-positive only as there is no cytokeratin present ) areas as recognized by the software following threshold values provided by the user . Next panels represent the assorted markers localized at pixels corresponding to SMI-selected masks and conforming to: active α5β1-integrin ( S-α5β1 act . ; in green ) , 3D-adhesions ( S-3D-adh . ; in red ) , pFAK-Y397 ( S-pFAK; in orange ) , and pSMAD2/3 ( S-pSMAD; in blue ) . ( B ) Graphs summarizing SMIA-CUKIE-generated data outputs representing median intensity levels of active α5β1-integrin ( green bullets ) , pFAK-Y397 ( orange bullets ) and pSMAD2/3 ( blue bullets ) from data conditions as in ( A ) ( ***p=0 . 0002 ) . ( C ) Graphs summarizing SMIA-CUKIE-generated data from marker intersections indicating mean intensity levels of α5β1-integrin activity localized away from 3D-adhesions ( green bullets ) ( ***p=0 . 0008 ) , pFAK-Y397 at 3D-adhesions ( orange bullets ) ( ***p=0 . 0002 ) , and nuclear pSMAD2/3 ( blue bullets ) ( ***p=0 . 0002 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 04910 . 7554/eLife . 20600 . 050Figure 12—figure supplement 1 . In vitro characterization of pancreatic and renal normal and CAF unextracted 3D fibroblastic cultures . Samples correspond to normal fibroblastic stellate cells ( A ) and CAFs ( B ) : depicted are spinning disc confocal-acquired images of indirect immunofluorescence of the assorted stromal markers . Images are shown as overlays to indicate the distribution of active α5β1-integrin ( α5β1 act . ; green ) with regards to 3D-adhesion locations ( 3D-adh . ; red ) , pFAK-Y397 ( pFAK; orange ) , pSMAD2/3 ( pSMAD; blue ) and nucleus ( yellow ) . Inserts correspond to monochromatic images of the corresponding marker . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05010 . 7554/eLife . 20600 . 051Figure 12—figure supplement 2 . In vitro uncovered traits correspond to in vivo PDAC and RCC stromal phenotypes . A seven-color simultaneous multi-channel immunofluorescent ( SMI; see Materials and methods ) approach was implemented to analyze FFPE tissue from the original patient surgical samples from which the fibroblasts used in vitro were harvested . ( A ) Normal tissue panel , representative images of PDAC and RCC FFPE samples . Note that the PDAC images correspond to pathological and matched normal samples from patient #1; fibroblasts harvested from this pair of samples were immortalized and used to generate all human KOs that were presented above . ( B ) Tumor-associated tissue panel , representative images . The first column of images in ( A ) and ( B ) are overlaid images including the three colors used for ‘masked’ locations ( see Materials and methods for details ) ; epithelial/tumoral areas are pseudocolored in cyan , stromal vimentin is magenta , and nuclei labeled using draq5 are shown in yellow . The SMIA-CUKIE software ( https://github . com/cukie/SMIA_CUKIE ) was instructed to render an intersection ‘mask’ image ( SMIA-mask ‘S’; white ) corresponding to pixel areas selected as stroma-positive and epithelial/tumoral-negative to exclude potential mesenchymal to epithelial transduced tumoral locations . Next , to the right of the SMIA mask image , are images of the corresponding markers showing only pixels corresponding to bona fide stromal ‘masks’ depicted in the mask image . Markers shown correspond to: active α5β1-integrin ( S- α5β1 act . ; green ) and 3D-adhesions ( S-3D-adh . ; insert red ) , followed next to the right by pFAK-Y397 ( S-pFAK; orange ) and pSMAD2/3 ( S-pSMAD; blue ) . Note that only pixels shown , which corresponded to the selected SMIA-mask ( white ) , were quantitatively analyzed by the SMIA-CUKIE software ( see Figure 12—figure supplement 3 for values ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05110 . 7554/eLife . 20600 . 052Figure 12—figure supplement 3 . Quantification of in vivo SMI approach validates in vitro findings . ( A ) Individual patient SMIA-CUKIE-generated intensity values from PDAC and RCC normal or tumor-associated tissue samples , representing stromal levels of active α5β1-integrin ( green bullets ) , pFAK-Y397 ( orange bullets ) and pSMAD2/3 ( blue bullets ) . ( B ) Stromal active α5β1-integrin away from 3D-adhesions in pancreatic ( left ) and renal ( right ) normal or tumor associated tissues . Significance asterisks represent the following: ( A ) PDAC ***p=0 . 0013 , **p=0 . 0371; RCC ****p<0 . 0001 , ***p=0 . 0031; ( B ) , **p=0 . 0371 and ****p<0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 052 Having validated the seven-color SMI approach in vitro , we used the same technique , combined with SMIA-CUKIE analyses , on formalin-fixed paraffin embedded ( FFPE ) samples matching the original tissues from the five PDAC and four RCC patients used to generate the 3D stroma models analyzed in vitro ( Figure 12—figure supplement 2 ) . This analysis indicated clear increases in total α5β1-integrin activity , pFAK-Y397 , and pSMAD2/3 in the stroma of PDAC and RCC compared to normal pancreatic and renal parenchyma ( Figure 12—figure supplement 3A ) . When the same comparisons were performed for α5β1-integrin activity specifically localized away from stromal 3D-adhesion-positive pixels , the results again indicated clear increases ( Figure 12—figure supplement 3B ) . To determine whether this signature of CAF activation had prognostic value , we analyzed 128 PDAC and 126 RCC surgical specimens , using tissue microarrays ( TMAs; Table 6 ) . To establish baseline factors that are prognostic for survival , we initially evaluated the annotated clinical data for specimens on the TMAs ( https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx ) using univariate ( Uni ) and multivariate ( MVA ) analyses . For PDAC specimens , association between pathological N stage , overall pathological stage and positive nodes with overall survival ( OS ) indicated a significant increased risk of death correlated with increasing levels of these variables , with Uni hazard ratios ( HRs ) of 2 . 5 for pathological N ( p=0 . 0002; 95% CI 1 . 5–4 . 2 ) , 1 . 3 for pathological stage ( p=0 . 01; 95% CI 1 . 1–1 . 6 ) and 1 . 15 for positive nodes ( p=0 . 0001; 95% CI 1 . 1–1 . 2 ) . Similar analyses looking at the association between clinical variables and recurrence-free survival ( RFS ) resulted in the identification of positive correlations for pathological N ( HR = 2 . 1; p=0 . 009; 95% CI 1 . 2–3 . 7 ) , pathological stage ( HR = 1 . 4; p=0 . 01; 95% CI 1 . 0–2 . 0 ) and positive nodes ( HR = 1 . 1; p=0 . 005; 95% CI 1 . 0–1 . 2 ) . Further , MVA analyses for OS in PDAC specimens showed an association for N stage: HR = 1 . 9; p=0 . 06; 95% CI 1 . 0–3 . 6 . 10 . 7554/eLife . 20600 . 053Table 6 . PDAC and RCC cohorts included in TMAs . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 053PDAC RCC Samples Number Samples Number Tumor128 ( 128 OS and 102 DSS ) Tumor126 ( 116 OS and 115 DSS ) Gender cases ( % ) Gender cases ( % ) Female76 ( 59 . 40 ) Female34 ( 27 ) Male52 ( 40 . 60 ) Male90 ( 71 . 4 ) N/A0 ( 1 . 34 ) N/A2 ( 1 . 6 ) Average age years ( range ) Average age years ( range ) 67 . 51 ( 37–91 ) 60 . 67 ( 23–82 ) TNM stage TNM stage N cases ( % ) N cases ( % ) 033 ( 25 . 80 ) 099 ( 78 . 57 ) 191 ( 71 . 10 ) 12 ( 1 . 59 ) 20 ( 0 ) 28 ( 6 . 35 ) 30 ( 0 ) 30 ( 0 ) N/A4 ( 3 . 10 ) N/A17 ( 13 . 49 ) T cases ( % ) T cases ( % ) 00 ( 0 ) 00 ( 0 ) 110 ( 7 . 81 ) 138 ( 30 . 16 ) 234 ( 26 . 56 ) 233 ( 26 . 19 ) 366 ( 51 . 56 ) 344 ( 34 . 92 ) 413 ( 10 . 15 ) 40 ( 0 ) N/A5 ( 3 . 91 ) N/A11 ( 8 . 73 ) M cases ( % ) M cases ( % ) 0109 ( 85 . 16 ) 085 ( 67 . 46 ) 16 ( 4 . 70 ) 134 ( 26 . 98 ) N/A13 ( 10 . 15 ) N/A7 ( 5 . 56 ) Overall stage cases ( % ) Overall stage cases ( % ) I17 ( 13 . 28 ) I32 ( 25 . 4 ) II38 ( 29 . 69 ) II26 ( 20 . 63 ) III53 ( 41 . 41 ) III25 ( 19 . 84 ) IV14 ( 10 . 94 ) IV35 ( 27 . 8 ) N/A6 ( 4 . 69 ) N/A8 ( 6 . 35 ) N/A: Not available . We then asked whether the levels and localization of the stromal biomarker signature developed here yielded clinically useful prognostic biomarkers for PDAC , testing the idea that stromal pSMAD2/3 , indicative of TGFβ signaling , would be associated with poor outcomes and active α5β1-integrin at 3D adhesions would be associated with better survival . For this , we conducted high-throughput SMI image acquisition and SMIA-CUKIE analysis of TMA PDAC samples , using univariate CART methodology to integrate the reporting levels of numerical outputs and intersections of the seven biomarkers of interest with OS or RFS . We observed significantly shorter OS in tumor surgical samples that had high stromal pSMAD2/3 values , whether values were quantified as mean or median stromal pSMAD2/3 intensity levels ( Figure 13 ) . Even though many patients did not show clinical improvement following surgery ( so that our cohort presented a bias towards fast recurrence ) , high stromal pSMAD2/3 levels also correlated with shorter RFS ( Figure 13—figure supplement 1 ) . In addition , higher mean quantified levels of stromal α5β1-integrin activity also correlated significantly with longer RFS ( Figure 13—figure supplement 2A ) . Interestingly , longer times to recurrence following surgery , quantified as the percentage area coverage relative to stromal occupied areas or as integrated intensities , were significantly associated with increased levels of integrin activity localized to stromal 3D-adhesion positive locations ( Figure 13—figure supplement 2B ) . These results suggest that stromal activation of TGFβ , represented by high stromal pSMAD2/3 levels and indicative of capacity for active production of D-ECM production , is a prognostic stromal trait for poor outcome , while increased stromal levels of active α5β1-integrin at 3D adhesion positive areas may constitute a patient-protective PDAC-associated desmoplastic phenotype . 10 . 7554/eLife . 20600 . 054Figure 13 . Stromal pSMAD2/3 levels are predictive of poor overall survival in PDAC patients . ( A ) CART-generated survival curves depicting overall survival ( OS ) as a function of stromal pSMAD2/3 expression in PDAC patients . Left and right curves were obtained using mean and median intensity levels , generated by SMIA-CUKIE , of stromal pSMAD2/3 related to OS , respectively . ( B ) Survival curves depicting pathological N , stage , and node status with PDAC patient OS . Colored tick-lines crossing Y axes indicate 0 . 5 survival marks and correspond to X axes locations that mark the median survival times obtained from the assorted curves . P values are shown . All patient data as well as SMIA-CUKIE generated data corresponding to human cohort constructed TMAs ( shown in Table 6 ) can be found in the online table at the following publically available link: https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Note that since our cohorts comprised of samples obtained from surgeries , early neoplastic stages were overrepresented . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05410 . 7554/eLife . 20600 . 055Figure 13—figure supplement 1 . Stromal pSMAD2/3 levels are predictive of short recurrence-free survival in PDAC patients . ( A ) CART-generated survival curves indicative of recurrence-free survival ( RFS ) as a function of stromal pSMAD2/3 expression in PDAC patients . Left and right curves were obtained using mean and median intensity levels , generated by SMIA-CUKIE , of stromal pSMAD2/3 related to RFS , respectively . ( B ) Survival curves depicting pathological N , stage , and node status plotted against PDAC patient RFS fraction . Colored tick-lines crossing Y axes indicate 0 . 5 survival marks and correspond to X axis locations that mark the median RFS times obtained from the assorted curves . P values are shown . All patient data as well as SMIA-CUKIE generated data corresponding to human cohort constructed TMAs ( shown in Table 6 ) can be found in the online table at the following publically available link: https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Note that since our cohorts comprised of samples obtained from surgeries , early neoplastic stages were overrepresented . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05510 . 7554/eLife . 20600 . 056Figure 13—figure supplement 2 . Stromal active α5β1-integrin levels localized at 3D-adhesions correlate with longer recurrence-free survival in PDAC patients . CART-generated survival curves depicting recurrence-free survival ( RFS ) as a function of active stromal α5β1-integrin levels in PDAC patients . Curves were obtained using ( A ) active α5β1-integrin median intensity levels and ( B ) percentage area coverage or integrated intensity of stroma populated by active α5β1-integrin localized at 3D-adhesions that were generated by SMIA-CUKIE , related to RFS . Colored tick-lines crossing Y axes indicate 0 . 5 survival marks and correspond to X axis locations that mark the median RFS obtained from the curves . P values are shown . All patient data as well as SMIA-CUKIE-generated data corresponding to human cohort constructed TMAs ( shown in Table 6 ) can be found in the online table at the following publically available link : https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Note that since our cohorts comprised of samples obtained from surgeries , early neoplastic stages were overrepresented . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05610 . 7554/eLife . 20600 . 057Figure 13—figure supplement 3 . Stromal levels and locations of p-SMAD2/3 , active α5β1-integrin , and pFAK correlate with short overall survival in RCC patients . ( A–C ) CART-generated survival curves depicting overall survival ( OS ) as a function of stromal assorted marker levels in RCC patients . ( A ) Left and middle curves correspond to mean and total intensity levels of stromal pSMAD2/3 and right curves to pSMAD2/3 mean intensity levels particularly localized at stromal nuclei , as a function of OS . ( B ) OS curves as a function of stromal active α5β1-integrin levels . Top left curves show mean intensity of active α5β1-integrin related to OS , top middle curves show mean intensity of active α5β1-integrin at 3D adhesions related to OS , and top right curves show the percentage area coverage of active α5β1-integrin at 3D adhesions related to OS . Bottom left curves depict median intensity levels of active α5β1-integrin away from 3D adhesions , while bottom right curves depict percentage area coverage of active α5β1-integrin away from 3D adhesions . ( C ) Curves depicting OS as a function of stromal pFAK . Top left curves show mean intensity of pFAK related to OS , top middle curves show percentage area coverage of pFAK related to OS , and top right curves show the total intensity pFAK related to OS . Bottom left curves depict median intensity levels of pFAK at 3D adhesions , bottom middle curves depict percentage area coverage of pFAK at 3D adhesions , and bottom right curves depict total intensity of pFAK at 3D adhesions . Colored tick-lines crossing Y axes indicate 0 . 5 survival marks and correspond to X axis locations that mark the median survival times obtained from the assorted curves . P values are shown . All patient data as well as SMIA-CUKIE-generated data corresponding to human cohort constructed TMAs ( shown in Table 6 ) can be found in the online table at the following publically available link: https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Since our cohorts were comprised of samples obtained from surgeries , early neoplastic stages were overrepresented . Note that the above-mentioned links host RCC clinical data that provided the following results , which were obtained prior to CART analyses to assure that in spite of the bias provided by the use of surgical samples , the cohort in question showed significant associations between clinical and outcome variables and OS and Disease-Specific Survival ( DSS ) : associations between pathological stage and OS had HRs of 2 . 4 ( Uni; p=1 . 11E-10; 95% CI 1 . 9–3 . 2 ) and 3 . 2 ( MVA; p=0 . 001; 95% CI 1 . 6–6 . 2 ) , respectively . Also , Uni analyses of pathological T , N and M showed significant correlation with OS ( T —HR = 2 . 0 , p=4 . 16E-05 , 95% CI 1 . 4–2 . 8; N — HR = 2 . 1 , p=0 . 0003 , 95% CI 1 . 4–3 . 1; and M — HR = 5 . 4 , p=3 . 79E-10 , 95% CI 3 . 2–9 . 1 ) . Similarly , Uni and MVA analyses suggested that shorter DSS times are associated with advanced pathological stage ( Uni —HR = 3 . 1 , p=7 . 94E-11 and 95% CI 2 . 2–4 . 3; MVA — HR = 3 . 4 , p<0 . 0001% and 95% CI 1 . 6–7 . 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 05710 . 7554/eLife . 20600 . 058Figure 13—figure supplement 4 . Stromal levels and locations of p-SMAD2/3 , active α5β1-integrin , and pFAK correlate with short disease specific survival in RCC patients . ( A-C ) CART-generated survival curves depicting disease specific survival ( DSS ) as a function of stromal assorted marker levels in RCC patients . ( A ) Left and right curves correspond to mean stromal-and stromal-nuclei- localized intensity levels of pSMAD , related to DSS . ( B ) DSS curves as a function of stromal active α5β1-integrin levels . Top left curves show mean intensity of active α5β1-integrin related to DSS , top middle curves show stromal percentage active α5β1-integrin related to DSS , and top right curves show the total stromal intensity levels of active α5β1-integrin related to DSS . Middle left , middle and right curves depict mean , percentage coverage and total intensity levels of active stromal α5β1-integrin at 3D adhesions , respectively , each related to DSS . Bottom row from left to right corresponds to mean , median , percentage coverage and total intensity levels of active stromal α5β1-integrin away from 3D adhesions , each related to DSS . ( C ) Survival curves depicting DSS as a function of stromal pFAK . Top from left to right: curves showing median , percentage coverage and total intensity levels of stromal pFAK related to DSS . Bottom from left to right: curves showing mean , median , percentage coverage and total intensity of stromal pFAK localized at 3D-adhesions related to DSS . Colored tick-lines crossing Y axes indicate 0 . 5 survival marks and correspond to X axis locations that mark the median survival times obtained from the assorted curves . P values are shown . All patient data as well as SMIA-CUKIE-generated data corresponding to human cohort constructed TMAs ( shown in Table 6 ) can be found in the online table at the following publically available link: https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Note that since our cohorts comprised of samples obtained from surgeries , early neoplastic stages were overrepresented . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 058 To investigate whether these patterns are also seen in stroma associated with an additional epithelial cancer , we analyzed the RCC cohort ( Table 6 ) . Initial Uni and MVA analyses on the clinical data tested whether , in spite of the bias generated by the retrospective use of surgical cases , the cohort in question showed significant associations between pathological stages and OS and Disease-Specific Survival ( DSS ) . Importantly , using CART analyses , we determined that RCC patients with high stromal levels of pFAK-Y397 localized at 3D-adhesions , higher nuclear pSMAD2/3 , or increased levels of active α5β1-integrin , measured as mean , median or total stromal intensity levels as well as stromal percentage area coverages , had significantly shorter OS and DSS ( Figure 13—figure supplements 3 and 4 ) . Together , these results strongly support the idea that the markers of an active desmoplastic phenotype , as defined herein , are useful in predicting patient outcomes .
Understanding the activity of and clinically exploiting tumor-associated stroma have for a long time posed challenges , given strong evidence for both tumor-limiting and tumor-promoting stromal functionality ( Mintz and Illmensee , 1975; Bissell and Hines , 2011; Langhans , 1879; Paget , 1889; Dvorak , 1986 ) . The data in this study are useful by providing insight into stromal function in several ways . First , by addressing prior studies that suggest that changes in tumor-associated ECM are necessary for TGFβ-dependent myofibroblastic activation ( Desmoulière et al . , 1993; Serini et al . , 1998 ) , they dissect the interaction of TGFβ with N-ECM versus D-ECM . These results indicate that TGFβ contributes to N-ECM assembly , and that it is essential solely for the phenotypic remodeling required for conversion to anisotropic D-ECM , but not for secretion of ECM components per se in the context of D-ECM production . This fact is supported by the observation that CAFs require TGFβ signaling for the myofibroblastic production of anisotropic D-ECM and for expression of αSMA , while the myofibroblastic features that are acquired by normal/naïve fibroblasts in response to D-ECM do not entail this signaling pathway . Second , our work addressed two cell-ECM receptor integrin heterodimers that have previously been shown to influence myofibroblastic activation , αvβ5 and α5β1 ( Asano et al . , 2006; Lygoe et al . , 2004; Dugina et al . , 2001 ) . Our study shows that both of these integrin heterodimers are abundant on the surface of both naïve fibroblasts and CAFs , but have distinct roles in the ability of naïve fibroblasts to respond to and of CAFs to assemble anisotropic D-ECM . In particular , our work integrated multiple approaches to emphasize an important role for αvβ5-integrin in the response of naïve fibroblasts to D-ECM and for CAFs in producing anisotropic matrix . We also found that while α5β1 counteracted the response of naïve fibroblasts to D-ECM , it was , not surprisingly , essential for matrix fibrillogenesis . Interestingly , in the context of extracted D-ECM imparting naïve-to-myofibroblastic activation , cross-signaling between the two integrins was apparent , with αvβ5 regulating the internalization of part of the elevated pool of activated α5β1 to intracellular endosomal compartments ( Figure 14 ) . Third , we also defined specific ECM responses as independent of , or dependent on , the canonical integrin effector FAK . Fourth , importantly , we demonstrated that the signaling relationships identified in this study were valid in multiple model systems , including human primary PDAC and RCC , as well as additional murine models . Fifth , we used high-content imaging and data analysis to demonstrate that interactions analyzed in vitro can be used to define an in vivo stromal signature that has prognostic value , addressing a significant clinical goal . 10 . 7554/eLife . 20600 . 059Figure 14 . Model depicting D-ECM-induced αVβ5 regulation of active α5β1-integrin localization during naïve-to-myofibroblastic activation . Essentially , naïve fibroblasts grown in N-ECMs do not trigger a surplus in active αVβ5 and α5β1-integrin conformations . Consequently , the non-pathological N-ECM spares regulation of αVβ5-integrin activity and assures physiological accumulation of low active α5β1-integrin levels at the PM , rendering fibroblasts inactive . D-ECMs induce an excess of active α5β1-integrin and regulate αVβ5-integrin-mediated relocation of active α5β1-integrin from the PM to intracellular ( e . g . , late endosomal ) pools , resulting in myofibroblastic activation . Last , this D-ECM activity is dependent on the activity of αVβ5-integrin , as inhibition of αVβ5-integrin maintains active α5β1-integrin at the PM ( similarly to N-ECM regulation of active α5β1-integrin locations ) , which averts D-ECM-induced myofibroblastic activation . The summarized data provide a possible explanation for the early model presented in Figure 3G , which called for a D-ECM control of αvβ5 regulation of active α5β1 and suggests that the alluded to regulation constitutes a re-localization of active α5β1 to intracellular endosomal compartments , allowing D-ECM-induced naïve-to-myofibroblastic activation . DOI: http://dx . doi . org/10 . 7554/eLife . 20600 . 059 There is strong evidence for desmoplastic stromal reciprocation , involving altered ECM ( e . g . , D-ECM ) and adjacent tumor and/or stromal cells during PDAC development and progression ( Xu et al . , 2009; Tape et al . , 2016; Alexander and Cukierman , 2016; Garcia et al . , 2014; Roskelley and Bissell , 1995 ) . Several prior studies have searched for proteomic signatures of normal vs . tumor-affected stroma ( Webber et al . , 2016 ) , in order to identify stromal traits that are associated with favorable or unfavorable clinical outcomes ( Erkan et al . , 2008; Moffitt et al . , 2015 ) . Some of these signatures have been proposed to serve as clinically relevant tumor sub-type indicators ( Choi et al . , 2014 ) . The desmoplastic 3D models used in this study allowed us to functionally dissect both mechanisms of D-ECM production by CAFs , including the specific induction of anisotropic ECMs , and D-ECM-induced responses such as myofibroblastic activation of naïve fibroblasts , mechanistically informing the significance of such signatures . A role for TGFβ in regulating the production of anisotropic ECM had been previously suggested ( Khan and Marshall , 2016 ) . Remodeling that causes isotropic ECMs to become increasingly anisotropic , together with increases in matrix tension and stiffness , are all features of chronic fibrosis that physically enhance the accessibility and activation of TGFβ by myofibroblastic integrins ( Klingberg et al . , 2014 ) . Another interesting study demonstrated that substrate stiffness influences the ability of a cell induced in vitro to undergo myofibroblastic activation , and can positively or negatively influence the degree to which a cell is able to accurately acquire the characteristics ( Achterberg et al . , 2014 ) of a CAF in vivo . In accord with this line of thinking , one may argue that specific ECM topographies , and in particular the overall physical properties of cell substrates , play key pathophysiological roles in promoting positive feedback loops involving myofibroblastic cells and D-ECMs in fibrous tumor-associated microenvironments , such as PDAC-associated desmoplasia ( Alexander and Cukierman , 2016 ) . Our results indicated that while TGFβ is necessary for normal fibroblasts to assemble N-ECM , it is essential solely for the phenotypic remodeling of anisotropic D-ECM by CAFs , and not for its production . Negating CAF capability to produce anisotropic D-ECM restricts tumor growth in vivo ( Goreczny et al . , 2016 ) , while increased levels of anisotropic D-ECM correlate with poor patient survival and cancer relapse ( Goetz et al . , 2011; Conklin et al . , 2011; Bredfeldt et al . , 2014 ) . Inhibition of the TGFβ type I receptor , which reduces the activity of a downstream canonical effector of TGFβ , SMAD2/3 , is known to hinder matrix fibrillogenesis ( Callahan et al . , 2002 ) . In our work , elevated fibroblast-expressed pSMAD2/3 and nuclear translocation of this protein in CAFs producing D-ECM correlated with short time to recurrence and reduced survival in both PDAC and RCC patient cohorts . Results from our study also suggest that while TGFβ-targeted therapeutic interventions may revert D-ECMs to their naturally tumor-suppressive isotropic phenotype , the same approach could ablate homeostatic N-ECM . Consideration of this possibility is important because total ablation of TGFβ induces multifocal inflammatory disease ( Shull et al . , 1992 ) and because clinical trials are currently assessing TGFβ-inhibitory drugs ( Herbertz et al . , 2015 ) . While the understudied αvβ1 heterodimer was recently recognized as a regulator of latent TGFβ in fibrosis ( Reed et al . , 2015 ) , αvβ5 and α5β1 integrins are involved in myofibroblastic activation ( Asano et al . , 2006; Lygoe et al . , 2004; Dugina et al . , 2001 ) . Both αvβ5 and α5β1 integrin receptors were abundant in primary fibroblasts collected from patients . Our results demonstrated that in response to D-ECM , αvβ5-integrin activity supports the relocalization of active α5β1-integrin to intracellular endosomal pools and that this vesicular accumulation is needed for the maintenance of naïve-to-myofibroblastic activation . In addition , genetic loss ( but not transient inhibition ) of α5β1 , which resulted in slow-growing naïve cells , altogether prevented de novo D-ECM induction of naïve-to-myofibroblastic transition . Interestingly , in other studies , α5β1-positive elongated ‘super mature’ adhesion structures ( Dugina et al . , 2001; Hinz et al . , 2003 ) , which control tension-dependent recruitment and retention of αSMA at stress fibers , were found to play a functional role in myofibroblastic activation ( Goffin et al . , 2006 ) . Together with our findings , these data suggest that α5β1 participates in the regulation of αSMA expression , whereas the αvβ5-regulated relocation of activated α5β1-integrin participates in αSMA localization to stress fibers . Other studies have shown that inhibition of αvβ5-integrin activates β1-integrin , counteracting ECM-dependent myofibroblastic αSMA stress fiber localization ( Wang et al . , 2012 ) . However , the nature of the specific β1-integrin heterodimer and the mechanistic details of this negative myofibroblastic regulation were previously unknown . We further defined the α5β1 activity that regulates αSMA localization as independent of FAK . Interestingly , a previously defined α5β1 integrin activity needed to form 3D-adhesions in both normal cell-derived ECMs and in vivo is also FAK-independent ( Cukierman et al . , 2001 ) . Intriguingly , our results indicate that D-ECM induces αvβ5-integrin-dependent redistribution of active α5β1-integrin to intracellular pools , within structures that include CD81-positive multivesicular ( Escola et al . , 1998 ) and late Rab7/Rab11 endosomes . We found that using SNAKA51 to stabilize α5β1-integrin activity resulted in reduced internalization , a finding that differs from results reported previously which indicated that treating cells with SNAKA51 internalized active integrin ( Mana et al . , 2016 ) . It is possible that these differences reflect differences in analytic time points . Mana et al . ( 2016 ) looked at a time point of 20 min after treatment , whereas we analyzed data after overnight incubation with SNAKA51 , which may have led to compensatory enrichment of activity at the PM in our study , and thus further investigation is required . However , it is interesting that engagement of the integrin-binding ligands RGD , osteopontin and cilengitide with αvβ3 and α5β1 results in the association of α5β1 with a Rab-coupling protein which regulates its localization concomitant with EGFR ( Caswell et al . , 2008 ) . It is therefore possible that the use of ALULA ( Clark et al . , 2005 ) in this study triggered activation of the Rab cellular trafficking machinery , in a manner restricted by αvβ5 activity during tumor-associated desmoplasia . The importance of identifying reliable biomarkers for early PDAC diagnostics ( Laeseke et al . , 2015 ) and improved RCC prognostic predictions is clear . Recently , increased stromal density was deemed to be a better prognostic outcome in a retrospective study looking at pancreatic cancer patients who underwent pancreaticoduodenectomy followed by adjuvant treatment , while assessing overall levels of stromal αSMA was found to be inconclusive ( Bever et al . , 2015 ) . In the future , it will be interesting to test whether the increased stromal density , perhaps in the context of specific relocalization of αSMA , corresponds to this study’s ‘patient-protective’ phenotype . This example , together with additional studies that suggest both tumor-protective and -detrimental roles for highly heterogeneous desmoplasia ( Özdemir et al . , 2014; Rhim et al . , 2014; Sherman et al . , 2014; Bever et al . , 2015; Dekker et al . , 2015; Sasson et al . , 2003 ) , underscore the need for better stromal categorization . The new software developed in this study to facilitate analysis of the discrete localizations of the various markers specifically in stroma ( freely downloadable from https://github . com/cukie/SMIA_CUKIE ) allows analysis of batch-sorted monochromatic images in bulk , and generation of qualitative and quantitative outputs to evaluate biomarkers located at any given mask and/or marker intersection . In this study , we used this software to identify predictive biomarker signatures for PDAC and RCC patients . Stromal increases in pSMAD2/3 and pFAK correlated with worse outcomes but , in spite of the bias introduced by the use of resectable surgical cases , we identified a patient-protective stroma phenotype in PDAC , in which increased active α5β1-integrin selectively localized at stromal 3D-adhesions is associated with improved outcomes . By contrast , intensities of active α5β1-integrin activity correlated with increasingly unfavorable OS as well as DSS in RCC patients . Hence , locations and intensities of stromal α5β1-integrin activity concomitant with constitutive active pFAK-Y397 could represent novel desmoplasia-dependent outcome-predicting stromal patterns . As our in vitro studies suggested a complex function for α5β1-integrin activity , and given the differences between the molecular characteristics of distinct tumor subtypes , we emphasize that individual tumor classes will require careful study before these biomarkers could be used as predictive clinical indicators . In summary , our study provides new insights into the intracellular and extracellular signaling processes that support desmoplastic formation , with mechanistic studies facilitated by the use of in vitro fibroblast-derived 3D ECMs . Specifically , the results suggest that αvβ5-integrin prevents FAK-independent α5β1-integrin activity from impairing D-ECM-induced naïve-to-myofibroblastic activation by relocating this activity to multivesicular and other late endosomes . β5-integrin's contribution to the ability of CAFs to produce functional anisotropic D-ECM , in which active α5β1-integrin levels are not altered , distinguishes between naïve-to-myofibroblastic activation and myofibroblastic CAFs . Importantly , this study supports the idea that stromal normalization , to produce a form that plays a tumor-suppressive role ( Mintz and Illmensee , 1975; Soto and Sonnenschein , 2011; Xu et al . , 2009; Bissell and Hines , 2011; Alexander and Cukierman , 2016 ) may be a favorable approach , especially as some recent attempts aimed at completely abolishing stroma have proven harmful to patients ( Bijlsma and van Laarhoven , 2015; Özdemir et al . , 2014; Rhim et al . , 2014 ) . This work also suggests specific protein targets that may be useful for stroma-normalizing therapies . For example , these data suggest that future therapeutic strategies might involve combinatorial treatments to inhibit TGFβ and αvβ5-integrin . Finally , the study also suggests that monitoring the level and localization of stromal pSMAD2/3 , as well as those of α5β1-integrin activity together with those of stromal pFAK , could assist in stratifying individuals that would benefit from αvβ5-integrin and other chronic fibrosis-directed therapeutics .
Human tissues were collected under exemption-approval of the Center’s Institutional Review Board after patients signed a written informed consent agreeing to donate samples to research . To protect patients' identities , samples were coded and distributed by the Institutional Biosample Repository Facility . All reagents ( see below ) were from Sigma-Aldrich ( St . Louis , MO ) unless listed otherwise . All primary and immortalized fibroblasts were cultured at 37°C under 5% CO2 using Dulbecco’s Modified Eagle’s Medium ( Mediatech ( Manassas , VA ) ) supplemented with 10% or 15% ( murine or human cells , respectively ) Premium-Select Fetal Bovine Serum ( Atlanta Biologicals ( Lawrenceville , GA ) ) , 2 mM L-glutamine and 100 u/ml-μg/ml penicillin-streptomycin . Panc1 , FAK+/+ , FAK−/− , SYF , and littermate wild-type control cells were from the American Tissue Culture Collection ( Manassas , VA ) . FAK-KD and hTert controls were a gift from David Schlaepfer ( UCSF , San Francisco , CA ) . A request to annotate the human fibroblastic cell lines that were generated in this study was issued to the Research Resource Identifiers for their quarterly year annotations . Also , these cells were submitted for CellCheckTM 16 Marker STR Profile and Inter-species Contamination Test for human cell line authentication to IDEXX Bioresearch ( Columbia , MO ) . Markers evaluated to authenticate the diversity of cell origins ( patients ) were: AMEL , CSF1PO , D13S317 , D16S539 , D18S51 , D21S11 , D3S1358 , D5S818 , D7S820 , D8S1179 , FGA , Penta_D , Penta_E , TH01 , TPOX , and vWA . Interspecies contamination testing included comparison of human samples versus mouse , rat , Chinese hamster and African green monkey DNA . Mycoplasma contamination status was negative . This study did not utilize cell lines listed in the Database of Cross-Contaminated or Misidentified Cell Lines ( Version 7 . 2 ) from the International Cell Line Authentication Committee ( ICLAC ) . Pan-keratin ( AE1/AE3 -Dako ( Carpinteria , CA ) ) negative and vimentin ( EPR3776 -Abcam ( Cambridge , MA ) ) positive fibroblastic cells were isolated from fresh surgical normal and tumoral tissue samples using the enzymatic tissue digestion approach as published ( Franco-Barraza et al . , 2016 ) . Fresh surgical tissues from normal or tumoral pancreas and from kidney or murine skin ( see below ) were collected into conical tubes containing Dulbecco’s phosphate-buffered saline with antibiotics at 4°C . Samples were minced and subjected to overnight gelatinase digestion . Digested tissues were subjected to 10 min 200 g centrifugation followed by three sequential size-exclusion filtrations using sterile nylon mesh strainers of pore sizes 500 μm , 100 μm and 40 μm . The resulting cells were characterized as naive or CAFs according to methods reported previously ( Gupta et al . , 2011; Goetz et al . , 2011; Amatangelo et al . , 2005 ) . Cells were used for no longer than 12 passages while maintained within their own derived ECMs ( if cells were not to be maintained in 3D ECMs , only 4–6 passages would have been used ) . Fibroblasts used in the study were harvested from five different patients for PDAC ( including two matched pairs ) and four for RCC models , while the fibroblasts used for the murine model of skin squamous-cell-carcinoma-associated desmoplasia were from our published study in which the D-ECM-induced naïve-to-myofibroblastic transition was first described ( Amatangelo et al . , 2005 ) . Note that PDAC fibroblastic cells from patient #1 were used in vitro throughout the study unless stated otherwise . We obtained cell-derived ECMs using our published protocols ( Franco-Barraza et al . , 2016 ) . Confluent fibroblastic cultures were maintained for 7 days in the presence of daily added and freshly prepared ascorbic acid at a concentration of 50 ìg/ml . The resulting ‘unextracted’ 8 day long multilayered 3D cultures were used as naïve or CAF-matrix—producing cultures in this study and were processed for phenotypic characterization and ECM alignment ( isotropy vs . anisotropy assessments ) . Cell extraction , to obtain N- and D-ECMs , was achieved using 0 . 5% ( v/v ) Triton X-100 complemented with freshly added 20 mM NH4OH . Extracted N- and D-ECMs were stored sealed , using Parafilm , in culturing plates with Dulbecco’s phosphate-buffered saline lacking CaCl2 and MgSO4 at 4°C . The resulting extracted matrices were used for D-ECM-induction of naïve-to-myofibroblastic activation . For the production of 3D matrices using inhibitors , the following were added during the 7 days of ECM production: TGFβ1R small molecule inhibitor ( SB431542 ) ( 25 μM ) ; DMSO; conformation-dependent anti-active α5-integrin functional antibody ( Clark et al . , 2005 ) ( 45 μg/ml SNAKA51 [a gift from Martin Humphries , Manchester University , UK] ) ; functional-blocking anti-αvβ5-integrin ( Clark et al . , 2005 ) ( 60 μg/ml ALULA [a gift from Drs Dean Sheppard ( UCSF , San Francisco , CA ) , and Shelia Violette ( Biogen , Cambridge , MA] ) ; or species-matched non-immunized isotypic antibodies . The Ambion PureLink kit ( Life Technologies ) was used , according to manufacturer’s instructions , to extract total cellular RNA from the various experimental conditions and tested for RNA quality using a Bioanalyzer ( Agilent ) . Contaminating genomic DNA was removed using Turbo DNA free from Ambion . RNA concentrations were determined with a spectrophotometer ( NanoDrop; Thermo Fisher Scientific ) . RNA was reverse transcribed ( RT ) using Moloney murine leukemia virus reverse transcriptase ( Ambion ) and a mixture of anchored oligo-dT and random decamers ( IDT ) . Two reverse-transcription reactions were performed for each experimental duplicate sample using 100 ng and 25 ng of input RNA . Taqman assays were used in combination with Taqman Universal Master Mix and run on a 7900 HT sequence detection system ( Applied Biosystems ) . Cycling conditions were 95°C , 15 min , followed by 40 ( two-step ) cycles ( 95°C , 15 s; 60°C , 60 s ) . Ct ( cycle threshold ) values were converted to quantities ( in arbitrary units ) using a standard curve ( four points , four-fold dilutions ) established with a calibrator sample . Values were averaged per sample and standard deviations were from a minimum of two independent PCRs . Polymerase ( RNA ) II ( DNA directed ) polypeptide F ( POLR2F ) was used as internal control . Identification numbers of commercial assays ( Life Technologies ) and sequences of the primers and probe for the POLR2F assay are: Hs00363100_m1 ( PALLD1 ) , Hs00426835_g1 ( ACTA2 ) , TGCCATGAAGGAACTCAAGG ( forward ) , TCATAGCTCCCATCTGGCAG ( reverse ) , and 6fam-CCCCATCATCATTCGCCGTTACC-bqh1 ( probe ) . Human naïve or assorted murine fibroblasts were pre-incubated for 45 min with functional antibodies , small molecule inhibitors or controls , prior to overnight culturing within assorted ECMs . Experiments were carried out in the presence or absence of 45 μg/ml SNAKA51 ( Clark et al . , 2005 ) , 60 μg/ml ALULA ( Su et al . , 2007 ) , 250 μg/m mAb16 ( Akiyama et al . , 1989 ) ( a gift from Kenneth Yamada [NIDCR/NIH , Bethesda , MD] ) , BMA5 ( Fehlner-Gardiner et al . , 1996 ) diluted as instructed by the manufacturer ( Millipore , Temecula , CA ) , IgG control , DMSO , or small molecule inhibitor of FAK ( PF573 , 228 [Slack-Davis et al . , 2007] ) . ECM-induced phenotypes were assessed by RT-qPCR , western blot or immunofluorescence as described in the corresponding Methods sections . An integrin-mediated cell adhesion assay was conducted to detect cell surface levels of heterodimeric integrin receptors on the assorted fibroblastic cells . The array , ECM531 from Millipore , is based on the use of selected monoclonal antibodies against assorted β-integrin heterodimers and testing amounts of cell adhesion to each particular antibody . Cells are incubated onto an array of wells coated with the various antibodies and then washed , before adherent cells are counted . The number of cells adhering to a specific antibody is directly related to the amount of the specific integrin receptor expressed at the cell surface . Numbers of adherent cells’ surfaces are evaluated by 540–570 nm absorbance of cells stained using a staining reagent provided with the kit according to the manufacturer'sinstructions . Quantification of TGFβ stored/deposited into unextracted fibroblast 3D cultures was performed using the DuoSet human TGFβ1 ELISA kit , as instructed by the manufacturer ( R and D Systems , #DY240-05 ) . All wash buffers and reagents were provided in the DuoSet Ancillary Reagent Kit 1 ( R and D systems , #DY007 ) . Briefly , whole cell lysates ( or conditioned media as controls ) were obtained from fibroblasts and loaded onto plates that were pre-coated with capture antibody overnight at RT . After 2 hr incubation of lysates , concentrated media and calibration curve standards , wells were rinsed three times with wash buffer and then incubated for 2 hr at RT with detection antibody . Next , after three more washes , streptavidin-HRP was incubated in the wells for 20 min at RT . After another three washes , substrate solution was added to the wells , and after 15 min , stop solution was added to halt the HRP-substrate reaction . Next , OD at 450 nm and 570 nm was obtained using the Synergy H1 plate reader ( BioTek ) . The OD at 570 nm was subtracted from the OD at 450 nm to correct for plate imperfections . Then the standard curve was constructed , and the concentration of TGFβ ( pg/mL ) in experimental samples was determined . Last , TGFβ concentration was normalized to the amount of protein loaded into each well and then normalized to control TAF levels , with the modal value referred to as one a . u . ( arbitrary unit ) . Au8-SNAKA51 and Au15-mAb11 gold-antibody complexes were conjugated as described in Handley et al . ( 1981 ) . Cells from functional assays were fixed with 2 . 0% paraformaldehyde in PBS , pH 7 . 4 , at 4°C overnight . Following antibody incubation , cells were fixed with 2 . 0% glutaraldehyde washed and post-fixed in 2 . 0% osmium tetroxide for 1 hr at room temperature . Dehydration through a graded ethanol series and propylene oxide was followed by embedding samples in EMbed-812 ( Electron Microscopy Sciences [Hatfield , PA] ) . Sections were stained with 2% alcoholic uranyl acetate and scanned with a FEI Tecnai electron microscope , while images were acquired using a Hamamatsu digital camera operated via AMT Advantage image capture software ( AMT 542 . 544; 18 Sep 2009 ) . The method for indirect immunofluorescence was as published previously ( Gupta et al . , 2011; Amatangelo et al . , 2005; Franco-Barraza et al . , 2016; Cukierman et al . , 2001 ) , only Triton was omitted for non-permeable conditions . Samples were either fixed/permeated or solely fixed as published ( Franco-Barraza et al . , 2016 ) . Following 60 min blocking , using Odyssey Blocking Buffer ( LI-COR Biosciences , Lincoln , NE ) containing 1% donkey serum , the following primary antibodies were incubated in combinations as indicated by the specific experiments for a period of 60 min: anti-pan-cytokeratin ( clones: AE1/AE3 ) ( AB_2631307 ) from Dako ( Carpinteria , CA ) , rabbit monoclonal anti-Vimentin EPR3776 ( AB_10004971 ) ( 1:200 ) from Abcam ( Cambridge , MA ) , rabbit polyclonal anti-human fibronectin ( AB_476961 ) ( 1∶200 ) and 1 A4 mouse anti-αSMA ( AB_476701 ) ( 1∶300 ) were from Sigma-Aldrich ( St . Louis , MO ) , mouse SNAKA51 Ab ( 45 μg/ml ) was a gift from M Humphries ( Clark et al . , 2005 ) , rat mAb11 ( 45 μg/ml ) from K Yamada ( Akiyama et al . , 1989 ) , rabbit polyclonal anti-palladin ( AB_2158782 ) ( 1:200 ) ( Proteintech , Chicago , IL ) , rabbit monoclonal anti-phospho FAK [Y397] ( AB_2533701 ) ( 1:200 ) ( Invitrogen , Camarillo , CA ) , rabbit monoclonal anti-phospho Smad2 [S465/467]/Smad3 [S423/425] ( AB_2631089 ) ( 1:200 ) ( Cell Signaling , Danvers , MA ) , rabbit monoclonal anti-Rab5 ( AB_10828212 ) ( 1:150 ) ( Cell Signaling , Danvers , MA ) , rabbit monoclonal anti-Rab7 ( AB_10831367 ) ( 1:25 ) ( Cell Signaling , Danvers , MA ) , rabbit monoclonal anti-Rab11 ( AB_10693925 ) ( 1:25 ) ( Cell Signaling , Danvers , MA ) , rabbit monoclonal anti-EEA1 ( 1:150 ) ( AB_10828484 ) ( Cell Signaling , Danvers , MA ) , rabbit monoclonal anti-CD81 ( AB_2275892 ) ( 1:35 ) ( Santa Cruz Biotechnologies , Dallas , Texas ) and species-matched non-immunogenic mouse IgG ( Abcam ) and Rat IgG ( Jackson ImmunoResearch Inc . , West Grove , PA ) were used at equal concentrations to their corresponding matching isotypes . Following three rinses of 5 min with PBS-Tween20 ( 0 . 05% ) , secondary antibodies were incubated for 60 min using donkey F ( ab' ) two fragments ( 1∶100 ) cross-linked to assorted fluorophores ( Jackson ImmunoResearch Laboratories Inc . ) . When mentioned , SYBR Green ( 1∶10 , 000 Invitrogen [Eugene , OR] ) or Draq-5 ( 1:10 , 000 Pierce Biotechnology [Rockford , lL] ) and/or fluorescent Phalloidin ( 2 . 5 μl/100 μl , Invitrogen [Eugene , OR] ) were included . Following washes with PBS and a final rinse with double-distilled water , samples were mounted using Prolong Gold anti-fading reagent from Life Technologies ( Carlsbad , CA ) . When needed , anti-αSMA and/or SNAKA51 primary antibodies were pre-linked to fluorescent dye crystals using the Mix-n-StainTM kit following the manufacturer's recommendations ( Biotium , Hayward , CA ) . Confocal spinning disk Ultraview ( Perkin-Elmer Life Sciences , Boston , MA ) images were acquired with a 60X ( 1 . 45 PlanApo TIRF ) oil immersion objective , under identical exposure conditions per channel using Volocity 6 . 3 . 0 ( SCR_002668 ) ( Perkin-Elmer Life Sciences , Boston , MA ) . Maximum reconstruction projections were obtained using MetaMorph 7 . 8 . 1 . 0 ( SCR_002368 ) ( Molecular Devices , Downingtown , PA ) as described previously ( Amatangelo et al . , 2005 ) . For monochromatic in vitro image analyses using SMIA-CUKIE , please refer to SMI methods below . Fibronectin channel monochromatic images were analyzed via ImageJ’s ( SCR_003070 ) OrientationJ plugin ( SCR_014796 ) ( http://bigwww . epfl . ch/demo/orientation/ ) as described previously ( Franco-Barraza et al . , 2016; Rezakhaniha et al . , 2012 ) . Numerical outputs were normalized by setting mode angles to 0˚ , and correcting angle spreads to fluctuate between −90˚ and 90˚ . Angle spreads for each experimental condition , corresponding to a minimum of three experimental repetitions and five image acquisitions per condition were plotted and their standard deviations calculated using Excel spreadsheets . Percentage of fibers oriented between −15˚ and 15˚ was determined for each normalized image-obtained data . See statistics section below . 16 bit TIFF files corresponding to the maximum projections of assorted monochromatic reconstituted z-stacks were analyzed using MetaMorh Offline software . On the basis of a noise/signal ratio , gray-scale levels of reference images ( control ) were optimized to reduce background to a minimum while avoiding signal saturation ( at the maximum point ) . An inclusive threshold for positive signal was set for control images . These levels and threshold values were used throughout the entire experiment and served as normalizing controls for each experiment . Intensity levels of αSMA or active α5-integrin were determined by threshold-masked images , evaluating total pixel intensities and integrated optical densities , using corresponding functions from the software’s integrated morphometry analysis ( IMA ) algorithm . The percentage of αSMA colocalization at F-actin stress fiber structures was calculated by querying αSMA-positive area coverages that overlapped ( i . e . , co-localized ) with phalloidin-positive stress fibers areas . For experiments questioning levels in murine cells , an ‘activated fibroblastic phenotype’ was determined by counting the percentage of cells presenting stress-fiber-localized αSMA . Output data from each analysis were normalized to corresponding control conditions and expressed as fold variations related to control . For 3D-adhesion length calculations: calibrated images ( one pixel = 0 . 11 μm ) were used while their gray-scale levels were optimized to reveal 3D-adhesion ( i . e . , mAb11 ) positive-stained structures . These were recognized as ‘objects’ by the software via threshold settings ( identical for all images ) and measured through the ‘dimension’ function of IMA . Objects with lengths ≥6 . 5 μm , signifying bona fide 3D-adhesion structures ( Cukierman et al . , 2001 ) , were included in the analysis . The more intricate analyses of multi-labeled 3D-adhesions were performed using the ‘line-scan’ function of the software . Channel-overlaid 8bit images were subjected to a transversal analysis ( 12 μm length ) to identify peaks of median gray levels of the stated particular markers along the line-scanned structure being analyzed . In vitro SMIA-CUKIE ( see ‘SMI approach’ below for additional information ) was used to query active α5-integrin localization ( i . e . , at or away from 3D-adhesions in vitro and where stroma-positive and tumoral-negative locations were identified in vivo ) . Areas corresponding to 3D-adhesion-positive pixels ( i . e . , mAb11 ) were assigned as masks areas , while SNAKA-51-positive pixels were designated active α5-integrin , pFAKY397 as PFAK , and pSmad2/3 S465 , 467/S423 , 467 as PSMAD or marker positive areas . Marker values corresponding to median , mean , standard deviation , total intensity , integrated intensity and area coverages relative to the mask , or relative to the image at mask , or away from mask locations were obtained as Excel spreadsheet outputs . See below for in vivo SMIA-CUKIE analyses using surgical FFPE samples . Slides were exposed to a short-wave UV lamp for about 30 min in a light-protected box to quench auto-fluorescence . They were then kept in a light-protected ( i . e . , dark ) box until used . Sections were deparaffinized in xylene and rehydrated in ascending graded alcohol to water dilutions . Sections were then treated with Digest-All ( Invitrogen ) and permeabilized in 0 . 5% TritonX-100 . After treating them with blocking buffer as in in vitro work ( see above ) , samples were first incubated with the above-mentioned Q-dot pre-labeled antibodies ( i . e . , Q655 SNAKA51 , Q565 mAb11 ) overnight at 4°C . Sections were washed as in in vitro work , and incubated with a mix of mouse monoclonal ‘cocktail’ containing anti-pan-cytokeratin ( clones AE1/AE3 , DAKO ) , anti-EpCam ( MOC-31EpCam , Novus Biologicals ( Littleton , CO ) ) , and anti-CD-70 ( 113–16 Biolegend ( San Diego , CA ) [Ryan et al . , 2010] ) to detect epithelial/tumoral locations , together with rabbit monoclonal anti-vimentin ( EPR3776 , Abcam ) antibodies for mesenchymal ( stromal ) components for 2 hr at RT . Note that pre-incubated Q-dot labeled antibodies could no longer be recognized by secondary antibodies , thus allowing for indirect immunofluorescent detection of tumoral and stromal masks . Secondary antibodies were as used as in in vitro work and included donkey anti-mouse Cy2 and donkey anti-rabbit Cy3 . Nuclei were stain using Draq-5 ( as in vitro ) . Sections were quickly dehydrated in graded alcohol and clarified in Toluene before mounting in Cytoseal-60 . Slides were cured overnight at RT before the imaging . FFPE sections of biological samples are known to give strong broad autofluorescence , thus obscuring the specific ( labeled ) fluorescent signal . To overcome this problem , images were collected using Caliper’s multispectral imaging system ( PerkinElmer ) , which utilizes a unique imaging module with Tunable Liquid Crystal . Two different systems namely Nuance-FX ( for 40x objective ) and Vectra ( for 20x objective and high throughput ) were used depending on the acquisition needs . A wavelength-based spectral library for each system and each tissue type ( i . e . , pancreas and kidney ) was created by staining control sections with individual fluorophores or by mock treating samples to include the specific autofluorescence spectra . Once a spectral library was constructed for each organ type , it was saved and used for the subsequent image acquisition and analysis . All excitations were achieved via a high-intensity mercury lamp using the following filters ( emission-excitation ) : for Nuance , DAPI ( 450-720 ) , FITC ( 500-720 ) , TRITC ( 580-720 ) , CY5 ( 680-720 ) ; for Vectra , DAPI ( 440-680 ) , FITC ( 500-680 ) , TRITC ( 570-690 ) , CY5 ( 680-720 ) . For emissions collection , ‘DAPI’ filter ( wavelength range 450–720 ) was used for all Q-dot labeled markers , while masks used the conventional FITC , TRITC , and CY5 filters . After collecting all image cubes ( including all channels ) , images were unmixed to obtain 16-bit greyscale individual stains monochromatic files . Using Photoshop’s 'Levels' and 'Batch Conversion' functions , the images were processed in bulk to render identically scaled 8-bit monochromatic images for each channel . The resulting images were sampled to set identical threshold values for each channel , which were used to feed the values needed for analyses in SMIA-CUKIE to signify positive-labeled pixels . SMIA-CUKIE ( SCR_014795 ) was written for the bulk analysis of high-throughput-acquired monochromatic images , corresponding to simultaneously labeled channels like the ones used in this study . As examples , masks in Figure 12 and Figure 12—figure supplement 2 were generated using SMIA-CUKIE and demonstrate how the software isolates stromal locations while omitting positive tumoral areas , based on mask value thresholds that are provided by the user . Images were sorted into ‘Batch Folders’ , each containing the five monochromatic images corresponding to the original ( unmixed ) sample . The newly written software , available at https://github . com/cukie/SMIA_CUKIE , was created to bulk process and analyze batches of monochromatic images providing localization ( masks ) , intensities , and similar quantifying values ( markers ) , including co-localizations of multichannel monochromatic immunofluorescent ( or IHC , etc . ) images . The software can identify intersection areas between an unlimited amount of masks , while queried marker values and locations can also be estimated for numerous interrogations . The software requires the identification of common nomenclatures to recognize each type of monochromatic image ( i . e vim for vimentin etc . ) . It also needs information regarding the available number of masks and markers deposited in the batch folders containing the images . In this work , three masks were used corresponding to nuclei , epithelium/tumor and stroma , as well as two markers corresponding to adhesion structures and active α5β1 integrin . For each of the masks and markers , the software requires a numeric threshold ( 0-255 ) , which indicates the value of pixel intensity that is to be considered positive for each channel ( corresponding to each mask and marker ) . The software then allows choosing amongst all possible query combinations . Alternatively , it provides an option for the user to write the desired tests to be queried . For example , when active α5β1 integrin values were requested at bona fide stromal locations , the software was instructed to look for ‘SNAKA under vimentin , NOTepi/tumor’ . In this example , ‘SNAKA’ was the nomenclature we used for the active integrin channel while vimentin and epi/tumor served as masks . After running this function , the software rendered an Excel file containing values of area coverage for the marker at the requested mask intersection ( stroma ) , as well as total area coverage related to the image . Similarly , values included mean , median , standard deviation , total and integrated intensities . In addition , the software can be instructed to provide image outputs corresponding to requested mask locations , as well as markers shown solely at the corresponding mask intersections ( as shown in Figure 12 and Figure 12 –figure supplement 2 ) . Data values obtained in this work are available online at https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . Finally , values were used for statistical analyses as described in the corresponding section . To knockout the β5-integrin-subunit-encoding gene from fibroblasts , CRISPR/CAS9 gene editing was performed to introduce a frameshift mutation that disrupts the reading frame causing a premature stop codon to be read , ultimately halting translation and knocking out this gene . First , gRNAs specific for β5 integrin were designed by targeting exon 3 . The first 200 base pairs of exon three were inserted into MIT Optimized CRISPR Design website: http://crispr . mit . edu/ and the top two scoring gRNAs were selected , based on the presence of a PAM site ( a CAS9 recognition site ) and limited off-target binding . The two independent gRNA sequences were as follows: gRNA1: ACCGAGAGGTGATGGACCGT gRNA 2: CACCGAGAGGTGATGGACCG For a non-targeting gRNA control , the following gRNA against eGFP was designed: CATGTGATCGCGCTTCTCGT . Once the gRNA sequences were designed and ordered ( Integrated DNA Technologies ) , they were cloned into the LentiCRISPR v2 vector ( LentiCRISPR v2 was a gift from Feng Zhang; Addgene plasmid # 52961 ) ( Sanjana et al . , 2014 ) . This is a dual-expression vector , expressing the CRISPR/CAS9 protein as well as the cloned gRNA sequence driven by the human U6 promoter . Briefly , DNA oligos representing the gRNA sequences are listed below , with overhangs compatible with the Esp3I restriction enzyme ( bold , underlined ) and an additional G added to the beginning of each gRNA , with a complementary C on the reverse oligo ( bold , italics ) , for efficient transcription driven by the human U6 promoter . Integrin β5 gRNA 1 . 1CACCGACCGAGAGGTGATGGACCGTIntegrin β5 gRNA 1 . 2AAACACGGTCCATCACCTCTCGGTCIntegrin β5 gRNA 2 . 1CACCGCACCGAGAGGTGATGGACCGIntegrin β5 gRNA 2 . 2AAACCGGTCCATCACCTCTCGGTGCeGFP gRNA 1 . 1CACCGCATGTGATCGCGCTTCTCGTeGFP gRNA 1 . 2AAACACGAGAAGCGCGATCACATGCIntegrin α5 gRNA 1 . 1CACCGCTCAGTGGAGTTTTACCGGCIntegrin α5 gRNA 1 . 2AAACGCCGGTAAAACTCCACTGAGCIntegrin α5 gRNA 2 . 1CACCGTCAGTGGAGTTTTACCGGCCIntegrin α5 gRNA 2 . 2AAACGGCCGGTAAAACTCCACTGACIntegrin αv gRNA 1 . 1CACCGATTCAATTGGCTGGCACCGGCGGIntegrin αv gRNA 1 . 2AAACCCGCCGGTGCCAGCCAATTGAATCIntegrin αv gRNA 2 . 1CACCGTGACTGGTCTTCTACCCGCCGGIntegrin αv gRNA 2 . 2AAACCCGGCGGGTAGAAGACCAGTCACCIntegrin β3 gRNA 1 . 1CACCGCCCAACATCTGTACCACGCGIntegrin β3 gRNA 1 . 2AAACCGCGTGGTACAGATGTTGGGCIntegrin β3 gRNA 2 . 1CACCGACCTCGCGTGGTACAGATGTIntegrin β3 gRNA 2 . 2AAACACATCTGTACCACGCGAGGTC Next , the gRNA oligos stocks were diluted to 100 uM , and 1 uL of each gRNA pair was added to a T4 PNK reaction mixture ( NEB , #M0201S ) for a 10 uL reaction . The reaction was allowed to run in a thermal cycler to phosphorylate and anneal the oligos , according to the following program: To prepare the vector for cloning , 5 ug of LentiCrispr v2 vector were simultaneously cut with Fast Digest Esp3I ( ThermoFisher , #FD0454 ) and dephosphorylated with Fast AP ( ThermoFisher , #EF0651 ) for 30 min at 37°C and subsequently run on a 1 . 5% agarose gel and purified for cloning using the GeneJet Gel Extraction Kit ( Thermofisher , #K0691 ) . To clone the annealed gRNA oligos into the vector , the oligos were diluted 1:200 in RNAase-free water ( ThermoFisher , #4387937 ) , and added to the quick ligation reaction mixture ( NEB , #M2200S ) , along with 1 µL of the digested and dephosphorylated vector , and the reaction was allowed to proceed for 10 min at room temperature . For bacterial transformation , 2 µL of the reaction was mixed with 50 µL of competent Stbl3 strain of Escherichia coli for 30 min on ice . The bacteria were then heat shocked for 45 s at 42°C and immediately transferred to ice for 2 min . Then , 50 µL of the transformed bacteria were spread onto an LB/agar dish containing 100 µg/mL ampicillin and incubated at 37°C overnight . The next day , single colonies were screened by colony PCR using the U6 promoter forward primer: GAGGGCCTATTTCCCATGATT and the corresponding reverse gRNA primers ( gRNA x . 2 ) . Positive clones were selected to expand for plasmid purification and sequencing . For functional lentiviral production , viruses were generated in 293T cells using the cloned LentiCrispr v2 plasmids , and two packaging plasmids: psPAX2 ( psPAX2 was a gift from Didier Trono; Addgene plasmid # 12260 ) and VSVg . Briefly , 10 µg of cloned LentiCrispr V2 , 5 µg of psPAX2 , and 2 µg of VSVg were mixed in 1 mL serum-free/antibiotic-free DMEM in an 1 . 5 mL Eppendorf tube . 30 µL of X-treme Gene 9 ( Roche , #06365787001 ) was added to the DNA mixture and gently mixed with the pipette tip . The mixture was allowed to sit for 45 min at room temperature . Next , the mixture was added drop-wise to a T75 flask containing 5 mL serum-free/antibiotic-free DMEM and 293T cells at ~85% confluence and slowly rocked for 30 s to evenly mix the DNA transfection mixture . The next morning , the serum-free media was removed from the 293T cell flasks , and 10 mL of fresh DMEM containing 10% FBS and 1% penicillin/streptomycin were added to each flask . Two days and 4 days post-transfection , the medium was collected and filtered through a 0 . 45 µM syringe filter ( Millipore , #SLHV013SL ) and then used immediately for viral infection of target cells , or stored at −80°C until needed . Target cells ( naïve or desmoplastic fibroblasts ) were seeded at ~40% confluence in 2 mL complete fibroblast media in a six-well plate . The following day , the target cells were infected with 2 mL of lentivirus for each corresponding CRISPR construct ( eGFP or integrin gRNAs ) in the presence of 10 ug/mL Polybrene ( Santa Cruz , #sc-134220 ) . As a control for the infection , cells were infected with a lentivirus overexpressing GFP , and the appearance of GFP-positive cells signified a successful infection . 24 hr later , the medium was replenished with fresh complete medium for each cell type . After 72 hr , puromycin selection ( 1 ug/mL for naïve fibroblasts , 2 ug/mL for desmoplastic fibroblasts ) of the infected cells began . The selection process lasted between 7 and 10 days; cells were expanded , and the efficiency of CRISPR/CAS9 knockout was assessed by western blotting . The cell lines with the greatest degree of target protein knockout were used for subsequent experiments . Briefly , various normal fibroblasts and CAFs were grown to confluency in six-well plates and were lysed in standard RIPA buffer . Lysates were then homogenized by sonication , allowed to rest on ice for 15 min , and centrifuged for 10 min at maximum speed in a microcentrifuge . Next , samples were diluted in 2x loading buffer ( Bio-Rad , Hercules , CA ) , boiled for 3 min , and loaded onto 4–20% gradient gels ( Bio-rad , Hercules , CA ) and run at 70 volts for 2 hr . Next , gels were subjected to semi-dry transfer to PVDF membranes ( Millipore , Billerica , MA ) and blocked for 1 hr at RT in 5% milk in 0 . 1% TBST . Membranes were then incubated in the one of the following primary antibodies overnight at 4°C: rabbit polyclonal anti-αv-integrin ( AB_2631308 ) ( 1:5000 ) ( Mybiosource , San Diego , CA ) , rabbit monoclonal anti-α5-integrin ( AB_2631309 ) ( 1:1000 ) ( Abcam , Cambridge , MA ) , rabbit polyclonal anti-β5-integrin ( AB_10806204 ) ( 1:1000 ) ( ) ( Millipore , Billerica , MA ) or rabbit polyclonal anti-β3-integrin ( 1:1000 ) ( AB_91119 ) ( Millipore , Billerica , MA ) . The following day , membranes were washed in 0 . 1% TBST , 5 times for 5 min each . Next , membranes were incubated for 2 hr in anti-rabbit IgG-HRP secondary antibodies ( Sigma , St . Louis , MO ) in 5% milk in 0 . 1% TBST . Blots were then washed again five times/5 min each . Finally , Immobilon Western Chemiluminescent HRP substrate ( Millipore , Billerica , MA ) was added to the blots and protein bands were captured by film or Protein Simple Digital Imaging System ( Protein Simple , San Jose , CA ) . Two-tailed non-parametric Mann-Whitney tests were used to query experimental significances for all in vitro data , comparing control conditions to experimental ones independently . All in vitro results were presented as medians with interquartile range . Each PDAC-related in vitro experiment was performed at least three times , using one patient matched CAF and normal fibroblasts . For verification , these series of experiments were also conducted , using one additional pair of normal and activated fibroblasts from a non-related patient , two normal fibroblasts isolated from two additional non-related patients , and one tumor tissue which provided one additional non-related activated ( desmoplastic ) fibroblastic cell . All cells characterized as normal fibroblasts were verified for their phenotype and challenged with D-ECMs produced by CAFs isolated from three of the above describedpatients . These included a total of five different patient sources which included seven surgical samples . The RCC-related in vitro assessments were performed employing a combination of two patients-derived CAFs and normal fibroblasts from another two different sources . All the image analyses were conducted in at least ten images containing no less than three cells per region per experimental condition for each above-mentioned repetition . Regarding statistics pertinent to the TMAs , to identify clinical and laboratory variables related to patient survival ( OS , DSS , and RFS ) , univariate and multivariable analyses were performed by constructing decision trees using the CART methodology . Clinical and laboratory variables were considered as predictors of survival time and are listed in data uploaded to https://www . foxchase . org/sites/fccc/files/assets/cukierman_Franco-Barraza%20SMIA-CUKIE-Dec-2016 . xlsx . The unified CART framework that embeds recursive binary partitioning into the theory of permutation tests was used . Significance-testing procedures were applied to determine whether no significant association between any of the clinical variables and the response could be stated , or whether the recursion would need to stop . Also , log-rank tests and univariate and multivariable Cox proportional hazards ( PH ) were used to correlate clinical and laboratory variables with survival; hazard ratios ( with 95% confidence intervals ) were calculated , as appropriate , for various comparisons . Goodness-of-fit of the PH model was evaluated based on Schoenfeld residuals . In order to account for potential batch effects , a ‘batch’ variable was included as an adjustment variable in all the analyses that were pertinent to the PDAC cohort . All tests were two-sided and used a significance level of 5% to test each hypothesis . Significant p-values in all figures were denoted by asterisks as follows: ****p<0 . 0001 extremely significant , ***p=0 . 0001–0 . 01 very significant , **p=0 . 01–0 . 05 significant and *p=0 . 05–0 . 10 marginally significant . Statistical analyses were performed using GraphPad Prism software , version 6 . 05 for Windows ( La Jolla , CA ) and the R packages survival and parity ( www . r-project . org ) . | Tumors are not entirely made out of cancerous cells . They contain many other components – referred to as tumor stroma – that may either encourage or hinder the tumor’s growth . Tumor stroma includes non-cancerous cells and a framework of fibrous sugary proteins , called the extracellular matrix , which surround and signal to cells while providing physical support . In the most common and aggressive form of pancreatic cancer , the stroma often makes up the majority of the tumor’s mass . Sometimes the stroma of these pancreatic tumors can protect the cancer cells from anti-cancer drugs . Researchers have therefore been interested in finding out exactly which aspects of the tumor stroma shield and support cancer cells , and which impede their growth and progression . Answering these questions could make it possible to develop new drugs that will change a tumor-supporting stroma into one that hinders the tumor’s growth and spread . The most abundant cells in the stroma of pancreatic tumors are called cancer-associated fibroblasts . Healthy specialized fibroblasts – known as pancreatic stellate cells – help to build and maintain the ‘normal’ extracellular matrix and so these cells normally restrict a tumor’s development . However , cancer cells can adapt healthy fibroblasts into cancer-associated fibroblasts , which produce an altered extracellular matrix that could allow the tumor to grow . Franco-Barraza et al . have now compared healthy and cancer-associated fibroblasts from patients’ pancreatic tumors . One of the main differences between these two cell types was the location of the activated form of a molecule called α5β1-integrin . Healthy fibroblasts , in a normal extracellular matrix , have active α5β1-integrin on the surface of the cell . However , a number of tumor-promoting signals , including some from the altered extracellular matrix , could force the active α5β1-integrins to relocate inside the fibroblasts instead . In further experiments , where the activated integrin was retained at the cell surface , the fibroblasts were able to resist the influence of the cancer-associated extracellular matrix . Then again , if the active α5β1-integrins were directed inside the cells , healthy cells turned into cancer-associated fibroblasts . With this information in hand , Franco-Barraza et al . examined tumor samples from over a hundred pancreatic cancer patients using a new microscopy-based technique that distinguishes cancer cells from stroma cells . The analysis confirmed the pattern observed in the laboratory: those patients who appeared to produce more normal extracellular matrix and have active α5β1-integrin localized mostly to the surface of the cells survived longer without the cancer returning than those patients who lacked these stroma traits . Samples from patients with kidney cancer also showed similar results and , as before , an altered extracellular matrix was linked to a worse outcome of the disease . Together these findings suggest that if future studies uncover ways to relocate or maintain active α5β1-integrin to the cell surface of fibroblasts they could lead to new treatments to restrict the growth of tumors in cancer patients . | [
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] | 2017 | Matrix-regulated integrin αvβ5 maintains α5β1-dependent desmoplastic traits prognostic of neoplastic recurrence |
Glycogen synthase kinase-3 ( GSK-3 ) is a key regulator of many cellular signaling pathways . Unlike most kinases , GSK-3 is controlled by inhibition rather than by specific activation . In the insulin and several other signaling pathways , phosphorylation of a serine present in a conserved sequence near the amino terminus of GSK-3 generates an auto-inhibitory peptide . In contrast , Wnt/β-catenin signal transduction requires phosphorylation of Ser/Pro rich sequences present in the Wnt co-receptors LRP5/6 , and these motifs inhibit GSK-3 activity . We present crystal structures of GSK-3 bound to its phosphorylated N-terminus and to two of the phosphorylated LRP6 motifs . A conserved loop unique to GSK-3 undergoes a dramatic conformational change that clamps the bound pseudo-substrate peptides , and reveals the mechanism of primed substrate recognition . The structures rationalize target sequence preferences and suggest avenues for the design of inhibitors selective for a subset of pathways regulated by GSK-3 .
Glycogen synthase kinase-3 ( GSK-3 ) is a highly conserved kinase ( Ali et al . , 2001 ) that in higher animals functions in insulin signaling , microtubule regulation , inflammatory pathways , and developmental programs including Hedgehog , Notch and Wnt signaling ( Jope and Johnson , 2004; Kaidanovich-Beilin and Woodgett , 2011 ) . GSK-3 has two isoforms in vertebrates , α and β , that share 97% identity in their catalytic cores but have different N- and C-terminal extensions . GSK-3 prefers pre-phosphorylated substrates , termed ‘primed’ substrates , with a phosphorylated serine or threonine residue at the position 4 residues C-terminal ( P+4 ) to the serine or threonine to be phosphorylated ( P+0 ) ( Fiol et al . , 1987 ) . Over 70 P+4 primed substrates for GSK-3 have been identified and confirmed as true biological GSK-3 targets ( Sutherland , 2011 ) . GSK-3 is a potential therapeutic target for diabetes and neurological disorders ( Amar et al . , 2011 ) , but its diversity of substrates complicates efforts to target a specific function . The overall structure of the GSK-3 catalytic domain is similar to other protein kinases , containing an N-terminal lobe composed primarily of β-strands in a β barrel conformation , and a C-terminal lobe composed mostly of α-helices ( Dajani et al . , 2001; ter Haar et al . , 2001 ) . At the interface of the N- and C- lobes , the C-helix and activation loop of GSK-3 form the putative substrate-binding site and help to position residues involved in the binding of ATP and substrate catalysis ( Dajani et al . , 2001; ter Haar et al . , 2001 ) . Crystal structures of GSK-3 obtained in the absence of nucleotide and/or peptide substrate reveal an ordered activation loop similar in conformation to that found in other kinases when the loop is phosphorylated . GSK-3 also shows no evidence for movements of the C-helix critical for positioning of catalytically important residues ( Jura et al . , 2011 ) . GSK-3 can be phosphorylated at a tyrosine ( Tyr216 ) on its activation loop , which increases enzymatic activity approximately fivefold , a modest increase compared to the activation of other kinases ( Dajani et al . , 2003 ) . GSK-3 differs from most kinases in that it is constitutively active and controlled by inhibition , rather than by specific activation conferred by phosphorylation of the activation loop . Phosphorylation of a serine present in a conserved sequence near the amino terminus of the protein ( Ser9 in GSK-3β ) is the major regulatory checkpoint in most pathways , including insulin signaling ( Kaidanovich-Beilin and Woodgett , 2011 ) . For example , in the absence of Ser9 phosphorylation , GSK-3 phosphorylates glycogen synthase and renders it less active , and inactivates eIF2B . Binding of insulin to its cell surface receptor results in the activation of the kinase AKT/PKB , which phosphorylates the conserved N-terminal peptide , resulting in increased glycogen and protein synthesis ( Cross et al . , 1995; Frame and Cohen , 2001 ) . The phosphorylated sequence , designated here as the pS9 peptide , is thought to auto-inhibit GSK-3 by acting as a pseudo-substrate that blocks binding of other substrates ( Dajani et al . , 2001; Frame et al . , 2001 ) . GSK-3 also plays a critical role in the Wnt/β-catenin pathway . Binding of a Wnt growth factor to its cell surface receptors Frizzled and LRP5/6 results in the stabilization of the transcriptional co-activator β-catenin and activation of target genes . In the absence of Wnt , β-catenin is found in a ‘destruction complex’ that includes GSK-3 , casein kinase 1 ( CK1 ) , the Adenomatous Polyposis Coli protein , and Axin . Axin has binding sites for β-catenin , GSK-3 and CK1 , and thereby scaffolds efficient phosphorylation of the N-terminus of β-catenin , which marks β-catenin for ubiquitylation and proteasomal degradation ( Stamos and Weis , 2013 ) . The association with Axin sequesters a fraction of cytosolic GSK-3 in the destruction complex , which likely prevents interactions with regulatory proteins not involved in Wnt signaling , including kinases that target Ser9 . Mutation of GSK-3β Ser9 does not affect Wnt signaling , and this residue does not become phosphorylated during Wnt stimulation ( Ding et al . , 2000; McManus et al . , 2005 ) . Although isoform-specific roles have been observed in other pathways ( e . g . , [McManus et al . , 2005; Patel et al . , 2008; Kaidanovich-Beilin et al . , 2009] ) , GSK-3α and β function redundantly in Wnt signaling ( Doble et al . , 2007 ) . Upon Wnt activation , Axin-bound GSK-3 translocates to the plasma membrane , where phosphorylated peptide repeat motifs in the cytoplasmic tail of the activated LRP5/6 receptor interact with the β-catenin destruction complex to inhibit its activity ( MacDonald and He , 2012 ) . The five repeats , designated a-e , have the consensus sequence P-P-P-S/T-P-X-S/T , where each of the S/T residues becomes phosphorylated in response to Wnt ( Niehrs and Shen , 2010 ) . Several kinases , including GSK-3 itself , may be responsible for phosphorylating the first Ser/Thr residue of the motif ( Zeng et al . , 2005; Chen et al . , 2009; Cervenka et al . , 2011 ) . Phosphorylation of this site forms a primed substrate recognition sequence for CK1 , which then phosphorylates the second Ser/Thr residue ( Davidson et al . , 2005 ) . Current models suggest that the phospho-LRP6 motifs can directly inhibit GSK-3 phosphorylation of β-catenin by engaging the kinase as a pseudo-substrate ( Piao et al . , 2008; Wu et al . , 2009; Kim et al . , 2013a ) . Although both phosphorylation sites in the consensus motif appear to be required for biological activity , only the first site is necessary for LRP6 to inhibit GSK-3 enzymatic activity ( Piao et al . , 2008 ) . Additionally , the five repeats function cooperatively in LRP6 , as removing any single motif from LRP6 results in significant decreases in Wnt signaling capability ( MacDonald et al . , 2008 ) . A major challenge has been to understand how GSK-3 is controlled such that its effects in one pathway are insulated from others . Some specificity is achieved through association with pathway-specific scaffolding proteins that bind GSK-3 and its substrate , such as the β-catenin destruction complex , which sequester GSK-3 from other pathways and lead to highly efficient phosphorylation . However , the inhibition by the LRP5/6 cytoplasmic domain demonstrates that other modes of inhibition can operate . We set out to assess the mechanistic similarities and differences between inhibition by N-terminal phosphorylation and inhibition mediated by the phosphorylated LRP5/6 motifs . There are only a handful of crystal structures of kinases bound to peptide substrates ( Madhusudan et al . , 1994; Hubbard , 1997; Lowe et al . , 1997; Brown et al . , 1999; Lippa et al . , 2008; Lopez-Ramos et al . , 2010; Soundararajan et al . , 2013 ) , and only one , a tyrosine kinase , with a primed substrate ( Davis et al . , 2009 ) . Here we show that the auto-inhibitory pS9 peptide sequence and the inhibitory LRP6 c- and e-motifs all bind to GSK-3 as pseudo-substrates . Unique to GSK-3 , binding is associated with a drastic conformational rearrangement of a highly conserved loop that engages the inhibitory peptides in a clamp-like structure . Peptide binding promotes a conformation of a glycine-rich loop that is associated with catalytic activity in other protein kinases . The LRP6 peptide complexes support a model in which phosphorylated receptor acts to inhibit GSK-3 directly and thereby promote β-catenin stabilization . The interactions between GSK-3 and the different inhibitory peptides help to explain substrate sequence preferences . The remodeling of the enzyme around peptide substrates suggests novel avenues for the development of GSK-3 inhibitors specific to particular pathways .
Inhibition constants of approximately 700 µM and 13 µM have been reported for the pS9 auto-inhibitory peptide and LRP6 a-motif peptide ( NPPPpSPApTERSH ) , respectively , but these were measured with different substrates and with different assays ( Dajani et al . , 2001; Piao et al . , 2008 ) . To compare the inhibitory activity of these two peptides directly in the same experimental system , we used a primed peptide substrate from eIF2B ( peIF2B ) that contains a single phosphorylation site , to avoid complications arising from processive phosphorylation of multiple sites present in many GSK-3 substrates , including glycogen synthase and β-catenin . We also used a longer GSK-3β pS9 peptide than used in Dajani et al . ( 2001 ) , residues 3–15 ( GRPRTTpSFAESCK ) : Glu12 is conserved , and we wanted to examine its potential contribution to inhibition in the context of the larger peptide . The doubly-phosphorylated LRP6 a-motif peptide inhibits GSK-3 with a Ki = 1 . 4 ± 0 . 2 μM , 40x better than the pS9 peptide ( 60 ± 11 μM; Figure 1A , B; Table 1 ) . The 12-fold decrease in Ki measured for the pS9 peptide compared to previously reported values might be due to the use of different substrate peptides , but might also be the result of using the longer pS9 peptide . The 40-fold stronger Ki for the LRP6 peptide compared to the pS9 peptide in the same experimental system supports the conclusion that the LRP5/6 motifs can act as direct GSK-3 inhibitors ( Piao et al . , 2008 ) . 10 . 7554/eLife . 01998 . 003Figure 1 . Inhibitory activities of pS9 and LRP6 a-motif peptides . ( A and B ) 32P incorporation into the peIF2b substrate over time for GSK-3β 26-383 , inhibited by the S9 peptide ( Ki = 60 ± 11 μM ) ( A ) or the LRP6 a-motif peptide ( Ki = 1 . 4 ± 0 . 2 μM ) . The number of replicates is either two or four for each inhibitor concentration , except for the 15 µM LrpA peptide timecourse , where there is only one measurement . ( C ) Determination of steady-state kinetic constants for GSK-3β 26-383 , GSK-3β 26-383F93A , GSK-3β 26-383F93G , GSK-3β 1-383 , and GSK-3β 1-383pS9 . Error bars represent the standard error of the mean . The number of replicates for each concentration is between 2 and 4 for all the concentration points , except for the 1 µM point of GSK-3 26-383 , the 80 µM point of GSK-3β 26-383F93A , and the 50 and 80 µM points of GSK-3β 26-383F93G , which have only one measurement . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 00310 . 7554/eLife . 01998 . 004Table 1 . Kinetic parameters for GSK-3β variantsDOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 004KM ( µM ) kcat ( s−1 , × 10−2 ) kcat/kM ( M−1•s−1 , × 103 ) GSK-3β 26-3838 . 5 ± 3 . 87 . 9 ± 1 . 29 . 3 ± 4 . 4GSK-3β 26-383F93A39 . 1 ± 13 . 85 . 1 ± 0 . 61 . 3 ± 0 . 5GSK-3β 26-383F93G21 . 3 ± 14 . 22 . 3 ± 0 . 41 . 1 ± 0 . 8GSK-3β 1-38327 . 8 ± 15 . 715 . 6 ± 3 . 65 . 6 ± 3 . 4GSK-3β 1-383pS921 . 7 ± 9 . 02 . 7 ± 0 . 41 . 2 ± 0 . 5 We determined crystal structures of GSK-3β residues 1-383 phosphorylated at the inhibitory pS9 residue by AKT at 2 . 1 Å resolution , as well as GSK-3β bound to phosphorylated LRP6 c- and e-motif inhibitory peptides at 2 . 3 Å resolution ( Table 2 ) . The LRP6 complexes were produced by replacing the first 12 residues of GSK-3β with the 8-residue c- or e-motif from LRP6 . The LRP6 peptides in these chimeric proteins were phosphorylated by the chimeric GSK-3 and by CK1 added to the purified protein . Phosphorylation in dilute solution proved to be very inefficient , so phosphorylation was accomplished during crystallization by adding the appropriate kinase to the wild-type or chimeric GSK-3β and dialyzing against high-molecular weight polyethylene glycol ( ‘Materials and methods’ ) . We confirmed that GSK-3β 1-383 that was partially phosphorylated in this manner by AKT was significantly less active than the non-phosphorylated enzyme ( Figure 1C; Table 1 ) . 10 . 7554/eLife . 01998 . 005Table 2 . X-ray crystallography data collection and refinement statisticsDOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 005Inhibitor-freepS9pS9-AlF3LRP6 c-motifLRP6 e-motifPDB code4NM04NM34NU14NM54NM7Data collection* Space groupP6122P6122P6122P6122P6122 Unit cell lengths a , c ( Å ) 81 . 3 , 280 . 881 . 0 , 281 . 181 . 0 , 280 . 581 . 7 , 280 . 982 . 0 , 280 . 3 BeamlineSSRL 11-1SSRL 11-1APS 23-ID-BSSRL 12-2SSRL 12-2 Wavelength ( Å ) 1 . 0331 . 0331 . 0331 . 001 . 00 Resolution range ( Å ) 39 . 1–2 . 5039 . 0–2 . 1046 . 8–2 . 5039 . 2–2 . 3039 . 4–2 . 30 ( last shell ) ( 2 . 60–2 . 50 ) ( 2 . 16–2 . 10 ) ( 2 . 60–2 . 50 ) ( 2 . 38–2 . 30 ) ( 2 . 38–2 . 30 ) Unique reflections20 , 06933 , 09319 , 92225 , 68525 , 932 CC1/2†0 . 999 ( 0 . 726 ) 1 . 00 ( 0 . 673 ) 0 . 999 ( 0 . 814 ) 0 . 999 ( 0 . 459 ) 1 . 00 ( 0 . 485 ) Rmerge‡0 . 106 ( 1 . 74 ) 0 . 100 ( 4 . 88 ) 0 . 303 ( 6 . 772 ) 0 . 141 ( 3 . 63 ) 0 . 136 ( 4 . 31 ) <I>/<σI>18 . 4 ( 1 . 2 ) 24 . 5 ( 0 . 8 ) 13 . 0 ( 0 . 7 ) 18 . 9 ( 1 . 0 ) 17 . 8 ( 0 . 9 ) Completeness ( % ) 100 ( 99 . 9 ) 99 . 9 ( 99 . 7 ) 100 ( 100 ) 99 . 7 ( 99 . 6 ) 100 ( 100 ) Multiplicity9 . 6 ( 9 . 9 ) 18 . 6 ( 17 . 9 ) 21 . 0 ( 21 . 6 ) 18 . 9 ( 18 . 1 ) 18 . 7 ( 18 . 2 ) Refinement No . reflections work/test set19 , 958/97932 , 924/165819 , 823/97425 , 608/128325 , 838/1292 Rwork/Rfree§0 . 191/0 . 2400 . 194/0 . 2420 . 191/0 . 2450 . 183/0 . 2320 . 183/0 . 234 Number of atoms GSK-327802885286228502858 Axin165153149155155 LrpC/E peptide–––4444 ADP2727272727 Mg2+22222 Cl−11–11 Glycerol3024122418 DTT88––8 AlF3––4–– NO3−––4–– Water16419798121106 B-factors ( Å2 ) GSK-352 . 655 . 075 . 169 . 769 . 7 Axin59 . 554 . 078 . 175 . 277 . 8 LrpC/E peptide–––101100 ADP65 . 350 . 357 . 572 . 256 . 5 Mg2+88 . 553 . 857 . 686 . 469 . 6 Cl−77 . 171 . 1–90 . 996 . 8 Glycerol65 . 279 . 493 . 194 . 697 . 2 DTT99 . 3102––131 AlF3––83 . 6–– NO3−––100 . 8–– Water46 . 452 . 361 . 164 . 364 . 6 Rmsd Bond lengths ( Å ) 0 . 0030 . 0050 . 0030 . 0020 . 004 Bond angles ( ° ) 0 . 630 . 910 . 630 . 630 . 76 Ramachandran plot ( % ) ¶ Favored regions96 . 596 . 996 . 195 . 695 . 8 Additional allowed regions3 . 53 . 13 . 93 . 94 . 2 Outliers000 . 50 . 50*Values in parentheses are for highest-resolution shell . Rmsd , root mean square deviation . †As defined in Aimless ( Evans and Murshudov , 2013 ) . ‡Rmerge = ΣhΣI|II ( h ) –< I ( h ) > |/ΣhΣI ( h ) , where II ( h ) is the Ith measurement of reflection h , and < I ( h ) > is the weighted mean of all measurements of h . §R = Σh|Fobs ( h ) –Fcalc ( h ) |/Σh|Fobs ( h ) | . Rwork and Rfree were calculated using the working and test reflection sets , respectively . ¶As defined in MolProbity ( Chen et al . , 2010 ) . All of the structures were obtained as complexes with the GSK-3 binding segment of Axin , which was used to promote solubility during protein expression in Escherichia coli and to avoid crystal packing interactions in Axin-free GSK-3 structures that likely block access to the substrate-binding pocket ( Dajani et al . , 2001; ter Haar et al . , 2001 ) . For direct comparison , we re-determined the crystal structure of the peptide inhibitor-free structure of the GSK-3 ( residues 1–383 ) /Axin complex using the same crystallization protocol for the inhibited complexes , at 2 . 5 Å resolution ( Table 2 ) . The overall structure of the GSK-3/Axin complexes is similar to those reported previously ( Dajani et al . , 2003; Tahtouh et al . , 2012 ) ( Figure 2A ) . In each structure , a molecule of ADP is sandwiched between the N- and C-terminal lobes . ATP was added to the protein preparation prior to crystallization , but may have hydrolyzed during crystallization . 10 . 7554/eLife . 01998 . 006Figure 2 . Inhibitory peptide binding to GSK-3 . ( A ) Overall structure of GSK-3 bound to inhibitory peptides . The superimposed LRP6 c-motif ( pink sticks ) , e-motif ( light green sticks ) and pS9 auto-inhibitory N-terminal peptide ( light blue sticks ) bind to the same substrate-binding pocket between the C-loop ( yellow ) and activation loop ( red ) . A molecule of ADP binds to the deep cleft located between the N-terminal ( white ) and C-terminal ( grey ) lobes , and the Axin helix ( purple ) binds at the C-lobe . The glycine-rich loop ( cyan ) and αC-helix are also indicated . The inset shows the protein sequences of the peptide residues that are visible in the structures . The P+4 phosphorylated residues are indicated in orange . The loop between the N-terminal peptides and the first β strand of the N-terminal lobe is partially disordered ( dotted line ) . Oxygen atoms are shown in red , nitrogen in blue , phosphorus in orange , and sulfur in yellow . ( B ) Surface representation of the substrate-binding pocket between the C-loop ( yellow ) and activation loop ( red ) of GSK-3 . The inhibitory peptides , pS9 auto-inhibitory N-terminal peptide ( light blue sticks ) , LRP6 c-motif ( pink sticks ) and e-motif ( light green sticks ) are superimposed , and the residues of the peptides are labeled according to the primed substrate numbering , with the phospho-serine or threonine at the P+4 position . Side chains of GSK-3 residues F93 , Y216 and I217 , which interact with the peptides , are also depicted as sticks . ( C ) Peptide inhibitor-free structure near the C-loop and activation loop . A molecule of glycerol is bound to three basic residues that interact with the phosphate at the substrate P+4 site . Hydrogen bonds are shown as dashed lines . ( D–F ) Interactions between GSK-3 and inhibitory peptides . The structural water molecules that interact between the carbonyl groups of Y216 and the P+1 proline residues of LRP6 c-motif and e-motif peptides are depicted as red spheres . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 006 Both the pS9 auto-inhibitory N-terminal peptide and the phosphorylated LRP6 motifs occupy the primed substrate binding pocket predicted from the presence of phosphate or sulfonate in earlier peptide-free structures ( Dajani et al . , 2001; Frame et al . , 2001; ter Haar et al . , 2001; Figure 2B–E ) . Much of the N-terminus is disordered: in the pS9 N-terminal peptide complex , residues 6RTTpSF are visible , but only the backbone of Arg6 is visible . In the LRP6 inhibitory peptide complexes , residues 1569PPPpTPR of the c-motif or 1604PPPpSPC of the e-motif are visible; the second phosphorylation site in these peptides is disordered and we were not able to ascertain whether it is phosphorylated in the crystallized protein . The pSer/pThr in the primed P+4 position of all three inhibitors binds to the site predicted from peptide-free structures that contain phosphate or sulfonate groups in this region ( Dajani et al . , 2001; ter Haar et al . , 2001 ) . Arg96 , Arg180 and Lys205 form hydrogen bonds with the phosphate group ( Figure 2C–E ) . In the inhibitor-free structure ( Figure 2C ) , these basic residues form hydrogen bonds with a molecule of glycerol present in the phosphate-binding site , and a previous structure of GSK-3 bound to a non-hydrolyzable ATP analog AMP-PNP shows that water molecules occupy this site ( PDB 1PYX; Bertrand et al . , 2003 ) . These observations suggest that the primed P+4 phosphate-binding site is essentially pre-formed in the enzyme . As in other kinases , the activation loop region ( residues 200–226 ) forms part of the binding site for substrate peptides . In addition to the basic side chains noted above , the backbone amide of Val214 also forms a hydrogen bond with the phosphate in the priming site . To accommodate the backbone of the inhibitory peptides , the side chain of Val214 adopts a rotamer that avoids steric clash with the phosphate . Residues in the P+1 , P+2 , and P+3 positions ( Arg-Thr-Thr in the pS9 peptide , or Pro-Pro-Pro in the LRP6 c- and e-motif peptides ) form an arch over the conserved Ile217 , and the P+1 residue packs against Tyr216 ( discussed in more detail below ) . In addition to these interactions , the LRP6 peptide complexes also reveal a water-mediated connection between the backbone carbonyl oxygens of the P+2 proline and Tyr216 . Comparison of the inhibited and peptide substrate-free GSK-3 structures shows that the loop immediately preceding the C-helix , designated here as the C-loop , undergoes a large movement toward the C-terminal lobe of the kinase and clamps down on top of the bound pS9 or LRP6 peptides ( Figure 3A ) . The α-carbon of residue Phe93 , which lies at the tip of the loop , moves 8 . 5 Å relative to substrate-free structures ( Figure 3A ) . In peptide substrate-free GSK-3 crystal structures , many of the side chains in the C-loop are disordered , and the loop has relatively high temperature factors , indicating conformational flexibility . It should be noted that crystals of GSK-3 grown in the absence of Axin have lattice contacts that would not allow the C-loop to adopt the conformation observed here ( e . g . , Dajani et al . , 2001; ter Haar et al . , 2001 ) . 10 . 7554/eLife . 01998 . 007Figure 3 . Conformational changes in GSK-3 upon inhibitor peptide binding . ( A ) Superposition of α-carbon traces of GSK-3 structures . The maximum deviation between the structures is at the C-loop region , which is color coded according to the key at the bottom of the panel . The dashed line indicates the maximum distance ( 8 . 5 Å ) between the α-carbon of residue Phe93 seen in peptide substrate-bound and peptide-free structures . The activation loops ( red ) align very well , indicating that there is no conformational change at the activation loop upon peptide substrate binding . ( B ) Changes of the C-loop upon peptide substrate binding . Superposition of the peptide substrate-free GSK-3 ( inhibitor-free; Table 2 ) ( orange ) and the GSK-3/pS9 peptide ( top panel ) , LRP6 c-motif ( middle panel ) , or LRP6 e-motif ( bottom panel ) complexes . Hydrogen bonds between the backbones of the P+4 and P+5 residues of the peptides and the backbone of the C-loop from R92 to K94 are shown as dashed lines; only the main-chain atoms of R92 and K94 are depicted . The change in the C-loop upon peptide binding alters the positions of the preceding β strands ( broad arrows ) . R96 adopts a conformation to accommodate the P+4 substrate phosphate . ( C ) Superposition of GSK-3 and active PKA structures . The catalytic residues , including K85 , E97 and the DFG motif , of the peptide-free GSK-3 ( orange ) , peptide-free GSK-3 with phosphorylated Y216 ( purple ) or peptide-bound GSK-3 ( light blue ) adopt similar conformations as those of the active PKA structures ( gold/brown ) . The double-headed arrow indicates the shorter length of the αC-helix of GSK-3 relative to that of PKA . The hydrogen bond between phosphorylated T197 ( pT197 ) and H87 is shown as dashed line; only H87 of the PKA/peptide complex is depicted for clarity . pT197 of PKA places a phosphate group in the space equivalent to that occupied by the substrate phosphate in GSK-3 . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 007 In all three inhibitory peptide complexes , Phe93 is sandwiched in between the P+2 and P+5 residues of the peptide ( Figures 2B and 3B ) . Hydrogen bonds form between the backbones of the P+4 and P+5 residues and the backbone of the C-loop from residues 92–94 ( Figure 3B ) . Asp90 also participates in stabilizing the alternate C-loop conformation by hydrogen bonding to the backbone amide of Arg92 . The movement of the C-loop toward the peptide is associated with a small ‘closing’ movement ( rmsd 0 . 5 Å , 0 . 6° rotation , not including the C-loop or glycine-rich loop ) of the N-terminal lobe toward the C-terminal lobe relative to the non-substrate bound structures of GSK-3 ( Figure 3A ) . Directly adjacent to the C-loop , Leu88 is pulled toward the inhibitory peptide , and Tyr127 moves downward and sits on top of Leu88 ( Figure 3B ) . These movements are transmitted along the corresponding β strands to which these residues attach , which also shift slightly ( Figure 3B ) . This shift of the β-sheet includes the glycine-rich loop that sits above the nucleotide . In crystal structures of GSK-3 lacking an inhibitory peptide , the glycine-rich loop of the N-terminal lobe , comprising residues 62–70 , adopts a position similar to that observed in the crystal structure of PKA bound to AMP ( Narayana et al . , 1997; Figure 4A , B ) In contrast , in both the pS9 peptide and LRP6 e-motif complexes we observe an additional , partially occupied conformation of the glycine-rich loop in which the loop has moved toward the ADP molecule and away from the rest of the N-terminal lobe , with the backbone amide of Ser66 moving by 4 Å compared to the peptide-free structure ( Figure 4A , C ) . This lower conformation could not be modeled in the LRP6 c-motif complex , although the refined Fo-Fc electron density suggests that it may be present at very low occupancy . In the pS9 complex , both Ser66 and Phe67 now adopt positions nearly identical to those of Ser53 and Phe54 of PKA bound to the transition state analog ADP+AlF3 and a peptide ( Madhusudan et al . , 2002; Figure 4B ) . Phe67 packs against the threonine at the P+2 residue of the pS9 peptide , and although Ser66 and Phe67 are poorly ordered in the LRP6 c- and e-motif complexes , modeling them in the same position as that observed in the pS9 complex shows that Phe67 can pack against the P+2 proline of the LRP6 peptides . Mutation of GSK-3β Phe67 to alanine severely impairs catalytic activity ( Ilouz et al . , 2006 and MDE , data not shown ) , suggesting that the packing of this residue against the substrate peptide is important for stabilizing a catalytically active conformation . 10 . 7554/eLife . 01998 . 008Figure 4 . Conformational change of the glycine-rich loop upon inhibitor peptide binding . ( A ) Left panel: superposition of α-carbon traces of five selected GSK-3 structures . The dashed line indicates the maximum deviation at the glycine-rich loops among the currently solved GSK-3 structures ( 4 Å at the α-carbon of residue S66 between the peptide-free ( orange/purple ) and the peptide-bound ( light blue ) GSK-3 ) . Some GSK-3 structures show an intermediate position of the glycine-rich loop ( green/pink ) due to interaction with another GSK-3 molecule in the crystal lattice ( not shown in the figure for clarity , please refer to PDB 1H8F/1PYX ) . The inset shows the close-up of the glycine-rich loops of the five selected GSK-3 structures . Upon peptide binding , F67 packs against the T7 or P1570/P1605 at the P+2 residue of the pS9 or LRP6 peptides . Right panel: superposition of α-carbon traces of three selected PKA structures . The glycine-rich loop of the nucleotide-free PKA ( light purple ) is at the ‘highest’ position , due to the open conformation of the inactive kinase structure . In the nucleotide-bound state ( gold ) , the glycine-rich loop moves toward the C-terminal lobe , and it moves further down by 4 Å upon binding to a peptide in its substrate-binding site ( brown ) . The inset shows the close-up of the glycine-rich loops of the three selected PKA structures . The equivalent residues of GSK-3 S66 and F67 , PKA S53 and F54 , are depicted as sticks . The ADP and AlF3 molecules are also shown . ( B ) Close-up comparisons of the glycine-rich loops in the nucleotide-bound state ( upper panel ) and nucleotide/peptide-bound transition state ( lower panel ) of GSK-3 and PKA . ( C ) Comparisons of the electron density ( gray mesh , 2Fo-Fc map contoured at 0 . 8 σ ) of the glycine-rich loops in the ADP-bound state ( upper panel ) and ADP–AlF3 transition state ( lower panel ) . The upper and lower conformations of the glycine-rich loops are indicated . GSK-3 S66 and F67 are depicted as sticks , and the ADP and AlF3 molecules are also shown . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 008 To confirm that the lower glycine-rich loop conformation corresponds to a catalytically competent conformation , we soaked crystals of the pS9 complex in AlF3 . The trigonal planar AlF3 molecule is coordinated by an ADP β-phosphate oxygen , as predicted for AlF3 mimicking the transition state of an in-line attack on the Ser/Thr residue of the substrate ( Madhusudan et al . , 2002 ) , and the lower conformation of the glycine-rich loop is well defined ( Figure 4C ) . Despite the slightly lower resolution of this structure ( Table 2 ) , the glycine-rich loop adopts the same conformation as that in the pS9 and LRP6 e-motif complexes , indicating that the presence of ADP and peptide inhibitor stabilize this conformation . The structural and mutational data strongly suggest that the inhibitory peptides bind as true pseudo-substrates and stabilize a catalytically competent conformation . The partial occupancy of the lower glycine-rich loop conformation , along with the disorder of residues 65–67 in the LRP6 complexes , is consistent with NMR studies of PKA , which show that the glycine-rich loop is relatively ordered in the upper conformation in the absence of peptide substrate , whereas it becomes more dynamic when the peptide is bound ( Masterson et al . , 2010 , 2011 ) . In the structures of GSK-3 bound to both ADP and pseudo-substrate peptides , the formation of the lower glycine-rich loop conformation occurs in the absence of a γ-phosphate on ATP or a transition-state mimic , but appears to be further stabilized by AlF3 ( Figure 4B , C ) . The interaction between Phe67 and the P+2 residue of a substrate , and the movement of the β strand adjacent to the glycine-rich loop that occurs when the C-loop engages the substrate peptide , may favor the lower conformation of the glycine-rich loop seen here . For example , Phe67 is found in several positions in peptide inhibitor-free GSK-3 structures , and some of them ( e . g . , PDB 1PYX ) would clash with the C-loop in the peptide-bound structures ( Figure 4A ) . We conclude that binding of the pseudo-substrate itself favors the lower glycine-rich loop conformation in GSK-3 , which strongly suggests that true substrates also favor this loop conformation and thereby promote catalysis ( summarized in Video 1 ) . Unlike PKA , however , substrate binding requires a significant conformational rearrangement of the C-loop in the N-terminal lobe . 10 . 7554/eLife . 01998 . 009Video 1 . Conformational changes in GSK-3 associated with peptide substrate binding . The video shows the ADP and Axin-bound enzyme changing between the inhibitor-free and pS9-bound states . Key structural elements are colored as in Figure 2A . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 009 The phosphorylated residue of the inhibitory peptides binds to the predicted P+4 binding pocket of primed GSK-3 substrates ( Dajani et al . , 2001; Frame et al . , 2001; ter Haar et al . , 2001 ) , and the position of the phosphate corresponds closely to that of phosphorylated Ser197 in the activation loop of PKA ( Figure 3C ) . However , we do not observe the P+0 residue in any of our structures , which leaves open the possibility that the observed binding mode is distinct from that of true substrates . We compared the orientation of the observed residues by superposition of several known Ser/Thr kinase:peptide substrate complex structures in which the P+0 residue is observed . The P+1 residue in these complexes , as well as that of the PKA inhibitor PKI , aligns very well with those of the GSK-3 inhibitory peptides , and is oriented similarly such that the P+0 residue would be correctly positioned to accept a phosphate from ATP ( Figure 5 ) . Combined with the effect of the conformation of the glycine-rich loop , it appears that the observed binding mode of the inhibitory peptides corresponds closely to the binding mode of true GSK-3 substrates . 10 . 7554/eLife . 01998 . 010Figure 5 . Comparison of GSK-3 inhibitory peptide orientation with other kinase: peptide complexes . Superposition of the GSK-3/LRP6 c-motif structure with the ( A ) PKA:PKI peptide ( PDB 1L3R ) ; ( B ) CDK2:p107 peptide ( PDB 1QMZ ) ; ( C ) AKT:GSK-3 peptide ( PDB 3CQU ) ; and ( D ) DYRK:consensus peptide ( PDB 2WO6 ) substrate complexes . The β-phosphate of the ADP molecule in the catalytic site of GSK-3 is shown at the top of each panel , with phosphorus in orange and oxygen in red . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 010 Approximately 50% of the total buried surface area of each inhibitory peptide is involved in interactions with the C-loop , suggesting that the C-loop is a key element of target recognition specificity . In addition to the main-chain interactions observed between GSK-3 and the inhibitory peptides , GSK-3β Phe93 packs against residues at the P+2 and P+5 positions of the inhibitor peptides . Given that the inhibitor peptides appear to bind as pseudo-substrates and stabilize an active conformation of GSK-3 , we tested the importance of Phe93 for GSK-3 activity by determining steady-state kinetic parameters for wild-type GSK-3β and the F93A and F93G mutants using peIF2b as the substrate ( Table 1; Figure 1C ) . The mutants show 7–9-fold reduction in catalytic efficiency ( kcat/KM ) , with significant increases in KM that suggest a loss of affinity for the peptide substrate . These data indicate that the interaction with Phe93 contributes strongly to substrate binding , and the reductions in kcat are consistent with the notion that peptide binding is required for formation of a catalytically competent enzyme . Unlike most loops in the N-terminal lobe of GSK-3 , the C-loop has been highly conserved throughout evolution ( Figure 6 ) . The position equivalent to Phe93 of mouse GSK-3β is strongly conserved; the only other residue found at this position is tyrosine , which would be able to form the same interactions with the P+2 and P+5 residues of the substrate peptide . A bulkier residue such as tryptophan at this position would clash with the substrate peptide backbone , and the decreased activity of the F93A mutant suggests that smaller residues would not extend far enough to interact with the substrate . In the absence of pseudo-substrate inhibitors , Phe93 does not interact with other parts of the kinase and is present on a solvent exposed , flexible loop . Thus , the conservation of Phe or Tyr residue at this position must be attributed to its role in substrate binding and concomitant formation of an active kinase conformation . 10 . 7554/eLife . 01998 . 011Figure 6 . GSK-3 sequence alignments near the C-loop . White letters on black background indicates identical residues , and bold letters denote similar residues . Secondary structure elements are shown above the alignment . The C-loop is marked in the red box , with Phe93 indicated by the asterisk . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 011 The interactions of the P+2 site with Phe67 and Phe93 , and the packing of the P+5 residue against Phe93 , are consistent with the sequence preferences of these positions seen in experimentally confirmed GSK-3 substrates ( Sutherland , 2011; Table 3; Figure 7A ) . The P+2 position has 43% hydrophobic and 79% uncharged residues , consistent with a relatively hydrophobic environment created by the two phenylalanines . The P+5 position has 63% hydrophobic and 81% uncharged residues , with a strong preference for proline , which makes extensive interactions with Phe93 in the LRP6 peptide complexes . Interestingly , only 5 of the 75 targets in Table 3 contain charged residues at both the P+2 and P+5 positions . Also , tryptophan is completely excluded at these positions , and phenylalanine and tyrosine only appear four times total out of the 75 sequences listed . We speculate that large aromatic residues might interact too strongly with Phe93 and thereby reduce turnover of the enzyme . Alternatively , at least in the P+2 position , large aromatic residues may sterically clash with Phe93 or the nearby Tyr216 or Phe67 residues . 10 . 7554/eLife . 01998 . 012Table 3 . Confirmed GSK-3 biological targets ( adapted from Sutherland , 2011 ) DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 012ProteinP+0 residueP+0 to P+5 sequenceUniprotKB #APC1501SCSSSL ( P25054 ) ATP-citrate lyase447TPAPSR ( P53396 ) 451SRTASFAxin317SANDSE ( O15169 ) 321SEQQSLBCL-3398SPSSSP ( P20749 ) 402SPSQSPβ-catenin33SGIHSG ( P35222 ) 37SGATTT41TTAPSLC/EBPα226TPPPTP ( P49715 ) 230TPVPSPC/EBPβ223SLSTSS ( P17676 ) 227SSSSSP231SPPGTPCi155 ( Gli3 ) 861SRRSSG ( P10071 ) 873SRRSSE903SRRSSECLASP2533SRESSR ( O71522 ) 537SRDTSPCRMP2514TPASSA ( Q16555 ) 518SAKTSPCRMP4514TPAGSA ( Q14195 ) 518SARGSPCREB129SRRPSY ( P16220 ) CRY2554SGPASP ( Q49AN0 ) Cytidine triphosphate synthetase571SGSSSP ( P17812 ) Dynamin I776TSSPTP ( Q05193 ) eIF2B540SRGGSP ( Q13144 ) FAK722SPRSSE ( Q05397 ) Glycogen Synthase641SVPPSP ( P13807 ) 645SPSLSR649SRHSSP653SPHQSEHeat shock factor 1303SPPQSP ( Q00613 ) HIF1α551STQDTD ( Q16665 ) hnRNP D83SPRHSE ( Q14103 ) IRS1337SRPASV ( P35568 ) c-jun239TPPLSP ( P05412 ) MAP1B1396SPLRSP ( P46821 ) Mcl1159SLPSTP ( Q07820 ) Mdm2242SDQFSV ( Q00987 ) 256SEDYSLMLK3789SPLPSP ( Q16584 ) c-myc58TPPLSP ( P01106 ) Myocardin451STSSSP ( Q8IZQ8 ) 455SPPISP459SPASSD626STFLSP630SPQCSP634SPQHSPNDRG1342SRSHTS ( Q92597 ) p130Rb948SHQNSP ( Q08999 ) 962SRDSSP982SAPPTPp5333SPLPSQ ( P04637 ) PITK1007SKTVSF ( O15084 ) Polycystin-276SPPLSS ( Q13563 ) Presenilin-1397SATASG ( P49768 ) 353STPESRPP1 G-subunit38SPQPSR42SRRGSEPTEN362STSVTP ( P60484 ) 366TPVDSDSnail96SGKGSQ ( O95863 ) 100SQPPSPSREBP1a426TPPPSD ( P36956 ) 430SDAGSPTau525SRSRTP ( P10693 ) 548TPPKSP713SPVVSG717SGDTSPTSC21379SQPLSK ( P49815 ) 1383SKSSSSVDAC51TTKVTG ( P21796 ) von Hippel-Lindau68SREPSQ ( P40337 ) Numbers listed are residue numbers of the P+0 residue of the human proteins , with sequences representing the P+0 through P+5 residues for each target sequence . UniprotKB accession numbers are listed to the right . 10 . 7554/eLife . 01998 . 013Figure 7 . GSK-3 substrate specificity . ( A ) Amino acid frequency logo diagram for each of the positions P+0 through P+5 for the 75 GSK-3 substrates listed in Table 3 . The diagram was created using WebLogo ( Crooks et al . , 2004 ) . ( B ) Conformational change of Y216 upon peptide binding . In the absence of peptide and Y216 phosphorylation , the ring of Y216 packs against V214 . In the presence of peptide , Y216 points outward , away from the peptide , and adopts an identical conformation to that seen when it is phosphorylated ( purple , GSK-3/Axin/ADZ structure; PDB 1O9U [Dajani et al . , 2003] ) . The ring of Y216 packs against the P+1 residue in each of the peptide complexes . ( C ) Model of basic P+1 residue interaction with phosphorylated Y216 . The phosphate group of phosphorylated Y216 could interact with the positively charged moiety of arginine at P+1 of the peptide substrate . As seen in PDB 1O9U , Arg220 and Arg223 would also chelate the phosphate group . Also shown are peptide-binding residues on the glycine-rich loop ( cyan ) , C-loop ( yellow ) and activation loop ( red ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01998 . 013 The GSK-3 prepared for crystallography was purified from E . coli , and we do not see evidence of phosphorylation of Tyr216 by mass spectrometry of the purified protein ( data not shown ) or in the electron density maps . Although partial phosphorylation of this residue has been observed previously in material produced in E . coli ( Wang et al . , 1994 ) , it may be that the presence of the bulky GST fused to the N-terminus of the Axin peptide , which is located near the GSK-3 active site , precludes GSK-3 from carrying out phosphorylation during expression by sterically blocking the GSK-3 active site . Crystal structures of GSK-3 containing non-phosphorylated Tyr216 show that the tyrosine side chain points toward the N-terminal lobe , with the tyrosine ring packing against Val214 ( Figure 7B; Dajani et al . , 2001; ter Haar et al . , 2001 ) . The presence of the pS9 or LRP6 peptides , however , precludes this positioning for Tyr216 . Instead , it points outward , away from the peptide , and adopts an identical conformation to that seen when it is phosphorylated , where it interacts with several basic residues ( Bax et al . , 2001; Dajani et al . , 2003; Figure 7B ) . In the inhibitory peptide complexes , the ring of Tyr216 packs against the P+1 residue . These observations are consistent with the modest increase in GSK-3 activity when Tyr216 is phosphorylated: in order to accommodate binding of the substrate peptide , Tyr216 must move away from its unphosphorylated position and break its interaction with Val214 . Having phosphorylated Tyr216 pre-positioned for substrate binding would not require the extra energy needed for this rearrangement . Alignment of known GSK-3 primed substrates demonstrates some preference for small , uncharged residues , especially proline , at the P+1 position ( Table 3 , Figure 7A ) , consistent with the non-polar interactions observed with the ring of Tyr216 . However , arginine is found in this position in 19% of the substrates , including the inhibitory pS9 peptide , where its β carbon packs against Tyr216 . When GSK-3 is phosphorylated at Tyr216 , the charged phosphate group on pTyr216 would be located near the distal end of the P+1 residue . Modeling shows that the phosphate group could interact with the positively charged moiety of arginine at P+1 ( Figure 7C ) . These observations suggest that GSK-3 substrate specificity may be controlled in part through Tyr216 phosphorylation status , with substrates containing smaller or hydrophobic residues at P+1 preferred when Tyr216 is not phosphorylated and basic residues preferred when phosphorylated . If so , potential tyrosine kinases mediating Tyr216 phosphorylation or factors that promote autophosphorylation of Tyr216 may favor phosphorylation of particular substrates and thereby provide an additional level of regulation of GSK-3 function ( Lochhead et al . , 2006; Kaidanovich-Beilin and Woodgett , 2011 ) .
The data presented here reveal the mode of binding of GSK-3 pseudo-substrate inhibitors , which imply that the catalytically active conformation can form only in the presence of a peptide substrate . Although the C-loop is often poorly ordered in non-peptide-bound structures , it interacts directly with the inhibitory peptides , and the loss of activity resulting from mutation of Phe93 demonstrates the critical nature of this interaction . The C-loop interactions , coupled with changes in the position of the glycine-rich loop , create an arrangement of catalytic residues comparable to those observed in PKA bound to the transition state mimic ADP-AlF3 and a peptide substrate ( Madhusudan et al . , 2002 ) . These observations , as well as comparison to structures of other kinases bound to substrates , indicate that the inhibitory peptides act as true pseudo-substrates that mimic binding to substrate , rather than stabilizing a distinct catalytically inactive conformation of GSK-3 . Phosphorylation of the conserved LRP5/6 cytoplasmic repeat motifs is essential for Wnt signal transduction and is in particular associated with inhibition of GSK-3 activity and subsequent stabilization of the transcriptional co-activator β-catenin ( MacDonald and He , 2012 ) . Several studies have shown that phosphorylated LRP5/6 can directly inhibit GSK-3 ( Piao et al . , 2008; Wu et al . , 2009 ) . This suggests a model in which recruitment of the destruction complex to the Wnt-bound receptors occurs when activated Dishevelled binds to Axin through their respective DIX domains . GSK-3 bound to Axin would phosphorylate LRP5/6 and generate its own inhibitor , thereby selectively inhibiting the pool of GSK-3 that phosphorylates β-catenin without affecting other GSK-3 activities ( Piao et al . , 2008 ) . The kinetic and structural data presented here demonstrate that the phosphorylated LRP6 repeat motifs act as direct inhibitors of GSK-3 catalytic activity . The 40-fold stronger inhibitory activity relative to that of phosphorylated GSK-3 N-terminus is consistent with the LRP5/6 motifs working in trans , as opposed to the presumed kinetic advantage of having the GSK-3 N-terminal inhibitory substrate part of the same polypeptide chain , although formation of membrane associated ‘signalosome’ complexes ( Bilic et al . , 2007 ) likely enhances the probability of encounter between Axin-bound GSK-3 and LRP5/6 . The prolines of the LRP5/6 cytoplasmic tail repeat motifs have been shown to be critical for Wnt signaling in cells ( MacDonald et al . , 2008 ) . The absolute conservation of prolines at the P+2 and P+5 positions of the five individual PPSP motifs of LRP6 , as opposed to the variation that can occur at the P+1 and P+3 positions , agrees well with the observed importance of the P+2 and P+5 sites for proper C-loop engagement , as well as the preponderance of prolines found at these positions in bona fide GSK-3 substrates ( Figure 7A , B ) . Although the second phosphorylation site in the LRP6 motif , which is three residues C-terminal to the phosphorylated Ser or Thr in the P+4 site , has been shown to be biologically important for LRP6 function , we do not observe it in either the c-motif or e-motif complex structures . Interestingly , a glutamate residue ( Glu12 ) is present at a similar position on the pS9 auto-inhibitory peptide , which we also do not observe . It is possible that the negative charge present at this position contributes to a relatively non-specific electrostatic interaction in a flexible region . However , previous experiments indicated that there was no increase in inhibitory activity toward GSK-3 of the doubly phosphorylated LRP6 peptide vs a peptide bearing only the first phosphorylated S/T ( Piao et al . , 2008 ) . The second phosphorylation site may instead provide an additional site of interaction for another protein , as this region of LRP6 is accessible to solvent . One example could be an additional domain of Axin itself , as Axin has been shown to interact directly with the phosphorylated PPSP motif in the absence of GSK-3 ( MacDonald and He , 2012; Kim et al . , 2013b ) . GSK-3 represents an important drug target for a variety of diseases , including diabetes , Alzheimer’s disease , and some cancers ( Medina and Castro , 2008; Takahashi-Yanaga and Sasaguri , 2009; Wada , 2009; Amar et al . , 2011 ) . Inhibiting GSK-3 in these pathways while not promoting oncogenesis through aberrant β-catenin signaling is a challenge . Many kinase inhibitors in use today are based on interactions with the nucleotide-binding region , with elaborations that target unique features of the particular enzyme . Such inhibitors would be non-selective towards the various substrates of GSK-3 . Some recently developed compounds target portions of the kinase not directly involved in nucleotide binding , especially activating or inhibiting conformations of the C-helix , for example clinically approved drugs toward the Abl and EGFR tyrosine kinases ( Jura et al . , 2011 ) . The peptide substrate-bound GSK-3 conformation observed in the crystal structures presented here suggests that it may be possible to target the three-dimensional structure formed by the rearranged C-loop in order to select for specific substrate-bound forms of GSK-3 . The projection of the P+3 substrate residue inward toward a pocket formed by the rearranged C-loop ( Figure 2B ) may provide a binding surface for small molecules that interact with a particular substrate sequence at this position . Such a compound would stabilize the bound conformation and prevent turnover of a specific substrate , enabling selective targeting of one pathway controlled by GSK-3 .
GST-Axin-GBD ( GSK-3 Binding Domain ) fusion protein was constructed by inserting the region encoding residues 383–402 of human Axin into a modified pGEX-KG vector containing a TEV cleavage site between the GST protein and the insert . The use of EcoRI at the 5′ end for the cloning inserts the sequence GGIL between the TEV cleavage site and residue 383 of Axin . Human LRP6 c- and e-motif–mouse GSK-3β ( cDNA from ATCC , Manassas , VA ) fusion proteins were constructed by replacing the first 12 residues of GSK-3β with 8 residues of the motif ( PPPPTPRS for c-motif or PPPPSPCT for e-motif ) by PCR and extending through residue 383 of GSK-3β . The construct also contains a non-cleavable 6xHis tag fused directly to the C-terminus of GSK-3β via PCR . This gene was then cloned into pET29b ( + ) using NdeI-NotI restriction sites . A shortened form of CK1ε ( residues 1–321 ) was constructed with a 6x-His tag fused directly to the C-terminus via PCR , and expressed in E . coli via the pET system in the pET21 vector ( Millipore , Billerica , MA ) . Purification was carried out with Ni-NTA agarose beads ( Qiagen , Germantown , MD ) according to the manufacturer’s protocol . Purified protein was stored at −20°C in 40% glycerol prior to use . Expression vectors for LRP6–GSK-3 fusion proteins , GSK-3 1-383 , or GSK-3 26-383 ( wild-type or F67A and F93 mutants; used for kinetics experiments ) were co-transformed with GST-Axin-GBD into chemically competent BL21 ( DE3 ) Codon-plus RIL cells ( Stratagene , La Jolla , CA ) and plated onto LB plates with 50 μg/ml kanamycin and 100 μg/ml ampicillin . A single colony was used to inoculate a 2L LB shaking culture containing the same antibiotics as listed above and grown to an approximate OD595 of 1 . 0–1 . 2 at 37°C . Cultures were then cooled to 16°C and induced with 0 . 1 mM IPTG . Fresh 100 μg/ml ampicillin was added and the cultures grown for 24 hr . Cell pellets were harvested via centrifugation and stored at −80°C until purification . Cell pellet from 4 l of culture was lysed in 40 ml of cold Lysis Buffer containing 20 mM Tris pH 7 . 5 , 300 mM NaCl , 5% glycerol , 0 . 01% Triton X-100 , 1 mg/ml lysozyme , 5 mg/l DNaseI ( Sigma , St . Louis , MO ) , EDTA-free Protease Inhibitor Cocktail ( Calbiochem , Billerica , MA ) , and 0 . 2 mM PMSF with an Emulsiflex homogenizer ( Avestin , Toronto , Canada ) . Lysate was centrifuged at 39000×g for 1 hr at 4°C to remove insoluble material . The clarified lysate was then added to a 10 ml bed volume of Glutathione-Agarose beads ( Sigma ) pre-equilibrated in Lysis Buffer and run under gravity flow . The beads were then washed with 200 ml of Wash Buffer containing 20 mM Tris pH 7 . 5 , 300 mM NaCl , 5% glycerol , and 0 . 1% β-mercaptoethanol . The GSK-3/GST-Axin complex was then eluted from the beads by adding 6 × 5 ml aliquots of Wash Buffer plus 20 mM reduced L-glutathione ( pH adjusted to 7 . 5 with NaOH ) . Elution fractions were analyzed by SDS-PAGE . TEV protease was added to the fractions containing the GSK-3/GST-Axin complex and allowed to incubate overnight at 4°C . Removal of the GST tag was confirmed by SDS-PAGE and samples were run over PD-10 columns pre-equilibrated with Wash Buffer according to manufacturer’s protocol ( GE Healthcare , Fairfield , CT ) to remove glutathione at room temperature . Samples were then passed over 10 ml of glutathione-agarose beads again and the flow-through collected in order to remove cleaved GST and any uncleaved GST-Axin complex . The resulting GSK-3/Axin complex was >95% pure as analyzed by SDS-PAGE , and its concentration was approximately 0 . 3–0 . 5 mg/ml . GSK-3β 1-383 used for the kinetics assays was produced by incubating 10 ml of the uncleaved protein with 16 , 000 units of lambda phosphatase ( New England Biolabs , Ipswich , MA ) and 2 mM MnCl2 overnight at 4°C , then for 2 hr at room temperature . Removal of the lambda phosphatase , TEV cleavage , and the rest of the purification were carried out as for the AKT-phosphorylated GSK-3β 1-383 . Analysis of the purified protein by western blotting showed little phosphorylation on pS9 compared to the protein before phosphatase treatment ( data not shown ) . Crystallization of the various GSK-3/Axin complexes was accomplished by dialysis . Purified GSK-3/Axin complex ( 12 ml ) was combined with 20 mM MgCl2 , and 400 μM ATP ( inhibitor-free structure ) , 30 μg of active AKT ( Millipore ) , 10 mM MgCl2 , and 200 μM ATP ( pS9 inhibitory peptide ) , or 50–100 μg of purified CK1 , 10 mM MgCl2 , and 200 μM ATP ( LRP6 c- and e-motif structures ) at room temperature and injected into a 3–12 ml size Slide-a-Lyzer cassette ( 7000 MWCO; Thermo Scientific , Waltham , MA ) . The cassette was placed into a 250 ml reservoir solution of 10% PEG 35 , 000 , 20 mM Tris pH 7 . 5 , 300 mM NaCl , 5% glycerol , 10 mM MgCl2 , 200 µM ATP , and 5 mM DTT at room temperature to facilitate phosphorylation , then incubated at 4°C . Crystals formed inside the cassette after approximately 72 hr . To form the pS9–AlF3 complex , the cassette was transferred to the same solution , with 200 μM ADP , 200 μM MgCl2 , 200 μM Al ( NO3 ) 3 , and 1 . 2 mM NaF replacing the ATP , and incubated for 1 week at 4°C . Crystals ranging from 20 to 750 µm in diameter were harvested by excising one side of the dialysis membrane on the cassette with a razor blade and adding a cryoprotectant solution composed of 10% PEG 35 , 000 , 20 mM Tris pH 7 . 5 , 300 mM NaCl , 20–22% glycerol , 10 mM MgCl2 , 200 µM ATP ( for the pS9–AlF3 complex , 200 μM ADP , 200 μM Al ( NO3 ) 3 , and 1 . 2 mM NaF were used instead of ATP ) and 5 mM DTT directly to the inside of the cassette . Crystals were mounted on cryoloops ( Hampton Research , Aliso Viejo , CA ) and frozen directly into liquid nitrogen . GSK-3β 1-383 phosphorylated at Ser9 ( GSK-3β 1-383pS9 ) used in kinase assays was produced by incubating GST-Axin/GSK-3β 1-383 with 15 µg AKT ( Millipore ) for approximately 48 hr in a dialysis cassette as described for crystallization , but prior to TEV cleavage . The protein solution was removed from the cassette when its volume reached ∼1 ml and diluted back to 12 ml with wash buffer supplemented with 10 mM MgCl2 and 200 µM ATP . The AKT was removed by applying the solution to glutathione-agarose beads . TEV cleavage and subsequent purification then proceeded as described above . Analysis of the purified protein by mass spectrometry showed that Ser9 was 24% phosphorylated . Diffraction data were measured from crystals at 100 K at the Stanford Synchrotron Radiation Lightsource ( SSRL ) beamlines 12-2 or 11-1 , or beamline 23ID-B of the Advanced Photon Source . Integration of images was performed with XDS ( Kabsch , 2010 ) , and scaling with Aimless ( Evans and Murshudov , 2013 ) . The crystals diffracted anisotropically , and resolution cutoffs were determined by choosing the resolution at which the half–dataset correlation CC1/2 was approximately 0 . 5 and Mn ( I/σ ) was approximately 1 . 3 or better in the best direction ( along the l-axis ) . The structures were solved and refined in the Phenix package ( Adams et al . , 2010 ) . The LRP6 c- and e-motif complexes were solved by molecular replacement using PDB ID 1O9U as the starting model . The inhibitor-free , pS9 and pS9-AlF3 structures were solved by rigid body refinement using the LRP6 c- and e-motif complexes . Bulk solvent parameters , TLS groups and individual temperature factors were applied throughout the refinement . Model building was carried out in Coot ( Emsley and Cowtan , 2004 ) . Data collection and refinement statistics are given in Table 2 . GSK-3β activity was measured by incorporation of 32P into a primed , synthetic eIF2b peptide ( Enzo Life Sciences , Farmingdale , NY ) . A typical reaction mixture contained 50 mM Tris pH 7 . 5 , 10 mM MgCl2 , 0 . 1% β-mercaptoethanol , and 112 . 5 μM γ32P-ATP at 0 . 02 μCi/μL ( Perkin Elmer , Waltham , MA ) and 224 nM GSK-3β . All measurements were performed at 30°C . The substrate peptide concentration was 20 μM or 80 µM for the inhibition assays and was varied for the determination of KM and kcat values . Inhibitor peptide was added at concentrations ranging from 50–1000 μM for the pS9 peptide and 5–20 μM for the LRP6 a-motif . For each measurement , a 20 μl sample was removed from the reaction and mixed immediately with 20 μl of 150 mM phosphoric acid . 35 μl of the resulting solution was then blotted onto a 96-well plate containing P81 cellulose phosphate paper and washed 5 or 10 times with 0 . 5% phosphoric acid . Scintillation fluid was then added , and the plate was counted in a Wallac MicroBetta 1450 liquid scintillation counter . Control reactions performed in the absence of substrate peptide showed that signal from GSK-3 autophosphorylation was not significant . Rates were calculated from the slope of the linear regression fit of 32P incorporation vs time . Ki values were calculated from initial rates by the method described in Zhou et al . ( 1997 ) . Briefly , an apparent Ki was calculated from the equationv0vi=1+[I]Kiappwhere v0 is the rate in the absence of inhibitor , vi is the rate in the presence of the inhibitor , [I] is the concentration of the inhibitor , and Kiapp is the apparent Ki . The actual Ki was then calculated from Kiapp asKi=Kiapp1+[S]KM Individual Ki values obtained from each independent experiment ( n = 27 for pS9 and n = 11 for LRP6 a-motif ) were averaged to obtain the reported value of Ki , with the error propagated from the errors in v0 , vi , and KM . | Cells need to be able to respond to changes in the body , such as changes in hormone levels or the arrival of a pathogen such as a virus . Proteins acting in signaling pathways—where one protein switches ‘on’ or ‘off’ the next protein in the pathway—allow the detection of different changes or signals to be translated in the appropriate response . The properties of a protein often depend on its shape , and many proteins change shape when they are switched ‘on’ and ‘off’ . Moreover , the ability to change shape allows a protein to interact with many other proteins and to be involved in many different signaling pathways . The enzyme GSK-3 is a protein that is involved in several pathways , and it controls other proteins by adding chemical tags , called phosphate groups , to them . Unlike many other enzymes , GSK-3’s default state is to be permanently switched on , and it can be switched off in a number of different ways . When a cell detects the hormone insulin , for example , another enzymes adds a phosphate group to a site near one end of GSK-3 to switch it off . Alternatively , when a cell recognises a different signaling molecule , called Wnt , a phosphate group is added to yet another protein , which then binds to and switches off GSK-3 . To explore the workings of GSK-3 in greater detail , Stamos et al . solved the three-dimensional structure of the enzyme that had been switched off in these two ways . In the insulin pathway , the region near to one end of GSK-3 that contains the added phosphate group was shown to bind to and block the site on the enzyme that usually binds to its target ( i . e . , to the next protein in the signaling pathway ) . In the Wnt pathway , remarkably , the same site on GSK-3 was blocked in a very similar way by the piece of the other protein with the phosphate group added . GSK-3 is a potential drug target for the treatment of several diseases , such as diabetes and neurological disorders . However , as this enzyme is involved in multiple pathways , it has been hard to find drugs to treat any one condition without side effects . Uncovering subtle differences in how GSK-3 can be controlled in different pathways could , in the future , help with efforts to develop more specific drugs to target GSK-3 to treat these diseases . | [
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] | 2014 | Structural basis of GSK-3 inhibition by N-terminal phosphorylation and by the Wnt receptor LRP6 |
COVID-19 patients can present with pulmonary edema early in disease . We propose that this is due to a local vascular problem because of activation of bradykinin 1 receptor ( B1R ) and B2R on endothelial cells in the lungs . SARS-CoV-2 enters the cell via ACE2 that next to its role in RAAS is needed to inactivate des-Arg9 bradykinin , the potent ligand of the B1R . Without ACE2 acting as a guardian to inactivate the ligands of B1R , the lung environment is prone for local vascular leakage leading to angioedema . Here , we hypothesize that a kinin-dependent local lung angioedema via B1R and eventually B2R is an important feature of COVID-19 . We propose that blocking the B2R and inhibiting plasma kallikrein activity might have an ameliorating effect on early disease caused by COVID-19 and might prevent acute respiratory distress syndrome ( ARDS ) . In addition , this pathway might indirectly be responsive to anti-inflammatory agents .
COVID-19 infects mainly elderly and people with cardiovascular risk , such as hypertension ( Guan et al . , 2020 ) . The clinical spectrum and imaging are so specific that MDs recognize this disease in an instant especially now that it is widespread . Every clinician recognizes that the virus does not cause disease similar to influenza , which carries the risk that designing targeted therapies based on the pathogenesis of influenza might fail in COVID-19 . Research on Sars-CoV pathogenesis which might be very similar to Sars-CoV-2 pathogenesis has focused the discussion on ACE inhibitors , recombinant ACE2 , and ARBs and how they could fit in the pathogenesis of COVID-19 , since these pathways were extensively studied in SARS ( Fang et al . , 2020; Batlle et al . , 2020 ) . For recombinant ACE2 this would be straight forward , it would at least be an attempt to bind and try to scavenge the virus ( Batlle et al . , 2020 ) . However , for ACE inhibitors and ARBs it is a much more complicated story . Since most of the attention was focused on the RAAS system and its interaction with modulating the vascular system and inflammation , the other major role of ACE and ACE2 for the regulation of the kinin-kallikrein system was lacking attention ( Jurado‐Palomo and Caballero , 2017; Marceau et al . , 2018 ) . Moreover , the notable clinical deterioration seems to be associated with increased inflammatory status . Here , we describe the clinical observations that brought the clues for explaining the potential pathophysiological mechanisms , and offer a rationale for targeted treatment at different stages of COVID-19 .
When patients are admitted with symptomatic COVID-19 infection fever , dry cough , and dyspnea are most commonly observed . Importantly , we observed that dyspnea and tachypnea can differ from hour to hour and a feeling of drowning is described with sometimes sudden recovery by patients . CT scans reveals unilateral or bilateral ground-glass opacities , that might progress to more clear consolidations throughout the disease . Fluid restriction improves oxygenation and ameliorates the feeling of dyspnea . Notably , plasma concentrations of D-dimers at this stage are increased without evidence of thromboembolic events . There is a phase during clinical admission where many patients are getting better , but some will worsen especially around day 9 , although this can also occur much earlier . This worsening seems to be accompanied specifically with further increases in IL-6 , CRP , ferritin , without elevated procalcitonin , indicative of a progressive innate inflammatory status , which is a clear different pattern of the first stage of the disease . In the ICU , there are several striking observations . In contrast to patients with common forms of ARDS , approximately 70% of patients with severe COVID-19 show an only slightly decreased pulmonary compliance ( L-type ) ( Gattinoni et al . , 2020a ) . Driving pressure is usually low . Recruitability is usually low and the use of high PEEP may therefore substantially increase functional residual capacity resulting in hyperinflation , high strain and considerable hypercapnia through an increase in dead space ventilation . Hereby mechanical ventilation may further contribute to lung damage . Only a minority of patients initially show the classical ARDS mechanical properties ( H-type ) with low compliance , high driving pressure and higher recruitability . Both L and H-type show high venous admixture . We and others have suggested that the L-type may progress to the H-type by a combination of negative intrathoracic pressure and increased lung permeability due to inflammation ( so called patient-self inflicted lung injury P-SILI ) ( Gattinoni et al . , 2020b ) .
We propose it all starts with ACE2 and its role in the kallikrein-kinin system , which to date has not been investigated in the pathogenesis of SARS or COVID-19 . The kinin-kallikrein system is a zymogen system that after activation leads to the release of the nona-petide bradykinin that after binding to the B2R on endothelial cells can lead to capillary leakage and thus angioedema . The prototype diseases of local peripheral transient increased bradykinin release are hereditary or acquired angioedema ( Jurado‐Palomo and Caballero , 2017 ) . The clinical picture of COVID-19 is in line with a single-organ failure of the lung that is due to edema at the site of inflammation . Moreover , the presence of an elevated D-dimer without thrombosis or microangiopathy is in line with the high D-dimers in angioedema . This most likely reflects the leakage of plasma substances involved in the coagulation cascade leading to fibrin and due to kallikrein activity is processed into D-dimer and leaks back into the circulation , reflecting subendothelial activation and kallikrein activity . The ACE2 and its role in the RAAS system has been suggested to play a role for more than 10 years in the pulmonary edema due to ARDS and SARS ( Imai et al . , 2005 ) . Pulmonary edema by ACE2 dysfunction was speculated to be due to increased hydrostatic pressure as a result of vasoconstriction of the pulmonary vasculature due to high angiotensin II ( a vasoconstrictor ) ( Imai et al . , 2005 ) . However , further experiments showed no difference in hydrostatic pressure and made the explanation of high angiotensin II with vasocontriction as a cause of pulmonary edema unlikely ( Imai et al . , 2005; Kuba et al . , 2005 ) . Increased bradykinin , however , could explain this observation without increased hydrostatic pressure . Notably , the RAAS system controls vasoconstriction and vasodilatation , and the bradykinin system controls permeability and vasodilatation , whereas ACE2 regulates both . Bradykinin ( BK ) is a linear nonapeptide that is formed by the proteolytic activity of kallikrein on kininogens ( Bhoola et al . , 1992 ) . Kallikreins are serine proteases and can be divided in plasma kallikrein and tissue kallikreins ( Figure 1 ) . The plasma and tissue kallikreins release the vasoactive peptides known as kinins ( all sorts of BKs ) that cause relaxation of vascular smooth muscle and increased vascular permeability ( Bhoola et al . , 1992; Marceau et al . , 2018 ) . Plasma kallikrein processes high‐molecular‐weight kininogen ( HMWK produced by the liver Bhoola et al . , 1992 ) into bradykinin ( BK ) , while tissue kallikrein processes low‐molecular‐weight kininogen ( LMWK produced by the liver Bhoola et al . , 1992 ) and results in Lys-BK ( Figure 1 ) . These are the ligands for the constitutively expressed bradykinin receptor B2 on endothelial cells ( Jurado‐Palomo and Caballero , 2017 ) . In addition , the enzymes ( carboxypeptidase M ( CPM ) and carboxypeptidase N ( CPN ) ) can further process BK and Lys-BK into des-Arg9-BK and Lys- des-Arg9-BK respectively , which are ligands for B1R , a bradykinin receptor on endothelial cells that is upregulated under proinflammatory conditions ( Jurado‐Palomo and Caballero , 2017 ) . These kinins have strong vasopermeable and vasodilatory capacity and need to be tightly controlled to prevent excessive angioedema . ACE and ACE2 both have roles in inactivating the ligands for the bradykinin receptors ( Gralinski et al . , 2018 ) . ACE mainly inactivates bradykinin which is the major ligand for B2Rs . ACE inhibition has been linked to systemic acquired angioedema since it can result in excessive presence of bradykinin that activates B2R ( Jurado‐Palomo and Caballero , 2017 ) . Interestingly , ACE2 does not inactivate bradykinin , but can inactivate Lys des-Arg9-BK and des-Arg9-BK which are potent ligands of the B1R in the lung ( Figure 1; Sodhi et al . , 2018 ) . In this way , it can be protective against pulmonary edema especially in the setting of inflammation , which is further supported by the role of ACE2 in acute pulmonary injury ( Imai et al . , 2005; Sodhi et al . , 2018 ) . When plasma leakage occurs due to tissue damage and tissue kallikrein activation in the setting of innate inflammation , plasma kallikrein will be activated locally resulting in the formation of bradykinin that stimulates the B2R and des-Arg9-BK that will further stimulate the B1R . ACE2 is almost undetectable in serum , but is expressed in the lung predominantly on pneumocytes type II ( Sodhi et al . , 2018 ) . The Sars-CoV-2 Spike ( S ) antigen binds to ACE2 and internalizes ( Walls et al . , 2020 ) . Since it has been reported and suggested that the expression of ACE2 and its capacity of enzyme activity is decreased in SARS-CoV and inflammatory conditions ( Sodhi et al . , 2018; Imai et al . , 2005; Kuba et al . , 2005 ) , it is tempting to speculate that Sars-CoV-2 interaction with ACE2 at the surface also downregulates ACE2 expression and function of ACE2 , subsequently leading to a deficiency to inactivate the B1R ligand locally in the lung , and might in this way directly link the virus to local pulmonary angioedema . Further supporting this concept are the reported findings of downregulation of ACE2 by SARS-CoV , and it has been suggested that this might be similar in SARS-CoV-2 ( Fu et al . , 2020; Glowacka et al . , 2010; Levi et al . , 2019 ) . In 2005 , it was proposed that the RAAS system was responsible for complications due to Sars-CoV . RAAS regulates vasodilatation and vasoconstriction , and it was hypothesized that increased angiotensin II as a result of ACE2 deficiency would result in pulmonary edema due to increased hydrostatic pressure since angiotensin II would cause vasoconstriction . However , there was no effect observed on the hemodynamics of the pulmonary vasculature in ACE2 deficiency , while there was clear vascular leakage . AT1R knockout mice and AT1R blockade were protected from lung edema due to inflammation but this was not explained by a mechanism linking AT1R to vascular leakage . Bradykinin might be the missing link , since AT1R can form heterodimers with the B2R and AT1R can synergize with B1R in the induction of ROS in endothelial cells ( Ceravolo et al . , 2014; Quitterer and AbdAlla , 2014 ) . We speculate that this dysregulated kinin pathway is present already early in COVID-19 disease . Patients can worsen clinically after days of illness which is accompanied by an increase in proinflammatory status often resulting in ICU admission and with necessity of supportive mechanical ventilation . Especially a strong innate immune response reflected by high levels of IL-6 and CRP seem to accompany this clinical worsening . This will not only result in more damage to the environment with neutrophil recruitment but will also further increase inflammation-induced B1R upregulation on endothelial cells especially via IL-1 . However , it must be kept in mind that targeting the innate immune response will not have a direct effect on the pulmonary edema that is driven by bradykinin , since kallikrein activity will be not affected , kinins will still be present , and B1R and B2R are still expressed on endothelial cells . This pathway might be less responsive to corticosteroids or adrenaline , meaning as long as the virus persists ACE2 dysfunction is present and the bradykinin pathway is active the pulmonary edema at the site of infection will persist . On the other hand , clinicians know how fast patients with bradykinin-related angioedema can recover with for example icatibant or when the trigger is gone that one can foresee a very fast recovery of pulmonary edema and recovery of hypoxia and disease when intervening with the plasma kallikrein-kinin pathway .
In our vision , as long as the virus persists the dysregulated kinin-kallikrein pathway is playing a role in disease via the absence of optimal ACE2 function in the lung . Maybe not everybody needs kallikrein-kinin blocking since they will recover once the viral load is resolved from the lung and there is no second inflammatory hit . However , when disease progresses which is accompanied by increased proinflammatory status which often results in critical illness we would argue that this timepoint has a rationale for strategies targeting the inflammation induced by innate immune responses . However this must be done in the presence of blocking the kallikrein-kinin pathway . Several targets might be amendable to intervention , namely 1 . at the level of blocking tissue and or plasma kallikrein activity and thus reducing the production of kinins , 2 . activating the degradation of kinins by treating with recombinant active enzymes such as ACE2 , 3 . at the level of B1R and B2R , 4 . by inhibiting the common downstream signaling of B1Rand B2R , and 5 . by suppressing local NO which is largely responsible for the endothelial leakage . By far , the most potent and logical would be to block B1R and B2R signaling . B2R inhibitors exist in the clinics . Icatibant is a selective B2R drug that is available in the US and Europe ( Firazyr ) and is licensed for the treatment of hereditary angioedema in adults , adolescents and children over the age of 2 years . It is a synthetic decapeptide with a structure similar to bradykinin , but with five non-proteinogenic amino acids ( European Medicines Agency , 2014 ) . The licensed dose of icatibant for hereditary angioedema is 30 mg by subcutaneous injection as a single dose . At current day , there is no licensed B1R drug ( Qadri and Bader , 2018 ) . Several B1R drugs have been tested in pre-clinical and in phase I/II trials as therapeutic target for inflammation related processes already since the 1970s . None of these drugs have made it to the market . This includes drugs like the Merck compound MK-0686 ( Kuduk et al . , 2007 ) that has been investigated in the reduction of pain , and the Sanofi compound safotibant that was discontinued in 2012 for the treatment of macular oedema and the Boehringer Ingelheim drug BI11382 ( Nasseri et al . , 2015 ) . Other products identified via open target ( accessible via http://www . opentarget . com ) and through literature review are ELN-441958 , SSR 240612 , NVP-SAA164 and R-715 ( Qadri and Bader , 2018 ) . Dual inhibition of both the B1R and B2R would be the way forward . But this would imply that specifically the drugs targeting B1R need to be become available and that they have to exert suitable pharmacodynamic action at concentrations that are non-toxic . Another option is the use of blocking plasma kallikrein , which in turn will result in less kinins ( both B2R and B1R ligands ) at the site of infection and subsequently less leakage via B1R and B2R . In addition , we should think about blocking innate cytokines that upregulate B1R on endothelial cells at the site of inflammation in combination with B1R and or B2R blockade . IL-1 ( consisting of IL-1α and IL-1β ) and TNF are potent inducers of B1R . Blockade of NF-κB translocation , TNF-α , or IL-1 prevented the functional and molecular up-regulation of B1R by LPS ( Passos et al . , 2004 ) . Therefore , one strategy could be with anakinra , which has an excellent safety profile and would make a lot of sense since it not only blocks IL-1β coming from infiltrating monocytes and macrophages , but also IL-1α . IL-1α is likely to be play a role locally due to its release from inflamed endothelial cells . Blocking TNF is an option , but has been associated with much more infectious complications . In addition , complement activation has been described and could play a role in this stage of disease , and this might be amendable to C5 blockade with eculizimab with which a randomized trial in COVID-19 is being performed ( NCT04288713 ) . Also corticosteroids are an option . Since we notice that some patients have persistent disease and at some point develop a proinflammatory profile especially a rise in CRP reflecting IL-6 elevation , which often leads to ICU admission this might be the timepoint to initiate potent anti-inflammatory therapy . For most patients this timepoint will be identified before the need of ICU admission and thus an anti-inflammatory drug might prevent them from ICU admission . This antiinflammatory strategy must be initiated together with the kallikrein-kinin pathway blockade and available antivirals as early as possible in disease . The anti-inflammatory strategies will buy time , but will not resolve the disease by themselves as long as the virus is present and or the bradykinin-induced angioedema is not resolved . A summary of these proposed targeted treatments and timing of treatment is depicted in Figure 2 . An overview of the hypothesis on which this strategy is based is illustrated by Figure 3A ( normal condition ) , 3B ( mild inflammation ) and 3C ( severe inflammation ) .
We are calling out for experts in the field of the kallikrein-kinin system and people involved in drug development to work together with SARS researchers who have the tools to test this hypothesis and interventions . This hypothesis explains the clinical spectrum that is so often observed and offers a rationale for treatment and more importantly timing of treatment . The bradykinin-driven pulmonary edema could be targeted by already available drugs such as icatibant or a plasma kallikrein inhibitor , such as lanadelumab . The cytokine-related clinical detoriation could respond to blocking the IL-1/IL-6 pathway . These treatment strategies , together with antiviral treatment , could prevent the development of ARDS in COVID-19 when started early and might be able to prevent ICU admission and the need for mechanical ventilation . | The COVID-19 pandemic represents an unprecedented threat to global health . Millions of cases have been confirmed around the world , and hundreds of thousands of people have lost their lives . Common symptoms include a fever and persistent cough and COVID-19 patients also often experience an excess of fluid in the lungs , which makes it difficult to breathe . In some cases , this develops into a life-threatening condition whereby the lungs cannot provide the body's vital organs with enough oxygen . The SARS-CoV-2 virus , which causes COVID-19 , enters the lining of the lungs via an enzyme called the ACE2 receptor , which is present on the outer surface of the lungs’ cells . The related coronavirus that was responsible for the SARS outbreak in the early 2000s also needs the ACE2 receptor to enter the cells of the lungs . In SARS , the levels of ACE2 in the lung decline during the infection . Studies with mice have previously revealed that a shortage of ACE2 leads to increased levels of a hormone called angiotensin II , which regulates blood pressure . As a result , much attention has turned to the potential link between this hormone system in relation to COVID-19 . However , other mouse studies have shown that ACE2 protects against a build-up of fluid in the lungs caused by a different molecule made by the body . This molecule , which is actually a small fragment of a protein , lowers blood pressure and causes fluid to leak out of blood vessels . It belongs to a family of molecules known as kinins , and ACE2 is known to inactivate certain kinins . This led van de Veerdonk et al . to propose that the excess of fluid in the lungs seen in COVID-19 patients may be because kinins are not being neutralized due to the shortage of the ACE2 receptor . This had not been hypothesized before , even though the mechanism could be the same in SARS which has been researched for the past 17 years . If this hypothesis is correct , it would mean that directly inhibiting the receptor for the kinins ( or the proteins that they come from ) may be the only way to stop fluid leaking into the lungs of COVID-19 patients in the early stage of disease . This hypothesis is unproven , and more work is needed to see if it is clinically relevant . If that work provides a proof of concept , it means that existing treatments and registered drugs could potentially help patients with COVID-19 , by preventing the need for mechanical ventilation and saving many lives . | [
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Biomarkers of low-grade inflammation have been associated with susceptibility to a severe infectious disease course , even when measured prior to disease onset . We investigated whether metabolic biomarkers measured by nuclear magnetic resonance ( NMR ) spectroscopy could be associated with susceptibility to severe pneumonia ( 2507 hospitalised or fatal cases ) and severe COVID-19 ( 652 hospitalised cases ) in 105 , 146 generally healthy individuals from UK Biobank , with blood samples collected 2007–2010 . The overall signature of metabolic biomarker associations was similar for the risk of severe pneumonia and severe COVID-19 . A multi-biomarker score , comprised of 25 proteins , fatty acids , amino acids , and lipids , was associated equally strongly with enhanced susceptibility to severe COVID-19 ( odds ratio 2 . 9 [95%CI 2 . 1–3 . 8] for highest vs lowest quintile ) and severe pneumonia events occurring 7–11 years after blood sampling ( 2 . 6 [1 . 7–3 . 9] ) . However , the risk for severe pneumonia occurring during the first 2 years after blood sampling for people with elevated levels of the multi-biomarker score was over four times higher than for long-term risk ( 8 . 0 [4 . 1–15 . 6] ) . If these hypothesis generating findings on increased susceptibility to severe pneumonia during the first few years after blood sampling extend to severe COVID-19 , metabolic biomarker profiling could potentially complement existing tools for identifying individuals at high risk . These results provide novel molecular understanding on how metabolic biomarkers reflect the susceptibility to severe COVID-19 and other infections in the general population .
The coronavirus disease 2019 ( COVID-19 ) pandemic affects societies and healthcare systems worldwide . Protection of those individuals who are most susceptible to a severe and potentially fatal COVID-19 disease course is a prime component of national policies , with stricter social distancing and other preventative means recommended mainly for elderly people and individuals with pre-existing disease conditions . The prominent susceptibility to severe COVID-19 for people at high age has been linked with impaired immune response due to chronic inflammation caused by ageing processes ( Akbar and Gilroy , 2020 ) . However , large numbers of seemingly healthy middle-aged individuals also suffer from severe COVID-19 ( Zhou et al . , 2020; Atkins et al . , 2020; Williamson et al . , 2020 ) ; this could partly be due to similar molecular processes related to impaired immunity . A better understanding of the molecular factors predisposing to severe COVID-19 outcomes may help to explain the risk elevation ascribed to pre-existing disease conditions . From a translational point of view , this might also complement the identification of highly susceptible individuals in general population settings beyond current risk factor assessment . Pneumonia is a life-threatening complication of COVID-19 and the most common diagnosis in severe COVID-19 patients . As for COVID-19 , the main factors that increase the susceptibility for severe community-acquired pneumonia are high age and pre-existing respiratory and cardiometabolic diseases , which can weaken the lungs and the immune system ( Almirall et al . , 2017 ) . Based on analyses of large blood sample collections of healthy individuals , biomarkers associated with the risk for severe COVID-19 are largely shared with the biomarkers associated with the risk for severe pneumonia , including elevated markers of impaired kidney function and inflammation and lower HDL cholesterol ( Ho et al . , 2020 ) . This may indicate that these molecular markers may reflect an overall susceptibility to severe complications after contracting an infectious disease . Comprehensive profiling of metabolic biomarkers , also known as metabolomics , in prospective population studies have suggested a range of blood biomarkers for cardiovascular disease and diabetes to also be reflective of the susceptibility for severe infectious diseases ( Ritchie et al . , 2015; Deelen et al . , 2019 ) . Metabolic profiling could therefore potentially identify biomarkers that reflect the susceptibility to severe COVID-19 among initially healthy individuals . However , such studies require measurement of vast numbers of blood samples collected prior to the COVID-19 pre-pandemic . Conveniently , a broad panel of metabolic biomarkers have recently been measured using nuclear magnetic resonance ( NMR ) spectroscopy in over 100 , 000 plasma samples from the UK Biobank . Here , we examined if NMR-based metabolic biomarkers from blood samples collected a decade before the COVID-19 pandemic associate with the risk of severe infectious disease in UK general population settings . Exploiting the shared risk factor relation between susceptibility to severe COVID-19 and pneumonia ( Ho et al . , 2020 ) , we used well-powered statistical analyses of biomarkers with severe pneumonia events to develop a multi-biomarker score that condenses the information from the metabolic measures into a single multi-biomarker score . Taking advantage of the time-resolved information on the occurrence of severe pneumonia events in the UK Biobank , we mimicked the influence of the decade lag from blood sampling to the COVID-19 pandemic on the biomarker associations , and used analyses with short-term follow-up to interpolate to a scenario of identifying individuals susceptible to severe COVID-19 in a preventative screening setting . Our primary aim was to improve the molecular understanding on how metabolic risk markers may contribute to increased predisposition to severe COVID-19 and other infections .
Figure 2A shows the associations of 37 biomarkers with severe pneumonia events occurring during the follow-up in the entire study population ( n = 105 146 ) . The biomarkers highlighted here are those with a regulatory approval for diagnostics use in the Nightingale Health NMR platform . These biomarkers span most of the different metabolic pathways captured with the NMR platform; results for all 249 metabolic measures quantified are shown in Figure 2—figure supplements 1–3 . Strong associations were observed across several metabolic pathways: increased plasma concentrations of cholesterol measures , omega-3 and omega-6 fatty acid levels , histidine , branched-chain amino acids and albumin were associated with lower susceptibility to contracting severe pneumonia . Increased concentrations of monounsaturated and saturated fatty acids , as well glycoprotein acetyls ( GlycA , a marker of low-grade inflammation ) were associated with elevated susceptibility to contracting severe pneumonia . Since all the biomarkers are quantified in the same single measurement , we examined if even stronger associations with severe pneumonia could be obtained using a combination of multiple biomarkers . We derived this multi-biomarker combination , denoted ‘infectious disease score’ , using logistic regression with LASSO for variable selection , considering the 37 clinically validated biomarkers in a half of the study population as the training set . This resulted in an infectious disease score comprised of the weighted sum of 25 biomarkers , with the weights selected by the machine learning algorithm ( Supplementary file 1 ) . Broadly similar results were obtained using all 249 metabolic measures quantified in the Nightingale Health NMR platform to derive the multi-biomarker score . The multi-biomarker infectious disease score was then tested for association with severe pneumonia in the other half of the study population . The magnitude of association for the infectious disease score was approximately twice as strong with severe pneumonia compared to any of the individual biomarkers ( Figure 2B ) . The odds for contracting severe pneumonia was increased 67% per 1-SD increment in the infectious disease score . This corresponds to close to fourfold higher risk for contracting severe pneumonia among people in the highest quintile of the infectious disease score , compared to those with a score in the lowest quintile . To assess the robustness of the multi-biomarker score association with severe pneumonia , we adjusted the analyses for prevalent diseases and performed analyses stratified by age and sex ( Figure 3 ) . The association was attenuated by ~10% in magnitude when adjusting for , or omitting , individuals with a diagnosis of prevalent diseases at time of blood sampling ( cardiovascular diseases , diabetes , lung cancer , COPD , liver diseases , renal failure , and dementia; panels 3A and 3B ) . The association was similar across age groups , and also for men and women analysed separately ( panels 3C and 3D ) . To mimic the influence of the decade-long lag from blood sample collection to the COVID-19 pandemic , we tested the association of the multi-biomarker infectious disease score with severe pneumonia events occurring during 7–11 years after the blood sampling ( Figure 4A ) . Since there were only few severe pneumonia events recorded with more than 9 years of follow-up , we could not fully mimic the decade long time lag to the COVID-19 pandemic . The risk elevation observed in this time-lag accounting scenario was only approximately half of that observed for severe pneumonia events occurring within the first 7 years ( odds ratio 1 . 43 vs 1 . 75 per 1-SD , respectively; and 2 . 59 vs 4 . 27 for individuals in the highest vs lowest quintile of the infectious disease score ) . To interpolate to a screening scenario conducted today , we also tested the association with short-term risk of severe pneumonia by analysing events occurring within the first 2 years after the blood sampling ( Figure 4B ) . The association magnitude in this analysis of short-term risk scenario was approximately twice as strong as for severe pneumonia events occurring more than 2 years after blood sampling ( odds ratio 2 . 21 vs 1 . 59 per 1-SD; 7 . 95 vs 3 . 35 for individuals in the highest vs lowest quintile of the infectious disease score ) . The elevated susceptibility to severe pneumonia associated with the multi-biomarker score was therefore three to four times stronger when examining short-term risk as compared to risk of severe pneumonia events occurring almost a decade after the blood sampling . The elevation in the short-term risk for severe pneumonia for high levels of the infectious disease multi-biomarker score remained strong when adjusting for BMI , smoking and prevalent diseases ( odds ratio 6 . 10 for individuals in the highest vs lowest quintile; Figure 4—figure supplement 1 ) . We further explored the risk gradient for a future onset of severe pneumonia along increasing levels of the infectious disease score , since non-linear effects could potentially facilitate the identification of thresholds for individuals at high susceptibility . Figure 5A shows the increase in the proportion of individuals who contracted severe pneumonia according to percentiles of the score . The risk increased prominently in the highest quintile , and particularly for the highest few percentiles . The time-resolved plot of the cumulative probability of severe pneumonia during follow-up is shown in Figure 5B . The susceptibility to severe pneumonia was particularly elevated among individuals with the very highest levels of the multi-biomarker infectious disease score . This was observed already during the first few years of follow-up , corroborating the results for long-term and short-term risk shown in Figure 3 . The prominent and immediate elevation in susceptibility to severe pneumonia was also observed when limiting analyses to individuals without chronic respiratory and cardiometabolic diseases at the time of blood sampling ( Figure 5—figure supplement 1 ) . Figure 6 shows the associations of the 37 clinically validated biomarkers and the infectious disease score with the future onset of severe COVID-19 ( defined as PCR-confirmed positive inpatient diagnosis ) . Many of the individual biomarkers had significant associations ( p-value<0 . 001 ) with increased risk for severe COVID-19 . These biomarkers for susceptibility to severe COVID-19 include lower levels of omega-3 omega-6 fatty acids as well as albumin , and higher levels of GlycA . We observed a high concordance in the overall pattern of COVID-19 biomarker associations with severe pneumonia ( Figure 2A ) , with a Spearman correlation of 0 . 89 between the overall biomarker association signatures for severe pneumonia and severe COVID-19 ( Figure 7 ) . The multi-biomarker infectious disease score derived for the future onset of severe pneumonia was also robustly associated with the future onset of severe COVID-19 . The odds ratio was 1 . 40 per 1-SD increment and 2 . 90 for comparing individuals in the highest quintile of the multi-biomarker infectious disease score to those in the lowest quintile . This magnitude of association with susceptibility to severe COVID-19 was similar to that observed with severe pneumonia events occurring during the interval of 7–11 years after the blood sampling . We further examined the association of the multi-biomarker infectious disease score with severe COVID-19 after adjustment or exclusion for prevalent diseases , and conducted stratified analyses for age and sex ( Figure 8 ) . The association with severe COVID-19 was attenuated , but remained significant when adjusted for BMI , smoking and prevalent diseases ( panel 7A ) . The association magnitudes were approximately 20% weaker when limiting the COVID-19 analyses to individuals without prevalent diseases at time of blood sampling ( panel 7B ) . There was no robust evidence of differences in association magnitude according to age ( panel 7C ) and odd ratios were broadly similar for men and women ( panel 7D ) . Finally , we examined the technical repeatability and biological stability of measuring the multi-biomarker infectious disease score . The measurement repeatability was high ( Pearson correlation 0 . 94 in blind duplicate samples; Figure 9A ) . Even though the blood samples were primarily non-fasting , the levels of the infectious disease score remained broadly stable during 4 years based on blood samples from repeat visits ( Pearson correlation 0 . 61 between baseline and repeat visit measurements; Figure 9B ) .
Most biomarker studies on COVID–19 have focused on characterising already infected patients and their disease prognosis ( Kermali et al . , 2020; Shen et al . , 2020; Messner et al . , 2020; Dierckx et al . , 2020 ) . In contrast , in the largest blood metabolic profiling study to date , we explored biomarker associations for susceptibility to severe pneumonia and COVID-19 in general population settings . We developed a multi-biomarker score for increased susceptibility to a severe infectious disease course , and demonstrated that this biomarker score captures an increased risk for COVID-19 hospitalisation a decade after the blood sampling . The overall signature of biomarker associations was similar for the susceptibility to severe COVID-19 and to severe pneumonia ( Figure 7 ) . The proportions of individuals with existing cardiometabolic diseases were also consistent for both of these infectious diseases ( Table 1 ) . We used these observations of a shared risk factor basis to draw an analogy between susceptibility to severe pneumonia and severe COVID-19 , and hereby infer potential implications for preventative screening . We therefore exploited the strong statistical power and time-resolved information on severe pneumonia events for more detailed analyses than was feasible with COVID-19 . This led to three important observations . First , the infectious disease multi-biomarker score was largely independent of prevalent chronic respiratory and cardiometabolic diseases ( Figure 3 ) . Second , the susceptibility to severe pneumonia was drastically elevated in the extreme tail of the multi-biomarker infectious disease score , with 5–10 times higher risk compared to individuals with normal levels of the multi-biomarker score ( Figure 5 ) . Such features might aid in establishing thresholds for identifying individuals most susceptible to a severe disease course . Third , the odds ratio of the multi-biomarker score for severe pneumonia events occurring after 7–11 years closely matched that of severe COVID-19 , for which all events occurred over decade after blood sampling ( Figure 4A ) . Yet , screening for the susceptibility to severe COVID-19 would require a strong association with the short-term risk . When confining the analyses of severe pneumonia to events occurring within the first 2 years after blood sampling , the short-term risk elevation was over four times stronger than that observed for long-term risk — individuals with high levels of the multi-biomarker score were almost 7-times more susceptible than people with low levels ( Figure 4B ) . If similar enhancement in short-term risk extend to COVID-19 , our results could potentially indicate applications for identification of individuals at high susceptibility to a severe COVID-19 disease course . However , the unavailability of metabolic biomarker data from blood samples drawn shortly prior to the pandemic prevents us from examining biomarker associations with short-term COVID-19 susceptibility , and our results should therefore be considered of hypothesis generating nature . We observed multiple blood biomarkers commonly linked with the risk for cardiovascular disease and diabetes ( Soininen et al . , 2015; Würtz et al . , 2017; Holmes et al . , 2018; Ahola-Olli et al . , 2019 ) to also be associated with increased susceptibility to both severe pneumonia and severe COVID-19 . The biomarkers span multiple metabolic pathways , including low concentrations of lipoprotein lipids , impaired fatty acid balance , decreased amino acid levels and high chronic inflammation . This is the first study to show that many of these blood biomarkers associate with susceptibility to severe infections , potentially indicating that fatty acids and amino acids should not be considered only as biomarkers for cardiometabolic risk . The associations of omega-3 and other fatty acids with the risk for severe COVID-19 may be particularly important , as these measures are more directly modifiable by lifestyle means than common markers of inflammation . The overall pattern of biomarker associations followed a characteristic metabolic signature reflective of an increased susceptibility to a severe infectious disease . This pattern of biomarker associations is broadly similar to what has previously been reported with the risk for all-cause mortality in smaller prospective cohort studies ( Deelen et al . , 2019 ) . It is therefore unlikely that the identified biomarker signature is specific to the risk for severe pneumonia and COVID-19 , or even specific to infectious diseases in general . We propose that the overall metabolic biomarker perturbations observed here reflect molecular signals of low-grade inflammation that exacerbate disease severity , in case of both infectious and chronic diseases ( Akbar and Gilroy , 2020; Bonafè et al . , 2020 ) . In line with this , prior studies have demonstrated that elevated levels of GlycA , the biomarker with the strongest weight in the infectious disease score , is associated with increased neutrophil activity and the long-term risk for fatal infections ( Ritchie et al . , 2015 ) . Such over-activity of immune response from pneumonia or COVID-19 infection is known to cause tissue damage and organ dysfunction through cytokine storm , a common complication of severe COVID-19 ( Mangalmurti and Hunter , 2020 ) . While the specific biological mechanisms underpinning the blood metabolic biomarker associations with chronic and infectious diseases remain poorly understood , we emphasize that the observational character of our study does not allow us to conclude whether the biomarkers are contributing causally to increase the risk or are merely indirect risk markers . Replication of novel biomarker associations is a key aspect in observational studies . We are not aware of other prospective studies with sufficient COVID-19 hospitalisation events and NMR-based metabolic biomarker data to address this . However , a preprint of the present study featured analysis of 195 severe COVID-19 cases , based on data available in UK Biobank back in June 2020 ( Julkunen et al . , 2020 ) . In the present updated analyses , with over three times the number of cases , all biomarker associations with susceptibility to severe COVID-19 were similar or stronger , and hereby provide a within-cohort replication of our initial findings . In addition , a recent study used the same metabolic biomarker panel in three cohorts of hospitalised patients and observed similar overall biomarker perturbations to be predictive of COVID-19 severity ( Dierckx et al . , 2020 ) . The study also reported the multi-biomarker infectious disease score to be among the strongest biomarkers for discriminating COVID-19 severity among already hospitalised patients . Our study has both strengths and limitations . Strengths include the large sample size , which enabled the analysis of biomarkers for susceptibility to severe COVID-19 based on pre-pandemic blood samples from general population settings . We used a validated metabolic profiling platform that enables simultaneous quantification of numerous metabolic biomarkers in a scalable low-cost setup . Although the number of hospitalised COVID-19 cases was in line with the prevalence in England , we acknowledge that the statistical power was limited for prediction analyses even with close to 100 , 000 samples linked with COVID-19 outcome data . Furthermore , the UK Biobank study participants are not fully representative of the UK population by demographic characteristics; the individuals were enrolled on a volunteer basis and are therefore more representative of healthier individuals than average ( Sudlow et al . , 2015; Fry et al . , 2017 ) . Even though this is generally not a concern for investigating risk associations ( Keyes and Westreich , 2019 ) , it does limit the statistical power to explore effects of ethnicity and old age . Other limitations include the decade long duration from blood sampling to the COVID-19 pandemic . While this limits inference on how well the biomarkers predict short-term risk for severe COVID-19 , our analogy with long-term risk for severe pneumonia indicates that the time lag likely attenuates the biomarker association magnitudes substantially . Conversely , the remarkably strong associations for short-term risk of severe pneumonia led us to speculate that similar enhancements in association magnitudes could also hold for severe COVID-19 . However , this inference should be further tested , in particular in the light of the bacterial origin of many severe pneumonia cases and the viral origin of COVID-19 . Weaker biomarker associations for severe COVID-19 compared to severe pneumonia may also arise from the UK Biobank COVID-19 data being influenced by ascertainment bias in terms of differential healthcare seeking and differential testing ( Griffith et al . , 2020 ) , whereas pneumonia is anticipated to have nearly complete case ascertainment ( Ho et al . , 2020 ) . In conclusion , a metabolic signature of perturbed blood biomarkers is associated with an increased susceptibility to both severe pneumonia and COVID-19 in blood samples collected a decade before the pandemic . The multi-biomarker score captures an elevated susceptibility to severe pneumonia within few years after blood sampling that is several times stronger than the risk elevation associated with many pre-existing health conditions , such as obesity and diabetes ( Ho et al . , 2020 ) . If the three- to fourfold elevation in short-term risk compared to long-term risk of severe pneumonia also applies to severe COVID-19 , then the metabolic biomarker profiling could potentially complement existing tools for identifying individuals most susceptible to a severe COVID-19 disease course . Regardless of the translational prospects , these results provide novel understanding on how metabolic biomarkers may reflect the susceptibility of severe COVID-19 and other infections .
Details of the design of the UK Biobank have been reported previously ( Sudlow et al . , 2015 ) . Briefly , the UK Biobank recruited 502 , 639 participants aged 37–70 years in 22 assessment centres across the UK . All study participants had to be able to attend the assessment centres by their own means , and there was no enrolment at nursing homes . All participants provided written informed consent and ethical approval was obtained from the North West Multi-Center Research Ethics Committee . Blood samples were drawn at baseline between 2007 and 2010 . The current analysis was approved under UK Biobank Project 30418 . No selection criteria were applied to the sampling . From the entire UK Biobank population , a random subset of non-fasting baseline plasma samples ( aliquot 3 ) from 118 466 individuals and 1298 repeat-visit samples were measured using high-throughput NMR spectroscopy ( Nightingale Health Plc; biomarker quantification version 2020 ) . This provides simultaneous quantification of 249 metabolic biomarker measures in a single assay , including routine lipids , lipoprotein subclass profiling with lipid concentrations within 14 subclasses , fatty acid composition , and various low-molecular-weight metabolites such as amino acids , ketone bodies , and glycolysis metabolites quantified in molar concentration units . Technical details and epidemiological applications of the metabolic biomarker data have been reviewed ( Soininen et al . , 2015; Würtz et al . , 2017 ) . The Nightingale NMR platform has received various regulatory approvals , including CE-mark , and 37 biomarkers in the panel have been certified for diagnostics use . We focused on this particular set of certified biomarkers , as we wanted to investigate if these markers of systemic metabolism — commonly linked to cardiometabolic diseases — could also be associated with future risk for severe infectious disease . Furthermore , these clinically validated biomarkers span most of the different metabolic pathways measured by the NMR platform and could facilitate potential translational applications as they are certified for diagnostics use and are measured simultaneously in a single assay . The mean and standard deviation of concentrations for 249 quantified metabolic biomarkers are given in Supplementary file 2 . Measurements of the metabolic biomarkers were conducted blinded prior to the linkage to the UK Biobank health outcomes . The metabolic biomarker data were curated and linked to UK Biobank clinical data in late-May 2020 . The metabolic biomarker dataset has been made available for the research community through the UK biobank in March 2021 . We combined ICD-10 codes J12–J18 to define the pneumonia endpoint . To strengthen the analogy with the analysis of severe COVID-19 , we focused on severe pneumonia events , defined as diagnosis in hospital or death records based on UK Hospital Episode Statistics data and national death registries ( 2507 incident cases in the current study ) . All analyses are based on the first occurrence of a diagnosis . Therefore , 2658 individuals with recorded hospitalisation of pneumonia prior to the blood sampling were excluded . Additionally , 346 individuals with pneumonia diagnosis recorded in primary care settings and by self-reports were also omitted from the analyses . The registry-based follow-up was from blood sampling in 2007–2010 through to 2016–2017 , depending on assessment centre ( 850 , 000 person-years ) . We used COVID-19 data available in the UK Biobank per 3rd of February 2021 , which covers test results from 16 March to 1st of February 2021 . These data include information on positive/negative PCR-based diagnosis results and explicit evidence in the microbiological record on whether the participant was an inpatient ( Resource UKBiobankD , 2020 ) . For the present analyses , we focused on PCR-positive inpatient diagnoses . These hospitalised cases are here denoted as severe COVID-19 ( 652 cases in the current study ) . COVID-19 data were not available for assessment centres in Scotland and Wales , so individuals from these centres were excluded . Individuals who had died during follow-up prior to 2018 were also excluded , since they were never exposed to COVID-19 . The entire study population of non-cases was used as controls in the statistical analyses ( n = 102 , 639 for severe pneumonia and n = 92 , 073 for severe COVID-19 , respectively ) . This choice of controls is consistent with the majority of publications examining risk factors for susceptibility to severe COVID-19 ( e . g . Ho et al . , 2020; Williamson et al . , 2020 ) . It allows to address the question of whether an initially healthy person with a high value of a given biomarker is at an increased risk of eventually getting the disease outcome ( severe pneumonia or COVID-19 hospitalisation ) compared to people from the general population with low levels of the biomarker . This choice of controls also overcomes biases that may arise from analyses using confirmed mild infections as the control group , such as collider bias caused by non-random testing of the control group compared to the rest of the study population ( Griffith et al . , 2020 ) . To examine the influence of prevalent diseases in the prospective analyses of severe pneumonia and severe COVID-19 , we used the following: prevalent cardiovascular disease ( ICD-10 codes I20–I25 , I50 , I60–I64 , and G45 ) , diabetes ( E10–E14 ) , lung cancer ( C33–C34 , D02 . 2 , Z85 . 1 ) , chronic obstructive pulmonary disease ( COPD; J43–J44 ) , liver diseases ( K70–K77 ) , renal failure ( N17–N19 ) , and dementia ( F00-F03 ) . Biomarker levels outside four interquartile ranges from median were considered as outliers and excluded . All 37 biomarkers were scaled to standard deviation ( SD ) units prior to analyses . For biomarker association testing with severe pneumonia and with severe COVID-19 ( as separate outcomes ) , we used logistic regression models adjusted for age , sex , and assessment centre . To examine the utility of multiple biomarkers in combination , we used a weighted sum of the biomarkers optimised for association with future risk of severe pneumonia; this multi-biomarker score was denoted as ‘infectious disease score’ . To minimise the collinearity of the biomarkers , the multi-biomarker score was trained using logistic regression with least absolute shrinkage and selection operator ( LASSO ) , which uses L1 regularisation that adds penalty equal to the absolute value of the magnitude of the coefficients . The multi-biomarker infectious disease score was trained using half of the study population with complete data available for the 37 clinically validated biomarkers ( n = 52 , 573 and 1257 severe pneumonia events ) using five-fold cross-validation to optimise the regularizsation parameter λ . The remaining half of the study population was used in validating the performance of the biomarker score in relation to future risk for severe pneumonia . The multi-biomarker infectious disease score was subsequently tested for association with severe pneumonia and COVID-19 in logistic regression models adjusted for age , sex , and assessment centre . We further examined the effect of additional adjustment for body mass index ( BMI ) and smoking status ( never , former , current ) and prevalent diseases . The associations were also examined by omitting individuals with prevalent diseases and stratified by age and sex . In the case of severe pneumonia , we further examined the association magnitudes according to follow-up time: we used severe pneumonia events occurring during 7–11 years after the blood sampling to mimic the decade long lag from blood sampling to the COVID-19 pandemic , and severe pneumonia events occurring within the first 2 years to interpolate to the scenario of preventative COVID-19 screening carried out today . In both scenarios , the confined follow-up times were arbitrarily chosen to be as short as possible while ensuring sufficient numbers of events . Finally , to explore potential non-linear effects , the infectious disease score was plotted as a proportion of individuals who contracted severe pneumonia during follow-up when binning individuals into percentiles of the infectious disease score ( Khera et al . , 2018 ) . The time-resolution was further examined by Kaplan-Meier curves of the cumulative risk for severe pneumonia . | National policies for mitigating the COVID-19 pandemic include stricter measures for people considered to be at high risk of severe and potentially fatal cases of the disease . Although older age and pre-existing health conditions are strong risk factors , it is poorly understood why susceptibility varies so widely in the population . People with cardiometabolic diseases , such as diabetes and liver diseases , or chronic inflammation are at higher risk of severe COVID-19 and other infections including pneumonia . These conditions alter the molecules circulating in the blood , providing potential ‘biomarkers’ to determine whether a person is more likely to develop a fatal infection . Uncovering these blood biomarkers could help to identify people who are prone to life-threatening infections despite not having ever been diagnosed with a cardiometabolic disease . To find these biomarkers , Julkunen et al . studied blood samples that had been collected from 105 , 000 healthy individuals in the United Kingdom over ten years ago . The data showed that individuals with biomarkers linked to low-grade inflammation and cardiometabolic disease were more likely to have died or been hospitalised with pneumonia . A score based on 25 of these biomarkers provided the best predictor of severe pneumonia . This biomarker score performed up to four times better within the first few years after blood sampling compared to predicting cases of pneumonia a decade later . The same blood biomarker changes were also linked with developing severe COVID-19 over ten years after the blood samples had been collected . The predictive value of the biomarker score was similar for both severe COVID-19 and the long-term risk of severe pneumonia . Julkunen et al . propose that the metabolic biomarkers reflect inhibited immunity that impairs response to infections . The results from over 100 , 000 individuals suggest that these blood biomarkers may help to identify people at high risk of severe COVID-19 or other infectious diseases . | [
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] | 2021 | Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population |
The fish-hunting marine cone snail Conus geographus uses a specialized venom insulin to induce hypoglycemic shock in its prey . We recently showed that this venom insulin , Con-Ins G1 , has unique characteristics relevant to the design of new insulin therapeutics . Here , we show that fish-hunting cone snails provide a rich source of minimized ligands of the vertebrate insulin receptor . Insulins from C . geographus , Conus tulipa and Conus kinoshitai exhibit diverse sequences , yet all bind to and activate the human insulin receptor . Molecular dynamics reveal unique modes of action that are distinct from any other insulins known in nature . When tested in zebrafish and mice , venom insulins significantly lower blood glucose in the streptozotocin-induced model of diabetes . Our findings suggest that cone snails have evolved diverse strategies to activate the vertebrate insulin receptor and provide unique insight into the design of novel drugs for the treatment of diabetes .
Insulin is a pancreatic hormone that is critical for glucose homeostasis . Secretion of insulin from pancreatic β-cells triggers the uptake of blood glucose into a variety of tissues , including the liver , skeletal muscle and adipose cells . Impairment of insulin secretion and/or insensitivity to the insulin produced can lead to the disease diabetes mellitus . To date , three major types of diabetes have been recognized: Type one diabetes ( T1D ) or autoimmune diabetes , Type two diabetes ( T2D ) and gestational diabetes mellitus ( GDM ) . Daily insulin injections are the only effective treatment for patients with T1D , late-stage T2D and some with GDM ( Mack and Tomich , 2017 ) . A major limitation of insulin therapy is its delayed action . The native hormone ( consisting of an A and B chain connected by disulfide bonds ) oligomerizes into a stable hexamer comprising three insulin dimers held together by two central zinc ions ( Adams et al . , 1969 ) . Following subcutaneous injection , the hexamer has to dissociate into the dimer , then monomer , in order to activate the insulin receptor . Hexamer-to-monomer conversion is a slow process that can lead to a significant delay in glucose control . This limitation has spurred efforts to design insulin analogues with reduced dimerization ( and thus oligomerization ) rates ( Owens , 2002 ) . However , despite decades of research the best fast-acting insulin formulations are not truly monomeric and still require 15–90 min to effectively lower blood glucose ( Elleri et al . , 2011 ) ; this is because the region involved in dimerization of the insulin molecule , the C-terminus of the insulin B chain , is also of critical importance for receptor activation ( Menting et al . , 2013 ) . Until now , removing this region of the B chain in order to generate a fast-acting analog could not be achieved without a near complete loss of biological activity ( Bao et al . , 1997; De Meyts et al . , 1978 ) . Novel insights for potentially solving this long-standing problem recently came from our discovery of an insulin peptide found in the venom of the fish-hunting cone snail , Conus geographus ( Safavi-Hemami et al . , 2015 ) . C . geographus belongs to a large genus of predatory marine snails that use their complex venoms for prey capture , defense and competitive interactions ( Olivera , 1997 ) . C . geographus insulin ( Con-Ins G1 ) was shown to rapidly induce insulin shock ( dangerously low blood sugar ) in its fish prey ( Safavi-Hemami et al . , 2015; Robinson and Safavi-Hemami , 2016 ) . Remarkably , Con-Ins G1 lacks the region of the B chain that is critical for both , dimerization and receptor engagement in human insulin ( Safavi-Hemami et al . , 2015; Menting et al . , 2016 ) . Despite this , Con-Ins G1 is a potent agonist of the human insulin receptor ( Menting et al . , 2016 ) . Structure-function studies provided a rationale for this conundrum: two residues within the B chain of Con-Ins G1 act as surrogates for the missing C-terminus of the B chain of human insulin ( Menting et al . , 2016 ) . Here , we demonstrate that fish-hunting cone snails have evolved a diverse set of B chain minimized insulins as part of their complex predation strategy . Remarkably , these insulins activate the vertebrate insulin receptor ( in fish , mouse and human ) but residues previously identified to serve as surrogates for the loss of the B region in Con-Ins G1 are variable . Molecular dynamics simulations reveal the modus operandi of these unique molecules . Our findings suggest the evolution of diverse molecular mechanisms of insulin receptor activation , providing a set of solutions to potentially solve a long-standing problem of designing truly monomeric , fast-acting insulin analogs for the treatment of diabetes .
We investigated three species of fish-hunting cone snails native to the Central Philippines: C . geographus and C . tulipa from the Gastridium clade and C . kinoshitai from the Afonsoconus clade ( Figure 1A ) . Reverse transcription PCR ( RT-PCR ) combined with whole transcriptome sequencing led to the identification of several distinct insulin sequences in the two cone snail species C . geographus and C . tulipa ( Safavi-Hemami et al . , 2015 ) . Of these sequences , the venom insulin Con-Ins G1 shared highest similarity with fish insulin and was previously selected for functional and structural characterization ( Safavi-Hemami et al . , 2015; Menting et al . , 2016 ) . Here , we synthesized and functionally characterized four additional venom insulins from these species with various degrees of sequence divergence: Con-Ins G3 from C . geographus and Con-Ins T1A , T1B and T2 from C . tulipa . Con-Ins G1 and G3 differ by 32 residues across the entire precursor ( 22 residues within the A and B chain , Figure 1—figure supplement 1 ) and likely represent paralogs originated by gene duplication ( Safavi-Hemami et al . , 2016 ) . Con-Ins T1A and T1B only differ by three residues across their entire precursor sequence and likely represent allelic variants of the same gene locus . Con-Ins T2 differs at 15 positions across the precursor ( 13 positions within the A and B chain ) and likely originates from a distinct gene locus ( Figure 1—figure supplement 1 ) . In addition to these sequences , we performed RT-PCR and whole transcriptome sequencing of the venom gland of C . kinoshitai leading to the identification of two new insulin sequences , named Con-Ins K1 and Con-Ins K2 . The two C . kinoshitai insulin sequences differ at 24 positions , 22 of which fall within the A and B chain ( Figure 1—figure supplement 1 ) . Compared to insulins from C . geographus and C . tulipa , the C-peptide regions of Con-Ins K1 and Con-Ins K2 are shorter suggesting an insertion/deletion event of 14 amino acids ( 14 codons , 42 nucleotides ) during the evolution of these peptides . As evident by a highly conserved N-terminal signal sequence , all venom insulins belong to the same gene superfamily of venom gland-specific insulins ( Safavi-Hemami et al . , 2016 ) . Sequence variability is highest within the A and B chain while the signal sequence is conserved ( Figure 1—figure supplement 2 ) . This juxtaposition between conserved and hypervariable regions is a common characteristic of cone snail venom toxins ( Woodward et al . , 1990 ) . Cleavage of the A and B chains and post-translational modifications were predicted from precursor sequences based on mass spectrometric sequence information available for venom insulins from C . geographus ( Safavi-Hemami et al . , 2015 ) . Unlike the endogenous insulins used by cone snails for insulin signaling ( Safavi-Hemami et al . , 2016 ) , all seven venom insulins exhibit the cysteine framework of vertebrate insulin ( with 4 and 2 cysteines in the A and B chain , respectively ) . Critically , all seven sequences lack the C-terminal region of the B chain involved in dimerization and receptor activation of human insulin ( Figure 1B ) . Insulins from C . geographus and C . tulipa share more sequence similarity to one another than insulins from C . kinoshitai , consistent with the close phylogenetic relationship of these two species ( Puillandre et al . , 2014 ) . Con-Ins K1 and K2 have longer A chain C-termini and B chain N-termini and differ in nearly every position from Con-Ins G1 ( Figure 1B ) . Notably , venom insulins are distinct from the endogenous signaling insulin expressed in the circumoesophageal nerve ring of cone snails ( Figure 1C ) ( Safavi-Hemami et al . , 2016 ) . This is also evident by the presence of a distinct signal sequence ( and distinct 5’ and 3’ untranslated regions ) between the venom insulin gene family and their endogenous homologs ( data not shown ) . All sequences were deposited to GenBank ( see Material and methods section for accession numbers ) . Overall , venom insulin sequences exhibit pronounced sequence divergence with very few conserved amino acids . The few relatively well conserved amino acids include the first four residues in the A chain , Gly8 , Ser9 and Leu18 in the B chain and all six cysteines ( see Sequence Logo in Figure 1 ) . Strikingly , the two residues ( TyrB15 and TyrB20 ) previously identified to serve as surrogates in Con-Ins G1 for the missing B chain C-terminus of human insulin are only moderately conserved ( TyrB15 ) or hypervariable ( TyrB20 ) . Since its first chemical synthesis in 1963 ( Meienhofer et al . , 1963 ) , insulin has remained challenging to synthesize with the correct intra- and intermolecular disulfide bonds . Here , all novel insulin sequences were successfully synthesized using Fmoc peptide chemistry . Con-Ins G3 and Con-Ins T2 were synthesized using procedures similar to those previously applied to Con-Ins G1 ( 11 ) . To simplify peptide synthesis for Con-Ins T1A , Con-Ins T1B , Con-Ins K1 and Con-Ins K2 , these peptides were synthesized using a selenocysteine replacement strategy in which the intra-molecular disulfide bond in the A-chain is substituted by a diselenide ( Sec-Sec ) bond ( Safavi-Hemami et al . , 2015 ) . We previously reported that there is no difference in activity between Con-Ins G1 containing a diselenide bond and native Con-Ins G1 ( 11 ) and similar observations have been made for other peptides ( Walewska et al . , 2009; Muttenthaler et al . , 2010 ) . Several different methods were applied for the formation of the first inter-molecular disulfide bridge ( i . e . , using DMSO , Cu ions and pre-activation of the B-chain with DTNP as recently described by others ( Liu et al . , 2014 ) ) . These methods allowed the accumulation of the desired intermediate product containing the first intermolecular disulfide bond in yields of up to 50% . When forming the second intermolecular disulfide bridge between the A and B chain in the presence of iodine we observed the formation of a small amount of an additional disulfide isomer for some venom insulins . This was most pronounced for Con-Ins K1 , where two products with the same mass were isolated in almost equimolar ratio . When synthesized with a disulfide instead of a diselenide bond only one final product was observed that was selected for subsequent functional analysis . The presence of insulins with structural similarity to vertebrate insulin in the venoms of these fish-hunting species strongly suggested that these compounds are used to induce hypoglycemic shock in fish prey . Indeed , behavioral observations of C . geographus and C . tulipa have demonstrated that these species prey on fish and release compounds into the water prior to prey capture ( Olivera et al . , 2015 ) ( Figure 1A ) . While no behavioral data is available for C . kinoshitai , phylogenetic analysis places this species within fish-hunters ( the majority of cone snail species prey on marine worms and some prey on snails ) ( Olivera et al . , 2015 ) . In order to determine whether venom insulins are capable of lowering blood glucose in fish via activation of the insulin receptor , cone snail insulins were tested in the streptozotocin ( STZ ) -induced model of diabetes in zebrafish ( Safavi-Hemami et al . , 2015 ) . Animals were first rendered hyperglycemic through i . p . injection of the β-cell poison STZ ( 1 . 5 g/kg ) ( Olsen et al . , 2010 ) , and the effects of subsequent injection of each venom insulin were examined . Following STZ treatment , blood glucose levels were significantly elevated from 65 . 9 ± 4 . 8 mg/dL ( n = 11 ) to 393 . 3 ± 10 . 2 mg/dL ( n = 7 ) . Administration of venom insulins at 65 ng peptide/g body weight significantly lowered blood glucose levels for all venom insulins tested ( Figure 2 ) with values ranging from 77 . 8 ± 39 . 8 mg/dL for Con-Ins T1A ( p<0 . 0001 , n = 5 ) to 199 . 2 ± 39 . 8 mg/dL for Con-Ins T2 ( p<0 . 0067 , n = 5 ) . For comparison , 65 ng of human insulin ( hIns ) /g body weight reduces blood glucose to 92 . 0 ± 17 . 4 mg/dL ( Safavi-Hemami et al . , 2015 ) . This demonstrates that venom insulins are capable of binding to and activating the fish insulin receptor , supporting their biological role in inducing insulin shock in fish prey . In order to determine if venom insulins were capable of binding to the human insulin receptor ( hIR ) the affinity of these compounds for the B isoform of hIR ( hIR-B ) was determined in competition assays based on the displacement of europium-labeled hIns from solubilized immunocaptured receptors ( Denley et al . , 2004 ) . All seven venom insulins bound to the hIR with affinities ranging from IC50 9 . 92 nM ( 7 . 75–12 . 68 nM; 95% CI ) for the most potent insulin Con-Ins T1A to IC50 275 . 2 nM ( 162 . 2–487 . 6 nM; 95 % CI ) for Con-Ins K2 ( n = 2 biological replicates with three technical replicates each; the IC50 indicates half-maximal inhibitory concentration; Figure 3 ) . For comparison , the affinity of hIns is ~20 fold higher than the most potent venom insulin Con-Ins T1A [hIns IC50 0 . 44 nM ( 0 . 40–0 . 49 nM; 95 % CI ) ] . The ability of venom insulins to activate insulin receptor signaling upon receptor binding was determined using an immunoassay of phosphorylated Akt ( pAkt ) Ser473 from lysates of mouse NIH 3T3 fibroblast cells overexpressing hIR-B . As previously shown , Con-Ins G1 potently activates the human insulins receptor , albeit at lower potency when compared to human insulin ( EC50 of Con-Ins G1 16 . 28 nM; 95% CI: 7 . 3–36 . 4 nM; EC50 of human insulin 1 . 5 nM; 95% CI: 1 . 1–2 . 1 nM , in which the EC50 indicates half-maximal effective concentration ) ( Figure 4A and E ) . The other insulin identified from C . geographus , Con-Ins G3 , was significantly less potent at the hIR-B with an EC50 of 242 . 0 nM , 95% CI: 101 . 3–578 . 5 nM ) . In contrast , all three insulins identified from C . tulipa were potently active at the hIR-B with activities comparable to Con-Ins G1 ( EC50 of Con-Ins T1A 12 . 0 , 95% CI: 9 . 7–15 . 0 nM; EC50 of Con-Ins T1B 12 . 0 nM , 95% CI: 10 . 2–13 . 8 nM; EC50 of Con-Ins T2 15 . 5 nM , 95% CI: 11 . 9–20 . 2 nM ) ( n = 4 technical replicates; Figure 4B and E ) . C . kinoshitai insulins showed variable activity with an EC50 of 30 . 45 nM ( 95 % CI 16 . 9–55 . 0 nM ) for Con-Ins K1 and 373 . 2 nM ( 95 % CI 61 . 6–2262 . 0 nM ) for Con-Ins K2 ( Figure 4C and E ) . The ability of Con-Ins T1A and Con-Ins G1 to induce hIR-B phosphorylation ( Tyr1150/1151 ) correlated with downstream receptor activation ( Con-Ins T1A and Con-Ins G1 were 10- and 15-times less potent in these assays when compared to hIns; Figure 4—figure supplement 1 ) . Overall , the ability of venom insulins to bind to the hIR correlates with downstream signaling activity although some differences can be observed . These include that Con-Ins T2 and Con-Ins K1 have higher and Con-Ins G3 lower activation potency than would be expected from their receptor binding potencies . These observations were not further explored in the current study but could indicate biased signaling of some venom insulins following receptor binding and/or partial receptor antagonism . As pointed out above , venom insulins lack the region of the B-chain that is known to be important for receptor activation for hIns . The ability of the venom insulins to induce downstream signaling was compared to a B-chain truncated analog of hIns , des-octa peptide insulin ( DOI ) ( Figure 4D and E ) . Of the seven venom insulins tested , five had significantly higher activity at the hIR-B than DOI suggesting the presence of structural motifs that enable receptor activation despite the peptides’ lack of the B-chain C-terminal segment . Based on their activity against the hIR-B and ability to lower blood glucose in fish , the most active venom insulin from each species was tested in the STZ-induced mouse model of T1D . Animals were rendered hyperglycemic through i . p . injection of STZ . Blood glucose was monitored over the course of 2–3 days prior to administration of human insulin ( n = 5 biological replicates ) and venom insulins ( n = 3 biological replicates for each venom insulin ) . Given their ~ 10 to 20-fold lower activity at the hIR-B over human insulin , venom insulins were initially injected at 10-times the effective concentration reported for human insulin ( 1 IU/kg body weight ) ( Gupta et al . , 2014 ) . At this concentration Con-Ins G1 , Con-Ins T1A and Con-Ins K1 effectively reversed hyperglycemia when measured every 15 min following injection over the course of 125 min ( Figure 5 ) . Given the severe drop in blood glucose , mice injected with Con-Ins T1A were fed at 90 min post-injection ( black arrow ) . Following this , Con-Ins T1A was also administered at the same concentration of human insulin ( equivalent to 1 IU/kg body weight ) . At this concentration , Con-Ins T1A effectively lowered blood glucose from 541 . 3 ± 52 mg/dL to 94 . 3 ± 29 mg/dL over 105 min ( Figure 5C ) . Consistent with hIR-B binding and activation data , Con-Ins K1 was less effective at lowering blood glucose at 10-times the concentration of human insulin ( Figure 5D ) . When tested at 20-times ( equivalent to 20 IU/kg body weight ) Con-Ins K1 showed a similar in vivo effect to human insulin . In order to develop an understanding of the structure and interaction of the most potent venom insulins identified from each species with the insulin receptor , we created a model of Con-Ins T1A and Con-Ins K1 based on the recent crystal structure of Con-Ins G1 , in complex with the human insulin micro-receptor ( hIR ) consisting of the first leucine-rich ( L1 ) ( residues 1–154 ) and the C-terminal segment ( αCT; residues 704–719 ) of the hIR α-chain ( Menting et al . , 2013; Menting et al . , 2014 ) ( Figure 6 , top panel ) . The model of Con-Ins T1A monomer was stable throughout the 50 ns molecular dynamics ( MD ) simulation , with notable flexibility of residues TyrB15 and PheB20; the remaining residues exhibited little movement , with the exception of the N-terminal region of the B chain , which exhibited a high degree of motility . The flexibility displayed by the sidechains of TyrB15 and PheB20 correlates with the weak electron density observed for the corresponding residues in the X-ray crystal structure of Con-Ins G1 ( 11 ) . The sidechains of TyrB15 and PheB20 are locked in position in the model of Con-Ins T1A bound to the hIR over 100 ns ( Figure 6 , middle panel ) , preferentially adopting a conformation consistent with these residues acting as a surrogate for the receptor-engaging residue PheB24 of the hIns . These positions are identical to positions previously identified in the model of G1 although TyrB20 is replaced by PheB20 in Con-Ins T1A ( Figure 6 , middle panel ) . Other residues that differ between Con-Ins T1A and G1 differ primarily at the interaction face of L1 and the insulins’ corresponding B chains ( shown in yellow in Figure 6 , middle panel ) . Residues ProB12 and IleB16 of T1A , threonine and methionine in Con-Ins G1 , respectively , bind pockets on the surface of L1 . In the model of Con-Ins K1 bound to the hIR , again residues at positions B15 and B20 again appear to play a role in receptor binding . However , aromatic residues present in Con-Ins G1 and T1A at these positions are replaced by leucine in Con-Ins K1 . Additionally , and likely compensatorily , the longer C-terminus of the A chain allows for a continuation of the C-terminal A chain helix orienting LeuA23 towards the hydrophobic core ( Figure 6 , bottom panel ) . The significant sequence divergence across Con-Ins K1 is compensatory both internally and in maintaining interactions with L1 . Coordinates for MD models are provided as pdb files in the supporting information ( Figure 6 , Figure 6—source data 1; Figure 6—source data 2; Figure 6—source data 3 ) .
Insulin and related peptides ( insulin-like peptides , insulin-like growth factors and relaxins ) form a large superfamily of peptide hormones ( Shabanpoor et al . , 2009 ) that is found throughout the animal kingdom ( Piñero-González and González-Pérez , 2011 ) . In vertebrates , insulin is synthesized in pancreatic β-cells and is the key regulator of carbohydrate and fat metabolism ( Blumenthal , 2010 ) . Insulin expression has also been detected in the brain where it functions in energy homeostasis and cognition ( Gerozissis and Kyriaki , 2003 ) . In vertebrates , the primary amino acid sequence , the length of the A and B chains , and the arrangements of cysteines that form disulfide bonds are highly conserved ( Blumenthal , 2010 ) . In contrast , sequences of invertebrate insulin-like peptides are more variable and are predominantly expressed in neuroendocrine tissues ( Smit et al . , 1998 ) . Most notably , insulins found in mollusks ( including cone snails ) differ from vertebrate insulins by having one additional disulfide bond between the A and B chain and are larger ( Smit et al . , 1998 ) . Thus , the discovery of an insulin in the cone snail C . geographus that unprecedentedly small and shared the disulfide framework with vertebrate insulin was surprising and indicated that this insulin had evolved to rapidly and effectively induce insulin shock in fish prey . The large majority of the ~800 extant cone snail species prey on worms . Fish- and snail-hunting behaviors are believed to have only evolved in very few clades within this large genus . Within fish-hunters , only a small subset of species expresses insulins that mimic fish insulins ( C . geographus and C . tulipa from the Gastridium clade ) . These species are known to use other specialized toxins to sedate and disorient their prey prior to capture . Con-Ins G1 was previously suggested to be part of this so-called ‘nirvana cabal’ that is released into the water to induce hypoglycemic shock and facilitate capture of the incapacitated prey . Here , we show that C . kinoshitai , a distantly related fish-hunter , also expresses insulins in its venom . The hunting strategy of this deep-water species has never been documented , but the presence of venom insulins suggests that C . kinoshitai may utilize these insulins for prey capture . We show that all seven venom insulins identified from these three fish-hunters are active at the vertebrate receptor , albeit with varying potencies . Cone snail venom peptides are known for their rapid rate of gene duplications and sequence diversification with nearly no overlap in each toxin repertoire , even between sister species ( Li et al . , 2017 ) . The drastic sequence divergences of venom insulins reported here is consistent with the rapid evolution of cone snail toxins . Notably , while Con-Ins G1 and Con-Ins T1A share sequence similarity with fish insulin , the other five venom insulins exhibit little to no sequence similarity with the fish hormone . Yet , all potently lower blood glucose in a zebrafish model of diabetes suggesting that , in cone snails , diverse strategies have evolved to bind to and activate the fish insulin receptor . Conus geographus is one of the largest fish-hunting cone snail species ( shell length of ~15 cm ) that can produce approximately 50 mg of venom in its long and convoluted venom gland . We have previously determined that venom insulins constitute ~1/25 of the total venom of this species ( Safavi-Hemami et al . , 2015 ) , corresponding to ~2 mg . While it remains to be experimentally determined how much venom is released into the water during each predation event , if all venom were injected , 2 mg of venom insulin would be sufficient to effectively lower blood glucose in ~85 , 500 zebrafish at the concentration used in this study ( 65 ng insulin/g body weight;~23 ng venom insulin per fish ) . We further show that three of these insulins ( Con-Ins G1 , Con-Ins T1A and Con-Ins K1 ) are capable of lowering blood glucose in a mouse model of T1D demonstrating their in vivo activity in a mammalian model system of the disease . Notably , each species expresses at least two different insulins with varying sequence divergence . The most pronounced sequence differences within the same species are observed between Con-Ins G1 and Con-Ins G3 from C . geographus ( 11 and 10 residues differ in the A and B chain , respectively ) . Both insulins lower blood glucose in zebrafish with comparable potencies . However , Con-Ins G3 is more than one order of magnitude less potent against the human IR-B than Con-Ins G1 . Similar observations were made for Con-Ins K1 and K2 from C . kinoshitai with Con-Ins K2 being ~10 times less active against the human IR-B than Con-Ins K1 . These species-specific differences may suggest that cone snails have evolved different insulins to target the insulin receptor in different species of prey . Fish insulin receptors share between ~75–96% identity ( for example , zebrafish IR is 96% and 75% identical to the receptor found in the grass carp Ctenopharyngodon idella and the green spotted puffer fish Tetraodon nigroviridis , respectively ) . Little is known about the specific diets of fish-hunting cone snails but aquarium observations suggest that they are not restricted to preying on a single fish species ( Cruz and Corpuz , 1978 ) . Thus , divergent venom insulins may have evolved to allow most efficient capture of different species of prey . Alternatively , venom insulins may target different isoforms of the insulin receptor within the same prey species . Zebrafish , and other fish species for which genome sequencing data is available , express at least two different isoforms of the insulin receptor ( insra and insrb ) ( Toyoshima et al . , 2008 ) that share 75% identity and are differentially expressed in a variety of tissue types ( Tseng et al . , 2013 ) . Humans and other mammals also express two isoforms of the insulin receptor ( IR-A and IR-B ) . However , the mammalian isoforms represent different splice variants of the same gene and their evolutionary origin is distinct from the two isoforms expressed in fish ( Al-Salam and Irwin , 2017 ) . Testing all venom insulins against different isoforms of the human and fish IR in the future could reveal isoform-specific activity profiles and lead to the generation of isoform-specific insulin receptor probes . The human and fish insulin receptors are ~65–75% identical and the fish hormone is known to potently activate the mammalian receptor ( Conlon , 2000 ) . Thus , a venom insulin that evolved to target the insulin receptor in fish has a high chance of also being active at the human receptor . However , activation of the fish and human receptor ( and likely all vertebrate insulin receptors ) requires a region of the C-terminus of the B chain ( Menting et al . , 2014 ) , particularly the conserved aromatic triplet FFY in position 24–26 ( PheB24-PheB25-TyrB26 ) ( Menting et al . , 2014 ) that is missing in all venom insulins identified here . Removal of this region from human insulin nearly abolishes its biological activity ( i . e . , an analog of human insulin missing the eight C-terminal residues of the B chain ( DOI ) retains less than 0 . 1% of native insulin bioactivity ( Bao et al . , 1997 ) ) . Despite lacking this region , all seven venom insulins bind to and activate the hIR-B and are capable of lowering blood glucose in a zebrafish model of T1D . Besides its role in receptor activation , the C-terminal segment functions in oligomerization of two insulin molecules into a homodimer . Three homodimers form a stable hexamer and insulin is stored and secreted from the β-cells of the pancreas in its hexameric form ( Adams et al . , 1969 ) . Self-association into insulin dimers is stabilized by hydrogen bonds and hydrophobic interactions in a short antiparallel β-sheet of the B-chain C-terminus . Hexamer formation is stabilized by two central zinc ions that form interactions with a histidine at position 10 of the B chains ( HisB10 ) ( Rc et al . , 1984 ) . Removal of the C-terminus of the B chain to prevent dimerization and thus hexamerization have led to fully monomeric insulin analogs ( e . g . , DOI ) , however , as mentioned above , these have low receptor binding activities and are poor drug leads . Of the seven venom insulins tested , five show higher activity at the hIR-B than the B-chain truncated human insulin analog DOI , suggesting the presence of structural motifs that enable receptor activation despite the molecules’ lack of the B-chain C-terminus . Identifying these motifs is of significant interest for the design of novel B-chain truncated ( thus monomeric ) analogs of human insulin that may retain activity at the hIR . To identify these motifs , we interrogated the mode of binding of Con-Ins T1A and Con-Ins K1 to the human insulin micro-receptor ( μIR ) ( Menting et al . , 2013 ) . Con-Ins T1A was chosen because of its superior activity profile over DOI and Con-Ins G1 in all in vitro and in vivo activity assays performed here . Con-Ins K1 was selected based on its higher activity over DOI and its very divergent primary structure compared to other venom insulins and the fish and human hormone . Models of these venom insulins that were stable over 100ns of MD indicated that they contain structural elements that act as surrogates for the B chain aromatic triplet of hIns; inclusive of the key hIR αCT residue Phe714 . These residues , Tyr B15 and PheB20 of Con-Ins T1A and , LeuB15 , LeuB20 and LeuA23 of Con-Ins K1 occupy space which is otherwise occupied by PheB24 in hIns . Notably , in these models these residues adopt similar rotated conformations as those represented within the model of Con-Ins G1 bound to the same elements of the hIR3 , consistent with the high degree of sequence and modelled structural similarity between Con-Ins T1A and G1 , but surprising given the sequence dissimilarity between Con-Ins K1 and G1 . However , Con-Ins K1 is significantly less potent than Con-Ins G1 and Con-Ins T1A suggesting that loss of aromatic side chains at positions B15 and B20 results in lower binding affinity and potency at the hIR . Comparative sequence alignment of a diverse set of venom insulin sequences allows for the interrogation of other residues that may play a role for vertebrate receptor activation but that may not easily identifiable by homology modeling . Amino acids that are conserved in the most potent venom insulins ( i . e . , Con-Ins T1A , T1B , T2 and G1 ) and different from the cone snail’s own signaling insulin include Glu4 ( modified to γ-carboxylated Glu ) , Lys/Arg9 , Ser12 in the A chain and Ser9 , Glu/Asp10 ( Glu modified to γ-carboxylated Glu ) , Glu/Asp17 ( Glu may be modified to γ-carboxylated Glu ) in the B chain . Four of these six residues have previously been shown to play a role for receptor activation by human insulin ( GluA4 , SerA12 , HisB10 , LeuB17 in human insulin ) ( De Meyts , 2015 ) . Most notably , mutation of HisB10 to Asp in human insulin leads to a dramatic increase in hIR activation ( Schwartz et al . , 1987 ) . An analog carrying this mutation , insulin X10 , was developed as a rapid-acting drug lead for diabetes but was ultimately halted due to its mitogenic properties ( Hansen et al . , 2011 ) . Cone snails appear to have evolved this strategy of introducing a ( double ) -negatively charged residue at position B10 to enhance vertebrate receptor activation millions of years ago . It seems likely that additional strategies remain to be uncovered . This could include Lys/Arg9 and γ-carboxylated Glu4 in the A chain and Glu/Asp17 in the B chain . Molecular modeling was performed using the human μIR ( Menting et al . , 2013 ) that includes structural elements of one of the two known ligand interaction sites on the insulin receptor ( site 1 and site 2 ) ( De Meyts , 2015; Whittaker et al . , 2008 ) . Recently published cryo-EM structures of insulin in complex with the human insulin receptor ( Scapin et al . , 2018; Weis et al . , 2018 ) may provide opportunities to fully investigate the mode of binding of venom insulins with the hIR and inform on additional structural motifs important for receptor activation . Ultimately , incorporating these structural motifs into DOI or other B chain-truncated analogs of human insulin is likely to lead to the generation of new classes of monomeric insulin analogs for the treatment of diabetes . Given their streamlined role in prey capture venom insulins may exhibit other advantageous properties that , if uncovered , could inform current drug design efforts . For example , venom insulins may have altered off-rates from the receptor , which would affect the ERK signaling properties and resultant mitogenic activities , or may be more stable in extracellular environments , such as blood . Additionally , it would be interesting to determine if venom insulins lack the negative cooperativity observed for human insulin upon receptor binding ( De Meyts et al . , 1978 ) . By investigating the venoms of three fish-hunting cone snail species this study characterized seven unique insulin sequences with pronounced sequence divergence and identified key structural elements for hIR activation . Given the large number of species found in the genus Conus ( ~800 species of which ~ 140 species prey on fish ) it is likely that additional insulins with unique modus operandi at the hIR will be identified from these venoms in the future providing a continuous resource for the design of new insulin analogs inspired by nature .
All studied specimens were collected in the central Philippines ( Figure 1A ) . Specimen identification was initially performed by morphological examination and later verified by sequence analysis of the cytochrome oxidase c subunit 1 ( COI ) gene as previously described ( Li et al . , 2017 ) . Venom glands were dissected and stored in RNAlater at −80°C until further processing . RNA extraction and sequencing of C . geographus and C . tulipa venom insulins was previously described ( Safavi-Hemami et al . , 2015 ) . Two specimens of C . kinoshitai were sequenced in this study . For RT-PCR sequencing , total RNA was extracted from the first specimen using TRIzol Reagent ( Life Technologies Corporation ) according to the manufacturer’s instructions . First-strand cDNA synthesis was performed using AMV reverse transcriptase ( Invitrogen ) with oligo-dT primer . RT-PCR was performed using the Clontech Advantage 2 PCR Kit . Oligonucleotides were designed based on insulin sequences obtained from the C . geographus venom gland transcriptome as previously described ( Safavi-Hemami et al . , 2015 ) [sense primer: 5′ ACA AGT CAG ATG ACG ACA TC 3′; antisense primer: 5′ ATT CCA T ( G , T ) C ATG ( G , C ) GT CAT T 3′] . PCR was carried out for 25 cycles at an annealing temperature of 51°C . To avoid the formation of heteroduplexes , amplicons were diluted 1:5 and subjected to three additional PCR cycles in the presence of fresh buffer , dNTPs , oligonucleotides , and polymerase ( Elleri et al . , 2011 ) . PCR amplicons were gel-purified ( Qiagen gel purification kit ) , cloned into the pGEM-T Easy Vector ( Promega ) , and transformed into Escherichia coli ( DH10B strain ) . Plasmids were purified ( DNA extraction kit; Viogene-Biotek Corporation ) and sequenced at the University of Utah Microarray and Genomic Analysis Core Facility using Sanger DNA sequencing . A total of 10 plasmids was sequenced per species . Sequences represented by at least two clones were considered for subsequent studies . For whole transcriptome sequencing , total RNA extraction was performed using the Direct-zol RNA extraction kit ( Zymo Research , Irvine , CA , USA ) , with on-column DNase treatment , according to the manufacturer’s instructions . cDNA library preparation and sequencing was performed by the University of Utah High Throughput Genomics Core Facility . Briefly , total RNA quality and quantity were first determined on an Agilent 2200 TapeStation ( Agilent Technologies ) . A dual-indexed library was constructed with the Illumina TruSeq Stranded mRNA Sample Prep Kit with oligo ( dT ) selection and an average insert size of approximately 150 bp . The library was validated on an Agilent 2200 TapeStation and using a qPCR assay ( Kapa Biosystems Library Quantification Kit for Illumina ) , and was multiplexed in a batch of 6 samples . 125 cycle paired-end sequencing was performed on an Illumina HiSeq2000 instrument ( San Diego , CA , USA ) at an 80% standard cluster density . Adapter trimming of de-multiplexed raw reads was performed using fqtrim ( v0 . 9 . 4 Release , available online: http://doi . org/10 . 5281/zenodo . 20552 ) , followed by quality trimming and filtering using prinseq-lite ( Schmieder and Edwards , 2011 ) . Error correction was performed using the BBnorm ecc tool , part of the BBtools package ( open source software , Joint Genome Institute ) . Trimmed and error-corrected reads were assembled using Trinity ( version 2 . 2 . 1 ) ( Haas et al . , 2013 ) with a k-mer length of 31 and a minimum k-mer coverage of 10 . Assembled transcripts were annotated using a blastx search ( Altschul et al . , 1990 ) ( E-value setting of 1e-3 ) against a combined database derived from UniProt , Conoserver ( Kaas et al . , 2012 ) , and an in-house cone snail venom transcript library . An in-house script was used to extract putative toxin transcripts ( including the venom insulin gene family ) , trim to open-reading frame , and discard redundant and partial sequences . Following assembly , venom insulin transcripts were manually examined using the Map-to-Reference tool of Geneious , version 8 . 1 . 7 ( 46 ) . All seqeucences characterized here have been deposited into the GenBank Nucleotide Database ( Accession Numbers: Con-Ins G1: AJD85832; Con-Ins G3: AJD85820; Con-Ins T1A: KP268600; Con-Ins T1B: KP268611; Con-Ins T2: MH879035; Con-Ins K1: MH879033; Con-Ins K2: MH87903 ) . Venom insulins were synthesized using solid phase peptide synthesis followed by reversed-phase chromatography and mass spectrometry to verify the identity of all synthetic peptides . Two approaches were used for peptide synthesis: Con-Ins G1 , Con-Ins G3 and Con-Ins T2 were synthesized following procedures similar to the method previously described for Con-Ins G1 ( Menting et al . , 2016 ) . Con-Ins T1A , Con-Ins T1B , Con-Ins K1 and Con-Ins K2 were synthesized using a selenocysteine replacement strategy similar to what was previously described for sCon-Ins G1 ( Safavi-Hemami et al . , 20168 ) . A detailed description of all methods used for peptide synthesis , purification , verification and quantification is provided in Appendix . To determine the extent of insulin signaling induced by the different venom insulins , pAkt Ser473 levels were measured in a mouse fibroblast cell line , NIH 3T3 , overexpressing human IR-B ( a gift from A . Morrione , Thomas Jefferson University ) . Cells were authenticated by western blotting to assess their level of IR expression compared with that of parent 3T3 cells: the NIH 3T3 cells showed an approximately ten-fold-higher level of expression than that of the parent . Cell lines are tested for mycoplasma contaminations every six months . NIH 3T3 cells were cultured in DMEM ( Thermo Fisher Scientific ) with 10% FBS , 100 U/mL penicillin-streptomycin ( Thermo Fisher Scientific ) and 2 μg/mL puromycin ( Thermo Fisher Scientific ) . For each assay , 40 , 000 cells per well were plated in 96-well plates with culture medium containing 1% FBS . 24 hr later , 50 μL of insulin solution in no FBS media ranging 0 . 86 μM - 0 . 82 pM was pipetted into each well following removal of the original medium . After a 30 min treatment , the insulin solution was removed , and the level of intracellular pAkt Ser473 was measured using the HTRF pAkt Ser473 kit ( Cisbio ) according to the manufacturer’s instructions . Briefly , the cells were first treated with cell lysis buffer ( 50 μL per well ) for 1 hr under mild shaking . 16 μL of cell lysate was then added to 4 μL of detecting reagent in a white 384-well plate . After a 4 hr incubation , the plate was read in a Synergy Neo plate reader ( BioTek ) , and data were processed according to the manufacturer’s protocol . Mean EC50 values and their 95% confidence intervals were calculated ( using GraphPad Prism , version 7 ) after curve fitting with a nonlinear regression ( one-site ) analysis . Competition binding assays were performed with solubilized immunocaptured hIR ( isoform B ) with europium-labeled human insulin and increasing concentrations of hIns or venom insulin peptides , as previously described ( Denley et al . , 2004 ) . Time-resolved fluorescence was measured with 340 nm excitation and 612 nm emission filters with a Polarstar Fluorimeter ( BMG Labtech ) . Mean IC50 values and their 95% confidence intervals were calculated with the statistical software package in GraphPad Prism ( version 7 ) after curve fitting with nonlinear regression ( one-site ) analysis . IR-B phosphorylation ( Tyr1150/1151 ) following insulin treatment was measured in a mouse fibroblast cell line , NIH 3T3 , overexpressing human IR-B ( a gift from A . Morrione , Thomas Jefferson University ) . NIH 3T3 cells were cultured in DMEM ( Thermo Fisher Scientific ) with 10% FBS , 100 U/mL penicillin-streptomycin ( Thermo Fisher Scientific ) and 2 μg/mL puromycin ( Thermo Fisher Scientific ) . For each assay , 45 , 000 cells per well were plated in 96-well plates with culture medium containing 1% FBS . 24 hr later , 50 μL of insulin solution in no FBS media ranging 1 μM - 0 . 82 pM was pipetted into each well following removal of the original medium . After a 15 min treatment , the insulin solution was removed , and the level of intracellular Tyr1150/1151 was measured using the HTRF Phospho-IR beta ( Tyr1150/1151 ) kit ( Cisbio ) according to the manufacturer’s instructions . Briefly , the cells were first treated with cell lysis buffer ( 50 μL per well ) for 1 hr under mild shaking . 16 μL of cell lysate was then added to 4 μL of detecting reagent in a white 384-well plate . After a 4 hr incubation , the plate was read in a Synergy Neo plate reader ( BioTek ) , and data were processed according to the manufacturer’s protocol . Mean EC50 values and their 95% confidence intervals were calculated ( using GraphPad Prism , version 7 ) after curve fitting with a nonlinear regression ( one-site ) analysis . STZ assays were performed on adult zebrafish ( strain AB ) . Studies were approved by the University of Utah Institutional Animal Care and Use Committee . Adult fish of 10–12 months in age and an average weight of 360 ± 80 mg were injected i . p . with 1 . 5 g/kg STZ ( Sigma Aldrich ) to cause hyperglycemia . Following STZ injection , animals were fasted for 40 hr and then injected with venoms insulins at 65 ng/g . This concentration was previously shown to be effective when using human insulin and the venom insulin Con-Ins G1 ( 8 ) . Blood glucose in mg/dL was measured 110 min later with a Bayer Contour meter . Data were analyzed in GraphPad Prism ( version 7 ) using unpaired t tests with Welch’s correction . STZ assays on mice were approved by the University of Utah Institutional Animal Care and Use Committee . Adult male mice ( CBA/CaJ and C57BL/6J strain ) between 12–21 weeks of age with an average weight of 23–37 . 1 g were injected i . p . with either one injection of 0 . 15 g/kg STZ ( Sigma Aldrich ) or one injection of 0 . 1 g/kg STZ followed by a second dose of 0 . 05 g/kg STZ after 3 days . Following STZ injections , blood glucose was monitored for 2–3 days until animals became hyperglycemic ( blood glucose 350–580 mg/dL ) . Animals were fasted for 4–6 hr and then injected with human insulin ( 1 IU/kg , 27 . 5 IU/mg , Sigma Aldrich ) ( Gupta et al . , 2014 ) and venom insulins at one-time ( 1X ) , ten-times ( 10X ) or twenty-times ( 20X ) the dose of human insulin . Blood glucose was measured ( in mg/dL ) every 15 min for 125 min following insulin injections using a Bayer Contour meter . Data were analyzed in Prism GraphPad software ( version 7 . 0 ) . Models of Con-Ins T1A and Con-Ins K1 in complex with the IR L1 module ( residues His1 to Glu154 ) and the IR αCT segment ( residues Thr704 to Ser719 of the IR-A isoform ) were created with MODELLER ( v9 . 16 ) ( Webb and Sali , 2016 ) , with templates of the crystal structure of Con-Ins G1 ( PDB 5JYQ ( Menting et al . , 2016 ) ) and the crystal structure of the IR site one components in complex with hIns ( PDB 4OGA ( Menting et al . , 2014 ) ) . Due to the sequence dissimilarity of the templates used , helical and distance restraints were applied to the C-terminus of the A-chain and to LeuB20 , respectively , to orient these residues towards the hydrophobic interior . All models included the post-translational modifications of Con-Ins T1A and Con-Ins K1 and a single N-linked N-acetyl-d-glucosamine residue at each of the IR residues Asn16 , Asn25 , Asn111 , Asn215 and Asn255 ( Sparrow et al . , 2008 ) . Molecular dynamics ( MD ) simulations were conducted as previously described . Briefly , simulations used GROMACS ( v5 . 1 . 2 ) ( Abraham et al . , 2015 ) with the CHARMM36 force field ( Best et al . , 2012; Guvench et al . , 2011 ) , and were initiated with the models of the Con-Ins T1A-IR and Con-Ins K1-IR complex that had the lowest MODELLER objective function . Ionizable residues were assumed to be in their charged state . Each system was solvated using the TIP3P water model in a cubic box extending 10 Å beyond all atoms . Sodium and chloride ions were added to neutralize the system and provide an ionic strength of 0 . 1 M . The protein and solvent ( including ions ) were coupled separately with velocity rescaling to a thermal bath at 300 K applied with a coupling time of 0 . 1 ps . All simulations were performed with a single nonbonded cutoff of 12 Å , the Verlet neighbor searching cut-off scheme was applied with a neighbor-list update frequency of 25 steps ( 50 fs ) ; the time step used in all the simulations was two fs . Periodic boundary conditions were used with the particle-mesh Ewald method to account for long-range electrostatics . All bond lengths were constrained with the P-LINCS algorithm ( Hess , 2008 ) . Simulations consisted of an initial minimization followed by 50 ps of MD with all protein atoms restrained . After positionally restrained MD , the simulations were continued without restraints for a further 100 ns . | Insulin is a hormone critical for maintaining healthy blood sugar levels in humans . When the insulin system becomes faulty , blood sugar levels become too high , which can lead to diabetes . At the moment , the only effective treatment for one of the major types of diabetes are daily insulin injections . However , designing fast-acting insulin drugs has remained a challenge . Insulin molecules form clusters ( so-called hexamers ) that first have to dissolve in the body to activate the insulin receptor , which plays a key role in regulating the blood sugar levels throughout the body . This can take time and can therefore delay the blood-sugar control . In 2015 , researchers discovered that the fish-hunting cone snail Conus geographus uses a specific type of insulin to capture its prey – fish . The cone snail releases insulin into the surrounding water and then engulfs its victim with its mouth . This induces dangerously low blood sugar levels in the fish and so makes them an easy target . Unlike the human version , the snail insulin does not cluster , and despite structural differences , can bind to the human insulin receptor . Now , Ahorukomeye , Disotuar et al . – including some of the authors involved in the previous study – wanted to find out whether other fish-hunting cone snails also make insulins and if they differed from the one previously discovered in C . geographus . The insulin molecules were extracted and analyzed , and the results showed that the three cone snail species had different versions of insulin – but none of them formed clusters . Ahorukomeye , Disotuar et al . further revealed that the snail insulins could bind to the human insulin receptors and could also reverse high blood sugar levels in fish and mouse models of the disease . This research may help guide future studies looking into developing fast-acting insulin drugs for diabetic patients . A next step will be to fully understand how snail insulins can be active at the human receptor without forming clusters . Cone snails solved this problem millions of years ago and by understanding how they have done this , researchers are hoping to redesign current diabetic therapeutics . Since the snail insulins do not form clusters and should act faster than currently available insulin drugs , they may lead to better or new diabetes treatments . | [
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] | 2019 | Fish-hunting cone snail venoms are a rich source of minimized ligands of the vertebrate insulin receptor |
Retinoic acid-inducible gene I ( RIG-I ) initiates a rapid innate immune response upon detection and binding to viral ribonucleic acid ( RNA ) . This signal activation occurs only when pathogenic RNA is identified , despite the ability of RIG-I to bind endogenous RNA while surveying the cytoplasm . Here we show that ATP binding and hydrolysis by RIG-I play a key role in the identification of viral targets and the activation of signaling . Using biochemical and cell-based assays together with mutagenesis , we show that ATP binding , and not hydrolysis , is required for RIG-I signaling on viral RNA . However , we show that ATP hydrolysis does provide an important function by recycling RIG-I and promoting its dissociation from non-pathogenic RNA . This activity provides a valuable proof-reading mechanism that enhances specificity and prevents an antiviral response upon encounter with host RNA molecules .
Retinoic acid-inducible gene I ( RIG-I ) is a cellular innate immune receptor that recognizes viral ribonucleic acid ( RNA ) in the cytoplasm and consequently initiates a host defense response ( Yoneyama et al . , 2004; Ablasser et al . , 2009; Baum et al . , 2010 ) . The RIG-I protein is comprised of three major domain groups , including tandem caspase activation and recruitment domains ( CARDs ) that mediate signaling , a modified DExD/H-box ATPase core , and a C-terminal domain ( CTD ) that provides RNA ligand specificity through high-affinity interaction with blunt-ended , triphosphorylated duplexes ( Hornung et al . , 2006; Lu et al . , 2010; Wang et al . , 2010; Jiang et al . , 2011; Kowalinski et al . , 2011; Luo et al . , 2011 , 2012b; Rawling et al . , 2015 ) . RIG-I surveys the cytoplasm in an autorepressed conformation , with the CARDs stacked against the helicase core ( Kowalinski et al . , 2011; Zheng et al . , 2015 ) ( Figure 1 ) . Upon RNA ligand recognition , the protein adopts a conformation that can bind and hydrolyze ATP ( Yoneyama et al . , 2004; Cui et al . , 2008; Gee et al . , 2008; Jiang et al . , 2011; Kowalinski et al . , 2011; Luo et al . , 2011 ) . These combined activities result in liberation of the CARDs for interaction with the adapter protein MAVS , causing MAVS oligomerization and subsequent downstream signaling ( Hou et al . , 2011; Peisley et al . , 2014; Rawling and Pyle , 2014 ) ( Figure 1 ) . 10 . 7554/eLife . 09391 . 003Figure 1 . Retinoic acid-inducible gene I ( RIG-I ) signaling activation by viral ribonucleic acid ( RNA ) . The RIG-I protein is comprised of four major domain groups including the caspase activation and recruitment domains ( CARDs ) ( black ) , and RNA-stimulated ATPase core ( green ) , a helical regulatory and binding domain inserted into the second lobe of the ATPase called HEL2i ( cyan ) , and a triphosphate recognition and RNA binding domain at the c-terminus annotated the C-terminal domain ( CTD ) ( orange ) . RIG-I is normally present in cells in an autorepressed conformation with the CARDs stacked against the HEL2i domain . Upon infection by a subset of RNA and DNA viruses , RIG-I binds 5′ triphosphorylated duplex termini of viral RNA ( black helix with three white circles ) deposited or transcribed in the cytoplasm . RNA binding stimulates ATP ( yellow star ) binding and hydrolysis by RIG-I to ADP ( yellow triangle ) . At some point in this process , RIG-I becomes competent to engage an immune signaling response through an interaction with the adaptor protein MAVS ( red ) , however the determinant step for signaling has not been conclusively demonstrated . Thus , the transition of RIG-I from autorepressed to signaling competent may occur at the stage of RNA binding , ATP binding , or ATP hydrolysis , and this is denoted here with aroows bearing question markes leading from each stage in RNA-stimulated ATPase activity to the immune signaling interaction . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 003 RIG-I requires both RNA and ATP ligands in order to become activated and initiate signaling . The precise molecular determinants for functional RNA recognition by RIG-I have been well characterized through structural , biochemical and cell-based approaches ( Schlee et al . , 2009; Luo et al . , 2012b; Kohlway et al . , 2013; Goubau et al . , 2014 ) . However , the specific roles of ATP binding and hydrolysis in activating RIG-I for immune signaling have not been defined or differentiated . Mutational studies have underscored the key role of conserved amino acids within the ATPase active-site of RIG-I . For example , residues that are required for ATP hydrolysis , such as the conserved lysine within helicase motif I ( the Walker A motif ) , or the conserved glutamate and aspartate residues within motif II , exhibit dramatically reduced signaling capabilities ( Yoneyama et al . , 2004; Bamming and Horvath , 2009; Rawling et al . , 2015 ) . In contrast , RIG-I signaling is not impaired by motif III mutations that significantly perturb ATPase activity ( Bamming and Horvath , 2009 ) , thereby making it difficult to establish a direct relationship between hydrolysis and signaling . Alternative roles proposed for ATP hydrolysis include translocation and filament formation ( Myong et al . , 2009; Patel et al . , 2013; Peisley et al . , 2013 ) , however these functions have not been shown to be absolutely necessary for signaling by RIG-I ( Kohlway et al . , 2013; Louber et al . , 2014 ) . Thus , the function of ATPase activity in RIG-I signaling remains unclear . To fully understand the determinants for RIG-I signal activation , the roles of ATP binding and hydrolysis must be established . While robust signaling is often associated with ATP hydrolysis activity ( Yoneyama et al . , 2004; Bamming and Horvath , 2009; Luo et al . , 2011; Kohlway et al . , 2013; Patel et al . , 2013; Peisley et al . , 2013; Rawling et al . , 2015 ) , the ability of an RNA ligand to stimulate RIG-I ATPase activity does not necessarily ensure immune-stimulatory activity . It has been demonstrated that duplex RNAs containing a 5′ hydroxyl can bind RIG-I and stimulate robust ATP hydrolysis ( Kohlway et al . , 2013; Rawling et al . , 2015 ) , yet these RNAs do not induce immune signaling by RIG-I , even at saturating concentrations ( Schlee et al . , 2009; Baum et al . , 2010; Goubau et al . , 2014 ) . Similarly , RIG-I ATPase activity can be stimulated by short RNAs bearing a 3′ overhang , as seen in miRNA precursors ( Schlee et al . , 2009; Wilson and Doudna , 2013 ) . There are many other cellular RNAs that might be expected to bind RIG-I , such as lncRNAs , which contain abundant stem-loop structures ( Somarowthu et al . , 2015 ) and 5′ monophosphorylated products of mRNA decapping ( Coller and Parker , 2004 ) . Therefore , a mechanism to discriminate between endogenous and pathogenic RNA must be utilized . The ability to differentiate between host and viral RNA is essential , as aberrant activation of RIG-I would result in unregulated signaling that could lead to auto-inflammation and other innate immune disorders . Thus , elucidating the mechanism for RNA ligand discrimination and understanding its linkage with ATP utilization are central to understanding the induction and regulation of RIG-I signaling . In this work , we use a series of biophysical and cell-based approaches , together with mutational analysis , to distinguish between the roles of ATP binding and ATP hydrolysis in RIG-I function . We show that RNA-stimulated ATP binding can activate RIG-I for immune signaling in the absence of hydrolysis , thus explaining the lack of a correlation between ATP hydrolysis and signaling . Using a novel RNA ligand lacking free ends , we establish that ATP-mediated signal activation requires RNA end capping by the CTD . We demonstrate that in the absence of end capping , ATP binding and hydrolysis promote RIG-I dissociation from RNA rather than signaling , thereby contributing to proofreading and rejection of sub-optimal RNA ligands , such as endogenous RNA . Additionally , we show that ATP-stimulated RNA dissociation is dependent on the CARD domains of RIG-I . We therefore propose a model in which ATP binding by RIG-I leads to conformational changes that can result in either the liberation of the CARDs for signaling or RIG-I dissociation from RNA , depending on the affinity of RIG-I for the bound RNA ligand . ATP binding and hydrolysis thus play important roles in RIG-I function by activating signaling and ensuring RNA target specificity .
To investigate the relationship between ATPase activity and immune signaling in RIG-I , we evaluated the catalytic and signaling capabilities of RIG-I ATPase site mutants . Lysine 270 , a conserved residue in the Walker A motif that coordinates ATP ( Walker et al . , 1982; Yoneyama et al . , 2004; Cordin et al . , 2006 ) , was mutated to either an alanine or arginine . Mutation of this lysine residue typically abolishes both ATP binding and hydrolysis in related DEAD-box proteins , making mutants at this conserved residue useful for studying the role of ATPase activity . Using a coupled ATPase assay ( Luo et al . , 2011; Kohlway et al . , 2013 ) , we determined the steady state ATP hydrolysis activity of RIG-I and the lysine mutants ( K270A and K270R ) when activated by a 5′ triphosphorylated , 10 bp hairpin ( 5′ppp10L ) . In agreement with previous studies on this and similar DExD/H-box proteins , we found that the RNA-stimulated ATPase activity of both mutations was eliminated , while wild type RIG-I activity was robust ( Figure 2A , Table 1 ) ( Yoneyama et al . , 2004; Cordin et al . , 2006 ) . Next , we measured the RNA binding affinity of K270A and K270R mutants for a FAM-labeled 10-mer RNA hairpin using fluorescence polarization , and found that both mutants bind RNA with affinities similar to the wild type protein ( Table 1 ) . Thus , we conclude that the observed ATP hydrolysis deficiencies are not a result of impaired RNA binding by these mutants . 10 . 7554/eLife . 09391 . 004Figure 2 . Walker A mutant RIG-I can induce IFN-β production in cells . ( A ) Steady state ATP hydrolysis by wild type , K270A and K270R RIG-I proteins stimulated with varying concentrations of the RNA hairpin 5′ppp10L . ( B ) Extrapolated dissociation constants for MANT-ATP binding by wild type and mutant RIG-I proteins bound to 5′ppp10L . ( C ) IFN-β induction in HEK 293T cells transfected with the indicated amount of the constitutive expression plasmid pUNO-hRIG-I containing either wild type or mutant RIG-I constructs . Cells expressing the indicated construct were challenged by transfection of 5′ppp10L . ( D ) Anti-RIG-I Western blot from HEK cell lysates . Plotted values are mean ± SD ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 00410 . 7554/eLife . 09391 . 005Figure 2—figure supplement 1 . Determining association rate constants for MANT-ATP binding by Wild Type and Lysine 270 mutant RIG-I . ( A–C ) An average of four traces monitoring the association of ( A ) 1 μM WT RIG-I/5′ppp10L ( red ) or 1 μM WT RIG-I/5′ppp30L ( black ) ( B ) 1 μM K270A/5′ppp10L ( blue ) and ( C ) 1 μM K270R/5′ppp10L ( green ) and 160 μM MANT-ATP . Data were fit to a double exponential equation to obtain an association rate constant kobs , with kobs = k1 , as this corresponds to the initial binding event between the protein and MANT-ATP . ( D–F ) Average linear fit of ( MANT-ATP ) vs kobs for ( D ) WT RIG-I bound to 5′ppp10L ( red ) or 5′ppp30L ( black ) , ( E ) K270A RIG-I bound to 5′ppp10L ( blue ) and ( C ) K270R bound to 5′ppp10L showing the ( MANT-ATP ) dependence of the observed rate constants . Plotted kobs values are mean ± SD ( n = 3 ) . The kon ( slope ) and koff ( intercept ) values were derived from three independent trials and averaged . Using these values ( koff/kon ) , a Kd was calculated for each protein/RNA combination . WT RIG-I/5′ppp10L: koff = 110 . 0 ± 6 , kon = 1 . 5 ± 0 . 2 , Kd ( calc . ) = 72 ± 4 μM . WT RIG-I/5′ppp30L: koff = 154 . 0 ± 7 , kon = 1 . 1 ± 0 . 2 , Kd ( calc . ) = 136 ± 6 μM . K270A/5′ppp10L: koff = 140 . 2 ± 6 , kon = 0 . 77 ± 0 . 1 , Kd ( calc . ) = 183 ± 8 μM . K270R/5′ppp10L: koff = 129 . 9 ± 31 , kon = 1 . 0 ± 0 . 2 , Kd ( calc . ) = 130 ± 30 μM . ( G ) Steady state ATP hydrolysis by wild type RIG-I bound to 5′ppp10L stimulated with varying concentrations of MANT-ATP . kcat = 3 . 9 ± 0 . 2 s−1 , KM , ATP = 115 ± 25 μM . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 00510 . 7554/eLife . 09391 . 006Table 1 . Measured physical and biochemical constantsDOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 006ConstructWT RIG-IK270AK270RΔCARDsSteady-state ATPase activity 5′ppp10L kcat ( ATP/RIG-I•s ) 14 . 1 ± 0 . 4inactiveinactive– KM , RNA ( nM ) 8 . 0 ± 1 . 0inactiveinactive– 5′ppp14L kcat ( ATP/RIG-I•s ) 14 ± 0 . 3inactiveinactive– KM , RNA ( nM ) 15 ± 1 . 4inactiveinactive– 5′ppp30L kcat ( ATP/RIG-I•s ) 7 . 0 ± 0 . 2inactiveinactive– KM , RNA ( nM ) 11 ± 1 . 4inactiveinactive– 5′ppp50L kcat ( ATP/RIG-I•s ) 6 . 5 ± 0 . 5inactiveinactive– KM , RNA ( nM ) 50 ± 16inactiveinactive– Dumbbell kcat ( ATP/RIG-I•s ) 21 ± 1 . 0––– KM , RNA ( μM ) 9 . 3 ± 1 . 4–––Maximum IFN-β production 5′ppp10L ( Firefly Luc/Renilla Luc ) 23 ± 1 . 822 ± 7 . 320 ± 0 . 2– 5′ppp14L ( Firefly Luc/Renilla Luc ) 16 ± 510 ± 0 . 121 ± 0 . 5– 5′ppp30L ( Firefly Luc/Renilla Luc ) 16 ± 70 . 5 ± 0 . 20 . 5 ± 0 . 2– 5′ppp50L ( Firefly Luc/Renilla Luc ) 46 ± 180 . 4 ± 0 . 10 . 6 ± 0 . 1– 5′OH10L ( Firefly Luc/Renilla Luc ) 3 . 7 ± 1 . 13 . 2 ± 0 . 18 . 9 ± 0 . 4– Dumbbell ( Firefly Luc/Renilla Luc ) 2 . 1 ± 0 . 5–––MANT-ATP binding 5′ppp10L ( μM ) 72 ± 4183 ± 8130 ± 30– 5′ppp30L ( μM ) 136 ± 6–––Equilibrium RNA binding 5′OH10L ( nM ) no nucleotide17 ± 1 . 042 ± 1130 ± 419 ± 1 . 0 with ATP–83 ± 782 ± 2– with ATPγS50 ± 5 . 098 ± 8 . 466 ± 6 . 128 ± 0 . 7 with ADP-AlF434 ± 4 . 960 ± 1950 ± 9 . 021 ± 3 . 3 with ADP32 ± 2 . 068 ± 1653 ± 4 . 624 ± 2 . 3 Dumbbell ( μM ) 2 . 1 ± 0 . 2–––Physical and biochemical constants for binding , catalysis , and cell-based signaling assays performed for wild type , Walker A-mutant , and ΔCARDs truncation constructs of RIG-I . Values represent mean ± SD ( n = 3 ) . See ‘Materials and methods’ for details of the experimental setups and data analysis used in each assay . CARDs , caspase activation and recruitment domains; RNA , ribonucleic acid . To determine if an inability to bind nucleotide by K270A and K270R causes the observed inhibition of hydrolysis , we measured ATP binding by WT and mutant RIG-I . To evaluate ATP binding , we monitored the association of WT and mutant RIG-I with a fluorescent ATP analog , MANT-ATP ( Figure 2—figure supplement 1G ) , in the presence of the 5′ppp10L ligand using stopped flow fluorescence spectroscopy ( Figure 2—figure supplement 1A–F ) . Using the observed rate constants , we extrapolated apparent dissociation constants ( Kd ) for MANT-ATP for each RIG-I construct ( Figure 2B , Table 1 , Figure 2—figure supplement 1D–F ) . Intriguingly , we found that Walker A mutants bind MANT-ATP with only a twofold weaker affinity than wild type RIG-I when stimulated by the 5′ppp10L RNA ( Figure 2B ) . Thus , mutations in the ATPase active site specifically disrupt the chemical mechanism of hydrolysis , but they do not significantly perturb ATP binding by RIG-I when bound to 5′ppp10L . We next evaluated the ability of K270A and K270R to stimulate IFN-β promoter activation using a cell-based assay system described previously ( see ‘Materials and methods’ , Murali et al . , 2008; Luo et al . , 2011 ) . Briefly , HEK 293T cells are transfected with RIG-I or RIG-I mutant and an IFN-β/firefly luciferase reporter construct , challenged with RNA ligands , and then analyzed for luciferase expression ( i . e . , IFN-β promoter activation ) using a luminescence assay . Several concentrations of each protein expression plasmid were transfected prior to dsRNA challenge in order to compare signaling efficiencies across wild type and mutant constructs . Intriguingly , wild type and Walker A mutant RIG-I proteins elicited similar levels of IFN-β promotor response at each of three concentrations tested when challenged by the 5′ppp10L hairpin ( Figure 2C , Table 1 ) . We performed Western blot analysis on cell lysates from IFN-β activation experiments and confirmed that wild type signaling levels for each mutant protein are not due to variable protein expression across the three constructs ( Figure 2D ) . We also evaluated IFN-β promotor activation when cells were challenged by a vehicle control , and observed no response . We thus determined that the observed IFN-β activation requires RNA transfection , and is not the result of high cellular concentrations of RIG-I . Therefore , signaling by the Walker A mutants represents a bona fide response to exogenous RNA , and we conclude that ATP hydrolysis is not required for signaling by RIG-I on a high-affinity RNA ligand . Previous studies have shown that RIG-I constructs containing mutations at lysine 270 will not induce a signaling response in cells ( Yoneyama et al . , 2004; Bamming and Horvath , 2009 ) , in direct contrast to the results shown above . As the previous studies used longer dsRNAs to challenge RIG-I , we next investigated whether the ATP binding and signaling activities of RIG-I are RNA length dependent . We performed the cell based analysis described above with wild-type RIG-I and the Walker A mutants using RNA ligands of increasing length , including 14- , 30- , and 50-mer duplex hairpins ( 5′ppp14L , 5′ppp30L , and 5′ppp50L respectively ) . Wild type RIG-I exhibits robust signaling behavior when challenged by any of the RNA ligands tested . In contrast , the K270A mutant exhibits a significant loss in IFN-β activation ( about twofold ) when challenged by the 14-mer , and a complete loss in signaling when challenged by a 30- or 50-mer RNA ( Figure 3A–C , Table 1 ) . For the K270R mutant , levels of IFN-β activation elicited by the 14-mer are comparable to those observed for the 10-mer; however , this mutant also exhibited no signaling when challenged by either the 30- or 50-mer hairpins ( Figure 3A–C ) . Thus , Walker A mutations in RIG-I are detrimental to signaling only on longer RNA ligands . 10 . 7554/eLife . 09391 . 007Figure 3 . The requirement for ATP in IFN-β promoter induction is ligand dependent . ( A–C ) IFN-β induction in HEK 293T cells transfected with the indicated amount of the constitutive expression plasmid pUNO-hRIG-I containing either WT or mutant RIG-I constructs . Cells expressing the indicated construct were challenged by transfection of ( A ) 5′ppp14L , ( B ) 5′ppp30L , or ( C ) 5′ppp50L . ( D–F ) Steady state ATP hydrolysis by wild type and mutant RIG-I proteins stimulated with varying concentrations of the RNA hairpin ( D ) 5′ppp14L , ( E ) 5′ppp30L , or ( F ) 5′ppp50L . ( G ) Relative affinities for MANT-ATP binding by wild type and mutant RIG-I proteins bound in the context of 5′ppp10L , 5′ppp30L normalized to WT RIG-I bound to 5′ppp10L ( Figure 1B ) . Plotted values are mean ± SD ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 00710 . 7554/eLife . 09391 . 008Figure 3—figure supplement 1 . RIG-I forms multimers on 5′ppp50L at high protein concentrations . An EMSA showing RIG-I binding to 5′ppp50L . Lane 1 contains 100 nM RNA , and lanes 2–5 contain wild type RIG-I incubated with 100 nM RNA at concentrations ranging from 100 nM to 1 μM . Multimer formation is demonstrated by the slower mobility bands ( denoted 2:1 and 3:1 ) that evolve at higher protein concentrations . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 00810 . 7554/eLife . 09391 . 009Figure 3—figure supplement 2 . Assessment of RNA quality . ( A ) RNA hairpins and the RNA dumbbell were run on a native polyacrylamide gel to visualize sample purity . ( B ) A 5′ end labeling reaction was performed on the RNA dumbbell in order to demonstrate complete ligation ( see ‘Materials and methods’ ) . A total of 2–5% incorporation of [32P-γ] ATP was observed ( lower spot ) based on radioactive signal retention normalized to an unwashed control ( upper spot ) . This indicates that the ligation reaction and subsequent purification yielded 95–98% pure RNA dumbbell . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 009 To investigate the relationship between ATPase activity and immune signaling on these longer RNAs , we measured the ATP hydrolysis activity of RIG-I when it is stimulated by RNAs of varying duplex length , using the coupled ATPase assay ( Luo et al . , 2011; Kohlway et al . , 2013 ) . Wild type RIG-I exhibits RNA-stimulated ATPase activity on all RNA ligands , although the rates of catalysis ( kcat ) vary considerably with duplex length ( Figure 3D–F ) . For example , while 5′ppp14L stimulates ATP hydrolysis with a rate constant of ∼15 s−1 , the analogous 30-mer displays a rate constant of only 8 s−1 . But , both RNA hairpins elicit nearly identical levels of IFN-β promoter induction , underscoring the lack of a direct apparent correlation between ATP hydrolysis and immune signaling . For the K270A and K270R mutants , all RNAs failed to stimulate measurable ATPase catalytic activity ( Figure 3D–F , Table 1 ) . We subsequently measured the ATP binding affinities of WT RIG-I and mutant proteins when bound to 5′ppp30L . In contrast to the 5′ppp10L-bound mutants , 5′ppp30L did not stimulate ATP binding by either K270A or K270R ( Figure 3G ) . By comparison , WT RIG-I is able to bind ATP in all cases , although its affinity for ATP weakens by two-fold with increasing RNA length ( Figure 3G , Figure 2—figure supplement 1A , D ) . These experiments show that the only difference between Walker A mutants that are bound to 5′ppp10L and 5′ppp30L is their ability to bind ATP , and this ATP binding ability is directly correlated with immune signaling . We therefore conclude that RNA-stimulated ATP binding is required to induce signaling by RIG-I . Walker A mutants can bind ATP and induce immune signaling only when interacting with 5′ppp10L and not the longer 5′ppp30L , suggesting that RNAs containing off-target , internal duplexes are detrimental to RIG-I activity . To directly investigate the importance of RIG-I binding at internal duplex sites , we assessed the enzymatic and signaling capabilities of RIG-I bound to RNA with no free ends . This RNA was created from two partially complementary strands that contain 5′ and 3′ regions incapable of base pairing . The two strands were annealed and ligated , creating a dumbbell-like structure that is characterized by a 14-bp duplex region flanked on each end by an 8 base , ssRNA loop region ( Figure 4A ) . 10 . 7554/eLife . 09391 . 010Figure 4 . RIG-I cannot signal from internal sites . ( A ) Schematic representation of RNA dumbbell synthesis . ( B ) IFN-β induction of WT RIG-I in HEK-293T cells challenged with 5′OH10L , 5′ppp10L , and the RNA dumbbell . ( C ) Equilibrium binding of RIG-I to the RNA dumbbell . The fraction bound RNA dumbbell is plotted agaings protein concentration . Values are mean ± SD ( n = 3 ) . ( D ) Steady state ATP hydrolysis by wild type RIG-I stimulated with varying concentrations of the RNA dumbbell . Data were fit to the Brigg–Haldane equation yielding values of kcat = 21 . 6 ± 1 . 4 s−1 , KM , RNA dumbell = 9 . 3 ± 1 . 4 μM . Plotted values are mean ± SD ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 010 We first measured the ability of the RNA dumbbell to elicit an IFN-β response in cells . The dumbbell was unable to induce signaling by wild type RIG-I ( Figure 4B , Table 1 ) , demonstrating that RIG-I cannot signal from an exclusively internal binding site . Longer dumbbells ( up to 50 bp ) also fail to induce signaling , indicating that this effect is not dependent on duplex length ( data not shown ) . We next determined the affinity of RIG-I for a fluorescently-labeled , 14-bp RNA dumbbell using an electrophoretic mobility shift assay ( see ‘Materials and methods’ ) . RIG-I exhibits weak affinity for the dumbbell , with a dissociation constant of 2 . 1 ± 0 . 22 μM ( Figure 4C ) . By comparison , RIG-I binds blunt-ended RNA termini orders of magnitude more tightly , with dissociation constants ranging from about 200 pM to around 20 nM for 5′ triphosphorylated and 5′ hydroxyl ligands , respectively ( Vela et al . , 2012 ) . Thus , while it is unable to signal from an internal duplex , RIG-I is still capable of binding such sites at very high concentrations . We then evaluated the ATP binding and hydrolysis activity of RIG-I bound to the 14-bp dumbbell . We found that ATP binding by RIG-I on the dumbbell is qualitatively the same as that observed when bound to duplex RNA termini , and that the dumbbell is capable of stimulating ATP hydrolysis at a rate constant that is comparable to 5′ppp10L at saturating RNA concentrations ( Figure 4D , Table 1 ) . These data show that ATP binding and hydrolysis is not impaired when wild-type RIG-I is bound to internal duplexes . Taken together with the inability of RIG-I to signal on the dumbbell , we conclude that ATP binding and hydrolysis does not always lead to signaling . Because ATP binding and hydrolysis do not induce signaling by RIG-I on an internal duplex , we wondered if these activities might serve a second functional role . A number of DExD/H-box helicases , including the RLR MDA5 , have been shown to use ATP binding and hydrolysis to promote dissociation from a bound ligand ( Liu et al . , 2008; Berke and Modis , 2012; Berke et al . , 2012 ) . Given the inability of RIG-I to signal on internal RNA duplexes , we hypothesized that this protein might also use ATPase activity to induce RNA dissociation in order to discriminate between optimal and non-productive RNA binding sites . A mechanism for differentiating target viral RNAs from diverse host RNAs would prevent undesirable induction of antiviral signaling and inflammation by endogenous RNA molecules . To investigate how ATP binding and hydrolysis affect RNA binding , we used fluorescence anisotropy to monitor RIG-I binding to a FAM-labeled 10-mer RNA hairpin alone or when bound to various nucleotides and nucleotide analogs . Equilibrium dissociation constants were determined in the presence of ADP , as well as the analogs ATPγS and ADP-AlF4 , which act as mimetics of ATP and the transition state of catalytic hydrolysis respectively ( Figure 5—figure supplement 1 ) . To ensure that ATPγS recapitulates interactions formed between RIG-I and ATP , we also determined RNA binding constants for Walker A mutants in the presence of ATP . WT RIG-I and Walker A mutant RIG-I exhibited significant losses in RNA binding affinity in the presence of all nucleotides tested . The most substantial changes in RNA Kd values were observed in the presence of the ATP analog ATPγS , although interactions with both the transition state analog ADP-AlF4 and ADP also impaired RNA binding by RIG-I ( Figure 5A , Table 1 ) . Further , ATP and ATPγS caused nearly identical losses in RNA affinity for both K270A and K270R RIG-I , indicating that ATPγS is a bona fide ATP mimetic for this system ( Table 1 ) . These experiments show that affinity of RIG-I for RNA is reduced by up to threefold in the presence of nucleotide . 10 . 7554/eLife . 09391 . 011Figure 5 . Nucleotide binding stimulates ligand dissociation in a CARDs-dependent manner . ( A ) Equilibrium dissociation constants for WT and mutant RIG-I binding to the fluorescent RNA hairpin 5′OH10L measured in the absence of nucleotide ( denoted RNA ) , or in the presence of saturating ATPγS , ADP-AlF4 , or ADP . ( B ) koff traces for WT RIG-I . Four traces monitoring displacement of RIG-I from 5′ppp10L ( black ) were averaged and fit to a double exponential equation . Also shown are koff traces in the presence of 3 mM ATPγS ( blue ) and 3 mM ADP ( red ) . ( C ) Relative dissociation constants for 5′OH10L binding by WT K270A , K270R , and ΔCARDs RIG-I proteins in the absence ( RNA ) or presence of saturating ATPγS normalized to RNA . ( D ) koff traces for RIG-I ΔCARDs performed as in ( B ) in the absence of nucleotide ( black ) or in the presence of ATPγS ( red ) or ADP ( blue ) . Plotted values are mean ± SD ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 01110 . 7554/eLife . 09391 . 012Figure 5—figure supplement 1 . Effect of nucleotide analogs on the ATPase activity of RIG-I . The relative rates of ATP hydrolysis ( kobs [with nucleotide]/kobs [without nucleotide] ) by RIG-I in the presence of ADP , ATPγS , ADP-AlF4 , and AMP-PNP determined using saturating RNA and ATP concentrations . Plotted values are mean ± SD ( n = 2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 012 While the equilibrium binding data are consistent with a nucleotide-stimulated dissociation mechanism , we next wanted to directly measure RIG-I dissociation from RNA to determine whether the dissociation rate constant is altered by nucleotide binding . To this end , microscopic rate constants for the dissociation of RIG-I ( koffRNA ) from 5′ppp10L were determined either alone or in the presence of various nucleotides and nucleotide analogs . Briefly , koffRNA values were measured using stopped flow fluorescence spectroscopy , with a pre-bound RIG-I:dsRNA-Cy3 complex rapidly mixed with an excess of unlabeled RNA , resulting in the release of RIG-I from Cy3-RNA and a concomitant decrease in fluorescence intensity ( see ‘Materials and methods’ and Figure 5B ) . We first measured the koffRNA for RIG-I in the absence of nucleotide to establish a baseline rate constant for dissociation . The observed time courses exhibited biphasic behavior and were best fit to a double exponential equation ( Figure 5B ) . RIG-I dissociates slowly from 5′ppp10L , with koff values of 27 . 8 ± 0 . 3 × 10−3 s−1 and 4 . 0 ± 0 . 2 × 10−3 s−1 , respectively ( Figure 5B , Table 2 ) . We interpret the biphasic nature of dissociation as representing two conformational populations of RIG-I present when bound to RNA , with most of the protein in a conformation corresponding to the slower dissociating species . 10 . 7554/eLife . 09391 . 013Table 2 . RNA dissociation rate constants for WT and mutant RIG-IDOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 013ProteinNucleotidek1 ( × 10−3 s−1 ) Amplitudek2 ( × 10−3 s−1 ) AmplitudeWT RIG-INone28 ± 1325%4 ± 0 . 275%ATPγS187 ± 345%26 ± 0 . 455%ADP187 ± 340%25 ± 0 . 360%K270ANone108 ± 180%5 . 3 ± 0 . 120%ATP327 ± 279%25 ± 0 . 321%ATPγS390 ± 375%35 ± 0 . 425%K270RNone92 . 9 ± 153%16 ± 0 . 147%ATP227 ± 270%14 ± 0 . 130%ATPγS224 ± 181%16 ± 0 . 219%ΔCARDsNone30 . 5 ± 0 . 534%2 . 1 ± 0 . 0266%ATPγS24 . 0 ± 0 . 327%2 . 2 ± 0 . 0173%ADP35 . 3 ± 0 . 523%1 . 7 ± 0 . 0177%Rate constants for the dissociation of RIG-I proteins measured using stopped-flow fluorescence spectroscopy as described in ‘Materials and methods’ . Dissociation was measured from a 5′ppp10L hairpin RNA in all cases . The data were fit to a biphasic exponential decay equation and . Values represent mean ± SD ( n = 3 ) . Having obtained basal koffRNA values for RIG-I dissociation from 5′ppp10L , we next measured the koffRNA in the presence of different nucleotides to evaluate the effects of ATP binding . As above , we rapidly mixed pre-formed RIG-I:Cy3-RNA complex with an excess of trap RNA , which now included saturating amounts of nucleotide . When we compared dissociation rate constants obtained in the presence of nucleotides to the basal RNA dissociation rate constant , we found that the presence of either ATPγS or ADP caused a five-fold increase in the rate of dissociation from 5′ppp10L ( Table 2 ) . In addition to the increased efficiency of dissociation , the relative contributions of the fast and slow populations to the ensemble dissociation rate constant also changed . While the faster rate constant comprised about 25% of the total change in fluorescence for basal koffRNA measurements , it now comprised 40–45% of the amplitude change in the presence of nucleotide . This further suggests that a rapidly dissociating population of RIG-I is favored in the presence of nucleotide . The koffRNA values for the Walker A mutants were also determined in the absence and presence of nucleotide . Dissociation of the 5′ppp10L RNA was stimulated for both K270R and K270A in the presence of ATP and ATPγS ( Table 2 ) as with WT RIG-I . The ATPase dead mutants provided an accurate means to test the effect of ATP binding on koffRNA , although with WT RIG-I we also observed similar RNA dissociation rate constants in the presence of ATP and ATPγS ( data now shown ) . As expected , these data demonstrate that ATP binding stimulates RIG-I dissociation from RNA , even in the absence of hydrolysis . We therefore conclude that one role of ATP binding is to challenge the RIG-I interaction with RNA and to promote dissociation . This activity may suppress RIG-I signaling from low affinity RNA binding sites , such as internal duplexes or non-triphosphorylated ends , liberating RIG-I to search for appropriate viral RNA targets . As a first step in establishing a physical mechanism for nucleotide-induced RNA dissociation , we examined whether the CARDs are important for this behavior . In a comparative binding analysis of the individual RIG-I domains , Vela et al . demonstrated that the CARDs antagonize RNA binding by helicase core and the CTD ( Vela et al . , 2012 ) . Further , available structural data suggests that RIG-I bound to both RNA and the transition state analog ADP-AlF4 cannot accommodate the CARDs:HEL2i interaction characteristic of the autorepressed state ( Kowalinski et al . , 2011; Luo , 2014; Rawling and Pyle , 2014 ) . We therefore hypothesized that nucleotide binding and subsequent dissociation from RNA may be related to the antagonism between the CARDs and the HEL/CTD domains . To test this hypothesis , we measured the equilibrium RNA binding affinities and pre-steady state RNA dissociation rates of a RIG-I construct lacking the CARDs ( ΔCARDs ) in the presence of various nucleotides . Compared to wild type and Walker A mutant constructs , where the presence of ATPγS resulted in an approximate threefold drop in affinity , ATPγS binding by the ΔCARDs construct lowered the affinity about 1 . 4-fold ( Figure 5C , Table 1 ) . Consistent with this observation , we detected no enhancement in the rate of RNA dissociation ( koff ) for ΔCARDs RIG-I in the presence of nucleotide ( Figure 5D , Table 2 ) . Taken together , these results suggest a significant role for the CARDs in mediating the enhanced RNA dissociation upon nucleotide binding .
To investigate the requirement for ATPase activity in RIG-I signaling , we produced RIG-I constructs with mutations at the key lysine residue of the highly conserved helicase motif I ( Walker A motif ) . In RIG-I , this residue ( K270 ) forms hydrogen bonds with the β and γ phosphates of ATP , and helps coordinate the Mg2+ ion required for chemistry ( Jiang et al . , 2011; Kowalinski et al . , 2011; Luo et al . , 2012b ) . Hence , mutation of this residue is expected to significantly weaken ATP binding and abrogate hydrolysis . As expected , RIG-I K270 mutants exhibit no ATP hydrolysis activity . Yet surprisingly , the K270 mutants retain the ability to bind ATP and to engage immune signaling when challenged by an optimized RNA ligand , 5′ppp10L ( Kohlway et al . , 2013 ) . Just as strikingly , the K270 mutants fail to bind ATP and they fail to induce signaling when bound to longer , suboptimal RNA ligands . Taken together , these data establish that ATP binding plays a critical role in the induction of signaling , and that ATP binding is controlled through simultaneous interactions between RIG-I and its RNA ligands , thereby providing a two-tier system for highly specific response to viral RNA . Although the activity of RIG-I K270R/5′ppp10L complexes provided a useful tool for showing that ATP binding , rather than hydrolysis , is strictly required for signaling , it raises the question of why 5′ppp10L is so special . Why does this short , high-affinity triphosphorylated RNA ligand uniquely activate the RIG-I protein , and what is the significance of this observation ? To answer this question , it is instructive to refer to the published literature on crystal structures of RIG-I complexes with RNA and ATP ligands . It has been reported that ATPase activity of wild type RIG-I is optimally activated by short RNA ligands ( Kato et al . , 2008; Kohlway et al . , 2013 ) , suggesting that these RNAs help ‘prime’ the protein for interactions with ATP . Many important ATP binding interfaces , such as the Q motif and motif VI ( Cordin et al . , 2004 , 2006; Luo et al . , 2011; Luo et al . , 2012a ) , are not perturbed by the K270 mutation , and therefore a strong ATP binding cleft can still be formed if it is reinforced by additional interactions with short RNA ligands . It is likely that the optimized 5′ppp10L ligand compensates for the deficiencies caused by removing lysine 270 , pre-organizing the active site for binding to ATP . Intriguingly , many viral genomes are predicted to form short duplex panhandle structures with mismatches , bulges , and loops near the duplex terminus , thereby mimicking the conformational constraints imposed by 5′ppp10L ( Luo et al . , 2012b ) . Structural data on human and duck RIG-I support a model in which interactions between RNA-bound RIG-I and an ATP molecule can lead to CARDs expulsion and subsequent signaling . Alignment of RIG-I in an apo conformation ( PDB 4A2W ) to structures of RIG-I bound only to RNA ( PDB 4AY2 ) or bound to RNA and the ATP analog ADP-AlF4 ( PDB 4A36 ) show that the CARDs of RIG-I can be accommodated in apo and RNA-bound states , but not when bound to both RNA and the ATP analog ( Jiang et al . , 2011; Kowalinski et al . , 2011; Luo et al . , 2011 , 2012b; Kolakofsky et al . , 2012; Luo , 2014; Rawling and Pyle , 2014 ) ( Figure 6 ) . When bound to ADP-AlF4 , RIG-I adopts a conformation that would force the CARDs and CTD domains into a steric collision , likely promoting CARDs ejection and RIG-I activation ( Figure 6 ) . Using the K270 mutant RIG-I and an optimized 5′ppp10L ligand , we have shown that the initial ATP binding event is sufficient to cause this conformational transition and activate RIG-I , even in the absence of ATP hydrolysis . 10 . 7554/eLife . 09391 . 014Figure 6 . Structural description of a nucleotide-mediated collision between the CARDs and CTD of RIG-I . ( Left ) Structural model of RIG-I in an ‘Open’ conformation produced by docking the CARDs of full-length duck RIG-I ( PDB: 4A2W ) to human RIG-I bound to the hairpin 5′ppp8L ( PDB: 4AY2 ) by alignment of the HEL2i domains . ( Right ) Structural model of RIG-I in a ‘Compact’ conformation produced by alignment of the CARDs , CTD and RNA from the ‘Open’ model to the HEL1 , HEL2 , HEL2i and Pincer domains of RIG-I bound to ADP-AlF4 ( PDB: 4A36 ) . The middle panels show van der Waals surface representations demonstrating motions in the HEL2i/CARDs relative to the CTD that result in a steric collision ( Top ) and motions in the HEL1 and HEL2 domains of the ATPase core ( Bottom ) that take place upon nucleotide-mediated compaction . CARDs are shown black/gray; HEL1 , green; HEL2 , blue; HEL2i , cyan; Pincer , red; CTD , orange; RNA , yellow; and ADP-AlF4 as purple spheres . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 014 Viral RNAs that productively activate RIG-I contain blunt duplex ends bearing a 5′ triphosphate moiety . It is clear that both of these features allow RIG-I to distinguish between pathogenic RNAs and potential host targets , such as capped RNA , single stranded RNAs , miRNA , or other processed RNAs in which the 5′ triphosphate moiety has been removed . However , the biophysical basis for this discrimination is not clearly understood . Analysis of our functional data and available crystallographic information also explain why RIG-I requires a blunt RNA duplex terminus for signaling . A RIG-I signaling model that involves ATP-mediated domain compaction depends upon a steric collision between the CARDs and the RNA-bound CTD in order to promote CARD expulsion ( Figure 7 , Left ) . Disrupting the CTD:RNA interface is expected to disrupt signaling by preventing this steric clash , and preventing CARD release . The tight binding affinity between the CTD and optimized RNA ligands derives from a complex network of specific interactions with the 5′ triphosphate moiety and stacking interactions with the terminal base pair of an RNA duplex ( Lu et al . , 2010; Jiang et al . , 2011; Kowalinski et al . , 2011; Luo et al . , 2011 , 2012b ) . By contrast , the CTD of RIG-I molecules bound at internal sites is effectively disengaged , and it is likely that all interactions between RIG-I and internal sites occur exclusively through contacts involving the helicase domain ( Vela et al . , 2012 ) . In such cases , the CTD will not provide a hard barrier for colliding with the CARDS , and these will fail to disengage upon ATP binding and domain compaction ( Figure 7 , Right ) . We tested this model by creating the dumbbell ligand as a model for internal binding by RIG-I , and we observed that wild type RIG-I binds internal duplexes much more weakly than duplex termini . Importantly , RIG-I binds the dumbbell RNA with similar affinity as the isolated helicase domain binds to optimal , terminal duplexes ( Vela et al . , 2012 ) , consistent with the fact that the CTD is disengaged when RIG-I binds internal sites . Nonetheless , the dumbbell ligand stimulates robust ATPase activity by wild-type RIG-I , indicating that the protein is capable of both binding and hydrolyzing ATP at internal duplexes , but it is incapable of signaling . While the ATP hydrolysis activity is likely to serve an important function ( vide infra ) , the failure of this ligand to induce signaling is consistent with a compaction and collision model that requires a tightly anchored CTD . We therefore conclude that at internal sites , RIG-I binds and hydrolyzes ATP , but the signaling domains remain in their auto-repressed conformation . 10 . 7554/eLife . 09391 . 015Figure 7 . RIG-I uses ATP-mediated compaction as signaling trigger and a proofreading mechanism . RIG-I surveys the cytoplasm in an auto-repressed conformation , with the signaling CARDs ( black ) sequestered through an interaction with the HEL2i domain ( cyan ) . Duplex RNA binding by RIG-I induces the formation of an active site pocket in the helicase core ( green ) , which allows the protein to bind ATP ( yellow star ) . ATP binding causes the domains of RIG-I to compact , bringing the CARDs into a steric collision with the CTD ( orange ) . If the CTD is engaged in a high-affinity interaction with a triphosphorylated duplex terminus ( as in viral RNA ) , this collision disengages the CARDS from the HEL2i domain , making them available for immune signaling ( left ) . If the CTD is engaged in a low-affinity interaction with a hydroxylated terminus or internal duplex binding site , the CARDs/CTD collision destabilizes RNA binding by RIG-I , resulting in dissociation and recycling ( right ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09391 . 015 The observation that monomeric RIG-I is incapable of signaling when bound to an internal duplex site does not preclude a possible role for additional RIG-I molecules which might load next to RIG-I that is bound at a blunt duplex terminus . Such a scenario could conceivably occur at very high concentrations of RIG-I . The proposed ‘priming’ behavior observed for the 5′ppp10L hairpin could potentially be recapitulated by tightly stacking additional copies of the protein against one another ( Figure 3—figure supplement 1 ) , thereby mimicking conformational constraints imposed by the short RNA and enhancing the signaling efficiency of the end-bound RIG-I . For example , we observe a significant and reproducible jump in RIG-I signaling activity when challenged by a 5′ppp50L hairpin compared to the 5′ppp30L hairpin . Even in this case , the requirement for CTD end-capping and the extremely weak affinity for internal duplex sites underscore the importance of an available duplex terminus for activating RIG-I signaling . Experiments performed using the dumbbell RNA demonstrate the importance of end binding in RIG-I activation , but they also show that RIG-I is capable of binding to sub-optimal duplexes , and that these RNA molecules stimulate non-productive ATPase activity . To deal with this issue , RIG-I has evolved a strategy for ‘rejecting’ non-pathogenic ligands by dissociating rapidly from these RNAs . RIG-I bound to ATP exhibits reduced equilibrium binding affinity for RNA , and rapid RNA dissociation . Furthermore , the observed enhancement in RNA dissociation kinetics is dependent on the CARDs of RIG-I , which suggests that a collision between the CARDs and the CTD further destabilizes RNA binding by RIG-I . Intriguingly , enhanced dissociation kinetics are observed for all RNAs tested , including those containing a 5′ triphosphorylated duplex terminus . Therefore , ATP-mediated turnover is not specific for a particular RNA ligand . Rather , it challenges and tests the RIG-I:RNA interaction for all ligands , and results in CARDs expulsion only when the CTD is rigidly constrained by high-affinity binding to the terminus of triphosphorylated viral RNAs ( Figure 7 ) . Using the energy from ATP hydrolysis as a recycling and proof-reading mechanism has been previously demonstrated for RNA helicases belonging to the DEAD-box family of proteins , which is the helicase subgroup that is phylogenetically most closely related to RIG-I ( Jankowsky and Fairman , 2007; Liu et al . , 2008; Jankowsky , 2011; Luo et al . , 2012a ) . Indeed , a number of DEAD-box proteins are capable of performing duplex unwinding in the presence of a non-hydrolyzable ATP mimetic ADP-BeF3 ( Liu et al . , 2008 ) . The model presented here mirrors these findings; in both RIG-I and DEAD-box proteins , ATP binding is required for a particular mechanical activity , while ATP hydrolysis is only required to ‘reset’ the protein , enabling it to search for another target or to engage in another round of activation . Thus , ATP binding represents the deterministic step in RIG-I activation , while hydrolysis helps the protein to efficiently sample potential RNA ligands , and reject those that cannot activate immune signaling . RIG-I integrates the input of ATP and RNA binding to select viral RNA over endogenous RNA , and subsequently initiate signaling . Endogenous RNAs , such as tRNAs , inverted repeat sequence elements , and pre-and mature miRNAs , are abundant in the cytoplasm and contain structured duplex regions to which RIG-I can readily bind ( Athanasiadis et al . , 2004; Wang and Carmichael , 2004; Watanabe et al . , 2008; Wang and Leung , 2009; Chiang et al . , 2010; Kirchner and Ignatova , 2015 ) . The high cellular concentrations of these RNAs means RIG-I will bind at these sites despite its relatively weak affinity for RNA that is not triphosphorylated . Importantly , RIG-I can bind these RNAs without activating signaling , due to the proofreading mechanism imparted by ATP hydrolysis . Thus , RIG-I is thus able to differentiate between non-pathogenic RNAs and viral RNAs using a finely tuned proofreading mechanism that is essential for preventing aberrant immune responses .
Oligoribonucleotides were synthesized on an automated MerMade synthesizer ( BioAutomation , Irving , TX , United States ) using standard phosphoramidite chemistry . The oligonucleotides were deprotected and gel purified as previously described ( Wincott et al . , 1995 ) . Fluorophore-labeled RNA hairpin used in stopped flow fluorescence spectroscopy was generated using amino-modified RNA ( on a U in the UUCG tetraloop ) , which is synthesized using a 3′ amino modifier with a C3 linker ( Glen Research , Sterling , VA , United States ) . Cy3 Mono NHS ester ( GE Healthcare , Little Chalfont , United Kingdom ) was conjugated to the modified oligonucleotides as per the manufacturer′s instructions . Cy3-labeled and unlabeled RNA were separated on a 20% denaturing polyacrylamide gel , and purified by gel extraction . Triphosphorylated RNA hairpins were prepared by in vitro transcription by T7 RNA polymerase using DNA oligomers containing 3′ 2′-O-methyl modifications as templates . After a 5 hr transcription at 37°C , the resulting transcripts were purified by gel extraction from a denaturing polyacrylamide gel ( 12–20% , depending on RNA transcript length ) . To assess purity , the purified RNA hairpins were then run on a 15% native polyacrylamide gel for 60 min at 100 V , stained in a 1:10 , 000 dilution of SYBR Gold Nucleic Acid Gel Stain ( Life Technologies , Carlsbad , CA , United States ) in 0 . 5× TBE Running Buffer ( Life Technologies ) and imaged on a Typhoon FLA 9500 biomolecular imager ( GE ) ( Figure 3—figure supplement 2 ) . All RNA sequences are listed in Supplementary file 1 . To create an RNA ligand without termini , an RNA dumbbell consisting of a 14 base pair stem region and two , 8 nucleotide single stranded loops was made . The synthesis of an RNA dumbbell requires two RNA strands , where two ligation events occur to yield an RNA without termini . The 14 base pair dumbbell was converted into two precursor RNA strands by placing nicks in the single strand loop regions to allow for straightforward ligation by T4 RNA ligase 1 ( for sequences see Supplementary file 1 ) . The two RNA strands containing 5′ monophosphates were synthesized using standard phosphoramidite chemistry as described above , with 5′ monophosphorylation performed on the synthesizer using Chemical Phosphorylation Reagent ( Glen Research , Sterling , VA , United States ) . After deprotection and gel purification , the two strands ( 30 μM each ) were annealed by rapid heating to 95°C followed by slow cooling to 30°C in annealing buffer ( 100 mM MOPS pH 7 . 5 , 10 mM DTT ) . The duplexed RNA was then ligated using T4 RNA ligase 1 ( New England Biolabs , Ipswich , MA , United States ) in ligation buffer ( 100 mM MOPS pH 7 . 5 , 10 mM DTT , 10% DMSO , 15 mM MgCl2 , 0 . 6 mM ATP ) for 12–15 hr at 16°C . The ligated RNA dumbbell was then purified to 95–98% purity ( see below , Figure 3—figure supplement 2 ) using denaturing polyacrylamide gel electrophoresis and gel extraction . For Cy3-labeled dumbbell , the same synthetic methods were employed as described above . One of the precursor RNA strands contains an amino-modified U in a single strand loop region , which is synthesized as described above for the amino-modified hairpin . After ligation and initial purification , Cy3 Mono NHS ester ( GE Healthcare ) was conjugated to the modified dumbbell . The Cy3-labeled RNA dumbbell was then isolated using denaturing polyacrylamide gel electrophoresis and gel extraction . To assess the purity of the RNA dumbbell , a 5′ end labeling reaction using [γ-32P] ATP ( Perkin Elmer , Waltham , MA , United States ) and T4 polynucleotide kinase ( New England Biolabs ) was performed ( Figure 3—figure supplement 2 ) . If both ligation events occurred and the dumbbell was formed , the labeling reaction would be unsuccessful as there are no free 5′ ends . In contrast , any impurities ( i . e . , hairpin or duplex RNA resulting from 1 or no ligation events , respectively ) in the sample would be 5′ end labeled . First , samples were treated with Antarctic Phosphatase ( New England Biolabs ) as per the manufacturer's instructions to remove any 5′ monophosphates . After heat inactivation , 15 pmol of the Antarctic phosphatase treated RNA was incubated with T4 PNK and excess [γ-32P] ATP at 37°C for 1 hr . To assess purity of the dumbbell in a quantifiable manner , the incorporation efficiency of [γ-32P] was determined using a filter spotting technique . Briefly , 1 μl of the reaction was spotted in duplicate on Whatman DE81 filter papers . One filter was washed by immersion in 25 ml 0 . 5 M sodium phosphate pH 7 . 0 buffer for 5 min ( four times ) . After drying , the washed and unwashed filters were exposed on a phosphorimaging screen and scanned using the Typhoon phosphorimager ( GE Healthcare ) . The percent incorporation was then determined by dividing quantified intensity for the washed filter by quantified intensity for the unwashed filter . Using the specific activity of [γ-32P] ATP , the pmol of RNA labeled was also calculated . Vector pUNO-hRIG-I for constitutive WT RIG-I expression in mammalian cells was purchased from Invivogen . Mutations were introduced into the parent plasmid using appropriate primers K270A forward: 5′-taagcagtgaaacaaaggttgctccacaacctgtaggagcac-3′ and K270A reverse: 5′-gtgctcctacaggttgtggagcaacctttgtttcactgctta-3′ or K270R forward: 5′-gcagtgaaacaaaggttcttccacaacctgtaggagc-3′ and K270R reverse: 5′-gctcctacaggttgtggaagaacctttgtttcactgc-3′ and PfuUltra Hotstart PCR Master Mix ( Agilent , Santa Clara , CA , United States ) per the manufacturer's protocol . Mutagenesis was validated by sequencing . Cell based experiments were conducted in HEK 293T cells because they do not express endogenous RIG-I ( proteinatlas . org ) . Cells were grown and maintained in 15 cm dishes containing Dulbecco's Modified Eagle Medium ( DMEM; Life Technologies ) supplemented with 10% heat-inactivated fetal bovine serum ( Hyclone , GE Healthcare ) and Non-Essential Amino Acids ( Life Technologies ) . IFN-β induction assays were conducted in 6-well format . Briefly , 2 . 5 ml of cells at 100 , 000 cells/ml were seeded in each well of a tissue culture treated 6-well plate . After 24 hr , each well of cells was transfected with the indicated amount of WT or mutant pUNO-hRIG-I , 30 ng pRL-TK constitutive Renilla luciferase reporter plasmid ( Promega , Madison , WI , United States ) , and 750 ng of an IFN-β/FireflyLuc reporter plasmid using the Lipofectamine 2000 transfection reagent ( Life Technologies ) per the manufacturer's protocol . Protein expression was allowed to proceed for 24 hr , at which point the cells were challenged by transfection of 2 . 5 μg of the indicated dsRNA , also using the Lipofectamine 2000 reagent . After 12 hr , cells were harvested for luminescence analysis . To assess IFN-β induction using a dual luciferase assay , cells were harvested and lysed as follows: Growth media was aspirated from each well , and 200 μl of passive lysis buffer ( Promega ) was added . Lysis proceeded for 15 min at room temperature . The lysates were transferred to a 96-well PCR plate ( Eppendorf , Hamburg , Germany ) and clarified by centrifugation . Next , 20 μl samples of the supernatant were transferred to a 96-well assay plate for analysis using the Dual-Luciferase Reporter Assay System ( Promega ) . Luminescence was measured using a Biotek Synergy H1 plate reader ( Biotek , Winooski , VT , United States ) . The resulting Firefly luciferase activity ( i . e . , the induction of IFN-β ) was normalized to the activity of the constitutively expressed Renilla luciferase to account for differences in confluency , viability and transfection efficiency across sample wells . For Western blot analysis , 20 μl of the appropriate HEK 293T cell supernatant was removed and combined with 5 μl SDS-PAGE loading dye . 15 μl samples of this mixture were run on a 4–20% Mini-PROTEAN TGX gel ( Bio-Rad , Hercules , CA , United States ) . Proteins were transferred to a PVDF membrane at 100 V for 60 min . Blocking was performed at 4°C for 4 hr in 5% BSA dissolved in TBS buffer . The membrane was then washed in TBST buffer 3× for 5 min each at 23°C . The membrane was next incubated in 3% BSA/TBS solution containing primary αRIG-I antibody ( Sigma , St . Louis , MO , United States ) at a 1:1000 dilution overnight at 4°C . The following morning , the membrane was washed in TBST 3× for 5 min each at 23°C . The membrane was then incubated in 3% BSA/TBS solution containing secondary αRabbit:HRP antibody at a 1:10 , 000 dilution for 30 min at 23°C . The membrane was washed in TBST 3× for 10 min each at 23°C , then treated with SuperSignal West Pico Chemiluminescent Substrate ( Thermo Scientific , Waltham , MA , United States ) per the manufacturer's protocol . Chemiluminescence was visualized by film . For expression , plasmids were transformed into Rosetta II ( DE3 ) Escherichia coli cells ( Novagen , Madison , WI , United States ) and grown in LB media supplemented with 50 mM Potassium Phosphate pH 7 . 4 and 1% glycerol . Expression was induced by the addition of isopropyl-β-D-thiogalactopyranoside to a final concentration of 0 . 5 mM . Cells were grown for 24 hr at 16°C , then harvested by centrifugation , resuspended in lysis buffer ( 20 mM Phosphate pH 7 . 4 , 500 mM NaCl , 10% glycerol , 5 mM β-mercaptoethanol [βME] ) to a final volume of 50 ml , and frozen at −80°C . For lysis , frozen pellets were thawed at room temperature , then resuspended in an additional 200 ml lysis buffer per 4L pellet . Cells were lysed by passage through a microfluidizer at 15 , 000 psi , and the lysate was clarified by ultracentrifugation at 100 , 000×g for 30 min . Soluble lysate was incubated on 2 . 5 ml Ni-NTA beads ( Qiagen , Valencia , CA , United States ) , washed with lysis buffer containing an additional 40 mM imidazole , then eluted in Ni elution buffer ( 25 mM HEPES pH 8 . 0 , 150 mM NaCl , 220 mM Imidazole , 10% glycerol , 5 mM βME ) . Eluted protein was bound to a HiTrap Heparin HP column ( GE Healthcare ) , washed in buffer containing 150 mM NaCl , and eluted stepwise at 0 . 65 M NaCl . The SUMO tag was then removed by incubation with SUMO protease for 2 hr at 4°C . Finally , monomeric protein was collected by passage over a HiPrep 16/60 Superdex 200 column ( GE Healthcare ) in gel filtration buffer ( 25 mM MOPS pH 7 . 4 , 300 mM NaCl , 5% glycerol , 5 mM βME ) . Peak fractions were concentrated to 10–20 μM using a centrifugal concentrator with a 50 kD molecular weight cutoff ( Millipore , Billerica , MA , United States ) . Concentrations were determined spectrophotometrically using an extinction coefficient of ε = 99 , 700 M−1 cm−1 at λ = 280 nm . Protein preparations were aliquoted , flash frozen using liquid nitrogen , and stored at −80°C . RIG-I ATPase activity was measured using an established absorbance-based coupled assay system . The RIG-I protein of interest was diluted into ATPase assay buffer ( 25 mM MOPS pH 7 . 4 , 150 mM NaCl , 5 mM DTT ) to a final concentration of 10 nM for KM , RNA experiments in the presence of a coupled assay mix ( 1 mM NADH , 100 U/ml lactic dehydrogenase , 500 U/ml pyruvate kinase , 2 . 5 mM phosphoenol pyruvic acid ) . For KM , RNA experiments , the RNA of interest was diluted into ME buffer ( 25 mM MES pH 6 . 0 , 0 . 5 mM EDTA ) over a 12-pt concentration series and added to the protein/NADH sample mix resulting in RNA concentrations varying from approximately 0 . 5 nM–500 nM . Samples were incubated for at least 2 hr at room temperature . The reaction was initiated by the addition of 5 mM ATP/5 mM MgCl2 to all wells . The rate of ATP hydrolysis was determined indirectly by monitoring the conversion of NADH to NAD+ which results in a loss of sample absorbance at 340 nM . The assay was performed in 96-well format and absorbance was measured over a 10 min time course using a Biotek Synergy H1 Plate Reader ( Biotek ) . Mean velocities were extrapolated for each time course and plotted as a function of either ATP or RNA concentration . These data were then fit to the quadratic solution of the Briggs-Haldane equation: ( 1 ) y=y0+ ( amp ) ∗x+p+KM− ( x+p+KM ) 2−4xp2p , where y0 = basal activity , defined as background catalytic velocity observed in the absence of RIG-I , amp = vmax − y0 = kcat , x = total ATP or RNA concentration , p = total protein concentration , and KM is the Michaelis constant for the variable substrate . The fluorescent RNA hairpin used in binding experiments ( TriLink Biotech , San Diego , CA , United States ) contained a 10 base pair duplex capped by a tetraloop with the sequence GGACGUACGUUU ( 6-FAM ) CGACGUACGUCC and included an internal fluorescent modification , carboxyfluorescein . Binding assays were carried out in 384-well plate format . Briefly , dsRNA was diluted into binding buffer ( 25 mM MOPS pH 7 . 4 , 150 mM NaCl , 5 mM DTT , 2 mM MgCl , 0 . 01% Triton X-100 ) to a concentration of 2 nM . The RIG-I protein construct of interest was then diluted into binding buffer over a 12-pt series of concentrations and mixed 1:1 with RNA samples ( final RNA concentration of 2 nM ) to a volume of 20 μl . Final RIG-I concentrations varyied from 1 . 5 nM to 1500 nM . Samples were equilibrated at room temperature for 2 hr . Fluorescence polarization was measured using a Biotek Synergy H1 plate reader ( Biotek ) . Samples were excited through a bandpass filter at 485/20 nM and fluorescence emission was measured through a bandpass filter at 528/20 nM . Polarization was calculated using the following Equation 2: ( 2 ) P=I∥−G∗I⊥I∥+G∗I⊥ , where I∥ is the intensity of the fluorescent light parallel to the plane of excitation , I⊥ is the intensity of fluorescent light perpendicular to the plane of excitation , and G is an empirically determined correction factor accounting for instrumental bias toward the detection of horizontally polarized light; in this case G = 0 . 87 . An individual experiment consisted of two replicates of each protein concentration for which polarization measurements were taken three times , yielding six values for each condition . The mean polarization values were then plotted against protein concentration and fit to a one-site total binding Equation 3: ( 3 ) y=y0+ymax∗xKd+x , where y0 represents the polarization value when [enzyme] = 0 nM , ymax represents the polarization achieved at a saturating enzyme concentration , and Kd is the dissociation constant . Three experiments were performed for each RIG-I construct , with each reported Kd value representing the mean and standard deviation across these experiments . For equilibrium nucleotide interference analysis , binding experiments were conducted identically as above , except that indicated nucleotides were present in the RNA/binding buffer mixture such that they achieved a final concentration 2 mM . To obtain RNA dissociation rate constants ( koffRNA ) for RIG-I , stopped-flow fluorescence spectroscopy was employed . Stopped-flow experiments were performed in buffer A ( 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM DTT , 0 . 1 mg/ml BSA ) at 24°C using a Kintek Auto-SF stopped-flow instrument ( Kintek , Austin , TX , United States ) supplied with a 150 W xenon arc lamp . For detection , the Cy3-labeled hairpin RNA was excited at 515 nm and the fluorescence emission was monitored at ≥570 nm using a 570 bandpass filter ( Newport Corporation , Irvine , CA , United States ) . Briefly , RIG-I was pre-incubated with Cy3-labeled hairpin RNA in equimolar amounts ( 400 nM ) at room temperature for 2–6 hr to form a protein-RNA complex . The protein-Cy3 RNA complex was then rapidly mixed with a 100-fold excess of unlabeled hairpin RNA for a specified period of time in which 2000 points were collected . For experiments with nucleotide , adenosine 5′-triphosphate ( ATP ) ( Sigma ) , adenosine 5′-diphosphate ( ADP ) ( Sigma ) or adenosine 5′-[γ-thio]triphosphate ( ATPγS ) ( Sigma ) and MgCl2 were included with the trap RNA and rapidly mixed with the protein-Cy3 RNA complex . The average fluorescence measurements ( 4–6 traces ) for each condition were then used in data analysis . Data was fit using non-linear regression to a single Equation 4 or double exponential Equation 5 using GraFit 5 , ( 4 ) y=A0e−kt+offset , ( 5 ) y=A0 ( 1 ) e1−kt+A0 ( 2 ) e2−kt+offset , where A is the amplitude , k is the rate constant , t is the reaction time ( s ) , and the offset is the fluorescence value ( V ) of free Cy3-RNA . Using stopped flow fluorescence spectroscopy , RIG-I binding to nucleotide was measured via Förster resonance energy transfer from RIG-I ( λex 290 ) to the MANT-ATP ( Invitrogen Life Technologies ) ( λem > 400 ) . RIG-I ( 1 μM ) bound to the specified RNA hairpin ( 1 μM ) in buffer A ( see above ) was mixed with varying concentrations of mant-nucleotide ( 10–160 μM ) under pseudo-first order conditions ( >4× [RIG-I] ) to measure the observed association rate constant . Upon binding ( λex 290 ) , the change in fluorescence was monitored through a 400 nm long-pass filter using the Kintek Auto-SF stopped flow ( Kintek ) , and the resulting traces were fit to a double exponential equation ( Equation 5 , above ) . The kobs ( k1 ) corresponding to the initial binding event ( obtained in triplicate ) is then plotted vs ( mant-nucleotide ) and fit to a linear Equation 6 using GraFit 5 , ( 6 ) y=mx+b , where the intercept y is koff and the slope m is kon . A calculated Kd can then be obtained ( koff/kon ) using the experimentally derived koff and kon values ( Figure 2—figure supplement 1D–F ) . For EMSAs performed to determine the equilibrium dissociation constants of WT RIG-I for the RNA dumbbell , reactions ( 20 μl ) containing 10 nM Cy3-labeled RNA dumbbell and varying concentrations of protein ( 15 nM–15 . 4 μM ) were incubated at room temperature for 30 min in RNA binding buffer ( 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM DTT , 0 . 1 mg/ml BSA ) . A portion of the reactions were loaded onto a precast 6% native polyacrylamide gel ( Invitrogen ) and run in 0 . 5× TBE at 100 V for 55 min at 4°C . The gels were imaged using the Typhoon FLA 9500 biomolecular imager ( GE Healthcare ) and analyzed using ImageQuant software ( GE Healthcare ) . The fraction bound RNA was quantified for each protein concentration and plotted vs protein concentration . The data was fit to a one-site specific binding Equation 7 using GraphPad Prism , ( 7 ) y=Bmax*X/ ( Kd+X ) , where y is specific binding , Bmax is the maximal fraction bound , X is the concentration of the Cy3-dumbbell RNA , and Kd is the dissociation constant . For EMSAs used to evaluate protein multimerization , wild type RIG-I was incubated with 100 nM RNA ligand for 30 min at room temperature in binding buffer ( 25 mM MOPS pH 7 . 4 , 150 mM NaCl , 5 mM DTT , 2% glycerol , 0 . 1 mg/ml BSA ) at concentrations ranging from 100 nM to 1 μM . After incubation , 1 μl of 20× loading dye ( binding buffer with 0 . 4% wt/vol bromophenol blue and 0 . 4% wt/vol xylene cyanol ) was added to each reaction , and 5 μl of each sample was run on a 6% DNA Retardation Gel ( Life Technologies ) for 60 min at 100 V . To visualize the RNA , the gel was stained in a 1:10 , 000 dilution of SYBR Gold Nucleic Acid Gel Stain ( Life Technologies ) in 0 . 5× TBE Running Buffer ( Life Technologies ) and imaged on a Typhoon FLA 9500 biomolecular imager ( GE ) . To screen non-hydrolyzable ATP analogs of RIG-I , the ability of these analogs to inhibit RIG-I ATPase activity was tested . The rates of ATP hydrolysis for RIG-I in the presence of ATP analogs ( ATPγS , ADPAlF4 , AMPPNP ) and ADP were determined using an established malachite green assay . RIG-I ( 50 nM ) was first pre-incubated with an excess of 5′ppp10L ( 500 nM ) for 2 . 5 hr at room temperature in buffer A ( 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM DTT ) with 0 . 1 mg/ml BSA . The reactions were then initiated by the addition of a 1:1 ATP:MgCl2 complex ( 1 mM each ) in the presence and absence of ATP analog ( 4 mM ) . For initial velocity measurements ( v0 ) , aliquots of the reaction were quenched at six time points between 15 s and 10 min . The reactions were quenched by the addition of 5× quench buffer ( 250 mM EDTA ) for a final concentration of 50 mM EDTA . Malachite green reagent was added ( 9:1 malachite green: reaction volume ) and allowed to age for 30 min at room temperature . The Abs . 650 was then measured using a Biotek Synergy 2 plate reader ( Biotek ) . The rate of ATP hydrolysis for each reaction was determined by calculating the kobs ( kobs = v0/[Etot] ) . The relative rates of ATP hydrolysis in the presence of the ATP analogs ( as compared to the rate of hydrolysis without analog ) reflect inhibition of RIG-I ATPase activity by the analogs . The malachite green assay was used to determine the steady state kinetic parameters ( kcat and KM ) of MANT-ATP hydrolysis for RIG-I . RIG-I ( 50 nM ) was first pre-incubated with a saturating concentration of 5′ppp10L ( 500 nM ) for 2 . 5 hr at room temperature in buffer A ( 25 mM HEPES pH 7 . 4 , 150 mM NaCl , 2 mM DTT ) with 0 . 1 mg/ml BSA . The reactions were then initiated by the addition of a 1:1 ATP:MgCl2 complex . For initial velocity measurements , aliquots of the reaction were quenched at six time points between 15 s and 10 min . The reactions were quenched by the addition of 5× quench buffer ( 250 mM EDTA ) for a final concentration of 50 mM EDTA . Malachite green reagent was added ( 9:1 malachite green: reaction volume ) and allowed to age for 30 min at room temperature . The Abs . 650 was then measured using a Synergy 2 plate reader ( BioTek ) . The steady state kinetic parameters , kcat and KM , were obtained by determining the initial velocity ( v0 ) as a function of ATP concentration , and fitting the data to Michaelis-Menten equation ( below ) using non-linear regression using GraphPad Prism , kobs=kcat{[S]/ ( KM+[S] ) } , where kobs = v0/[Etot] , and Etot is the total protein concentration . | When a virus invades a cell , it commandeers the cell's replication machinery to make copies of the virus' genetic material . Some viruses , such as those that cause influenza or measles , store their genetic information in the form of ribonucleic acid ( RNA ) molecules . When a virus is first detected inside an animal , specialized cells are activated and sent to destroy the invader . This is known as the innate immune response . Animal cells contain a sensor known as RIG-I , and when RIG-I detects and binds to viral RNA , it starts a signaling process that activates the innate immune system . RIG-I can also bind to RNA molecules made by the host cell , but this binding does not cause RIG-I to activate the immune response . Although there have been many studies into how RIG-I tells the difference between cell and virus RNA ( this process is also known as ‘proofreading’ ) , many of these have overlooked the role of a molecule called ATP . ATP stores energy for the cell , which is released in a process called hydrolysis . Binding to RNA causes the shape of RIG-I to change so that it can also bind to ATP , and RIG-I cannot signal to trigger an immune response unless it is bound to ATP . Now , Rawling , Fitzgerald and Pyle have investigated ATP's role in immune signaling in more detail , and have found that ATP plays two distinct roles . Binding to ATP is necessary for RIG-I to start signaling in response to viral RNA , as it activates or ‘springs open’ the signaling regions formed when RIG-I binds to RNA . ATP hydrolysis is not involved in signaling; instead , it helps to remove RIG-I from the cell's RNA molecules . This recycles RIG-I and prevents it from becoming activated at the wrong time . With a clear mechanistic description of RIG-I proofreading in place , it will now be possible to investigate how certain viruses take advantage of this system to evade detection . Further investigations could also look at how the dysregulation of RIG-I proofreading may be related to autoimmune disorders and cancer . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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] | 2015 | Establishing the role of ATP for the function of the RIG-I innate immune sensor |
SOX2 positive pituitary stem cells ( PSCs ) are specified embryonically and persist throughout life , giving rise to all pituitary endocrine lineages . We have previously shown the activation of the STK/LATS/YAP/TAZ signalling cascade in the developing and postnatal mammalian pituitary . Here , we investigate the function of this pathway during pituitary development and in the regulation of the SOX2 cell compartment . Through loss- and gain-of-function genetic approaches , we reveal that restricting YAP/TAZ activation during development is essential for normal organ size and specification from SOX2+ PSCs . Postnatal deletion of LATS kinases and subsequent upregulation of YAP/TAZ leads to uncontrolled clonal expansion of the SOX2+ PSCs and disruption of their differentiation , causing the formation of non-secreting , aggressive pituitary tumours . In contrast , sustained expression of YAP alone results in expansion of SOX2+ PSCs capable of differentiation and devoid of tumourigenic potential . Our findings identify the LATS/YAP/TAZ signalling cascade as an essential component of PSC regulation in normal pituitary physiology and tumourigenesis .
SOX2 is a crucial transcription factor involved in the specification and maintenance of multiple stem cell populations in mammals . Pituitary stem cells express SOX2 and contribute to the generation of new endocrine cells during embryonic development and throughout postnatal life ( Andoniadou et al . , 2013; Rizzoti et al . , 2013 ) . The pituitary gland is composed of three parts , the anterior , intermediate and posterior lobes ( AL , IL and PL , respectively ) . The AL and IL contain hormone-secreting cells , which are derived from an evagination of the oral ectoderm expressing SOX2 , termed Rathke’s pouch ( RP ) . SOX2+ cells , both in the embryonic and adult pituitary , can differentiate into three endocrine cell lineages , which are marked by transcription factors PIT1 ( POU1F1 ) ( Li et al . , 1990 ) , TPIT ( TBX19 ) ( Pulichino et al . , 2003 ) and SF1 ( NR5A1 ) ( Ingraham et al . , 1994 ) , and differentiate into hormone-secreting cells ( somatotrophs , lactotrophs , thyrotrophs , corticotrophs , melanotrophs and gonadotrophs , which express growth hormone , prolactin , thyrotropin , adrenocorticotropin , melanotropin and gonadotropin , respectively ) . SOX2+ PSCs are highly proliferative during the first 2–3 weeks of life , in concordance with major organ growth , after which they reach a steady low proliferative capacity that contributes to maintain normal homeostasis and physiological adaptation of the pituitary gland ( Levy , 2002; Nolan et al . , 1998 ) . Contrary to other organs , where somatic stem cells are shown to be able to become transformed into cancer stem cells , the roles of SOX2+ PSCs in tumourigenesis remain poorly understood , possibly due to the patchy knowledge of the pathways regulating SOX2+ PSC fate and proliferation . Pituitary tumours are common in the population , representing 10–15% of all intracranial neoplasms ( Bronstein et al . , 2011; Daly et al . , 2006 ) . Adenomas are the most common adult pituitary tumours , classified into functioning , when they secrete one or more of the pituitary hormones , or non-functioning if they do not secrete hormones . In children , adamantinomatous craniopharyngioma ( ACP ) is the most common pituitary tumour . Targeting oncogenic beta-catenin in SOX2+ PSCs in the mouse generates clusters of senescent SOX2+ cells that induce tumours resembling ACP in a paracrine manner , that is the tumours do not derive from the targeted SOX2+ PSCs ( Andoniadou et al . , 2013; Gonzalez-Meljem et al . , 2017 ) . Up to 15% of adenomas and 50% of ACP display aggressive behaviour with invasion of nearby structures including the hypothalamus and visual tracts , associated with significant morbidity and mortality ( Lasolle et al . , 2017 ) . Pituitary carcinomas exhibiting metastasis are rare but can develop from benign tumours ( Veldhuis , 2013; Pernicone et al . , 1997; Heaney , 2014 ) . Whether SOX2+ cells can cell autonomously contribute to pituitary neoplasia has not been hitherto demonstrated . The Hippo pathway controls stem cell proliferation and tumourigenesis in several organs such as in the liver ( Zhou et al . , 2009; Lu et al . , 2018 ) , intestines ( Zhou et al . , 2011 ) and lung ( Lin et al . , 2015; Nantie et al . , 2018 ) . In the core phosphorylation cascade , STK3/4 kinases phosphorylate and activate LATS1/2 serine/threonine-protein kinases , which in turn phosphorylate co-activators Yes-associated protein ( YAP1 , a . k . a . YAP ) and WW domain-containing transcription regulator protein 1 ( WWTR1 , a . k . a . TAZ ) that are subsequently inactivated through degradation and cytoplasmic retention ( Meng et al . , 2016 ) . Active YAP/TAZ associate with TEAD transcription factors , promoting the transcription of target genes such as Cyr61 and Ctgf ( Zhao et al . , 2008; Zhang et al . , 2009; Zhou et al . , 2016 ) . YAP/TAZ have been shown to promote proliferation and the stem cell state in several organs , and can also lead to transformation and tumour initiation when overexpressed ( Camargo et al . , 2007; Schlegelmilch et al . , 2011; Dong et al . , 2007 ) . The involvement of YAP/TAZ in the function of tissue-specific SOX2+ stem cells during development and homeostasis has not been shown . We previously reported strong nuclear localisation of YAP and TAZ exclusively in SOX2+ stem cells of developing Rathke's pouch and the postnatal anterior pituitary of mice and humans , and enhanced expression in human pituitary tumours composed of uncommitted cells , including ACPs and null-cell adenomas ( Lodge et al . , 2016; Xekouki et al . , 2019 ) , which do not express any of the lineage transcription factors PIT1 , TPIT or SF1 . In these populations we detected phosphorylation of YAP at serine 127 ( S127 ) indicating LATS kinase activity . Together these point to a possible function for LATS/YAP/TAZ in normal pituitary stem cells and during tumourigenesis . Here , we have combined genetic and molecular approaches to reveal that deregulation of the pathway can promote and maintain the SOX2+ PSC fate under physiological conditions and that major disruption of this axis transforms SOX2+ PSCs into cancer-initiating cells giving rise to aggressive tumours .
To determine if YAP and TAZ function during embryonic development of the pituitary , we used genetic approaches to perform gain- and loss-of-function experiments . We first expressed a constitutive active form of YAP ( S127A ) using the Hesx1-Cre driver , which drives Cre expression in Rathke’s pouch ( RP ) and the hypothalamic primordium from 9 . 5dpc , regulated by administration of doxycycline through the reverse tetracycline-dependent transactivator ( rtTA ) system ( R26rtTA/+; see Materials and methods for details , Scheme Figure 1A ) . Analyses were restricted to embryonic time points . As expected , we confirmed accumulation of total YAP protein but not of TAZ or pYAP ( S127 ) , throughout the developing pituitary and hypothalamus of Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ ( hereafter YAP-TetO ) embryos at 15 . 5dpc , but not of Cre-negative controls ( Figure 1B , Figure 1—figure supplement 1A ) . Likewise , the YAP downstream target Cyr61 ( Figure 1B ) was also upregulated . Morphologically , YAP-TetO mutants displayed a dysplastic anterior pituitary , which was more medially compacted and lacked a central lumen , making it difficult to distinguish between the developing anterior and intermediate lobes ( Figure 1C ) . Immunofluorescence staining against SOX2 at 15 . 5dpc demonstrated loss of SOX2 in the most lateral regions of control pituitaries ( arrows in Figure 1C ) , where cells are undergoing commitment; yet mutant pituitaries had abundant SOX2 positive cells in the most lateral regions ( arrowheads in Figure 1C ) . Immunostaining for LHX3 , which is expressed in the developing anterior pituitary ( Sheng et al . , 1996 ) , was used to demarcate AL and IL tissue . Staining using antibodies against lineage markers PIT1 , TPIT and SF1 revealed a concomitant reduction in committed cell lineages throughout the gland ( Figure 1D; PIT1 0 . 35% in mutants compared with 30 . 21% in controls ( Student’s t-test p<0 . 0001 , n = 3 for each genotype ) , TPIT 1 . 03% in mutants compared with 9 . 81% in controls ( Student’s t-test p=0 . 0012 , n = 3 for each genotype ) , SF1 0 . 34% in mutants compared with 4 . 14% in controls ( Student’s t-test p=0 . 0021 , n = 3 for each genotype ) ) . We therefore conclude that sustained activation of YAP prevents lineage commitment and is sufficient to maintain the progenitor state during embryonic development . We did not obtain any live Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ pups at birth when treated with doxycycline from 5 . 5dpc ( n = 5 litters ) . To bypass the embryonic lethality of these early inductions , we commenced doxycycline treatment from 9 . 5dpc , the onset of RP formation ( Figure 1—figure supplement 1B ) . Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ pups were viable and were maintained on doxycycline until P24 , at which point the experimental end point was reached due to excessive weight loss and animals had to be culled following UK Home Office Regulations . Histological analyses of pituitaries revealed multiple anterior lobe cysts per gland , localising predominantly in the ventral AL ( n = 4 ) ( Figure 1—figure supplement 1C ) . These structures developed in YAP-accumulating regions and were lined by SOX2+ cells ( Figure 1—figure supplement 1D ) . The proportion of SOX2+ cells throughout the AL was increased , as was the percentage of SF1+ cells , whereas PIT1+ cell numbers were significantly decreased and differentiated cells of the TPIT lineage , identified by ACTH antibody staining , were unaffected ( Figure 1—figure supplement 1E ) . The total number of cycling Ki-67+ cells showed a trend towards a decrease in Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ mutants relative to controls , which did not reach significance ( Figure 1—figure supplement 1F ) . The cystic structures observed in Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ mutants were reminiscent of Rathke’s cleft cyst ( RCC ) , which is a benign developmental anomaly of the pituitary characterised by the presence of ciliated and secretory cells , expression of cytokeratins and frequent expression of p63 . Immunostaining revealed that cysts were lined by cytokeratin+ cells using the AE1/AE3 pan-cytokeratin cocktail and basal cells were positive for nuclear p63 in Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ mutant pituitaries ( Figure 1—figure supplement 1G ) . Staining using antibodies against ARL13B and Acetylated α-Tubulin ( Lys40 ) marking cilia , revealed multi-ciliated cells along the cyst lining ( Figure 1—figure supplement 1H ) . Combined staining using Alcian Blue and the Periodic Acid-Schiff technique ( AB/PAS ) to recognise mucins , detected royal blue-stained mucous cells lining the cysts ( Figure 1—figure supplement 1H ) . Taken together , we conclude that sustained activation of YAP during embryonic and postnatal pituitary development , promotes maintenance and abnormal expansion of SOX2+ epithelia during development , resulting in the formation of cysts that resemble RCC . Next , we generated embryos null for TAZ and conditionally lacking YAP in the Hesx1 expression domain ( Figure 1—figure supplement 2A–E ) . Hesx1Cre/+;Yapfl/fl;Taz-l- double mutants were obtained at expected ratios during embryonic stages until 15 . 5dpc , however the majority of Taz-/- mutants with or without compound Yap deletions showed lethality at later embryonic and early postnatal stages ( Tian et al . , 2007 ) ( Supplementary file 1 ) . The developing pituitary gland of Hesx1Cre/+;Yapfl/fl;Taz-l- double mutants appeared largely normal at 13 . 5dpc by histology ( Figure 1—figure supplement 2A ) . Immunostaining against SOX2 to mark embryonic progenitors and postnatal stem cells did not reveal differences in the spatial distribution of SOX2+ cells between double mutants compared to controls ( Hesx1+/+;Yapfl/fl;Taz+/+ and Hesx1+/+;Yapfl/fl;Taz+/- ) at 13 . 5dpc , 16 . 0dpc ( Figure 1—figure supplement 2B ) or P28 , even in regions devoid of both TAZ and active YAP ( Figure 1—figure supplement 2C , D ) . This suggests that YAP/TAZ are not required for SOX2+ cell specification or survival . Likewise , analysis of commitment markers PIT1 and SF1 as well as ACTH to identify cells of the TPIT lineage , did not show any differences between genotypes ( Figure 1—figure supplement 2E ) . Together , these data suggest there is no critical requirement for YAP and TAZ during development for the specification of SOX2+ cells or lineage commitment , but that YAP functions to promote the SOX2 cell identity . Since sustained activation of YAP led to an embryonic phenotype , we reasoned that YAP/TAZ need to be regulated during embryonic development . To determine if STK and LATS kinases are important in YAP/TAZ regulation we carried out genetic deletions in the pituitary . Conditional deletion of Stk3 and Stk4 ( also called Mst2 and Mst1 ) in Hesx1Cre/+;Stk3fl/fl;Stk4fl/fl embryos did not lead to a pituitary phenotype ( Figure 2—figure supplement 1 ) . A reduction of over 75% in total STK3/4 proteins in mutants was confirmed by western blot on total lysates from Hesx1+/+;Stk3fl/fl;Stk4fl/fl controls and Hesx1Cre/+;Stk3fl/fl;Stk4fl/fl mutants ( Figure 2—figure supplement 1B ) . Mutant pituitaries were macroscopically normal at birth ( Figure 2—figure supplement 1A ) , and showed comparable expression patterns of TAZ , YAP , pYAP to controls lacking Cre , without distinct accumulation of YAP or TAZ ( Figure 2—figure supplement 1C ) . The distribution of SOX2+ cells was comparable between mutants and controls ( Figure 2—figure supplement 1C ) . Normal lineage commitment was evident by immunofluorescence staining for PIT1 , TPIT and SF1 at P10 ( Figure 2—figure supplement 1D ) . Mutant animals remained healthy and fertile until P70 , at which point pituitaries appeared histologically normal ( Figure 2—figure supplement 1E ) . Since deletion of Stk3/4 at embryonic stages does not affect embryonic or postnatal pituitary development , we conclude these kinases are not critical for YAP/TAZ regulation in the pituitary . We next focused on perturbing LATS kinase function , as we have previously shown strong expression of Lats1 in the developing pituitary and postnatal kinase activity in SOX2+ stem cells ( Lodge et al . , 2016 ) . However , Hesx1Cre/+;Lats1fl/fl embryos showed unaffected pituitary development and normal localisation and levels of YAP and TAZ as assessed by immunofluorescence ( Figure 2—figure supplement 2A , B ) when compared with controls . mRNA in situ hybridisation against Lats2 at P2 , revealed abundant Lats2 transcripts upon conditional deletion of Lats1 , suggesting a compensatory upregulation of Lats2 in the absence of LATS1 ( Figure 2—figure supplement 2C ) , similar to previous reports of elevated YAP/TAZ signalling inducing Lats2 expression ( Moroishi et al . , 2015 ) . To overcome potential functional redundancy , we deleted both Lats1 and Lats2 in RP . Deletion of Lats2 alone ( Hesx1Cre/+;Lats2fl/fl ) , did not reveal any developmental morphological anomalies ( Figure 2—figure supplement 2D ) and pups were identified at normal Mendelian proportions ( Supplementary file 2 ) . Similarly , deletion of any three out of four Lats alleles did not affect pituitary development and were identified at normal ratios , similar to other tissues ( Lavado et al . , 2018 ) . Homozygous Hesx1Cre/+;Lats1fl/fl;Lats2fl/fl mutants were identified at embryonic stages at reduced Mendelian ratios and were absent at P0-P2 , suggesting embryonic and perinatal lethality ( Supplementary file 2 ) . Haematoxylin/eosin staining of the developing pituitary gland in Hesx1Cre/+;Lats1fl/fl;Lats2fl/fl mutants revealed overgrowth of RP by 13 . 5dpc compared to controls lacking Cre ( Figure 2A , n = 4 ) . Total TAZ and YAP proteins accumulated throughout the developing gland in double mutants ( arrowheads ) but only in the SOX2+ periluminal epithelium of controls ( arrows ) . The same regions showed a marked reduction in pYAP-S127 staining , which is observed in SOX2+ cells of the control ( Figure 2A ) . These findings are in line with LATS1/2 normally regulating YAP and TAZ in the pituitary and demonstrate successful deletion in RP . The mutant pituitary was highly proliferative ( Figure 2B , Figure 2—figure supplement 2F; Ki-67 index average 47 . 42% ± 1 . 73 SEM in control versus 76 . 04% ± 9 . 11 SEM in the double mutant , p=0 . 0067 , Student’s t-test ) and the majority of cells expressed SOX2 ( Figure 2A , C ) but not SOX9 ( Figure 2B , Figure 2—figure supplement 2F ) . By 15 . 5dpc the pituitary was grossly enlarged and exerting a mass effect on the brain , had cysts and displayed areas of necrosis ( asterisks Figure 2 , Figure 2—figure supplement 2E , n = 5 ) . Staining for Endomucin to mark blood vessels revealed poor vascularisation in Hesx1Cre/+;Lats1fl/fl;Lats2fl/fl mutants compared to the ample capillaries seen in the control ( Figure 2C ) , which may account for the necrosis . This could be due to a direct inhibition of vascularisation or a consequence of the rapid growth of this embryonic tumour . We frequently observed ectopic residual pituitary tissue at more caudal levels , reaching the oral epithelium and likely interfering with appropriate fusion of the sphenoid , similar to other phenotypes involving pituitary enlargement ( arrows Figure 2C ) ( Andoniadou et al . , 2012; Sajedi et al . , 2008; Gaston-Massuet et al . , 2008 ) . Immunofluorescence to detect active ( non-phosphorylated ) YAP revealed abundant staining throughout the pituitary at 15 . 5dpc , compared to the control where active YAP localises in the SOX2 epithelium ( Figure 2C ) . Immunofluorescence using specific antibodies against lineage commitment markers PIT1 , TPIT and SF1 at 15 . 5dpc revealed very few cells expressing PIT1 , TPIT and SF1 in the double mutant ( Figure 2D; PIT1 9 . 14% in mutants compared with 51 . 4% in controls ( Student’s t-test p<0 . 0001 ) ; TPIT 4 . 0% in mutants compared with 11 . 4% in controls ( Student’s t-test p<0 . 007 ) ; SF1 2 . 1% in mutants compared with 6 . 5% in controls ( Student’s t-test p>0 . 05 ) n = 3 mutants and five controls ) , suggesting failure to commit into the three lineages . These data suggest that the LATS/YAP/TAZ axis is required for normal embryonic development of the anterior pituitary and that LATS1/2 kinases control proliferation of SOX2+ progenitors and their progression into the three committed lineages . Postnatal analysis of Hesx1Cre/+;Lats1fl/fl pituitaries revealed that by P56 , despite developing normally during the embryonic period , all glands examined exhibited lesions of abnormal morphology consisting of overgrowths , densely packed nuclei and loss of normal acinar architecture ( n = 15 ) . To minimise the likely redundancy by LATS2 seen at embryonic stages , we generated Lats1 mutants additionally haploinsufficient for Lats2 ( Hesx1Cre/+;Lats1fl/fl;Lats2fl/+ ) . These pituitaries also developed identifiable lesions accumulating YAP and TAZ ( Figure 3—figure supplement 1A ) , which were observed at earlier time points ( P21 n = 4 ) , the earliest being 10 days , indicating increased severity . The number of lesions observed per animal was similar between the two models at P56 ( 3–8 per animal ) . Deletion of Lats2 alone ( Hesx1Cre/+;Lats2fl/fl ) , which is barely expressed in the wild type pituitary , did not result in any defects ( Figure 3—figure supplement 1B ) . We focused on the Hesx1Cre/+;Lats1fl/fl;Lats2fl/+ double mutants for further analyses . Histological examination of Hesx1Cre/+;Lats1fl/fl;Lats2fl/+ pituitaries confirmed the abnormal lesions were tumours , characterised by frequent mitoses , focal necrosis , and a focal squamous differentiation , as well as the occasional presence of cysts ( Figure 3A ) . These lesions were identical to those in Hesx1Cre/+;Lats1fl/fl pituitaries ( not shown ) . These tumours accumulated YAP/TAZ and upregulated expression of targets Cyr61 and Ctgf ( Figure 3B ) , confirming the validity of the genetic manipulation ( Figure 3B ) . Tumours were also frequently observed in the anterior and intermediate lobe ( Figure 3—figure supplement 1C ) . Analysis of proliferation by Ki-67 immunostaining revealed an elevated mitotic index of 7–28% in tumours ( mean 15 . 46 , SEM ±2 . 74 ) , compared to 2 . 97% ( SEM ±1 . 2 ) mean in control pituitaries not carrying the Lats1 deletion ( Figure 3C ) . In keeping with the morphological evidence of epithelial differentiation ( Figure 3A ) , the tumours were positive for cytokeratins using AE1/AE3 ( multiple keratin cocktail ) ( Figure 3—figure supplement 1E ) . Furthermore , the tumours showed focal morphological evidence of squamous differentiation and showed positive nuclear p63 staining , frequently expressed in squamous carcinomas ( Figure 3—figure supplement 1E ) . In contrast , the tumours did not show immunohistochemical evidence of adenomas , that is , they were negative for neuroendocrine markers , which all types of adenomas are typically positive for: the neuroendocrine marker synaptophysin and neuron-specific enolase ( Figure 3—figure supplement 1F ) . The lesions were also negative for chromogranin A , a neuroendocrine granule marker often expressed in clinically non-functioning pituitary adenomas . Tumours were also negative for vimentin , expressed by spindle cell oncocytoma , an uncommitted posterior pituitary tumour ( Figure 3—figure supplement 1F ) . Moreover , immunostaining against PIT1 , TPIT and SF1 showed only sparse positive cells within the lesions , suggesting lack of commitment into endocrine precursors and supporting the undifferentiated nature of the tumour cells ( Figure 3D ) . Consistent with a tumourigenic phenotype , and role for LATS1 genomic stabilisation ( Pefani et al . , 2014 ) , staining for gamma-H2A . X detected elevated DNA damage in cells of the mutant pituitaries compared with controls ( Figure 3—figure supplement 1D ) . The absence of adenoma or oncocytoma markers together with the histological appearance , observation of focal necrosis and a high mitotic index support the features of squamous carcinoma . Tumour regions were mostly composed of SOX2 positive cells , a sub-population of which also expressed SOX9 ( Figure 3E , Figure 3—figure supplement 1A; 85–97% of cells , 7 tumours across four pituitaries ) . Close examination of the marginal zone epithelium , a major SOX2+ stem cell niche of the pituitary , revealed a frequent ‘ruffling’ resembling crypts , likely generated through over-proliferation of the epithelial stem cell compartment ( Figure 3F ) . To determine if the cell of origin of the tumourigenic lesions is a deregulated SOX2+ stem cell , we carried our specific deletion of LATS1/2 in postnatal SOX2+ cells using the tamoxifen-inducible Sox2-CreERT2 driver , combined with conditional expression of membrane-GFP in targeted cells ( Sox2CreERT2/+;Lats1fl/fl;Lats2fl/+;R26mTmG/+ ) . Tamoxifen induction at P5 or P21 , led to abnormal lesions in the anterior pituitary within three months in all cases . We focused our analyses on inductions performed at P5 , from which time point all animals developed lesions by P35 ( Figure 4A ) . Similar to observations in Hesx1Cre/+;Lats1fl/fl;Lats2fl/+ animals , these areas strongly accumulated YAP and TAZ ( Figure 4B ) , activated expression of targets Cyr61 and Ctgf , displayed ruffling of the AL epithelium ( Figure 4C , Figure 4—figure supplement 1E ) and lacked lineage commitment markers ( Figure 4D , Figure 4—figure supplement 1A ) . These lesions showed a similar marker profile to Hesx1-Cre-targeted tumours , with positive p63 and AE1/AE3 staining ( Figure 4—figure supplement 1B ) . Lineage tracing confirmed expression of membrane GFP in tumourigenic lesions , characterised by the accumulation of YAP and expansion of SOX2+ cells , suggesting they were solely derived from SOX2+ cells ( Figure 4E , Figure 4—figure supplement 1C ) . Taken together , our data support that LATS kinase activity is required to regulate the pituitary stem cell compartment . Loss of LATS1 is sufficient to drive deregulation of SOX2+ pituitary stem cells , generating highly proliferative non-functioning tumours with features of carcinomas . Conditional deletion of LATS1/2 kinases in the pituitary has revealed how these promote an expansion of SOX2+ stem cells in the embryonic and postnatal gland at the expense of differentiation . To establish if this effect was mediated through YAP alone , we used the tetracycline-controlled conditional YAP-TetO system to promote YAP ( S127A ) protein levels in postnatal pituitaries of Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ mice . We treated YAP-TetO animals with doxycycline from P21 to P105 ( 12 week treatment , Figure 5A ) . We did not observe the formation of tumours at any stage analysed ( n = 12 , Figure 5—figure supplement 1A ) . Similarly , we did not observe the formation of lesions when treating from P5 . This is in contrast with the unequivocal tumour formation observed in Sox2CreERT2/+;Lats1fl/fl;Lats2fl/+ mice . Elevation of YAP protein levels was confirmed following three weeks of doxycycline treatment ( P42 ) , displaying patchy accumulation , likely a result of genetic recombination efficiencies ( Figure 5B ) . Consistent with pathway activation , there was robust elevation in the expression of transcriptional targets Cyr61 and Ctgf following treatment ( Figure 5—figure supplement 1B ) , however at significantly lower levels compared to Sox2CreERT2/+;Lats1fl/fl;Lats2fl/+ deletions ( Figure 5—figure supplement 1E ) , and there was no elevation in phosphorylated inactive YAP ( Figure 5B ) . Immunofluorescence against SOX2 demonstrated a significant increase in the number of SOX2+ cells as a proportion of the anterior pituitary ( Figure 5B , F; 18 . 0% compared to 12 . 1% in controls , p=0 . 0014 ) , a finding recapitulated by SOX9 that marks a subset of the SOX2 population ( Figure 5B ) . This increase in the percentage of SOX2+ cells was maintained at all stages analysed ( Figure 5F ) and did not affect the overall morphology of the pituitary . At P42 we observed a significant increase in proliferation among the SOX2+ pituitary stem cells from 3% in controls to 15% in mutants ( p=0 . 027 ) . SOX2+ cells make up 10% of all cycling cells ( Ki-67% ) in normal pituitaries , however in mutants this increased to 25% , suggesting a preferential expansion of the SOX2+ population , rather than an overall increase in proliferation ( Figure 5C ) . No additional marked differences were observed in samples analysed at P63 ( 6 weeks of treatment , n = 3 ) , however longer treatment ( P21 to P105 ) resulted in sporadic regions of expanded SOX2+ cells ( Figure 5—figure supplement 1C ) . These regions did not express the commitment marker PIT1 and were identifiable by haematoxylin/eosin staining . In contrast to tumour lesions generated following loss of LATS kinases , these were not proliferative , were positive for pYAP and did not accumulate high levels of YAP/TAZ ( n = 6 lesions ) . Together these results suggest that the sustained expression of constitutive active YAP can activate the proliferation of SOX2 stem cells , but in contrast to deletion of LATS1 , this alone is not oncogenic . To establish if the expansion of pituitary stem cells following forced expression of YAP is reversible , we administered doxycycline to YAP-TetO animals for three weeks ( P21 to P42 ) by which point there is a robust response , followed by doxycycline withdrawal for three weeks ( until P63 ) to allow sufficient time for YAP levels to return to normal ( scheme Figure 5D ) . Immunofluorescence against total YAP protein confirmed restoration of the normal YAP expression pattern and levels after recovery ( Figure 5E ) , and mRNA in situ hybridisation detected a reduction in expression of YAP/TAZ targets Cyr61 and Ctgf ( Figure 5—figure supplement 1D ) . Following recovery from high levels of YAP , the number of SOX2+ cells reduced to comparable levels as in controls ( around 10% of the total anterior pituitary ) ( Figure 5E , F ) . This suggests that the effects of YAP overexpression on the stem cell population are transient following three weeks of treatment ( Figure 5F ) . Finally , to determine if SOX2+ cells could differentiate into hormone-producing cells after the reduction in YAP levels , we expressed constitutive active YAP only in SOX2+ cells while lineage tracing this population ( Sox2CreERT2/+;R26rtTA/mTmG;Col1a1tetO-Yap/+ ) . We induced SOX2+ cells by low-dose tamoxifen administration at P21 and treated with doxycycline for three weeks , followed by doxycycline withdrawal for a further three weeks ( Figure 5G ) . Larger clones of SOX2 derivatives were observed at P63 in Sox2CreERT2/+;R26rtTA/mTmG;Col1a1tetO-Yap/+ animals compared to controls , and these still contained SOX2+ cells ( Figure 5H ) . Following withdrawal , we were able to detect GFP+ derivatives of SOX2+ cells , which had differentiated into the three lineages ( PIT1 , SF1 and ACTH , marking corticotrophs of the TPIT lineage ) ( Figure 5I ) . Taken together , these findings confirm that sustained expression of YAP is sufficient to maintain the SOX2+ state and promote activation of normal SOX2+ pituitary stem cells in vivo , driving expansion of this population .
Here we establish that regulation of LATS/YAP/TAZ signalling is essential during anterior pituitary development and can influence the activity of the stem/progenitor cell pool . LATS kinases , mediated by YAP and TAZ , are responsible for controlling organ growth , promoting an undifferentiated state and repressing lineage commitment . Loss of both Lats1 and Lats2 , encoding potent tumour suppressors , leads to dramatic tissue overgrowth during gestation , revealing a function for these enzymes in restricting growth during pituitary development . The involvement of YAP/TAZ and dysfunction of the kinase cascade is emerging in multiple paediatric cancers , which are often developmental disorders ( Ahmed et al . , 2017 ) . Loss of LATS1 heterozygosity has been reported in a range of human tumours ( Lee et al . , 1990; Chen et al . , 2005; Theile et al . , 1996; Mazurenko et al . , 1999 ) leading to an increase in YAP/TAZ protein levels . Previous global deletion of Lats1 in mice resulted in a variety of soft tissue sarcomas and stromal cell tumours ( St John et al . , 1999 ) . The anterior lobe of these animals appeared hyperplastic with poor endocrine cell differentiation leading to combined hormone deficiencies , but the presence of tumours was not noted . We report that loss of Lats1 alone is sufficient to drive anterior and intermediate lobe tumour formation . This phenotype is accelerated following additional deletion of one copy of Lats2 . Phenotypically identical tumour lesions were generated when the genetic deletions were carried out embryonically in RP , or at postnatal stages . Interestingly , tissue-specific loss of Stk3 and Stk4 , which regulate LATS activation in other tissues ( Hu et al . , 2017 ) , did not lead to any pituitary defects despite reduction in STK3/4 levels . These data suggest that perhaps the residual activity of STK3/4 is sufficient for LATS1/2 activation . Alternatively , regulation of LATS1/2 by kinases other than STK3/4 is possible in the pituitary , meaning deletion of Stk3/4 alone is insufficient to result in significant LATS function impairment . Similar situations have been reported in other organs where LATS are functioning ( Hu et al . , 2017 ) . The resulting non-secreting tumours in our mouse models are composed predominantly of SOX2+ stem cells and display signs of squamous differentiation . Rare cases of squamous cell carcinoma have been reported as primary pituitary tumours ( Saeger et al . , 2007 ) , but more frequently , arising within cysts that are normally non-neoplastic epithelial malformations ( Lewis et al . , 1983; O'Neill et al . , 2016 ) . In the embryonic YAP-TetO model , where constitutive active YAP ( S127A ) was expressed during pituitary development , cysts phenocopying Rathke’s cleft cyst , develop by postnatal stages . Target elevation is not as high in YAP-TetO pituitaries , as following the deletion of LATS1/2 , indicating that signalling levels are likely to be critical for progression between these phenotypes . Although human pituitary carcinomas are only diagnosed as such after metastasis , the tumours generated in our LATS1/2 mouse models fit their histopathological profile . Genetic lineage tracing identified SOX2+ cells as the cell of origin of the tumours; this observation could have ramifications regarding involvement of the LATS/YAP/TAZ pathway in the establishment or progression of human pituitary tumours composed of uncommitted cells . In cancer stem cells of osteosarcoma and glioblastoma , SOX2 antagonises upstream Hippo activators , leading to enhanced YAP function ( Basu-Roy et al . , 2015 ) . We recently reported enhanced expression of YAP/TAZ in a range of non-functioning human pituitary tumours , compared to functioning adenomas , and that Lats1 knock-down in GH3 pituitary mammosomatotropinoma cells results in repression of the Gh and Prl promoters ( Xekouki et al . , 2019 ) . Therefore , YAP/TAZ , perhaps in a positive feedback loop with SOX2 , are likely to function both to promote the maintenance of an active pituitary stem cell state as well as to inhibit differentiation . By dissecting the downstream requirement for YAP in pituitary regulation by the LATS/YAP/TAZ axis , we found that expression of constitutively active YAP ( S127A ) is sufficient to push SOX2+ pituitary stem cells into an activated state , leading to expansion of the stem cell cohort ( see Model , Figure 6 ) . YAP has previously been indicated to promote the stem cell state in other tissues , for example pancreas , neurons and mammary glands ( Panciera et al . , 2016 ) . However , this does not fully recapitulate the LATS deletion phenotypes , as it did not lead to the formation of tumours during the time course of YAP activation ( 12 weeks ) . Interestingly , since the levels of target activation are significantly greater in Lats1/2 deletions that in YAP-TetO activation , initiation of tumourigenesis may be associated with levels of signalling rising above a threshold . However , the temporal control of expressing the mutation is critical , as seen in other tumour models ( Han et al . , 2016 ) . Instead , the findings identify an isolated role for YAP in promoting the expansion of the SOX2+ stem cell pool and restoring their proliferative potential to levels akin to the most active state during postnatal pituitary growth . Activity of YAP/TAZ is reduced in dense tissues , resulting in a decrease in stemness . One mechanism through which this is achieved is by crosstalk with other signalling pathways regulating stem cell fate ( Papaspyropoulos et al . , 2018; Heallen et al . , 2011 ) . For example , a decrease in YAP/TAZ activity removes inhibition on Notch signalling , resulting in higher levels of differentiation and a drop in stem cell potential ( Totaro et al . , 2017 ) . In the pituitary , Notch plays a role in the maintenance of the SOX2 stem cell compartment and is involved in regulating differentiation ( Zhu et al . , 2015; Nantie et al . , 2014; Cheung et al . , 2013; Batchuluun et al . , 2017 ) . The downstream mechanisms of YAP action on SOX2+ pituitary stem cells , as well as the likely crosstalk with other signalling pathways remain to be explored . In summary , our findings highlight roles for LATS/YAP/TAZ in the regulation of pituitary stem cells , where fine-tuning of their expression can make the difference between physiological stem cell re-activation and tumourigenesis , of relevance to other organs . We reveal this axis is involved in the control of cell fate commitment , regulation of regenerative potential and promotion of tumourigenesis . These findings can aid in the design of treatments against pituitary tumours and in regenerative medicine approaches targeting the regulation of endogenous stem cells .
Animal husbandry was carried out under compliance of the Animals ( Scientific Procedures ) Act 1986 , Home Office license and KCL ethical review approval . The Hesx1Cre/+ Andoniadou et al . , 2007 , Sox2CreERT2/+ Andoniadou et al . , 2013 , Yapfl/fl 25 , Taz-/- Tian et al . , 2007 ( JAX:011120 ) , R26mTmG/+ Muzumdar et al . , 2007 ( JAX:007576 ) , ROSA26rtTA/+ Yu et al . , 2005 ( JAX:016999 ) , Col1a1tetO-Yap/+ Jansson and Larsson , 2012 ( MGI:5430522 ) , Stk3fl/fl; Stk4fl/fl Lu et al . , 2010 ( JAX:017635 ) , and Lats1fl/fl Heallen et al . , 2011 ( JAX:024941 ) and Lats2fl/fl Heallen et al . , 2011 ( JAX:025428 ) have been previously described . Tamoxifen ( Sigma , T5648 ) was administered to experimental mice by intraperitoneal injection at a single dose of 0 . 15 mg/g body weight , or two equal doses on sequential days , depending on the experiment . Mice for growth studies were weighed every week . For embryonic studies , timed matings were set up where noon of the day of vaginal plug was designated as 0 . 5dpc . For YAP-TetO experiments , crosses between Hesx1Cre/+;R26+/+;Col1a1+/+ and Hesx1+/+;R26rtTA/rtTA;Col1a1tetO-Yap/ tetO-Yap animals were set up to generate Hesx1Cre/+;R26rtTA/+;Col1a1tetO-Yap/+ offspring ( hereby YAP-TetO ) and control littermates , or crosses between Sox2CreERT2/+;R26mTmG/mTmG;Col1a1+/+ and Sox2+/+; R26rtTA/rtTA;Col1a1tetO-Yap/ tetO-Yap animals were set up to generate Sox2CreERT2/+;R26rtTA/mTmG;Col1a1tetO-Yap/+ offspring . While treated with the tetracycline analogue doxycycline , YAP-TetO expressed rtTA from the ROSA26 locus in Cre-derived cells , enabling YAP-S127A expression from the Col1a1 locus . For embryonic studies between 5 . 5dpc and 15 . 5dpc ( scheme , Figure 1A ) , doxycycline ( Alfa Aesar , J60579 ) was administered to pregnant dams in the drinking water at 2 mg/ml , supplemented with 10% sucrose . For postnatal analyses animals were treated with doxycycline or vehicle ( DMSO ) as described , from the ages specified for individual experiments on the Hesx1Cre/+ driver , or directly following tamoxifen administration for animals on the Sox2CreERT2/+ driver . Both male and female mice and embryos where included in the studies . Embryos and adult pituitaries were fixed in 10% neutral buffered formalin ( Sigma ) overnight at room temperature . The next day , tissue was washed then dehydrated through graded ethanol series and paraffin-embedded . Embryos up to 13 . 5dpc were sectioned sagittal and all older embryo and postnatal samples were sectioned frontal , at a thickness of 7 µm for immunofluorescence staining , or 4 µm for RNAscope mRNA in situ hybridisation . Sections were selected for the appropriate axial level , to include Rathke’s pouch or pituitary , as described previously ( Lodge et al . , 2016 ) . The RNAscope 2 . 5 HD Reagent Kit-RED assay ( Advanced Cell Diagnostics ) was used with specific probes: Ctgf , Cyr61 , Lats2 ( all ACDBio ) . Sections were dewaxed in histoclear and rehydrated through graded ethanol series from 100% to 25% ethanol , then washed in distilled H2O . Sections were stained with Haematoxylin QS ( Vector #H3404 ) for 1 min , and then washed in water . Slides were then stained in eosin in 70% ethanol for 2 min and washed in water . Slides were dried and coverslips were mounted with VectaMount permanent mounting medium ( Vector Laboratories H5000 ) . Slides were deparaffinised in histoclear and rehydrated through a descending graded ethanol series . Antigen retrieval was performed in citrate retrieval buffer pH6 . 0 , using a Decloaking Chamber NXGEN ( Menarini Diagnostics ) at 110°C for 3mins . Tyramide Signal Amplification ( TSA ) was used for staining using antibodies against YAP ( 1:1000 , Cell Signaling #4912S ) , pYAP ( 1:1000 , Cell Signaling #4911S ) , TAZ ( 1:1000 , Atlas Antibodies #HPA007415 ) and SOX2 ( 1:2000 , Abcam ab97959 ) with EMCN ( 1:1000 , Abcam ab106100 ) staining as follows: sections were blocked in TNB ( 0 . 1M Tris-HCl , pH7 . 5 , 0 . 15M NaCl , 0 . 5% Blocking Reagent ( Perkin Elmer FP1020 ) ) for 1 hr at room temperature , followed by incubation with primary antibody at 4°C overnight , made up in TNB . Slides were washed three times in TNT ( 0 . 1MTris-HCl pH7 . 5 , 0 . 15M NaCl , 0 . 05% Tween-20 ) then incubated with secondary antibodies ( biotinylated anti-rabbit ( 1:350 Abcam ab6720 ) and anti-Rat Alexa Fluor 555 ( 1:300 , Life Technologies A21434 ) for 1 hr at room temperature and Hoechst ( 1:10000 , Life Technologies H3570 ) . Slides were washed again then incubated in ABC reagent ( ABC kit , Vector Laboratories PK-6100 ) for 30 mins , followed by incubation with TSA conjugated fluorophore ( Perkin Elmer NEL753001KT ) for ten minutes . Slides were washed and mounted with VectaMount ( Vector Laboratories H1000 ) . For regular immunofluorescence , sections were blocked in blocking buffer ( 0 . 15% glycine , 2 mg/ml BSA , 0 . 1% Triton-X in PBS ) , with 10% sheep serum ( donkey serum for goat SOX2 antibody ) for 1 hr at room temperature , followed by incubation with primary antibody at 4°C overnight , made up in blocking buffer with 1% serum . Primary antibodies used were against SOX2 ( 1:250 , Immune Systems Ltd GT15098 ) , active YAP ( 1:300 , Abcam ab205270 ) , GFP ( 1:300 , Abcam ab13970 ) , Ki-67 ( 1:300 , Abcam ab16667 ) , SOX9 ( 1:300 , Abcam ab185230 ) , PIT1 ( 1:1000 , Gift from S . Rhodes , Indiana University ) , TPIT ( 1:1000 , Gift from J . Drouin , Montreal ) , SF1 ( 1:200 , Life Technologies N1665 ) , Gamma H2A . X ( 1:1000 , Abcam ab2893 ) , Vimentin ( 1:300 , Cell Signaling #5741 ) , Caspase ( 1:300 , Cell Signaling #9661S ) . Slides were washed in PBST then incubated with secondary antibodies for 1 hr at room temperature . Appropriate secondary antibodies were incubated in blocking buffer for 1 hr at room temperature ( biotinylated anti-rabbit ( 1:350 , Abcam ab6720 ) , biotinylated anti-mouse ( 1:350 , Abcam ab6788 ) , anti-chicken 488 ( 1:300 , Life Technologies A11039 ) , anti-goat 488 ( 1:300 , Abcam ab150133 ) . Slides were washed again using PBST and incubated with fluorophore-conjugated Streptavidin ( 1:500 , Life Technologies S21381 or S11223 ) for 1 hr at room temperature , together with Hoechst ( 1:10000 , Life Technologies H3570 ) . Slides were washed in PBST and mounted with VectaMount ( Vector Laboratories , H1000 ) . Immunohistochemistry for the remaining antigens were undertaken on a Ventana Benchmark Autostainer ( Ventana Medical Systems ) using the following primary antibodies and antigen retrieval: AE1/AE3 ( 1:100 , Dako M351529 ) , CC1 ( 36 min , Ventana Medical Systems 950–124 ) ; Chromogranin ( 1:400 , Dako M086901 ) , CC1 ( 36 min , Ventana Medical Systems 950–124 ) ; NCAM ( 1:15 , Novocastra NCL-L-CD56-504 ) , CC1 ( 64 min , Ventana Medical Systems 950–124 ) ; NSE ( 1:1000 , Dako M087329 ) , CC1 ( 36 min , Ventana Medical Systems 950–124 ) ; p63 ( 1:100 , A . Menarini Diagnostics ) , CC1 ( 64 min , Ventana Medical Systems 950–124 ) and Synaptophysin ( 1:2 , Dako M731529 ) , CC2 ( 92 min , Ventana Medical Systems 950–124 ) . Targets were detected and viewed using the ultraView Universal DAB Detection Kit ( Ventana Medical Systems , 760–500 ) according to manufacturer’s instructions . Following deparaffinisation and rehydration , sections were taken through distilled water then placed in Alcian Blue solution ( 1% Alcian Blue ( Alfa Aeser J60122 ) in 3% acetic acid ( VWR International 20103 ) ) for 20 min . Sections were then placed in 1% periodic acid ( VWR 29460 ) for 10 min , washed in distilled water and transferred to Schiff’s reagent ( Thermo Fisher Scientific 88017 ) for 10 min , followed by washing in distilled water for 5 min . Sections were then routinely dried , cleared and mounted . Dissected anterior pituitaries were flash frozen in liquid nitrogen and stored at −80°C . Frozen pituitaries were each lysed in 30 µl of lysis buffer ( 5 mM Tris , 150 mM NaCl , 1% protease and phosphatase inhibitor ( Abcam ab201119 ) , 5 μM EDTA , 0 . 1% Triton-X , pH7 . 6 ) and sonicated at 40% power , twice for ten cycles of: two seconds on/two seconds off , using a Vibra-Cell Processor ( Sonics ) . Protein concentration was determined using the Pierce BCA protein assay kit ( Thermo #23227 ) and all samples were diluted to 4 mg/ml in Laemmli buffer ( Biorad #161–0747 ) . Proteins were denatured at 95°C for 5 min . Samples were run on a 10% Mini-PROTEAN TGX polyacrylamide gel ( BioRad #4561033 ) , then transferred using Trans-Blot Turbo transfer machine ( BioRad ) onto polyvinylidene difluoride membranes ( BioRad #1704156 ) . Membranes were blocked with 5% non-fat dairy milk ( NFDM ) in TBST ( 20 mM Tris , 150 mM NaCl , 0 . 1% Tween-20 , pH7 . 6 ) , cut , then incubated with primary antibodies overnight at 4°C as follows: anti-STK3/STK4 ( 1:5000 , Bethyl Laboratories #A300-466A ) or Cyclophilin B ( 1:1000 , R and D Systems #MAB5410 ) in 5%NFDM . The next day , membranes were washed in TBST , incubated with secondary antibodies HRP-conjugated anti-Rabbit ( 1:2000 , Cell Signaling #7074 ) or HRP-conjugated anti-Mouse ( 1:2000 , Cell Signaling #7076 ) in 5% NFDM for 1 hr at room temperature . After washing in TBST , membranes were treated with Clarity Western ECL substrate ( Biorad #170–5060 ) and bands visualised using the ChemiDoc Touch Imaging System ( BioRad ) . Protein abundance was analysed using ImageLabs ( BioRad ) . Wholemount images were taken with a MZ10 F Stereomicroscope ( Leica Microsystems ) , using a DFC3000 G camera ( Leica Microsystems ) . For bright field images , stained slides were scanned with Nanozoomer-XR Digital slide scanner ( Hamamatsu ) and images processed using Nanozoomer Digital Pathology View . Fluorescent staining was imaged with a TCS SP5 confocal microscope ( Leica Microsystems ) and images processed using Fiji ( Schindelin et al . , 2012 ) . Cell counts were performed manually using Fiji cell counter plug-in; 5–10 fields were counted per sample , totalling over 1500 nuclei , across 3–7 pituitaries . Statistical analyses and graphs were generated in GraphPad Prism ( GraphPad Software ) and the following tests were performed to determine significance: Student’s t-tests between controls and mutants for Figures 1D and 2D , Figure 2—figure supplement 2D and 2E ( n = 3 of each genotype ) , Figure 4—figure supplement 1 ( n = 4 of each genotype ) and Figure 5C ( n = 4–5 of each genotype ) ; unpaired t-test for Figure 2—figure supplement 2A ( n = 3 per genotype ) and Figure 2—figure supplement 2F ( n = 6 sections across two samples per genotype ) ; two-tailed t-test for Figure 3C ( n = 3 controls , seven mutants ) ; two-way ANOVA with Sidak’s multiple-comparison test for Figure 5F ( n = 4–5 of each genotype ) . For quantification of target expression by RNAscope mRNA in situ hybridisation ( Figure 5—figure supplement 1 ) , the area of positive staining ( red fluorescence ) from 4 μm sections was determined from images using thresholding in Fiji , and quantified as a percentage of total pituitary area in the same image . For statistical testing , one-way ANOVAs with Tukey’s multiple comparisons were performed ( n = 4 mutants per genotype ) . Error bars in graphs show ±standard error of the mean , unless otherwise indicated . Quantification of STK3/4 by western blot was carried out on two control ( Stk3fl/fl;Stk4fl/fl ) and three mutant ( Hesx1Cre/+;Stk3fl/fl; Stk4fl/fl ) samples . A Student’s t-test was carried out on normalised band intensities . Chi-squared tests were used to determine significant deviations of observed from expected genotypes presented as tables in Supplementary files 1 and 2 . | The pituitary is a gland inside the head that releases hormones that control major processes in the body including growth , fertility and stress . Diseases of the pituitary gland can prevent the body from producing the appropriate amounts of hormones , and also include tumours . A population of stem cells in the pituitary known as SOX2 cells divide to make the specialist cells that produce the hormones . This population forms as the pituitary develops in the embryo and continues to contribute new hormone-producing cells throughout life . Signals from inside and outside the gland control how the pituitary develops and maintain the correct balance of different types of cells in the gland in adults . In 2016 , Lodge et al . reported that a cascade of signals known as the Hippo pathway is active in mouse and human pituitary glands , but its role remained unclear . Here , Lodge et al . use genetic approaches to study this signalling pathway in the pituitary of mice . The results of the experiments show that the Hippo pathway is essential for the pituitary gland to develop normally in mouse embryos . Furthermore , in adult mice the Hippo pathway is required to maintain the population of SOX2 cells in the pituitary and to regulate their cell numbers . Increasing the level of Hippo signalling in mouse embryos and adult mice led to an expansion of SOX2 stem cells that could generate new specialist cell types , but a further increase generated aggressive tumours that originated from the uncontrolled growth of SOX2 cells . These findings are the first step to understanding how the Hippo pathway works in the pituitary , which may eventually lead to new treatments for tumours and other diseases that affect this gland . The next step towards such treatments will be to carry out further experiments that use drugs to control this pathway and alter the fate of pituitary cells in mice and other animals . | [
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] | 2019 | Homeostatic and tumourigenic activity of SOX2+ pituitary stem cells is controlled by the LATS/YAP/TAZ cascade |
Clonally transmissible cancers are tumour lineages that are transmitted between individuals via the transfer of living cancer cells . In marine bivalves , leukaemia-like transmissible cancers , called hemic neoplasia ( HN ) , have demonstrated the ability to infect individuals from different species . We performed whole-genome sequencing in eight warty venus clams that were diagnosed with HN , from two sampling points located more than 1000 nautical miles away in the Atlantic Ocean and the Mediterranean Sea Coasts of Spain . Mitochondrial genome sequencing analysis from neoplastic animals revealed the coexistence of haplotypes from two different clam species . Phylogenies estimated from mitochondrial and nuclear markers confirmed this leukaemia originated in striped venus clams and later transmitted to clams of the species warty venus , in which it survives as a contagious cancer . The analysis of mitochondrial and nuclear gene sequences supports all studied tumours belong to a single neoplastic lineage that spreads in the Seas of Southern Europe .
Cancers are clonal cell lineages that arise due to somatic changes that promote cell proliferation and survival ( Stratton et al . , 2009 ) . Although natural selection operating on cancers favours the outgrowth of malignant clones with replicative immortality , the continued survival of a cancer is generally restricted by the lifespan of its host . However , clonally transmissible cancers – from now on , transmissible cancers – are somatic cell lineages that are transmitted between individuals via the transfer of living cancer cells , meaning that they can survive beyond the death of their hosts ( Murchison , 2008 ) . Naturally occurring transmissible cancers have been identified in dogs ( Murgia et al . , 2006; Murchison et al . , 2014; Baez-Ortega et al . , 2019 ) , Tasmanian devils ( Murchison et al . , 2012; Pye et al . , 2016 ) and , more recently , in marine bivalves ( Metzger et al . , 2015; Metzger et al . , 2016; Yonemitsu et al . , 2019 ) . Hemic neoplasia ( HN ) , also called disseminated neoplasia , is a type of leukaemia cancer found in multiple species of bivalves , including oysters , mussels , cockles , and clams ( Carballal et al . , 2015 ) . Although these leukaemias represent different diseases across bivalve species , they have been classically grouped under the same term because neoplastic cells share morphological features ( Carballal et al . , 2015 ) . Some HNs have been proven to have a clonal transmissible behaviour ( Metzger et al . , 2015 ) , in which neoplastic cells , most likely haemocytes ( i . e . the cells that populate the haemolymph and play a role in the immune response ) , are likely to be transmitted through marine water . In late stages of the disease , leukaemic cells invade the surrounding tissues and , generally , animals die because of the infection ( Carballal et al . , 2015 ) , although remissions have also been described ( Burioli et al . , 2019 ) . Despite the observation that leukaemic cells are typically transmitted between individuals from the same species , on occasion they can infect and propagate across populations from a second , different bivalve species ( Metzger et al . , 2016; Yonemitsu et al . , 2019 ) . Hence , these cancers represent a potential threat for the ecology of the marine environment , which argues for the necessity of their identification and characterization for their monitoring and prevention . Here , we use multiplatform next-generation genome sequencing technologies , including Illumina short reads and Oxford Nanopore long reads , together with cytogenetics , electron microscopy , and cytohistological approaches to identify , characterize , and decipher the evolutionary origin of a new marine leukaemia that is transmitted between two different clam species that inhabit the Seas of Southern Europe , namely warty venus ( Venus verrucosa ) and striped venus ( Chamelea gallina ) ( Video 1 ) .
We investigated the prevalence of HN in the warty venus clam ( V . verrucosa ) , a saltwater bivalve found in the Atlantic Coast of Europe and the Mediterranean Sea . We collected 345 clam specimens from six sampling regions in the Atlantic and the Mediterranean coasts of Europe across five different countries , including Spain , Portugal , France , Ireland , and Croatia ( Figure 1a; Supplementary file 1 ) . Cytohistological examination identified HN-like tumours in eight specimens from two sampling points in Spain ( Figure 1b–e; Figure 1—figure supplement 1 ) . Three HN-positive specimens ( ERVV17-2995 , ERVV17-2997 , and ERVV17-3193 ) were collected in Galicia , northwest of the Iberian Peninsula in the Atlantic Ocean , and another five specimens ( EMVV18-373 , EMVV18-376 , EMVV18-391 , EMVV18-395 , and EMVV18-400 ) were collected in the Balearic Islands , bathed by the Mediterranean Sea ( Figure 1a; Supplementary file 1 ) . Four of these specimens ( ERVV17-2995 , ERVV17-3193 , EMVV18-391 , and EMVV18-395 ) showed a severe form of the disease – classified as N3 stage – which is characterized by high levels of neoplastic cells infiltrating the gills , different levels of infiltration of the digestive gland and gonad , and low/very low infiltration of the mantle and foot ( Figure 1d , e; Figure 1—figure supplement 1 ) ; one specimen ( EMVV18-400 ) was found that was affected with an intermediate form of the disease – N2 stage – characterized by low levels of neoplastic cells infiltrating the gill vessels , digestive gland , and gonad , but not the foot ( Figure 1—figure supplement 1 ) ; and three specimens ( ERVV17-2997 , EMVV18-373 , and EMVV18-376 ) were diagnosed with a light form of the disease – N1 stage – characterized by low levels of neoplastic cells infiltrating the gills vessels only , and no infiltration in the remaining tissues ( Figure 1—figure supplement 1 ) . Electron microscopy analysis through gill’s ultrathin sections from two neoplastic warty venus specimens ( ERVV17-2995 and ERVV17-3193 ) revealed tumour cells with a round shape and a pleomorphic nucleus , which are morphological features that generally characterize bivalves’ HN ( Figure 1f; Figure 1—figure supplement 2 ) . Finally , one additional neoplastic warty venus specimen ( EVVV11-02 ) was included in the study . The animal , which was sampled in 2011 in Galicia and came from a private collection , showed abnormal metaphases in the gills that were suggestive of HN . Although the species typically shows a 2n = 38 karyotype with metacentric chromosomes that are homogeneous in size ( García-Souto et al . , 2015 ) , the tumoural metaphases from this individual showed around 100 chromosomes that were variable in size and shape ( Figure 1g ) . To obtain some biological insights into the clonal dynamics of this cancer , we carried out whole-genome sequencing with Illumina paired-ends in DNA samples isolated from the tumoural haemolymph from eight out of nine neoplastic specimens mentioned above ( Table 1 ) . Their feet were also sequenced , as foot typically represents the tissue with lower infiltration of neoplastic cells , making it a good candidate tissue to act as ‘matched-normal’ ( i . e . host tissue ) . As for the animal with an abnormal karyotype ( EVVV11-02 ) that was compatible with HN , we sequenced the only tissue available , which were gills ( Table 1 ) . Only one neoplastic specimen ( EMVV18-373 ) that had a very low proportion of tumour cells in its haemolymph was excluded from the sequencing . Then , we mapped the paired-end reads onto a dataset containing non-redundant mitochondrial Cytochrome C Oxidase subunit 1 ( Cox1 ) gene references from 118 Venerid clam species . In six out of eight sequenced neoplastic specimens , the results revealed an overrepresentation ( >99% ) of reads in the sequenced tissues mapping to Cox1 DNA sequences that exclusively identified two different clam species ( Figure 2a ) : the expected one , warty venus clam ( V . verrucosa ) , and a second , unexpected one , the striped venus ( C . gallina ) , a clam that inhabits the Mediterranean Sea ( Figure 2b ) . Preliminary analysis by PCR and capillary sequencing of Cox1 in the haemolymph of two neoplastic specimens , EMVV18-373 and EVVV11-02 , revealed an electropherogram with overlapping peaks apparently containing two different haplotypes that match the reference Cox1 sequences for warty and striped venus ( Figure 2c ) . These results suggested cancer contagion between the two clam species of the family Veneridae . Hence , to decipher the origins of this clam neoplasia , we further analysed the mitochondrial DNA ( mtDNA ) from the two species involved and the tumours . Firstly , we performed multiplatform genome sequencing , including Illumina short reads and Oxford Nanopore long reads , on canonical individuals from the two species to obtain a preliminary assembly of the mitogenomes of V . verrucosa and C . gallina . These reconstructions resulted in 18 , 092- and 17 , 618-bp long mtDNA genomes for the warty venus and the striped venus clam , respectively ( Figure 2—figure supplement 1 ) . The comparative analysis of the nucleotide sequences from both mitogenomes confirms that , although both species are relatively close within the subfamily Venerinae ( Canapa et al . , 1996 ) , they represent distinct sister species , showing a Kimura’s two-parameter nucleotide distance ( K2P ) equal to 21 . 13% . Then , we mapped the paired-end sequencing data from the six neoplastic specimens with evidence of interspecies cancer transmission onto the two reconstructed species-specific mtDNA genomes . This approach confirmed the coexistence of two different mtDNA haplotypes in the six examined neoplastic samples , matching the canonical mtDNA genomes from the two clam species . For example , in a N2-stage specimen ( EMVV18-400 ) , this analysis revealed different proportion of tumour and host mtDNA molecules in the two tissue types sequenced ( Figure 2d ) . Here , the striped venus mtDNA results the most abundant in the haemolymph , in which tumour cells are dominant over the remaining cell types , and the lower in the matched-normal tissue ( i . e . infiltrated foot ) , where tumour cells represent a minor fraction of the total . Similar results were obtained for the remaining five neoplastic individuals ( Figure 2—figure supplement 2 ) . To further investigate the evolutionary origins and geographic spread of this cancer , we sequenced with Illumina paired-ends an additional set of eight healthy ( i . e . non-neoplastic ) clams from three different Veneridae species , including five more warty venus specimens ( EMVV18-385 , IGVV19-666 , FGVV18-183 , CSVV18-1052 , and PLVV18-2249 ) from five different countries , two striped venus specimens ( IMCG15-69 and ECCG15-201 ) from two countries , and one specimen ( EVCS14-09 ) from its sibling species Chamelea striatula , a type of striped venus clam that inhabits the Atlantic Ocean from Norway to the Gulf of Cadiz in Spain . This made a total of 16 Veneridae specimens sequenced , all listed in Table 1 ( see also Supplementary file 1 ) . The complete mitochondrial genomes from all tumoural and healthy V . verrucosa specimens ( 13 individuals ) , 2 C . gallina , and 1 from its sibling species C . striatula , were individually de novo assembled from the sequencing reads . As expected , this approach reconstructed two different haplotypes in six out eight sequenced neoplastic animals , supporting the presence of mtDNA from two different species . Despite the high sequencing coverage obtained for these individuals ( Table 1 ) , we did not find foreign reads in the N1 tumours ( ERVV17-2997 and EMVV18-373 ) , most likely due to a low proportion of neoplastic cells in the haemolymph and the matched-normal tissue . Then , we performed a phylogenetic analysis based on the alignment of these mitochondrial genomes ( 13 coding and 2 RNA gene sequences , altogether encompassing ~14 kb ) . The results show that tumour and non-tumour sequences from neoplastic warty venus specimens define two well-differentiated clades , and that tumoural warty venus sequences are all identical and closer to striped venus mtDNA than to its own ( warty venus ) ( Figure 2e ) . Overall , these data support the existence of a single cancer clone originated in the striped venus clam C . gallina that was transmitted to V . verrucosa . Transmissible cancers are known to occasionally acquire mitochondria from transient hosts ( Strakova et al . , 2016; Strakova et al . , 2020 ) , which can lead to misinterpretation of their evolutionary history . Thus , we looked for nuclear markers to confirm the striped venus origin of this cancer lineage . We performed a preliminary draft assembly of the warty venus and the striped venus nuclear ‘reference’ genomes , using the paired-end sequencing data from two non-neoplastic animals . Then , we used bioinformatic approaches to find single copy nuclear genes that were homologous between the two species , identifying two confident candidate genomic regions ( see Methods ) : a 2 . 9-kb long region from DEAH12 , a gene that encodes for an ATP-dependent RNA helicase , and a 2 . 2-kb long fragment from the Transcription Factor II Human-like gene , TFIIH . With the idea of finding differentially fixed single-nucleotide variants ( SNVs ) between both species , we performed PCR amplification and capillary sequencing on a 441 bp fragment from the DEAH12 , and a 559 bp fragment from TFIIH , in 2 cohorts of non-neoplastic warty venus specimens ( 12 for DEAH12 and 15 for TFIIH ) , 2 cohorts of non-neoplastic striped venus ( 9 for DEAH12 and 12 for TFIIH ) , and 1 specimen of its sister species C . striatula . This analysis provided 14 and 15 sites , respectively , for the DEAH12 and the TFIIH loci , with fixed SNVs ( allele frequency >95% ) that allowed to discriminate between the 3 relevant species and the tumour ( Figure 3a ) . These variants were employed to identify the Illumina reads from each sequenced warty venus neoplastic specimens that were specific for either warty venus or striped venus , which allowed to obtain the consensus sequences that corresponded to the tumour tissue and the non-affected tissue from each neoplastic individual . At the end of this process , we performed Maximum Likelihood phylogenetic reconstructions from these individual nuclear consensus sequences . On the one hand , the phylogeny for the DEAH12 locus confirmed both the monophyly of the tumoural sequences and their closer relationship to C . gallina than to the host species ( Figure 3b ) , which were also observed in the mtDNA analysis . However , the phylogeny derived from the TFIIH locus showed that , although the tumours remained monophyletic , they were positioned in a basal branch relative to C . gallina and V . verrucosa ( Figure 3b ) . Hence , to resolve these differences we also obtained a multilocus species tree based on the alignment of both the mtDNA and the two nuclear genes . This new phylogeny confirmed that warty venus tumours are closer to striped venus specimens than to non-neoplastic warty venus sequences from the same diseased specimens , while the non-neoplastic sequences conformed a more distant warty venus lineage ( Figure 3c ) . To obtain further evidence on the striped venus origin of this clam’s neoplasia , we performed a comparative screening of tandem repeats in the genomes of C . gallina and V . verrucosa using fluorescence in situ hybridization ( FISH ) ( Figure 3d; Figure 3—figure supplement 1 ) . We focused on two satellite DNA repeats , namely CL4 and CL17 . The satellites represent repeats of 332- and 429-bp long monomers , respectively , and were identified in a preliminary bioinformatics screening of the striped venus reference genome ( see methods ) . This FISH approach revealed that the mentioned repeats are very abundant in heterochromatic regions from the genomes of the canonical striped venus and the neoplastic warty venus specimens tested ( Figure 3d; Figure 3—figure supplement 1 ) . However , the repeats were absent in the metaphases from all the healthy warty venus individuals ( Figure 3d; Figure 3—figure supplement 1 ) . These results suggest that the relevant chromosomes with CL4 and CL17 satellites found in neoplastic warty venus specimens derive from C . gallina , supporting that a tumour originated in C . gallina was transmitted to V . verrucosa . To find out whether this cancer is present in the clam species where it first arose , we performed a screening for its presence in natural populations of striped venus clams from the species C . gallina ( n = 213 ) and C . striatula ( n = 9 ) at five additional sampling points across two countries ( Supplementary file 1 ) , including Spain ( n = 115 ) and Italy ( n = 107 ) . Histological analyses did not show any traces of HN in these specimens . The virtual absence of this tumour in natural populations of striped venus clams may suggest that today this leukaemia is being mainly , if not exclusively , transmitted between specimens of the recipient species , warty venus . However , further sampling in other regions across the striped venus area of distribution may be necessary to confirm these findings . Overall , the results provided here reveal the existence of a transmissible leukaemia originated in a striped venus clam , most likely C . gallina , which was transmitted to a second species , the warty venus clam ( V . verrucosa ) , and among whose specimens it currently propagates . We identified this parasitic cancer in warty venus clams from two sampling points that are more than 1000 nautical miles away in the coasts of Spain , bathed by two different seas , the Atlantic Ocean and the Mediterranean Sea . The analysis of mitochondrial and nuclear gene sequences revealed no nucleotide diversity within the seven tumours sequenced , which supports that all belong to the same neoplastic lineage that spreads between Veneridae clams in the Seas of Southern Europe . Although we ignore the age of this cancer clone , we can confirm it arose before 2011 , when the neoplastic warty venus specimen EVVV11-02 was collected . The apparent lack of genetic variation between all tumours , even from distant sampling points , suggests either that this cancer is very recent , or that it may have been unintentionally scattered by the action of man , a way of transmission that has been proposed for other bivalve transmissible cancers ( Yonemitsu et al . , 2019 ) .
We collected 570 clam specimens from three different species , from the following countries and locations ( Supplementary file 1 ) . V . verrucosa clams were collected in Spain ( Galicia , n = 90; Balearic Islands , n = 67 ) , France ( Granville , n = 100 ) , Croatia ( Split , n = 18 ) , Portugal ( Oeiras , n = 19 ) , and Ireland ( Carna , n = 50 ) . C . gallina clams were collected in Spain ( Cadiz , n = 50; Mallorca , n = 50 ) and Italy ( Naples , n = 50; Cattolica , n = 57 ) . C . striatula clams were collected in Spain ( Combarro , n = 9 ) . Additionally , we recruited samples from the following specimens from private collections: one V . verrucosa clam collected in 2011 in Spain ( Islas Cies ) , four C . gallina collected in 2015 in Italy ( San Benedetto de Tronto ) , five C . gallina collected in 2015 in Spain ( Huelva ) , and one C . striatula collected in 2014 in Spain ( Marin ) . We followed standard cytological and/or histological protocols to test and diagnose HN in the clam specimens . However , only histological examination resulted decisive for the diagnosis , particularly in early stages of the disease . Briefly , for each animal , we extracted 300–2000 ml of haemolymph from the posterior adductor muscle using a 5 ml syringe with a 23 G needle . The haemolymph ( 50 ml ) was diluted in cold Alserver’s antiaggregant solution to a 1:4 concentration , and spotted by centrifugation ( 130 × g , 4°C , 7 min ) onto a microscope slide using cytology funnel sample chambers to produce a cell monolayer . Haemolymph smears were fixed and stained with Hemacolor solutions from Sigma-Aldrich and subsequently examined with a light microscope for the diagnosis of HN . Tissues ( visceral mass , gills , mantle , and foot ) were dissected , fixed in Davidson’s solution and embedded in paraffin . Then , 5-mm thick sections from each tissue were microdissected and stained with Harris’ haematoxylin and eosin and examined using a light microscope for histopathological analysis . HN was diagnosed and classified according to three disease stages ( i . e . N1 , N2 , or N3 ) as follows . N1 stage: small groups of leukaemic cells were detected only in the vessels of the gills and in the connective tissue surrounding the digestive tubules . N2 stage: leukaemic cells spread to different organs , conforming small groups in the connective tissue that surrounds the digestive gland and the gonadal follicles , branchial sinuses , and mantle . N3 stage: leukaemic cells invade the filaments , completely deforming the plica structure in the gill , invade the connective tissue surrounding the gonadal follicles and the digestive gland; in the mantle , they invade the connective tissue , but in the muscle fibres of the mantle and foot , cells appear isolated or in small groups and in lower intensity than in other tissues . Four V . verrucosa specimens ( two non-neoplastic , ERVV17-2993 and ERVV17-2992 , and two with high grade of HN , ERVV17-2995 and ERVV17-3193 ) were processed for transmission electron microscopy as follows: 2 mm sections of gills and digestive glands were fixed in 2 . 5% glutaraldehyde seawater for 2 hr at 4°C . Then , tissues were post-fixed in 1% osmium tetroxide in sodium cacodylate solution and embedded in Epon resin . Ultrathin sections were stained with uranyl acetate and lead citrate and examined in a JEM-1010 transmission electron microscope . Mitotic chromosomes of a neoplastic V . verrucosa specimen ( EVVV11-02 ) were obtained as follows . After colchicine treatment ( 0 . 005% , 10 hr ) , gills were dissected , treated with a hypotonic solution , and fixed with ethanol and acetic acid . Small pieces of fixed gills were disaggregated with 60% acetic acid to obtain cell suspensions that were spread onto preheated slides . Chromosome preparations were stained with DAPI ( 0 . 14 mg/ml ) and PI ( 0 . 07 mg/ml ) for 8 min , mounted with antifade medium , and photographed . A comparative screening of tandem repeats was performed on the genomes of C . gallina and V . verrucosa using RepeatExplorer ( Novák et al . , 2010 ) on a merged short-read dataset of both species ( 500 , 000 reads each ) . Short reads of healthy and neoplastic animals were mapped onto both satellite consensus sequences using BWA , filtered according to their mapping quality ( q > 60 and AS >70 ) and their abundance assessed by means of samtools/bamtools . Satellites CL4 and CL17 were selected for FISH purposes and FISH probes were PCR amplified ( CL4F: TCAGAAACCGCTATTTTTCAC , CL4R: AAATGATGCTACGAACCTCC and CL17F: ATTCCAGAAATGTACATGAACAC , CL17R: ATTTTTGCACCAGATGTTCAC , respectively ) and directly labelled with digoxigenin-11-dUTP ( 10× DIG Labeling Mix , Roche Applied Science ) . FISH experiments were performed as described in reference ( García-Souto et al . , 2015 ) . In total , we performed whole-genome sequencing on 23 samples from 16 clam specimens , which includes 8 neoplastic and 8 non-neoplastic animals by Illumina paired-end libraries of 350 bp insert size and reads 150 bp long . First we assembled the mitochondrial genomes of one V . verrucosa ( FGVV18_193 ) , one C . gallina ( ECCG15_201 ) , and one C . striatula ( EVCS14_02 ) specimens with MITObim v1 . 9 . 1 ( Hahn et al . , 2013 ) , using gene baits from the following Cox1 and 16S reference genes to prime the assembly of clam mitochondrial genomes: V . verrucosa ( Cox1 , with GenBank accession number KC429139; and 16S: C429301 ) , C . gallina ( Cox1: KY547757 , 16S: KY547777 ) , and C . striatula ( Cox1: KY547747 , 16S: KY547767 ) . These draft sequences were polished twice with Pilon v1 . 23 ( Walker et al . , 2014 ) , and conflictive repetitive fragments from the mitochondrial control region were resolved using long read sequencing with Oxford Nanopore technologies ( ONT ) on a set of representative samples from each species and tumours . ONT reads were assembled with Miniasm v0 . 3 ( Li , 2016 ) and corrected using Racon v1 . 3 . 1 ( Vaser et al . , 2017 ) . Protein-coding genes , rDNAs and tDNAs were annotated on the curated mitochondrial genomes using MITOS2 web server ( Bernt et al . , 2013 ) , and manually curated to fit ORFs as predicted by ORF-FINDER ( Rombel et al . , 2002 ) . Then , we employed the entire mtDNAs of V . verrucosa ( FGVV18_193 ) and C . gallina ( ECCG15_201 ) as ‘references’ to map reads from individuals with neoplasia , filter reads matching either mitogenome and assemble and polish their two ( healthy and tumoural ) mitogenomes individually as above . Further healthy individuals were later sequenced and their mitogenomes assembled , to further investigate the geographic and taxonomic spread of this neoplasia . We retrieved a dataset of 3745 sequences comprising all the barcode-identified venerid clam Cox1 fragments available from the Barcode of Life Data System ( BOLD , http://www . boldsystemns . org/ ) . Redundancy was removed using CD-HIT ( Fu et al . , 2012 ) , applying a cut-off of 0 . 9 sequence identity , and sequences were trimmed to cover the same region . Whole-genome sequencing data from both healthy and tumoural warty venus clams were mapped onto this dataset , containing 118 venerid species-unique sequences , using BWA-mem , filtering out reads with mapping quality below 60 ( -q60 ) , and quantifying the overall coverage for each sequence with samtools idxstats . PCR primers were designed with Primer3 v2 . 3 . 7 ( Kõressaar et al . , 2018 ) to amplify a fragment of 354 bp from the Cox1 mitochondrial gene of V . verrucosa and C . gallina ( F: CCT ATA ATA ATT GGK GGA TTT GG , R: CCT ATA ATA ATT GGK GGA TTT GG ) . PCR products were purified with ExoSAP-IT and sequenced by Sanger sequencing . We further mapped the paired-end sequencing data from healthy and neoplastic tissues from all neoplastic samples onto the ‘reference’ mitochondrial genomes of V . verrucosa and C . gallina ( two of the previously assembled ones , FGVV18_193 and ECCG15_201 ) using BWA-mem v0 . 7 . 17-r1188 ( Li and Durbin , 2009 ) with default parameters . Duplicate reads were marked with Picard 2 . 18 . 14 and removed from the analysis . Read coverage depth was computed with samtools v1 . 9 ( Li et al . , 2009 ) , summarized by computing the average in windows of 100 bp size and plotted with R v3 . 5 . 3 . We ran the MEGAHIT v1 . 1 . 3 assembler ( Li et al . , 2015 ) on the Illumina paired-end sequencing data to obtain partial nuclear genome assemblies of V . verrucosa ( FGVV18_193 ) , C . gallina ( ECCG15_201 ) , and C . striatula ( EVCS14_02 ) . Then , single copy genes were predicted with Busco v . 3 . 0 . 2 ( Seppey et al . , 2019 ) . Candidate genes were considered if they ( 1 ) were present in the genomes of the three species , and ( 2 ) showed variant allele frequencies ( VAFs ) at exclusively 0 , 0 . 5 , or 1 . 0 in all the sequenced healthy ( non-neoplastic ) specimens . Under this criteria , two loci were finally selected: a 3914-bp long fragment of DEAH12 , a gene encoding for an ATP-dependent RNA helicase and a 2 . 2-kp length fragment of the Transcription Factor II Human-like gene , TFIIH . PCR primers were designed with Primer3 v2 . 3 . 7 to amplify and sequence a 441-bp region of the DEAH12 nuclear gene ( DEAH12_F: AGGTATGCTGAAACAAACACTT and DEAH12_R: ACGACAAATTTGATACCTGGAAT ) and a 559-bp fragment of the TFIIH gene ( TFIIH_F: TGGCATCTTTGTTACGGAC and TFIIH_R: CTTGTGRTTCTGTATCTGATCAATAA ) on neoplastic specimens from V . verrucosa and healthy animals from both species ( DEAH12: 11 V . verrucosa and 9 C . gallina; TFIIH: 15 V . verrucosa and 12 C . gallina ) . We screened for differentially fixed SNVs between both species using the dapc function in the R package Exploratory Analysis of Genetic and Genomic Data adegenet ( Jombart and Ahmed , 2011 ) . These variants were later employed to filter the Illumina short reads matching either V . verrucosa or C . gallina genotypes from the neoplastic animals , and to obtain consensus sequences from tumour and healthy tissue in each sequenced specimen . Read filtering was performed with samtools fillmd , while GATK mutect2 ( Benjamin et al . , 2019 ) was used for variant calling . Only variants with VAFs close to fixation ( >0 . 9 ) were considered when building the consensus sequences . Mitochondrial sequences for 13 coding genes and 2 rDNA genes from the 23 recovered mitogenomes ( 6 neoplastic , 17 from host and healthy specimens ) were extracted from the paired-end sequencing data by mapping reads onto the previously reconstructed canonical mtDNAs for V . verrucosa and C . gallina ( Figure 2—figure supplement 1 ) , concatenated , and subjected to multiple alignment with MUSCLE v3 . 8 . 425 ( Edgar , 2004 ) . The best-fit model of nucleotide substitution for each individual gene was selected using JModelTest2 ( Darriba et al . , 2012 ) and a partitioned Bayesian reconstruction of the phylogeny was performed with MrBayes v3 . 2 . 6 ( Ronquist et al . , 2012 ) . Two independent Metropolis-coupled Markov Chain Monte Carlo ( MCMC ) analyses with four chains in each were performed . Each chain was run for 10 million generations , sampling trees every 1000 generations . Convergence of runs was assessed using Tracer ( Rambaut et al . , 2018 ) . DEAH12 and TFIIH sequences were subjected to multiple alignment using MUSCLE v3 . 8 . 425 . Then , a ‘species/population tree’ was inferred with the starBEAST multispecies coalescent model , as implemented in BEAST v2 . 6 . 2 ( Bouckaert et al . , 2019 ) . This analysis was performed using a Yule speciation prior and strict clock , with the best-fit model of nucleotide substitution obtained with jModelTest2 on both the concatenated mitochondrial haplotypes ( 13 protein-coding and 2 rRNAs genes ) and unphased data from DEAH12 and TFIIH nuclear fragments . The four mitochondrial groups observed on the mitogenome analysis ( V . verrucosa , C . gallina , C . striatula , and Tumour ) were defined as tips for the species tree . A single MCMC of 10 million iterations , with sampling every 1000 steps , was run . A burn-in of 10% was implemented to obtain ESS values above 200 with Tracer v1 . 7 . 1 and the resulting posterior distributions of trees were checked with DENSITREE v2 . 1 ( Bouckaert , 2010 ) . A maximum clade credibility tree was obtained with TreeAnnotator ( Bouckaert et al . , 2019 ) to summarize information on topology , with 10% burn-in and Common Ancestors for the node heights . | In humans and other animals , cancer cells divide excessively , forming tumours or flooding the blood , but they rarely spread to other individuals . However , some animals , including dogs , Tasmanian devils and bivalve molluscs like clams , cockles and mussels , can develop cancers that are transmitted from one individual to another . Despite these cancers being contagious , each one originates in a single animal , meaning that even when the cancer has spread to many individuals , its origins can be traced through its DNA . Cancer contagion is rare , but transmissible cancers seem to be particularly common in the oceans . In fact , 7 types of contagious cancer have been described in bivalve species so far . These cancers are known as ‘hemic neoplasias’ , and are characterized by the uncontrolled division of blood-like cells , which can be released by the host they developed in , and survive in ocean water . When these cells encounter individuals from the same species , they can infect them , causing them to develop hemic neoplasia too There are still many unanswered questions about contagious cancers in bivalves . For example , how many species do the cancers affect , and which species do the cancers originate in ? To address these questions , Garcia-Souto , Bruzos , Díaz et al . gathered over 400 specimens of a species of clam called the warty venus clam from the coastlines of Europe and examined them for signs of cancer . Clams collected in two regions of Spain showed signs of hemic neoplasia: one of the populations was from the Balearic Islands in the Mediterranean Sea , while the other came from the Atlantic coast of northwestern Spain . Analyzing the genomes of the tumours from each population showed that the cancer cells from both regions had likely originated in the same animal , indicating that the cancer is contagious and had spread through different populations . The analysis also revealed that the cancer did not originally develop in warty venus clams: the cancer cells contained DNA from both warty venus clams and another species called striped venus clams . These two species live close together in the Mediterranean Sea , suggesting that the cancer started in a striped venus clam and then spread to a warty venus clam . To determine whether the cancer still affected both species , Garcia-Souto , Bruzos , Díaz et al . screened 200 striped venus clams from the same areas , but no signs of cancer were found in these clams . This suggests that currently the cancer only affects the warty venus clam . These findings confirm that contagious cancers can jump between clam species , which could be threat to the marine environment . The fact that the cancer was so similar in clams from the Atlantic coast and from the Mediterranean Sea , however , suggests that it may have emerged very recently , or that human activity helped it to spread from one place to another . If the latter is the case , it may be possible to prevent further spread of these sea-borne cancers through human intervention . | [
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] | 2022 | Mitochondrial genome sequencing of marine leukaemias reveals cancer contagion between clam species in the Seas of Southern Europe |
The DNA repair protein RAD52 is an emerging therapeutic target of high importance for BRCA-deficient tumors . Depletion of RAD52 is synthetically lethal with defects in tumor suppressors BRCA1 , BRCA2 and PALB2 . RAD52 also participates in the recovery of the stalled replication forks . Anticipating that ssDNA binding activity underlies the RAD52 cellular functions , we carried out a high throughput screening campaign to identify compounds that disrupt the RAD52-ssDNA interaction . Lead compounds were confirmed as RAD52 inhibitors in biochemical assays . Computational analysis predicted that these inhibitors bind within the ssDNA-binding groove of the RAD52 oligomeric ring . The nature of the inhibitor-RAD52 complex was validated through an in silico screening campaign , culminating in the discovery of an additional RAD52 inhibitor . Cellular studies with our inhibitors showed that the RAD52-ssDNA interaction enables its function at stalled replication forks , and that the inhibition of RAD52-ssDNA binding acts additively with BRCA2 or MUS81 depletion in cell killing .
Understanding of synthetically lethal relationships between genome caretakers will help to define the molecular mechanisms underlying the maintenance of genomic integrity and may lead to the advancement of personalized cancer treatments . Depletion of the human DNA repair protein RAD52 is synthetically lethal with defects in tumor suppressors , BRCA1 , BRCA2 , or PALB2 ( Feng et al . , 2011; Lok et al . , 2012; Cramer-Morales et al . , 2013 ) . Importantly , this synthetic lethality requires both copies of the tumor suppressor gene to be defective and should not manifest in the heterozygous cells . Therefore , specific RAD52 inhibitors are expected to selectively kill cancerous cells lacking one of these three tumor suppressors . Replacing or supplementing standard radiation and chemotherapies with the RAD52 inhibitors will help to decrease the toxicity associated with these treatments . BRCA1 and BRCA2 are tumor suppressors that are commonly mutated or depleted in hereditary and sporadic breast cancers , and have important roles in homologous recombination ( HR ) ( Prakash et al . , 2015 ) , a template directed pathway that accurately repairs DNA lesions affecting both strands of the DNA duplex ( Couedel et al . , 2004; Moynahan and Jasin , 2010; Jasin and Rothstein , 2013; Kowalczykowski , 2015; Heyer , 2015 ) . BRCA1 regulates repair pathway choice after DNA damage by promoting HR ( Kass and Jasin , 2010; Prakash et al . , 2015 ) . BRCA2 is a recombination mediator , which facilitates assembly of the RAD51 nucleoprotein filament on ssDNA downstream of BRCA1 activities ( Couedel et al . , 2004; Jensen et al . , 2010; Liu et al . , 2010; Thorslund et al . , 2010; Prakash et al . , 2015 ) . PALB2 mediates interactions between BRCA1 and BRCA2 proteins , acts as a scaffold connecting numerous tumor suppressors ( Park et al . , 2014 ) , stimulates Polη-dependent DNA synthesis ( Buisson et al . , 2014 ) and RAD51 recombinase activity ( Dray et al . , 2010 ) . Finally , the three tumor suppressors cooperate with Fanconi Anemia proteins in the repair of inter-strand DNA cross-links ( Kim and D'Andrea , 2012 ) . Rad52 was identified in yeast as the main recombination mediator and the central player in the single-strand annealing pathway of mutagenic homology-directed DNA repair ( Mortensen et al . , 2009 ) . In contrast to the severe recombination and repair phenotypes observed in yeast , deletion of RAD52 has only a mild effect on recombination in vertebrates ( Rijkers et al . , 1998; Yamaguchi-Iwai et al . , 1998; Yanez and Porter , 2002 ) . Although it is clear that RAD52 is important for survival and uncontrolled proliferation of BRCA-deficient cancer cells , the molecular mechanism by which RAD52 allows BRCA-deficient cells to survive is unknown . The proposed mechanisms included the putative RAD52 recombination mediator function and its role in single-strand annealing pathway of homology-directed DSB repair ( Lok and Powell , 2012 ) . Functional interactions between BRCA1 , BRCA2 , PALB2 and RAD52 , as well as the ability of RAD52 to promote BRCA-independent cell survival , are commonly expected to involve HR-related mechanisms . The recent discovery that BRCA proteins act together with the Fanconi Anemia pathway to support and protect replication forks points to a potentially more complex scenario ( Schlacher et al . , 2011; 2012 ) . Additionally , RAD52 cooperates with the structure-selective nuclease MUS81/EME1 to generate DNA double-strand breaks ( DSBs ) essential for the recovery of stalled replication forks in the absence of the replication check point ( Murfuni et al . , 2013 ) . Known biochemical functions of human RAD52 include annealing of two complementary ssDNA strands in the presence of replication protein A ( RPA ) ( Van Dyck et al . , 2001; Grimme et al . , 2010 ) and the ability to pair ssDNA to complementary homologous regions in supercoiled DNA ( Kagawa et al . , 2001; Murfuni et al . , 2013 ) . Putative recombination mediator activity of RAD52 ( Benson et al . , 1998 ) should also require ssDNA binding . Therefore , if the cellular functions of the RAD52 protein depend on the ssDNA binding , then inhibition of the RAD52-ssDNA interaction should have similar consequences as RAD52 depletion . RAD52 forms an oligomeric ring ( Kagawa et al . , 2002; Lloyd et al . , 2002; Singleton et al . , 2002; Stasiak et al . , 2000 ) , where the primary ssDNA binding site is located in the narrow groove spanning the ring circumference ( Lloyd et al . , 2005; Mortensen et al . , 2002 ) . We designated this ssDNA-binding groove as the feature to be targeted by small molecule inhibitors . While disrupting the protein-ssDNA interaction with small molecules presents a formidable challenge ( Yap et al . , 2012 ) that has only been overcome in a handful of cases , the ssDNA binding groove of RAD52 ( for reasons discussed below ) is a promising target and is distinct from the ssDNA binding sites of other ssDNA binding proteins . Here , we report the development of a novel FRET-based high throughput screening ( HTS ) assay that led to the identification of compounds that disrupt the RAD52-ssDNA interaction . Initial HTS hits were biochemically validated in RAD52 functional assays and tested in two separate cellular assays . Two available high resolution crystal structures ( PDB: 1H2I and 1KNO ) of the conserved ssDNA-binding domain of RAD52 highlight the unique nature of this target ( Singleton et al . , 2002; Kagawa et al . , 2002 ) . The ssDNA-binding region is continuous around the circumference of the ring and has shallow sub-pockets that are repeating in each monomer . While the truncated version of RAD52 in the crystal structures may differ from the full length RAD52 , it likely recapitulates the structural features of the ssDNA-binding groove . Computational docking followed by all atom-simulated annealing placed all identified RAD52 inhibitors into two distinct sub-pockets within the ssDNA-binding groove . Compounds ‘1’ ( ( − ) −Epigallocatechin ) and ‘6’ ( Epigallocatechin-3-monogallate ) predicted to bind within the RAD52 ssDNA-binding site , inhibited the formation of the RAD52-dependent DSBs in hydroxyurea ( HU ) -stressed , checkpoint deficient cells to the same level as RAD52 depletion . Moreover , ‘1’ acts additively with the MUS81 depletion to kill cells treated with hydroxyurea ( HU ) , which perturbs replication , and with checkpoint inhibitor UCN01 . These data strongly suggest that the ssDNA binding activity of RAD52 is required for recovery of stalled replication forks in checkpoint deficient cells . We also show that ‘1’ selectively kills cells depleted of BRCA2 , further supporting the importance of the RAD52-ssDNA interaction in BRCA deficient cells and the potential therapeutic value of RAD52 inhibition . Finally , in order to validate the strength of our hypotheses about the structural nature of the RAD52-inhibitor complex , we developed a validated in silico screening campaign , based on our HTS results , using a library of four thousand natural products . We describe the discovery of NP-004255 , a macrocyclic compound , which we show by NMR WaterLOGSY and biophysical assays to be a completely novel and effective inhibitor of the RAD52-ssDNA interaction . The implication of these findings for the discovery of novel therapeutics that specifically inhibit the activity of RAD52 is discussed .
To identify compounds that disrupt the RAD52-ssDNA interaction we adapted a previously developed FRET-based assay ( Grimme and Spies , 2011; Grimme et al . , 2010 ) to the HTS format . The RAD52-ssDNA interaction is independent of sequence and involves a binding site size of 4 nucleotides per monomer ( Singleton et al . , 2002 ) . Our FRET-based assay relies on the ability of RAD52 to bind and wrap ssDNA around the narrow groove spanning the circumference of the protein ring ( Grimme and Spies , 2011; Grimme et al . , 2010 ) . Förster Resonance Energy Transfer ( FRET ) donor ( Cy3 ) and acceptor ( Cy5 ) fluorophores are positioned at the ends of a 30-mer ssDNA ( Cy3-dT30-Cy5 ) . When this ssDNA forms a stoichiometric complex with RAD52 ( one 30-mer ssDNA molecule per one heptameric ring of RAD52 ) , the two fluorophores are brought close to one another resulting in an increase in the FRET signal . The assay was successfully adapted to the 384-well plates HTS format . In each well , we recorded the fluorescence signal of the Cy3 dye , which was excited directly , and the signal of Cy5 dye , which was excited via the energy transfer from Cy3 . The apparent FRET signal was then calculated as described in the Materials and methods . The separation between the positive control ( a stoichiometric complex of RAD52 with Cy3-dT30-Cy5 substrate challenged with an excess of unlabeled ssDNA ( Poly dT100 ) ) and the negative control ( an unperturbed stoichiometric complex of RAD52 with Cy3-dT30-Cy5 ) initially resulted in a Z’ factor of 0 . 66 when calculated for the whole plate . Further optimization increased the Z’ factor calculated for the control rows in the screening experiments to 0 . 94 , indicating excellent reliability of the assay ( Figure 1a ) . Using this assay , we screened the MicroSource SPECTRUM collection , which contains 2320 drug and drug-like synthetic compounds as well as natural products , which represent a wide structural diversity and a range of known biological activities . The screening was carried out at 15 µM concentration of each compound in the library . Of the 2320 compounds examined , 96 were identified as preliminary hits . The results for a one plate in the collection are shown in Figure 1b with initial hits that were validated in the follow up experiments highlighted in green . These preliminary hits were selected based on the criterion of their separation from the negative control ( RAD52 + Cy3-dT30-Cy5 ) of at least 5 S . D . The 96 preliminary hits were assembled into a 'cherry picked plate' and were tested in two more rounds of screening Figure 1c . Compounds that showed reproducible and nearly complete inhibition were re-tested at a range of small-molecule concentrations . Seven of the compounds tested showed a promising decrease in FRET in the re-screening assays and six were selected for biochemical validation ( shown in green ) . One compound was excluded due to a low molecular weight and promiscuous binding observed in the follow-up biochemical assays . Additionally , we selected six compounds that elicited the FRET values below the positive control . These molecules were expected either to be 'false positives' ( i . e . molecules that are fluorescent in the Cy3 channel or interact with DNA ) or to have a significant absorbance in the region of Cy3 emission and/or Cy5 excitation . We purchased 12 compounds and confirmed their chemical structures by 1D NMR . The identified compounds and their chemical structures are listed in the Table 1 . 10 . 7554/eLife . 14740 . 003Figure 1 . High throughput screening of the MicroSource SPECTRUM collection identifies 12 compounds that inhibit the RAD52-ssDNA interaction . ( a ) Control lanes from a 384 well: 16 negative control wells contain stoichiometric RAD52- Cy3-dT30-Cy5 complexes ( red filled circles ) , while 16 positive control wells contain a stoichiometric RAD52- Cy3-dT30-Cy5 complex challenged with unlabeled polydT100 ( blue filled circles ) . Red and blue lines with error bars at the ends indicate the average and the standard deviation for the negative and positive controls , respectively . Z’ factor of 0 . 94 was calculated for these control lanes , indicating excellent reliability of the assay . ( b ) A representative 384 well plate from the HTS screen highlighting ‘1’ , ‘5’ , ‘6’ , ‘7’ , and ‘15’ . Red and blue lines with error bars at the ends indicate the average and the standard deviation for the negative and positive controls , respectively . ( c ) Average of cherry-picked rescreening of compounds identified from screening all plates in the MicroSource SPECTRUM collection highlighting all 12 identified hits ( green filled circles ) along with a number of false positive compounds ( blue filled circles ) that either showed poor reproducibility in subsequent rescreening or a linear dependence of the signal on the compound concentration . Red and blue lines with error bars at the ends indicate the average and the standard deviation for the negative and positive controls , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 00310 . 7554/eLife . 14740 . 004Table 1 . The twelve hits from the FRET-based HTS assay aimed at finding inhibitors of the RAD52-ssDNA interaction . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 004#Small molecule name; CAS #Small molecule structureIC50 ( DNA binding ) ; FRET value at saturationIC50 ( Annealing extent ) SAEM ΔG ( kcal/mol ) ‘1’ ( − ) −Epigallocatechin; 970-74-1ssDNA: 1 . 8 ± 0 . 1 µM; 0 . 45 ± 0 . 004 ssDNA-RPA: 1 . 6 ± 0 . 1 µM;ssDNA: 4 . 9 ± 0 . 4 µM ssDNA-RPA 4 . 8 ± 1 . 8 µM;−8 . 60‘3’Methacycline Hydrochloride; 3963-95-92 . 0 ± 0 . 17 µM; 0 . 47 ± 0 . 013 . 8 ± 0 . 2 µM−4 . 61‘4’Rolitetracycline; 751-97-329 ± 8 . 2 µM; 0 . 56 ± 0 . 04NI−10 . 5‘5’ ( − ) −Epicatechin gallate; 1257-08-5255 ± 16 nM; 0 . 41 ± 0 . 00420 ± 0 . 7 µM−9 . 87‘6’Epigallocatechin-3-monogallate; 989-51-5ssDNA: 277 ± 22 nM; 0 . 46 ± 0 . 01 ssDNA-RPA: 1 . 6 ± 0 . 5 µM;ssDNA: 6 . 7 ± 2 . 1 µM ssDNA-RPA: 3 . 7 ± 0 . 5 µM;−10 . 69‘7’ ( − ) −Epicatechin; 490-46-01 . 45 ± 0 . 11 µM; 0 . 51 ± 0 . 01NI−9 . 03‘14’Oxidopamine; 28094-15-7 1199-18-4779 ± 51 nM; 0 . 50 ± 0 . 01NI−5 . 71‘15’Quinalizarin; 81-61-8563 ± 40 nM; 0 . 51 ± 0 . 015 . 6 ± 0 . 6 µM−9 . 17‘16’Cisapride Monohydrate; 260779-88-2 81098-60-41 . 06 ± 0 . 05 µM; 0 . 50 ± 0 . 01NI−8 . 39‘17’Cedrelone; 1254-85-9>300 µMNI−10 . 0‘18’Asiatic Acid; 464-92-6 18449-41-7>800 µM>100 µM−11 . 33‘19’Gossypetin; 489-35-0913 ± 58 nM; 0 . 49 ± 0 . 016 . 0 ± 2 . 3 µM−9 . 30 In order to determine how the selected compounds affect known RAD52 functions we performed FRET-based assays that recapitulate the HTS screen , yet have higher precision and yield a calibrated FRET signal . We titrated increasing amounts of each compound into a cuvette containing preformed stoichiometric RAD52-Cy3-dT30-Cy5 complexes ( 1 nM Cy3-dT30-Cy5 and 8 nM RAD52 ) . As the compounds bind and disrupt the ssDNA-RAD52 interaction we observed a decrease in FRET between the DNA-tethered Cy3 and Cy5 fluorophores . At each concentration of the compound , the FRET signal was adjusted for the change in Cy3 and Cy5 fluorescence in the presence of the compound , but in the absence of protein . From the hyperbolic inhibition curves we then calculated IC50 value for each compound under these conditions ( Figure 2b and 3b , Table 1 ) . IC50 values were in the nano-molar range for compounds ‘5’ , ‘6’ , ‘14’ , ‘15’ and ‘19’ . We calculated IC50 values in the micro-molar range for compounds ‘1’ , ‘3’ , ‘7’ , ‘13’ , and ‘16’ . Compound ‘4’ had IC50 value in the mid micro-molar range . Compounds ‘17’ and ‘18’ were poor inhibitors of ssDNA binding with IC50 values in the high micro-molar range . These compounds were likely false positives in our HTS screen . 10 . 7554/eLife . 14740 . 005Figure 2 . Biochemical characterization of ‘1’ . ( a ) Aromatic region of the 1D 1H NMR spectrum of compound ‘1’ alone ( black ) and the WaterLOGSY spectrum of 20 μM compound ‘1’ in the presence of 3 . 3 μM RAD52 ( red ) . The nonexchangeable proton peaks are labeled using atom names as indicated on the structure of compound ‘1’ . ( b ) IC50 values for inhibition of ssDNA binding and wrapping were determined using FRET-based assays that follow the change in geometry of a Cy3-dT30-Cy5 substrate ( black circles ) . The computed IC50 value is shown below the curve . Titration of the RAD52-dsDNA with ‘1’ ( grey boxes ) shows no perturbation of the dsDNA binding . ( c ) IC50 values for inhibition of RAD52-mediated ssDNA annealing were determined by fitting the dependence of the extent of oligonucleotide-based annealing reaction carried in the presence of increasing concentration of ‘1’ . ( d ) Aromatic region of the 1D 1H NMR spectrum of compound ‘1’ alone ( black ) and the WaterLOGSY spectrum of 20 μM compound ‘1’ in the presence of 3 . 3 μM RPA ( red ) . ( e ) Titration of the RAD52-RPA- Cy3-dT30-Cy5 complex with ‘1’ ( black circles ) . The computed IC50 value is shown below the curve . Green squares show titration of the RPA- Cy3-dT30-Cy5 complex with ‘1’ . ( f ) IC50 values for inhibition of RAD52-mediated annealing of the RPA-coated ssDNA were determined by fitting the dependence of the extent of the annealing reaction carried out in the presence of increasing concentration of ‘1’ . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 00510 . 7554/eLife . 14740 . 006Figure 2—figure supplement 1 . Stoichiometric complexes of RAD52 with ssDNA , RPA-coated ssDNA and dsDNA yield characteristic FRET values . ( a ) FRET measurements were performed by titrating RAD52 protein into a solution containing 1 nM Cy3-dT30-Cy5 ssDNA . The Cy3 fluorescence was excited directly and the emissions of Cy3 and Cy5 dyes were measured , and the respective FRET values calculated as described in the Materials and methods . The highest separation in the FRET signal of unbound ssDNA and the RAD52-ssDNA complex was achieved around 8 nM RAD52 . At this concentration , one RAD52 heptameric ring binds and wraps the ssDNA oligonucleotide bringing the Cy3 and Cy5 dyes close to one another . The details and control experiments for these measurements can be found in ( Grimme et al . , 2010; Grimme and Spies , 2011 ) . Each data point represents the average and standard deviation for at least three independent titrations . ( b ) The titrations were performed similarly to those shown in a , except 1 nM RPA was added to a solution containing 1 nM Cy3-dT30-Cy5 ssDNA prior to titration of RAD52 . Under our experimental conditions , RPA forms stoichiometric complexes with the 30-mer DNA oligonucleotide with 1 RPA coating 1 molcule of ssDNA . The data points and error bars represent averages and standard deviation for three or more independent titrations . ( c ) The titrations were performed similarly to those shown in a , except 1 nM dsDNA ( Cy3-Oligo28-Cy5 annealed to Oligo28-REV ) was used as a substrate . Stoichiometric complexes are achieved at 10 nM RAD52 . The data points and error bars represent averages and standard deviation for three or more independent titrations . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 00610 . 7554/eLife . 14740 . 007Figure 2—figure supplement 2 . RAD52 FRET based ssDNA annealing assay in the presence of small molecules . ( a ) Schematic of the FRET based annealing reaction . In two half-reactions , stoichiometric amounts of RAD52 were incubated with Target28Cy3 and Probe28Cy5 oligonucleotides , respectively . Upon mixing of the two half-reactions , RAD52 facilitated annealing of the two complementary oligonucleotides , which can be observed as an increase in FRET between Cy3 and Cy5 dyes . ( b ) Annealing reactions performed in the absence ( blue ) or presence of increasing concentrations of ‘1’ . The average of three or more independent annealing reactions is shown for each curve . Grey continuous lines show fits to double exponentials . Light green circles correspond to the DNA only reaction; dark green circles represent the annealing reaction containing the DNA and 100 µM ‘1’ . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 00710 . 7554/eLife . 14740 . 008Figure 2—figure supplement 3 . None of the tested compounds affect the oligomeric state of RAD52 protein . A possible effect of the identified compounds on the oligomeric state of RAD52 protein was probed in the dynamic light scattering experiments , which measured the average hydrodynamic radius of RAD52 ( 15 . 8 μM ) alone or in the presence of equimolar concentrations of each compound . Measurements were recorded at 25°C in buffer containing 50 mM Tris-HCl pH7 . 5 , 200 mM KCl , 1 mM DTT , and 0 . 5 mM EDTA . A total of 10 measurements with 10 accumulations were collected , had monomodal distributions , and sum of squares ( SOS ) values of ≤ 0 . 65 . Measurements were recorded with a DynaPro NanoStar ( Wyatt Tech . Corp . ) and the hydrodynamic radius were calculated by the DYNAMICS software . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 00810 . 7554/eLife . 14740 . 009Figure 2—figure supplement 4 . Compounds ‘1’ and ‘6’ have no effect on the interaction between RAD52 and RPA proteins . Ni-NTA Agarose ( 15 uL buffer equilibrated bead slurry ) was incubated with 3 μM RAD52 , 3 μM RPA in the presence or absence of 3 μM ‘1’ or ‘6’ , in the binding buffer ( 30 mM Tris-Acetate pH7 . 5 , 1 mM βME , 150 mM KCl , 30 mM Imidazole , 5% glycerol , and 0 . 2% Nonidet P40 substitute ) . After 30 min incubation on a neutator at 4°C samples were spun down , and the aliquots of unbound ( 'free' ) proteins from each reaction were saved . Then the beads were washed and the bound proteins were eluted with 20 uL elution buffer ( the same as the binding buffer , but with 400 mM Imidazole ) and saved for gel electrophoresis . Free proteins and proteins co-eluted from the beads ( 'bound' ) were separated on the 12% SDS PAGE gel . Lane 1 is a loading control , which shows RAD52 and the three subunits of RPA ( RPA70 , RPA35 , and RPA14 ) . The proteins and the compounds present in each reaction are indicated in the table above the gel . The carton on the left of the gel schematically depicts the experiment: RAD52 protein binds to the Ni-NTA beads through the interaction with its 6xHis tag; RPA is untagged and can be retained on the beads only through a specific interaction with RAD52 ( Grimme et al . , 2010 ) . The experiment was repeated three times ( a representative gel is shown ) and no change in the ratio of RAD52 and RPA co-eluted from the beads in the presence and absence of ‘1’ or ‘6’ was detected . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 009 To determine how the selected compounds affect the ssDNA annealing function of RAD52 we performed FRET-based annealing assays ( Grimme et al . , 2010; Grimme and Spies , 2011 ) . These assays utilize two complementary single stranded 28-nucleotide-long substrates , which contain either Cy3 ( T-28 ) or Cy5 ( P-28 ) incorporated into the middle of the respective DNA strand . When the substrates are annealed by RAD52 , the Cy3 and Cy5 dyes are separated by 3 base pairs and yield a high FRET signal ( Figure 2—figure supplement 2 ) . Negative controls containing T-28 and P-28 with the compounds in the absence of RAD52 displayed no change in FRET suggesting the small molecules do not promote ssDNA annealing by themselves . The annealing reactions were initiated by mixing two half reactions and observing the change in FRET over time in the presence of varying concentrations of small molecules . An increasing FRET value over time indicates the formation of the dsDNA duplex which brings the two dyes in close proximity ( Figure 2—figure supplement 2 ) . Fitting the annealing data to a double exponential allowed us to calculate and compare the final extent of annealing at varying concentrations of each compound compared to RAD52 alone . We plotted the final extent of annealing vs the concentration of the compound and calculated an IC50 of annealing inhibition . A full set of the annealing time courses recorded at different concentrations of ‘1’ is shown in Figure 2—figure supplement 2 . As the concentration of the compound increases , the final extent of annealing is reduced compared to RAD52 alone . Since we showed previously that ssDNA wrapping around the RAD52 ring is necessary for the most efficient annealing ( Grimme et al . , 2010; Honda et al . , 2011 ) , it was expected that a compound that interferes with ssDNA access to the ssDNA binding groove of RAD52 would compete with ssDNA annealing , thus shifting the equilibrium away from the dsDNA product . It is notable that the IC50 values for DNA annealing were generally higher than IC50 values for the ssDNA binding . We attribute this to the dynamic nature of the RAD52-ssDNA complex as well as to RAD52 ability to bypass regions of heterology and other obstacles during the homology search process ( Rothenberg et al . , 2008 ) . To confirm specificity of the two compounds ( ‘1’ and ‘6’ ) selected for the in-depth follow-up characterization as disruptors of the RAD52-ssDNA interaction , we tested the ability of these compounds to interfere with the RAD52-dsDNA interaction , which involves a different site on the RAD52 ring ( Kagawa et al . , 2008; Grimme et al . , 2010 ) . At the stoichiometric RAD52: dsDNA ratio , ( 1 nM dsDNA: 10 nM RAD52 ) the dsDNA is bent upon RAD52 binding , which allows us to distinguish the RAD52-bound and free dsDNA ( Figure 2—figure supplement 1 ) . Interestingly , ‘1’ had no effect on the RAD52-dsDNA interaction ( Figure 2b open grey squares ) , which indirectly confirms its specificity for the ssDNA-binding groove of RAD52 . In contrast , ‘6’ was able to displace dsDNA from the RAD52-dsDNA complex ( Figure 3b open grey squares ) . Dynamic light scattering experiments conducted in the presence of equimolar concentrations of each compound and RAD52 showed that the presence of these compounds neither breaks up the oligomeric ring of RAD52 nor causes protein aggregation ( Figure 2—figure supplement 3 ) . Notably , this means that our compounds act differently from the RAD52 inhibitor 6-hydroxy-DL-dopa ( Chandramouly et al . , 2015 ) , which disrupts supramolecular assembly of the RAD52 protein . We further confirmed that the inhibition of the ssDNA binding does not occur due to aggregation of compounds as annealing FRET trajectories in the presence of 0 . 01% Triton X-100 are identical to those in the absence of Triton X-100 . 10 . 7554/eLife . 14740 . 010Figure 3 . Biochemical characterization of ‘6’ . ( a ) Aromatic region of the 1D 1H NMR spectrum of compound ‘6’ alone ( black ) and the WaterLOGSY spectrum of 40 μM compound ‘6’ in the presence of 3 . 3 μM RAD52 ( red ) . The nonexchangeable proton peaks are labeled using atom names as indicated on the structure of compound ‘6’ . ( b ) IC50 values for inhibition of ssDNA binding and wrapping were determined using FRET-based assays that follow the change in geometry of a Cy3-dT30-Cy5 substrate ( black circles ) . The computed IC50 value is shown above the curve . Titration of the RAD52-dsDNA with ‘6’ ( grey boxes ) shows that this inhibitor also perturbs the RAD52-dsDNA interaction . ( c ) IC50 values for inhibition of RAD52-mediated ssDNA annealing were determined by fitting the dependence of the extent of oligonucleotide-based annealing reaction carried in the presence of increasing concentration of ‘6’ . ( d ) Aromatic region of the 1D 1H NMR spectrum of compound ‘6’ alone ( black ) and the WaterLOGSY spectrum of 40 μM compound ‘6’ in the presence of 3 . 3 μM RPA ( red ) . ( e ) Titration of the RAD52-RPA- Cy3-dT30-Cy5 complex with ‘6’ ( black circles ) . The computed IC50 value is shown below the curve . Green squares show titration of the RPA- Cy3-dT30-Cy5 complex with ‘6’ . ( f ) IC50 values for inhibition of RAD52-mediated annealing of the RPA-coated ssDNA were determined by fitting the dependence of the extent of the annealing reaction carried out in the presence of increasing concentration of ‘6’ . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 010 To confirm that the selected compounds bind RAD52 , we employed water-ligand observation with gradient spectroscopy ( WaterLOGSY ) , an NMR technique , which is based on transfer of magnetization from bulk water to the protein-bound compound of interest ( Dalvit et al . , 2001 , 2000 ) . In WaterLOGSY spectrum , if a compound binds to a protein , the compound will receive negative nuclear Overhauser effects ( NOEs ) due to the slow tumbling of the protein-compound complex , leading to a positive WaterLOGSY peak . In contrast , if a compound does not bind to a protein , the compound will receive positive NOEs due to the fast tumbling of the compound itself , resulting in a negative WaterLOGSY peak . Figure 2a and Figure 3a show that both ‘1’ and ‘6’ physically interact with RAD52 protein . When the aromatic region of the 1D 1H NMR spectrum of compound ‘1’ alone ( black ) and the WaterLOGSY spectrum of compound ‘1’ in the presence of RAD52 ( red ) are compared , positive WaterLOGSY peaks are observed for the compound ‘1’ , indicating the binding of ‘1’ to RAD52 ( Figure 2a ) . Similarly , the binding of ‘6’ to RAD52 is evident from the positive WaterLOGSY peaks that are clearly detected for this compound ( Figure 3a ) . Notably , ‘6’ also binds to RPA as shown by the positive WaterLOGSY peaks ( Figure 3d ) , thought it does not interfere with the RPA-ssDNA interaction ( Figure 3e ) , while ‘1’ neither binds to RPA as shown by the negative WaterLOGSY peak ( Figure 2d ) nor interferes with the RPA-ssDNA interaction ( Figure 2e ) . In the cell , ssDNA is typically found in complex with Replication protein A ( RPA ) , the major eukaryotic ssDNA-binding protein essential for DNA replication , repair and recombination ( Wold , 1997; Oakley and Patrick , 2010; Chen and Wold , 2014 ) . The RPA-ssDNA complex is a physiologically relevant substrate for the RAD52-mediated strand annealing . To confirm that compounds ‘1’ and ‘6’ can inhibit the RAD52 binding to and annealing of the RPA-coated ssDNA we added stoichiometric amounts of RPA ( 1 RPA per 30 nucleotides of ssDNA ) to the FRET-based ssDNA binding/wrapping and ssDNA annealing experiments described above . RPA binds ssDNA with high affinity and extends the ssDNA to its contour length . In our assays such an extension manifests as a distinct FRET state of ~0 . 3 , which is readily distinguished from a FRET state of ~0 . 48 of free Cy3-dT30-Cy5 ssDNA , as well as ~0 . 63 FRET of the stoichiometric ssDNA-RPA-RAD52 complex ( see Figure 2—figure supplement 1 and ( Grimme and Spies , 2011 ) for details ) . Notably , neither ‘1’ nor ‘6’ affected the RPA-ssDNA interaction over the range of the tested compound concentrations ( Figure 2e and Figure 3e ) . Both , however , inhibited the RAD52 binding to and wrapping of RPA-coated ssDNA with IC50 values identical to those determined without RPA ( Figure 2e and Figure 3e ) . Similarly , we confirmed that both ‘1’ and ‘6’ inhibit the RAD52-mediated annealing of RPA-coated ssDNA with the IC50 values comparable to the inhibition of ssDNA annealing ( Figure 2f and Figure 3f ) . Notably , this inhibition is not due to the disruption of the RAD52-RPA interaction as neither ‘1’ nor ‘6’ interfered with the interaction between the two proteins ( Figure 2—figure supplement 4 ) . In order to gain insight into the binding determinants of the various polyphenol hits obtained from the HTS screening , we undertook a computational investigation using the structure of the oligomeric ring formed by the conserved ssDNA-binding domain of RAD52 ( PDB 1KNO ) . We utilized a layered approach involving docking and all atom-simulated annealing with explicit solvent , using a knowledge based force field ( Krieger et al . , 2004 ) . The long circular ssDNA binding groove of the RAD52 oligomeric ring yielded excellent 'druggability' scores ( ~4 . 0 ) , based on the pocket metric of Sugo et al . , ( Soga et al . , 2007 ) . Docking approaches generally generate many potential poses , and many false positives . Initially , top scoring poses in either Triangle Matcher ( placement ) , London dG ( affinity scoring function ) or MM/GBSA ( physics based scoring ) were retained for further analysis ( see Materials and methods section for details ) . An all atom force field-based protocol was employed to distinguish binding affinities from a variety of docking poses that possessed various docking metrics . We compared the different scoring metrics , such as Triangle Matcher placement scores followed by rescoring with the affinity function versus the computationally expensive force field-based ligand refinement and subsequent MM/GBSA scoring . The use of all atom simulations ( including explicit solvent models ) , when combined with docking , has been shown to significantly boost docking procedures' ability to predict and rank compound affinities . Therefore , we can compare the differences in free energy changes due to ligand binding for each of the poses , an ability that is not all within the realm of classical docking procedures ( Ellingson et al . , 2015; Whalen et al . , 2011; Warren et al . , 2006; Head , 2010 ) . Thirty four unique docking poses were selected for comparison for each compound . Comparisons of the results of the various scoring metrics for docking of ligands to the RAD52 complex showed a clear lack of consensus between the three methods , except for compound ‘6’ , which resulted in a single pose scoring the highest in all three methods . To determine which methodology consistently provides the most accurate scoring , we employed a conservative approach , in which the 34 selected top scoring complexes were further subjected to all atom-simulated annealing studies , using explicit solvent and salt conditions . The binding affinities computationally determined using the Simulated Annealing Energy Minimization ( SAEM ) Docking approach are listed in the Table 1 ) . Interestingly , in most cases , the RAD52-ligand complex resulting from the best placed docking pose , rather than the more computationally intensive MM/GBSA ( physics-based ) scoring function , yielded the final SAEM-generated RAD52-ligand complex with the lowest energy . Compounds ‘1’ and ‘6’ yielded complexes with unique binding sub-pockets or 'hotspots' along the RAD52 binding groove , suggesting that they may have distinct biological activities and/or efficacies with regard to their ability to compete with ssDNA binding ( Figure 4 ) . The SAEM-generated complexes indicate that compounds ‘1’ and ‘6’ occupy complex pockets lying at the interface of two RAD52 monomers . Notably , all final compound placements include interactions , directly or through the interstitial water molecules , with key RAD52 residues , which have been previously shown to be involved in ssDNA binding ( Lloyd et al . , 2005 ) ( Figure 4 ) . In particular , R55 , Y65 , K152 , R153 and R156 found in the vicinity of the docked compounds ( Figure 4b ) have been shown to impact ssDNA binding ( Lloyd et al . , 2005 ) . Additional participants in the binding of our inhibitors include K141 and K144 residues that are important to distinct cellular functions of yeast Rad52 . A highly conserved K144 corresponds to K159 in S . cerevisiae Rad52 . Its K159A substitution results in severe deficiency in mitotic recombination , mild γ-ray sensitivity , but unperturbed recombination between direct repeats ( Mortensen et al . , 2002 ) . K141 corresponds to S . cerevisiae R156 , whose substitution to alanine causes γ-ray sensitivity only ( Mortensen et al . , 2002 ) . 10 . 7554/eLife . 14740 . 011Figure 4 . Virtual screening places the RAD52 inhibitors within the ssDNA binding groove . ( a ) Three individual monomers of the RAD52-NTD undecameric ring ( PDB 1KNO ) are colored yellow , green and blue respectively . ‘1’ and ‘6’ occupy similar sites at the interface of two subunits . Two grey lines in each panel indicate the approximate boundaries of the ssDNA-binding groove . ( b ) . MOE ligand maps highlight water mediated interactions as well as interactions with amino acids . ‘1’ likely mediates interactions through R55 , V128 , E140 , and E145 , as well as through water contacts made with G59 , M56 , and K141 . ‘6’ likely binds via hydrogen bonding via D149 and I166 as well as through water interactions with E140 , K144 , and R153 . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 011 All of the tested inhibitors yielded complexes in which interstitial solvent plays a role in the binding of the ligand . Unlike classic enzyme pockets , which often have large desolvated volumes , the RAD52 ssDNA binding groove cannot truly be evaluated for the ability to bind to compounds without understanding the role of solvent in its various sub-pockets . The SAEM method used here was specifically designed to capture these complex recognition parameters . Unlike the case of deeply buried waters that occur in many active site pockets of enzymes , the waters along the RAD52 ssDNA-binding groove are mostly not involved in productive interstitial H-bonding with the ligand , but rather , represent a van der Waals binding surface , suggesting opportunities for future ligand improvement . In human cells , RAD52 may perform both limited recombination-mediator function in the RAD51-dependent pathway ( Lok and Powell , 2012; Benson et al . , 1998; Feng et al . , 2011 ) as well as additional RAD51-independent roles ( Lok and Powell , 2012; Murfuni et al . , 2013; McIlwraith and West , 2008 ) . One of these HR independent roles of RAD52 involves stimulation of MUS81/EME1-dependent DSB formation at replication forks stalled by hydroxyurea ( HU ) treatment in the absence of cellular checkpoints ( Murfuni et al . , 2013 ) . Since the most likely targets of these MUS81/EME1/RAD52-dependent DSBs are the DNA structures produced by RAD52 ( Murfuni et al . , 2013 ) , we expected this activity to depend on the RAD52-ssDNA interaction ( Figure 5a ) . To confirm this , we assessed DSB formation in checkpoint deficient cells using the neutral comet assay . These assays monitor MUS81/EME1/RAD52-dependent DSB formation upon induction of replication stress by HU treatment in primary fibroblasts immortalized by hTERT expression and treated with UNC01 to inhibit CHK1 kinase ( Figure 5b ) . Our data show that increasing amounts of ‘1’ and ‘6’ decrease the mean tail moment indicative of the decrease in the MUS81/EME1/RAD52-dependent DSB formation ( Figure 5 ) . Importantly , these inhibitors recapitulate RAD52 depletion by inhibiting RAD52-MUS81-dependent DSBs at stalled replication forks ( Figure 5a–b ) . Notably , even at 500 nM , ‘1’ had the same effect of reduction in DSBs as siRNA depletion of RAD52 . These data strongly support the idea that inhibiting the RAD52-ssDNA interaction in cells recapitulates the effects of RAD52 depletion with respect to its role at the distressed replication forks . It also confirms our previous supposition that the target of MUS81/EME1-mediated cleavage under these conditions are indeed the structures annealed by RAD52 . Interestingly , the concentrations of ‘1’ sufficient to inhibit the MUS81/EME1/RAD52-mediated DSBs correlate well with the IC50 values for inhibition of ssDNA binding/wrapping in vitro ( compare Figure 5c with Figure 2b and e ) . These values are significantly lower than those required for inhibiting annealing of short , complementary oligonucleotides ( Figure 2c and f ) . Higher concentration of ‘6’ required to inhibit MUS81/EME1/RAD52-mediated DSBs ( Figure 5d ) is likely due to the particular chemical nature of this compound , which is a promiscuous binder; not only does it interact with RPA and binds within the dsDNA binding site of RAD52 , but has been identified as an inhibitor in 192 different HTS assays ( Pubchem ) . We previously reported that concomitant depletion of RAD52 and MUS81 gives raise to the MUS81-independent DSBs ( Murfuni et al . , 2013 ) . In agreement with a specific activity towards RAD52 , treatment of the MUS81-depleted cells with “1” resulted in an appearance of the MUS81-independent DSBs upon replication stress induced by CHK1 inhibition . Due to its lower capacity to inhibit the MUS81/EME1/RAD52-mediated DSBs , and its expected off-target effects , we have eliminated ‘6’ from further analysis and focused all our subsequent cellular studies on ‘1’ , which appeared more specific in our biochemical studies and had no Pubchem hits . The fact that none of the compounds we tested showed additive effects on DSBs with RAD52 siRNA depletion , suggests that the effect of these inhibitors is specific to RAD52 at least with respect to recovery from replication stress . Furthermore , accumulation of anaphase bridges , a phenotype associated with impairment of the RAD52-independent mitotic function of MUS81/EME1 was completely unaffected by inhibition of RAD52 , whereas it was strongly stimulated by MUS81 silencing ( Figure 5—figure supplement 1 ) . This observation strongly suggests that the suppression of the DSB formation is not due to direct inhibition of MUS81 , but is mediated through RAD52 inhibition . 10 . 7554/eLife . 14740 . 012Figure 5 . Inhibiting the RAD52-ssDNA interaction interferes with RAD52/MUS81-mediated DSB formation essential for replication fork recovery in check point deficient cells . ( a ) Representative images showing fields of cells from the comet assay for untreated , as well as from UCN01 ( 300 nM ) and HU ( 2 mM ) treated cells in the presence and absence of ‘1’ , ‘6’ , and siRAD52 . ( b ) ‘1’ and ‘6’ at 1 or 25 μM recapitulate RAD52 depletion . GM1604 cells , transfected or not with siRNAs against RAD52 , were treated as indicated , in the presence or absence of the inhibitor . At the end of treatment , DSBs were analyzed by neutral comet assay . Data are presented as the mean ± SEM from two independent experiments; p values are shown in the graph when differences are statistically significant . ( c–d ) Inhibitors ‘1’ and ‘6’ , respectively decrease the mean tail moment following HU treatment with IC50 values ranging from mid nanomolar to low micromolar . Cells were treated as in ( b ) . Data are from three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 01210 . 7554/eLife . 14740 . 013Figure 5—figure supplement 1 . Compound ‘1’ does not affect MUS81 activity . ( a ) GM01604 wild-type fibroblasts were transfected with CTRL or MUS81 siRNA . Forty eight hours after transfection the fibroblasts were lysed and analysed by WB with the indicated antibodies . ( b ) Forty eight hours after transfection , GM01604 cells were exposed to 0 . 2 µM aphidicolin ( APH ) for 24h in the presence ( grey bars ) or absence ( white bars ) of increasing concentration of ‘1’ added in the last 3h before aphidicolin treatment . Results are presented as mean ± SEM from two independent replicates . ( c ) The presence of anaphase bridges , as shown in the representative images , was scored in DAPI-stained cells . The white bars indicate 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 013 Compounds‘1’ and ‘6’ are able to interfere with MUS81-dependent DSBs formation under pathological replication , mimicking RAD52 depletion ( Figure 5 ) . To address whether inhibition of the RAD52-ssDNA binding reduces viability of MUS81-depleted cells , as reported for RAD52 depletion ( Murfuni et al . , 2013 ) , we evaluated cell death after inducing replication stress by pharmacological CHK1 inhibition ( Figure 6 ) . In cells transfected with the control ( Ctrl ) siRNAs , replication stress induced by HU treatment resulted in 20% cell death , which was increased similarly by MUS81 or BRCA2 knock-down by nearly two-fold . Treatment with ‘1’ also potentiated the effect of the combined HU+UCN01 treatment and , strikingly enhanced cell death observed in MUS81-depleted cells . Inhibition of RAD52 increased cell death of MUS81-depleted cells even under unperturbed cell growth to approximately the same level as RAD52 depletion by siRNA ( Murfuni et al . , 2013 ) . Strikingly , no additive effect on cell death was detected in cells depleted of RAD52 and treated with the RAD52 inhibitor , as compared with the cells transfected with the RAD52 siRNAs alone ( Figure 6b ) . 10 . 7554/eLife . 14740 . 014Figure 6 . Inhibition of ssDNA binding by RAD52 is sufficient to stimulate cell death in the absence of the MUS81 nuclease or BRCA2 tumor suppressor . ( a ) The WB shows the analysis of RNAi . ( b ) Evaluation of cell death after replication stress . Forty eight hours after transfection with the BRCA2 or MUS81 siRNAs , alone or in combination , the GM01604 cells were treated with compound ‘1’ or solvent ( DMSO ) . Where indicated , the CHK1 inhibitor UCN-01 and HU was added and the cells were treated for 6h , followed by 18 hr of recovery in drug-free medium . Compound ‘1’ was present during the 6h of treatment . Cell viability was evaluated by LIVE/DEAD assay as described in 'Materials and methods' . Data are presented as percentage of dead cells and are mean values from three independent experiments . Error bars represent SEM . The numbers shown in the graph represent the p value; the first p value of each pair refers to untreated cells while the second to the treated cells ( 2 way ANOVA ) . ( c ) Representative images of live cells ( green ) and dead cells ( red ) . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 014 Depletion of RAD52 not only enhances cell death of MUS81-depleted cells , but also reduces viability of BRCA2-deficient cells , making RAD52 an attractive target for potential treatment of BRCA2-deficient tumors ( Feng et al . , 2011; Warren et al . , 2006 ) . Treatment with ‘1’ acted additively with loss of BRCA2 resulting in ~80% of cell death after replication stress ( Figure 6b and c ) . Concomitant depletion of BRCA2 and MUS81 also resulted in an additive effect on cell death , even under an unperturbed cell growth ( Figure 6b and c ) . To further investigate the effect of ‘1’ on cell viability under the conditions of pathological replication , we depleted cells with BRCA2 or RAD52 siRNAs and challenged them with HU for 18h in the presence or absence of ‘1’ . As reported in Figure 7 , and in agreement with previous reports , co-depletion of BRCA2 and RAD52 increased cell death with respect to each single depletion . Notably , RAD52 inhibition mimicked RNAi-mediated gene knockdown and induced a substantial increase in cell death of BRCA2-depleted cells after prolonged replication arrest , further supporting the possibility that inhibiting the RAD52-ssDNA interaction may be a useful strategy for targeting BRCA2-deficient tumors . 10 . 7554/eLife . 14740 . 015Figure 7 . Inhibition of ssDNA binding to RAD52 is sufficient to stimulate cell death in the absence of the BRCA2 tumor suppressor . ( a ) Western blot analysis of BRCA2 , RAD52 , and GAPDH ( loading control ) protein levels in GM01604 cells treated with Ctrl , BRCA2 , and RAD52 siRNAs . ( b ) Evaluation of cell death after replication stress in cells treated with ‘1’ . GM01604 cells were transfected with the RAD52 or BRCA2 siRNAs , alone or in combination , and 48h thereafter treated as indicated . The ‘1’ inhibitor or solvent ( DMSO ) was added to media 1h prior to replication stress . Cell viability was evaluated by LIVE/DEAD assay as described in the 'Materials and methods' . Data are presented as percentage of dead cells and are mean values from three independent experiments . Error bars represent standard error . The numbers shown in the graph represent the p value; the first p value of each pair refers to untreated cells while the second to the treated cells ( 2 way ANOVA ) . ( c ) Representative images: live cells are stained green , while dead cells are red . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 015 We hypothesized that the computationally determined RAD52-'1' and RAD52-'6' complexes could be used to validate an in silico workflow directed towards identifying a novel inhibitor of the RAD52-ssDNA interaction . This approach should facilitate further discovery of novel drug lead compounds that possess similar or improved activities as '1' and '6' , but with a fundamentally different chemical space . Natural products have an unrivaled history in drug discovery , and often represent the first and most significant hits against a metabolic pathway . The AnalytiCon Discovery MEGx Natural Products Screen Library , which is the in silico version of an actual library of purified natural products from plant , fungal and microbial sources , was subjected to an in silico screening campaign ( Figure 8a ) . The campaign was designed based on the ability to optimally minimize false positives and false negatives , and to maximize true positives and true negatives . More specifically , the experimental hits identified in the HTS campaign described above , constitute the true positives , while specifically selected decoy compounds constitute the true negatives . The details of this optimization approach , known as Receiver Operator Characteristic ( ROC ) , are described in the Materials and methods section . The ROC procedure in this in silico screening study was designed to challenge the value of the docking and scoring methods by employing decoy compounds ( so called 'DUDS' compounds; see Materials and methods section ) , which possess similar chemical properties , but different topologies than true positives . Importantly , the ROC approach uses the experimental HTS hits to optimize the in silico screening assay vis-à-vis minimizing false positives ( which are rampant in all docking-based in silico screening approaches ) . The ROC curve for '1' ( Figure 8b ) shows that the optimized in silico selection process is nearly ideal in separating false positives from true positives . Finally , anin silico screen of the AnalytiCon Discovery MEGx Natural Products data base resulted in 9 compounds that had poses with scores better than those of compound '1' . The best scoring of those structures was ordered from AnalytiCon Discovery GmbH ( Potsdam , Germany ) for in vitro inhibition studies . The compound that was identified , NP-004255 is known as corilagin , and is a member of the class of secondary plant metabolites called ellagitannins . Corilagin is a macrocyclic ester consisting of three trihydroxylated phenolic moieties . NP-004255 binds to RAD52 in a similar manner as '1' and '6' , in that it uses a buried interstitial water network , and is able to adopt a conformation that fits nicely into the ssDNA binding groove ( Figure 8d and e ) . The binding and inhibitor activity of this prediction was validated by both NMR and FRET-based assays , as described below . 10 . 7554/eLife . 14740 . 016Figure 8 . In silico screening campaign identifies novel small molecule that inhibits the RAD52-ssDNA interaction . ( a ) Docking workflow involved RAD52-NTD undecameric ring ( PDB 1KNO ) pre-processing , AnalytiCon Discovery MEGx Natural Products Screening Library pre-processing , and classical docking using the Dock utility of MOE . Top ranking poses ( those with the lowest energy scores from the London dG scoring function ) were subjected to a refining docking step involving force field-based energy minimization . From these complexes , predicted binding free energies were calculated . Workflow validation involved RAD52-NTD pre-processing , pre-processing of ‘1’ and associated decoys ( DUD-E ) , followed by classical docking and score ranking . ROC curves were then generated and analyzed . Scores of the conformations of inhibitor compounds and of conformations of their respective decoy compounds were compared . ( b ) Docking scores ( kcal/mol ) for the individual conformations of compound ‘1’ and decoy compounds were binned and plotted as histograms . The low docking scores , as indicated by the more negative predicted free energies of ‘1’ when compared to decoys , indicate more favorable poses , and highlight a distinct separation between true positives and true negatives . ( c ) Receiving-operating characteristic ( ROC ) curve shows that the classifier used , i . e . the scoring function , was close to optimal in distinguishing compound ‘1’ conformations from those of decoys confirmed by AUC analysis yielding a value of 0 . 9973 . ( d ) Electrostatic surface potential of three monomers of the RAD52-NTD undecameric ring ( PDB 1KNO ) depicting NP-004255 within the ssDNA binding groove . ( e ) . MOE ligand maps highlight water mediated interactions with E145 and D149 as well as via hydrogen bonding with amino acids R55 . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 016 The biochemical assays that were carried out for inhibitors ‘1’ and ‘6’ , were repeated for NP-004255 ( Figure 9 ) . The WaterLOGSY spectra suggest that similar to ‘1’ and ‘6’ , NP-004255 physically interacts with RAD52 ( Figure 9a ) , while the FRET-based competition assay ( Figure 9b ) confirmed that this natural product does indeed inhibit the RAD52-ssDNA interaction with an IC50 = 1 . 5 ± 0 . 2 µM , which is a potency similar to ‘1’ . Also similar to ‘1’ , the macrocycle compound was specific for the RAD52-ssDNA complex and had no effect on the RAD52-dsDNA interaction ( Figure 9b ) . 10 . 7554/eLife . 14740 . 017Figure 9 . Biochemical characterization of NP-004255 . ( a ) Aromatic region of the 1D 1H NMR spectrum of compound NP-004255 alone ( black ) and the WaterLOGSY spectrum of 40 μM compound NP-004255 in the presence of 3 . 3 μM RAD52 ( red ) . The nonexchangeable proton peaks are labeled using atom names as indicated on the structure of compound NP-004255 . ( b ) IC50 values for inhibition of ssDNA binding and wrapping were determined using FRET-based assays that follow the change in geometry of a Cy3-dT30-Cy5 substrate ( black circles ) . The computed IC50 value is shown above the curve . Titration of the RAD52-dsDNA with NP-004255 ( grey boxes ) shows that this inhibitor does not perturb the RAD52-dsDNA interaction . ( c ) Aromatic region of the 1D 1H NMR spectrum of compound NP-004255 alone ( black ) and the WaterLOGSY spectrum of 40 μM compound NP-004255 in the presence of 3 . 3 μM RPA ( red ) . ( d ) Titration of the RAD52-RPA- Cy3-dT30-Cy5 complex with NP-004255 ( black circles ) . The computed IC50 value is shown below the curve . Green squares show titration of the RPA- Cy3-dT30-Cy5 complex with NP-004255 indicating NP-004255 does not perturb the RPA-ssDNA interaction . DOI: http://dx . doi . org/10 . 7554/eLife . 14740 . 017 Similar to ‘6’ , NP-004255 also bound RPA ( Figure 9c ) , but did not affect the RPA-ssDNA complex ( Figure 9d ) . It did , however , inhibit RAD52 binding to RPA-coated ssDNA with an IC50 = 0 . 5 ± 0 . 1 µM , i . e . it was more effective in perturbing this interaction that that involving the protein-free ssDNA .
Maintenance of genetic integrity , as well as the ability to accurately and timely repair damaged DNA and complete DNA replication are essential for all living organisms ( Heyer , 2015; Abbas et al . , 2013 ) . While these basic processes and the central protein players are conserved , significant variation exists between eukaryotic lineages . The mechanisms that ensure faithful DNA replication and repair are exceedingly more complex in mammalian cells compared to simpler eukaryotes with more alternative interconnected pathways that may share proteins as well as regulatory enzymes . HR and the pathways that employ the machinery of HR are expected to be responsible for the most accurate repair of the most deleterious DNA lesions including DSBs , DNA interstrand cross-links , and damaged replication forks ( Head , 2010; Li and Heyer , 2008; Couedel et al . , 2004; Moynahan and Jasin , 2010; Jasin and Rothstein , 2013 ) . In yeast , Rad52 functions as a recombination mediator by facilitating replacement of RPA with Rad51 recombinase on ssDNA and thereby allowing the formation of the Rad51 nucleoprotein filament , which is the active species in the DNA strand exchange reaction . Analogously , RAD51 nucleoprotein filament formation and its activity in human cells is facilitated by a recombination mediator , BRCA2 ( Xia et al . , 2001; Yang et al . , 2005; Carreira et al . , 2009 ) with multiple RAD51 paralogs playing roles in ensuring assembly and stability of the active RAD51 nucleoprotein filament ( Yang et al . , 2005; Chun et al . , 2013 ) . Whether , and how , human RAD52 substitutes for BRCA2 mediator activity remains unclear . Synthetic lethality between BRCA2 defects and RAD52 depletion suggests that either RAD52 is indeed a recombination mediator , or that it participates in an alternative pathway that becomes prominent in the absence of BRCA2 function , such as for example SSA ( single-strand annealing ) ( Feng et al . , 2011; Lok and Powell , 2012 ) . More intriguingly , RAD52 depletion is also synthetically lethal with defects in BRCA1 , a tumor suppressor that acts upstream of BRCA2 in HR and at the branch point in the DSB repair that promotes homology-directed DNA repair through HR or SSA over the NHEJ ( non-homologous end joining ) ( Singleton et al . , 2002 ) . It is unknown which pathway ( s ) allow survival and proliferation of BRCA-deficient cells . These pathways , however , have to depend on the activities or interactions of RAD52 . We showed recently that human RAD52 plays an important role in allowing cellular recovery under conditions of pathological replication ( Murfuni et al . , 2013 ) . Similarly , a sub-pathway of HR that is Rad51 ( Rhp51 ) independent , but Mus81/Eme1/Rad52 ( Rad22 ) dependent has been described in yeast and represents an important mechanism of DNA repair during replication in fission yeast ( Doe et al . , 2004; Vejrup-Hansen et al . , 2011 ) . Whether this pathway , at least in part , compensates for the BRCA-deficiency in human cells remains to be determined . We chose to target the well characterized ssDNA binding activity of RAD52 , which we expected to underlie RAD52 functions both in supporting replication and in promoting the survival of BRCA-deficient cells . The ssDNA-binding groove of RAD52 is an interesting target for small-molecule binding in that it spans the circumference of the RAD52 oligomeric ring and offers a repetitive pattern of potential binding pockets . This deep and circular groove surprisingly yields reasonable druggability scores , as described in the Results section . Nevertheless , it is a highly exotic cavity , and very distinct from enzyme and receptor pockets . The ssDNA binding groove consists of an alternating arrangement of hydrophobic and hydrophilic regions . Twelve compounds that inhibit RAD52-ssDNA interaction identified in this study ( Table 1 ) are predicted to bind within the ssDNA binding groove of RAD52 ring . In retrospect , it is not surprising that molecules such as the current suite of polyphenols have high affinity for this cavity . Additionally , although there are a number of hydrophobic regions in the ssDNA binding groove , one does not see the kind of significant desolvation that is usually found in enzyme active sites . Nevertheless , a number of waters are revealed in the crystal structure , and are maintained in the docking simulations . However , the nature and importance of these water networks to ligand optimization is not known . It appears there is significant room for improvement in terms of matching the shape of the binding groove with the van der Waals surface of prospective ligands . It will be interesting to see whether such chemical space is extant or may be designed to optimize this unusual surface . The computational studies on the complexation of '1' and '6' with RAD52 indicate the presence of a ubiquitous layer of interstitial water interactions with RAD52 , yet these ligands are almost completely shielded from bulk solvent . The presence of these extensive interstitial water contacts further complicates hypotheses concerning which , if any , RAD52 functional groups are dominating the binding energy contacts . Rather , it may be that our HTS-generated hits possess the right combination of Van der Waals shape complementarity , and the ability to be both hydrogen bond donors and acceptors ( with both interstitial waters and functional moieties ) in the narrow DNA binding groove . Indeed , it may be that this shape complementarity and the ability to utilize the resident waters dominates the binding determinants ( both for the identified ligands , as well as the native ssDNA substrate ) . Our iterative approach of compound discovery followed by the in silico screening was clearly successful in expanding the chemical space of our lead compounds , but more importantly provides a platform for strengthening the structure-activity relationship in an exceedingly challenging target pocket . Indeed , the discovery of the secondary plant metabolite , NP-004255 , a macrocycle , as a means to effectively compete with a native substrate macromolecule ( ssDNA and the ssDNA-RPA complex ) may prove to be a new strategy in the field of disrupting protein-nucleic acid interactions . It has to be noted , however , that although our work provides a solid understanding of how shape complementarity and utilization of interstitial water networks drives complexation , there are limitations to these approaches . Specifically , the current experimental and computational studies do not address the underlying thermodynamics and kinetics that control ligand competition in the ssDNA binding groove . A fascinating aspect of the competition between small molecules and macromolecules for an extended and partially solvated binding pocket , as in RAD52 , is the extent to which small molecules may exploit the potential enthalpy-entropy compensation of a large and floppy native DNA ligand . Additionally , residence times of lead compounds may be a critical factor in the successful design of therapeutic lead compounds . These factors will be addressed in the future studies that will be focused on the thermodynamics and kinetics of small molecule binding to the RAD52 protein , and in the studies that will confirm the ligand placement through high resolution structures . Recently , Chandramouly and colleagues ( Chandramouly et al . , 2015 ) identified a small-molecule RAD52 inhibitor , 6-hydroxy-DL-dopa , that acts differently from the molecules reported here . This inhibitor interferes with RAD52 oligomerization and the supramolecular assembly by an unresolved mechanism . It may act by binding at the RAD52 monomer-monomer interface , or at a different site on the protein and act allosterically . The existence of the distinct classes of RAD52 inhibitors , exemplified by ‘1’ and 6-hydroxy-DL-dopa , suggests that disrupting the RAD52-ssDNA interaction or the integrity of the RAD52 oligomeric ring bears negative consequences for the RAD52 cellular functions . Considering that the efficient homology search and the DNA strand annealing requires the two complementary DNA strands ( or the complementary ssDNA-RPA complexes ) to be wrapped around the two different RAD52 oligomeric rings ( Grimme et al . , 2010; Rothenberg et al . , 2008 ) , this is not surprising , and offers an exciting opportunities for development of more specific and potent agents for targeting recombination-deficient tumors . While this manuscript was under review , two studies reporting small-molecule inhibitors of RAD52 were published . In the first study , Huang et al ( Huang et al . , 2016 ) carried out an HTS campaign to identify 17 compounds that inhibit RAD52-mediated annealing in vitro with IC50 values ranging between 1 . 7 and 17 µM , physically bind RAD52 , and selectively , albeit at high concentrations , inhibit the single-strand annealing pathway of DSB repair over homologous recombination . In another study , Sullivan et al ( Sullivan et al . , 2016 ) reported an in silico docking screen of a library of drug-like compounds . Among 36 predicted small-molecules , 9 compounds inhibited RAD52-ssDNA interaction in vitro , and 1 in cells . As with all previous publications , the authors screened different libraries , resulting in compounds that represent a different chemical space from those that emerged from our campaign . Since the expected role of RAD52 in the recovery of stalled replication forks in the absence of cellular checkpoint is to produce an intermediate that can be cleaved by MUS81/EME1 nuclease , we predicted that the ssDNA binding/annealing activity of RAD52 is required to fulfill this role . As expected , we found that ‘1’ and ‘6’ recapitulate inhibition of DSB formation by siRNA mediated RAD52 depletion ( Figure 5 ) . In the case of ‘1’ , 1 µM of the inhibitor was sufficient to achieve the same level in DSB reduction as siRNA treatment . Notably , no further inhibition was observed when the cells were treated with both siRNA and the small-molecule inhibitor suggesting that the effect is specific to RAD52 . A higher concentration of ‘6’ was required to achieve the level of reduction in the RAD52/MUS81 dependent DSBs comparable with siRNA depletion of RAD52 . This may be due to metabolic instability of this compound or due to potential off-target binding . For this reason , we placed an increased focus on ‘1’ . The ability of ‘1’ to inhibit DSB formation , which is required for the recovery of damaged replication forks in checkpoint deficient cells confirms that the ability of RAD52 to bind ssDNA is required for MUS81-dependent cleavage at stalled replication forks . Moreover , it is consistent with the mechanism we previously proposed whereby , in these cells , RAD52 used its ssDNA-binding activity to create a substrate for MUS81/EME1 and to recruit this structure selective nuclease . Consistent with our previous finding ( Murfuni et al . , 2013 ) , RAD52 inhibition with ‘1’ acted additively with MUS81 depletion eliciting an effect comparable with RAD52 depletion . At 1 µM concentration of the inhibitor , approximately 40% of untreated and 60% of checkpoint-deficient , HU treated cells were dead ( Figure 6b ) . This is notable because the inhibitor interferes only with the biochemical function of RAD52 , namely its ability to bind ssDNA , while leaving the protein itself and its cellular concentration unperturbed , and also because even temporary loss of this biochemical activity during the exposure to replication stress is sufficient to exert the additive effect on viability . This result also illustrates the potentially powerful utility of these inhibitors in elucidating the function of RAD52 in the cell . We observed that inhibition of RAD52 during replication stress , which is induced by blocking DNA synthesis in the absence of the CHK1 activity , in a MUS81 knock-down background results in a comparable effect on viability as the concomitant depletion of both proteins . This observation strengthens the hypothesis that loss of MUS81 and RAD52 produces an additive lethal effect on replication stress ( Murfuni et al . , 2013 ) because , while RAD52 and MUS81 collaborate to resolve demised forks , MUS81 is subsequently required for resolution of recombination intermediates in a RAD52-independent pathway ( Figure 5—figure supplement 1 ) . More interestingly , ‘1’ was able to act at least additively with BRCA2 knock-down ( Figure 6b ) . An increase in cell death when BRCA2 depletion was combined with ‘1’ was comparable to or even exceeded that of MUS81 depletion by siRNA . Notably , the effect of ‘1’ was further enhanced by replication stress induced by short HU treatment and concomitant CHK1 inhibition , as well as by a prolonged exposure to HU . Treatments inducing replication stress are widely used in cancer therapy ( e . g . CPT , Gemcitabin ) . Therefore , RAD52 inhibitors could be useful in combination with drugs which elicit replication stress . Tumors in which MUS81 is mutated or downregulated have been described ( Wu et al . , 2011 ) . While it is unclear whether the role RAD52 plays in supporting the survival of the MUS81-deficient cells is akin to its role in supporting viability in the absence of BRCA1 , BRCA2 or PALB2 , RAD52 inhibitors may represent a new means of treatment for the MUS81-deficient tumors as well as the BRCA-deficient tumors . In addition to its obvious uses in cancer therapy , the RAD52-ssDNA binding inhibitors can be utilized as molecular probes to assist in distinguishing the cellular pathways that depend on the main biochemical activity of RAD52 . RAD52 may act together with other HR proteins , such as RAD51 paralogs to ensure formation of the active RAD51 nucleoprotein filament during RAD51-dependent HR . An understanding of the common players which might bind RAD52 in the absence of BRCA1 or BRCA2 and how they are regulated in the BRCA-deficient cells may require development of specific inhibitors of RAD52 protein-protein interactions and/or combining our inhibitors with other treatments that challenge homology directed DNA repair and replication .
The HPLC purified ssDNA substrate ( Cy3-dT30-Cy5 ) , Target28Cy3 ( T-28 ) ( 5′-ATAGTTATGGTGAGGACCC/iCy3/CTTTGTTTC-3′ ) , Probe28Cy5 ( P-28 ) ( 5′ GAAACAAAGGGGTCC/iCy5/ TCACCATAACTAT-3′ ) Oligo28-REV ( 5’- ( Cy5 ) -GCAATTAAGCTCAAGCCATCCGCAACG- ( Cy3 ) -3’ , Cy3-Oligo28-Cy5 ( 5’-CGTTGCGGATGGCTTAGAGCTTAATTGC-3’ , and Poly dT100 were purchased from Integrated DNA Technologies ( Coralville , IA , USA ) . All chemicals were reagent grade ( Sigma-Aldrich , St . Louis , MO ) . All compounds were purchased from MicroSource Discovery systems , Inc ( Gaylordsville , CT , USA ) and Sigma-Aldrich . The NP-004255 was from AnalytiCon Discovery GmbH ( Potsdam , Germany ) . Purity and structures of the purchased compounds were assessed from 1H NMR spectra collected on a Varian Unity Inova 600 MHz NMR spectrometer at 0 . 5 mM concentrations diluted into DMSO-d6 . The 6xHis-tagged human RAD52 protein was expressed and purified as previously described ( Rothenberg et al . , 2008 ) , except a size exclusion chromatography ( HiPrep 16/60 Sephacryl S-300 HR GE Healthcare Life Sciences , Pittsburgh , PA , USA ) step was added between the heparin and Resource S columns to remove low molecular weight impurities . RAD52 protein concentration was determined by measuring absorbance at 280 nm using extinction coefficient 40 , 380 M−1 cm−1 . RPA protein was purified as described in ( Henricksen et al . , 1994; Grimme et al . , 2010 ) ( and its concentration was determined by measuring absorbance at 280 nm using extinction coefficient 84 , 000 M−1 cm−1 . HTS against the MicroSource Spectrum collection ( Microsource , Gaylordsville , CT ) was performed in nine 384 well plates . All measurements were carried out in the RAD52-HTS buffer containing 20 mM Hepes pH7 . 5 , 1 mM DTT , and 0 . 1 mg/mL BSA . Each 384 well plate contained two columns of negative and positive controls as follow: Columns 1 and 24 were the positive controls , which contained 100 nM RAD52 ( monomers ) and 15 nM ( molecules ) Cy3-dT30-Cy5 ssDNA in the RAD52-HTS buffer . Columns 2 and 23 in addition to 100 nM RAD52 and 15 nM Cy3-dT30-Cy5 also contained 10 nM poly ( dT ) -100 . These were designated as negative controls as 10 nM poly ( dT ) -100 was sufficient to fully inhibit formation of the wrapped RAD52- Cy3-dT30-Cy5 complex under the selected experimental conditions ( data not shown ) . Using a Multiflo dispenser ( Biotek ) , 50 µL of the positive and negative controls were dispensed into their respective wells . Then 15 nM Cy3-dT30-Cy5 and 100 nM RAD52 in RAD52-HTS buffer were dispensed into each well . Next , 1 µL of each compound dissolved in DMSO at 833 µM concentration ( for a final concentration of 15 µM compound ) was dispensed into the wells in columns 3 – 22 using a Microlab Star liquid handling robot ( Hamilton ) and mixed 3 times . Thus , 320 compounds were assayed per 384 well . The plate was incubated at 25°C for 30 min and the fluorescent signal of the Cy3 ( λex ( Cy3 ) = 530 nm; λem ( Cy3 ) = 565 nm ) and the Cy5 ( λem ( Cy5 ) = 660 nm ) dyes were recorded using a Wallac , Envision Manager . The apparent FRET was calculated asFRETapp=ICy5ICy5+ ICy3 Assay performance was assessed across the screen using the following parameters: The signal-to-noise ratio S/N= ( μn−μp ) SDn , the signal-to-background ratio S/B=μnμp , and the Z’-factor Z′=1−3∗ ( SDn+SDp ) ( μn−μp ) , where SDp and SDn are standard deviations , and μn and μp are means of the negative and positive control ( Zhang et al . , 1999 ) . Compounds from the wells that showed apparent FRET values at least 5 SD lower than the positive control were considered potential hits and were selected for the follow up analysis . Ninety six compounds were re-screened to assess reproducibility of hits . Twenty two of these compounds were removed due to high background signal . Twelve compounds , which showed a reproducible reduction in FRET from a screening of the individual plates and re-screening in cherry picked plates , were selected for biochemical validation . Compound binding to RAD52 and RPA proteins was analyzed using water-ligand observation with gradient spectroscopy ( WaterLOGSY ) NMR experiments ( Dalvit et al . , 2001 , 2000 ) . The WaterLOGSY spectra of compounds in the presence of RAD52 or RPA were acquired using a water NOE mixing time of 1 s and a T2 relaxation filter of 50 ms just before data acquisition to suppress the broad signals derived from protein . The protein buffer used in the experiments contains 10 mM Tris-d11 , 75 mM KCl , 0 . 25 mM EDTA , pH 7 . 5 , and 10% D20 . All NMR data were acquired on a Bruker Avance II 800 MHz NMR spectrometer equipped with a sensitive cryoprobe and recorded at 25°C . The 1H chemical shifts were referenced to 2 , 2-dimethyl-2-silapentane-5-sulfonate ( DSS ) . NMR spectra were processed using NMR Pipe ( Delaglio et al . , 1995 ) and analysed using NMR View ( Johnson and Blevins , 1994 ) . FRET-based ssDNA binding , dsDNA binding , and annealing assays were carried out as previously described ( Grimme et al . , 2010; Grimme and Spies , 2011 ) using Cary Eclipse spectrofluorimeter ( Varian ) at 25°C in buffer containing 30 mM Tris-Acetate pH7 . 5 , 1 mM DTT , and 0 . 1 mg/mL BSA . Cy3 dye was excited at 530 nm and its emission was monitored at 565 nm . Emission of Cy5 acceptor fluorophore excited through the energy transfer from Cy3 donor is monitored at 660 nm simultaneously with emission of Cy3 dye . Both the excitation and the emission slit widths were set to 10 nm . To confirm that selected compounds inhibit RAD52 mediated binding and wrapping of ssDNA , compounds were titrated into stoichiometric complex containing 1 nM T30 and 8 nM RAD52 . All experiments were performed in triplicates , and the data are shown as averages and standard deviations for three independent measurements . To remove possible experimental artifacts associated with chromogenic or fluorogenic compounds , as well as with the compounds that may quench or enhance Cy3 or Cy5 fluorescence , we also performed control titrations whereby we titrated each compound into 1 nM Cy3-dT30-Cy5 in the absence of RAD52 . For each compound concentration we subtracted the difference in the FRET signal in the presence and absence of the compound from the respective FRET signal in the presence of RAD52 . The FRET signal corrected for the compound fluorescence was calculated using the equation:FRETapp=4 . 2∗ICy54 . 2∗ICy5+ 1 . 7∗ICy3 as previously described ( Grimme et al . , 2010; Grimme and Spies , 2011 ) and plotted as a function of compound concentration and fitted to the following inhibition model”FRET ( [small molecule] ) =FRET0−FRETmin1+10 ( LogIC50−Log ( [small molecule] ) ∗HillSlope ) , where FRET0 is the initial FRET value of the RAD52-ssDNA complex in the absence of the compound and FRETmin is the FRET value at complete inhibition . FRET values were calculated as an average of three or more independent annealing reactions plotted against the concentration of the compound . Inhibition of the RAD52-dsDNA interaction was assayed in a similar experiments , except the stoichiometric complexes containing 1 nM molecules of Cy3-Oligo28-Cy5 duplex DNA and 10 nM RAD52 . To assess if selected compounds inhibit RPA-ssDNA mediated binding and wrapping by RAD52 compounds were titrated into stoichiometric complexes containing 1 nM T30 , 1 nM RPA , and 10 nM RAD52 . In all experiments , the FRET values for each data point were corrected for the effects of the compounds on the respective substrate in the absence of RAD52 . Annealing of complementary oligonucleotides by RAD52 was monitored under identical conditions as the binding assays described above . For each assay , the reaction master mixture containing 8 nM RAD52 protein in the presence and absence of the compounds at varying concentrations was prepared at room temperature and divided into two half reactions . Following baseline buffer and protein measurements , 0 . 5 nM of T-28 ssDNA substrate was added to the reaction cuvette and the signal was allowed to stabilize . The annealing reaction was initiated upon addition of the second half-reaction pre-incubated with 0 . 5 nM P-28 ssDNA substrate . The fluorescence of Cy3 and Cy5 were measured simultaneously over the reaction time course ( 500 s ) . FRETapp values were calculated as an average of three or more independent annealing reactions plotted against time ( s ) . The average FRET values were fitted to a double exponential to calculate the final extent of annealing using Graphpad Prism4 software . The calculated annealing extent was plotted as a function of compound concentration and fitted to the same model as we used to determine IC50 values for the inhibition of DNA annealing . Our initial computational workflow employed a combination of classical docking , using the Triangle Matcher approach and scoring using the London dG scoring function ( an empirical scoring function which attempts approximate the binding energy of the docked ligand ) in MOE 2013 . 08 ( Chemical Computing Group , 2013 ) , followed by force field ( MMFF94x [Halgren , 1996] ) -based ligand refinement and finally rescoring using an MM/GBSA-based approach ( which is described in more detail below ) . Initially , top scoring poses in either Triangle Matcher ( Placement ) , London dG ( affinity scoring function ) or MM/GBSA ( physics-based scoring ) were retained for further analysis . Often the top scoring poses from each metric were highly distinct from one another , suggesting that a generally poor consensus between the different metrics used in this early phase of the work flow . This lack of consensus in the scoring of the possible ligand binding in the sub-pockets within the DNA-binding groove of RAD52 ( PDB: 1KNO ) motivated us to apply the much more computationally rigorous all atom-simulated annealing studies , which are detailed below . All lead ligands were subjected to docking using MOE 2013 . 08 to a portion of the DNA binding groove of RAD52 spanning nearly a quarter of the circumference ( 3 adjacent monomers of the protein ring ) . The top 30 poses for each docked and scored ( London dG scoring function ) were subjected to energy minimization with a rigid RAD52 receptor using the MMFF94x force field , followed by rescoring ( in order to estimate the ΔG of binding ) of each distinct pose with the MM/GBSA methodology ( Naïm et al . , 2007 ) , which includes an implicit solvation energy calculation and captures changes in the solvent exposed surface area of the pose , which is a highly parameterized version of the popular MM/PBSA and MM/GBSA methodologies ( Steinbrecher and Labahn , 2010; Wang and Kollman , 2000 ) . The all atom-simulated annealing energy minimization ( here referred to SAEM for brevity ) , which is followed by a local docking protocol ( as described below ) is a customized protocol that was automated with a script using the Python-based Yanaconda scripting language , and use of the Yamber03 knowledge-based force field ( Krieger et al . , 2004 ) . Briefly , each complex was placed into a simulation cell and solvated , and charge-neutralized to yield physiological conditions , followed by an optimization of the solvent and H-bonding network , and finally a phased simulated annealing minimization was performed ( a similar process is described in Whalen et al . ( 2011 ) ) . No restraints were placed in any of these systems ( i . e . all atoms in the ligand and the entire RAD52 complex , ions and solvent were free to move in the simulation ) . The affinity of the ligand in this optimized complex was then determined by scoring with AutoDock VINA ( Trott and Olson , 2010 ) . Water molecules that were interstitial were automatically retained in the VINA scoring . To induce RAD52-MUS81-dependent cleavage at arrested replication forks ( Murfuni et al . , 2013 ) , hTERT-immortalized wild-type human fibroblasts ( GM01604 ) were treated with 2 mM HU and 300 nM UCN01 for 6h , in the presence and absence of varying concentrations of ‘1’ or ‘6’ . Where indicated , the GM01604 cells were cells were transfected with siRNAs directed against GFP ( Ctrl ) , or against RAD52 ( Qiagen ) 48 hr prior to induction of replication stress and/or inhibitor treatment . After that cells were subjected to neutral comet assay as described in Murfuni et al ( Murfuni et al . , 2013 ) . Slides were analyzed by a computerized image analysis system ( Comet IV , Perceptive UK ) . To assess the quantity of DNA damage , computer-generated tail moment values ( tail length x fraction of total DNA in the tail ) were used . Apoptotic cells ( smaller comet head and extremely larger comet tail ) were excluded from the analysis to avoid artificial enhancement of tail moment . A minimum of 100 cells were analyzed for each compound concentration point . GM01604 cells were transfected with siRNAs directed against GFP ( Ctrl ) , or against MUS81 ( Qiagen ) , BRCA2 ( Sigma-Aldrich ) , and RAD52 ( Qiagen ) 48 hr prior to addition of 1 µM ‘1’ . Where indicated , the conditions of pathological replication were induced by treating cells with 2 mM HU and 300 nM UCN01 for 6 hr or by 18 hr treatment with 2 mM HU , in the presence or absence of ‘1’ . Viability was evaluated by the LIVE/DEAD assay ( Sigma-Aldrich ) according to the manufacturer’s instructions . Cell number was counted in randomly chosen fields and expressed as percent of dead cells ( number of red nuclear stained cells divided by the total cell number ) corrected for the cell loss observed in the population . For each time point , at least 200 cells were counted . The AnalytiCon Discovery MEGx Natural Products Screen Library , which is the in silico version of an actual library of purified natural products from plant , fungal and microbial sources , which is available for purchase , was subjected to an in silico screening campaign . The campaign was designed based on the ability to optimally minimize false positives and false negatives , and to maximize true positives and true negatives . More specifically , the experimental hits identified in the HTS campaign described above , constitute the true positives , while specifically selected decoy compounds constitute the true negatives . Decoy compounds were generated using the Database of Useful Decoys – Enhanced ( DUD–E ) website ( Mysinger et al . , 2012 ) . Decoys are compounds that resemble active ligands in physicochemical properties , but are distinct in chemical topology to true binders , so that separation bias is avoided ( Huang et al . , 2006 ) . Decoys are property-matched to compounds of interest using molecular weight , estimated water-octanol partition coefficient ( miLogP ) , rotatable bonds , hydrogen bond acceptors , hydrogen bond donors , and net charge ( Mysinger et al . , 2012 ) . An average of 50 decoys are obtained for each ligand . In order to validate the selected protocol ( Figure 1b ) , we employed a statistical method in which 'Receiver Operating Characteristic' or ROC curves are used to optimize the balance of true positives , false positives , true negatives and false negatives ( Varnek et al . , 2008 ) . ROC curves were created using MatLab ( R2015a; Mathworks , Natick , MA , USA ) from scoring ranks of active versus inactive poses for each of the best HTS hits ( Varnek et al . , 2008 ) . This plot represents the percentage of true positives versus percentage of false positives for a wide range of choices of score cutoffs . This procedure also allows the determination of the best score threshold for cutoff of compounds regarding the particular protein target . A database containing the AnalytiCon Natural Products compound library , and a control selected from the initial HTS hits , were created and preprocessed for virtual docking ( as described above ) . The top 30 final poses , generated using the Dock utility of MOE ( as described above ) were written to an output database . Poses of all compounds were ranked based on their scores . Compounds with the poses most favorable to binding , i . e . the poses with the lowest energy scores from the London dG scoring function were selected for further analysis . Those poses with better scores than the highest scoring pose of our control ( ie , a 'true positive' in the ROC curve context ) were selected , and then subjected to a refining docking step involving force field-based energy minimization with the MMFF94x force field in MOE . Binding energies were ranked , and evaluated . The docking scores ( kcal/mol ) were used for determining the ROC threshold values ( see ( DeLong et al . , 1988 ) , for precise description of the how to determine the threshold value ) . Each original compound of interest and its poses were to be the only 'predicted positives' , and the DUDs ( decoys ) and its poses were to be the 'predicted negatives'; any poses above the threshold were to be the 'actual positives' and the poses below the threshold were to be the 'actual negatives' . The curves were analyzed using the metric of the area under the curves ( AUC ) ( DeLong et al . , 1988 ) . The scores of the poses for the most active compounds exhibited bimodal frequency distribution ( Figure 8b ) , and the docking protocols’ ability to distinguish between active compounds and decoys was verified ( Figure 8c ) . We have determined a protocol for sorting compounds of interest among a database that provides reliable results with high cutoff limits . | Cells are constantly in danger of losing or scrambling critical genetic information because of DNA damage . To cope with this stress , cells have numerous DNA repair systems . One of these systems – homology-directed DNA repair – involves the proteins BRCA1 and BRCA2 , which are often missing or defective in breast and ovarian cancers . The BRCA-deficient cancer cells can still survive , but become “addicted” to other DNA repair proteins – among them a protein called RAD52 . It might be possible to kill these cancer cells using drugs that stop RAD52 from working . Such treatments would have the benefit of not harming normal healthy cells , as these cells contain working BRCA proteins and can survive without RAD52 . It is not currently known exactly how RAD52 allows the BRCA-deficient cells to survive , but this probably depends on RAD52’s ability to bind to single strands of DNA . Small molecules that block the interaction between the RAD52 protein and DNA might therefore help to kill cancer cells . Hengel et al . developed a high throughput biophysical method to search through a large collection of small molecules to find those that prevent RAD52 from binding to DNA . The best potential drug leads were then tested in laboratory-grown human cells and using biophysical and biochemical techniques . Computational approaches were also used to model how these molecules block the interaction between RAD52 and DNA at the atomistic level . Hengel et al . then used the information about how the small molecules bind to RAD52 to perform further computational screening . This identified a natural compound that competes with single-stranded DNA to bind to RAD52 . The activity of this molecule was then validated using biophysical methods . The methods used by Hengel et al . provide the foundation for further searches for new anticancer drugs . Future studies that employ the small molecule drugs identified so far will also help to determine exactly how RAD52 works in human cells and how it helps cancer cells to survive . | [
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] | 2016 | Small-molecule inhibitors identify the RAD52-ssDNA interaction as critical for recovery from replication stress and for survival of BRCA2 deficient cells |
The liver X receptors ( LXRs ) are transcriptional regulators of lipid homeostasis that also have potent anti-inflammatory effects . The molecular basis for their anti-inflammatory effects is incompletely understood , but has been proposed to involve the indirect tethering of LXRs to inflammatory gene promoters . Here we demonstrate that the ability of LXRs to repress inflammatory gene expression in cells and mice derives primarily from their ability to regulate lipid metabolism through transcriptional activation and can occur in the absence of SUMOylation . Moreover , we identify the putative lipid transporter Abca1 as a critical mediator of LXR's anti-inflammatory effects . Activation of LXR inhibits signaling from TLRs 2 , 4 and 9 to their downstream NF-κB and MAPK effectors through Abca1-dependent changes in membrane lipid organization that disrupt the recruitment of MyD88 and TRAF6 . These data suggest that a common mechanism-direct transcriptional activation-underlies the dual biological functions of LXRs in metabolism and inflammation .
The liver X receptors ( LXRs ) are members of the nuclear receptor superfamily that play pivotal roles in sterol homeostasis in mammals . LXRs control the expression of a battery of genes involved in cholesterol , fatty acid and phospholipid metabolism through direct binding to LXR response elements ( LXREs ) in their target promoters ( Calkin and Tontonoz , 2012; Hong and Tontonoz , 2014 ) . In addition to their ability to activate the expression of genes linked to lipid metabolism , LXRs also have the ability to antagonize inflammatory gene expression triggered by Toll-like receptor ( TLR ) activation ( Joseph et al . , 2003 , 2004; Castrillo et al . , 2003a ) . LXRs are not known to act as direct ligand-dependent repressors , that is , there is no well-documented example of LXR/RXR heterodimers binding to an LXRE in a gene promoter and repressing transcription in response to ligand . Rather , they have been proposed to act on inflammatory promoters through mechanisms that do not involve DNA-binding domain recognition of LXREs . One proposed mechanism , termed ‘transrepression’ , postulates that LXRs become SUMOylated in response to LXR agonist and that this SUMOylated LXR monomer stabilizes repressive nuclear complexes on the promoters of inflammatory genes . Key features of this model include the requirement for receptor SUMOylation , the involvement of an LXR monomer , and the mechanistic separation of the transcriptional activation and repression functions of LXRs ( Ghisletti et al . , 2007; Lee et al . , 2009; Venteclef et al . , 2010 ) . Although several lines of evidence have supported the transrepression model , the requirement for receptor SUMOylation in the repressive actions of LXRs on inflammatory genes in cells and animals remains to be tested . The potential involvement of additional or alternative mechanisms has also not been excluded . We demonstrate here that the ability of LXRs to antagonize endogenous inflammatory gene expression in cultured cells and in vivo is a consequence of changes in cellular lipid metabolism . We show that the ability of LXRs to activate transcription of the Abca1 sterol transporter , and thereby alter membrane cholesterol homeostasis , has a secondary effect on inflammatory signaling through inhibition of NF-κB and MAPK signaling pathways downstream of TLRs . These data present a unified view of LXR-dependent gene regulation in which direct transcriptional activation underlies the dual biological functions of LXRs in metabolism and inflammation . They further emphasize the importance of local membrane composition in the activation of TLR signaling pathways .
LXRs activate gene expression as heterodimers with RXRs . Previous studies have proposed that LXRs ‘transrepress’ inflammatory gene expression by acting as a monomer ( Ghisletti et al . , 2007; Lee et al . , 2009; Venteclef et al . , 2010 ) . Unexpectedly , we found that siRNA-mediated knockdown of RXRα and RXRβ in primary mouse macrophages blunted the ability of LXR agonist to repress the LPS-induced expression of the endogenous inflammatory genes Nos2 , Il1β , Ccl2 , Cxcl1 , Tnfα , Cox2 and Il6 ( Figure 1A and Figure 1—figure supplement 1A , B ) . This observation suggested that LXR was acting to repress inflammation as a heterodimer with RXR in our system . We therefore explored alternative mechanisms for the repressive effects of LXR on inflammatory gene expression . 10 . 7554/eLife . 08009 . 003Figure 1 . RXR and transactivation are required for LXR-dependent inflammatory repression . ( A ) Bone marrow-derived macrophages from wild-type mice were transfected with siRNA targeting RXRα and RXRβ ( siRXRαβ or control ( siCtrl ) for 48 hr , pretreated with GW3965 ( 1 µM ) overnight , and then stimulated with LPS ( 10 ng/ml ) for 4 hr . ( B ) Immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , AF1-deletion mutant ( ∆AF1 ) , DNA-binding domain deletion mutant ( ∆DBD ) , ligand-binging domain deletion mutant ( ∆LBD ) , AF2-deletion mutant ( ∆AF2 ) or control mock were pretreated with the LXR agonist GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . ( C ) Immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , L439A/E441A mutant or control mock were pretreated with the LXR agonist GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 00310 . 7554/eLife . 08009 . 004Figure 1—figure supplement 1 . Effects of RXR knockdown on LXR-mediated inflammatory repression . ( A , B ) Bone marrow-derived macrophages from wild-type mice were transfected with siRNA targeting RXRα and RXRβ ( siRXRαβ or control ( siCtrl ) for 48 hr , pretreated with GW3965 ( 1 µM ) overnight , and then stimulated with vehicle or LPS ( 10 ng/ml ) for 4 hr as indicated . Gene expression was analyzed by real-time PCR . N = 4 per group . ( C ) Immunoblot analysis of LXRα and LXRβ protein in HepG2 cells treated with vehicle or GW3965 ( 2 µM ) overnight and immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , human LXRβ or control mock . ( D ) Immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα and LXRβ were treated with the LXR agonist GW3965 ( 1 µM ) overnight . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . ( E ) Immunoblot analysis of LXR proteins in immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRs or indicated mutants . Note , the epitope recognized by the LXRα antibody is in the DBD and that recognized by the LXRβ antibody is in the AF-1 domain . Therefore , these respective deletion mutants are not detected . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 004 To test the structural requirements for LXR-dependent repression of endogenous inflammatory genes , we stably reconstituted immortalized mouse embryonic fibroblasts ( MEFs ) and immortalized primary bone marrow-derived macrophages ( iBMDM ) from mice lacking LXRα and LXRβ with wild-type and mutant LXRs ( Figure 1 , Figure 1—figure supplement 1 ) . The synthetic LXR agonist GW3965 did not induce the canonical LXR target gene Abca1 , nor did it repress LPS-induced inflammatory gene expression , in LXR-deficient MEFs or macrophages ( Figures 1B , C , 2A , B ) ( Castrillo et al . , 2003a , 2003b; Joseph et al . , 2004 ) . However , when LXR-deficient cells were reconstituted with wild-type LXRα , GW3965 treatment simultaneously induced Abca1 expression and repressed LPS-induced inflammatory gene expression ( Ccl2 and Cxcl1 ) ( Figure 1B , C ) . These results establish that both the activating and repressive effects of GW3965 under our experimental conditions are entirely mediated by LXRs . This is important to emphasize because many synthetic LXR agonists , including GW3965 and T0901317 , can exhibit LXR-independent effects on gene expression , especially when used at concentrations higher than employed here . It is also important to note that this approach does not involve supraphysiologic overexpression of LXRs , as the level of reconstituted protein expression in these cells was within the physiologic range and restored target gene expression to physiologic levels ( Figure 1—figure supplement 1C , D ) . 10 . 7554/eLife . 08009 . 005Figure 2 . Transactivation but not sumoylation is required for LXR-mediated inflammatory repression . ( A ) Immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , sumoylation site mutants ( K328R/K434R ( KK ) , K328R , K434R ) , wild-type human LXRβ , sumoylation site mutants ( K410R/K448R ( KK ) , K410R , K448R ) , or mock control were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . ( B , C ) Immortalized bone marrow-derived macrophages from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , K328R/K434R ( KK ) mutant , L439A/E441A mutant , or mock control were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR ( B ) and Agilent microarrays ( C ) . Selected genes that are annotated with the ‘Immune system process’ GO term from the array studies are presented as a heatmap ( ≧ twofold change ) . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 00510 . 7554/eLife . 08009 . 006Figure 2—figure supplement 1 . Effect of lysine mutants on LXR-mediated inflammatory repression . ( A ) Immortalized bone marrow-derived macrophages from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα K328R/K434R mutant ( KK ) , LXRα L439A/E441A mutant , or mock control were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . ( B ) Immortalized MEFs from Lxrα−/−Lxrβ−/− mice reconstituted with wild-type human LXRα , K328R/K434R mutant ( KK ) , K180R mutant , K177R/K178R/K180R mutant ( 3 KR ) or control mock were pretreated with the LXR agonist GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 00610 . 7554/eLife . 08009 . 007Figure 2—figure supplement 2 . Repression does not require Ubc9 or Hdac4 . Immortalized bone marrow-derived macrophages were transduced with control siRNA or siRNA targeting Ubc9 or Hdac4 as indicated . ( A ) Validation of mRNA knockdown for Ubc9 or Hdac4 . ( B ) Regulation of inflammatory gene expression by LXR agonists in presence or absence of Ubc9 or Hdac4 . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 007 To identify the domains important for LXR-dependent repression , we reconstituted LXR-deficient cells with various domain-deletion mutants of LXR . Agonist treatment induced Abca1 and repressed the levels of Ccl2 and Cxcl1 in AF1-deletion mutant cells . However , both the activation and repression activities of LXR were completely abolished in DNA binding domain- ( DBD ) , ligand binding domain- ( LBD ) or AF2-deletion mutant cells ( Figure 1B ) . Thus , the DBD , LBD and AF2 domains , but not the AF1 domain , are critical for both transactivation and repression by LXR . Furthermore , an LXR mutant defective in its ability to recruit co-activators , L439A/E441A ( Tzukerman et al . , 1994; Bastie et al . , 2000 ) , was unable to induce Abca1 or to repress inflammatory genes in both MEFs and iBMDM ( Figure 1C , Figure 2B , Figure 2—figure supplement 1A ) , strongly suggesting that gene activation and inflammatory repression are mechanistically linked . In contrast to activation-defective LXR proteins , mutants of LXRα and LXRβ that lack the SUMOylation sites previously identified ( Ghisletti et al . , 2007 ) were capable of inducing Abca1 and repressing inflammatory genes in both MEFs and iBMDM ( Figure 2A , B and Figure 2—figure supplement 1A ) . We considered the possibility that alternative SUMOylation sites might be employed , however , mutation of three additional residues in the hinge region of the receptor predicted to be likely SUMOylation sites also had no effect on LXR-dependent repression of Cxcl1 or Ccl2 ( Figure 2—figure supplement 1B ) . We also considered the possibility that SUMOylation might be required for a distinct subset of inflammatory genes in our system . However , transcriptional profiling revealed that LXR SUMOylation site mutants were capable of mediating ligand-dependent repression of a broad array of inflammatory genes ( e . g . , those annotated with the ‘Immune system process’ GO term; Figure 2C ) . We further tested whether the SUMOylation machinery reported to target LXR was required for inflammatory repression in our system . Knocking down Ubc9 or Hdac4 by siRNA in iBMDM did not block repression ( Figure 2—figure supplement 2 ) . Thus , SUMOylation-dependent transrepression could not account for the inhibitory actions of LXR on inflammatory gene expression in our system . Given our data suggesting that gene activation and inflammatory repression by LXRs were mechanistically linked , we hypothesized that there must be one or more direct LXR target genes whose action has a secondary effect on inflammatory gene expression . We therefore used siRNA to knockdown a panel of LXR target genes in primary BMDM and tested whether inflammatory repression was compromised . Surprisingly , we found that knockdown of Abca1 , a gene critical for cellular cholesterol efflux ( Oram , 2003 ) , substantially impaired the ability of LXR agonists to repress inflammatory gene expression ( Figure 3A ) . By contrast , knockdown of Abcg1 or Apoe , two other LXR target genes involved in metabolism , had no effect on inflammatory repression ( Figure 3A and Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 08009 . 008Figure 3 . ABCA1 induction is critical for LXR-mediated repression . ( A ) Bone marrow-derived macrophages from wild-type mice were transfected with siRNA targeting Abca1 , Abcg1 or control ( Ctrl ) for 48 hr , pretreated with GW3965 ( 1 µM ) overnight , and then stimulated with LPS ( 10 ng/ml ) for 4 hr . ( B , C ) Bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR ( B ) and Agilent microarrays ( C ) . Selected genes from the array studies that are annotated with the ‘Immune system process’ GO term are presented as a heatmap ( ≥twofold changes shown ) . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 00810 . 7554/eLife . 08009 . 009Figure 3—figure supplement 1 . Loss of Abcg1 or ApoE does not compromise LXR-mediated repression . ( A ) Bone marrow-derived macrophages from wild-type mice were transfected with siRNA targeting Abcg1 or control ( Ctrl ) for 48 hr , pretreated with GW3965 ( 1 µM ) overnight , and then stimulated with LPS ( 10 ng/ml ) for 4 hr . ( B ) Bone marrow-derived macrophages from wild-type or Apoe−/− mice were pretreated with GW3965 ( 1 µM ) overnight , and then stimulated with LPS ( 10 ng/ml ) for 4 hr . ( C ) Bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice were treated with dexamethasone ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 009 We corroborated these results by analyzing the effects of LXR agonist in BMDM from myeloid-specific Abca1−/− mice . Again , the ability of LXR agonist to repress inflammation was markedly inhibited in the genetic absence of Abca1 ( Figure 3B ) , but not Abcg1 ( Figure 3—figure supplement 1A ) . To comprehensively analyze the impact of Abca1 deficiency on inflammatory gene expression , we performed transcriptional profiling . This analysis revealed that most of the genes annotated with the ‘Immune system process’ GO term were not repressed by LXR activation in Abca1-deficient macrophages ( Figure 3C ) , suggesting that LXR-induced Abca1 expression is broadly important for LXR-mediated repression . We also tested whether Abca1 was required for inflammatory repression by the synthetic glucocorticoid receptor ( GR ) agonist dexamethasone . The ability of dexamethasone to repress inflammatory gene expression was preserved in Abca1−/− cells , indicating that distinct mechanisms are involved in transcriptional repression by LXR and GR ( Figure 3—figure supplement 1C ) . Prior work has shown that LXR-dependent induction of Abca1 contributes to the maintenance of sterol homeostasis by promoting the efflux of cellular cholesterol to extracellular acceptors such as ApoA-I and ApoE ( Santamarina-Fojo et al . , 2001; Oram , 2003 ) . Tangier disease is a severe HDL deficiency syndrome that is caused by mutations in the ABCA1 gene . We also tested the ability of LXR agonist to inhibit inflammatory gene expression in skin fibroblasts from a patient with Tangier disease that lack functional ABCA1 protein . LXR target genes were induced at similar levels in normal ( healthy donor ) and Tangier fibroblasts in response to activation by synthetic agonist ( Figure 4A ) . While LXR activation with ligand repressed inflammatory gene expression in normal cells , this response was abrogated in Tangier fibroblasts ( Figure 4B ) . 10 . 7554/eLife . 08009 . 010Figure 4 . Intracellular cholesterol content affects LXR-mediated repression . ( A , B ) Skin fibroblasts from a healthy donor ( normal ) and a Tangier disease patient ( Tangier ) were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . ( C ) Immortalized bone marrow-derived macrophages from Abca1−/− mice reconstituted with wild-type Abca1 , N935S mutant , C1447R mutant or mock control were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 010 To further examine the function of Abca1 was critical for inflammatory repression , we reconstituted iBMDM from myeloid-specific Abca1−/− mice with wild-type Abca1 or two different Abca1 point mutants that lacks cholesterol efflux ability , N935S and C1477R ( Singaraja et al . , 2006; Kannenberg et al . , 2013 ) . When Abca1-deficient cells were reconstituted with wild-type Abca1 , inflammatory gene expression was repressed by LXR activation; however , this effect was lost in cells expressing the N935S or C1477R mutants ( Figure 4C ) . These data suggest that cholesterol transport by Abca1 is critical for inflammatory repression in human cells and that effective LXR-dependent inhibition of inflammation requires the ATP-dependent transporter function of Abca1 . The requirement for Abca1 in LXR-dependent inflammatory repression suggests that membrane cholesterol content or distribution is mechanistically linked to inflammatory gene repression . To test this idea , we loaded cells with cholesterol using cyclodexrin-cholesterol complexes or depleted cells of cholesterol by incubating them with hydroxypropyl-β-cyclodextrin ( Klein et al . , 1995; Christian et al . , 1997 ) . As expected , cholesterol addition activated LXR target genes and repressed SREBP target genes , whereas cholesterol removal inhibited LXR target genes and induced SREBP target genes ( Figure 5 ) . Remarkably , increasing the cholesterol content of the cell also enhanced the induction of inflammatory genes by LPS . On the other hand , decreasing cholesterol content inhibited the induction of inflammatory genes ( Figure 5 ) . These data are consistent with the hypothesis that regulation of membrane cholesterol content by LXRs may modulate inflammatory responses . 10 . 7554/eLife . 08009 . 011Figure 5 . Manipulation of membrane cholesterol content affects inflammatory responses . Bone marrow-derived macrophages were incubated with cyclodextrin cholesterol ( CD-Chol , 100 µM ) or hydroxypropyl-β-cyclodextrin ( CD , 10 mM ) for 1 hr and stimulated with LPS ( 10 ng/ml ) for 4 hr . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 011 To further explore the connection between LXR-dependent target gene activation and inflammatory repression , we analyzed signaling pathways downstream of TLR4 in macrophages . We found that treatment of macrophages with LXR agonist inhibited the activation of ERK , p38 and JNK MAP kinases downstream of TLR4 activation ( Figure 6A , B ) . Reduced levels of phospho-ERK , phospho-p38 and phospho-JNK were observed in the setting of LXR activation . Importantly , this effect was mediated by LXRs , as it was not observed in LXR-deficient macrophages . To test whether inhibition of MAPK signaling was mechanistically linked to the regulation of Abca1 expression by LXR , we repeated these studies in macrophages derived from myeloid-specific Abca1-deficient mice . Remarkably , the ability of LXR agonist to inhibit ERK , p38 and JNK activation was severely impaired in cells lacking Abca1 ( Figure 6C , D ) . 10 . 7554/eLife . 08009 . 012Figure 6 . Ligand activation of LXR inhibits LPS-induced MAP kinase activation through Abca1 induction . ( A–D ) Bone marrow-derived macrophages from Lxrα−/−Lxrβ−/− and control wild-type mice ( A , B ) , or bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice ( C , D ) were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 20 min or 1 hr . Whole cell lysates were harvested and protein expression was analyzed by immunoblotting with the indicated antibodies ( A , C ) . Protein expression was quantified by Image Quant TL7 . 0 ( B , D ) . N = 4–6 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 012 We also found that LXR agonist suppressed the activation of NF-κB signaling downstream of TLR4 . Immunoblot analysis of whole cell lysates and nuclear extracts revealed that treatment of macrophages with GW3965 reduced the ratio of phospho-IκBα to total IκBα and reduced the nuclear abundance of p65 ( Figure 7A , B ) . These effects of agonist were also LXR-dependent , as they were not observed in LXR-deficient macrophages . Furthermore , the ability of LXR agonist to suppress NF-κB signaling was abolished in Abca1-deficient macrophages ( Figure 7C , D ) . Analysis of p65 localization by confocal immunofluorescence microscopy confirmed the ability of GW3965 to inhibit p65 nuclear translocation in an LXR-dependent and Abca1-dependent manner ( Figure 7E , F ) . 10 . 7554/eLife . 08009 . 013Figure 7 . Ligand activation of LXR inhibits LPS-induced NF-κB activation through Abca1 induction . ( A–F ) Bone marrow-derived macrophages from Lxrα−/−Lxrβ−/− and control wild-type mice ( A , B , E ) , or bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice ( C , D , F ) were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 20 min . Whole cell lysates and nuclear lysates were harvested and protein expression was analyzed by immunoblotting with the indicated antibodies ( A , C ) . Protein expression was quantified by Image Quant TL7 . 0 ( B , D ) . N = 4–6 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . Nuclear translocation of p65 was assessed by staining of p65 ( green ) and DAPI ( blue ) ( E , F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 013 To address whether p65-binding to its target inflammatory gene promoters was affected by LXR activation , we employed chromatin immunoprecipitation ( ChIP ) assays . The binding of p65 to the Nos2 , IL1b and Ccl2 gene promoters was increased by LPS treatment as expected , whereas no binding was observed on the hemoglobin beta ( Hbb2 ) gene promoter , which does not contain a p65 binding site ( Figure 8 ) . We found that LXR activation suppressed LPS-induced p65 recruitment to the Nos2s , Ccl2 and Il1b gene promoters . Moreover , the inhibitory effect of LXR agonist on p65 recruitment was lost in Abca1-deficient macrophages ( Figure 8 ) . Together , the data of Figures 6–8 demonstrate that LXR activation inhibits signaling through TLR4 , and that Abca1 expression is critical for this phenomenon . 10 . 7554/eLife . 08009 . 014Figure 8 . LXR activation decreases p65 occupancy on inflammatory gene promoters . Recruitment of p65 to inflammatory gene promoters was assessed by ChIP-qPCR assays . Chromatin from wild-type or Abca1−/− cells was precipitated with p65 antibody or control IgG . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 014 We next tested the hypothesis that LXR agonist treatment relocalizes plasma membrane cholesterol away from detergent-resistant microdomains or rafts , thereby preventing the localization of TLR4 signaling molecules and impairing downstream signaling . BMDM from wild-type or LXR-deficient mice were treated overnight with GW3965 , and then cell membranes were fractionated and the cholesterol content of each fraction determined . We found that LXR activation reduced the cholesterol content of Flotillin-enriched detergent-resistant membrane microdomains in wild-type type ( <47% reduction ) but not LXR-deficient macrophages ( Figure 9A ) . Furthermore , this reduction in cholesterol content of detergent-resistant membrane microdomains was not observed in Abca1-deficient macrophages ( Figure 9B ) . On the other hand , the expression of ganglioside GM1 , a marker of lipid rafts , was not altered by LXR agonist ( Figure 9C ) . These results suggest that LXR-dependent ABCA1 expression reduces the cholesterol content of detergent-resistant membrane microdomains without affecting the abundance of lipid rafts in the plasma membrane . 10 . 7554/eLife . 08009 . 015Figure 9 . Ligand activation of LXR decreases cholesterol content in lipid rafts and increases membrane lipid mobility . ( A , B ) Bone marrow-derived macrophages from Lxrα−/−Lxrβ−/− and control wild-type mice ( A ) , or bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice ( B ) were pretreated with GW3965 ( 1 µM ) overnight . Lipid raft and non-raft fractions were isolated using the detergent method and each fraction was analyzed by Western blotting . The free cholesterol concentration in each fraction was determined and normalized to protein concentration . N = 5 per group . ( C ) The abundance of lipid rafts in the plasma membrane was analyzed by flow cytometry after staining with cholera toxin B ( CTB ) . ( D ) Wild-type or Abca1−/− iBMDM were treated with oxidized LDL ( 50 µg/ml ) for 48 hr , and GW3965 ( 1 µM ) overnight . GP images ( left ) were obtained from fluorescence-lifetime imaging microscopy ( FLIM ) . The GP scale used to pseudocolor the intensity image is shown at right . Plasma membrane fluidity was determined with GP and number of pixel in plasma membrane ( right ) . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 015 The cholesterol content of the plasma membrane affects its fluidity and rigidity , and therefore could conceivably affects signaling through membrane receptors ( Simons and Toomre , 2000; Fessler and Parks , 2011 ) . Laurdan is a fluorescent lipophilic molecule that can be used to detect changes in membrane dynamics due to its sensitivity to the polarity of the membrane environment ( Parasassi and Gratton , 1995; Vest et al . , 2006; Golfetto et al . , 2013 ) . Changes in membrane dynamics shift the laurdan emission spectrum , which can be quantified by the generalized polarization ( GP ) calculated from the spectrum shifts ( Parasassi et al . , 1990 ) . To examine if membrane dynamics were affected as a result of LXR-dependent reductions in raft cholesterol content , we stained living primary macrophages with laurdan . As shown in Figure 9D , plasma membrane rigidity was decreased in response to LXR activation , consistent with the altered cholesterol distribution . Furthermore , the ability of LXR agonist treatment to affect plasma membrane fluidity in primary macrophages was largely dependent on Abca1expression , as we did not observe a shift in laurdan signal in Abca1−/− macrophages ( Figure 9D ) . To gain additional insight into the inflammatory signaling pathways impacted by LXR , we tested the influence of LXR agonist treatment on gene expression induced by TLR2 ( Pam3CSK4 ) , TLR9 ( CpG ) and TLR3 ( Poly I:C ) agonists . LXR activation blunted the response to TLR2 and TLR9 ( Figure 10A , B ) , but did not affect the induction of viral response genes by TLR3 ( Figure 10C ) . Furthermore , loss of Abca1 expression in macrophages inhibited the ability of LXR agonist to repress gene expression stimulated by TLR2 and TLR9 as well as by TLR4 . These observations suggested that LXR-dependent membrane lipid remodeling was targeting a component of the inflammatory signaling cascade common to TLRs 2 , 4 and 9 . 10 . 7554/eLife . 08009 . 016Figure 10 . Inhibition of TLR2 and TLR9 signaling by LXR requires Abca1 . Bone marrow-derived macrophages from myeloid-specific Abca1−/− and control wild-type mice ( A , B , C ) were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with Pam3CSK4 ( 100 ng/ml ) , LPS ( 10 ng/ml ) , CpG ( 1 µM ) or polyI:C ( 10 µg/ml ) for 4 hr as indicated . Gene expression was analyzed by real-time PCR . N = 4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 016 The adaptor molecule MyD88 is commonly engaged by TLRs 2 , 4 and 9 and links these receptors to the downstream activation of MAPKs and NF-κB , and ultimately to the induction of cytokine gene expression . Thus , MyD88 emerged as potential common component of inflammatory signaling pathways targeted by LXR and Abca1 . Flow cytometry revealed that TLR4 expression in plasma membrane was not affected by LXR activation ( Figure 11A ) . We therefore tested whether the recruitment of key adaptor molecules to lipid rafts was altered by LXR activation . We performed cell fractionation studies to assess the ability of MyD88 and TRAF6 to associate with lipid rafts in response to TLR4 activation by LPS . We confirmed the ability of our antibodies to detect MyD88 and TRAF6 using MyD88-deficient and siRNA to TRAF6 ( Figure 11—figure supplement 1 ) . LPS-dependent recruitment of MyD88 and TRAF6 in Flotillin-1-enriched detergent-resistant membrane microdomains was markedly reduced by treatment of iBMDM with GW3965 ( Figure 11B ) . Furthermore , the ability of LXR agonist to inhibit recruitment of MyD88 and TRAF6 was lost in Abca1-deficient macrophages . These observations suggest that a reduction in raft cholesterol content in response to LXR activation and Abca1 expression leads to alterations in membrane dynamics and/or raft structure and to the disruption of functional TLR4 complexes . Therefore , downstream MAPK and NF-κB signaling and inflammatory gene expression are repressed . 10 . 7554/eLife . 08009 . 017Figure 11 . LXR activation inhibits recruitment of MyD88 and TRAF6 to lipid rafts . ( A ) Wild-type iBMDM were pretreated with GW3965 ( 1 µM ) overnight and TLR4 expression was analyzed by flow cytometry . ( B ) Wild-type or Abca1−/− iBMDM were pretreated with GW3965 ( 1 µM ) overnight , followed by stimulation with LPS ( 10 ng/ml ) for 10 min . Lipid raft and non-raft fractions were isolated using detergent method and each fraction was analyzed by Western blotting . Results are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 01710 . 7554/eLife . 08009 . 018Figure 11—figure supplement 1 . Specificity of MyD88 and TRAF6 antibodies . ( A ) Immunoblot analysis of wild-type and MyD88−/− bone marrow macrophages . ( B ) Immunoblot analysis of wild-type bone marrow macrophages transduced with siRNA targeting TRAF6 or control siRNA . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 018 To test whether Abca1 was required for the ability of LXR agonists to repress inflammatory gene expression in vivo , we pretreated wild-type or myeloid-specific Abca1−/− mice with vehicle or GW3965 for 3 days and then challenged them with LPS . Analysis of gene expression in spleen and lung 2 hr after LPS treatment revealed that an array of inflammatory genes was induced by LPS as expected ( Figure 12 ) . This induction was substantially attenuated by LXR agonist treatment in wild-type mice , whereas little if any inflammatory repression was observed in mice lacking expression of Abca1 in macrophages ( Figure 12 ) . Taken together , these results suggest that the ability of LXRs to repress inflammation in macrophages derives primarily from their ability to regulate cellular cholesterol metabolism through Abca1 . They further reveal an unexpected connection between Abca1-dependent membrane cholesterol distribution and TLR4-mediated inflammatory signaling . 10 . 7554/eLife . 08009 . 019Figure 12 . Abca1 contributes to LXR anti-inflammatory effects in vivo . Myeloid-specific Abca1−/− and control wild-type mice were gavaged with GW3965 ( 40 mg/kg ) for 3 days , followed by challenge with LPS ( 1 mg/kg ) by intraperitoneal injection . 2 hr later , spleens ( upper ) and lungs ( lower ) were harvested , RNA isolated and gene expression analyzed by real-time PCR . N = 3–4 per group . *p < 0 . 05 , **p < 0 . 01 , NS , not significant . Error bars represent means ± SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 08009 . 019
LXRs reciprocally regulate lipid metabolism and inflammation . Both of these processes are central to the pathogenesis of metabolic diseases such as atherosclerosis and diabetes ( Castrillo et al . , 2003a , 2003b; Joseph et al . , 2004 ) . LXRs have been proposed to repress inflammatory gene expression in trans by tethering to NF-κB transcriptional complexes in a SUMOylation-dependent manner ( Ghisletti et al . , 2007; Lee et al . , 2009; Venteclef et al . , 2010 ) . This has heretofore been believed to be the sole mechanism underlying LXR's anti-inflammatory effects . The present study suggests that LXR-mediated transcriptional repression of endogenous inflammatory genes is principally dependent on LXR's capacity for transactivation . Our studies also identify the sterol transporter Abca1 as an important mediator of LXR's anti-inflammatory effects , both in vitro and in vivo . We find that Abca1 induction represses TLR-induced NF-κB and MAPK pathway activation by reducing the cholesterol content of detergent-resistant membrane domains . These findings reveal a previously unrecognized mechanism for LXR-mediated inflammatory repression in which inflammatory repression is secondary to activation-dependent changes in cellular lipid metabolism . Although we cannot exclude the possibility that trans-repression may operate in certain contexts , LXR agonist did not repress endogenous inflammatory gene expression in the absence of RXRα and RXRβ in our system , suggesting that LXR/RXR heterodimers were mediating repression . This led us to explore additional mechanisms for LXR inflammatory crosstalk . We found that an LXR L439A/E441A mutant that lacks co-activator recruitment capacity was unable to repress inflammation . These studies suggested that repression was mechanistically linked with transcriptional activation , and that repression might be a secondary phenomenon . We examined an array of endogenous gene expression in LXR-null cells reconstituted with various LXRs mutants and found that mutants that could not undergo SUMOylation at the previously identified lysines ( LXRα K328 and K434 and LXRβ K410 and K448 ) were still competent to repress inflammatory gene expression . Interestingly , the nuclear receptor SF-1 can be modified by SUMO in its hinge region and this modification has been shown to be functionally important for gene repression in vivo ( Campbell et al . , 2008; Lee et al . , 2011 ) . In contrast to the LXR transrepression model , however , the repressive effects of SUMOylated SF-1 are mediated by direct binding of SF-1 to response elements in its target promoters . Nevertheless , we considered the possibility that alternative SUMOylation sites in the hinge region of LXR ( LXRα K177/K178/K180 ) might be used for transrepression . However , mutation of the three hinge lysines to arginine did not alter LXR-dependent repression of endogenous inflammatory genes . Prior studies from our group suggested that LXR-mediated inflammatory repression was not mediated by the inhibition of nuclear translocation or DNA binding of NF-κB and AP-1 ( Castrillo et al . , 2003a , 2003b ) . These conclusions were drawn based on the analysis of electrophoretic mobility shift assays . In retrospect , these studies likely had technical limitations that prevented us from appreciating the effects of LXR on NF-κB signaling . Our prior studies did not define which NF-κB isoforms were bound to our DNA probe , and according to a recent comprehensive analysis of DNA binding by NF-κB proteins ( Siggers et al . , 2012 ) , the sequence we used is not predicted to have high affinity for the LPS-induced p65-p50 heterodimer ( Hoffmann and Baltimore , 2006 ) . Our subsequent analysis of IκB phosphorylation , p65 nuclear translocation , and p65 recruitment to gene promoters has revealed previously unrecognized impairments in NF-κB and MAPK signaling in the setting of LXR activation . The observation that gene activation was required for LXR-mediated inflammatory repression led us to identify the cholesterol transport protein Abca1 as a key mediator of the phenomenon . Accumulating evidence suggests that changes in membrane lipid content can affect inflammatory responses ( Westerterp et al . , 2013; Rong et al . , 2015 ) . Our membrane dynamics and biochemical studies indicate that LXR-dependent Abca1 induction reduces raft cholesterol content and alters membrane fluidity , suggesting a biophysical basis for the ability of LXR to inhibit TLR signaling . Previous studies have reported that Abca1-deficient macrophages contain more cholesterol and TLR4 protein in plasma membrane lipid rafts and that this results in enhanced inflammatory signaling upon LPS treatment ( Koseki et al . , 2007; Zhu et al . , 2008 , 2010 ) . Our data are consistent with these results and provide an additional mechanism whereby changes in Abca1 expression can affect TLR signaling . We found that Abca1 expression is important for the ability of LXR to inhibit signaling from TLRs 2 , 4 and 9–all of which use MyD88 as a key adaptor . We showed that LXR activation inhibits recruitment of the adaptor proteins MyD88 and TRAF6 to rafts and thereby blocks TLR-induced activation of MAP kinases and NF-κB . LXR activation leads to the induction of an entire cascade of genes involved in cellular lipid homeostasis ( Calkin and Tontonoz , 2012; Hong and Tontonoz , 2014 ) . Although Abca1 is clearly a major contributor , our data do not exclude the involvement of other factors . In our view , it is likely that additional LXR target genes involved in lipid handling may also contribute to effects on inflammation . One attractive additional player is the phospholipid-remodeling enzyme Lpcat3 , which we have recently shown affects inflammatory signaling in hepatocytes in the setting of obesity ( Rong et al . , 2013 ) . In conclusion , these studies outline a mechanistic connection between regulated plasma membrane cholesterol distribution and TLR-mediated inflammatory signaling . They further support a unified view of the role of LXR in transcriptional regulation in which direct activation underlies the dual biological functions of these nuclear receptors in metabolism and inflammation . | Inflammation is a normal part of the immune response to infection or tissue damage . However , increased inflammation has been linked to diseases such as obesity , diabetes and atherosclerosis ( in which the walls of the arteries become hardened ) . These same diseases have also been linked to problems with the production or breakdown of fatty molecules , such as cholesterol . Transcription factors are proteins that bind to DNA to control gene expression . A transcription factor called LXR regulates the production and breakdown of cholesterol in response to changing levels of cholesterol in the body . LXR has also been shown to inhibit inflammatory responses , but previous studies suggested that these two actions of LXR are independent of each other . Ito et al . have now challenged these findings by showing that LXR inhibits inflammation via changes in the metabolism of cholesterol and other fatty molecules . The experiments used genetically engineered immune cells , called macrophages , and mice to show that activating LXR causes cholesterol molecules to move between the membranes in a cell . This in turn leads to changes in the signals sent by proteins found at the cell surface , and eventually to a reduction of inflammation responses . Future work will focus on better understanding the link between LXR's effects on metabolism and inflammation in models of human diseases such as diabetes and atherosclerosis . | [
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TMEM16A and TMEM16B are calcium-activated chloride channels ( CaCCs ) with important functions in mammalian physiology . Whether distant relatives of the vertebrate TMEM16 families also form CaCCs is an intriguing open question . Here we report that a TMEM16 family member from Drosophila melanogaster , Subdued ( CG16718 ) , is a CaCC . Amino acid substitutions of Subdued alter the ion selectivity and kinetic properties of the CaCC channels heterologously expressed in HEK 293T cells . This Drosophila channel displays characteristics of classic CaCCs , thereby providing evidence for evolutionarily conserved biophysical properties in the TMEM16 family . Additionally , we show that knockout flies lacking subdued gene activity more readily succumb to death caused by ingesting the pathogenic bacteria Serratia marcescens , suggesting that subdued has novel functions in Drosophila host defense .
TMEM16A ( Caputo et al . , 2008; Schroeder et al . , 2008; Yang et al . , 2008 ) and , a different family member , TMEM16B ( Pifferi et al . , 2009 ) encode the classic calcium-activated chloride channels ( CaCCs ) in various mammalian tissues . Previously observed in a variety of organisms from green algae ( Fromm and Lautner , 2007; Shiina and Tazawa , 1987 ) to Xenopus ( Miledi and Parker , 1984; Kline , 1988 ) , these channels are activated by an increase in cytosolic calcium with outward rectification at low calcium levels , and they preferentially permeate larger anions ( Large and Wang , 1996; Qu and Hartzell , 2000 ) . In mammals , TMEM16A regulates fluid secretion in submandibular glands , ( Yang et al . , 2008; Romanenko et al . , 2010 ) as well as on the epithelia of airway surfaces ( Rock et al . , 2009 ) . This channel also modulates arterial ( Manoury et al . , 2010; Bulley et al . , 2012 ) , tracheal ( Huang et al . , 2012a ) , and gastrointestinal smooth muscle tone ( Hwang et al . , 2009 ) , and has been observed to play a role in noxious heat sensing in the peripheral nervous system ( Cho et al . , 2012 ) . TMEM16B is expressed in photoreceptor terminals ( Stohr et al . , 2009 ) , where CaCCs are hypothesized to stabilize presynaptic membrane potential ( Lalonde et al . , 2008 ) . This channel also gives rise to the majority of recorded CaCC current in hippocampal pyramidal neurons ( Huang et al . , 2012b ) and olfactory sensory neurons ( Billig et al . , 2011 ) . Besides TMEM16A and B , only one other mammalian family member , TMEM16F , has been biophysically characterized in vitro and in vivo ( Yang et al . , 2012 ) , which begs the question of whether or not the rest of the mammalian TMEM16 family encodes CaCCs ( like TMEM16A and B ) or small-conductance calcium-activated non-selective cation ( SCAN ) channels ( like TMEM16F ) . Outside of mammals , even less is known about the TMEM16 family . Ubiquitous in eukaryotes , TMEM16 family members regulate a bewildering variety of physiological functions . Ist2p , the single ortholog of the TMEM16 family in Saccharomyces cerevisiae , has been shown to function in endoplasmic reticulum–plasma membrane tethering ( Wolf et al . , 2012 ) . A member of the TMEM16 family in Drosophila , Axs , is found on the meiotic spindle and regulates meiotic chromosomal segregation ( Kramer and Hawley , 2003 ) . Xenopus TMEM16A functions to block polyspermy in fertilized oocytes and is to date the only non-mammalian TMEM16 member described as a CaCC ( Schroeder et al . , 2008 ) . We thus have a limited understanding of both the biophysical and functional aspects of the TMEM16 family and whether these properties are evolutionarily conserved . In an attempt to uncover TMEM16 family members with CaCC or SCAN channel activity , we cloned and heterologously expressed TMEM16 members from various genetically tractable organisms for electrophysiological inspection . A Drosophila melanogaster TMEM16 ortholog , CG16718 , was found to be a CaCC upon heterologous expression in HEK 293T cells . In addition , we observe that this channel plays a role in host defense in Drosophila , a function that has not been previously reported in the TMEM16 family . Given the physiological role of this newly identified CaCC , we chose to give CG16718 the name subdued .
Multiple sequence alignment with the mammalian TMEM16 family shows that Subdued is most similar to TMEM16A and B ( Figure 1A , B ) , sharing 32 . 8% and 31 . 7% identity with these channels , respectively . 10 . 7554/eLife . 00862 . 003Figure 1 . CG16718 ( Subdued ) aligns closely with mammalian CaCCs TMEM16A and B ( Mus musculus ) . ( A ) Multiple sequence alignment of protein sequences was done with ClustalW2 and phylogenetic tree construction was done in PHYLIP 3 . 67 ( Drawgram ) . ( B ) Putative transmembrane segments are highlighted with boxes , and mutated residues ( Figure 4 ) featured in this report are marked in color on the primary sequence alignment Y489 is shown in green and Q672 is shown in red . Bracketed values at the end of the alignment indicate the number of residues in the whole channel . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 003 Heterologous expression of Subdued would have been ideally done in the commonly used Drosophila-derived S2 cell line . However , this cell line was reported to robustly express bestrophins that give rise to endogenous calcium-activated chloride currents ( Chien et al . , 2006 ) , potentially confounding the analysis . Alternatively , Subdued was expressed in human embryonic kidney ( HEK ) 293T cells , a common heterologous expression system for electrophysiological studies of CaCCs ( Caputo et al . , 2008; Schroeder et al . , 2008; Yang et al . , 2008 ) . 48 hr post-transfection , HEK 293T cells expressing Subdued were recorded from using whole-cell patch clamp . Upon activation with 20–200 µM free intracellular calcium and symmetric NaCl solutions , the cells showed large time-dependent currents . In zero-calcium pipette solutions , no currents were observed in the transfected cells ( Figure 2A ) . For this study , all recordings were done with 200 µM calcium unless otherwise mentioned . Mock-transfected cells showed little to no CaCC ( data not shown ) . Voltage ramps showed that Subdued was outwardly rectifying , giving rise to more current at depolarized potentials ( Figure 2B ) . In these experiments with calcium infused from the whole-cell patch clamp pipette into the cytosol , rectification decreased over time as current amplitude increased . Run-up of current likely results from the process of calcium diffusing from the pipette solution into the cell , suggesting that rectification is also calcium-dependent , as has been shown for CaCCs ( Yang et al . , 2008 ) . 10 . 7554/eLife . 00862 . 004Figure 2 . Subdued is a calcium and voltage-dependent channel . ( A ) Subdued-transfected HEK 293T cells were used for recording in whole cell patch clamp experiments . No current was observed without calcium in the pipette ( left ) , but large time dependent currents were observed ( right ) when calcium was added to the pipette ( 200 µM free calcium ) . Unless otherwise mentioned , all recordings in this study were done with 200 µM free calcium in the pipette and symmetrical NaCl in the pipette and bath solution . ( B ) Representative traces showing outward rectification of Subdued current in voltage ramps from −100 to +100 mV . Traces 1–4 were taken sequentially and show an increase in current and decrease in rectification over time ( 20 µM free calcium in pipette ) . The ramps were done at a rate ( dV/dT ) of 0 . 067 V/s . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 004 Given that the mammalian TMEM16 family contains both anion and cation channels ( Yang et al . , 2012 ) , we wanted to determine the ionic selectivity of the channel . This was done by varying concentrations of NaCl externally while keeping the intracellular NaCl concentration constant at 140 mM . A series of positive reversal potentials ( Erev ) was obtained upon decreasing the external NaCl concentration , indicating anionic selectivity ( Figure 3A , B ) . Using the Goldman-Hodgkin-Katz equation ( Figure 3A inset ) , which describes experimentally obtained reversal potentials as a function of ion concentrations and their respective permeabilities ( Px , where X is any ion in the system ) , PNa/PCl was calculated to be 0 . 16 , indicating a small permeability for cations , as has been found for mammalian TMEM16A and B ( Pifferi et al . , 2009; Yang et al . , 2012 ) . Substitution of chloride with larger halide anions in the external solution revealed that Subdued preferentially permeates the larger anions SCN− , I− and Br− relative to Cl− ( Figure 3C ) . This hallmark feature of CaCCs implicates hydration energy as a factor in anionic selectivity , a feature shared with cystic fibrosis transmembrane conductance regulator ( CFTR ) channels ( Qu and Hartzell , 2000 ) . However , classic CaCC blockers niflumic acid ( NFA ) , flufenamic acid ( FFA ) and 5-nitro-2- ( 3-phenylpropylamino ) benzoic acid ( NPPB ) as well as a more recently developed TMEM16A inhibitor T16Ainh-A01 ( Namkung et al . , 2011 ) did not block the channel ( data not shown ) . Benzbromarone , a TMEM16A blocker identified from a high throughput screen ( Huang et al . , 2012a ) , blocked Subdued current significantly and reversibly ( Figure 3D ) . 10 . 7554/eLife . 00862 . 005Figure 3 . Subdued displays hallmark ionic selectivity of classic CaCCs and is blocked by a known CaCC inhibitor . ( A ) NaCl gradients were introduced across the membrane by varying NaCl concentrations of external solutions . The reversal potential ( Erev ) at each concentration was obtained and fitted to the Goldman-Hodgkin-Katz ( GHK ) equation from which the PNa/PCl was determined to be 0 . 16 . ( B ) Representative I/V plots obtained by varying external NaCl ( in mM ) . A diagram of the voltage protocol used to measure Erev is shown below . After a 750 ms activating pre-pulse to +100 mV , instantaneous tail currents were measured from test voltages −100 to +100 mV in 20 mV steps . After an initial estimate of Erev using this protocol , test potentials and voltage increments were refined , while pre-pulse conditions and the length of voltage steps remained constant . For each NaCl concentration , n = 4 to n = 9 . ( C ) Subdued preferentially permeates larger anions with the selectivity sequence: SCN > I > Br > Cl . Bi-ionic conditions were introduced by varying the external solution . Erev and permeability ratios were obtained by the same methodology as described for ( B ) . Representative I/V plots obtained by varying the anion in external solutions using the same voltage protocols as described in ( B ) are shown . ( D ) Subdued is significantly and reversibly blocked by 20 µM benzbromarone ( n = 4 , p<0 . 05 , Student’s t-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 005 To obtain evidence that Subdued is directly responsible for the observed currents , we introduced mutations onto the channel and observed that the mutant channels produced currents that had different properties from the wild-type channel . A Q672K mutation produced currents that had significantly slower activation kinetics as compared to wild type ( Figure 4A , B ) . Interestingly , the corresponding mutation on mammalian TMEM16F produced a highly similar effect on channel kinetics ( Yang et al . , 2012 ) . A Y489H mutation decreased selectivity for chloride as evident from the decreased shift in Erev upon introduction of a chloride gradient across the membrane ( Figure 4C ) . The position of both mutations relative to the first five putative transmembrane domains can be seen on Figure 1B , where Y489 is marked in green and Q672 in red . Transmembrane domains are boxed and bolded , and were predicted using the TOPCONS program ( Bernsel et al . , 2009 ) . 10 . 7554/eLife . 00862 . 006Figure 4 . Mutations of subdued change properties of observed currents . ( A ) Different kinetic properties in the wild-type ( WT ) and Q672K mutant channel revealed by a voltage step protocol ( 750 ms in 15 mV increments ) . ( B ) Semi-log plots of mean activation time constants ( τ ) as a function of voltage . τ was derived from the single exponential fitting of the current traces obtained from 750 ms ( WT , n = 6 ) and 5 s ( Q672K , n = 4 ) voltage steps . Time constants at 60 and 75 mV were significantly different for the two channels ( p<0 . 001 , Student’s t-test ) . ( C ) A Y489H mutation decreases chloride selectivity compared to the WT channel . A representative I/V plot showing the shift in Erev in a 20 mM external NaCl solution . WT Erev was determined to be ( 25 ± 4 ) mV , n=5; the Y489H mutant Erev was significantly different at ( 11 ± 2 ) mV , ( n = 4 , p<0 . 05 , Student’s t-test ) . Data were obtained using methodology described in Figure 2B . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 006 FlyAtlas data show that Subdued is expressed at moderate levels in a broad variety of tissues both in larvae and adults , making it difficult to predict a physiological function for this gene in Drosophila ( Chintapalli et al . , 2007 ) . From FlyBase-curated data , we noticed that a genome-wide screen for genetic determinants of gut immunity in Drosophila reported subdued as a susceptibility hit ( Cronin et al . , 2009 ) . In this study , ubiquitous RNAi of subdued caused increased lethality upon ingestion of Db11 , a particular strain of Serratia marcescens isolated from moribund flies ( Flyg et al . , 1980 ) . Serratia marcescens is a strain of Gram-negative bacteria that is a common cause of nosocomial infections , and the Db11 strain has been shown to be virulent in flies . Importantly , although Db11 kills Drosophila in less than 24 hr when introduced via septic injury , its virulence is attenuated when introduced via ingestion ( Nehme et al . , 2007 ) . Survival rates of different fly strains can thus be monitored over a span of a few days , allowing inspection of their relative host defense responses to Db11 . Wild-type flies and two knockout strains generated from independent crosses , KO2 and KO11 , were fed Db11 mixed with sucrose solution , and their survival monitored for 8 days , as in previous studies of fly immunity ( Cronin et al . , 2009 ) . Confirming the susceptibility phenotype previously reported , the knockout strains had significantly higher lethality upon being fed Db11 ( Figure 5A ) . Over the same timescale as the infection experiment , UV-killed Db11 did not bring about early lethality in any of the strains ( Figure 5—figure supplement 1 ) . We also determined that subdued mRNA was indeed expressed in the gut of wild-type flies but not the genetic knockout strains ( Figure 5—figure supplement 3 ) . 10 . 7554/eLife . 00862 . 007Figure 5 . Subdued plays a role in host defense in Drosophila melanogaster . ( A ) Subdued knockout flies display susceptibility to Serratia infection . Wild-type ( WT ) and knockout ( KO ) flies were fed on a Db11/sucrose solution and their survival monitored for 192 hr post-infection . Two independently generated KO strains , KO2 and KO11 , were used . WT flies lived significantly longer compared to KO2 and KO11 ( n = 4 , p<0 . 001 , two-way ANOVA ) . ( B ) KO flies accumulate higher titers of bacteria in the whole animal . 20 whole flies were homogenized 48 hr post-infection . Serially diluted homogenates were plated on agar and inspected for Db11 colony forming units ( CFU ) . Significantly more bacteria were recovered from the KO flies ( n = 7 , Student’s t-test , p<0 . 01 ) . ( C ) KO flies do not consume more food than WT flies . Four fly guts were dissected and homogenized from vials of 20 flies fed with Db11/sucrose solution containing 0 . 5% wt/vol erioglaucine disodium salt ( FDC Blue #1 ) 72 hr post-infection . The amount of food eaten by the flies was estimated by measuring absorbance of the dye . KO flies tended to consume significantly less food than WT flies ( n = 5 , p<0 . 01 , repeated measures one-way ANOVA and Tukey’s multiple comparison test ) . No significant difference was observed between the KO strains . ( D ) The homogenate obtained from experiments described in ( C ) was inspected for Db11 colony forming units ( CFU ) . Significantly higher amounts of bacteria were recovered from KO compared to WT fly guts ( n = 6 , p<0 . 05 , repeated measures one-way ANOVA and Tukey’s multiple comparison test ) . No significant difference was observed between KO strains . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 00710 . 7554/eLife . 00862 . 008Figure 5—figure supplement 1 . Knockout ( KO ) flies do not display significant lethality relative to wild-type ( WT ) flies upon ingestion of UV-killed Db11 . Db11/sucrose solution was irradiated with UV for 15 min prior to administration to the flies . While non-irradiated Db11 caused high mortality in KO strains 96 hr post-infection , UV-irradiated Db11 did not bring about noticeable lethality in all three strains at this time point . Increased lethality at later time points could arise from general reduced fitness of the KO strains . UV-irradiated bacterial feeding was done in a separate Db11-free incubator ( n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 00810 . 7554/eLife . 00862 . 009Figure 5—figure supplement 2 . Feeding flies gentamicin greatly reduces Db11 counts from gut dissections . Nehme et al . observed that after 24 hr of Db11 feeding , a significant amount of bacteria had entered the hemocoel and adhered to dissected guts in hemocyte-impaired flies . To explore the possibility that KO flies accumulated bacteria in the hemocoel which then adhered to the guts , flies were fed with Db11/sucrose solution for 24 hr and then switched to vials containing 500 µg/ml gentamicin with Db11/sucrose solution . After another 48 hr , four fly guts were dissected and the bacteria recovered from the guts were plated and counted . The low Db11 CFU counts from flies fed with gentamicin show that hemocoel-resident Db11 that adhere to the gut do not contribute greatly to the population of bacteria recovered from gut dissections . The data also suggest that it is unlikely that KO flies have severe impairment of hemocyte and hemocoel defenses . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 00910 . 7554/eLife . 00862 . 010Figure 5—figure supplement 3 . Drosophila guts express the subdued gene . RNA extraction was performed on four whole dissected guts and subdued expression was determined by performing reverse transcription and quantitative PCR ( A , n = 3 , p<0 . 001 ) , using KO guts as a control . For further verification , a different primer set was used to perform regular PCR using the same cDNA as a template ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00862 . 010 We hypothesized that since the knockout flies did not display prominent structural abnormalities in the alimentary canal , it was possible that the susceptibility to Db11 infection arose from defects in host defense . At 48 hr post-infection , whole flies were homogenized , and the homogenates were serially diluted and plated on LB agar plates with antibiotic selection . Colony forming units ( CFU ) were counted on each plate to estimate the number of Db11 bacteria present in the whole fly . Since only live flies were homogenized , this assay reports on the active host response the flies mount against Db11 infection . Significantly higher amounts of Db11 were isolated from knockout flies compared to wild type ( Figure 5B ) . To control for the possibility that the higher titers of bacteria isolated from knockout flies could arise from increased feeding , we performed a feeding assay in which a food dye was introduced into the bacteria/sucrose solution ( Ha et al . , 2009 ) . After 72 hr of feeding , flies were dissected to isolate the intact guts and crop , which were homogenized and analyzed for food dye content as a read-out for food consumption . Knockout fly guts did not contain more food dye and surprisingly , slightly but significantly less food dye was recovered from the guts of knockout compared to the wild-type flies ( Figure 5C ) . Thus , we surmised that the knockout flies are unlikely to consume more food than wild-type flies . Differences in whole animal bacterial titers are thus likely to result from disparities in host defense . To see if bacteria also accumulated more in the guts as well as the whole animal , homogenates obtained from the feeding assay described above were serially diluted and plated on LB agar plates with antibiotic selection . Knockout fly guts had a significantly higher number of CFU of Db11 than wild-type flies ( Figure 5D ) . Since it was reported that injection of latex beads into the hemocoel of Db11-fed flies to impair hemocytic phagocytosis causes significant Db11 proliferation in the hemocoel and adherence of Db11 to the gut wall ( Nehme et al . , 2007 ) , defective hemocoel defenses could also give rise to an apparent increase in the CFU counts from gut dissections . However , homogenate Db11 counts were greatly reduced upon feeding the flies with gentamicin ( Figure 5—figure supplement 2 ) . This control reveals no significant contributions of hemocoel-resident Db11 to CFU counts from dissected guts . This result also rules out severe hemocoel defense impairment as an explanation for increased in vivo proliferation of Db11 .
In this study , we report that Subdued ( CG16718 ) , an ortholog of the TMEM16 family in Drosophila melanogaster , is a calcium-activated chloride channel with biophysical properties resembling those of classic CaCCs . This channel is activated by internal calcium , with a lower bound of [Ca2+]in = 20 µM in whole cell patch clamp experiments in which current was observed . No significant currents were observed when an EGTA-buffered zero calcium solution was used as the internal solution . Relative to mammalian TMEM16A ( Yang et al . , 2008 ) , TMEM16B ( Pifferi et al . , 2009 ) or Xenopus TMEM16A ( H Yang , personal communication , March 2013 ) , Subdued is one to two orders of magnitude less calcium sensitive in whole cell patch clamp experiments . This could either reflect a true biophysical property of the channel or could be an indication that the non-native HEK 293T expression system used in our experiments lacks auxiliary subunits required for higher calcium sensitivity . It would be interesting to test if directed mutagenesis of Subdued can tune its calcium sensitivity within the realm of its mammalian and Xenopus counterparts . Subdued rectifies outwardly , passing larger currents at more positive voltages . The channel permeates mainly chloride with a PNa/PCl of 0 . 16 , and preferentially permeates larger anions relative to smaller ones , giving the selectivity series SCN− > I− > Br− > Cl− . A Y489H mutation affected the ionic selectivity of the channel , making it more permeable to Na+ . This suggests that perhaps the Y489 residue is pore lining , or has an allosteric effect on the structure of the pore . The Y489H mutant also gave rise to smaller currents compared to the wild-type channel , a reflection of either decreased unitary channel conductance or decreased membrane expression . Additionally , a Q672K mutation produced a dramatic slowing of activation kinetics , a phenomenon also observed when mutating the corresponding residue in mammalian TMEM16F ( Yang et al . , 2012 ) . The observation that mutations to Subdued alter the properties of the currents strongly points to this protein as a pore-forming subunit of the recorded CaCCs . Pharmacologically , Subdued is not blocked by the CaCC blockers NFA , FFA , NPPB or T16Ainh-A01 . This might arise from structural differences in Subdued , perhaps in the pore , relative to its mammalian and Xenopus counterparts . However benzbromarone blocks Subdued significantly and could potentially be used to interrogate the location and properties of the channel pore . In conclusion , as a distantly related TMEM16 family member , Subdued will be useful as a tool in structure/function studies to parse out conserved or divergent biophysical properties such as calcium- and voltage-dependent gating , permeation and ion selectivity . To study the function of the channel in Drosophila , we generated subdued knockout strains . Confirming results from a previous genome-wide RNAi study , the knockout was found to be more susceptible to gut infection by a strain of Serratia marcescens , Db11 ( Cronin et al . , 2009 ) . An earlier study proposed that the cause of lethality is bacterial proliferation leading to invasion of the gut tissue and subsequent gut distension and escape of bacteria into the hemolymph ( Nehme et al . , 2007 ) . In the case of the subdued knockout , susceptibility arises , at least in part , from deficient host defense , since in vivo proliferation of Db11 was higher in knockout fly guts as well as in the whole animal as compared to wild-type flies . Additionally , we observed that slightly but significantly less food dye was recovered from the guts of the knockout flies . This might arise from lower food consumption by knockout flies , but could also be an indication of increased gut tissue damage due to greater numbers of Db11 in the gut , leading to leakage and diffusion of food dye into the hemolymph . It remains to be determined if higher Db11 titers in the whole animal also result from defective immune responses within the hemolymph . Tissue-specific RNAi of subdued using gut or hemocytes drivers did not recapitulate the whole animal RNAi phenotype ( Cronin et al . , 2009 ) , suggesting that Subdued is likely to exert its protective function in a multitude of tissues . One potential function for Subdued is in the regulation of the secretion of cationic antimicrobial peptides ( AMPs ) , a process that occurs widely on epithelial surfaces and is known to play critical roles in host defense ( Lemaitre and Hoffmann , 2007 ) . This hypothesis is consistent with the abundant mRNA expression of subdued in various epithelial tissues ( Chintapalli et al . , 2007 ) . The susceptibility observed in the subdued knockout flies could also be a consequence of a deficiency in dual oxidase ( DUOX ) –mediated immunity . The DUOX system is reported to be critical in generating reactive oxygen species ( ROS ) in Drosophila gut epithelia ( Ha et al . , 2005 ) . This study reported that strong antimicrobial ROS species are generated by the peroxidase homology domain ( PHD ) of Drosophila DUOX in a chloride-dependent manner . These ROS species are likely to be the highly reactive hypohalites OCl or OSCN , the in vivo production of which requires trans-epithelial anion transport . Additionally , the Drosophila DUOX system has also been shown to mobilize downstream of the Gαq-coupled signaling pathway ( Ha et al . , 2009 ) , implicating other calcium-dependent responses in the Drosophila immune response . Following Db11 infection of subdued knockouts , it is possible that Gαq receptor stimulation fails to elicit sufficient amounts of halide transport onto gut epithelia due to a deficiency in CaCCs , reducing PHD-mediated generation of antimicrobial hypohalites and leading to increased bacterial proliferation and higher lethality . There remains the possibility that subtle structural deficits also contribute to the susceptibility of subdued knockouts to Db11 infection . Developmental defects in gut epithelial integrity ( Bonnay et al . , 2013 ) or the peritrophic matrix lining the gut ( Kuraishi et al . , 2011 ) might result in the susceptibility phenotype . The subdued knockout flies did not have significant defects in gut epithelial polarity and integrity under basal conditions as assessed by immunostaining for Armadillo and Discs Large ( Dlg ) to observe adherens and septate junction structure ( Tepass et al . , 2001; Hortsch and Margolis , 2003 ) ( data not shown ) . However , Subdued might function in gut epithelial or peritrophic matrix integrity only upon Db11 challenge to the gut , a possibility that will be explored in future study .
CG16718 was subcloned from BDGP Drosophila Gene Collection cDNA clone LD10322 , which yields the ORF of the RA splice variant of CG16718 . Fresh HEK 293T cells ( ATCC , Manassas , VA , USA ) were transfected with CG16718-eGFPN1 ( CG16718 tagged at the C-terminus with EGFP ) for 24 hr ( Fugene , Madison , WI , USA ) and recovered in fresh media for another 24 hr . Patch pipets ( World Precision Instruments , Sarasota , FL , USA ) were pulled from a Sutter P-97 puller and re-polished . Pipettes had resistances of 3–5 MΩ for whole cell patch clamp experiments . The bath was grounded via a 3 M KCl agar bridge connected to an Ag-AgCl reference electrode . Data were acquired using a Multiclamp 700B amplifier controlled by Clampex 10 . 2 via Digidata 1440A ( Axon Instruments , Sunnyvale , CA , USA ) . The standard internal solution contained ( in mM ) 130 NaCl , 10 HEPES , 5 . 6 CaCl2 , 5 EGTA , 5 MgATP , 1 Na2GTP , 10 phosphocreatine , pH 7 . 2 . The standard external solution was 140 NaCl , 10 EGTA , 2 MgCl2 and 10 HEPES , pH 7 . 2 . The free calcium concentration was calculated to be 200 µM with WEBMAXC software ( http://www . stanford . edu/∼cpatton/webmaxcS . htm ) and was confirmed with a calcium electrode ( Orion 4-Star , Thermo Scientific , Waltham , MA , USA ) . For Figure 2B , the internal solution contained ( in mM ) 130 NaCl , 10 HEDTA , 10 HEPES , 7 . 55 CaCl2 , pH 7 . 2 , with a free calcium concentration of 20 µM . External solutions contained various concentrations ( in mM ) of 140 NaCl ( the default symmetric NaCl condition ) or 140 NaX , 10 EGTA , 2 MgCl2 and 10 HEPES , pH 7 . 2 . Sucrose was added to balance osmolarity for the low NaCl solutions . NFA , FFA , NPPB , T16Ainh-A01 and benzbromarone were obtained from Sigma ( St . Louis , MO , USA ) . All experiments were performed at room temperature ( 22–24°C ) , and data were analyzed in pCLAMP 10 . 0 and GraphPad Prism 5 . The CG16718 deficiency line was generated by following closely the heat-shock driven FLP-recombinase methodology previously reported ( Parks et al . , 2004 ) . The fly strains used were PBac{RB}CG16718e02779 and P{XP}CG16718d03361 ( Harvard Medical School ) . Flies were reared on standard cornmeal-agar with dead yeast . Infection assays were performed as described in Cronin et al . ( 2009 ) , with some modifications . Batches of 20 adult flies were used for each line assayed . The food solution containing Db11 was prepared from culture grown exponentially at 37°C in LB ( Luria Bertani ) medium supplemented with 60 µg/ml ampicillin . This culture was diluted with a freshly prepared sterile-filtered 0 . 05 M sucrose solution to a final OD ( 600 nm ) = 0 . 1 . Absorbent filters ( 37 mm; Millipore , Billerica , MA , USA ) were thoroughly soaked with the bacteria/sucrose solution and one filter was placed into each vial . 20 flies that were 2 days old were then transferred to each vial , which was then placed at 29°C to start the infection assay . Flies were transferred to new vials with freshly prepared bacteria/sucrose solution every 4 days . w1118 flies were used as a control . Data were analyzed with GraphPad Prism 5 . Vials of 20 flies ( 2–4 days old ) were fed with bacteria/sucrose solution as described above . After 48 hr the flies were cold-anesthetized , transferred to eppendorf tubes and homogenized in 500 µl of LB media . After a 4 s spin at 200×g , homogenates were serially diluted into LB , and the serial dilutions plated onto LB-ampicillin plates ( 60 µg/ml ) . The plates were incubated at 37°C for 18–24 hr , and the plates with a colony count between 100–500 were chosen for inspection . For gut-specific CFU assays , homogenates from the feeding assay were plated instead . Data were analyzed with GraphPad Prism 5 . For the feeding assay , 0 . 5% wt/vol of erioglaucine disodium salt ( Sigma , St Louis , MO , USA ) was added to the sucrose solution to be used in the infection assay and the final solution used to dilute Db11 bacteria as described above . After 72 hr of exposure to the bacteria , the flies were cold anesthetized and four female flies were dissected , and the whole gut including the crop was isolated from each fly . The tissue was homogenized in 50 µl of PBS and spun at 8000×g for 10 min . Absorbance of the supernatant at 625 nm was measured using the UV-Vis module of Nanodrop 1000 ( Thermo Scientific , Waltham , MA , USA ) . Data were analyzed with GraphPad Prism 5 . The SuperScript III First Strand Synthesis System ( Invitrogen , Carlsbad , CA , USA ) was used for RT-PCR . Random hexamers were used to prime cDNA synthesis . The following primers were used for qPCR ( Figure 5—figure supplement 3 ) : subdued—forward: 5′-GCGAATCAATGACTTTGAAC-3’ , reverse: 5′-CCCTGGTGATGATTTTGGTG-3′ β-tubulin—forward: 5′-ATGAGGGAAATCGTTCACATCCAAG-3′ , reverse: 5′-CCCGCGCTGTCCTTGTCGAT-3′ The program used for qPCR is as follows: incubation at 50°C for 2 min and 95°C for 10 min; followed by 40 cycles of denaturation at 95°C for 15 s , annealing and extension at 60°C for 1 min . Dissociation curves of each sample were measured to confirm the specificity of the PCR products . The following primers were used for regular PCR amplification of cDNA: subdued—forward: 5′-GATCGATTGACCACGGACATTCCTGG-3′ , reverse: 5′-CAACGCCCCCACGCTCCAACTGCC-3′ | Ions are at the root of most processes that occur in the body , so they must be able to move in and out of cells . However , because they have an electric charge , ions are not usually able to pass through the fatty membrane that encloses all cells . Instead , they must be imported or exported by a variety of dedicated proteins in the cell membrane . These include ion channels – proteins that , under certain conditions , open to form pores – and ion transporters . Calcium-activated chloride channels are ion channels that permeate chloride ions when the concentration of calcium ions inside the cell increases . Two important calcium-activated chloride channels in mammals belong to the TMEM16 family of proteins , which is conserved in many organisms . However , to date all the examples of TMEM16 proteins forming calcium-activated chloride channels have been found in vertebrates . Moreover , it is not known how many members of the TMEM16 family can act as ion channels . Wong et al . have now isolated a protein belonging to the TMEM16 family from fruit flies and , in a series of experiments on human cells , showed that it acts as a calcium-activated chloride channel . Previous work has shown that fruit flies lacking this protein , which is called Subdued , are more susceptible than wild-type flies to a pathogenic bacterium called Serratia marrescens , which implies that the Subdued ion channel might be involved in the immune system . Indeed , Wong et al . found that the mutant flies died more often than wild-type flies after eating these bacteria; the mutant files also had higher levels of the bacteria in their digestive tracts . These results will be of interest to researchers trying to understand how TMEM16 ion channels evolved to be involved in processes as diverse as vision , the secretion of bodily fluids and the immune system . | [
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] | 2013 | Subdued, a TMEM16 family Ca2+-activated Cl− channel in Drosophila melanogaster with an unexpected role in host defense |
Neurons in higher cortical areas , such as the prefrontal cortex , are often tuned to a variety of sensory and motor variables , and are therefore said to display mixed selectivity . This complexity of single neuron responses can obscure what information these areas represent and how it is represented . Here we demonstrate the advantages of a new dimensionality reduction technique , demixed principal component analysis ( dPCA ) , that decomposes population activity into a few components . In addition to systematically capturing the majority of the variance of the data , dPCA also exposes the dependence of the neural representation on task parameters such as stimuli , decisions , or rewards . To illustrate our method we reanalyze population data from four datasets comprising different species , different cortical areas and different experimental tasks . In each case , dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure .
In many state of the art experiments , a subject , such as a rat or a monkey , performs a behavioral task while the activity of tens to hundreds of neurons in the animal’s brain is monitored using electrophysiological or imaging techniques . The common goal of these studies is to relate the external task parameters , such as stimuli , rewards , or the animal’s actions , to the internal neural activity , and to then draw conclusions about brain function . This approach has typically relied on the analysis of single neuron recordings . However , as soon as hundreds of neurons are taken into account , the complexity of the recorded data poses a fundamental challenge in itself . This problem has been particularly severe in higher-order areas such as the prefrontal cortex , where neural responses display a baffling heterogeneity , even if animals are carrying out rather simple tasks ( Brody et al . , 2003; Machens et al . , 2010; Hernández et al . , 2010; Mante et al . , 2013; Rigotti et al . , 2013 ) . Traditionally , this heterogeneity has often been neglected . In neurophysiological studies , it is common practice to pre-select cells based on particular criteria , such as responsiveness to the same stimulus , and to then average the firing rates of the pre-selected cells . This practice eliminates much of the richness of single-cell activities , similar to imaging techniques with low spatial resolution , such as MEG , EEG , or fMRI . While population averages can identify some of the information that higher-order areas process , they ignore much of the fine structure of the single cell responses ( Wohrer et al . , 2013 ) . Indeed , most neurons in higher cortical areas will typically encode several task parameters simultaneously , and therefore display what has been termed mixed selectivity ( Rigotti et al . , 2013; Pagan and Rust , 2014; Park et al . , 2014; Raposo et al . , 2014 ) . Instead of looking at single neurons and selecting from or averaging over a population of neurons , neural population recordings can be analyzed using dimensionality reduction methods ( for a review , see Cunningham and Yu , 2014 ) . In recent years , several such methods have been developed that are specifically targeted to electrophysiological data , working on the level of single spikes ( Pfau et al . , 2013 ) , accommodating different time scales of latent variables ( Yu et al . , 2009 ) , or accounting for the dynamics of the population response ( Buesing et al . , 2012a; 2012b; Churchland et al . , 2012 ) . However , these approaches reduce the dimensionality of the data without taking task parameters , i . e . , sensory and motor variables controlled or monitored by the experimenter , into account . Consequently , mixed selectivity remains in the data even after the dimensionality reduction step . The problem can be addressed by dimensionality reduction methods that are informed by the task parameters ( Machens et al . , 2010; Machens , 2010; Brendel et al . , 2011; Mante et al . , 2013; Raposo et al . , 2014 ) . We have previously introduced a dimensionality reduction technique , demixed principal component analysis ( dPCA ) ( Brendel et al . , 2011 ) , that emphasizes two goals . It aims to find a decomposition of the data into latent components that ( a ) are easily interpretable with respect to the experimentally controlled and monitored task parameters; and ( b ) preserve the original data as much as possible , ensuring that no valuable information is thrown away . Here we present a radically modified version of this method , and illustrate that it works well on a wide variety of experimental data . The new version of the method has the same objectives as the older version ( Brendel et al . , 2011 ) , but is more principled , more flexible , and has an analytical solution , meaning that it does not suffer from any numerical optimization problems . Furthermore , the new mathematical formulation highlights similarities to and differences from related well-known methods such as principal component analysis ( PCA ) and linear discriminant analysis ( LDA ) . The dPCA code is available at http://github . com/machenslab/dPCA for Matlab and Python .
We illustrate the classical approaches to analyzing neural activity data from higher-order areas in Figure 1 . To be specific , we consider recordings from the prefrontal cortex ( PFC ) of monkeys performing a somatosensory working memory task ( Romo et al . , 1999; Brody et al . , 2003 ) . In this task , monkeys were required to discriminate two vibratory stimuli presented to the fingertip . The stimuli F1 and F2 were separated by a 3 s delay , and the monkeys had to report which stimulus had a higher frequency by pressing one of the two available buttons ( Figure 1a ) . 10 . 7554/eLife . 10989 . 003Figure 1 . Existing approaches to population analysis , illustrated with recordings from monkey PFC during a somatosensory working memory task ( Romo et al . , 1999 ) . ( a ) Cartoon of the paradigm , adapted from Romo and Salinas ( 2003 ) . Legend shows 12 experimental conditions . ( b ) Average per-condition firing rates ( PSTHs ) for four exemplary neurons out of N=832 . Colors refer to stimulus frequencies F1 and line styles ( dashed/solid ) refer to decision , see legend in ( a ) . ( c ) Fraction of cells , significantly ( p<0 . 05 , two-way ANOVA ) tuned to stimulus and decision at each time point . ( d ) Left: Distribution of stimulus tuning effect sizes across neural population at F1 period ( black arrow in c ) . Significantly tuned neurons are shown in dark gray . Right: Same for decision at F2 period ( gray arrow in c ) . ( e ) The average of zero-centered PSTHs over all significantly tuned neurons ( for neurons with negative effect size , the sign of PSTHs was flipped ) . Arrows mark time-points that were used to select the significant cells . ( f ) Fraction of cells , significantly ( p<0 . 05 , linear regression ) tuned to stimulus and decision at each time point . ( g ) Distribution of regression coefficients of neural firing rates to stimulus ( during F1 period ) and decision ( during F2 period ) . ( h ) Stimulus and decision components produced by the method of Mante et al . ( 2013 ) . Briefly , neural PSTHs are weighted by the regression coefficients . ( i ) Fraction of variance captured by the first 20 principal components . ( j ) Distributions of weights used to produce the first six principal components ( weights are elements of the eigenvectors of the N×N covariance matrix ) . ( k ) First six principal components ( projections of the full data onto the eigenvector directions ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 003 When we focus on the neural representation of the stimulus F1 and the decision , we have to take 12 experimental conditions into account ( six possible values of F1 and two possible decisions ) . For each of these conditions , we can average each neuron’s spike trains over trials and then smooth the resulting time series in order to estimate the neuron’s time-dependent firing rate ( also known as peri-stimulus time histogram or PSTH ) . We find that the PSTHs of many neurons are tuned to the stimulus F1 , the decision , or both ( Figure 1b; so-called mixed selectivity ) , and different neurons generally show different tuning . Our goal is to characterize and summarize the tuning of all N recorded neurons . The most standard and widespread approach is to resort to a statistical test ( e . g . a two-way analysis of variance or ANOVA ) , in order to check whether the firing rate of a neuron depends significantly on the frequency F1 or on the monkey’s decision . Such a test can be run for each neuron and each time point , in which case the population tuning over time is often summarized as the fraction of cells significantly tuned to stimulus or decision at each time point ( p<0 . 05 , Figure 1c ) . In addition to providing such a 'summary statistics' , this approach is also used to directly visualize the population activity . For that purpose , one selects the subset of neurons significantly tuned to stimulus or decision ( e . g . by focusing on a particular time point , Figure 1d ) and then averages their PSTHs . The resulting 'population average' is shown in Figure 1e , where we also took the sign of the effect size into account . The population average is generally thought to demonstrate the 'most typical' firing pattern among the cells encoding the corresponding parameter . Importantly , this method yields one single population average or 'component' for each parameter . Each such component can be understood as a linear combination ( or a linear readout ) of the individual PSTHs , with all Ns significant neurons for a parameter having the same weights ± 1/Ns and all others having weight zero . In a related approach , the firing rates of each neuron at each time point are linearly regressed on stimulus and decision ( Figure 1f ) ( Brody et al . , 2003 ) . Mante et al . ( 2013 ) suggested to use the regression coefficients of all N neurons ( Figure 1g ) as weights to form linear combinations of PSTHs representing stimulus and decision tuning ( Figure 1h ) . This approach , which the authors call 'targeted dimensionality reduction' ( TDR ) , also yields one component per task parameter: in our example , we obtain one component for the stimulus and one for the decision ( Figure 1h; see Materials and methods for details ) . Both of these approaches are supervised , meaning that they are informed by the task parameters . At the same time , they do not seek to faithfully represent the whole dataset and are prone to losing some information about the neural activities . Indeed , the two components from Figure 1e explain only 23% of the total variance of the population firing rates and the two components from Figure 1h explain only 22% ( see Materials and methods ) . Consequently , a naive observer would not be able to infer from the components what the original neural activities looked like . While such supervised approaches can be extended in various ways to produce more components and capture more variance , a more direct way to avoid this loss of information is to resort to unsupervised methods such as principal component analysis ( PCA ) . This method extracts a set of principal components ( PCs ) that are linear combinations of the original PSTHs , just as the population averages above . However , the weights to form these linear combinations are chosen so as to maximize the amount of explained variance ( first six components explain 69% of variance , see Figure 1i–k ) . The principal components can be thought of as 'building blocks' of neural activity: PSTHs of actual neurons are given by linear combinations of PCs , with the first PCs being more informative than the later ones . However , since PCA is an unsupervised method , information about stimuli and decisions is not taken into account , and the resulting components can retain mixed selectivity and therefore fail to highlight neural tuning to the task parameters . The most striking observation when comparing supervised and unsupervised approaches is how different the results look . Indeed , PCA paints a much more complex picture of the population activity , dominated by strong temporal dynamics , with several stimulus- and decision-related components . At the same time , none of the methods can fully demix the stimulus and decision information: even the supervised methods show decision-related activity in the stimulus components and stimulus-related activity in the decision components ( Figure 1e , h ) . To address these problems , we developed a modified version of PCA that not only compresses the data , but also demixes the dependencies of the population activity on the task parameters . We will first explain that these two goals generally constitute a trade-off , then suggest a solution to this trade-off for a single task parameter , and then generalize to multiple task parameters . The trade-off between demixing and compression is illustrated in Figure 2 , where we compare linear discriminant analysis ( LDA , Figure 2a , b ) , PCA ( Figure 2c , d ) , and dPCA ( Figure 2e–h ) . We will first focus on a single task parameter and seek to reduce the activity of N=2 neurons responding to three different stimuli . For each stimulus , the joint activity of the two neurons traces out a trajectory in the space of firing rates as time progresses ( Figure 2b ) . The aim of 'demixing' in this simplified case is to find a linear mapping ( decoder ) of the neural activity that separates the different stimuli ( Figure 2a ) and ignores the time-dependency . We can use LDA in order to determine a projection of the data that optimally separates the three stimuli . However , LDA will generally not preserve the 'geometry' of the original neural activity: firing patterns for stimuli 1 and 2 are close to each other and far away from stimulus 3 , whereas in the LDA projection all three stimuli are equally spaced ( Figure 2b ) . More generally , decoding is always prone to distorting the data and therefore tends to impede a proper reconstruction of the original data from the reduced description . 10 . 7554/eLife . 10989 . 004Figure 2 . Linear dimensionality reduction . ( a ) Linear discriminant analysis maps the firing rates of individual neurons onto a latent component that allows us to decode a task parameter of interest . Shades of grey inside each neuron show the proportion of variance due to the various task parameters ( e . g . stimulus , decision , and time ) , illustrating mixed selectivity . In contrast , the LDA component is maximally demixed . ( b ) At any moment in time , the population firing rate of N neurons is represented by a point in the N-dimensional space; here N=2 . Each trial is represented by a trajectory in this space . Colors indicate different stimuli and dot sizes represent time . The LDA component for stimulus is given by the projection onto the LDA axis ( black line ) ; projections of all points are shown along this line . All three stimuli are clearly separated , but their geometrical relation to each other is lost . ( c ) Principal component analysis linearly maps the firing rates into a few principal components such that a second linear transformation can reconstruct the original firing rates . ( d ) The same set of points as in ( b ) is projected onto the first PCA axis . However , the stimuli are no longer separated . Rather , the points along the PCA axis have complex dependencies on stimulus and time ( mixed selectivity ) . The PCA axis minimizes the distances between the original points and their projections . ( e ) Demixed principal component analysis also compresses and decompresses the firing rates through two linear transformations . However , here the transformations are found by both minimizing the reconstruction error and enforcing a demixing constraint on the latent variables . ( f ) The same set of points as in ( b ) projected onto the first dPCA decoder axis . The three stimuli are clearly separated ( as in LDA ) , but some information about the relative distances between classes is preserved as well ( as in PCA ) . ( g ) The same data as in ( b ) linearly decomposed into the time effect , the stimulus effect , and the noise . ( h ) The dPCA projection from ( f ) has to be mapped onto a different axis , given by the dPCA encoder , in order to reconstruct the stimulus class means ( large colored circles ) . The decoder and encoder axes together minimize the reconstruction error between the original data and the stimulus class means . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 004 The aim of compression is to find a linear mapping ( decoder ) that reduces the dimensionality and preserves the original data as much as possible ( Figure 2c , d ) . Using PCA , we determine a projection of the data that minimizes the reconstruction error between the projections and the original points . In contrast to LDA , PCA seeks to preserve the geometry of the neural activity , and thereby yields the most faithful reduction of the data ( Figure 2d ) . However , the PCA projection does not properly separate the stimuli and mixes the time-dependency with the stimulus-dependency . The wildly different projection axes for LDA ( Figure 2b ) and PCA ( Figure 2d ) seem to suggest that the goals of demixing and compression are essentially incompatible in this example . However , we can achieve both goals by assuming that the reconstruction of the original data works along a separate encoder axis ( Figure 2f , h ) . Given this additional flexibility , we first choose a decoder axis that reconciles the decoding and compression objectives . Once projected onto this axis , all three stimuli are separated from each other , as in LDA , yet their geometrical arrangement is approximately preserved , as in PCA ( Figure 2f ) . In turn , when reconstructed along the encoder axis , the projected data still approximates the original data ( Figure 2h ) . To define these ideas more formally , we assume that we simultaneously recorded the spike trains of N neurons . Let 𝐗 be our data matrix with N rows , in which the i-th row contains the instantaneous firing rate ( i . e . binned or smoothed spike train ) of the i-th neuron for all task conditions and all trials ( assumed to be centered , i . e . , with row means subtracted ) . Classical PCA compresses the data with a decoder matrix 𝐃 . The resulting principal components can then be linearly de-compressed through an encoder matrix 𝐃⊤ , approximately reconstructing the original data ( Hastie et al . , 2009 ) . The optimal decoder matrix is found by minimizing the squared error between the original data , 𝐗 , and the reconstructed data , 𝐃⊤𝐃𝐗 , given byLPCA=∥X−D⊤DX∥2 . In the toy example of Figure 2 , the data matrix 𝐗 is of size 2×15 , and the decoder matrix 𝐃 is of size 1×2 . Crucially , the information about task parameters does not enter the loss function and hence PCA neither decodes nor demixes these parameters . In our method , which we call demixed PCA ( dPCA ) , we make two changes to this classical formulation . First , we require that the compression and decompression steps reconstruct not the neural activity directly , but the neural activity averaged over trials and over some of the task parameters . In the toy example , the reconstruction target is the matrix of stimulus averages , 𝐗s , which has the same size as 𝐗 , but in which every data point is replaced by the average neural activity for the corresponding stimulus , as shown in Figure 2h . Second , we gain additional flexibility in this quest by compressing the data with a linear mapping 𝐃 , yet decompressing it with another linear mapping 𝐅 ( Figure 2e ) . The respective matrices are chosen by minimizing the loss functionLdPCA=∥Xs−FDX∥2 . Accordingly , for each stimulus , the neural activities are projected close to the average stimulus , which allows us both to decode the stimulus value and to preserve the relative distances of the neural activities . In order to see how this approach preserves all aspects of the original data , and not just some averages , we note that the data in our toy example included both stimulus and time . The matrix 𝐗s can be understood as part of a linear decomposition of the full data 𝐗 into parameter-specific averages: a time-varying part , 𝐗t , that is obtained by averaging 𝐗 over stimuli , and a stimulus-varying part , 𝐗s , that is obtained by averaging 𝐗 over time . Any remaining parts of the activity are captured in a noise term ( Figure 2g ) . In turn , we can find separate decoder and encoder axes for each of these averages . Once more than N=2 neurons are considered , these decoder and encoder axes constitute a dimensionality reduction step that reduces the data into a few components , each of which properly decodes one of the task parameters . In turn , the original neural activity can be reconstructed through linear combinations of these components , just as in PCA . The key ideas of this toy example can be extended to any number of task parameters . In this manuscript , all datasets will have three parameters: time , stimulus , and decision , and we will decompose the neural activities into five parts: condition-independent , stimulus-dependent , decision-dependent , dependent on the stimulus-decision interaction , and noise ( see Figure 8 in the Materials and methods ) :X=Xt+Xst+Xdt+Xsdt+Xnoise=∑ϕXϕ+Xnoise . Individual terms are again given by a series of averages . This decomposition is fully analogous to the variance ( covariance ) decomposition done in ANOVA ( MANOVA ) . The only important difference is that the standard ( M ) ANOVA decomposition for three parameters A , B , and C , would normally have 23=8 terms corresponding to the main effects of A , B , C , pairwise interactions AB , BC , and AC , three-way interaction ABC , and the noise . Here we join some of these terms together , as we are not interested in demixing those ( see Materials and methods ) . Once this decomposition is performed , dPCA finds separate decoder and encoder matrices for each term ϕ by minimizing the loss functionLdPCA=∑ϕ∥𝐗ϕ-𝐅ϕ𝐃ϕ𝐗∥2 . Each term within the sum can be minimized separately by using reduced-rank regression , the solution of which can be obtained analytically in terms of singular value decompositions ( see Materials and methods ) . Each row 𝐝 of each 𝐃ϕ yields one demixed principal component 𝐝𝐗 and , similar to PCA , we order the components by the amount of explained variance . Note that the decoder/encoder axes corresponding to two different task parameters ϕ1 and ϕ2 are found independently from each other and may end up being non-orthogonal ( in contrast to PCA where principal axes are all orthogonal ) . In a nutshell , the loss function ensures that each set of decoder/encoder axes reconstructs the individual , parameter-specific terms , 𝐗ϕ , thereby yielding proper demixing , and the data decomposition ensures that the combination of all decoder/encoder pairs allows to reconstruct the original data , 𝐗 . There are a few other technical subtleties ( see Materials and methods for details ) . ( 1 ) We formulated dPCA for simultaneously recorded neural activities . However , all datasets analyzed in this manuscript have been recorded sequentially across many sessions , and so to apply dPCA we have to use 'pseudo-trials' . ( 2 ) Similar to any other decoding method , dPCA is prone to overfitting and so we introduce a regularization term and perform cross-validation to choose the regularization parameter . ( 3 ) The data and variance decompositions from above are exact only if the dataset is balanced , i . e . , if the same number of trials were recorded in each condition . If this is not the case , one can use a re-balancing procedure . ( 4 ) A previous version of dPCA ( Brendel et al . , 2011 ) used the same variance decomposition but a different and less flexible loss function . The differences are layed out in the Materials and methods section . We first applied dPCA to the dataset presented above ( Romo et al . , 1999; Brody et al . , 2003 ) , encompassing 832 neurons from two animals . As is typical for PFC , each neuron has a distinct response pattern and many neurons show mixed selectivity ( some examples are shown in Figure 1b ) . Several previous studies have sought to make sense of these heterogeneous response patterns by separately analyzing different task periods , such as the stimulation and delay periods ( Romo et al . , 1999; Brody et al . , 2003; Machens et al . , 2010; Barak et al . , 2010 ) , the decision period ( Jun et al . , 2010 ) , or both ( Hernández et al . , 2010 ) . With dPCA , however , we can summarize the main features of the neural activity across the whole trial in a single figure ( Figure 3 ) . 10 . 7554/eLife . 10989 . 005Figure 3 . Demixed PCA applied to recordings from monkey PFC during a somatosensory working memory task ( Romo et al . , 1999 ) . ( a ) Cartoon of the paradigm , adapted from Romo and Salinas ( 2003 ) . ( b ) Demixed principal components . Top row: first three condition-independent components; second row: first three stimulus components; third row: first three decision components; last row: first stimulus/decision interaction component . In each subplot , the full data are projected onto the respective dPCA decoder axis , so that there are 12 lines corresponding to 12 conditions ( see legend ) . Thick black lines show time intervals during which the respective task parameters can be reliably extracted from single-trial activity ( using pseudotrials with all recorded neurons ) , see Materials and methods . Note that the vertical scale differs across rows . Ordinal number of each component is shown in a circle; explained variances are shown as percentages . ( c ) Cumulative variance explained by PCA ( black ) and dPCA ( red ) . Demixed PCA explains almost the same amount of variance as standard PCA . Dashed line shows an estimate of the fraction of 'signal variance' in the data , the remaining variance is due to noise in the PSTH estimates ( see Materials and methods ) . ( d ) Variance of the individual demixed principal components . Each bar shows the proportion of total variance , and is composed out of four stacked bars of different color: gray for condition-independent variance , blue for stimulus variance , red for decision variance , and purple for variance due to stimulus-decision interactions . Each bar appears to be single-colored , which signifies nearly perfect demixing . Pie chart shows how the total signal variance is split among parameters . ( e ) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes . Stars mark the pairs that are significantly and robustly non-orthogonal ( see Materials and methods ) . Bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components . Most of the correlations are close to zero . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 005 Just as in PCA , we can think of the demixed principal components ( Figure 3b ) as the 'building blocks' of the observed neural activity , in that the activity of each single neuron is a linear combination ( weighted average ) of these components . These building blocks come in four distinct categories: some are condition-independent ( Figure 3b , top row ) ; some depend only on stimulus F1 ( second row ) ; some depend only on decision ( third row ) ; and some depend on stimulus and decision together ( bottom row ) . The components can be easily seen to demix the parameter dependencies , which is exactly what dPCA aimed for . Indeed , the components shown in Figure 3b are projections of the PSTHs of all neurons onto the most prominent decoding axes; each projection ( each subplot ) shows 12 lines corresponding to 12 conditions . As intended , condition-independent components have all 12 lines closely overlapping , stimulus components have two lines for each stimulus closely overlapping , etc . The overall variance explained by the dPCA components ( Figure 3c , red line ) is very close to the overall variance explained by the PCA components ( black line ) . Accordingly , we barely lost any variance by imposing the demixing constraint , and the population activity is accurately represented by the obtained dPCA components . The dPCA analysis captures the major findings previously obtained with these data: the persistence of the F1 tuning during the delay period ( component #5; Romo et al . , 1999; Machens et al . , 2005 ) , the temporal dynamics of short-term memory ( components ##5 , 10 , 13; Brody et al . , 2003; Machens et al . , 2010; Barak et al . , 2010 ) , the 'ramping' or 'climbing' activities in the delay period ( components ##1–3; Brody et al . , 2003; Machens et al . , 2010 ) ; and pronounced decision-related activities ( component #6 , Jun et al . , 2010 ) . We note that the decision components resemble derivatives of each other; these higher-order derivatives likely arise due to slight variations in the timing of responses across neurons ( see Appendix B for more details ) . The first stimulus component ( #5 ) looks similar to the stimulus components that we obtained with standard regression-based methods ( Figure 1e , h ) but now we have further components as well . Together they show how stimulus representation evolves in time . In particular , plotting the first two stimulus components against each other ( see Video 1 ) illustrates how stimulus representation rotates in the neural space during the delay period so that the encoding subspaces during F1 and F2 periods are not the same ( but far from orthogonal either ) . 10 . 7554/eLife . 10989 . 006Video 1 . Stimulus representation in the somatosensory working memory taskTwo leading stimulus dPCs in the somatosensory working memory task ( components #5 and #10 as horizontal and vertical axis correspondingly ) . Each frame of this movie corresponds to one time point t . Each dot is the average between two decision conditions with the same F1 stimulus . Fading 'tails' show last sections of the trajectories . See Figure 3 for the color code . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 006 As explained above , the demixed principal axes are not constrained to be orthogonal . The angles between the encoding axes are shown in Figure 3e , upper-right triangle; we discuss them later , together with other datasets . Pairwise correlations between components are all close to zero ( Figure 3e , lower-left triangle ) , as should be expected since the components are considered to represent independent signals . To assess whether the condition tuning of individual dPCA components was statistically significant , we used each component as a linear decoder to classify conditions . Specifically , stimulus components were used to classify stimuli , decision components to classify decisions , and interaction components to classify all 12 conditions . We used cross-validation to measure time-dependent classification accuracy and a shuffling procedure to assess whether it was significantly above chance ( see Materials and methods ) . Time periods of significant tuning are marked in Figure 3b with horizontal black lines . We next applied dPCA to recordings from the PFC of monkeys performing a visuospatial working memory task ( Qi et al . , 2011 , 2012; Meyer et al . , 2011 ) . In this task , monkeys first fixated a small white square at the centre of a screen , after which a square S1 appeared for 0 . 5 s in one of eight locations around the centre ( Figure 4a ) . After a 1 . 5 s delay , a second square S2 appeared for 0 . 5 s in either the same ( 'match' ) or the opposite ( 'non-match' ) location . Following another 1 . 5 s delay , a green and a blue choice target appeared in locations orthogonal to the earlier presented stimuli . Monkeys had to saccade to the green target to report a match condition , and to the blue one to report a non-match . 10 . 7554/eLife . 10989 . 007Figure 4 . Demixed PCA applied to recordings from monkey PFC during a visuospatial working memory task ( Qi et al . , 2011 ) . Same format as Figure 3 . ( a ) Cartoon of the paradigm , adapted from Romo and Salinas ( 2003 ) . ( b ) Demixed principal components . In each subplot there are ten lines corresponding to ten conditions ( see legend ) . Color corresponds to the position of the last shown stimulus ( first stimulus for t<2 s , second stimulus for t>2 s ) . In non-match conditions ( dashed lines ) the colour changes at t=2 s . Solid lines correspond to match conditions and do not change colors . ( c ) Cumulative variance explained by PCA and dPCA components . Dashed line marks fraction of signal variance . ( d ) Explained variance of the individual demixed principal components . Pie chart shows how the total signal variance is split between parameters . ( e ) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes , bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 007 We analyzed the activity of 956 neurons recorded in the lateral PFC of two monkeys performing this task . Proceeding exactly as before , we obtained the average time-dependent firing rate of each neuron for each condition . Following the original studies , we eliminated the trivial rotational symmetry of the task by collapsing the eight possible stimulus locations into five locations that are defined with respect to the preferred location of each neuron ( 0° , 45° , 90° , 135° , or 180° away from the preferred location , see Materials and methods ) . As a consequence , we obtained ten conditions: five possible stimulus locations , each paired with two possible decisions of the monkey . The dPCA results are shown in Figure 4 . As before , stimulus and decision are well separated at the population level despite being intermingled at the single-neuron level; at the same time dPCA captures almost the same amount of variance as PCA . One notable difference from before is the presence of strong interaction components in Figure 4b . However , these interaction components are in fact stimulus components in disguise . In match trials , S2 and S1 appear at the same location , and in non-match trials at opposite locations . Information about S2 is therefore given by a non-linear function of stimulus S1 and the trial type ( i . e . decision ) , which is here captured by the interaction components . Here again , our analysis summarizes previous findings obtained with this dataset . For instance , the first and the second decision components show tuning to the match/non-match decision during the S2 period and in the subsequent delay period . Using these components as fixed linear decoders , we achieve single-trial classification accuracy of match vs . non-match of 75% for t>2 ( cross-validated , see Materials and methods , Figure 12 ) , which is approximately equal to the state-of-the-art classification performance reported previously ( Meyers et al . , 2012 ) . Constantinidis et al . have also recorded population activity in PFC before starting the training ( both S1 and S2 stimuli were presented exactly as above , but there were no cues displayed and no decision required ) . When analyzing this pre-training population activity with dPCA , the first stimulus and the first interaction components come out close to the ones shown in Figure 4 , but there are no decision and no 'memory' components present ( data not shown ) , in line with previous findings ( Meyers et al . , 2012 ) . These task-specific components appear in the population activity only after extensive training . Next , we applied dPCA to recordings from the OFC of rats performing an odor discrimination task ( Feierstein et al . , 2006 ) . This behavioral task differs in two crucial aspects from the previously considered tasks: it requires no active storage of a stimulus , and it is self-paced . To start a trial , rats entered an odor port , which triggered delivery of an odor with a random delay of 0 . 2–0 . 5 s . Each odor was uniquely associated with one of the two available water ports , located to the left and to the right from the odor port ( Figure 5a ) . Rats could sample the odor for as long as they wanted ( up to 1 s ) , and then had to move to one of the water ports . If they chose the correct water port , reward was delivered following an anticipation period of random length ( 0 . 2–0 . 5 s ) . 10 . 7554/eLife . 10989 . 008Figure 5 . Demixed PCA applied to recordings from rat OFC during an olfactory discrimination task ( Feierstein et al . , 2006 ) . Same format as Figure 3 . ( a ) Cartoon of the paradigm , adapted from Wang et al . ( 2013 ) . ( b ) Each subplot shows one demixed principal component . In each subplot there are four lines corresponding to four conditions ( see legend ) . Two out of these four conditions were rewarded and are shown by thick lines . ( c ) Cumulative variance explained by PCA and dPCA components . ( d ) Explained variance of the individual demixed principal components . Pie chart shows how the total signal variance is split between parameters . ( e ) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes , bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 008 We analyzed the activity of 437 neurons recorded in five rats in four conditions: two stimuli ( left and right ) each paired with two decisions ( left and right ) . Two of these conditions correspond to correct ( rewarded ) trials , and two correspond to error ( unrewarded ) trials . Since the task was self-paced , each trial had a different length; in order to align events across trials , we restretched ( time-warped ) the firing rates in each trial ( see Materials and methods ) . Alignment methods without time warping led to similar results ( data not shown ) . Just as neurons from monkey PFC , neurons in rat OFC exhibit diverse firing patterns and mixed selectivity ( Feierstein et al . , 2006 ) . Nonetheless , dPCA was able to demix the population activity ( Figure 5 ) . In this dataset , interaction components separate rewarded and unrewarded conditions ( thick and thin lines in Figure 5b , bottom row ) , i . e . , correspond to neurons tuned either to reward , or to the absence of reward . The overall pattern of neural tuning across task epochs agrees with the findings of the original study ( Feierstein et al . , 2006 ) . Interaction components are by far the most prominent among all the condition-dependent components , corresponding to the observation that many neurons are tuned to the presence/absence of reward . Decision components come next , with the caveat that decision information may also reflect the rat’s movement direction and/or position , as was pointed out previously ( Feierstein et al . , 2006 ) . Stimulus components are less prominent , but nevertheless show clear stimulus tuning , demonstrating that even in error trials there is reliable information about stimulus identity in the population activity . Curiously , the first interaction component ( #4 ) already shows significant tuning to reward in the anticipation period . In other words , neurons tuned to presence/absence of reward start firing before the reward delivery ( or , on error trials , before the reward could have been delivered ) . We return to this observation in the next section . Kepecs et al . ( 2008 ) extended the experiment of Feierstein et al . ( 2006 ) by using odor mixtures instead of pure odors , thereby varying the difficulty of each trial ( Uchida and Mainen , 2003 ) . In each trial , rats experienced mixtures of two fixed odors with different proportions ( Figure 6a ) . Left choices were rewarded if the proportion of the 'left' odor was above 50% , and right choices otherwise . Furthermore , the waiting time until reward delivery ( anticipation period ) was increased to 0 . 3–2 s . 10 . 7554/eLife . 10989 . 009Figure 6 . Demixed PCA applied to recordings from rat OFC during an olfactory categorization task ( Kepecs et al . , 2008 ) . Same format as Figure 3 ( a ) Cartoon of the paradigm , adapted from Wang et al . ( 2013 ) . ( b ) Each subplot shows one demixed principal component . In each subplot there are ten lines corresponding to ten conditions ( see legend ) . Six out of these ten conditions were rewarded and are shown with thick lines; note that the pure left ( red ) and the pure right ( blue ) odors did not have error trials . Inset shows mean rate of the second interaction component during the anticipation period . ( c ) Cumulative variance explained by PCA and dPCA components . ( d ) Explained variance of the individual demixed principal components . Pie chart shows how the total signal variance is split between parameters . ( e ) Upper-right triangle shows dot products between all pairs of the first 15 demixed principal axes , bottom-left triangle shows correlations between all pairs of the first 15 demixed principal components . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 009 We analyzed the activity of 214 OFC neurons from three rats recorded in 8 conditions , corresponding to four odor mixtures , each paired with two decisions ( left and right ) . During the presentation of pure odors ( 100% right and 100% left ) rats made essentially no mistakes , and so we excluded these data from the dPCA computations ( which require that all parameter combinations are present , see Discussion ) . Nevertheless , we displayed these additional two conditions in Figure 6 . The dPCA components shown in Figure 6b are similar to those presented in Figure 5b . Here again , some of the interaction components ( especially the second one , #5 ) show strong tuning already during the anticipation period , i . e . before the actual reward delivery . The inset in Figure 6b shows the mean value of the component #5 during the anticipation period , separating correct ( green ) and incorrect ( red ) trials for each stimulus . The characteristic U-shape for the error trials and the inverted U-shape for the correct trials agrees well with the predicted value of the rat’s uncertainty in each condition ( Kepecs et al . , 2008 ) . Accordingly , this component can be interpreted as corresponding to the rat’s uncertainty or confidence about its own choice , confirming the results of Kepecs et al . ( 2008 ) . In summary , both the main features of this dataset , as well as some of the subtleties , are picked up and reproduced by dPCA . One of the key advantages of applying dPCA to these four datasets is that we can now compare them far more easily than was previously possible . This comparison allows us to highlight several general features of the population activity in prefrontal areas . First , most of the variance of the neural activity is always captured by the condition-independent components that together amount to 65–90% of the signal variance ( see pie charts in Figures 3–6d; see Materials and methods for definition of 'signal variance' ) . These components capture the temporal modulations of the neural activity throughout the trial , irrespective of the task condition . Their striking dominance in the data may come as a surprise , as such condition-independent components are usually not analyzed or shown ( cf . Figure 1e , h ) , even though condition-independent firing has been described even in sensory areas ( Sornborger et al . , 2005 ) . These components are likely explained in part by an overall firing rate increase during certain task periods ( e . g . during stimulus presentation ) . More speculatively , they could also be influenced by residual sensory or motor variables that vary rhythmically with the task , but are not controlled or monitored ( Renart and Machens , 2014 ) . The attentional or motivational state of animals , for instance , often correlates with breathing ( Huijbers et al . , 2014 ) , pupil dilation ( Eldar et al . , 2013 ) , body movements ( Gouvêa et al . , 2014 ) , etc . Second , even though dPCA , unlike PCA , does not enforce orthogonality between encoding axes corresponding to different task parameters , most of them turned out to be close to orthogonal to each other ( Figures 3–6e , upper triangle ) , as has been observed before ( Brendel et al . , 2011; Rishel et al . , 2013; Raposo et al . , 2014 ) . Nevertheless , many pairs were significantly non-orthogonal , meaning that neurons expressing one of the components tended to also express the other one . Throughout the four datasets , we identified 277 pairs of axes ( among the first 15 axes ) corresponding to different parameters . Of these , 38 , i . e . 14% , were significantly non-orthogonal with p<0 . 001 ( 8 out of 53 if we do not take time axes into account ) . Third , all dPCA components in each of the datasets are distributed across the whole neural population ( as opposed to being exhibited only by a subset of cells ) . For each component and each neuron , the corresponding encoder weight shows how much this particular component is exhibited by this particular neuron . For each component , the distribution of weights is strongly unimodal , centred at zero ( Figure 7a ) , and rather symmetric ( although it is skewed to one side for some components ) . In other words , there are no distinct sub-populations of neurons predominantly expressing a particular component; rather , each individual neuron can be visualized as a random linear combination of these components . We confirmed this observation by applying a recently developed clustering algorithm ( Rodriguez and Laio , 2014 ) to the population of neurons in the 15-dimensional space of dPC weights . In all cases , the algorithm found only one cluster ( Figure 7b ) . An alternative clustering analysis with Gaussian mixture models yielded similar results ( data not shown ) . This absence of any detectable clusters of neurons has been noted before ( Machens et al . , 2010 ) and was recently observed in other datasets as well ( Raposo et al . , 2014 ) . 10 . 7554/eLife . 10989 . 010Figure 7 . Encoder weights for the leading dPCA components across the neural population . ( a ) Distributions of encoder weights for the 15 leading dPCA components across the neural population , in each of the four datasets . Each subplot shows 15 probability density curves , one curve per component ( bin width 0 . 005 ) . The distribution corresponding to the first component is highlighted in red . ( b ) Clustering of neurons by density peaks ( Rodriguez and Laio , 2014 ) . For each dataset we took the first 15 dPCA components , and then ran the clustering algorithm in the 15-dimensional space of encoding weights . The clustering algorithm works in two steps: first , it computes a local density for each point ( i . e . , for each neuron ) , using a Gaussian kernel with σ2=0 . 01 . Second , for each point it finds the minimal distance to a point with higher local density ( if there is no such point , then the distance to the furthest point is taken ) . Each subplot shows local density on the horizontal axis plotted against distance to the next point with higher density on the vertical axis; each dot corresponds to one of the N neurons . Cluster centres are characterized by high local density and large distance to the point of even higher density; they should appear as outliers in the upper-right corner of the plot ( see Rodriguez and Laio , 2014 , for details ) . In each case , there is only one such outlier ( bigger dot ) , indicating a single cluster . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 010
The method presented here is conceptually based on our previous work ( Machens , 2010; Machens et al . , 2010; Brendel et al . , 2011 ) , but is technically very different . The original approach from Machens et al . ( 2010 ) only works for two parameters of interest , such as time and stimulus . Machens ( 2010 ) suggested a partial generalization to multiple parameters and Brendel et al . ( 2011 ) introduced the full covariance decomposition and developed a probabilistic model . However , all of them imposed orthogonality on the decoder/encoder axes ( and as a result did not distinguish them ) , a constraint that cannot be easily relaxed . While we have previously argued that orthogonality is a desirable feature of the decomposition , we now believe that it is better not to impose it upfront . First , by looking across many datasets , we have learnt that encoding subspaces can sometimes be highly non-orthogonal ( Figures 3–6e ) and hence not demixable under orthogonality constraints . Second , by not imposing orthogonality , we can easier identify components that are truly orthogonal . Third , removing the orthogonality constraint allowed us to obtain a simple analytical solution in terms of singular value decompositions ( see Materials and methods ) and hence to avoid local minima , convergence issues , and any additional optimization-related hyperparameters . To demonstrate these advantages , we ran the algorithm of Brendel et al . ( 2011 ) , dPCA-2011 , on all our datasets . The resulting components were similar to the components presented here , with the amount of variance captured by the first 15 components being very close; but the achieved demixing was worse . For each component we defined a demixing index ( see Materials and methods ) that is equal to 1 if the component is perfectly demixed . For all datasets , these indices were significantly higher with our current dPCA-2015 method than with dPCA-2011 . Moreover , dPCA-2011 failed to find some weak components at all . For comparison , see Figure 14 in the Materials and methods . Another method , called 'targeted dimensionality reduction' ( TDR ) has recently been suggested for neural data analysis and is similar in spirit to dPCA in that it looks for demixing linear projections ( Mante et al . , 2013 ) . As mentioned above , the original application of this method yields only one component per task parameter and ignores the condition-independent components . While TDR can be extended in various ways to yield more components , no principled way of doing it has been suggested so far . Comparison of dPCA with TDR on our datasets shows that dPCA demixes the task-parameter dependencies better than TDR ( see Figure 14 in the Materials and methods ) . For an in-depth discussion of the relationship between dPCA and LDA/MANOVA , we refer the reader to the Methods . Briefly , LDA is a one-way technique , meaning that only one parameter ( class id ) is associated with each data point . Therefore , LDA cannot directly be applied to the demixing problem . While LDA could be generalized to deal with several parameters in a systematic way , such a generalization has not been used for dimensionality reduction of neural data and does not have an established name in the statistical literature ( we call it factorial LDA ) . We believe that for the purposes of dimensionality reduction , dPCA is a superior approach since it combines a reasonably high class separation with low reconstruction error , whereas LDA only optimizes class separation without taking the ( potential ) reconstruction error into account ( see Figure 2 ) . MANOVA , on the other hand , is a statistical test closely related to LDA that deals with multiple parameters . However , it deals with isolating the contribution of each parameter from residual noise rather than from the other parameters , and is therefore not suited for demixing . While we believe that dPCA is an easy-to-use method of visualizing complex data sets with multiple task parameters , several limitations should be kept in mind . First , dPCA as presented here works only with discrete parameters , and all possible parameter combinations must be present in the data . This limitation is the downside of the large flexibility of the method: apart from the demixing constraint , we do not impose any other constraints on the latent variables and their estimation remains essentially non-parametric . In order to be able to treat continuous parameters or missing data ( missing parameter combinations ) , we would need to further constrain the estimation of these latent variables , using e . g . a parametric model . One simple possibility is to directly use a parametric model for the activity of the single neurons , such as the linear model used in Mante et al . ( 2013 ) , in order to fill in any missing data points , and then run dPCA subsequently . Second , the number of neurons needs to be sufficiently high in order to obtain reliable estimates of the demixed components . In our datasets , we found that at least ∼100 neurons were needed to achieve satisfactory demixing . The number is likely to be higher if more than three task parameters are to be demixed , as the number of interaction terms grows exponentially with the number of parameters . This trade-off between model complexity and demixing feasibility should be kept in mind when deciding how many parameters to put into the dPCA procedure . In cases when there are many task parameters of interest , dPCA is likely to be less useful than the more standard parametric single-unit approaches ( such as linear regression ) . As a trivial example , imagine that only N=1 neuron has been recorded; it might have strong and significant tuning to various parameters of interest , but there is no way to demix ( or decode ) these parameters from the recorded 'population . ' Third , even with a large number of neurons , a dataset may be non-demixable , in which case dPCA would fail . For instance , if the high-variance directions of the stimulus and the decision parts of the neural activities fully overlap , then there is no linear decoder that can demix the two parameters . Finally , dPCA components corresponding to the same parameter ( e . g . successive stimulus components ) are here chosen to be orthogonal , similarly to PCA . This can make successive components difficult to interpret ( e . g . the second and the third stimulus components in Figure 3 ) . To make them more interpretable , the orthogonality constraint could be replaced with some other constraints , such as e . g . requiring each component to have activity 'localized' in time . This problem may be addressed in future work .
In each of the datasets analyzed in this manuscript , trials can be labeled with two parameters: 'stimulus' and 'decision' . Note that a 'reward' label is not needed , because its value can be deduced from the other two due to the deterministic reward protocols in all tasks . In this situation , for each stimulus s ( out of S ) and decision d ( out of Q ) , we have a collection of K trials with N neurons recorded in each trial . For each trial k ( out of K ) and neuron n ( out of N ) we have a recorded spike train . We denote the filtered ( or binned ) spike train by x ( t ) , and assume that it is sampled at T time points t . To explicitly denote all task parameters , we will write either x ( t , s , d , k ) or xtsdk for the filtered spike train of one neuron and 𝐱tsdk for the vector of filtered spike trains of all N neurons . The latter notation is more compact and also highlights the tensorial character of the data . These data can be thought of as KSQ time-dependent neural trajectories ( K trials for each of the SQ conditions ) in the N-dimensional space ℝN ( Figure 2b ) . The number of distinct data points in this N-dimensional space is KSQT . We collect the full data with all single trials in a matrix 𝐗 of size N×KSQT , i . e . N rows and KSQT columns . Averaging all K trials for each neuron , stimulus , and decision , yields mean firing rates ( PSTHs ) that can be collected in a smaller matrix 𝐗~ of size N×SQT . Consider one single neuron first . We can decompose its filtered spike trains , xtsdk , into a set of averages ( which we call marginalizations ) over various combinations of parameters . We will denote the average over a set of parameters {a , b , …} by angular brackets ⟨⋅⟩ab… . Let us define the following marginalized averages:x¯=⟨xtsdk⟩tsdk=x¯⋅⋅⋅⋅x¯t=⟨xtsdk−x¯⟩sdk=x¯t⋅⋅⋅−x¯⋅⋅⋅⋅x¯s=⟨xtsdk−x¯⟩tdk=x¯⋅s⋅⋅−x¯⋅⋅⋅⋅x¯d=⟨xtsdk−x¯⟩tsk=x¯⋅⋅d⋅−x¯⋅⋅⋅⋅x¯ts=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩dk=x¯ts⋅⋅−x¯t⋅⋅⋅−x¯⋅s⋅⋅+x¯⋅⋅⋅⋅x¯td=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩sk=x¯t⋅d⋅−x¯t⋅⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯sd=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩tk=x¯⋅sd⋅−x¯⋅s⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯tsd=⟨xtsdk−x¯−x¯t−x¯s−x¯d−x¯ts−x¯td−x¯sd⟩k=x¯tsd⋅−x¯ts⋅⋅−x¯⋅sd⋅−x¯t⋅d⋅=+x¯t⋅⋅⋅+x¯⋅s⋅⋅+x¯⋅⋅d⋅−x¯⋅⋅⋅⋅ϵtsdk=xtsdk−⟨xtsdk⟩k=xtsdk−x¯tsd⋅ . Here x¯ is simply the overall mean firing rate of our neuron , x¯t is the average time-varying firing rate once the overall mean has been subtracted , etc . The right-hand side shows the same averaging procedure in the more explicit form using ANOVA-style notation , in which averages of x over everything apart from the explicitly mentioned parameters , e . g . , the stimulus s , are denoted by terms of the form x¯⋅s⋅⋅ . One can directly see that the original neural activities are given by the sum of all marginalizations:xtsdk=x¯+x¯t+x¯s+x¯d+x¯ts+x¯td+x¯ds+x¯tsd+ϵtsdk . This decomposition is identical to the one used in factorial ANOVA ( Rutherford , 2001; Christensen , 2011 ) where task parameters are called factors . The ANOVA literature uses a slightly different notation with task parameters ( t , s , d , k ) replaced by indices ( i , j , k , l ) and with Greek letters designating individual terms:xijkl=μ+αi+βj+γk+δij+ζjk+ηik+θijk+ϵijkl . We will use our notation , though , to keep the connection with the task parameters more explicit . For the purposes of demixing neural signals in the context of our datasets , we combine some of these terms together . Indeed , demixing a time-independent pure stimulus term x¯s from a stimulus-time interaction term x¯ts makes little sense because we expect all neural components to change with time . Hence , we group the terms as follows ( without changing the notation ) :xtsdk=x¯+x¯t+x¯s+x¯ts⏟x¯ts+x¯d+x¯td⏟x¯td+x¯sd+x¯tsd⏟x¯tsd+ϵtsdk . Here the first term on the right-hand side is the mean firing rate , the last term is the trial-to-trial noise , and we call the other terms condition-independent term , stimulus term , decision term , and stimulus-decision interaction term . This decomposition is illustrated in Figure 8 for several exemplary neurons ( we only show the decomposition of the PSTH part , leaving out the noise term ) . 10 . 7554/eLife . 10989 . 012Figure 8 . Marginalization procedure . PSTHs of three exemplary neurons from the somatosensory working memory task decomposed into marginalizations . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 012 We apply this marginalization procedure to every neuron , splitting the whole data matrix 𝐗 into parts . Assuming from now on that the data matrix is centered ( i . e . x¯=0 for all neurons ) , we can write the decomposition in the matrix formX=Xt+Xts+Xtd+Xtsd+Xnoise=∑ϕXϕ+Xnoise . Here t , ts , td , and tsd are labels and not indices , and all terms are understood to be matrices of the same N×KSQT size , so e . g . 𝐗t is not an N×T sized matrix , but the full size N×KSQT matrix with N×T unique values replicated KSQ times . Crucially , the marginalization procedure ensures that all terms are uncorrelated and that the N×N covariance matrix 𝐂=𝐗𝐗⊤/ ( KSQT ) is linearly decomposed into the sum of covariance matrices from each marginalization ( see Appendix A for the proof ) :C=Ct+Cts+Ctd+Ctsd+Cnoise=∑ϕCϕ+Cnoise . Here all covariance matrices are defined with the same denominator , i . e . 𝐂ϕ=𝐗ϕ𝐗ϕ⊤/ ( KSQT ) . Given a decomposition 𝐗=∑ϕ𝐗ϕ+𝐗noise , the loss function of dPCA is given byL=∑ϕLϕ withLϕ=∥𝐗ϕ-𝐅ϕ𝐃ϕ𝐗∥2 , where each 𝐅ϕ is an encoder matrix with qϕ columns and each 𝐃ϕ is a decoder matrix with qϕ rows . Here and below , matrix norm signifies Frobenius norm , i . e . ∥𝐗∥2=∑i∑jXij2 . In the remaining discussion , it will often be sufficient to focus on the individual loss functions Lϕ , in which case we will drop the indices ϕ on the decoder and encoder matrices for notational convenience , and simply write 𝐅 and 𝐃 . Without any additional constraints , the decoder and encoder are only defined up to their product 𝐅𝐃 of rank q . To make the decomposition unique , we will assume that 𝐅 has orthonormal columns and that components are ordered such that their variance ( row variance of 𝐃𝐗 ) is decreasing . The reason for this choice will become clear below . This loss function penalizes the difference between the marginalized data 𝐗ϕ and the reconstructed full data 𝐗 , i . e . , the full data projected with the decoders 𝐃 onto a low-dimensional latent space and then reconstructed with the encoders 𝐅 ( see Video 2 ) . The loss function thereby favours variance in marginalization ϕ and punishes variance coming from all other marginalizations and from trial-to-trial noise . Given that the marginalized averages are uncorrelated with each other , we can make this observation clear by writing , Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FDXϕ∥2+∥FD ( X−Xϕ ) ∥2 . Here the first term corresponds to the non-explained variance in marginalization ϕ and the second term corresponds to the variance coming from all other marginalizations and from trial-to-trial noise . The dPCA objective is to minimize both . 10 . 7554/eLife . 10989 . 013Video 2 . Illustration of the dPCA algorithm . Illustration of the dPCA algorithm using the somatosensory working memory task . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 013 We note that the loss function Lϕ is of the general form ∥𝐗ϕ-𝐀𝐗∥2 , with 𝐀=𝐅𝐃 . For an arbitrary N×N matrix 𝐀 , minimization of the loss function amounts to a classical regression problem with the well-known ordinary least squares ( OLS ) solution , 𝐀OLS=𝐗ϕ𝐗 ( 𝐗𝐗 ) ⊤-1⊤ . In our case , 𝐀=𝐅𝐃 is an N×N matrix of rank q , which we will make explicit by writing 𝐀q . The dPCA loss function therefore amounts to a linear regression problem with an additional rank constraint on the matrix of regression coefficients . This problem is known as reduced-rank regression ( RRR ) ( Izenman , 1975; Reinsel and Velu , 1998; Izenman , 2008 ) and can be solved via the singular value decomposition . To see this , we write Xϕ−AqX= ( Xϕ−AOLSX ) + ( AOLSX−AqX ) . The first term , 𝐗ϕ-𝐀OLS𝐗 , consists of the regression residuals that cannot be accounted for by any linear transformation of 𝐗 . It is straightforward to verify that these regression residuals , 𝐗ϕ-𝐀OLS𝐗 , are orthogonal to 𝐗 ( Hastie et al . , 2009 , Section 3 . 2 ) and hence also orthogonal to ( 𝐀OLS-𝐀q ) 𝐗 . This orthogonality allows us to split the loss function into two terms , ∥Xϕ−AqX∥2=∥Xϕ−AOLSX∥2+∥AOLSX−AqX∥2 , where the first term captures the ( unavoidable ) error of the least squares fit while the second term describes the additional loss suffered through the rank constraint . Since the first term does not depend on 𝐀q , the problem reduces to minimizing the second term . To minimize the second term , we note that the best rank-q approximation to 𝐀OLS𝐗 is given by its first q principal components ( Eckart-Young-Mirsky theorem ) . Accordingly , if we write 𝐔q for the matrix of the q leading principal directions ( left singular vectors ) 𝐮i of 𝐀OLS𝐗 , then the best approximation is given by 𝐔q𝐔q⊤𝐀OLS𝐗 and hence 𝐀q=𝐔q𝐔q⊤𝐀OLS . To summarize , the reduced-rank regression problem posed above can be solved in a three-step procedure: Conveniently , the extracted decoder/encoder pairs do not depend on how many pairs are extracted: the i-th pair is given by 𝐟=𝐮i and 𝐝=𝐮i⊤𝐀OLS , independent of q . Indeed , this feature motivated the above choice that 𝐅 should have orthonormal columns . A standard way to avoid overfitting in regression problems is to add a quadratic penalty to the cost function , which is often called ridge regression ( RR ) . This approach can be used in reduced-rank regression as well . Specifically , we can add a ridge penalty term to the loss function Lϕ:Lϕ=∥Xϕ−FDX∥2+μ∥FD∥2 . The RR solution modifies the OLS solution from above toARR=XϕX⊤ ( XX⊤+μI ) −1 . In turn , the reduced-rank solution can be obtained as described above: 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀RR where 𝐔q are the first q principal directions of 𝐀RR𝐗 . We found it convenient to define μ= ( λ∥𝐗∥ ) 2 , since this makes the values of λ comparable across datasets . As explained below , we used cross-validation to select the optimal value of λ in each dataset . The data and variance decomposition carried out by the marginalization procedure can break down when the dataset is unbalanced , i . e . , when the number of data points ( trials ) differs between conditions . We illustrate this problem with a two-dimensional toy example in Figure 9 . We assume two task parameters ( factors ) , each of which can take only two possible values . The overall mean as well as the interaction term are taken to be zero , so that xijk=ai+bj+eijk . Since the number of trials , K=Kij , depends on the condition , the trial index runs through the values k=1…Kij . As shown in Figure 9a , all three terms on the right-hand side exhibit zero correlation between x1 and x2 . A balanced dataset with the same number of data points in each of the four possible conditions ( Figure 9b ) also has zero correlation . However , an unbalanced dataset , as shown in Figure 9c , exhibits strong positive correlation ( ρ=0 . 8 ) . Accordingly , the covariance matrix of the full data can no longer be split into marginalized covariances . To avoid this and other related problems , we can perform a 're-balancing' procedure by reformulating dPCA in terms of PSTHs and noise covariance . 10 . 7554/eLife . 10989 . 014Figure 9 . Balanced and unbalanced data . ( a ) In this toy example there are two task parameters ( factors ) , with two possible values each . Parameter A ( left ) is represented by the size of the dot , parameter B ( middle ) is represented by the color of the dot , noise is Gaussian with zero mean and zero correlation ( right ) . Interaction term is equal to zero . ( b ) Balanced case with N=10 data points in each of the four parameter combinations . Overall correlation is zero . ( c ) Unbalanced case with N=10 for two parameter combinations and N=100 for the other two . Overall correlation is 0 . 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 014 In the balanced case , the dPCA loss function Lϕ can be rewritten as the sum of two terms with one term depending on the PSTHs and another term depending on the trial-to-trial variations , Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FD ( X−Xnoise ) ∥2+∥FDXnoise∥2 , where we used the fact that 𝐗ϕ and 𝐗-𝐗noise are orthogonal to 𝐗noise ( see Appendix A ) . We now define 𝐗PSTH=𝐗-𝐗noise which is simply a matrix of the same size as 𝐗 with the activity of each trial replaced by the corresponding PSTH . In addition , we observe that the squared norm of any centered data matrix 𝐘 with n data points can be written in terms of its covariance matrix 𝐂Y=𝐘𝐘⊤/n , namely ∥Y∥2=tr[YY⊤]=ntr[CY]=ntr[CY1/2CY1/2]=n∥CY1/2∥2 , and soLϕ=∥Xϕ−FDXPSTH∥2+KSQT∥FDCnoise1/2∥2 . The first term consists of K replicated copies: 𝐗PSTH contains K replicated copies of 𝐗~ ( which we defined above as the matrix of PSTHs ) and 𝐗ϕ contains K replicated copies of 𝐗~ϕ ( which we take to be a marginalization of 𝐗~ , with 𝐗~=∑ϕ𝐗~ϕ ) . We can eliminate the replications and drop the factor K to obtainLϕ=∥X~ϕ−FDX~∥2+SQT∥FDCnoise1/2∥2 . In the unbalanced case , we can directly use this last formulation where all occurrences of 𝐗 have been replaced by 𝐗~ . This is especially useful for neural data , where some combinations of task parameters may occur more often than others . The 're-balanced' dPCA loss function treats all parameter combinations as equally important , independent of their occurrence frequency . It stands to reason to 're-balance' the noise covariance matrix as well by defining it as follows:𝐂~noise=1SQT∑sdt𝐂noise ( s , d , t ) =⟨𝐂noise ( s , d , t ) ⟩sdt , where 𝐂noise ( s , d , t ) is the covariance matrix for the ( s , d , t ) parameter combination . This formulation , again , treats noise covariance matrices from different parameter combinations as equally important , independent of how many data points there are for each parameter combination . Putting everything together and including the regularization term as well , we arrive at the following form of the dPCA loss function:Lϕ=∥X~ϕ−FDX~∥2+SQT∥FDC~noise1/2∥2+μ∥FD∥2 . This loss function can be minimized as described in the previous section . Specifically , the full rank solution with 𝐀=𝐅𝐃 becomesARR=X~ϕX~⊤ ( X~X~⊤+SQT⋅C~noise+μI ) −1 . The reduced-rank solution can then be obtained by setting 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀 , where 𝐔q are the first q principal directions of 𝐀RR𝐗~ . Even when using the re-balanced formulation of the loss function , we still need data from all possible parameter combinations . In neurophysiological experiments , however , one may run into situations where not all combinations of stimuli could be presented to an animal before it decided to abort the task , or where an animal never carried out a particular decision , etc . This problem is particularly severe if individual task parameters can take many values . What should one do in these cases ? The key problem here is that dPCA as formulated above makes no assumptions about how the firing rates of individual neurons depend on the task parameters . ( Nor is there an explicit assumption about how the demixed components depend on the task parameters . ) If some task conditions have not been recorded , then the only way out is to add more assumptions , or , more formally , to replace the non-parametric estimates of individual neural firing rates ( or demixed components ) by parametric estimates . We could for instance fit a simple linear model to the firing rate of each neuron at each time step ( Mante et al . , 2013; Brody et al . , 2003 ) , x ( t , s , d ) =α ( t ) +β ( t ) s+γ ( t ) d+ϵ and then use this model to 'fill in' the missing data . More sophisticated ways of dealing with missing data could be envisaged as well and may provide a venue for future research . For sequentially recorded datasets , the matrix 𝐗 cannot be meaningfully constructed . However , we can still work with the PSTH matrix 𝐗~ that can be decomposed into marginalizations: X~=∑ϕX~ϕ . Consequently , we can use the same formulation of the loss function as in the simultaneously recorded unbalanced case ( see above ) . The only difference is that the noise covariance matrix is not available ( noise correlations cannot be estimated when neurons are recorded in different sessions ) . In this manuscript we took as 𝐂noise the diagonal matrix with individual noise variances of each neuron on the diagonal . We used the re-balanced version 𝐂~noise ( average noise covariance matrix across all conditions ) , but found that the difference between re-balanced and non-rebalanced noise covariance matrices was always minor and did not noticeably influence the dPCA solutions . As all datasets analyzed in this manuscript were sequentially recorded , we always reported fractions of the PSTH variance ( as opposed to the total PSTH+noise variance ) explained by our components , i . e . fractions of variance explained in 𝐗~ . We defined the fraction of explained variance in a standard way:R2=∥𝐗~∥2-∥𝐗~-𝐅𝐃𝐗~∥2∥𝐗~∥2 . This formula can be used to compute the fraction of variance explained by each dPCA component ( by plugging in its encoder 𝐟 and decoder 𝐝 ) ; these are the numbers reported on Figures 3–6b , d and used to order the components . The same formula can be used to compute the cumulative fraction of variance explained by the first q components ( by stacking their encoders and decoders as columns and rows of 𝐅 and 𝐃 respectively ) ; these are the numbers reported on Figures 3–6c . Note that the cumulative explained variance is close to the sum of individually explained variances but not exactly equal to it since the dPCA components are not completely uncorrelated . The same formula holds for standard PCA using 𝐅=𝐃⊤=𝐔pca , i . e . , the matrix of stacked together principal directions ( Figures 3–6c ) . Using the decomposition X~=∑ϕX~ϕ , we can split the fraction of explained variance into additive contributions from different marginalizations:R2=∑ϕ∥𝐗~ϕ∥2-∥𝐗~ϕ-𝐅𝐃𝐗~ϕ∥2∥𝐗~∥2 . We used this decomposition to produce the bar plots in Figures 3–6d , showing how the explained variance of each single dPCA component is split between marginalizations . Following the approach of Machens et al . ( 2010 ) , we note that our PSTH estimates 𝐗~ must differ from the 'true' underlying PSTHs due to the finite amount of recorded trials . Hence , some fraction of the total variance of 𝐗~ is coming from this residual noise . We can estimate this fraction as follows . Our estimate of the noise variance of the n-th neuron is given by C~nn , the n-th diagonal element of 𝐂~noise . There are on average K~n=1SQ∑Knsd trials being averaged to compute the PSTHs for this neuron . So a reasonable estimate of the residual noise variance of the n-th neuron is C~nn/K~n . Accordingly , we define the total residual noise sum of squares asΘ=SQT⋅∑nC~nnK~n . In turn , the fraction of total signal variance is computed as 1-Θ/∥𝐗~∥2 which is the dashed line shown in Figures 3–6c . Note that each component likewise has contributions from both signal and noise variance , and hence the fraction of total signal variance does not constitute an upper bound on the number of components . The residual noise variance is not split uniformly across marginalizations: the fraction falling into marginalization ϕ is proportional to the respective number of degrees of freedom , Kϕ . This can be explicitly computed; for a centered dataset with S stimuli , Q decisions , and T time points the total number of degrees of freedom ( per neuron ) is SQT-1 and is split into T-1 for time , ST-T for stimulus , QT-T for decision , and SQT-ST-QT+T for the stimulus-decision interaction ( compare with the formulas in the Marginalization Procedure section ) . Accordingly , we computed the residual noise sum of squares falling into marginalization ϕ asΘϕ=KϕSQT-1Θ . The pie charts in Figures 3–6d show the amount of variance in each marginalization , with estimated contributions of the residual noise variance subtracted: ( ∥𝐗~ϕ∥2-Θϕ ) / ( ∥𝐗~∥2-Θ ) . To display the percentage values on the pie charts , percentages were rounded using the 'largest remainder method' , so that the sum of the rounded values remained 100% . We defined the demixing index of each component as maxϕ{∥dX~ϕ∥2}/∥dX~∥2 . This index can range from 1/4 to 1 ( since there are four marginalizations ) and the closer it is to 1 , the better demixed the component is . As an example , for the somatosensory working memory dataset , the average demixing index over the first 15 PCA components is 0 . 76 ± 0 . 16 ( mean ± SD ) , and over the first 15 dPCA components is 0 . 98 ± 0 . 02 , which means that dPCA achieves much better demixing ( p=0 . 0002 , Mann-Whitney-Wilcoxon ranksum test ) . For the first 15 components of dPCA-2011 ( Brendel et al . , 2011 ) it was 0 . 95 ± 0 . 03 , significantly less than for the current dPCA ( p=0 . 0008 ) . This difference may seem small , but is clearly visible in the projections by the naked eye . For comparison , the average demixing index of individual neurons in this dataset is 0 . 55 ± 0 . 18 . In other datasets these numbers are similar , and the same differences were significant in all cases . In Figures 3–6e , stars mark the pairs of components whose encoding axes 𝐟1 and 𝐟2 are significantly and robustly non-orthogonal . These were identified as follows: In Euclidean space of N dimensions , two random unit vectors ( from a uniform distribution on the unit sphere ) have dot product ( cosine of the angle between them ) distributed with mean zero and standard deviation N-1/2 . For large N the distribution is approximately Gaussian . To avoid the problems inherent to multiple comparisons , we chose a conservative significance level of p<0 . 001 , which means that two axes are significantly non-orthogonal if |f1⋅f2|>3 . 3/N1/2 . Coordinates of 𝐟1 quantify how much this component contributes to the activity of each neuron . Hence , if cells exhibiting one component also tend to exhibit another , the dot product between the axes f1⋅f2>0 is positive ( note that 𝐟1⋅𝐟2 is approximately equal to the correlation between the coordinates of 𝐟1 and 𝐟2 ) . Sometimes , however , the dot product has large absolute value only due to several outlying cells . To ease interpretation , we marked with stars only those pairs of axes for which the Kendall ( robust ) correlation was significant at p<0 . 001 level ( in addition to the above criterion on 𝐟1⋅𝐟2 ) . Brief descriptions of experimental paradigms are provided in the Results section and readers are referred to the original publications for all further details . Here we describe the selection of animals , sessions , and trials for the present manuscript . In all experiments neural recordings were obtained in multiple sessions , so most of the neurons were not recorded simultaneously . All four datasets used in this manuscript have been made available at http://crcns . org ( Romo et al . , 2016; Constantinidis et al . , 2016; Feierstein et al . , 2016; Uchida et al . , 2016 ) . For our analysis , we only selected neurons which had been recorded in each possible condition ( combination of parameters ) , which avoids the missing data problems explained above . Additionally , we required that in each condition there were at least Kmin>1 trials , to reduce the standard error of the mean when averaging over trials , and also for cross-validation purposes . The cutoff was set to Kmin=5 for both working memory datasets , and to Kmin=2 for both olfactory datasets ( due to less neurons available ) . We have further excluded very few neurons with mean firing rates over 50 Hz , as such neurons can bias the variance-based analysis . Firing rates above 50 Hz were atypical in all datasets ( number of excluded neurons for each dataset: 5 / 2 / 1 / 0 ) . This exclusion had a minor positive effect on the components . We did not apply any variance-stabilizing transformations , but if the square-root transformation was applied , the results stayed qualitatively the same ( data not shown ) . No other pre-selection of neurons was used . This procedure left 832 neurons ( 230 / 602 for individual animals , order as above ) in the somatosensory working memory dataset , 956 neurons ( 182 / 774 ) in the visuospatial working memory dataset , 437 neurons in the olfactory discrimination dataset ( 166 / 30 / 9 / 106 / 126 ) , and 214 neurons in the olfactory categorization dataset ( 67 / 38 / 109 ) . The spike trains were filtered with a Gaussian kernel ( σ=50 ms ) and sampled at 100 Hz to produce single-trial instantaneous firing rates . In the visuospatial working memory dataset we identified the preferred location of each neuron as the location that evoked maximum mean firing rate in the 500 ms time period while the first stimulus was shown . The neural tuning was shown before to have a symmetric bell shape ( Qi et al . , 2011; Meyer et al . , 2011 ) , with each neuron having its own preferred location . We then re-sorted the trials ( separately for each neuron ) such that only five distinct stimuli were left: preferred location , 45° , 90° , 135° , and 180° away from the preferred location . In both olfactory datasets trials were self-paced . Accordingly , trials last different amounts of time , and firing rates cannot simply be averaged over trials . We used the following time warping ( re-stretching ) procedure to equalize the length of all trials and to align several events of interest ( Figure 10 ) separately in each dataset . We defined five alignment events: odor poke in , odor poke out , water poke in , reward delivery , and water poke out . First , we aligned all trials on odor poke in ( T1=0 ) and computed median times of the four other events Ti , i=2…5 ( for the time of reward delivery , we took the median over all correct trials ) . Second , we set ΔT to be the minimal waiting time between water port entry and reward delivery across the whole experiment ( ΔT=0 . 2 s for the olfactory discrimination task and ΔT=0 . 3 s for the olfactory categorization task ) . Finally , for each trial with instantaneous firing rate x ( t ) we set ti , i=1…5 , to be the times of alignment events on this particular trial ( for error trials we took t4=t3+ΔT ) , and stretched x ( t ) along the time axis in a piecewise-linear manner to align each ti with the corresponding Ti . 10 . 7554/eLife . 10989 . 015Figure 10 . Re-stretching ( time warping ) procedure . We defined several alignment events ( such as odour poke in , odour poke out , etc . ) and for each trial found the times ti of these events . After aligning all trials on t1=0 ( left ) we computed median times Ti for all other events . Then for each trial we re-stretched the firing rate on each interval [ti , ti+1] to align it with [Ti , Ti+1] ( right ) . After such re-stretching , all events are aligned and the trials corresponding to one condition can be averaged . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 015 We made sure that time warping did not introduce any artifacts by considering an alternative procedure , where short ( ± 450 ms ) time intervals around each ti were cut out of each trial and concatenated together; this procedure is similar to the pooling of neural data performed in the original studies ( Feierstein et al . , 2006; Kepecs et al . , 2008 ) . The dPCA analysis revealed qualitatively similar components ( data not shown ) . As noted above , we renormalized the regularization parameter μ= ( λ∥𝐗∥ ) 2 , and then used cross-validation to find the optimal value of λ for each dataset . To separate the data into training and testing sets , we held out one random trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test ( as the neurons were not recorded simultaneously , we do not have recordings of all N neurons in any actual trial ) . Remaining trials were averaged to form a training set of PSTHs 𝐗~train and an estimate of the noise covariance matrix 𝐂~train . Note that 𝐗test and 𝐗~train have the same dimensions . We then performed dPCA on 𝐗~train for various values of λ between 10-7 and 10-3 ( on a logarithmic grid ) . For each λ , we selected ten components in each marginalization ( i . e . 40 components in total ) to obtain 𝐅ϕ ( λ ) and 𝐃ϕ ( λ ) , and computed the normalized reconstruction error LCV ( λ ) on the test set ( see below ) . We repeated this procedure ten times for different train-test splittings and averaged the resulting functions LCV ( λ ) . In all cases the average function L¯CV ( λ ) had a clear minimum ( Figure 11 ) that we selected as the optimal λ . The values of λ selected for each dataset were 2 . 6⋅10-6 / 5 . 8⋅10-6 / 5 . 8⋅10-6 / 5 . 8⋅10-6 . We also performed the same procedure in each marginalization separately , but in all datasets the optimal values of λ were similar across marginalizations ( Figure 11 ) . We therefore chose to use the same value of λ for all marginalizations . 10 . 7554/eLife . 10989 . 016Figure 11 . Cross-validation errors depending on the regularization parameter λ . Each subplot corresponds to one dataset and shows mean ( solid lines ) and min/max ( boundaries of shaded regions ) of the relative cross-validation errors for ten repetitions . Different colors refer to different marginalizations ( see legend ) , the minima are marked by dots . Black color shows all marginalizations together , i . e . LCV ( λ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 016 Interestingly , for all our datasets minλ{L¯CV ( λ ) } was only slightly smaller than L¯CV ( 0 ) , so the regularization term had almost no influence . Presumably , this result stems from our diagonal ( and thus non-singular ) noise covariance matrices , and therefore does not necessarily hold for simultaneously recorded data . To compute LCV ( λ ) , we used 𝐗test to predict 𝐗~train:LCV ( λ ) =∑ϕ∥𝐗~train , ϕ-𝐅ϕ ( λ ) 𝐃ϕ ( λ ) 𝐗test∥2∥𝐗~train∥2 . This is the residual training-set variance not explained by the test data . Note that it would not make sense to exchange 𝐗test and 𝐗~train in this formula: the decoder and encoder are fitted to the training data , and should only be applied to the test data for the purposes of cross-validation . An alternative approach , in which we predicted the test data rather than the training data , yielded similar results ( data not shown ) . We used decoding axis 𝐝 of each dPC in stimulus , decision , and interaction marginalizations as a linear classifier to decode stimulus , decision , or condition respectively . Black lines on Figures 3–6b show time periods of significant classification . A more detailed description follows below . We used 100 iterations of stratified Monte Carlo leave-group-out cross-validation , where on each iteration we held out one trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test and averaged over remaining trials to form a training set 𝐗~train ( see above ) . After running dPCA on 𝐗~train , we used decoding axes of the first three stimulus/decision/interaction dPCs as a linear classifier to decode stimulus/decision/condition respectively . Consider e . g . the first stimulus dPC: first , for each stimulus , we computed the mean value of this dPC separately for every time-point . Then we projected each test trial on the corresponding decoding axis and classified it at each time-point according to the closest class mean . The proportion of test trials ( out of SQ ) classified correctly resulted in a time-dependent classification accuracy , which we averaged over 100 cross-validation iterations . Note that this is a stratified procedure: even though in reality some conditions have many fewer trials than others , here we classify exactly the same number of 'pseudo-trials' per condition . At the same time , as the coordinates of individual data points in each pseudo-trial are pooled from different sessions , the influence of noise correlations on the classification accuracies is neglected , similar to Meyers et al . ( 2012 ) . We then used 100 shuffles to compute the distribution of classification accuracies expected by chance . On each iteration and for each neuron , we shuffled all available trials between conditions , respecting the number of trials per condition ( i . e . all ∑sdKnsd trials were shuffled and then randomly assigned to the conditions such that all values Knsd stayed the same ) . Then exactly the same classification procedure as above ( with 100 cross-validation iterations ) was applied to the shuffled dataset to find mean classification accuracy for the first stimulus , decision , and interaction components . All 100 shuffling iterations resulted in a set of 100 time-dependent accuracies expected by chance . The time periods when actual classification accuracy exceeded all 100 shuffled decoding accuracies in at least ten consecutive time bins are marked by black lines on Figures 3–6 . Components without any periods of significant classification are not shown . See Figure 12 for classification accuracies in each dataset . The Monte Carlo computations took ∼8 hr for each of the larger datasets on a 6 core 3 . 2 Ghz Intel i7-3930K processor . 10 . 7554/eLife . 10989 . 017Figure 12 . Cross-validated time-dependent classification accuracies of linear classifiers ( black lines ) given by the first three stimulus/decision/interaction dPCs ( columns ) in each dataset ( rows ) . Shaded gray regions show distribution of classification accuracies expected by chance as estimated by 100 iterations of shuffling procedure . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 017 The two-way ANOVA shown in Figure 1c–e was performed as follows . The two factors were stimulus ( with six levels ) and decision ( with two levels ) , the interaction term was included , and a separate ANOVA was run for the firing rate of each neuron at each time point . Significance level was set at α=0 . 05 . Effect size was defined as partial omega squared with a sign given by the sign of the correlation coefficient between firing rate and the corresponding parameter . It can take values between -1 and 1 , with 0 meaning no effect . For one-way ANOVA with a single two-level factor ( which is a t-test ) , it would reduce to the signed R2 between firing rate and factor level . For Figure 1f–g , we ran linear regressions for the firing rate of each neuron at t=0 . 25 s and t=3 . 75 s , taking F1 stimulus value in Hz as one predictor , decision as another one , and including an interaction effect . Predictors were standardized ( so regression coefficients in Figure 1g are standardized coefficients ) . The components shown in Figure 1f were constructed following the 'targeted dimensionality reduction' method presented in Mante et al . ( 2013 ) ( see below for more details ) . To compute the proportion of explained PSTH variance , we arranged the two components ( obtained by either method ) into a matrix 𝐙 of 2×SQT size . Both the PSTH data matrix 𝐗~ and 𝐙 were centered by subtracting row means . Linear regression was used to find reconstruction weights 𝐁=𝐗~𝐙⊤ ( 𝐙𝐙⊤ ) -1 minimizing reconstruction error ∥𝐗~-𝐁𝐙∥2 . Then the proportion of explained variance was computed as R2=1-∥𝐗~-𝐁𝐙∥2/∥𝐗~∥2 . The PCA on Figure 1i–k was done on the centered PSTH data matrix 𝐗~ . Let its singular value decomposition be 𝐗~=𝐔𝐒𝐕⊤ . Then each subplot on Figure 1j is a histogram of elements of one column of 𝐔 and each subplot on Figure 1j is one column of 𝐕 . To understand the differences of dPCA with respect to other ( demixing ) methods , we will make several explicit comparisons . The first method we will consider is performing a series of standard PCAs in each marginalization separately . This procedure can be understood in two ways: after performing PCA on 𝐗ϕ and obtaining the matrix 𝐔ϕ for the k leading principal directions , we can use this matrix to project either the marginalized data or the full data . In the first case we obtain the principal components of the corresponding marginalization , 𝐔ϕ𝐗ϕ . However , while these components provide a particular decomposition or visualization of the data , they do not constitute readouts of the neural activity , since they are based on projecting the marginalized data . One particular advantage of the dPCA formulation is that it operates on the raw data , so that the decoders ( and encoders ) can actually be used on single trials . In turn , the visualization of the data found through dPCA also provides insights into the utility of the respective population code for the brain . In the second case we obtain 𝐔ϕ𝐗 components from the full data , so that these components could be obtained by projecting single-trial activities . However , now there is no guarantee that these components will be demixed . For a simple counter-example , consider Figure 13: the stimulus marginalization 𝐗s consists of three points ( one for each stimulus ) located roughly on a horizontal axis , and so the first principal axis of 𝐗s is roughly horizontal . It is easy to see that the projection of the full data onto this axis will be not only stimulus- , but also time-dependent . 10 . 7554/eLife . 10989 . 018Figure 13 . Toy example illustrating the pseudo-inverse intuition . ( a ) Firing rate trajectories of two neurons for three different stimuli . ( b ) Same data with dPCA decoding and encoding axes . The encoding axes are approximately equivalent to the axes of the principal components in this case . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 018 Nonetheless , we can obtain a reasonable approximation to the dPCA solution using PCA in each marginalization . Namely , 𝐔ϕ can be taken to constitute the encoders 𝐅ϕ . In turn , the decoders 𝐃ϕ are obtained by a pseudo-inverse 𝐃=𝐔+ , where 𝐔 is a matrix with 4k columns obtained by joining together all 𝐔ϕ . We found that this procedure provides a close approximation of the actual decoder and encoder matrices , provided one chooses a reasonable value of k: choosing k too small results in poor demixing , and choosing k too large results in overfitting . In our datasets , k=10 provides a good trade-off . This approximate solution highlights the conditions under which dPCA will work well , i . e . , result in well-demixed components that capture most of the variance of the data: the main principal axes of different marginalizations 𝐗ϕ need to be non-collinear . In other words , principal subspaces of different marginalizations should not overlap . Next , we compare dPCA with 'targeted dimensionality reduction' ( TDR ) , the method proposed by Mante et al . ( 2013 ) . Briefly , the algorithm underlying TDR works as follows: We applied TDR to all our datasets and observed that dPCA consistently outperforms it in terms of capturing variance and demixing task parameters . First , unlike dPCA , TDR yields only one component per task parameter . Second , even this component tends to retain more mixed selectivity than the corresponding dPCA component . Some representative components are shown in Figure 14 . 10 . 7554/eLife . 10989 . 019Figure 14 . Some demixed components as given by three different demixing methods ( rows ) in various datasets and marginalizations ( columns ) . Empty subplots mean that the corresponding method did not find any components . All projections were z-scored to make them of the same scale . Barplots on the right show fractions of variance in each marginalization for each component ( stimulus in blue , decision in red , interaction in purple , condition-independent in gray ) : ∥𝐝𝐗~ϕ∥2/∥𝐝𝐗~∥2 . Barplots consisting of a single colour correspond to perfect demixing . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 019 Linear Discriminant Analysis ( LDA ) is usually understood as a one-way technique: there is only one parameter ( class id ) associated with each data point , whereas in this manuscript we dealt with three parameters simultaneously . Therefore , LDA in its standard form cannot directly be applied to the demixing problem . We can , however , use the same data and covariance decompositionX=∑ϕXϕ+XnoiseC=∑ϕCϕ+Cnoise that dPCA is using and construct a separate LDA for each marginalization ϕ . To the best of our knowledge , this framework does not have an established name , so we call it factorial LDA . Let us first consider the case of finding demixed components for marginalization 𝐗ϕ . We will denote the remaining part of the data matrix as 𝐗-ϕ=𝐗-𝐗ϕ and the remaining part of the covariance matrix as 𝐂-ϕ=𝐂-𝐂ϕ . In turn , the goal of LDA will be to find linear projections that have high variance in 𝐂ϕ and low variance in 𝐂-ϕ . In LDA , these matrices are usually called between-class and within-class covariance matrices ( Hastie et al . , 2009 ) . The standard treatment of LDA is to maximize the multivariate signal-to-noise ratiotr ( DCϕD⊤[DC−ϕD⊤]−1 ) , where 𝐃 is the matrix with discriminant axes in rows . The well-known solution is that 𝐃LDA is given by the leading eigenvectors of 𝐂-ϕ-1𝐂ϕ ( stacked together as rows ) , or , equivalently , as eigenvectors of 𝐂-1𝐂ϕ . More useful for our purposes is the reformulation of LDA as a reduced-rank regression problem ( Izenman , 2008; De la Torre , 2012 ) . When classes are balanced , it can be formulated asLLDA=∥𝐆ϕ-𝐅𝐃𝐗∥2 , where 𝐆ϕ is a class indicator matrix . This matrix has as many rows as there are possible values of parameter ϕ and specifies which data point is labeled with which parameter value: Gij=1 if the j-th data point belongs to class i ( has i-th value of the parameter ϕ ) and Gij=0 otherwise . In the toy example shown in Figure 2 , there are three classes with five points each , and so 𝐆ϕ will be a 3×15 matrix of zeros and ones . In this reformulation of LDA , the main interest is in the decoder matrix 𝐃 , whereas the encoder matrix 𝐅 , which serves to map the low-dimensional representation onto the class indicator matrix , plays only an auxiliary role . In contrast , the dPCA loss function isLdPCA=∥𝐗ϕ-𝐅𝐃𝐗∥2 , where 𝐗ϕ is the matrix of the same size as 𝐆ϕ with j-th column being the class centroid of the class to which the j-th point belongs . This comparison highlights the difference between the two methods: LDA looks for decoders that allow to reconstruct class identity ( as encoded by 𝐆ϕ ) whereas dPCA looks for decoders that allow to reconstruct class means ( as encoded by 𝐗ϕ ) . Figure 2b , f , h provides a toy example of a situation when these two goals yield very different solutions: the LDA projection separates the three classes better than the dPCA projection , but the dPCA projection preserves the information about the distance between classes . Using the explicit solution for reduced-rank regression , one can show that LLDA does indeed have eigenvectors of 𝐂-1𝐂ϕ as a solution 𝐃LDA for decoder ( see Section 8 . 5 . 3 in Izenman , 2008 ) . Following the similar logic for LdPCA , one can derive the corresponding expression for the dPCA decoder: 𝐃dPCA is given by the eigenvectors of 𝐂-1𝐂ϕ2 ( personal communication with Maneesh Sahani ) . A statistical test known as MANOVA can be seen as another possible factorial generalization of LDA . Given the same data and covariance decomposition , MANOVA tests if the effect of ϕ is statistically significant by analyzing eigenvalues of 𝐂noise-1𝐂ϕ . The eigenvectors of this matrix can in principle serve as decoders , but these projections are optimized to separate the contribution of ϕ from noise , not from the contributions of noise and other parameters . Hence , MANOVA is not the appropriate method for demixing purposes . While the toy example of Figure 2 illustrates that dPCA and LDA will in principle have very different solutions , we note that in all datasets considered here factorial LDA and dPCA yielded very similar components . This may reflect several pecularities of the data: for instance , the population activity for different values of the same parameter was spaced rather evenly , and all decisions were binary . Nevertheless , we emphasize that dPCA is better suited for ( demixed ) dimensionality reduction due to its focus on reconstructing the original data , as explained and discussed in the Results ( Figure 2 ) . Demixed PCA as presented here is conceptually based on our previous work . Machens et al . ( 2010 ) suggested a demixing method called difference of covariances ( DOC ) that can only handle two parameters , e . g . stimulus s and time t . Given PSTHs 𝐱 ( s , t ) , DOC first constructs stimulus-dependent and time-dependent marginalizations 𝐱¯ ( s ) =⟨𝐱 ( s , t ) ⟩t and 𝐱¯ ( t ) =⟨𝐱 ( s , t ) ⟩s , and then computes the difference between the stimulus and time covariance matrices 𝐂s=⟨𝐱¯ ( s ) 𝐱¯ ( s ) ⊤⟩ and 𝐂t=⟨𝐱¯ ( t ) 𝐱¯ ( t ) ⊤⟩ , 𝐒=𝐂s-𝐂t . Eigenvectors of S with maximum ( positive ) eigenvalues correspond to directions with maximum stimulus variance and minimum decision variance . Vice versa , eigenvectors with minimum ( negative ) eigenvalues correspond to directions with maximum decision variance and minimum stimulus variance . In the toy example presented in Figure 2 DOC finds the axis that is very close to the first PCA axis of class centroids ( which is also very close to the dPCA encoder axis shown on the figure ) , providing worse demixing than both LDA and dPCA . A possible extension of DOC to more than two parameters is described in Machens ( 2010 ) . Here the PSTHs are assumed to depend on M parameters , and the method constructs M marginalizations by averaging over all parameters except one . The respective covariance matrices 𝐂ϕ are then formed as above . The extension of DOC seeks to find the matrix of orthogonal directions 𝐔 such thatL=∑ϕtr ( Uϕ⊤CϕUϕ ) is maximized subject to 𝐔⊤𝐔=𝐈 where 𝐔=[𝐔1…𝐔M] . For M=2 this can be shown to be equivalent to the original DOC . Note that Machens ( 2010 ) did not address the interaction terms . The connection between the current dPCA and the DOC approach can be made more explicit if we consider the full covariance decomposition C=∑ϕCϕ and introduce into the dPCA loss function an additional constraint that both encoder and decoder should be given by the same matrix with orthonormal columns: 𝐅ϕ=𝐃ϕ⊤=𝐔ϕ . Then∥Xϕ−UϕUϕ⊤X∥2=∥Xϕ−UϕUϕ⊤Xϕ∥2+∥UϕUϕ⊤X−ϕ||2=∥Xϕ∥2−∥UϕUϕ⊤Xϕ||2+∥UϕUϕ⊤X−ϕ||2∼−tr ( Uϕ⊤ ( Cϕ−C−ϕ ) Uϕ ) , where the first equality follows from properties of the decomposition , the second equality from the properties of the orthonormal matrices 𝐔ϕ , and the third equality uses the definition of the covariance . This derivation shows that the difference of covariances 𝐂ϕ-𝐂-ϕ emerges from the dPCA loss function if the decoder and encoder are given by the same set of orthogonal axes . However , such axes 𝐔ϕ from different marginalizations ϕ will in general not be orthogonal to each other , whereas both DOC and its generalization insisted on orthogonal axes . Both the original DOC and its extension ignored interaction terms . Brendel et al . ( 2011 ) introduced interaction terms and the full covariance splitting C=∑ϕCϕ as described in this manuscript , and developed a probabilistic dPCA model based on probabilistic PCA ( PPCA ) ; to remove ambiguity we call this method dPCA-2011 . Similar to PPCA , dPCA-2011 assumes that the data are described by a linear model with Gaussian residuals , i . e . p ( 𝐱|𝐳 ) =𝒩 ( 𝐖𝐳 , σ2𝐈 ) , but the prior over the components z is chosen such that the components are sparsely distributed over marginalizations . In other words , the prior is chosen such that those components are favored that have variance in only one marginalization . Under the constraint that decoding directions W are orthogonal , the model can be fit using the expectation-maximization algorithm . However , the probabilistic formulation of Brendel et al . ( 2011 ) still suffers from the orthogonality constraint . As explained in the Discussion , the orthogonality constraint is too rigid and can prevent successful demixing if parameter subspaces are sufficiently non-orthogonal . Indeed , we applied dPCA-2011 to all our datasets and observed that dPCA-2015 showed better demixing ( Figure 14 ) . Moreover , dPCA-2011 failed to find any decision components in the visuospatial working memory task . In addition , the formulation of dPCA in this manuscript is radically simplified compared to Brendel et al . ( 2011 ) , features an analytic solution and is easier to compare with other linear dimensionality reduction techniques . Above we presented marginalization procedure for three parameters . In order to generalize it for an arbitrary number of parameters , we introduce a more general notation . We denote as Ψ the set of parameters ( in the previous section Ψ={t , s , d}; note that the trial index is not included into Ψ ) and write x¯ψ to denote a decomposition term that depends on a subset of parameters ψ⊆Ψ . In particular , x¯∅=x¯ . In full analogy to the 3-parameter case , each term can be iteratively computed viax¯ψ=⟨x−∑τ⊂ψx¯τ⟩Ψ∖ψ=⟨x⟩Ψ∖ψ−∑τ⊂ψx¯τ , where ⟨⋅⟩Ψ∖ψ denotes averaging over all parameters that are not elements of ψ and averaging over the trial index . This equation can be rewritten in a non-iterative way by expanding the sum; this yields the expression with alternating signs that is similar to our ANOVA-style equations above: ( ⋆ ) x¯ψ=∑τ⊆ψ ( −1 ) |τ|⋅⟨x⟩ ( Ψ∖ψ ) ∪τ . One can verify that this formula correctly describes the 3-parameter case presented above; the general case can be proven by induction . The noise term is defined viaxnoise=x−∑ψx¯ψ=x−⟨x⟩∅ . This decomposition has several useful properties . First , the average of any marginalization x¯ψ over any parameter γ∈ψ is zero . This can be seen from the equation ( ⋆ ) because after averaging over γ all terms will split into pairs with opposite signs ( indeed , for each τ∋γ there is another τ′=τ∖γ ) . Second , all marginalizations are pairwise uncorrelated , i . e . their covariance is zero: ⟨x¯ψx¯χ⟩Ψ=0 . This can be seen from equation ( ⋆ ) because x¯ψ and x¯χ both consist of an even number of terms with alternating signs , so their product will also consist of an even number of terms with alternating signs , and after averaging over Ψ all terms will become equal to ± x¯2 and cancel each other . Third , from the definition of the noise term it follows that any marginalization x¯ψ is uncorrelated with the noise term: ⟨x¯ψx¯noise⟩Ψ=0 . The fact that all marginalizations and the noise are pairwise uncorrelated allows to segregate the variance of x ( here we assume that x is centered , i . e . x¯=0 ) :var[x]=⟨x2⟩Ψ=⟨ ( ∑ψx¯ψ+xnoise ) 2⟩Ψ=∑ψ⟨x¯ψ⟩Ψ+⟨xnoise2⟩Ψ=∑ψvar[x¯ψ]+var[xnoise] . Turning now to the multivariate case , if we replace x with x∈RN , everything remains true but variances should be replaced by covariance matrices:C=⟨xx⊤⟩Ψ=∑ψCψ+Cnoise . Note that in ANOVA literature one usually talks about decomposing sums of squares ∑x2 and in MANOVA literature about decomposing scatter matrices ∑𝐱𝐱⊤ , because ( co ) variances of different terms are computed from these sums using different denominators ( depending on the corresponding number of degrees of freedom ) and do not add up . We do not make this distinction and prefer to talk about decomposing the ( co ) variance , i . e . all ( co ) variances here are defined with the same denominator equal to the total number of sample points . Consider the decision components in the somatosensory working memory task , Figure 3 . Here the second and the third components are closely resembling the first and second temporal derivatives of the leading decision component . To illustrate why these components are likely to be artifacts of the underlying sampling process , consider a highly simplified example in which a population of N neurons is encoding a one-dimensional bell-shaped signal z ( t ) in the population vector 𝐚 , i . e the population response is given by 𝐲 ( t ) =𝐚z ( t ) . In this case , the population response lies in the one-dimensional subspace spanned by 𝐚 and the covariance matrix has rank one:𝐂=⟨𝐲 ( t ) 𝐲⊤ ( t ) ⟩t=𝐚𝐚⊤⟨z ( t ) 2⟩t . Now consider the case in which the neurons are not recorded simultaneously but are pooled from different sessions . In behavioural experiments it is unavoidable that the onset of ( self-timed ) neural responses will vary by tenths or hundreds of milliseconds . Hence , the individual response yi ( t ) of neuron i will experience a small time-shift τi so that yi ( t ) =aiz ( t+τi ) , see Figure 15 for an example with Gaussian tuning curves . If τi is small we can do a Taylor expansion around t , yi ( t ) =aiz ( t ) +aiτiz′ ( t ) +𝒪 ( τi2 ) . 10 . 7554/eLife . 10989 . 020Figure 15 . Fourier-like artifacts in PCA . ( Left ) In this toy example , single neuron responses are generated from the same underlying Gaussian but are randomly shifted in time . ( Right ) First three PCA components of the population data . While the leading component resembles the true signal , higher order components look like higher Fourier harmonics . They are artifacts of the jitter in time . DOI: http://dx . doi . org/10 . 7554/eLife . 10989 . 020 where we neglect higher-order corrections for simplicity , but the extension is straight-forward . Let 𝝉 be the vector of time-shifts of all neurons and let 𝐛=𝐚∘𝝉 be the element-wise vector product of 𝐚 and 𝝉 , i . e . [𝐚∘𝝉]i=aiτi . Then the population response can be written asy ( t ) ≈az ( t ) +bz′ ( t ) . Hence , the covariance matrix becomes approximatelyC≈aa⊤⟨z2 ( t ) ⟩t+bb⊤⟨z′2 ( t ) ⟩t , where we assumed for simplicity that 𝐚⟂𝐛 . In other words , time-shifts between observations will result in additional PCA components that roughly resemble the temporal derivatives of the source component . The dPCA code is available at http://github . com/machenslab/dPCA for Matlab and Python . All four datasets used in this manuscript have been made available at http://crcns . org ( Romo et al . , 2016; Constantinidis et al . , 2016; Feierstein et al . , 2016; Uchida et al . , 2016 ) . Our preprocessing and the main analysis scripts ( Matlab ) are available at http://github . com/machenslab/elife2016dpca . | Many neuroscience experiments today involve using electrodes to record from the brain of an animal , such as a mouse or a monkey , while the animal performs a task . The goal of such experiments is to understand how a particular brain region works . However , modern experimental techniques allow the activity of hundreds of neurons to be recorded simultaneously . Analysing such large amounts of data then becomes a challenge in itself . This is particularly true for brain regions such as the prefrontal cortex that are involved in the cognitive processes that allow an animal to acquire knowledge . Individual neurons in the prefrontal cortex encode many different types of information relevant to a given task . Imagine , for example , that an animal has to select one of two objects to obtain a reward . The same group of prefrontal cortex neurons will encode the object presented to the animal , the animal’s decision and its confidence in that decision . This simultaneous representation of different elements of a task is called a ‘mixed’ representation , and is difficult to analyse . Kobak , Brendel et al . have now developed a data analysis tool that can ‘demix’ neural activity . The tool breaks down the activity of a population of neurons into its individual components . Each of these relates to only a single aspect of the task and is thus easier to interpret . Information about stimuli , for example , is distinguished from information about the animal’s confidence levels . Kobak , Brendel et al . used the demixing tool to reanalyse existing datasets recorded from several different animals , tasks and brain regions . In each case , the tool provided a complete , concise and transparent summary of the data . The next steps will be to apply the analysis tool to new datasets to see how well it performs in practice . At a technical level , the tool could also be extended in a number of different directions to enable it to deal with more complicated experimental designs in future . | [
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"neuroscience"
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Heterochromatic domains are enriched with repressive histone marks , including histone H3 lysine 9 methylation , written by lysine methyltransferases ( KMTs ) . The pre-replication complex protein , origin recognition complex-associated ( ORCA/LRWD1 ) , preferentially localizes to heterochromatic regions in post-replicated cells . Its role in heterochromatin organization remained elusive . ORCA recognizes methylated H3K9 marks and interacts with repressive KMTs , including G9a/GLP and Suv39H1 in a chromatin context-dependent manner . Single-molecule pull-down assays demonstrate that ORCA-ORC ( Origin Recognition Complex ) and multiple H3K9 KMTs exist in a single complex and that ORCA stabilizes H3K9 KMT complex . Cells lacking ORCA show alterations in chromatin architecture , with significantly reduced H3K9 di- and tri-methylation at specific chromatin sites . Changes in heterochromatin structure due to loss of ORCA affect replication timing , preferentially at the late-replicating regions . We demonstrate that ORCA acts as a scaffold for the establishment of H3K9 KMT complex and its association and activity at specific chromatin sites is crucial for the organization of heterochromatin structure .
Origin recognition complex-associated ( ORCA/LRWD1 ) , a protein required for the initiation of DNA replication , preferentially localizes to heterochromatic regions in post-replicated cells ( Bartke et al . , 2010; Shen et al . , 2010; Vermeulen et al . , 2010 ) . We and others have demonstrated that ORCA and ORC associate with centromeric and telomeric heterochromatin in mammalian cells ( Shen et al . , 2010 ) . Furthermore , using a stable isotope labeling by amino acids in cell culture ( SILAC ) -based proteomic approach , ORCA-ORC complex has been shown to bind the repressive histone lysine methylation marks , specifically H3K9me3 , H3K27me3 , and H4K20me3 ( Bartke et al . , 2010; Vermeulen et al . , 2010 ) that are known to be enriched at heterochromatic sites . ORCA contains a WD domain , a structure known to interact with nucleosomes/histones ( Wysocka et al . , 2005 ) . We have previously demonstrated that the WD domain of ORCA is crucial for its binding to heterochromatin . Furthermore , ORCA is critical for stabilizing ORC binding to chromatin ( Shen et al . , 2010 ) . ORC , a hetero-hexameric complex , in addition to serving as the landing pad for the assembly of pre-replicative complex at the origins of DNA replication , participates in sister chromatid cohesion , heterochromatin organization , and chromosome segregation ( Bell et al . , 1993; Shimada et al . , 2002; Sasaki and Gilbert , 2007 ) . In metazoans , ORC also facilitates the association of heterochromatin protein 1 ( HP1 ) to the H3K9me3-containing pericentric heterochromatin ( Pak et al . , 1997; Prasanth et al . , 2004 , 2010 ) . Thus , it is obvious that ORC-ORCA complex associates with heterochromatin , but the mechanism underlying the recruitment of this multiprotein complex to the condensed chromatin and the functional relevance of such association has remained elusive for decades . Histone lysine methylation , catalyzed by lysine methyltransferases ( KMTs ) , plays key roles in the epigenetic regulation of chromatin organization , transcription , and replication ( Black et al . , 2012 ) . Methylation of H3K9 is an abundant and stable modification and is an important regulator of heterochromatin formation , gene silencing , and DNA methylation ( Martin and Zhang , 2005 ) . The methyl modifications on H3K9 exist in distinct mono- , di- , and tri-methyl states ( H3K9me1 , H3K9me2 , and H3K9me3 , respectively ) , with each responsible for governing distinct cellular functions . In general , H3K9me1 and H3K9me2 are associated with gene expression/repression at euchromatic regions , whereas the H3K9me3 , enriched at pericentric heterochromatin , is required for heterochromatin assembly and gene silencing ( Martin and Zhang , 2005 ) . The major KMTs catalyzing these modifications are G9a and GLP , responsible for H3K9me2 ( Tachibana et al . , 2001; Rice et al . , 2003; Shinkai and Tachibana , 2011 ) ; SETDB1 , which establishes H3K9 di- and tri-methylation in euchromatin ( Schultz et al . , 2002 ) and Suv39H1/H2 that establishes H3K9me3 from mono- or di-methylated H3K9 ( Rea et al . , 2000; Peters et al . , 2003 ) . While the idea of G9a and Suv39H1 acting in distinct , primarily in non-overlapping chromatin contexts held sway for a long time , this concept has been recently challenged by the discovery of a complex consisting of multiple H3K9 KMTs ( Fritsch et al . , 2010 ) . The multimeric complex contains all four H3K9 KMTs G9a , GLP , Suv39H1 , and SETDB1 and is recruited to both pericentromeric heterochromatin and promoter of a set of G9a-repressed genes where it aids in gene repression by maintaining H3K9me2 and H3K9me3 marks ( Fritsch et al . , 2010 ) . Furthermore , destabilizing even one of these KMTs resulted in the disintegration of the multimeric complex and loss of the enzymatic activity of this complex ( Fritsch et al . , 2010 ) . How this multimeric KMTs complex is recruited to specific chromatin sites remained to be determined . In the broader context , the functional significance of the crosstalk between chromatin modifying and replication machineries has remained largely unexplored . Here , we demonstrate that ORCA associates with H3K9 KMTs in a chromatin context-dependent manner . By using a highly sensitive and quantitative single-molecule pull-down ( SiMPull ) approach ( Jain et al . , 2011; Shen et al . , 2012 ) , we demonstrate that ORCA preferentially binds to H3K9me3 and ORCA-ORC , and multiple H3K9 KMTs exist in a single complex . Furthermore , ORCA is required for the formation and/or maintenance of the H3K9 KMT complex . Our results indicate that ORCA is required for the integrity of global chromatin architecture . In the absence of ORCA , human cells show alterations in the binding and activity of KMTs at sites enriched for these factors with concomitant reduction in H3K9me2 and H3K9me3 marks . Finally , we observe that the cells lacking ORCA display abnormal heterochromatin organization and alteration in the replication timing , specifically at the late-replicating regions . We propose that ORCA is a scaffold protein that is required for the establishment as well as maintenance of heterochromatin .
In order to address if ORCA interacts with the machinery that causes the establishment of heterochromatin , we used a candidate approach to investigate the interaction of ORCA with individual H3K9 KMTs that catalyze H3K9 repressive modifications . We observed robust interaction of endogenous ORCA with endogenous G9a and Suv39H1 ( Figure 1Aa , Ab , Figure 1—figure supplement 1Aa , Ab ) . 1 . 31% of total G9a was found to be in a complex with ORCA . Quantitation was based on the amount of G9a immunoprecipitated with ORCA ( based on 100% efficiency of ORCA IP , Figure 1—figure supplement 1B ) ( n = 7 ) . Similarly , 1 . 44% of total Suv39H1 was in a complex with ORCA ( n = 4 ) . Note that only about 0 . 2% of the endogenous H3K9 KMTs co-purified with Suv39H1 ( Fritsch et al . , 2010 ) . Co-immunoprecipitation ( co-IP ) using T7-tagged ORCA and Flag-tagged H3K9 KMTs revealed interaction of ORCA with G9a , GLP , and Suv39H1 , all enzymes involved in the establishment of heterochromatin ( Figure 1Ba , Bb ) . In addition , we carried out IP from cell lines stably expressing Flag-tagged-G9a or GLP . IP from nuclear extracts using Flag antibody to determine the association of endogenous ORCA with the KMTs . ORCA along with Orc2 and MCMs was found to interact with the KMTs ( Figure 1—figure supplement 1C ) . However , ORCA did not associate with the arginine methyltransferase PRMT5 ( Figure 1—figure supplement 1D ) , showing the specificity of the interactions . 10 . 7554/eLife . 06496 . 003Figure 1 . ORCA interacts with multiple repressive histone lysine methyltransferases . ( A ) a IP using origin recognition complex-associated ( ORCA ) Ab from U2OS cells . ORCA , G9a , and Suv39H1 were analyzed by immunoblotting ( IB ) . b IP using G9a Ab from U2OS cells . Endogenous ORCA , G9a , and Suv39H1 were analyzed by IB . ( B ) a and b Immunoprecipitation ( IP ) using ORCA antibody ( Ab ) from cells expressing T7-ORCA and different Flag-KMTs: a H3K9 KMTs G9a; b H3K9 KMT GLP and Suv39H1 . ( C ) U2OS 2-6-3 CLTon cells co-transfected with individual YFP-LacI-KMTs and CFP-ORCA . Inset represents 150% magnification of the boxed region . ( D ) IP using T7 ab from cells co-expressing T7-ORCA and; a Flag-G9a or b Flag-Suv39H1 in the presence ( + ) or absence ( − ) of EtBr . ( E ) Direct interaction of ORCA and a G9a or b SUV39H1 using purified proteins . ‘*’ denotes cross reacting band and ‘’denotes ORCA . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 00310 . 7554/eLife . 06496 . 004Figure 1—figure supplement 1 . Interaction of ORCA with histone methyltransferases . ( A ) a . Endogenous ORCA IP in untransfected ( UT ) U2OS and in U2OS cells expressing full length ( FL ) or the SET domain of HA-Suv39H1 . b . Endogenous ORCA IP in untransfected ( UT ) U2OS and in U2OS cells expressing full length ( FL ) or the Ankyrin ( ANK ) domain of HA-G9a . ( B ) IB showing efficient depletion of endogenous ORCA from U2OS nuclear extract by using ORCA antibody . ( C ) Flag IP in Hela cells stably expressing Flag-HA-G9a or Flag-HA-GLP . IPs were conducted using nuclear soluble ( S ) or chromatin ( P ) fractions and endogenous ORCA , ORC2 , MCM3 , Geminin and PCNA were analyzed by IB . ( D ) ORCA does not interact with arginine methyltransferase ( RMT ) PRMT5 . IP using T7 Ab from cells expressing T7-ORCA and HA-PRMT5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 004 In order to show functional co-recruitment of ORCA and the H3K9 KMTs , we used an in vivo cell system ( CLTon ) that uses a 200 copy transgene array-containing lac operator repeats stably integrated into human osteosarcoma ( U2OS ) cells as a single heterochromatic locus that can be visualized by Cherry-lac repressor ( LacI ) . Furthermore , transcriptional activation using doxycycline ( DOX ) causes the decondensation of the locus ( Janicki et al . , 2004; Shen et al . , 2010 ) . We tethered the triple fusion proteins of YFP-LacI-KMTs to the heterochromatic locus and examined if these enzymes could recruit ORCA to the locus . This approach corroborated the interaction of ORCA with G9a ( Figure 1C ) . We next examined whether ORCA and H3K9 KMTs ( G9a and Suv39H1 ) assembly requires intact DNA . Co-IPs from cells expressing G9a and ORCA or Suv39H1 and ORCA were carried out in the presence or absence of ethidium bromide ( EtBr ) . EtBr selectively inhibits DNA-dependent protein interactions ( Lai and Herr , 1992 ) . ORCA continued to show interaction with G9a as well as Suv39H1 even in the presence of EtBr ( Figure 1Da , Db ) , indicating that these interactions were DNA-independent . The DNA-independent interactions were also corroborated by co-IP experiments in the presence of the nuclease benzonase ( data not shown ) . Furthermore , the interaction of ORCA with G9a as well as Suv39H1 was direct and independent of DNA , as evident by the direct interaction of purified ORCA with G9a/Suv39H1 proteins ( Figure 1Ea , Eb ) . Recent studies have demonstrated that in addition to Suv39H1 , G9a/GLP may participate in the establishment of pericentric heterochromatin ( Vassen et al . , 2006; Dong et al . , 2008; Kondo et al . , 2008 ) . Since ORCA is enriched at heterochromatic regions , we carried out detailed functional characterization of the interaction of ORCA with Suv39H1 and also with G9a in order to dissect the biological relevance of these associations . To map the interaction domains of ORCA with G9a and Suv39H1 , we generated several truncation mutants of ORCA ( Figure 2Aa ) , G9a ( Figure 2Ab ) , and Suv39H1 ( Figure 2Ac ) . Using co-IP experiments , we observe that the WD repeats of ORCA ( truncation mutants 128–647 and 270–647 aa ) interacted with G9a ( Figure 2—figure supplement 1Aa ) and Suv39H1 ( Figure 2—figure supplement 1Ab ) . We found that the deletion of any one of the WD domains in ORCA resulted in loss of binding to heterochromatin consistent with the fact that the intact β-propeller structure of WD is crucial to maintain its functionality . We also observed that the leucine-rich repeats ( LRR ) -containing fragment of ORCA ( 1–127 aa ) , but not the one containing the linker ( 1–270 aa ) , interacted with G9a ( Figure 2—figure supplement 1Aa ) but not with Suv39H1 ( Figure 2—figure supplement 1Ab ) . Co-IP experiments demonstrated that the ankyrin repeat ( 619–965 aa ) of G9a ( Figure 2Ba ) and the SET domain ( 151–412 aa ) of Suv39H1 were necessary for interaction with ORCA ( Figure 2Bb ) . 10 . 7554/eLife . 06496 . 005Figure 2 . ORCA associates with KMTs in a chromatin context-dependent manner . ( A ) a Schematic representation of various truncation mutants of ORCA containing a T7-epitope on the N-terminus . The specific domains that can associate with G9a and Suv39H1 based on–IB ( Figure 2—figure supplement 1Aa , Ab ) is depicted as ‘+’ . b Schematic representation of various truncation mutants of G9a containing a HA-epitope on the N-terminus . The interaction domain of G9a that interacts with ORCA ( Figure 2Ba ) is denoted as ‘+’ . c Schematic representation of various truncation mutants of Suv39H1 containing a HA-epitope on the N-terminus . The interaction domain of Suv39H1 that interacts with ORCA ( Figure 2Bb ) is denoted as ‘+’ . ( B ) a IP in U2OS cells expressing various HA-G9a mutants and T7-G9a using T7 Ab and analysis by T7 and HA-IB . b IP in U2OS cells expressing various HA-Suv39H1 mutants and T7-G9a using T7 Ab and analysis by T7 and HA IB . ‘*’ denotes the cross-reacting band . ( C ) a Cells co-transfected with YFP-LacI ( negative control ) and CFP-G9a or YFP-LacI-ORCA or the truncation mutants along with CFP G9a in CLTon cells . b The % of cells with CFP-G9a recruited to the locus is plotted . ( D ) a Cells co-transfected with YFP-LacI ( negative control ) and CFP-ORCA or YFP-LacI-G9a wild type and the mutants , which are catalytically inactive along with CFP-ORCA in CLTon cells . b The % of cells with CFP-ORCA recruited to the locus . ( E ) a U2OS 2-6-3 CLTon cells co-transfected with YFP-LacI-ORCA and CFP-G9a in the presence and absence of doxycycline . b The % of cells with CFP-G9a recruited to the locus in both conditions . Scale bars equal 10 μm . Inset represents 150% magnification of the boxed region . Error bars represent s . d . , n = 3 . ****p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 00510 . 7554/eLife . 06496 . 006Figure 2—figure supplement 1 . WD domain of ORCA interacts with H3K9 KMTs . ( A ) a . IP in U2OS cells expressing various T7-ORCA mutants and FLAG-G9a using T7 Ab and analysis of G9a by IB . b . IP in U2OS cells expressing various T7-ORCA mutants and HA-Suv39H1 using T7 Ab and analysis of Suv39H1 by IB . ( B ) a . Cells were co-transfected CFP-LacI and YFP-G9a ( negative control ) or CFP-LacI-ORCA along with YFP-G9a truncation mutants . b . The % of cells with YFP-G9a truncation mutants recruited to the locus is plotted . Note the significant reduction in the recruitment of YFP-G9a ( aa1-965 ) , the mutant lacking the SET domain , to the locus . Error bars represent s . d , n=3 . ****p<0 . 0001 . span=""> <0 . 0001 . span=""> ( C ) Localization of H3K9me2 in CLTon cells in cells with ( + ) or without ( - ) the expression of YFP-LacI-G9a . Note the H3K9me2 accumulation in YFP-LacI-G9a expressing cells . ( D ) IP in U2OS cells expressing T7-ORCA and GFP-G9a-full length and ∆SET mutant using T7 Ab and analysis of GFP-G9a by IB . ( E ) Localization in CLTon cells expressing CFP-LacI-ORCA , of HP1α and YFP-CDK9 , at heterochromatic ( -Dox ) as well as decondensed locus ( +Dox ) . the expression of YFP-LacI-G9a . Note the loss of HP1α and accumulation of YFP-CDK9 upon decondensation of the locus . Scale bar , 10µm . Inset represents 150% magnification of the boxed region . ( F ) Tethering of YFP-LacI-Orc3 recruits Orc2 at heterochromatic ( -Dox ) as well as decondensed locus ( +Dox ) . Scale bar , 10µm . Inset represents 150% magnification of the boxed region . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 006 To address if the chromatin context affected the interaction between ORCA and G9a , we used the CLTon cells and examined the interaction of YFP-LacI-fused full-length and various truncation mutants of ORCA tethered to the heterochromatic locus , with full-length or truncation mutants of G9a ( Figure 2Ca–Db , Figure 2—figure supplement 1Ba , Bb ) . The WD domain of ORCA was able to recruit CFP-G9a ( Figure 2Ca , Cb ) corroborating our IP results ( Figure 2—figure supplement 1Aa ) . Interestingly , when CFP-LacI-ORCA was co-transfected with YFP-G9a mutants ( Figure 2—figure supplement 1Ba ) , not only did the mutant YFP-G9a-1-618 , which lacks the ankyrin repeats , show significantly reduced interaction but also YFP-G9a-1-965 , which has an intact ankyrin repeat but lacks the SET domain , showed significantly reduced association with the locus ( Figure 2—figure supplement 1Ba , Bb ) . We next addressed if the interaction of ORCA with G9a at heterochromatic regions required the catalytic domain of G9a in addition to its ankyrin repeats . YFP-LacI-G9a triple fusion protein was found to be enzymatically active as is evident by the accumulation of H3K9me2 at the CLTon locus upon the tethering of G9a full-length construct ( Figure 2—figure supplement 1C ) . Tethering of YFP-LacI-G9a-ΔSET or YFP-LacI-G9a-H1166K ( a point mutant , which abolishes the catalytic ability of G9a ) to the locus ( Figure 2Da ) failed to recruit CFP-ORCA ( Figure 2Da , Db ) . However , co-IP experiments demonstrated that T7-ORCA could interact with GFP-G9a-ΔSET ( Figure 2—figure supplement 1D ) . These data suggest that while the ankyrin repeat of G9a is sufficient for the association with ORCA ( Figure 2Ba ) , the interaction of ORCA and G9a at the heterochromatin also requires the methylating ability of G9a . Similarly , the interaction between ORCA and Suv39H1 requires the SET catalytic domain ( Figure 2Bb ) . Since we observed the interaction between ORCA and the H3K9 KMTs at the heterochromatic locus , we next asked if the interaction occurred in a chromatin context-dependent manner . We tethered ORCA to the CLTon locus and examined the recruitment of G9a upon induction of transcription from the decondensed locus ( Figure 2Ea ) . In the absence of DOX , ∼80% of cells showed CFP-G9a recruitment to the locus when YFP-LacI-ORCA was tethered ( Figure 2Ea , Eb ) . Upon transcriptional activation , there was a striking reduction ( ∼10% ) in CFP-G9a association in the YFP-LacI-ORCA-tethered decondensed locus ( Figure 2Ea , Eb ) . ORCA-tethered decondensed locus , in addition to G9a , also failed to recruit HP1α , but contained Cdk9 , a component of the pTEFB kinase complex , which is part of the transcription elongation complex ( Figure 2—figure supplement 1E ) Two components of the ORC , Orc2 , and Orc3 that require each other for their stability associate with one another at both the condensed and the open chromatin ( Figure 2—figure supplement 1F ) . These results indicate that while ORCA can interact directly with G9a and Suv39H1 ( Figure 1Ea , Eb ) , the interactions could be dependent on or regulated by chromatin within the cells . In a cellular milieu , the fraction of ORCA directly interacting with G9a or Suv39H1 , independent of chromatin , is likely to be a very small pool , and therefore , too weak to be detected at the CLTon locus . Our earlier work demonstrated the existence of a subset of multiple H3K9 KMTs in a single complex and functional cooperation between these molecules to regulate heterochromatin function and gene expression ( Fritsch et al . , 2010 ) . Since ORCA interacts with different H3K9 KMTs , we investigated if ORCA is an integral component of this multi-KMT complex . For this purpose , we employed the process of SiMPull analysis ( Figure 3Aa , Ab ) ( Jain et al . , 2011 ) . This method is extremely sensitive and is a tour de force to examine protein complexes and also to accurately calculate the stoichiometry of proteins within the complexes ( Shen et al . , 2012 ) . This approach obviates the need for ensemble experiments that require IPs with large quantities of cell lysates . Our initial estimates predicted that three grams of Flag-HA-Suv39H1-expressing Hela-S3 cell pellet , which is ∼3 billion cells , is required for a single glycerol gradient sedimentation to obtain other H3K9 KMT signals detectable by Western blotting ( Fritsch et al . , 2010 ) . However , a relatively higher sensitivity can be achieved by the SiMPull approach by using only a million cells . We first measured the stoichiometry of ORCA bound ORC and H3K9 KMTs , respectively . We used cells co-expressing T7-ORCA and YFP-ORC1 ( Figure 3Ba–Bd ) or T7-ORCA and YFP-KMTs to perform SiMPull ( Figure 3Ca–Dd ) . ORCA complexes containing YFP-ORC1 or YFP-KMTs were visualized as isolated fluorescent spots by single-molecule total internal reflection fluorescence ( TIRF ) microscopy ( Figure 3Bb , Cb , Db ) . Pulldown by a control antibody ( anti-HA ) showed very low non-specific level of fluorescence , thereby demonstrating the high specificity of SiMPull assay . Individual fluorescence spots showed single- or multi-step decreases in fluorescence intensity corresponding to photobleaching of individual YFP molecules in a single complex ( a representative schematic of the photobleaching analysis is shown in Figure 3Aa ) . After photobleaching analysis of many co-immunoprecipitated YFP-ORC1 , YFP-G9a , and YFP-Suv39H1 , we found that primarily one molecule each of ORC1 , G9a , and Suv39H1 interacts with a single molecule of ORCA ( Figure 3Bd , Cd , Dd ) . In addition , we also observed that in a small population , ORCA associates with two molecules of G9a ( Figure 3Cd , Ce ) , suggesting that the ORCA-interacting-G9a may also be present as a homodimer . 10 . 7554/eLife . 06496 . 007Figure 3 . ORCA and H3K9 KMTs exist in one multimeric complex . ( A ) a Representative single-molecule fluorescence time trajectories for YFP-tagged molecules that exhibit one-step , two-step , and three-step photobleaching . b Key to the schematics of the SiMPull assay . ( B ) a and b Schematic and total internal reflection fluorescence ( TIRF ) images of YFP molecules pulled down from U2OS cell lysates expressing T7-ORCA and YFP-ORC1 using biotinylated T7 Ab . The same lysate incubated with biotinylated HA Ab served as the control . c Average number of YFP fluorescent molecules per imaging area ( 5000 μm2 ) . d Photobleaching step distribution for YFP-ORC1 bound to T7-ORCA . Note 1:1 ratio of ORCA to Orc1 . e Intensity profiles of the YFP-ORC1 molecules bound to T7-ORCA . ( C ) a–d ORCA-G9a pulldown . Shown are YFP molecules pulled down from U2OS cell lysates expressing T7-ORCA and YFP-G9a . Note 1:1 or 1:2 ratio of ORCA to G9a . e Intensity profiles of YFP-G9a molecules bound to T7-ORCA . ( D ) a–d ORCA-Suv39H1 pulldown . Shown are YFP molecules pulled down from U2OS cell lysates expressing T7-ORCA and YFP-Suv39H1 . Note 1:1 ratio of ORCA to Suv39H1 . e Intensity profiles of YFP-Suv39H1 molecules bound to T7-ORCA . ( E ) a–c Determination of ORCA complexes containing both ORC and G9a by SiMPull and colocalization analyses . a Schematic of YFP and mCherry molecules pulled down from U2OS cell lysates expressing T7-ORCA , YFP-ORC1 , and mCherry-G9a using biotinylated T7 Ab . The same lysate incubated with biotinylated HA Ab served as the control . b Average number of YFP and mCherry fluorescent molecules per imaging area ( 5000 μm2 ) . c Note 39 ± 5% overlap . Transfection condition used as indicated in Figure 3—figure supplement 1Aa , lane3 . ( F ) a–c Determination of ORCA complexes containing multiple H3K9 KMTs by SiMPull and colocalization analyses . a Schematic of YFP and mCherry molecules pulled down from U2OS cell lysates expressing T7-ORCA , YFP-Suv39H1 , and mCherry-G9a using biotinylated T7 Ab . The same lysate incubated with biotinylated HA Ab served as the control . b Average number of YFP and mCherry fluorescent molecules per imaging area ( 5000 μm2 ) . c Note 55 ± 7% colocalization . Transfection condition used as indicated in Figure 3—figure supplement 1Ba , lane3 . Scale bars , 10 μm . Error bars represent s . d . , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 00710 . 7554/eLife . 06496 . 008Figure 3—figure supplement 1 . ORC-ORCA-H3K9 KMTs exist in a single complex . ( A ) a . Titration of T7-ORCA , mCherry-G9a and YFP-ORC1 plasmids in U2OS cells . b-c . Determination of ORCA complexes containing both ORC and G9a by SiMPull and colocalization analyses . b . Average number of YFP and mCherry fluorescent molecules per imaging area ( 5000µm2 ) . c . Note 41±4% overlap . Transfection condition used as indicated in Fig . S3Aa , lane 5 . ( B ) a . Titration of T7-ORCA , mCherry-G9a and YFP-Suv39H1 plasmids in U2OS cells . b-c . Determination of ORCA complexes containing both G9a and Suv39H1 by SiMPull and colocalization analyses . b . Average number of YFP and mCherry fluorescent molecules per imaging area ( 5000µm2 ) . c . Note 46±11% overlap . Transfection condition used as indicated in Fig . S3Ba , lane 5 . ( C ) Sequential IP of HA-Orc1 followed by T7-ORCA from U2OS extracts expressing T7-ORCA , HA-Orc1 and Flag-G9a . IB of G9a corroborated the presence of Orc1-ORCA-G9a triple complex . ( D ) Sequential IP of HA-G9a followed by T7-ORCA from U2OS extracts expressing T7-ORCA , HA-G9a and Flag-Suv39H1 . IB of Suv39H1 corroborated the presence of G9a-ORCA-Suv39H1 triple complex . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 008 Next , we investigated whether ORCA bound to KMTs also contains ORC by performing SiMPull using biotin-conjugated anti-T7 antibody with cells co-transfected with YFP-ORC1 , mCherry-G9a , and T7-ORCA ( Figure 3Ea ) . To mimic endogenous expression levels of these proteins , we first systematically titrated the levels of plasmid transfected to obtain an expression of the candidate protein that is similar to endogenous levels ( Figure 3—figure supplement 1Aa ) . Based on this analysis , we transfected 2 × 106 cells with 100 ng of each plasmid and then carried out SiMPull ( Figure 3—figure supplement 1Aa , lane3 ) . Complexes of T7-ORCA that contain YFP-Orc1 were detected in the green imaging channel and those containing mCherry-G9a were detected in the red imaging channel ( Figure 3Eb , Ec ) . After overlaying the two channels , 39 ± 5% of YFP-ORC1 molecules colocalized with mCherry-G9a , indicating that all three proteins , ORC , ORCA , and G9a are found in a substantial fraction of single complexes ( Figure 3Ec ) . SimPull from cells that were transfected with higher concentration of plasmid showed similar extent of co-localization ( Figure 3—figure supplement 1Ab , Ac ) . The results are consistent with our earlier study showing that ORCA is protected from ubiquitin-mediated proteolysis when bound to ORC and as a result is always associated with ORC ( Shen and Prasanth , 2012 ) . Next , we tested whether multiple H3K9 KMTs exist in a single complex with ORCA using triple transfections of T7-ORCA , YFP-Suv39H1 , and mCherry-G9a in U2OS cells ( Figure 3Fa , Fc , plasmid titrations: Figure 3—figure supplement 1Ba , lane3 used for the experiment ) . Interestingly , we could observe ∼55 ± 7% of YFP-Suv39H1 colocalized with mCherry-G9a , ( Figure 3Fc ) . Similar results were obtained with higher concentration of plasmid transfection: ( Figure 3—figure supplement 1Bb , Bc ) , suggesting the existence of a significant amount of ORCA-G9a-Suv39H1 complex . The true degree of cohabitation may be even higher because the fluorescent proteins may not all mature into active chromophores . This leads to dark molecules and appearance of either only green or only red spots even though both the KMTs are present in a complex . In addition , unequal expression of the transfected KMTs or the presence of endogenous KMTs in the complexes may also lead to a reduction in cohabitation detected by SiMPull . Finally , only a subset of G9a and Suv39H1 may exist as a single complex with ORCA ( similar to reported data [Fritsch et al . , 2010] ) . Elucidation of three different proteins in a single complex is one of the promised capabilities of SiMPull ( Jain et al . , 2011 ) , and the data we present here constitute one of the first demonstrations of such a capability . In order to corroborate our SimPull observations on the existence of ORC-ORCA-H3K9 KMTs and G9a-ORCA-Suv39H1 in a single complex , we utilized sequential IPs . We carried out triple transfections of T7-ORCA , HA-ORC1 , and Flag-G9a in U2OS cells , followed by immunoprecipitation of HA-ORC1 . Following HA peptide elution , the eluate was used for T7-Ab immunoprecipitation . T7-ORCA was immunoprecipitated , and a robust co-IP of Flag-G9a was detected ( Figure 3—figure supplement 1C ) . This further confirmed the existence of ORC-ORCA-H3K9 KMTs in a single complex . Similarly , we performed triple transfections of T7-ORCA , HA-G9a , and Flag-Suv39H1 in U2OS cells followed by immunoprecipitation of HA-G9a . Following HA peptide elution , the eluate was used for T7-Ab immunoprecipitation . T7-ORCA was immunoprecipitated , and a robust co-IP of Flag-Suv39H1 was detected ( Figure 3—figure supplement 1D ) , further confirming the existence of multiple H3K9 KMTs in a single complex with ORCA . The exogenous expression of Suv39H1 did not affect the association of G9a and ORCA; similarly , the exogenous expression of G9a did not compromise the association of Suv39H1 and ORCA ( Figure 1—figure supplement 1Aa , Ab ) . Previous studies indicated that ORCA along with ORC associates with heterochromatic regions ( Prasanth et al . , 2010; Shen et al . , 2010 ) and also specifically binds to repressive histone and DNA marks ( Vermeulen et al . , 2010 ) . Since ORCA interacts with both H3K9me2 and H3K9me3-catalyzing enzymes , we examined the direct binding of ORCA to these marks . We performed peptide pull-downs with N-terminal peptides of histone H3 with the K9 differentially modified with acetylation or mono- , di- , or tri-methylation ( Figure 4—figure supplement 1A ) . Iodoalkyl agarose-conjugated peptides were incubated with purified His-tagged-ORCA . We found that ORCA displayed increased interaction with mono- , di- , and tri-methylated H3K9 compared to unmodified or K9-acetylated H3 peptides ( Figure 4—figure supplement 1A ) . To get a more quantitative estimation of the affinity of ORCA for differentially methylated H3K9 , we have employed SiMPull for the first time as a potential substitute for isothermal calorimetry . Biotinylated histone H3 N-terminal tails were immobilized on passivated quartz slides followed by passing lysates expressing full-length YFP-ORCA or the fragment 1–127 aa , which contains only the LRR and lacks WD domain necessary for binding to methylated histones ( Figure 4Aa ) . The level of YFP-ORCA expression was quantitated by a direct anti-GFP pull-down with both the lysates ( Figure 4—figure supplement 1Ba ) and analyses of the average number of molecules pulled down ( Figure 4—figure supplement 1Bb , Bc ) . The lysates were then diluted so that the expression of the YFP-tagged proteins is nearly equal and passed through the flow chambers containing the immobilized peptides . Analysis of the average number of molecules pulled down by the peptides revealed that ORCA has the highest affinity for H3K9me3 followed by for me2 and me1 ( Figure 4Ab , Ac ) . YFP-ORCA 1–127 aa showed a low-basal binding to all the peptides corroborating the necessity of WD domain of ORCA for specifically recognizing methylated histones . 10 . 7554/eLife . 06496 . 009Figure 4 . ORCA binds and regulates levels of H3K9 methylation . ( A ) a Schematic of experimental setup for peptide pulldown and analyses by SiMPull . b TIRF images of YFP-ORCA WT and 1–127 aa pulled down by H3K9 modified peptides . Note that the YFP-ORCA WT and 1–127 aa truncation mutant expressing lysates were diluted so that the concentration of the overexpressed proteins is comparable ( 200 and 800 times , respectively for WT and 1–127 aa ) . c Average number of fluorescent molecules per imaging area . Scale bars , 10 μm . ( B ) a Chromatin fractionation in ORCA-depleted U2OS cells followed by IB analysis of H3K9me2 and me3 . b Chromatin fractionation in ORCA-depleted diploid fibroblasts , WI38 followed by IB analysis of H3K9me2 and me3 . Splicing factor , SRSF1 is shown as a loading control . Error bars represent s . d . , n = 3 . S and S2-cytosolic; S3-nuclear soluble and MNase sensitive; P: nuclear; P3: nuclear insoluble and MNase resistant fraction . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 00910 . 7554/eLife . 06496 . 010Figure 4—figure supplement 1 . ORCA binding to H3K9 peptides . ( A ) Peptide pulldown using baculovirally purified His-ORCA and N-terminal histone H3 peptides , which are unmodified or acetylated , mono- , di- or tri methylated at K9 . ( B ) a . Schematic of GFP pulldown to quantitate YFP-ORCA expression levels by SiMPull . b . TIRF images of YFP-ORCA WT and aa1-127 pulled down by GFP Ab . Note that the aa1-127 truncation mutant is much more highly expressed compared to WT ( 5000 fold dilution of aa1-127 shows greater number of molecules/imaging area compared to 2000 fold diluted WT . c . Quantitation of average number of fluorescent molecules ( YFP-ORCA WT and aa1-127 ) per imaging area in ( Bb ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 010 To further determine whether ORCA is required for the establishment of these histone marks on chromatin , depletion of ORCA ( siRNA-mediated knockdowns [KDs] ) was carried out both in cancerous cells ( U2OS ) and diploid fibroblasts ( WI-38 ) , and the total levels of H3K9me2 and H3K9me3 were analyzed by immunoblotting ( IB ) ( Figure 4Ba , Bb ) . In both the cell lines , upon depletion of ORCA , the levels of H3K9me2 decreased while H3K9me3 remained unchanged at the resolution of Western blotting ( Figure 4Ba , Bb ) . To determine the involvement of ORCA in the genome-wide status of H3K9 methylation , we conducted H3K9me3 ChIP-sequencing upon ORCA depletion . We observed a significant decrease in H3K9me3 ChIP-seq signal upon ORCA KD ( Figure 5A , B ) . Around 18% of the detected peaks showed highly significant ( more than fivefold decrease ) changes in H3K9 tri-methylation in cells lacking ORCA ( Figure 5B , Supplementary file 1 and total H3K9 in Figure 5—figure supplement 1Aa ) . Interestingly , several regions did not show significant change in H3K9me3 association upon ORCA KD ( Figure 5—figure supplement 1Ab ) . Since ORCA associates with heterochromatin , we specifically analyzed the number of reads of satellite repeats in H3K9me3 ChIP and found that there was a significant reduction of this mark at these regions of the genome upon ORCA KD ( Figure 5Ca ) . Furthermore , H3K9me3 showed less association both with the telomeric ( TAR1 ) and centromeric ( SST1 ) repetitive DNA in cells lacking ORCA ( Figure 5Cb , Cc ) . 10 . 7554/eLife . 06496 . 011Figure 5 . Loss of ORCA leads to significant reduction in H3K9 methylation . ( A ) Model-based analysis of ChIP-sequencing ( MACS ) 1 . 4 peaks analysis of H3K9me3 ChIP-seq in control and ORCA-depleted cells . ( B ) Regions showing greater than fivefold decrease in H3K9me3 upon ORCA knockdown ( KD ) plotted along the length of the chromosomes in which they reside . ( C ) a Normalized number of reads of repetitive sequences in control and ORCA KD H3K9me3 ChIP-seq . Normalized number of reads of-b Telomeric repetitive sequences and c Centromeric repetitive sequences in control and ORCA KD H3K9me3 ChIP-seq . ( D ) a–d Representative regions showing significant decrease in reads in H3K9me3 ChIP on ORCA KD compared to the control . ( E ) a HA-ORCA ChIP at H3K9me3-target sites and ( b ) C-FOS . ( F ) a Suv39H1 ChIP and b IgG ChIP at regions showing decrease in H3K9me3 . Error bars represent s . d . , n = 3 . C-FOS is shown as negative control . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 01110 . 7554/eLife . 06496 . 012Figure 5—figure supplement 1 . ORCA depletion causes changes in chromatin architecture . ( A ) a . Regions with H3K9me3 peaks detected by ChIP-seq plotted along the length of the chromosomes in which they reside . Chromosome scale indicated at the bottom of the chromosomes . b . Regions showing less than 1 . 3 fold decreases in H3K9me3 upon ORCA knockdown plotted along the length of the chromosomes in which they reside . Chromosome scale is indicated at the bottom of the chromosomes . ( B ) a-c . Representative regions showing significant decrease in the reads in H3K9me3 ChIP on ORCA knockdown . c-FOS , a region which doesn’t show decrease in H3K9me3 is also shown . ( C ) a-b . H3K9me2 ChIP at regions showing decrease in H3K9me3 . ( D ) Chromatin fractionation in ORCA depleted U2OS cells and G9a and Suv39H1 IB analyses . SRSF1 , a splicing factor , was used as loading control . ( E ) a . G9a ChIP and b . IgG ChIP at regions showing alterations in H3K9me2 and me3 . Error bars represent s . d , n=3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 012 Our attempts on H3K9me2 ChIP-seq did not succeed because of the technical challenge associated with sequencing the broad H3K9me2 peaks . Similar problems with H3K9me2 ChIP-seq have been previously reported by other studies ( Yuan et al . , 2009 ) . As an alternate , regions that showed significant reduction of H3K9me3 in the ChIP-seq experiment ( as evident by the wiggle plots; Figure 5Da–Dd and Figure 5—figure supplement 1Ba , Bb ) were chosen for H3K9me2 ChIP-qPCR ( quantitative PCR ) validation ( Supplementary file 2 ) . These regions also consistently showed a significant reduction of H3K9me2 , corroborating the decrease seen in Western blotting ( Figure 5—figure supplement 1Ca , Cb ) . C-FOS , a gene that does not associate with these repressive histone marks , was used as a negative control ( Figure 5—figure supplement 1Bc–Cb ) . To determine whether the H3K9-targets are directly regulated by ORCA , we conducted ChIP using HA antibody in HA-ORCA expressing stable cell line . This allowed us to address if ORCA is associated with H3K9-occupied genomic sites . We observed a strong enrichment of ORCA at the H3K9-enriched loci ( Figure 5Ea ) , while ORCA binding to C-FOS , a region devoid of H3K9 , was comparable to that of IgG ( Figure 5Eb ) . To understand the mechanism of reduction of H3K9 methyl marks upon ORCA depletion , we first determined whether the protein stability or chromatin association of G9a and Suv39H1 was altered upon ORCA loss . Our data revealed that the total cellular levels of G9a and Suv39H1 were not reduced upon ORCA KD ( Figure 5—figure supplement 1D ) . We next addressed if the loading of these KMTs onto chromatin is impaired upon ORCA KD . To investigate this , we performed G9a and Suv39H1 ChIP upon ORCA KD and analyzed the association of these enzymes to the loci that show H3K9me2 and H3K9me3 reductions . Suv39H1 showed a decrease at these loci ( Figure 5Fa , Fb ) , indicating that the loading of Suv39H1 to these regions is reduced upon ORCA depletion . G9a association with these regions showed either no alteration or an increase at some regions ( Figure 5—figure supplement 1Ea , Eb ) , indicating that the reduction in H3K9me2 that was observed was possibly due to impaired catalytic activity of G9a . Next , we addressed if ORCA facilitates the assembly of the multimeric H3K9 KMT megacomplex . To address this , the association between the components of the KMT megacomplex , namely G9a and Suv39H1 , was evaluated in cells that were treated with control or ORCA siRNAs . Flag-G9a and HA-Suv39H1 were expressed , and HA IP was carried out in control and ORCA-depleted ( ORCA KD ) cells . ORCA KD showed close to 50% reduction of Suv39H1 that co-immunoprecipitated with G9a ( Figure 6Aa , Ab ) . This observation suggested that the stability of the KMT complex requires ORCA . We used SiMPull to obtain an accurate quantitative estimate of the reduction ( Figure 6Ba–Bc ) . YFP-Suv39H1 and mCherry-G9a were expressed in cells depleted of ORCA . GFP pull-down was carried out , and the number of mCherry-G9a molecules associated with Suv39H1 was calculated ( Figure 6Bb , Bc ) . ORCA KD led to ∼50% reduction in the complexes containing YFP-Suv39H1 and mCherry-G9a ( note , 24 ± 3% mCherry-G9a pulled down by YFP-Suv39H1 in control vs 15 ± 1% in ORCA knock-down cells; Figure 6Bc ) . These results support the argument that ORCA acts as a scaffold protein that is necessary for stabilizing a subset of the complexes containing multiple H3K9 KMTs . 10 . 7554/eLife . 06496 . 013Figure 6 . ORCA is a scaffold for G9a-Suv39H1 complexes . ( A ) a–b HA-IP in control and ORCA-depleted U2OS cells co-expressing with HA-G9a and Flag-Suv39H1 . ( B ) a TIRF images of GFP SiMPull in control and ORCA-depleted U2OS cells co-transfected with YFP-Suv39H1 and mCherry-G9a . The same lysates incubated with biotinylated HA Ab served as the control . b Average number of YFP fluorescent molecules per imaging area ( 5000 μm2 ) . c The % of mCherry-G9a pulled down by YFP Suv39H1 in control and ORCA KD . ( C ) a TIRF images of GFP SiMPull in U2OS cells transiently transfected with YFP-Suv39H1 , mCherry-G9a , and T7-ORCA full-length or truncation mutant 1–270 or 128–647 . The same lysates incubated with biotinylated HA Ab served as the control . b Average number of YFP fluorescent molecules per imaging area ( 5000 μm2 ) . c The % of mCherry-G9a pulled down by YFP-Suv39H1 . The % of mCherry-G9a pulled down by YFP-Suv39H1 in WT-ORCA is 25 ± 1%; 1–270 ORCA is 14 ± 3%; and 128–647 ORCA is 29 ± 6% . Scale bars , 20 μm . Error bars represent s . d . , n = 3 . **p < 0 . 01 , ***p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 013 To further confirm the role of ORCA as a scaffold protein , we addressed if over-expressing ORCA leads to any increase in G9a and Suv39H1-containing complexes . We performed triple transfections of YFP-Suv39H1 , mCherry-G9a , and T7-ORCA . YFP-Suv39H1 SimPull was carried out , and the number of mCherry-G9a molecules pulled down was analyzed as a percentage of YFP-Suv39H1 pull-down ( Figure 6Ca , Cc ) . The presence of full-length T7-ORCA showed 25 ± 1% association between Suv39H1 and G9A . The mutant T7-ORCA ( 1–270 ) that does not interact with either G9a or Suv39H1 , when expressed along with G9a and Suv39H1 showed 14 ± 3% of mCherry-G9a in complex with YFP-Suv39H1 . By contrast , the other T7-ORCA mutant ( 128–647 ) that interacts with both G9a and Suv39H1 stabilized mCherry-G9a and YFP-Suv39H1 complexes in the cell ( 29 ± 6%; Figure 6Cb , Cc ) . These results collectively indicated that ORCA , by acting as a scaffold protein , stabilizes the association of multiple KMTs in a single complex . In the absence of ORCA , the integrity of this complex is compromised , leading to the reduction in the KMT-associated enzymatic activity and a subsequent reduction of H3K9me2 and H3K9me3 patterns on chromatin . In general , chromatin at the nuclear periphery is significantly enriched with H3K9me2 , whereas H3K9me3 is preferentially enriched around nucleolus ( Yokochi et al . , 2009 ) . Typically , both of these regions replicate late during S-phase indicating that in general repressive histone marks-containing differentially condensed chromatin influences replication timing and chromatin positioning ( Julienne et al . , 2013 ) . Therefore , we investigated whether the changes in H3K9me2 and H3K9me3 deposition in specific chromatin regions , upon ORCA depletion , also influences their replication timing . We depleted ORCA in U2OS cells and then synchronized the cells so as to analyze the spatio-temporal regulation of replication during S-phase ( Figure 7A ) . Samples were collected at 4 , 8 , 12 hr post release from aphidicolin arrest with BrdU pulse-labeling prior to sample collection . This was followed by immunofluorescence to score for cells in early , mid , and late S-phase of cell cycle ( Figure 7—figure supplement 1A ) . At 8 hr and 12 hr time points post-aphidicolin release , ORCA depletion caused dramatic reduction in cells showing late replication patterns ( Figure 7B ) . BrdU ( Bromodeoxyuridine ) -PI flow cytometry results showed a significant reduction in BrdU incorporation in ORCA-depleted cells without significant changes in the early S-phase ( Figure 7C ) . To determine if the changes in late replication pattern are a reflection of changes in the heterochromatin organization , we examined the replication timing of regions that showed a reduction in H3K9me2 and H3K9me3 upon ORCA KD . Initial analysis of the available repli-seq data set from various human cell lines in UCSC Genome Browser and ENCODE consortium revealed that the replication timing of large domains remains the same across cell lines . We , therefore , compared the H3K9me3 ChIP-seq data set to the HeLa repli-seq data set ( Figure 7D ) . HeLa-S3 G1b and HeLa-S3 S1 are deep sequencing data sets for late G1 and early S replicating regions in HeLa-S3 cells ( Hansen et al . , 2010 ) . The chromosomal regions that are replicating at these two stages are shown in black ( early ) and late ( gray ) along the length of the chromosome ( Figure 7D and Figure 7—figure supplement 1B ) . 10 . 7554/eLife . 06496 . 014Figure 7 . Loss of ORCA causes defects in heterochromatin organization . ( A ) IB showing efficient siRNA-mediated KD of ORCA . ( B ) Distribution of S-phase cells displaying early , mid , and late replication patterns in control and ORCA KD cells . Error bars represent s . d . , n = 3 independent experiments with 500 BrdU positive cells scored in each . ( C ) BrdU-PI flow cytometry of control and ORCA KD cells . ( D ) Replication timing of genomic regions that show reduced H3K9me3 upon ORCA KD . Gray bars represent late-replicating domains , and black bars denote early replicating domains . HeLa-S3 G1b and HeLa-S3 S1 are late G1 and early S cell cycle fractions that together represent the early replicating regions of the genome . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 01410 . 7554/eLife . 06496 . 015Figure 7—figure supplement 1 . Depletion of ORCA alters the replication timing . ( A ) Patterns of BrdU incorporation in S phase . Examples of early ( 1 ) , mid ( 2 and 3 ) and late ( 4 and 5 ) S patterns . Scale bar , 10µm . ( B ) Replication timing of genomic regions that show reduced H3K9me3 upon ORCA knockdown . Gray bars represent late replicating domains and black bars denote early replicating domains . ( C ) BrdU ChIP in S phase in control and ORCA knockdown cells . Note the changes in replication timing of CELSR3 ( b ) and FAM20A ( c ) upon loss of ORCA . C-FOS locus is used as a control region whose replication timing remains unaffected upon loss of ORCA ( a ) . Fold enrichment in the graph represents the % input of BrdU ChIP over % input of rIgG ChIP . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 015 Using the data set mentioned above , we examined the replication timing of the regions that showed reduction in H3K9me3 by ChIP-seq . On chromosome 19 , the total H3K9me3 peaks in the control sample and the regions , which show greater than fivefold decrease in H3K9me3 upon ORCA depletion are represented as black bars above the HeLa-S3 G1b and HeLa-S3 S1 tracks . Upon ORCA depletion , most of the affected H3K9me3 peaks resided in late-replicating domains ( Figure 7D and Figure 7—figure supplement 1B ) . This coupled with the loss of late-replication patterns by BrdU IF in ORCA-depleted cells made us hypothesize that ORCA could also regulate the replication of late-replicating regions . Loss of ORCA could be causing either changes in replication timing of late-replicating regions or the complete loss of replication of these regions . To investigate these possibilities , we conducted BrdU ChIP in control and ORCA-depleted U2OS cells ( Figure 7—figure supplement 1Ca–Cc ) . We depleted ORCA and arrested the cells using aphidicolin . The cells were then released into S-phase , pulse-labeled with BrdU , and analyzed by BrdU ChIP at different time points post-release ( 0 , 4 , 8 , and 12 hr ) . The replication timing of various loci that showed significant reduction in H3K9me2 and me3 ( Figure 5D and Figure 5—figure supplement 1Ca ) was assessed by quantitative PCR . We observed changes in replication timing of these loci ( Figure 7—figure supplement 1Cb , Cc shows two representative loci CELSR3 and FAM20A ) upon loss of ORCA . For example , in control cells a significant population of CELSR3 locus replicates in late S ( 12 hr post release ) as evident by the BrdU ChIP signal at 12 hr . Upon ORCA KD , there is a significant increase in the population of the locus replicating during early S ( 4-hr time point ) and a concomitant reduction in BrdU ChIP signal at mid and late S ( 8 and 12 hr time points ) ( Figure 7—figure supplement 1Cb ) . The replication timing of C-FOS , a region that serves as a control showing no changes in H3K9me2 and me3 upon loss of ORCA , remains unaffected ( Figure 7—figure supplement 1Ca ) , suggesting that the replication timing changes observed in ORCA-depleted cells may not be because of the direct role of ORCA/ORC in establishing the pre-replicative complex . We have previously demonstrated that ORCA plays a key role in replication initiation ( Shen et al . , 2012 ) . We addressed whether the observed defects in heterochromatin organization and replication patterning in cells lacking ORCA are due to defects in preRC assembly or reflect the more direct role of ORCA in heterochromatin organization . While it is well-known that ORC ( along with ORCA ) associates with heterochromatic regions in post-replicated cells in metazoans ( Prasanth et al . , 2004; Shen et al . , 2010 ) , its direct role in heterochromatin organization vs heterochromatin replication licensing has remained to be understood . In order to understand ORCA's role in chromatin organization and if it is independent of its role in preRC function , we wanted to deplete ORCA after the establishment of pre-replication complex ( post-G1 phase ) but before DNA synthesis began . Depletion of a protein within a short , specific time window by RNA interference is challenging because even if the mRNA levels are dramatically reduced , the protein levels could persist for significantly longer times . This necessitates the use of a strategy that utilizes post-translation degradation process for reducing proteins levels efficiently . To achieve this , we utilized a commercially available Proteotuner kit ( Clonetech ) . Briefly , an siRNA resistant version of T7-ORCA ( T7-ORCA-siRNA NTV: non-targetable version ) was tagged with a destruction signal or DD ( destabilization domain ) tag , a destabilization domain of the FKBP protein ( Figure 8A; [Banaszynski et al . , 2006] ) . This signal is recognized by the proteosomal machinery and results in the rapid degradation of the exogenous ORCA . In the presence of a ligand , Shield1 , the DD tag is masked by the binding of Shield1 , thereby preventing the degradation of the exogenous ORCA protein . 10 . 7554/eLife . 06496 . 016Figure 8 . Heterochromatin organization role of ORCA is independent of its role in preRC assembly . ( A ) Schematic of depletion of ORCA using the proteotuner system . ( B ) Western blotting showing the levels of endogenous and exogenous ORCA in the presence of control and ORCA siRNA . β′′ , a nuclear speckle protein , serves as the loading control . Note that the DD-T7-ORCA-siRNA non-targetable version ( NTV ) is stabilized upon the addition of Shield1 . ( C ) Chromatin fractionation and IB showing the levels of chromatin bound Orc2 in control and ORCA siRNA-treated cells ( either in the absence or presence of exogenous ORCA ) . Note the reduction in chromatin bound Orc2 in the absence of ORCA and the rescue of its levels upon expression of exogenous ORCA . Also note the increase in the soluble pool of Orc2 in the absence of ORCA and the decrease of its levels upon expression of exogenous ORCA . Splicing factor , SRSF1 is shown as a loading control . ( D ) IB showing the levels of endogenous and exogenous ORCA at G1/S and 12 hr post-release from aphidicolin . H3 is used as loading control . ( E ) a–b Patterns of BrdU incorporation in control and ORCA-depleted cells in late S-phase . The white arrowheads indicate preferential incorporation of BrdU incorporation at perinucleolar regions upon loss of ORCA . Scale bar , 10 μm . b % increase in S-phase cells displaying early and % decrease of the mid and late replication patterns in ORCA-depleted cells compared to control cells . Error bars represent s . d . , n = 3 independent experiments with ∼450 BrdU positive cells scored in each . ( F ) a H3K9me3 and HP1α immunofluorescence in control and ORCA-depleted cells . The white arrowheads indicate H3K9me3 and HP1α immunofluorescence at perinucleolar regions upon loss of ORCA . Representative regions in control and ORCA-depleted cells marked by white dotted squares ( 1 , 2 , and 3 ) are shown at 3× magnification on the right . Scale bar , 10 μm . b The % of cells with HP1α at nucleolar periphery in control and ORCA-depleted cells . Error bars represent s . d . , n = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 01610 . 7554/eLife . 06496 . 017Figure 8—figure supplement 1 . BrdU incorporation preferentially at perinucleolar regions in cells lacking ORCA . ( A ) IP of DD-T7-ORCA siRNA NTV from U2OS cells using T7 Ab . DD-T7-ORCA siRNA NTV and endogenous Orc were analyzed by IB . ( B ) Patterns of BrdU incorporation in control and ORCA depleted cells in late S phase . Scale bar , 10µm . ( C ) H3K9me3 and HP1α immunofluorescence in control and ORCA depleted cells . Scale bar , 10µm . ( D ) Flow cytometry of control and ORCA knockdown cells at 0 , 4 , 8 and 12h post release from Aphidicolin block . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 017 In order to determine whether DD-T7-ORCA-siRNA NTV is functional and can substitute for endogenous ORCA , we examined whether it could efficiently rescue ORC levels on chromatin upon depletion of endogenous ORCA ( Shen et al . , 2010 ) . We transfected DD-T7-ORCA-siRNA NTV into U2OS along with siRNA to KD endogenous ORCA . Following two rounds of siRNA treatment ( 48 hr ) in the presence of DD-T7-ORCA-siRNA NTV ( Figure 8B ) , we examined the loading of ORC2 on chromatin and compared it to ORC2 loading in control and ORCA-depleted cells . We observed that while ORC2 loading was affected upon ORCA depletion ( levels of ORC2 decrease in the chromatin fraction with concomitant increase in the supernatant fraction ) , the expression of DD-T7-ORCA-siRNA NTV construct rescued this phenotype by restoring the levels of ORC2 on chromatin to an extent comparable to that of the control ( Figure 8C ) . In addition , we also carried out immunoprecipitation of DD-T7-ORCA and found that it efficiently interacts with endogenous ORC2 ( Figure 8—figure supplement 1A ) , further confirming that DD-T7-ORCA is functional . To determine if the role of ORCA in heterochromatin organization is independent of its role in preRC assembly , we transfected U2OS cells with DD-T7-ORCA-siRNA NTV . We then depleted endogenous ORCA by using ORCA siRNA , while the levels of exogenous DD-T7-ORCA-siRNA NTV were maintained by growing the cells in the presence of Shield1 . We synchronized the cells at early S by using aphidicolin and then degraded exogenous DD-T7-ORCA at early S by removing shield from the medium . Removal of Shield1 resulted in the loss of exogenous ORCA ( in addition to endogenous ORCA that was removed by siRNA treatment [Figure 8D] ) . The cells were then allowed to progress through S-phase and chromatin organization and replication were examined at different time points during S-phase . Specific depletion of ORCA only in post-G1 cells also resulted in reduction in the H3K9me2 levels ( Figure 8D ) . This demonstrates that the heterochromatin is disorganized in the absence of ORCA in post-G1 cells . In these cells , we examined the replication patterning by BrdU immunofluorescence . We observed a decrease in cells showing mid- and late- patterns of DNA replication and a concomitant increase in cells showing early patterns ( Figure 8Eb ) , similar to our previous observations ( Figure 7B ) . In cells lacking ORCA , a large number of cells showing mid-replication patterns showed BrdU staining preferentially at perinucleolar regions ( Figure 8Ea , Figure 8—figure supplement 1B ) . Furthermore , we observed that H3K9me3 and HP1α were mislocalized and formed perinucleolar rings in cells lacking ORCA ( Figure 8Fa , Fb , Figure 8—figure supplement 1C ) . Such localization was reminiscent of HP1α localization in Orc1- and Orc5-depleted cells ( Prasanth et al . , 2010 ) . Moreover , both control and ORCA-depleted cells progressed through S-phase at comparable rates ( Figure 8—figure supplement 1D ) indicating that the observed defects in HP1α localization and BrdU incorporation upon loss of ORCA are due to defects in heterochromatin and not due to indirect effects of defects in S-phase progression . Based on the results , we propose that the observed defects in heterochromatin organization in ORCA-depleted post-G1 cells are independent of its known functions in preRC assembly .
ORCA , a key player in the initiation of DNA replication , associates with multiple components of the pre-replicative complex ( Shen et al . , 2012 ) . The ORCA-ORC complex associates with heterochromatin , including telomeric and centromeric regions , even after replication has been accomplished suggesting that ORCA-ORC complex may play key roles in heterochromatin organization in addition to its role in pre-RC . The WD-repeat-containing domain ( also found in ORCA ) mediates interaction of proteins with nucleosomes/histones ( Suganuma et al . , 2008 ) . For example , WDR5 , a component of the MLL/SET1 KMT complex , binds to H3K4me2 using WD repeats ( Ruthenburg et al . , 2006 ) . Similarly , HIRA , a WD-repeat-containing protein , binds to core histones and controls transcription ( Lorain et al . , 1998 ) . Here , we demonstrate that ORCA associates with multiple H3K9 KMTs , binds to methylated H3K9 , and regulates both the organization and replication of repressed chromatin marked with H3K9me2 and H3K9me3 . Recently , a H3K9 KMTs multimeric complex has been identified that has been shown to be recruited to major satellite repeats and a subset of promoters of G9a-repressed genes and a functional cooperation of these enzymes is crucial for the regulation of G9a target genes ( Fritsch et al . , 2010 ) . We demonstrate that ORCA-ORC associates with the H3K9 KMT-containing complex and in the absence of ORCA , this complex disintegrates . The loss of the enzymatic activity of this complex causes changes in the H3K9me2 and H3K9me3 profile in human cells . Based on this , we propose that ORCA is a scaffold protein that stabilizes the H3K9 KMT complex . Recent work suggests that in mouse cells ORCA associates with pericentric heterochromatin via its association to H3K9me3 and maintains silencing at the major satellite repeats ( Chan and Zhang , 2012 ) . Based on our results , we speculate that the changes in transcription of satellite repeats upon ORCA-depletion are likely caused by the changes in the heterochromatin structure . Immunoprecipitation experiments demonstrate that the WD-repeat of ORCA and the ankyrin repeat of G9a are crucial for the interaction between these two proteins . Ankyrin repeats of G9a also contain methyl-lysine binding modules and can therefore generate as well as read the same epigenetic mark ( Collins et al . , 2008 ) . We have observed that the SET domain of G9a and its catalytic activity is essential for the binding of ORCA to heterochromatin , suggesting that the chromatin modifications initiated by the KMT provide docking sites for ORCA . These in turn may facilitate the recruitment of accessory factors that stabilize the interaction and help the propagation of heterochromatin . We propose that ORCA recognizes repressive histone marks , binds to KMTs that in turn facilitate the propagation of the histone mark . The newly established marks then become docking sites for ORCA and the whole process is repeated and this results in the spreading of heterochromatin ( Figure 9A ) . 10 . 7554/eLife . 06496 . 018Figure 9 . Model depicting the role of ORCA in organizing heterochromatin . Model representing mode of regulation of heterochromatin by ORCA . DOI: http://dx . doi . org/10 . 7554/eLife . 06496 . 018 Tri-methylation of H3K9 , mono-methylation of H3-K27 , and tri-methylation of H4-K20 are enriched at pericentric heterochromatin ( Peters et al . , 2003; Rice et al . , 2003; Schotta et al . , 2004 ) . It is well established that H3K9 tri-methylation is a prerequisite for the subsequent H4K20 tri-methylation at the pericentric heterochromatin and this in turn sets the chromatin for binding of other key heterochromatin proteins including HP1 ( Lachner et al . , 2001; Fischle et al . , 2005; Stewart et al . , 2005 ) . It is interesting to note that ORCA also interacts with Suv420H1 and H2 , enzymes that catalyze H4K20 di- and tri-methylation , respectively ( data not shown ) . It has been previously reported that Suv420H2 is a structural component of the heterochromatin and is required for chromatin compaction as well as cohesin recruitment ( Hahn et al . , 2013 ) . Recently , Reinberg and co-workers have proposed that the H4K20 me1/2/3 is also crucial for the regulation and timing of replication origin firing and that ORCA and Orc1 directly recognize these chromatin sites ( Beck et al . , 2012 ) . We are currently addressing the functional relevance of ORCA and Suv420H1/2 interaction in heterochromatin organization and replication progression . Work from Jenuwein and co-workers have pointed to the idea that Prdm3 and Prdm16 , H3K9 mono-methyltransferases , are also required for mammalian heterochromatin formation ( Pinheiro et al . , 2012 ) . Similarly , mono-methylation of H3 at K9 catalyzed by SETDB1 has been shown to be a favored substrate for Suv39h for K9 tri-methylation , which can then establish heterochromatin ( Loyola et al . , 2009 ) . The mechanism by which the Suv39h or H3K9me3 is targeted to pericentromere has been a long-standing question . It is generally assumed that HP1 is a key regulator of heterochromatin organization that is required for establishment and maintenance of this compacted form of chromatin . Spreading of the heterochromatin is thought to involve a mechanism where HP1 dimerizes , interacts with Suv39h1/2 , and also recruits de novo DNA methyltransferase activity ( Almouzni and Probst , 2011 ) . The fact that HP1 associates with heterochromatin in a transient manner has suggested that other perhaps constitutively bound factors contribute to the organization of heterochromatin ( Cheutin et al . , 2003 ) . In addition , recent work has demonstrated that pericentric heterochromatin can be generated independent of Suv39h-HP1 binding ( Muramatsu et al . , 2013 ) . A transcription factor-based mechanism has also recently been suggested as an intrinsic mechanism for the formation of heterochromatin in mouse cells ( Bulut-Karslioglu et al . , 2012 ) . Based on our results , we propose that in human cells , ORCA facilitates the recruitment , accumulation , and also the propagation of the heterochromatin by a self-sustaining loop mechanism , whereby ORCA binds to specific chromatin marks , associates with Suv39H1 , that in turn propagates more H3K9me3 marks , generating more docking sites for ORCA ( Figure 9 ) . We previously demonstrated that ORCA can facilitate the binding of ORC to chromatin and in the absence of ORCA , the binding of ORC to chromatin is compromised ( Shen et al . , 2010 ) . However , it remains to be determined if the loss of chromatin-bound ORC in ORCA-depleted cells occurs at specific origins or at heterochromatic sites or both . Our data unequivocally demonstrate that ORCA binds to H3K9-methylated chromatin and facilitates the recruitment of KMTs as well as other components of ORC and MCMs to these sites . It is possible that the regulation of these epigenetic modifications by ORCA may provide identity to repressed chromatin and this in turn is crucial for proper replication . This idea is supported by our observation that ORCA associates with repressive KMTs only in a heterochromatic environment . It is known that G9a mediates di-methylation of H3K9 at late-replicating chromatin and this occurs predominantly at the nuclear periphery . H3K9me2 and H3K9me3 are enriched at the late-replicating facultative or constitutive heterochromatin , respectively ( Wu et al . , 2005 ) . The reduction of these marks upon loss of ORCA leads to changes in the replication timing only of these regions as indicated by the significant decrease in late-replication patterns upon ORCA KD . This is very similar to previous reports that show that loss of Rif1 causes reduction in mid-replication patterns and global changes in replication timing primarily due to Rif1's role in organizing chromatin loops ( Cornacchia et al . , 2012; Yamazaki et al . , 2012; Kumar and Cheok , 2014 ) . ORCA could be functioning in a similar fashion as an organizer of heterochromatin , and therefore , in cells lacking ORCA replication timing is altered . Furthermore , using the proteoTuner system , we demonstrate that the role of ORCA in chromatin organization is independent of its role in preRC assembly . Heterochromatin is formed as a result of a maturation process that requires several steps and ORCA acts early in this process . Our results demonstrate that ORCA is a chromatin reader that facilitates the assembly of the writer KMT complex and its associated partners to specific chromatin sites . These together regulate key cellular events , including DNA replication and heterochromatin organization .
Human G9a ( hG9a ) full-length and mutant clones were obtained using PCR using pCDNA3 Flag G9a plasmid provided very kindly by Dr Martin Walsh . The mutants were cloned into pCGN , pEYFP , pECFP and pEmCherry vectors ( Clonetech , Mountain View , CA ) , and pEYFP-LacI vector . pEGFP G9a full-length and ∆SET constructs were also kindly provided by Dr Walsh . Mouse G9a ( mG9a ) full-length was amplified from pSV2 YFP mG9a ( Dr David Spector's lab ) ( Janicki et al . , 2004 ) . pEYFP LacI mG9a ∆SET and H1166K constructs were cloned by amplification from respective pSG5 HA mG9a constructs kindly provided by Dr Michael Stallcup . Flag GLP was kindly provided by Dr Dan Levy . Human Suv39H1 full-length and mutant clones were obtained using PCR using Flag-Suv39H1 plasmid provided very kindly by Dr Rama Natarajan ( Villeneuve et al . , 2008 ) . The mutants were cloned into pCGN vector and pEYFP vector ( Clonetech , Mountain View , CA ) . pSV2-YFP-mSuv39H1 has been described previously ( Janicki et al . , 2004 ) . Myc-EZH2 was kindly provided by Dr Francois Fuks , Free University Brussels . EZH2 was PCR amplified and cloned into pEYFP-LacI vector . Flag Suv420H1 . 1 and H2 constructs were kindly provided by Dr Craig Mizzen . pEGFP-LacI vector was a kind gift from Dr Miroslav Dundr ( Kaiser et al . , 2008 ) and used for making pEYFP-LacI vector . T7-ORCA mutants , pEYFP and CFP ORCA , pECFP-LacI and pECFP-LacI-ORCA have been described previously ( Shen et al . , 2010 ) . YFP-LacI-Orc2 was cloned by amplifying and inserting Orc2 into pEYFP-LacI vector . YFP-Orc1 construct has been described previously ( Hemerly et al . , 2009 ) . T7-ORCA-siRNA NTV was cloned into pPTuner IRES2 ( Clonetech , Mountain View , CA ) vector . All the cloned constructs were confirmed by sequencing and used for immunoprecipitation and/or cell biological experiments . U2OS cells were grown in Dulbecco's modified Eagle's medium ( DMEM ) containing high glucose and supplemented with 10% fetal bovine serum ( FBS—HyClone GE , Pittsburg , PA ) . WI38 cells were also grown in the same medium but supplemented with non-essential amino acids . Hela suspension cells ( Hela-XLP GLP ) were grown in DMEM supplemented with 5% fetal calf serum and penicillin-streptomycin . U2OS-2-6-3 CLTon cells were grown in DMEM with 10% Tet system approved FBS ( Clonetech , Mountain View , CA ) . For arresting cells at G1/S , aphidicolin ( stock 10 mg/ml ) was added to the cells at a final concentration of 5 μg/ml for 12 hr . Cells were then washed three times with PBS and released into S-phase with medium ( DMEM + 10%FBS ) lacking aphidicolin . Cells were then collected at 4 , 8 , and 12 hr post-G1/S block for immunofluorescence and flow cytometry analysis . siRNA transfection for ORCA depletion: cells were grown to 30% confluency and siRNA against ORCA or control luciferease gene ( Shen et al . , 2010 ) was delivered into the cells at a final concentration of 100 nM using Lipofectamine RNAimax ( Invitrogen , Carlsbad , CA ) . The siRNAs were delivered twice at the gap of 24 hr , and the cells were collected 24 hr after the second round of transfection for subsequent analysis . U2OS cells were transfected with of DD-T7-ORCA-siRNA NTV construct along with siRNA against ORCA . 5 hr later Shield1 ( 0 . 5 μM ) was added to the medium . 24 hr after the first round of KD , a second round of ORCA siRNA treatment was carried out in the presence of Shield1 . Samples were collected 24 hr later for chromatin fractionation to examine ORC loading . As described above , DD-T7-ORCA-si NTV ( 500 ng plasmid was transfected ) was expressed in U2OS cells grown on coverslips in the presence of Shield1 ( 0 . 25 μM ) . This was followed by the addition of fresh medium containing aphidicolin ( 5 μg/ml ) + Shield1 ( 0 . 25 μM ) . 14 hr post-aphidicolin block , cells were washed thrice with PBS containing aphidicolin , with or without Shield1 , respectively . This was followed by performing control and ORCA depletions in Opti-MEM containing aphidicolin , with or without Shield1 . The control and ORCA-depleted cells were washed with PBS containing or lacking Shield1 , respectively . Then , the cells were released into S-phase using medium with or without Shield1 for control and ORCA-depleted cells followed by late S sample collection 12 hr later for Western blotting and immunofluorescence analysis . His ORCA , G9a , and Suv39H1 viruses were generated by using pFastBac HT-B-His-tagged-ORCA , G9a , and Suv39H1 , respectively ( Shen et al . , 2012 ) ( by following Bac-to-Bac baculovirus expression system—Invitrogen ) . Virus production was carried out in Sf9 cells with viruses collected 72 hr post-infection ( multiplicity of infection 5 to 10 ) . Protein expression was carried out in Hi5 cells by collecting cells 65 hr post-infection . Nuclei were collected by using Hypotonic lysis buffer ( 20 mM potassium phosphate buffer pH 7 . 5 , 5 mM KCl , 1 . 5 mM MgCl2 , and 5 mM b-mercaptoethanol ) making nuclear extracts in PK50 buffer ( 20 mM potassium phosphate buffer pH 7 . 5 , 50 mM KCl , 0 . 02% NP-40 , 10% glycerol , 5 mM b-mercaptoethanol with protease and phosphatase inhibitors ) ( Siddiqui and Stillman , 2007 ) . 45% ammonium sulfate precipitation was carried out followed by reconstitution of His-ORCA , G9a , and Suv39H1 in PK50 buffer . Cells were fixed with 2% formaldehyde in phosphate buffered saline ( PBS , pH 7 . 4 ) for 15 min in room temperature ( RT ) followed by permeabilization with 0 . 5% Triton X-100 in PBS for 7 min on ice or pre-extracted before fixing with 0 . 5% Triton X-100 in Cytoskeletal buffer ( CSK: 100 mM NaCl , 300 mM Sucrose , 3 mM MgCl2 , 10 mM PIPES at pH 6 . 8 ) for 5 min on ice . Blocking was then done for 30 min with 1% Normal goat serum ( NGS ) in PBS . Primary antibody incubation was then carried out for 1 hr in a humidified chamber followed by secondary antibody incubation for 25 min . The cells were then stained with DAPI ( 4' , 6-Diamidino-2-Phenylindole ) and mounted using VECTASHIELD ( Vector Laboratories Inc . , Burlingame , CA ) . The following antibodies were used for immunofluorescence: BrdU ( 1:500; mAb BU-33 , Sigma , St . Louis , MO ) , Lamin ( 1:750 ) , H3K9me2 ( 1:100; 07-212 , Millipore , Billerica , MA ) , H3K9me3 ( 1:200 , Millipore 07-523 ) , HP1α ( 1:100 , Millipore 3584 ) . BrdU immunofluorescence: after primary and secondary antibody incubation for lamin immunofluorescence , cells were fixed with 2% formaldehyde solution in PBS . This was followed by acid denaturation of DNA using 4 N HCl for 25 min at RT . Three washes with PBS and two washes with PBS-NGS followed . This was followed by incubation of the cells with anti-BrdU antibody followed by secondary antibody incubation and mounting . Zeiss Axioimager z1 fluorescence microscope ( Carl Zeiss Inc . , Jena , Germany ) equipped with chroma filters ( Chroma technology , Bellows Falls , CA ) was used for observing the cells and statistics . Axiovision software ( Zeiss ) was used for digital imaging using Hamamatsu ORCA cooled CCD camera . Cells were also examined on the Delta vision optical sectioning deconvolution instrument ( Applied precision , Pittsburgh , PA ) on an Olympus microscope . For co-IP with transiently transfected HKMTs and ORCA , co-transfections were carried out in U2OS cells . Cells were lysed , 24 hr post-transfection , in IP buffer ( 50 mM Tris pH 7 . 4 , 150 mM NaCl , 10% glycerol , 0 . 1% NP-40 , 1 mM DTT ( Dithiothreitol ) supplemented with the protease and phosphatase inhibitors ) . After pre-clearing with Gammabind Sepharose beads for 1 hr , the lysates were incubated with appropriate antibody-conjugated agarose overnight . The beads were washed in the same IP buffer and finally denatured by the addition of Laemmli buffer . The complex was analyzed by Western blotting . For immunoprecipitations and IB the following antibodies were used anti-GFP ( 1:500; Covance , Princeton , NJ ) , anti-Flag M2 ( 1:500 , Sigma ) , anti-HA 12CA5 ( 1:100 ) and anti-T7 ( 1:5000; Novagen , Billerica , MA ) , anti-ORCA pAb 2854-1 AP ( 1:500 ) , anti-G9a ( 1:500 , Sigma ) , anti-Suv39h1 ( 1:200 , Cell Signaling , Danvers , MA ) , anti-ORC2 pAb 205-6 ( 1:1000 ) , anti-Geminin ( 1:1000 , Santa Cruz , Dallas , TX ) , anti-MCM3 TB3 ( 1:750 ) , anti-α-tubulin ( 1:10 , 000 , Sigma–Aldrich ) , anti-H3K9me2 ( 1:200 , Millipore 07-212 ) , anti-H3K9me3 ( 1:500 , Millipore 07-523 ) , anti-SF2 AK96 ( 1:750 ) , anti-PCNA mAb PC10 ( 1:750 ) antibodies . For IP in the presence of EtBr , lysates were made with IP buffer described above followed by addition of EtBr ( stock 10 mg/ml , working 50 μg/ml ) . After pulldown , beads were washed with IP buffer supplemented with 80 μg/ml of EtBr . For Benzonase treatment , cells ( grown in 6-cm plates ) were lysed for 10 min in IP buffer ( 50 mM HEPES pH 7 . 9 , 10% glycerol , 200 mM NaCl , 0 . 1% Triton X-100 , 1 mM MgCl2 ) supplemented with protease and phosphatase inhibitors . 1000 U of Benzonase ( Sigma ) was then added followed by nutation at RT for 20 min . EDTA ( final concentration 5 mM ) was added to stop the reaction followed by centrifugation at 12 , 500 rpm , 5 min at 4°C . The supernatant was used for subsequent antibody incubation . The nuclear extraction protocol has been described previously ( Robin et al . , 2007; Fritsch et al . , 2010 ) . We carried out HA immunoprecipitation in HeLa cells stably expressing Flag-HA-GLP and Flag-HA-G9a stable by retroviral transduction . First , nuclear extract was made using an equivalent of 20 g of dry cell pellet , which approximately corresponds to 3 billion cells . Cells were resuspended in hypotonic buffer ( 10 mM Tris pH 7 . 6 , 1 . 5 mM MgCl2 , 10 mM KCl ) with the volume of hypotonic buffer being equal to the packed volume of cells . The suspension was then dounced 20 times using tight pestle followed by adding one third the packed volume of sucrose buffer ( 20 mM Tris pH 7 . 6 , 15 mM KCl , 60 mM NaCl , 0 . 34 M Sucrose , 0 . 15 mM Spermine , 0 . 5 mM Spermidine ) . Then , centrifugation was carried out ( 9000 rpm , 7 min ) . The supernatant was discarded , and the nuclei were resuspended in low salt buffer ( 20 mM Tris pH 7 . 6 , 25% glycerol , 1 . 5 mM MgCl2 , 0 . 2 mM EDTA , 20 mM NaCl ) . This was followed by adding high salt buffer ( 20 mM Tris pH 7 . 6 , 25% glycerol , 1 . 5 mM MgCl2 , 0 . 2 mM EDTA , 900 mM NaCl ) dropwise while vortexing to make the final salt concentration 300 mM . After incubation on ice for 30 min , one third the volume of sucrose buffer was added followed by centrifugation at 1000 rpm , 10 min , 4°C . The supernatant is the nuclear soluble fraction . The pellet ( chromatin bound fraction ) was resuspended in sucrose buffer and CaCl2 ( final concentration 1 mM ) was added . The sample was then preheated for 1 min at 37°C and MNase was added to a final concentration of 0 . 0025 U/μl . The sample was then incubated for exactly 12 min at 37°C followed by placing the tubes on ice . 0 . 5 M EDTA was added at a final concentration of approximately 3 . 6 μM . The samples were then sonicated ( Bioruptor , high amplitude 5cycles: each cycle 1 min ON , 1 min OFF ) . Protease and phosphatase inhibitors were added , and the samples ( nuclear soluble and chromatin bound fractions ) were ultracentifuged at 40 , 000 rpm for 30 min . The supernatants were transferred to a fresh tube . Tagged-H3K9 HMTs complexes were then purified by immunoprecipitation using anti-FLAG antibody immobilized on agarose beads ( cat# A2220 , Sigma ) . After elution with the FLAG peptide ( Ansynth , The Netherlands ) , the bound complexes containing nucleosomes were further affinity purified on anti-HA antibody-conjugated agarose ( cat# A2095 , Sigma ) and eluted with the HA peptide ( Ansynth , The Netherlands ) . The samples were then analyzed by IB . Cells ( HeLa for gel filtration and U2OS for SiMPull ) were lysed in hypotonic buffer ( 10 mM HEPES-NaOH pH 7 . 9 , 10 mM KCl , 2 mM MgCl2 , 0 . 34 M Sucrose , 10% glycerol , 0 . 1% Triton X-100 ) supplemented with 1 mM DTT , protease and phosphatase inhibitors . The lysate was incubated at 4°C for 5 min after which nuclei were collected by centrifuging at 1500×g for 5 min . The pellet was then resuspended in nuclear extraction buffer ( 10 mM HEPES-NaOH pH 7 . 9 , 2 mM MgCl2 , 1 mM EGTA , 25% glycerol , 350 mM NaCl , 0 . 1% Triton X-100 , and 1 mM DTT ) supplemented with protease and phosphatase inhibitors . The supernatant was collected by centrifugation at 12 , 000 rpm for 5 min . The lysate was then used for co-localization studies by SiMPull . Baculovirally purified His-ORCA and His-G9s/His-Suv39H1 were diluted using PK 50 buffer and incubated together for 2 hr at 4°C in the presence of ORCA antibody or pre-bleed . ORCA containing complexes were then pulled down followed by washes with PK 150 buffer ( 20 mM potassium phosphate buffer pH 7 . 5 , 150 mM KCl , 0 . 5% NP-40 , 10% glycerol , 5 mM b-mercaptoethanol with protease and phosphatase inhibitors ) . The samples were finally denatured by the addition of Laemmli buffer . The complexes were analyzed by Western blotting . U2OS cells were grown in 6-cm plates to approximately 50% confluency followed by incorporation of BrdU ( stock 10 mM; working 50 μM ) for 1 hr at 37°C . Cells were then harvested at 3500 rpm , 15 min followed by washing with 1% BSA ( Bovine serum albumin ) in PBS ( Phosphate buffered saline ) ( pH 7 . 4 ) . The cells were then resuspended in 0 . 9% NaCl ( final cell density: 2 × 106 cells/ml ) . The cells were then fixed by adding chilled 100% ethanol to a final concentration of 50% ( left overnight at −20°C ) . After spinning down the fixed cells , DNA was denatured by resuspending in 2 N HCl + 0 . 5% Triton X—100 and incubating for 30 min at RT . The cells were then pelleted and resuspended in 0 . 1 M Sodium tetraborate pH 8 . 5 . This was followed by centrifugation at 3500 rpm , 15 min at 4°C followed by resuspending the cells in PBS + 1% BSA + 0 . 5% tween 20 . Anti-BrdU FITC antibody ( 1ug Ab/106 cells ) was added and the cells were incubated at RT for 1 hr . 1 ml of PBS + 1% BSA + 0 . 5% Tween 20 was added after that followed by spinning down the cells and proceeding with RNase A treatment and PI staining as described in the previous section . Human Histone H3 ( amino acids 1–15 ) peptides were synthesized ( Biomer technology , Pleasanton , CA ) with a Cysteine at the N terminus . The K9 ( Lysine at position 9 ) of the peptides was unmodified , acetylated , mono- , di- , or tri-methylated . The peptides were dissolved in water , quantitated using reverse phase chromatography , lyophilized and stored at −20°C as 1-mg aliquots . For coupling the peptides to SulphoLink Coupling Resin ( Thermo Scientific , Waltham , MA ) , the peptides were reduced first . For this , 1 mg of each peptide was dissolved in 3 ml of coupling buffer ( 50 mM Tris pH 8 . 7 , 5 mM EDTA , final pH adjusted to 8 . 5 ) supplemented with TCEP HCl ( Thermo Scientific ) and allowed to incubate at RT for 1 hr . 2 ml of the beads was washed with 5 ml coupling buffer ( three 10 min washes ) and resuspended in 1 ml of coupling buffer . 3 ml of the reduced peptides was added to the slurry followed by mixing immediately to distribute the peptide throughout the slurry . The mixture was incubated overnight at RT with gentle mixing . The beads were then spun down ( 2000 rpm , 2 min ) , washed with 6 ml of coupling buffer ( three 5 min washes ) , resuspended in 5 ml coupling buffer + 1 ml L-Cysteine HCl . The mixture was incubated overnight at RT . The beads were then spun down and washed with 5 ml of 1 M NaCl ( three 5 min washes ) . This was followed by washing two 5 min washes with 5 ml of storage solution ( 0 . 05% NaN3 in water ) and final suspension in 5 ml of storage solution and storage at 4°C . 50 μl packed volume of beads ( 300 μl of bead slurry ) was washed with PK 150 buffer ( 20 mM potassium phosphate buffer pH 7 . 5 , 150 mM KCl , 0 . 02% NP-40 , 10% glycerol , 5 mM β-mercaptoethanol ) and incubated with baculovirally purified His-ORCA for 2 hr at RT . This was followed with five washes with PK 150 buffer . The beads were then resuspended in Laemmli buffer and analyzed by Western blotting . SiMPull experiments were carried out in flow chambers prepared on quartz microscope slides , which were passivated with methoxy-polyethylene glycol ( mPEG ) doped with 1% biotin-PEG ( Lysan Bio , Inc , Arab , Al ) ( Jain et al . , 2011 ) . Appropriate biotinylated antibody was immobilized on PEG passivated surfaces at approximately 20 nM concentration for 20 min after coating the flow chambers with 0 . 2 mg/ml NeutrAvidin for 5 min . Antibodies were immobilized on NeutrAvidin ( Thermo ) -coated flow chambers either by incubating with biotinylated T7 antibody ( Novagen ) for 10 min . RIPA buffer lysed samples were then incubated in the chamber for 20 min and washed twice with the buffer ( 10 mM Tris-HCl pH 8 . 0 , 50 mM NaCl 0 . 1 mg/ml BSA ) . Single-molecule data were acquired by a prism-type TIRF microscope and analyzed using scripts written in Matlab . For ORCA-Orc1; ORCA-G9a; ORCA-Suv39H1 SiMPull analysis , lysates were made from the cells transiently transfected with T7-ORCA with YFP-Orc1 , YFP-G9a , or YFP-Suv39H1 , respectively . For multimeric complex assembly analysis using SiMPull , cells were transfected with T7-ORCA , YFP-Orc1 , and mCherry-G9a or T7-ORCA , YFP-Suv39H1 , and mCherry-G9a . For peptide pulldown experiments , biotinylated peptides were immobilized instead of antibodies . Cells lysed in RIPA buffer or nuclear extracts ( depending on the experiment ) were then incubated in the flow chamber for 20 min followed by wash with T300 buffer ( 20 mM Tris-HCl , pH 8 . 0 , 300 mM NaCl , 0 . 1 mg/ml bovine serum albumin [BSA] ) . Single molecules were visualized by prism-type TIRF microscope and analyzed using Matlab scripts ( https://github . com/vasuagg/SiMPull_Analysis ) . Cell lysate was appropriately diluted in T300 buffer to obtain optimal single molecule density on the surface . Single molecule data were acquired as the average number of YFP or mCherry fluorescent molecules per imaging area ( 5000 μm2 ) as shown in the histograms . The error bars represent standard deviation of the mean values from 20 imaging areas . Number of fluorescence photobleaching steps was determined for each YFP-tagged molecule and accumulated to obtain the stoichiometry of the complex . Colocalization percentage between YFP and mCherry was calculated as the number of coaligned molecules of one fluorescent molecule with respect to the fluorescent molecules found in lower density on the surface . This was needed since the number of YFP and mCherry tagged proteins was not pulled down to the same extent due to their independent interaction with ORCA . Colocalization criterion was set at 2 pixels , which correspond to a diffraction limited spot ( ∼300 nm ) for our TIRF setup . Error bars represent standard deviation of the mean values obtained from three independent experiments . For pulled down experiments performed using H3K9 peptides , the expression level of YFP-WT ORCA and YFP-mutant-ORCA was compared in the beginning by performing a direct pulldown by anti-GFP . The peptides pulldown was then performed at appropriate lysate dilution such that protein expression was same for WT ( Wild type ) and mutant ORCA . H3K9me2 and me3 ChIPs: formaldehyde ( Sigma ) was added to culture medium to a final concentration of 1% . Crosslinking was allowed to proceed for 10 min at RT and stopped by the addition of glycine at a final concentration of 0 . 125 M . Fixed cells were washed and harvested with PBS . Chromatin was prepared by two subsequent extraction steps ( 10 min at 4°C ) with Buffer 1 ( 50 mM Hepes/KOH pH 7 . 5; 140 mM NaCl; 1 mM EDTA; 10% glycerol; 0 . 5% NP-40; 0 . 25% Triton ) and Buffer 2 ( 200 mM NaCl; 1 mM EDTA; 0 . 5 mM EGTA; 10 mM Tris pH 8 ) . Nuclei were then pelleted by centrifugation , resuspended in Buffer 3 ( 50 mM Tris pH 8; 0 . 1% SDS; 1% NP-40; 0 . 1% Na-Deoxycholate; 10 mM EDTA; 150 mM NaCl ) and subjected to sonication with Bioruptor Power-up ( Diagenode , Denville , NJ ) yielding genomic DNA fragments with a bulk size of 150–300 bp . Chromatin was precleared with Protein A/G ultralink beads ( 53 , 133 , Pierce , Grand Island , NY ) for 2 hr at 4°C and immunoprecipitation with the specific antibodies carried out overnight at 4°C . Immune complexes were recovered by adding pre-blocked protein A/G ultralink beads and incubated for 2 hr at RT . Beads were washed twice with low salt buffer ( 0 . 1% SDS; 1% Triton; 2 mM EDTA; 20 mM Tris pH 8; 150 mM NaCl ) , twice with high salt buffer ( 0 . 1% SDS; 1% Triton; 2 mM EDTA; 20 mM Tris pH 8; 500 mM NaCl ) , once with LiCl wash buffer ( 10 mM Tris pH 8 . 0; 1% Na-deoxycholate; 1% NP-40 , 250 mM LiCl; 1 mM EDTA ) , and twice with TE + 50 mM NaCl . Beads were eluted in TE + 1% SDS at 65°C , and cross-link was reversed O/N at 65°C . The eluted material was phenol/chloroform extracted and ethanol precipitated . DNA was resuspended in water and q-PCR performed using PowerSYBR Green PCR Master mix ( Applied Biosystems , Pittsburgh , PA ) and analyzed on a 7300 PCR System ( Applied Biosystems ) . ChIP-qPCR results were represented as percentage ( % ) of IP/input signal ( % input ) . HA-ORCA ChIPs were carried out using HA-ORCA stable cell lines in U2OS using a similar protocol with the following modifications . All the washing steps after immune complexes pulldown were done once followed by two washes with TE . Beads were eluted with 1% SDS + 0 . 1 M NaHCO3 at 65°C , and cross-link was reversed O/N at 65°C . The eluted material was purified using Qiagen gel purification kit and qPCR carried out . G9a and Suv39H1 ChIPs were carried out using double-crosslinking protocols . The first crosslinking was carried out using disuccinimidyl glutarate ( DSG ) ( Santa Cruz; stock 50 mM DSG in DMSO ) and the second crosslinking using formaldehyde . U2OS cells were grown in 10-cm plates to 80% confluency , washed twice with PBS ( pH 7 . 4 ) . Freshly made crosslinking solution ( 2 mM DSG + 1 mM MgCl2 in PBS-pH 8 . 0 ) was added for 45 min at RT . The cells were then washed twice with PBS ( pH 7 . 4 ) and 10 ml of freshly made crosslinking solution ( 1% formaldehyde , 15 mM NaCl , 150 μM EDTA , 75 μM EGTA , 15 μM HEPES pH 7 . 9 ) was added for 10 min at RT . Then 3 ml of freshly made 1 M Glycine was added for 5 min at RT followed by two cold washes with PBS ( pH 7 . 4 ) . The cells were then pelleted in PBS ( supplemented with protease inhibitors ) followed by lysis with 300 μl of SDS lysis buffer ( 1% SDS , 10 mM EDTA , 50 mM Tris pH 8 . 0 ) . The lysate was then subjected to sonication with Bioruptor Power-up ( Diagenode ) . Chromatin was precleared with Dynabeads protein G ( Life Technologies , Grand Island , NY ) for 2 hr at 4°C and immunoprecipitation with the specific antibodies carried out overnight at 4°C . Immune complexes were recovered by adding pre-blocked Dynabeads ( 1 mg/ml BSA , 0 . 4 mg/ml salmon sperm DNA ) and incubated for 2 hr at 4°C . Beads were washed once with low salt buffer ( 0 . 1% SDS; 1% Triton; 2 mM EDTA; 20 mM Tris pH 8; 150 mM NaCl ) , once with high salt buffer ( 0 . 1% SDS; 1% Triton; 2 mM EDTA; 20 mM Tris pH 8; 500 mM NaCl ) , once with LiCl wash buffer ( 10 mM Tris pH 8 . 0; 1% Na-deoxycholate; 1% NP-40 , 250 mM LiCl; 1 mM EDTA ) , and twice with TE ( 10 mM Tris pH 8 . 0 , 1 mM EDTA ) . Beads were eluted in elution buffer ( 1% SDS , 0 . 1 M sodium bicarbonate in water ) at 65°C twice , 10 min each . The eluates were pooled ( 250 μl ) , NaCl added ( final concentration 0 . 2 M ) and cross-link was reversed O/N at 65°C . The eluted material was Rnase A treated ( 10 μg/ml , 1 hr at 37°C ) followed by proteinase K treatment ( 4 μl 0 . 5 M EDTA , 8 μl 1 M Tris pH 6 . 9 , 1 μl proteinase K 20 mg/ml ) at 42°C for 2 hr . DNA was purified using QIAquick PCR purification kit ( Qiagen ) and q-PCR performed SYBR Green PCR Master mix and analyzed on a 7300 PCR System ( Applied Biosystems ) . ChIP-qPCR results were represented as percentage ( % ) of IP/input signal ( % input ) . Two rounds of ORCA KD were carried out , 24 hr apart . The cells were then arrested using aphidicolin for 12 hr followed by release into S-phase and samples were collected 0 , 4 , 8 , and 12 hr post release for BrdU ChIP . Prior to each time point collection , cells were pulsed for 2 hr with BrdU ( 10 μM ) . Cells for each time point were then lysed with 300 μl of SDS lysis buffer ( 1% SDS , 10 mM EDTA , 50 mM Tris pH 8 . 0 ) . The lysates were subjected to sonication with Bioruptor Power-up ( Diagenode ) . 100 μl sheared chromatin aliquots were then placed on 95°C heat block for 10 min . This was followed by snap chilling the samples for 10 min . The samples were then diluted , precleared and processed further in a manner identical to the ChIP protocol described in the previous section . qPCRs were carried out with purified DNA of input , BrdU ChIP , and mouse IgG ChIP samples obtained at 0 , 4 , 8 , and 12 hr post-aphidicolin release time points . The qPCR signals of BrdU and mouse IgG samples were calculated as percent input values . Fold enrichment of BrdU ChIP over mouse IgG ChIP was calculated for each time point . H3K9me2 and me3 ChIP were performed as above . Five to fifteen nanograms of ChIP DNA or un-enriched whole cell extract ( Input ) were prepared for sequencing on an Illumina HiSeq2000 . Libraries were constructed with the Truseq DNA sample prep kit V2 ( Illumina , San Diego , CA ) with the following modifications: 10 ng of ChIP DNA were used as input material . DNA fragments were blunt-ended , 3′-end A-tailed and ligated to indexed TruSeq adaptors . The adaptors were diluted in a ratio of 1:20 to adjust for the input amount of DNA . Indexed adaptors allow for sequencing of multiple samples on the same lane ( multiplexing ) . The adaptor-ligated ChIP DNAs were individually size selected on a 2% agarose gel ( Ex-Gel , Life Technologies , CA ) to obtain the ligated fragments 300–800 bp in length . Size-selected DNAs were amplified by PCR to selectively enrich for those fragments that have adapters on both ends . Amplification was carried out for 15 cycles with the Kapa HiFi polymerase ( Kapa Biosystems , Woburn , MA ) to reduce the likeliness of multiple identical reads due to preferential amplification . The final libraries were quantitated by qPCR on an ABI 7900 , to allow for accurate quantitation and maximization of number of clusters in the flowcell . Final amplified libraries were also run on Agilent bioanalyzer DNA 7500 LabChips ( Agilent , Santa Clara , CA ) to determine the average fragment size and to confirm the presence of DNA of the expected size range . The libraries were pooled and loaded onto a lane of an 8-lane flowcell for cluster formation and sequenced on an Illumina HiSeq2000 . The libraries were sequenced from one end of the molecules to a total read length of 100 nt . The raw . bcl files were converted into demultiplexed compressed . fastq files using CASAVA 1 . 8 . 2 . The complete ChIP-seq data are available at http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE68129 . | The genetic material inside cells is contained within molecules of DNA . In animals and other eukaryotes , the DNA is tightly wrapped around proteins called histones to form a compact structure known as chromatin . There are two forms of chromatin: loosely packed chromatin tends to contain genes that are highly active in cells , while tightly packed chromatin—called heterochromatin—tends to contain less-active genes . How tightly DNA is packed in chromatin can be changed by adding small molecules known as methyl tags to individual histone proteins . Enzymes called KMTs are responsible for attaching these methyl tags to a specific site on the histones . Before a cell divides , it duplicates its DNA and these methyl tags , so that they can be passed onto the newly formed cells . This enables the new cells to ‘remember’ which genes were inactive or active in the original cell . A protein known as ORCA associates with heterochromatin , but it is not clear what role it plays in controlling the structure of chromatin . Giri et al . studied ORCA and the KMTs in human cells . The experiments show that ORCA recognizes the methyl tags and binds to the KMTs in regions of heterochromatin , but not in regions where the DNA is more loosely packed . Next , Giri et al . used a technique called single-molecule pull-down , which is able to identify individual proteins within a group . These experiments showed that several KMT enzymes can bind to a single ORCA protein at the same time . ORCA stabilizes the binding of KMTs to chromatin , which enables the KMTs to modify the histones within it . Cells lacking ORCA had fewer methyl tags on their histones , which altered the structure of the chromatin . This also affected the timing with which DNA copying takes place in cells before the cell divides . Giri et al . 's findings suggest that ORCA acts as a scaffold for the KMTs and that its activity at specific sites on chromatin is important for the organization of heterochromatin . The next step is to identify the exact regions in the genome where the timing of DNA copying is regulated by ORCA . | [
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] | 2015 | The preRC protein ORCA organizes heterochromatin by assembling histone H3 lysine 9 methyltransferases on chromatin |
KCC2 is a neuron-specific K+-Cl– cotransporter essential for establishing the Cl- gradient required for hyperpolarizing inhibition in the central nervous system ( CNS ) . KCC2 is highly localized to excitatory synapses where it regulates spine morphogenesis and AMPA receptor confinement . Aberrant KCC2 function contributes to human neurological disorders including epilepsy and neuropathic pain . Using functional proteomics , we identified the KCC2-interactome in the mouse brain to determine KCC2-protein interactions that regulate KCC2 function . Our analysis revealed that KCC2 interacts with diverse proteins , and its most predominant interactors play important roles in postsynaptic receptor recycling . The most abundant KCC2 interactor is a neuronal endocytic regulatory protein termed PACSIN1 ( SYNDAPIN1 ) . We verified the PACSIN1-KCC2 interaction biochemically and demonstrated that shRNA knockdown of PACSIN1 in hippocampal neurons increases KCC2 expression and hyperpolarizes the reversal potential for Cl- . Overall , our global native-KCC2 interactome and subsequent characterization revealed PACSIN1 as a novel and potent negative regulator of KCC2 .
GABA and glycine are the key inhibitory neurotransmitters of the mature nervous system , and most synaptic inhibition is mediated by Cl- permeable GABAA and glycine receptors . This hyperpolarizing inhibition results from the inward gradient for Cl- established primarily by the K+-Cl- cotransporter KCC2 , which exports Cl- to maintain low intracellular Cl- [Cl-]i ( Rivera et al . , 1999; Doyon et al . , 2016 ) . KCC2 is a member of the nine-member family of cation-chloride cotransporters and is unique among the members because it is present exclusively in neurons of the CNS , and mediates the electroneutral outward cotransport of K+ and Cl- . KCC2 protein is encoded by the SLC12A5 gene , which via alternative splicing results in two transcript variants encoding the isoforms KCC2a and KCC2b ( Payne et al . , 1996; Uvarov et al . , 2007 ) . During embryonic development , KCC2 expression is low and GABA and glycine act as excitatory neurotransmitters; however , during early postnatal development KCC2 expression is dramatically upregulated and GABA and glycine become inhibitory ( Ben-Ari , 2002; Blaesse et al . , 2009 ) . Excitation-inhibition imbalance underlies numerous neurological disorders ( Kahle et al . , 2008; Nelson and Valakh , 2015 ) , and in many of these disorders , the decrease in inhibition results from a reduction in KCC2 expression . In particular , KCC2 dysfunction contributes to the onset of seizures ( Huberfeld et al . , 2007; Kahle et al . , 2014; Puskarjov et al . , 2014; Stödberg et al . , 2015; Saitsu et al . , 2016 ) , neuropathic pain ( Coull et al . , 2003 ) , schizophrenia ( Tao et al . , 2012 ) , and autism spectrum disorders ( ASD ) ( Cellot and Cherubini , 2014; Tang et al . , 2016a; Banerjee et al . , 2016 ) . Despite the critical importance of this transporter in maintaining inhibition and proper brain function , our understanding of KCC2 regulation is rudimentary . In addition to its canonical role of Cl- extrusion that regulates synaptic inhibition , KCC2 has also emerged as a key regulator of excitatory synaptic transmission . KCC2 is highly localized in the vicinity of excitatory synapses ( Gulyás et al . , 2001; Chamma et al . , 2013 ) and regulates both the development of dendritic spine morphology ( Li et al . , 2007; Chevy et al . , 2015; Llano et al . , 2015 ) and function of AMPA-mediated glutamatergic synapses ( Gauvain et al . , 2011; Chevy et al . , 2015; Llano et al . , 2015 ) . Thus , a dysregulation of these non-canonical KCC2 functions at excitatory synapses may also contribute to the onset of neurological disorders associated with excitation-inhibition imbalances . KCC2 is regulated by multiple posttranslational mechanisms including phosphoregulation by distinct kinases and phosphatases ( Lee et al . , 2007; Kahle et al . , 2013; Medina et al . , 2014 ) , lipid rafts and oligomerization ( Blaesse et al . , 2006; Watanabe et al . , 2009 ) , and protease-dependent cleavage ( Puskarjov et al . , 2012 ) . KCC2 expression and function is also regulated by protein interactions , including creatine kinase B ( CKB ) ( Inoue et al . , 2006 ) , sodium/potassium ATPase subunit 2 ( ATP1A2 ) ( Ikeda et al . , 2004 ) , chloride cotransporter interacting protein 1 ( CIP1 ) ( Wenz et al . , 2009 ) , protein associated with Myc ( PAM ) ( Garbarini and Delpire , 2008 ) , 4 . 1N ( Li et al . , 2007 ) , the glutamate receptor subunit GluK2 , its auxiliary subunit Neto2 ( Ivakine et al . , 2013; Mahadevan et al . , 2014; Pressey et al . , 2017 ) , cofilin1 ( CFL1 ) ( Chevy et al . , 2015; Llano et al . , 2015 ) , the GABAB receptor subunit GABABR1 ( Wright et al . , 2017 ) , metabotropic glutamate receptor subunits mGluR1/5 ( Farr et al . , 2004; Banke and Gegelashvili , 2008; Mahadevan and Woodin , 2016; Notartomaso et al . , 2017 ) , and RAB11 ( Roussa et al . , 2016 ) . However , since KCC2 exists in a large multi-protein complex ( MPC ) ( Mahadevan et al . , 2015 ) , it is likely that these previously identified interactions do not represent all of the components of native-KCC2 MPCs . In the present study , we performed unbiased affinity purifications ( AP ) of native-KCC2 coupled with high-resolution mass spectrometry ( MS ) using three different KCC2 epitopes from whole-brain membrane fractions prepared from developing and mature mouse brain . We found that native KCC2 exists in macromolecular complexes comprised of interacting partners from diverse classes of transmembrane and soluble proteins . Subsequent network analysis revealed numerous previously unknown native-KCC2 protein interactors related to receptor recycling and vesicular endocytosis functions . We characterized the highest-confidence KCC2 partner identified in this screen , PACSIN1 , and determined that PACSIN1 is a novel and potent negative regulator of KCC2 expression and function .
In order to determine the composition of native KCC2 MPCs using AP-MS , we first determined the detergent-based conditions that preserve native KCC2 following membrane extraction . In a non-denaturing Blue-Native PAGE ( BN-PAGE ) , native-KCC2 migrated between 400 kDa – 1000 kDa in the presence of the native detergents C12E9 ( nonaethylene glycol monododecyl ether ) , CHAPS ( 3-[ ( 3-Cholamidopropyl ) dimethylammonio]−1-propanesulfonate hydrate ) 1 , and DDM ( n-dodecyl β-d-maltoside ) . However , all other detergent compositions previously used for KCC2 solubilization resulted in KCC2 migration at lower molecular weights ( Figure 1a ) . This indicates that native detergent extractions are efficient at preserving higher order KCC2 MPCs . Upon further analysis using standard SDS-PAGE , we observed that the total KCC2 extracted was greater in C12E9 and CHAPS-based detergent extractions in comparison with all other detergents ( Figure 1a , Figure 1—figure supplements 1 and 2 ) , hence we restricted our further analysis to C12E9 and CHAPS-based membrane preparations . To determine which of these two detergents was optimal for our subsequent full-scale proteomic analysis , we performed AP-MS to compare the efficacy of C12E9 versus CHAPS-solubilized membrane fractions . Immunopurification was performed on membrane fractions prepared from adult ( P50 ) wild-type ( WT ) mouse brain , using a well-validated commercially available C-terminal KCC2 antibody ( Williams et al . , 1999; Gulyás et al . , 2001; Woo et al . , 2002; Mahadevan et al . , 2014 ) and a control IgG antibody . In both detergent conditions , we recovered maximum peptides corresponding to KCC2 from the KCC2 pull downs ( KCC2-AP ) , in comparison to the control IgG pull downs ( IgG-AP ) , confirming the specificity of the C-terminal KCC2 antibody ( Figure 1b and Figure 1—source data 1 ) . However , upon further examination , two key pieces of evidence indicated that C12E9 -based conditions are optimal for proteomic analysis of native KCC2 . First , we observed peptides corresponding to both KCC2 isoforms-a and -b; in C12E9-based samples , but we could only detect peptides corresponding to KCC2 isoform-a in KCC2-APs from CHAPS-based samples . Second , we observed a higher enrichment of peptides corresponding to previously identified KCC2 interactors belonging to the family of Na+/K+ ATPases ( ATP1A1-3 ) , and the family of creatine kinases ( CKB , CKMT1 ) , and cofilin1 ( CFL1 ) in the KCC2-AP from C12E9-based samples . Based on these results , we concluded that C12E9-based solubilization conditions yield more KCC2-specific binding partners and fewer IgG-specific binding partners compared to CHAPS , and thus provide a higher stringency for KCC2 AP-MS . Thus , we performed all subsequent proteomic analysis of native KCC2 on samples solubilized with C12E9 . To focus our proteomic analysis on KCC2b , which is the abundant isoform primarily responsible for the shift from excitatory to inhibitory GABA during early postnatal development ( Uvarov et al . , 2007; Kaila et al . , 2014 ) , we used a multi-epitope approach that allowed us to distinguish the KCC2 isoforms ( Figure 2a ) . The C-terminal antibody recognizes both isoforms ( Uvarov et al . , 2007; 2009 ) , so we chose to use another antibody that is specifically raised against the unique N-terminal tail of the KCC2b isoform . Lastly , we used a phosphospecific antibody for serine 940 ( pS940 ) , as phosphorylation of this residue increases KCC2 surface expression and/or transporter function ( Lee et al . , 2007; Lee et al . , 2011 ) . We validated these three KCC2 antibodies ( C-terminal , N-terminal , and pS940 ) for KCC2-immunoenrichment ( Figure 2—figure supplement 1 ) ( Mahadevan et al . , 2014 ) . Moreover , by taking a multi-epitope approach , we significantly increased the likelihood of detecting KCC2 interactions that may be missed during single-epitope AP approaches . We performed 34 rounds of AP-MS including both developing ( P5 ) and mature/adult ( P50 ) WT mouse brain lysates ( Figure 2—source data 1 ) . We could not use KCC2-knockout brains since these animals die at birth , so as an alternative we used a mock IP for each sample condition in the absence of the KCC2 antibody using parallel preimmunization immunoglobulin ( IgG/IgY ) as negative controls . We obtained 440 potential KCC2 protein interactors with 99% confidence and a 1% false discovery rate . We identified KCC2 peptides spanning the entire sequence of KCC2 with ~44% sequence coverage , primarily at the C- and N-terminal tails ( Figure 2b ) ; and in both the developing and mature brain , KCC2 peptides were the most abundant peptides identified in the KCC2-IPs ( Figure 2c ) . While the KCC2 C-terminal antibody recovered peptides from both isoforms of KCC2 , the N-terminal KCC2b-specific antibody did not recover any KCC2a isoform-specific peptides , indicating the specificity of the antibodies used , and the success of KCC2-immunoenrichment in our AP-assays . To build the KCC2 interactome , all potential KCC2 protein interactors were filtered according to their spectral count enrichment in the KCC2-APs , and normalized to IgG IPs . In the first pass filter grouping , we included proteins with at least two unique peptides and peptide-spectrum matches and a 3-fold increase in KCC2 spectral counts in the KCC2-AP in comparison to IgG-AP ( Figure 3—source data 1 ) . This yielded ~75 putative-KCC2 partners . In the second pass filter grouping , we identified additional putative-KCC2 partners by including those with only one unique peptide , or less than three-fold KCC2-AP enrichment , if they met one of the following criteria: ( a ) the protein was a previously validated KCC2 physical/functional interactor; ( b ) the protein family already appeared in the first-pass filter; ( c ) the protein appeared as a single-peptide interactor across multiple experiments ( e . g . multiple antibodies , or in lysates from both age timepoints , or in both replicates ) . Including these additional proteins from the second pass filtering yielded 186 putative-KCC2 partners . We next eliminated the 36 proteins that have been previously identified as commonly occurring spurious interactors in LC/MS experiments as indicated in the CRAPome database ( Figure 3—source data 2 ) ( Mellacheruvu et al . , 2013 ) . By applying these filtering criteria and processes , we established a total list of 150 putative KCC2 partners in our present LC-MS assay ( Figure 3—source data 3 ) . More than half of these KCC2 interactors were exclusively enriched in KCC2-APs from the mature brain ( 85 proteins , ~57% overlap ) , while approximately one-third ( 41 proteins , ~27% overlap ) were identified across both the developing and mature brain ( Figure 3—figure supplement 1 ) . Only relatively small percentages were exclusively enriched in the developing brain ( 24 proteins , ~16% overlap ) . Next , we segregated the 150 putative KCC2 partners into high-confidence ( platinum , gold ) , moderate-confidence ( silver ) , or lower confidence ( bronze ) putative KCC2-interactors ( Figure 3 , Table 1 and Figure 3—source data 4 ) . This segregation was based on the largest probability of a bait-prey pair across all replicate purifications , as indicated by the MaxP score ( Choi et al . , 2012 ) , and the presence of a particular protein across replicates , and spectral enrichment of a particular protein across all experiments . Platinum KCC2-partners are those proteins enriched in a minimum of two out of three replicates , show 5 + fold spectral enrichment , and a MaxP SAINT score ≥0 . 89 . Gold KCC2-partners were those proteins enriched in only one replicate with normalized spectral count enrichments ≥ 5 and a MaxP SAINT score ≥0 . 89 . Silver KCC2-partners were those with normalized spectral count enrichments between 3 and 5 , and a MaxP score between 0 . 7 and 0 . 89 . Bronze KCC2-parterns were all remaining proteins that were not designated as Platimum , Gold or Silver . Lastly , we added 31 proteins that have been previously established as KCC2-physical/functional partners but were not identified in our present LC-MS assay ( Figure 3—source data 4 ) . The proteins identified in the present screen ( 150 ) and the proteins previously established as key KCC2 physical/functional partners ( 31 ) , together constitutes the 181 members of the proposed KCC2 interactome . All 181 proteins were included in subsequent network analyses . To interpret the potential functional role of KCC2-protein interactors , we first segregated them based on their abundance at excitatory and inhibitory synapses . To perform this analysis , we mapped the KCC2 interactome to the excitatory synapse-enriched postsynaptic density ( PSD ) , Nlgn1 , Lrrtm1 , and Lrrtm2 proteomes ( Collins et al . , 2006; Loh et al . , 2016 ) , or the inhibitory synapse-enriched iPSD , GABAAR , GABABR , NLGN2 , Slitrk3 , and GlyR proteomes ( Heller et al . , 2012; Del Pino et al . , 2014; Kang et al . , 2014; Loh et al . , 2016; Nakamura et al . , 2016; Schwenk et al . , 2016; Uezu et al . , 2016 ) ( Figure 4—source data 1 ) . Interactome mapping revealed that ~43% of proteins in the KCC2 interactome ( 77/181 ) were exclusively enriched at excitatory synapses , while only ~2% of proteins ( 4/181 ) were exclusively enriched at inhibitory synapses ( Figure 4a , b ) . However , ~15% proteins ( 30/181 ) were mapped to both excitatory and inhibitory synapses , whereas ~39% proteins ( 70/181 ) did not map to either synapses . To further examine the KCC2 interactome based on cellular functions , we performed an Ingenuity Pathway Analysis ( IPA ) to segregate the KCC2-interactors into highly enriched Gene Ontology ( GO ) classes . Performing this IPA analysis revealed that KCC2 partners segregate into multiple cellular and molecular functional nodes , which we then combined into three broad categories that collectively had high p values: ion homeostasis , dendritic cytoskeleton rearrangement , and receptor trafficking ( Figure 4c–e; Figure 4—source data 2 ) . KCC2 has been previously associated with both ion homeostasis and dendritic spine morphology , and consistent with this previous work we identified previously characterized KCC2 functional or physical interactors , including subunits of the sodium/potassium ( Na+/K+ ) ATPase , including the previously characterized KCC2 interactor ATP1A2 ( Ikeda et al . , 2004 ) , and Cofilin1 , which was recently demonstrated to be important for KCC2-mediated plasticity at excitatory synapses ( Chevy et al . , 2015; Llano et al . , 2015 ) . The third category , receptor trafficking , has a denser network ( clustering coefficient of 0 . 63 and an average of ~4 . 4 neighbors ) in comparison to the other networks , suggesting a tight link between KCC2 and proteins in this node . Notably , this analysis revealed multiple novel putative-KCC2 partners , including PACSIN1 , SNAP25 , RAB11FIP5 , CSNK2A1 , DNM1 and AP2B1 . All these novel putative interactors have established functions in membrane recycling and/or trafficking of glutamate receptor subunits ( Carroll et al . , 1999; Lee et al . , 2002; Vandenberghe et al . , 2005; Pérez-Otaño et al . , 2006; Selak et al . , 2009; Sanz-Clemente et al . , 2010; Anggono et al . , 2013; Bacaj et al . , 2015 ) . In order to determine the spatiotemporal expression profiles of the KCC2 interactome , we first performed transcriptomic analysis and hierarchical clustering of high-resolution human brain RNAseq data ( Kang et al . , 2011; Willsey et al . , 2013 ) ( obtained from http://brain-map . org ) . We observed that SLC12A5 mRNA is expressed with several members of the receptor trafficking node in the hippocampus ( Figure 5a ) , amygdala , thalamus , cerebellum and cortex ( Figure 5—figure supplement 1; Figure 5—source data 1 ) . The RNAseq data does not distinguish between isoforms KCC2a and KCC2b , which equally represented in the neonatal brain , while KCC2b is the predominant isoform in the adult brain . In order to independently validate the utility of the KCC2 interactome , we proceeded to biochemical and functional analysis . We focused this validation analysis on proteins in the receptor trafficking category for two reasons: ( i ) the most abundant putative-KCC2 partner , PACSIN1 ( PKC and CSNK2A1 substrate in neurons; also called as Syndapin1 ) is present in the receptor trafficking node; and ( ii ) the tightest KCC2-subnetwork exists in receptor trafficking node , indicating a dense interconnectivity between these proteins . To biochemically and functionally validate our KCC2 interactome , we chose to focus on the putative KCC2-PACSIN1 interaction . The rationale for this selection was based on the following: ( 1 ) PACSIN one is the most abundant KCC2 interactor in our LC-MS assay ( next only to KCC2 ) , with a high normalized spectral count ratio and a high MaxP score , and with extensive amino acid sequence coverage ( Figure 5—figure supplement 2 ) ; ( 2 ) PASCIN1 is a substrate for PKC , and PKC is a key regulator of KCC2 ( Lee et al . , 2007 ) ; ( 3 ) PASCIN1 is a substrate for CSNK2A1 , and our LC-MS assay revealed CSNK2A1 also as a putative KCC2-interactor; ( 4 ) PACSIN1 is abundant at both excitatory and inhibitory synapses ( Pérez-Otaño et al . , 2006; Del Pino et al . , 2014 ) ; and ( 5 ) PACSIN1 was identified as an abundant KCC2 interactor using two antibodies ( N-terminal and pS940 ) in multiple replicates . To independently verify whether KCC2 associated with PACSIN1 , we performed a co-immunoprecipitation assays from adult whole brain native membrane preparations . We found that PACSIN1 was immunoprecipitated with an anti-KCC2b antibody , but not with control IgY antibodies ( Figure 5b ) . Using a previously well-validated PACSIN1 antibody ( Anggono et al . , 2013 ) , we confirmed this interaction in the reverse direction , indicating the existence of a KCC2-complex with PACSIN1 in vivo ( Figure 5b ) . Consistent with our ability to co-immunoprecipitate KCC2 and PACSIN1 , we found that the expression profiles of KCC2 and PACSIN1 are temporally aligned in the mouse brain ( Figure 5c ) . To determine whether native-KCC2 complexes are stably associated with PACSIN1 , we performed an antibody-shift assay coupled with two-dimensional BN-PAGE ( 2D BN-PAGE ) , which is a strategy that has been used previously to examine the native assemblies of several transmembrane protein multimeric complexes ( Schwenk et al . , 2010 , 2012 ) , including that of native-KCC2 ( Mahadevan et al . , 2014 , 2015 ) . Using this approach , we first verified that the addition of N-terminal KCC2b antibodies could shift a proportion of native-KCC2 to higher molecular weights , in comparison to IgY control antibodies ( Figure 5d ) . Next , we observed that this antibody-induced shift in native-KCC2b using N-terminal antibody also shifted a population of native-PACSIN1 to comparable higher molecular weights ( Figure 5d ) . Collectively , these experiments establish native-PACSIN1 as a novel KCC2-binding partner in whole brain tissue . The PACSIN family of proteins contains three members that share ~90% amino acid identity ( Modregger et al . , 2000 ) . PACSIN1 is neuron-specific and is broadly expressed across multiple brain regions; PACSIN2 is ubiquitous and is abundant in cerebellar Purkinje neurons ( Anggono et al . , 2013; Cembrowski et al . , 2016 ) , and PACSIN3 is restricted to muscle and heart ( Modregger et al . , 2000 ) . To determine which members of the PACSIN family binds to KCC2 , we transfected PACSIN constructs ( Anggono et al . , 2013 ) , with myc-KCC2b in COS-7 cells and performed co-immunoprecipitation . We observed that KCC2 robustly associates with PACSIN1 , weakly interacts with PACSIN2 , and does not interact with PACSIN3 ( Figure 5e ) . Mouse PASCIN1 contains a membrane-binding F-BAR domain ( aa 1–325 ) , a SH3 domain ( aa 385–441 ) that binds to phosphorylated targets , and a VAR ( variable ) region ( aa 326–384 ) ( Kessels and Qualmann , 2004; 2015 ) . To determine the PACSIN1 region that is required for KCC2 binding we repeated our co-immunoprecipitation assays in COS- 7 cells , but this time we used previously characterized PACSIN1 deletion constructs ( Anggono et al . , 2013 ) ( Figure 5f ) . We discovered that removing either the SH3 ( lane 3 ) or the F-BAR region ( lane 6 ) did not disrupt the KCC2:PACSIN1 interaction , indicating that they are not necessary for KCC2 binding . In an analogous result , neither the SH3 domain alone ( lane 7 ) nor F-BAR domain alone ( lane 2 ) could interact with KCC2 . However , KCC2 robustly co-precipitated with PACSIN1 when the VAR region alone was co-expressed with KCC2 ( lane 5 ) , indicating that the VAR region is sufficient to mediate the KCC2 interaction . KCC2 dysregulation has emerged as a key mechanism underlying several brain disorders including seizures ( Fiumelli et al . , 2013; Stödberg et al . , 2015; Saitsu et al . , 2016 ) , neuropathic pain ( Coull et al . , 2003 ) , schizophrenia ( Tao et al . , 2012 ) , and autism spectrum disorders ( ASD ) ( Cellot and Cherubini , 2014; Tang et al . , 2016a ) . However , there are currently no existing KCC2 enhancers approved for clinical use , and thus there is a critical need to identify novel targets for the development of KCC2 enhancers . To determine whether PACSIN1 may be a potential target for regulating KCC2 function , we assayed for KCC2 function following PACSIN1 knockdown . We chose to assay for the canonical KCC2 function of Cl- extrusion , as the loss of Cl- homeostasis and thus synaptic inhibition , is causal for several neurological disorders ( Coull et al . , 2003; Huberfeld et al . , 2007; Tao et al . , 2012; Cellot and Cherubini , 2014; Toda et al . , 2014; Kahle et al . , 2014; Puskarjov et al . , 2014; Stödberg et al . , 2015; Banerjee et al . , 2016; Saitsu et al . , 2016; Tang et al . , 2016b ) . We assayed KCC2-mediated Cl- extrusion by performing patch clamp recordings of the reversal potential for GABA ( EGABA ) , which is principally determined by [Cl−]i ( Kaila , 1994 ) . Whole-cell patch clamp recordings were obtained from cultured hippocampal neurons that endogenously express KCC2 . EGABA was determined from current–voltage ( IV ) curves that were created by eliciting inhibitory postsynaptic currents ( IPSCs ) , by puffing GABA ( 20 µM ) at the soma while progressively step-depolarizing the postsynaptic holding potential ( through current injection via the whole-cell patch pipette ) . A linear regression of the IPSC amplitude was used to calculate the voltage dependence of IPSCs; the intercept of this line with the abscissa was taken as EGABA ( mV ) , and the slope of this line as GABA conductance ( pS ) . KCC2 function is best determined when the transporter is driven to extrude Cl- , which we achieved by loading the intracellular compartment with Cl- via the whole-cell patch pipette ( Doyon et al . , 2016 ) . To determine whether PACSIN1 regulates KCC2-mediated Cl- transport , we virally transduced neurons with plasmids containing a previously validated PACSIN1 shRNA to knockdown PACSIN1 ( Anggono et al . , 2013 ) , or a scrambled shRNA , which served as the control recordings . Neurons were transduced at 5–7 days in vitro ( DIV ) and all the recordings were performed at 11–14 DIV . Neurons were selected for recording based on the presence of reporter fluorescence ( ~60% of neurons were transduced ) . When neurons expressed the PASCIN1 silencing shRNA , EGABA was significantly hyperpolarized compared to control neurons expressing the scrambled shRNA ( Figure 6a , b; control shRNA: −28 . 62 ± 3 . 07 mV , n = 9; PACSIN1 shRNA: −37 . 86 ± 1 . 73 mV , n = 11; t ( 18 ) =2 . 744 , p=0 . 013 ) . We found no significant change in the GABAAR conductance ( Figure 6a , c; control shRNA: 6 . 93 ± 1 . 32 pS , n = 9; PACSIN1 shRNA: 12 . 96 ± 2 . 71 pS , n = 11; t ( 18 ) =1 . 86 , p=0 . 079 ) , which indicates that the effect is on Cl- transport and not GABA conductance . In addition , we found no significant change in the resting membrane potential ( RMP ) , ( Figure 6d; control shRNA: −63 . 41 ± 1 . 48 mV , n = 11; PACSIN1 shRNA: −62 . 77 ± 2 . 21 mV , n = 9; t ( 18 ) =0 . 246 , p=0 . 808 ) , which indicates the change in the driving force for Cl- was due to a change in EGABA and not RMP . Together , these data indicate that knocking down PACSIN1 increases KCC2-mediated Cl- transporter , which results in hyperpolarized EGABA . The electrophysiology experiments performed above using Cl- loading through the patch pipette are important to test KCC2 function under an ionic load ( Doyon et al . , 2016 ) . To determine the impact of PACSIN1 on KCC2 function under resting conditions , we repeated the electrophysiology recordings performed above to determine EGABA , but this time , we used the gramicidin-perforated patch clamp technique to maintain the neuronal Cl- gradient ( Kyrozis and Reichling , 1995 ) . Similar to our recordings above , we found a significant hyperpolarizing shift in EGABA in neurons expressing PACSIN1 shRNA versus control cells expressing scrambled shRNA ( Figure 6e; control shRNA: −46 . 93 ± 2 . 78 mV , n = 7; PACSIN1 shRNA: −−72 . 70 ± 4 . 70 mV , n = 6; t ( 11 ) =4 . 9115 , p=0 . 0005 ) . Taken together , our whole-cell and gramicidin-perforated patch clamp recordings reveal that knocking down KCC2 in cultured hippocampal neurons leads to a hyperpolarization of EGABA that strengthens inhibition , which results from an increase in KCC2-mediated Cl- extrusion . Due to the known role of PACSIN1 as an endocytic regulatory protein ( Anggono et al . , 2006; 2013; Pérez-Otaño et al . , 2006; Del Pino et al . , 2014; Widagdo et al . , 2016 ) , we hypothesized that PACSIN1 negatively regulates KCC2 function by altering its expression in the surface membrane . To test our hypothesis , we performed immunofluorescent staining of endogenous KCC2 in cultured hippocampal neurons transduced with transduced with PACSIN1 shRNA or scrambled shRNA ( control cells ) . We observed a significant increase in KCC2 fluorescence in neurons expressing PASCIN1 shRNA in comparison with controls ( Figure 6e; control shRNA: 58 . 86 ± 2 . 53 A . U . , n = 32; PACSIN1 shRNA: 74 . 05 ± 2 . 53 A . U . , n = 32; t ( 31 ) =5 . 272 , p<0 . 0001 ) . Taken together , our electrophysiological recordings and immunofluorescence results demonstrate that a reduction in PACSIN1 results in increased KCC2 expression and an increase in the strength of inhibition ( hyperpolarization of EGABA ) . If PACSIN1 is a bona fide negative regulator of KCC2 expression , then overexpressing PACSIN1 should produce a reduction in KCC2 expression . To test this prediction , we again performed immunofluorescent staining of endogenous KCC2 in cultured hippocampal neurons transduced with either eGFP ( control ) or PASCIN1-eGFP . We observed a remarkable loss of KCC2 immunofluorescence when PACSIN1 was overexpressed in comparison to control eGFP ( Figure 6f; control eGFP: 62 . 1 ± 2 . 7 A . U . , n = 23; PACSIN1-eGFP: 11 . 31 ± 3 . 17 A . U . , n = 16; t ( 37 ) =12 . 13 , p<0 . 0001 ) , which supports the conclusion that PACSIN1 negatively regulates KCC2 expression .
We determined that the mouse brain KCC2 functional interactome is comprised of 181 proteins; and by mapping the KCC2 interactome to excitatory and inhibitory synapse proteomes and performing ingenuity pathway analysis , we determined that KCC2 partners are highly enriched at excitatory synapses and form a dense network with proteins involved in receptor trafficking . We validated the utility of the KCC2-interactome that we presented , by verifying the biochemical interaction between PACSIN1 and KCC2 , and by demonstrating that PACSIN1 participates in regulation of the level of expression of KCC2 . Functional validation of the KCC2-PACSIN1 interaction revealed that PACSIN1 robustly and negatively regulates KCC2 expression . While ion channels and GPCRs are known to predominantly exist in large multi-protein complexes in the CNS ( Husi et al . , 2000; Berkefeld et al . , 2006; Collins et al . , 2006; Müller et al . , 2010; Schwenk et al . , 2010; 2012; 2014; 2016; Nakamura et al . , 2016; Pin and Bettler , 2016 ) , similar studies on CNS solute carrier proteins ( transporters ) are still in their infancy ( César-Razquin et al . , 2015b; Comstra et al . , 2017; Haase et al . , 2017 ) . Based on the critical importance of SLC transporters as therapeutic targets in both rare and common diseases ( César-Razquin et al . , 2015a; Lin et al . , 2015 ) , including that of KCC2 in human neurological diseases ( Blaesse et al . , 2009; Medina et al . , 2014 ) , our present study also fills a general gap in the field of CNS transporter proteomics . A common caveat of isoform-counting in shot-gun proteomic experiments such as ours , is the problem of protein inference ( Nesvizhskii and Aebersold , 2005 ) . While we were able to discriminate KCC2 isoforms-a and -b , based on the presence of their unique peptides in their N-terminus ( Figure 1c ) , it is not possible to categorize the remaining peptides to either isoform a or b due to their extensive shared homology . Therefore , targeted proteomics such as selected-reaction monitoring would be required to accurately establish the abundances of KCC2 isoforms . We report that native-detergents C12E9 and CHAPS extract KCC2 isoforms differentially ( Figure 1b ) . We also report that native-detergents C12E9 and CHAPS pull-down different subsets of proteins along with some common interactors ( Figure 1b ) . It is intriguing to note that there were several putative KCC2-partners uniquely identified with CHAPS ( SLC44A1 , ATP2B4 , RNF8 , JAGN1 , CD47 , SLC2A1 ( Figure 1b ) . Although we did not perform exhaustive proteomics with CHAPS-based KCC2 extractions , because of the presence of these high-confidence proteins in the CHAPS-based KCC2 LC/MS , we did include these proteins in the KCC2-interactome . This demonstrates that detergent stabilities of KCC2 protein complexes are distinct , in line with other recent ion channel proteomic studies ( Müller et al . , 2010; Schulte et al . , 2011; Schwenk et al . , 2012 ) . Our KCC2 LC/MS identified previously established KCC2 proteins interactors , including ATP1A2 ( Ikeda et al . , 2004 ) , CFL1 ( Chevy et al . , 2015; Llano et al . , 2015 ) , and CKB ( Inoue et al . , 2004; 2006 ) , which add confidence to the validity of this interactome . We were initially surprised at the absence of other previously established KCC2 interactors , including: Neto2 ( Ivakine et al . , 2013; Mahadevan et al . , 2015 ) , GluK2 ( Mahadevan et al . , 2014; Pressey et al . , 2017 ) , 4 . 1N ( Li et al . , 2007 ) , beta-pix ( Chevy et al . , 2015; Llano et al . , 2015 ) , RCC1 ( Garbarini and Delpire , 2008 ) , or signaling molecules PKC ( Lee et al . , 2007 ) , WNK , SPAK and OSR ( Friedel et al . , 2015 ) . The absence of these previously identified interactors may be due to any of the following caveats , which have been well recognized in previous ion channel and GPCR proteomic studies: ( 1 ) these interactions may be weak , transient , mediated by posttranslational modifications ( Schulte et al . , 2011 ) , or mediated by intermediary partners; ( 2 ) these interactions are under-represented in the whole brain membrane fractions because they are restricted to specific brain regions; ( 3 ) antibody-epitope binding blocked endogenous interactions; ( 4 ) despite using the C12E9-based solubilization strategy that is known to stabilize ion pumps and transporters ( Romero , 2009; Babu et al . , 2010; Ramachandran et al . , 2013 ) particular interactions may be better preserved by other detergent conditions . Single particle tracking of surface KCC2 has revealed that ~ 66% of KCC2 is located synaptically ( Chamma et al . , 2012 , 2013 ) . While the density of surface KCC2 was not reportedly different between excitatory and inhibitory synapses , KCC2 was shown to dwell longer at excitatory synapses . Our observation that KCC2 interacting proteins are primarily enriched at excitatory synapses in comparison to inhibitory synapses is in line with this increased confinement of KCC2 at excitatory synapses . The presence of KCC2 at excitatory synapses raises some interesting questions: How does KCC2–mediated Cl- extrusion regulate hyperpolarizing inhibition if it is preferentially localized near excitatory synapses ? Why are the KCC2 partners exclusive to the inhibitory synapses , less represented when compared with excitatory synapses ? One potential answer to both these questions is that: because of the difficulty in identifying components of inhibitory synapses , our knowledge of the proteins present at these structures is incomplete . Despite the fact that our network mapping incorporated 444 proteins known to be enriched at inhibitory synapses ( Heller et al . , 2012; Del Pino et al . , 2014; Kang et al . , 2014; Loh et al . , 2016; Nakamura et al . , 2016; Schwenk et al . , 2016; Uezu et al . , 2016 ) , it is possible that we identified a smaller representation of inhibitory synapse-specific KCC2 partners in our present study . In this context , it is interesting to note that KCC2 itself was not identified in any of the above inhibitory synapse-enriched proteomes , and we assigned KCC2 as a member of the inhibitory synapse during network analyses , because of its established function at this locus . Another possibility is that KCC2 ‘moon-lights’ between inhibitory and excitatory synapses , as previously suggested ( Blaesse and Schmidt , 2015 ) . Our interactome supports this hypothesis as we identified 29 proteins ( excluding KCC2 ) that are enriched at both synapses . However , future studies are required to systematically examine whether the KCC2 complexes containing these putative moon-lighting proteins are similar or distinct complexes within these loci . While the notion that excitatory and inhibitory synapses are distinct structures is widely accepted , emerging evidence from cortex suggests this may not be strictly true ( Chiu et al . , 2013; Higley , 2014 ) . Under circumstances where excitatory and inhibitory synapses are in close physical proximity , the molecular complex involving KCC2 and these moonlighting proteins are ideally placed to execute cell-intrinsic E/I balance regulation , a hypothesis stemming from our present study that requires rigorous experimental testing . Ever since the first discovery that KCC2 participates in the regulation of dendritic spine structures ( Li et al . , 2007 ) , several studies have demonstrated 4 . 1N as a critical mediator of this non-canonical transporter-independent KCC2 function ( Horn et al . , 2010; Gauvain et al . , 2011; Chamma et al . , 2013; Fiumelli et al . , 2013 ) . Recently however , additional molecular players underlying this phenomenon , including COFL1 , and ARHGEF7 ( Beta-pix ) have been identified to interact with KCC2 ( Chevy et al . , 2015; Llano et al . , 2015 ) . In the present study , we identify diverse high confidence ( Platinum , Gold ) cytoskeletal organizers belonging to distinct protein families such as CRMPs , 14-3-3 isoforms , SRCIN1 , VCP , KIF21B , previously unsuspected to mediate KCC2-dependant non-canonical function . However , the precise relation between KCC2-dependant non-canonical functions and these putative partners in not currently known . PACSIN1 is a well-established endocytic adapter protein that regulates the surface expression of distinct glutamate ( Anggono et al . , 2006 , 2013; Pérez-Otaño et al . , 2006; Widagdo et al . , 2016 ) and glycine receptors ( Del Pino et al . , 2014 ) . We reveal PACSIN1 as a novel negative regulator of KCC2 expression in hippocampal neurons . We previously reported that native-KCC2 assembles as a hetero-oligomer that migrates predominantly above ~400 kDa ( Mahadevan et al . , 2014; 2015 ) . Similar to KCC2 , native-PACSIN1 also migrates above ~400 kDa ( Kessels and Qualmann , 2006 ) . Here , we report that while SLC12A5 and PACSIN1 mRNA transcripts increase in parallel in multiple brain regions throughout development , PACSIN1 overexpression remarkably decreases total KCC2 abundance . How does PACSIN1 exist in a stable complex with KCC2 when it negatively regulates KCC2 expression ? Since KCC2 and PACSIN1 are both dynamically regulated by phosphorylation and PKC ( Anggono et al . , 2006; Lee et al . , 2007; Clayton et al . , 2009; Kahle et al . , 2013 ) , we predict that upon KCC2 phosphorylation , PACSIN1 uncouples from KCC2 rendering it incapable of negatively regulating KCC2 . Numerous pathological situations are associated with decreased KCC2 phosphorylation at Ser940 ( Wake et al . , 2007; Lee et al . , 2011; Sarkar et al . , 2011; Toda et al . , 2014; Ford et al . , 2015; Mahadevan et al . , 2015; Silayeva et al . , 2015; Leonzino et al . , 2016; Mahadevan and Woodin , 2016 ) , resulting in decreased transporter expression and/or function . It will be important to determine whether any of these neurological deficits stem from PACSIN1-mediated decreases in KCC2 . In the present study , we demonstrate that PACSIN1 shRNA increases KCC2 expression and strengthens inhibition , indicating that PACSIN1 is a target for intervention to upregulate KCC2 during pathological states . In conclusion , the KCC2 interactome as presented here , serves as a molecular framework for systematically exploring how KCC2 up and down states can be dynamically regulated by its native molecular constituents , thereby providing a blueprint for subsequent detailed functional investigations .
All experiments were performed in accordance with guidelines and approvals from the University of Toronto Animal Care Committee and the Canadian Council on Animal Care ( University of Toronto Protocol #20012022 ) . Animals of both sexes from wild-type mice , C57/Bl6 strain ( Charles River Laboratories ) were used throughout . Animals were housed in the Faculty of Arts and Science Biosciences Facility ( BSF ) in a 12 hr light: 12 hr d cycle , with 2–5 animals/cage . All biochemical preparations and centrifugations were performed at 4°C as previously described ( Ivakine et al . , 2013; Mahadevan et al . , 2014 , 2015 ) . Systematic analysis of detergent solubility , and migration of native-KCC2 from crude membrane fractions were performed according to the workflow described in Figure 1—figure supplement 1 . The following eight detergents ( or detergent combinations ) were used to solubilize whole brain membranes: C12E9 ( 1 . 5%; nonaethylene glycol monododecyl ether , Sigma-Aldrich , St . Louis , MO , #P9641 ) , CHAPS ( 1 . 5%; 3-[ ( 3-Cholamidopropyl ) dimethylammonio]−1-propanesulfonate hydrate , Sigma-Aldrich #C3023 ) , DDM , ( 1 . 5%; DDM , n-dodecyl β-d-maltoside , Sigma-Aldrich #D4641 ) , DOC ( 1%; sodium deoxycholate , Sigma-Aldrich #D6750 ) , NP40 ( 1%; nonyl phenoxypolyethoxylethanol , Thermo Fisher Scientific # 28324 ) , Triton-X-100 ( 1%; 4- ( 1 , 1 , 3 , 3-Tetramethylbutyl ) phenyl-polyethylene glycol , t-Octylphenoxypolyethoxyethanol , Polyethylene glycol tert-octylphenyl ether , Sigma-Aldrich #X-100 ) , Triton-X-100 ( 1% ) +DOC ( 1% ) , SDS ( 0 . 1%; Sodium dodecyl sulphate , ( Sigma-Aldrich #71725 ) +DOC ( 1% ) +NP40 0 . 5% ) . Mice ( ~P5 , P50 ) were sacrificed , and brains were removed and homogenized on ice in PBS using a glass-Teflon homogenizer , followed by brief low-speed centrifugation . Soft-pellets were re-suspended in ice-cold lysis buffer [Tris·HCl , 50 mM , pH 7 . 4; EDTA , 1 mM; protease and phosphatase inhibitor mixture ( Roche ) ] , homogenized , and centrifuged for 30 min at 25 , 000 × g . Membrane pellets were re-suspended in solubilization buffer ( 4Xw/v ) [Tris·HCl , 50 mM , pH 7 . 4; NaCl , 150 mM; EDTA , 0 . 05 mM; selected detergent ( s ) , and protease and phosphatase inhibitor mixture ( Roche ) ] , solubilized for 3 hr on a rotating platform at 4°C , and centrifuged for 1 hr at 25 , 000 × g . For KCC2 and control co-immunoprecipitations , 20–100 μl GammaBind IgG beads were incubated on a rotating platform with the following antibodies ( 5–100 μg antibody ) for 4 hr at 4°C in cold 1X PBS: Following antibody binding , 20 mM DMP ( dimethyl pimelimidate , ThermoFisher 21667 ) in cold 1X PBS was used to crosslink antibodies with the beads , according to manufacturer’s instructions . The crosslinking reaction was stopped by adding 50 mM Tris·HCl to quench excess DMP , and the antibody-conjugated beads were thoroughly washed with the IP buffer . 1–10 mg of pre-cleared mouse brain membrane fractions were incubated with KCC2 or control antibody-conjugated beads on a rotating platform for 4 hr at 4°C . After co-immunoprecipitation , the appropriate unbound fraction was saved for comparison with an equal amount of protein to calculate the IP-efficiency ( Figure 2—figure supplement 1 ) . The beads were washed twice with IP buffer containing detergent , and twice with IP-buffer excluding the detergent . The last wash was performed in 50 mM ammonium bicarbonate . Co-immunoprecipitation experiments for validating KCC2 and PACSIN1 was performed similar to the above procedure , in the absence of DMP-crosslinking . In a subset of validation experiments , anti-PACSIN1 antibody ( Synaptic Systems #196002 , RRID AB_2161839 ) , was used for reverse co-IP . The break-down of LC/MS replicates are as follows: Mass spectrometry for the creation of the KCC2 interactome ( Figures 2 and 3 ) was performed at the SPARC Biocentre at SickKids Research Institute ( Toronto , Ontario ) . Mass spectrometry for the determination of optimal detergents for native KCC2 extraction ( Figure 1 ) was performed in the lab of Dr . Tony Pawson at the Lunenfeld-Tanenbaum Research Institute ( LTRI ) , Mount Sinai Hospital ( Toronto , ON ) and in the CBTC ( University of Toronto ) . Details on the individual experiments performed in each facility is located in Figure 2—source data 1 . For all MS experiments , proteins were eluted from beads by treatment with double the bead volume of 0 . 5 M ammonium hydroxide ( pH 11 . 0 ) , and bead removal by centrifugation; this procedure was repeated 2x . The combined supernatants were dried under vacuum , reduced with DTT , and the free cysteines were alkylated with iodoacetamide . The protein concentration was measured , and trypsin was added at a ratio of 1:50; digestion occurred overnight at 37°C . The peptides were purified by C18 reverse phase chromatography on a ZipTip ( Millipore , Bellerica , MA ) . Specifics of the MS in the three facilities are below: All MS/MS samples were analyzed using Sequest ( Thermo Fisher Scientific , San Jose , CA , USA; version 1 . 4 . 0 . 288 ) and X ! Tandem ( The GPM , thegpm . org; version CYCLONE ( 2010 . 12 . 01 . 1 ) ) . Sequest was set up to search Uniprot-mus +musculus_reviewed_Oct172015 . fasta ( unknown version , 25231 entries ) assuming the digestion enzyme trypsin . X ! Tandem was set up to search the Uniprot-mus +musculus_reviewed_Oct172015 database ( 25248 entries ) also assuming trypsin . Sequest and X ! Tandem were searched with a fragment ion mass tolerance of 0 . 020 Da and a parent ion tolerance of 10 . 0 PPM . Carbamidomethyl of cysteine was specified in Sequest and X ! Tandem as a fixed modification . Deamidated of asparagine and glutamine and oxidation of methionine were specified in Sequest as variable modifications . Glu->pyro Glu of the n-terminus , ammonia-loss of the n-terminus , gln->pyro Glu of the n-terminus , deamidated of asparagine and glutamine and oxidation of methionine were specified in X ! Tandem as variable modifications . Scaffold ( version Scaffold_4 . 7 . 2 , Proteome Software Inc . , Portland , OR ) was used to validate MS/MS-based peptide and protein identifications . Peptide identifications were accepted if they could be established at greater than 95 . 0% probability . Peptide Probabilities from X ! Tandem were assigned by the Peptide Prophet algorithm ( Keller et al . , 2002 ) with Scaffold delta-mass correction . Peptide probabilities from Sequest were assigned by the Scaffold Local FDR algorithm . Protein identifications were accepted if they could be established at greater than 95 . 0% probability and contained at least one identified peptide . Protein probabilities were assigned by the Protein Prophet algorithm ( Nesvizhskii et al . , 2003 ) . Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony . Proteins were annotated with GO terms from gene_association . goa_uniprot ( downloaded Dec 14 , 2015 ) ( Ashburner et al . , 2000 ) . In addition , peak lists obtained from MS/MS spectra were identified independently using OMSSA version 2 . 1 . 9 ( Geer et al . , 2004 ) , X ! Tandem version X ! Tandem Sledgehammer ( 2013 . 09 . 01 . 1 ) ( Craig and Beavis , 2004 ) , Andromeda version 1 . 5 . 3 . 4 ( Cox et al . , 2011 ) , MS Amanda version 1 . 0 . 0 . 5242 ( Dorfer et al . , 2014 ) , MS-GF +version Beta ( v10282 ) ( Kim and Pevzner , 2014 ) , Comet version 2015 . 02 rev . 3 ( Eng et al . , 2013 ) , MyriMatch version 2 . 2 . 140 ( Tabb et al . , 2007 ) and Tide ( Diament and Noble , 2011 ) . The search was conducted using SearchGUI version 2 . 2 . 2 ( Vaudel et al . , 2011 ) . Protein identification was conducted against a concatenated target/decoy version ( Elias and Gygi , 2010 ) of the Mus musculus ( 24797 , >99 . 9% ) , Sus scrofa ( 1 , <0 . 1% ) complement of the UniProtKB ( Apweiler et al . , 2004 ) ( version of December 2015 , 24798 , Mus Musculus ) canonical and isoform sequences ) . The decoy sequences were created by reversing the target sequences in SearchGUI . The identification settings were as follows: Trypsin with a maximum of two missed cleavages; 10 . 0 ppm as MS1 and 0 . 5 Da as MS2 tolerances; fixed modifications: Carbamidomethylation of C ( +57 . 021464 Da ) , variable modifications: Deamidation of N ( +0 . 984016 Da ) , Deamidation of Q ( +0 . 984016 Da ) , Oxidation of M ( +15 . 994915 Da ) , Pyrolidone from E ( -−18 . 010565 Da ) , Pyrolidone from Q ( -−17 . 026549 Da ) , Pyrolidone from carbamidomethylated C ( -−17 . 026549 Da ) and Acetylation of protein N-term ( +42 . 010565 Da ) , fixed modifications during refinement procedure: Carbamidomethylation of C ( +57 . 021464 Da ) . Peptides and proteins were inferred from the spectrum identification results using PeptideShaker version 1 . 9 . 0 ( Vaudel et al . , 2015 ) . Peptide Spectrum Matches ( PSMs ) , peptides and proteins were validated at a 1 . 0% False Discovery Rate ( FDR ) estimated using the decoy-hit distribution . Spectrum counting abundance indexes were estimated using the Normalized Spectrum Abundance Factor ( Powell et al . , 2004 ) adapted for better handling of protein inference issues and peptide detectability . While the two independent protein algorithm searches largely matched with each other , a small subset of proteins were identified with high confidence using the SearchGUI/Peptideshaker platforms that were not identified with the ThermoFisher Scientific/Scaffold platforms . The mass spectrometry data along with the identification results have been deposited to the ProteomeXchange Consortium ( Vizcaíno et al . , 2014 ) via the PRIDE partner repository ( Martens et al . , 2005 ) at https://www . ebi . ac . uk/pride/archive/ with the dataset identifier PXD006046 . Protein candidates from replicate LC/MS screens were subject to the following criteria to build the KCC2 interactome . First pass filter grouping: at least two unique peptides and fold change of total spectra above 1 . 5 . Second pass filter grouping: for proteins with only one unique peptide , consider whether ( a ) the protein isoform is an already validated KCC2 interactor in literature; ( b ) the protein isoform already appears in the first pass filter; ( c ) the protein isoform appears as a single-peptide interactor across experiments ( using the same epitope KCC2 IPs/different epitope KCC2 IPs/different developmental time KCC2 IPs ) . If a particular protein isoform matches any of the above criteria , it gets shifted to the first pass filter grouping . Finally , the proteins that appear in the KCC2 interactome that are previously identified spurious interactors as identified in the CRAPome database ( Mellacheruvu et al . , 2013 ) were further eliminated . For the existing proteins , a MaxP-SAINT score ( Choi et al . , 2012 ) was assigned and proteins were grouped as Platinum , Gold , Silver or Bronze interactors prior to subsequent PPI ( protein-protein interaction ) network analysis . See Figure 3—figure supplement 3 for a detailed description of the path towards constructing the KCC2 interactome . Protein interactions were integrated with curated , high-throughput and predicted interactions from I2D ver . 2 . 3 database ( Brown and Jurisica , 2007 ) , FpClass high-confidence predictions ( Kotlyar et al . , 2015 ) and from the BioGRID database ( Stark et al . , 2006 ) . Networks were visualized using Cytoscape ver . 3 . 3 . 0 ( Shannon et al . , 2003; Cline et al . , 2007 ) . Components of the KCC2 interactome were mapped to the excitatory synapse-enriched PSD , Nlgn1 , Lrrtm1 and Lrrtm2 proteomes ( Collins et al . , 2006; Loh et al . , 2016 ) , or the inhibitory synapse-enriched GABAAR , GABABR , Nlgn2 , Slitrk3 and GlyR proteomes ( Heller et al . , 2012; Del Pino et al . , 2014; Kang et al . , 2014; Loh et al . , 2016; Nakamura et al . , 2016; Schwenk et al . , 2016; Uezu et al . , 2016 ) . In the PPI networks , the thickness of the black radial lines in the foreground denotes the number of spectral enrichment ( KCC2/IgG ) in the log scale ranging from 2 . 13 for the highly enriched interactor to 0 . 08 for the least enriched interactor . For representing the previously established KCC2 physical/functional interactors not identified in this study , an arbitrary value of 0 . 05 was used for indicating the thickness of black radial lines ( See Figure 4—source data 1 ) . Grey radial lines in the PPI network background denotes the previously identified physical/co-expression networks across all interactome members as identified from BioGRID and FpClass databases . Venn diagrams were made using Venny , online tool ( http://bioinfogp . cnb . csic . es/tools/venny/ ) ; heat maps were made using Morpheus online tool provided by the Broad Institute ( https://software . broadinstitute . org/morpheus/ ) . HEK-293 and COS7 cells obtained from the ATCC were authenticated using Short Tandem Repeat ( STR ) profiling and checked for mycoplasma contamination . For co-immunoprecipitation experiments , cells were transfected with KCC2b-MYC , eGFP control , eGFP-PACSIN1/2/3 , or eGFP-PACSIN1-deletion constructs ( 0 . 25 μg/construct ) using Lipofectamine ( Invitrogen ) at 70% confluency . Thirty-six hours after transfection , cells were washed with ice-cold 1 × PBS and lysed in modified RIPA buffer [50 mM Tris·HCl , pH 7 . 4 , 150 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , 0 . 1% SDS , 0 . 5% DOC , and protease inhibitors ( Roche ) ] . Lysed cells were incubated on ice for 30 min and were centrifuged at 15 , 000 × g for 15 min at 4°C . Cell lysates or solubilized membrane fractions ( ∼0 . 2–0 . 5 mg protein ) were incubated with N-terminal KCC2b or anti-myc ( CST #9B11 , RRID AB_331783 ) antibodies on a rotating platform ( 4 hr , 4°C ) . Lysates were subsequently incubated with 20 μl GammaBind IgG beads ( GE Healthcare ) on a rotating platform ( 1 hr at 4°C ) . After incubation , beads were washed twice with modified RIPA buffer , and twice with modified RIPA buffer minus detergents . Bound proteins were eluted with SDS sample buffer and subjected to SDS/PAGE along with 10% of input fraction and immunoblotted . Figure 5e is representative of four independent biological replicates; Figure 5f is representative of three independent biological replicates . Native-membrane fractions were prepared similarly as described ( Swamy et al . , 2006; Schwenk et al . , 2012; Mahadevan et al . , 2014; 2015 ) . Antibody-shift assay and 2D BN-PAGE analysis of native-KCC2 complexes were performed as described previously ( Mahadevan et al . , 2014; 2015 ) . Briefly , 50 μg - 100 μg of C12E9 solubilized complexes were pre-incubated for 1 hr with 10 μg of anti-N-terminal KCC2b antibody or chicken IgY whole molecule , prior to the addition of Coomassie blue G250 . 1D-BN-PAGE was performed as described above using home-made 4% and 5% bis-tris gels as described ( Swamy et al . , 2006 ) . After the completion of the gel run , excised BN- PAGE lanes were equilibrated in Laemmli buffer containing SDS and DTT for 15 min at room temperature to denature the native proteins . After a brief rinse in SDS- PAGE running buffer , the excised BN-PAGE lanes were placed on a 6% or 8% SDS- PAGE gel for separation in the second dimension . After standard electro-blotting of SDS- PAGE-resolved samples on nitrocellulose membrane , the blot was cut into two molecular weight ranges; the top blots were subjected to western blotting analysis with Rb anti- KCC2 , and the bottom blots with Rb anti-PACSIN1 . Antibody-shift experiments ( Figure 5d ) using hippocampal membranes are representative from two independent biological replicates . All PACSIN constructs used for overexpression and shRNA constructs have been previously validated for specificity ( Anggono et al . , 2013; Widagdo et al . , 2016 ) . The PACSIN1 shRNA-targeting sequence ( sh#1 , 5′-GCGCCAGCTCATCGAGAAA-3′ ) or control shRNA sequence was inserted into the pSuper vector system ( Oligoengine ) as described previously ( Anggono et al . , 2013 ) . The efficiency and specificity of the PACSIN1 and control shRNA constructs were tested in HEK 293 T cells overexpressing GFP-PACSIN1 , and they were subsequently cloned into pAAV-U6 for lentiviral production ( serotype AAV2/9 ) . Low-density cultures of dissociated mouse hippocampal neurons were prepared as previously described ( Acton et al . , 2012; Mahadevan et al . , 2014 ) . Electrophysiological recordings were performed using pipettes made from glass capillaries ( World Precision Instruments , Sarasota , FL ) , as previously described ( Acton et al . , 2012; Mahadevan et al . , 2014 ) . Neuronal transduction with viral vectors was performed at DIV 5–7 and the recordings were performed at DIV 11–14 . For Cl− loading experiments in whole-cell configuration , pipettes ( 5–7 MΩ ) were filled with an internal solution containing the following ( in mM ) : 110 K+-gluconate , 30 KCl , 10 HEPES , 0 . 2 EGTA , 4 ATP , 0 . 3 GTP , and 13 phosphocreatine ( pH 7 . 4 with KOH , 300 mOsm ) . For gramicidin-perforated recordings , pipettes were filled with an internal solution containing the following ( in mM ) : 130 K+-gluconate , 10 KCl , 10 HEPES , 0 . 2 EGTA , 4 ATP , 0 . 3 GTP , 13 phosphocreatine , and 50 μg/ml gramicidin ( pH 7 . 4 with KOH , 300 mOsm ) . Cultured neurons were continuously perfused ( at ~1 mL/min ) with standard extracellular solution containing: 150 NaCl , 3 KCl , 3 CaCl2:2H2O , 2 MgCl2:6H2O , 10 HEPES , and 5 glucose ( pH 7 . 4 with NaOH , 300 mOsm ) . Cultured neurons were selected for electrophysiology based on the following criteria: ( 1 ) a healthy oval or pyramidal-shaped cell body; ( 2 ) multiple clearly identifiable processes; ( 3 ) a cell body and proximal dendrites that were relatively isolated; and ( 4 ) reporter fluorescence ( if applicable ) . Recordings started when the series resistance dropped below 50 MΩ . Recordings were amplified with an Axon Instruments Multiclamp 700B and digitized using an Axon Instruments Digidata 1322a ( Molecular Devices; Sunnyvale , CA ) . To determine the reversal potential for GABA ( EGABA ) , neurons were held at −60 mV under whole-cell voltage clamp and the membrane potential was stepped in +10 mV increments from −80 to −40 mV ( this holding potential was set through current injection via the whole-cell patch pipette ) . During each membrane potential step , a 20 μM GABA puff was applied onto the soma using a picospritzer ( Parker , Hollis , NH , USA ) . The IPSC amplitude represents the maximum current measured during the recordings performed for the EGABA measurement . Using Clampfit ( version 9 . 2; Molecular Devices; Sunnyvale , CA , USA ) , two cursors were placed on the recording trace ( one just before the current , and the other at the peak of the current ) , and the peak amplitude was exported to Prism ( version 5 . 01 ) and graphed against the holding potential . A linear regression of the IPSP amplitude versus membrane/holding potential was achieved using Prism and the intercept of this line with the abscissa was taken as EGABA , and the slope of this line was taken as the synaptic conductance . For resting membrane potential , a whole-cell patch clamp was achieved , the amplifier was set to I = 0 , and the corresponding potential was measured under current clamp mode . Electrophysiological values have not been corrected for the liquid junction potential of ~7 mV . DIV 12–14 cultured hippocampal neurons with were first rinsed with 1X PBS , and fixed in 4% paraformaldehyde for 10 min on ice followed by washing thrice with 1X PBS . Neurons were then permeabilized with 1X PBS containing 10% goat serum and 0 . 5% Triton X-100 for 30 min , followed by a 45 min incubation with rabbit anti-KCC2 ( Millipore 07–432 ) antibodies at 37˚C to detect endogenous proteins . Finally , neurons were washed thrice with 1X PBS and incubated with Alexa-fluor 555-conjugated goat anti-rabbit antibody for 45 min at 37˚C . Neurons were imaged on a Leica TCS SP8 confocal system with a Leica DMI 6000 inverted microscope ( Quorum Technologies ) . Cultured neurons were selected for immunostaining based on the following criteria: ( 1 ) with a healthy oval or pyramidal-shaped cell body; ( 2 ) multiple clearly identifiable processes; ( 3 ) a cell body and proximal dendrites that were relatively isolated; ( 4 ) reporter fluorescence ( if applicable ) . Images were acquired using 3D Image Analysis software ( Perkin Elmer ) . Images were obtained using a 63 × 1 . 4 NA oil immersion objective . Imaging experiments were performed and analyzed in a blinded manner . Using ImageJ , four bisecting lines were drawn across the center of the cell ( Figure 6—figure supplement 1 ) . The peak values of each line ( 2 values/line ) were used to calculate peak fluorescent intensity of KCC2 at the membrane . Fluorescence intensity is plotted in arbitrary units ( a . u . ) . For electrophysiology and immunostaining data ( Figure 6 ) , ‘n’ values report the number of neurons , and were obtained from a minimum of three independent sets of cultured neurons ( produced from different litters ) . Example recordings in Figure 6a are representative of n = 9 ( shRNA control ) and n = 11 ( PACSIN1 shRNA ) . Example recordings in Figure 6e are representative of n = 32 ( shRNA control ) and n = 32 ( PACSIN1 shRNA ) . Example recordings in Figure 6f are representative of n = 23 ( eGFP ) and n = 16 ( PACSIN1-eGFP ) . Data in Figure 6b , c , e and f are mean ± SEM . Statistical significance was determined using either SigmaStat or GraphPad Prism ( version 5 . 01 ) software . Statistical significance in Figure 6b , c , d , e and f was determined using Student’s t-tests ( two-tailed ) ; all data sets passed the normal distribution assumptions test . Statistical significance is noted as follows: *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Exact p and t values are reported in the Results text . Figures 1a , 5b , c and d are representative of two independent biological replicates . Figure 5f is representative of three independent biological replicates . Figure 5e is representative of four independent biological replicates . | Neurons in the brain talk to each other by releasing chemicals called neurotransmitters . These neurotransmitters can either increase ( 'excite' ) or decrease ( 'inhibit' ) the activity of other neurons . Inhibitory neurotransmission uses the chemical GABA as a neurotransmitter . When a neuron releases GABA it is like applying the brake in your car – you can slow down subtly to stay under the speed limit , or stomp on it to avoid an accident . The brain needs to carefully control the amount of inhibition so that the animal can learn and produce complex behaviours . For GABA to inhibit the activity of a neuron , the neuron must maintain a low amount of chloride ions inside . A transporter protein called KCC2 shuttles chloride out of cells; if this transporter fails to work , chloride builds up in the neuron and prevents inhibition so that GABA neurotransmission switches from inhibitory to excitatory . This breakdown of GABA inhibition is a hallmark of abnormal brain activity during conditions such as epilepsy , pain and some forms of autism . Despite the fact that neurons need KCC2 for inhibition in the brain , we do not know much about how this transporter works . Since the activity of a protein is determined in part by the other proteins it interacts with , it is therefore important to identify all the proteins that interact with KCC2 – termed the KCC2 interactome . To discover these protein interactions , Mahadevan et al . performed a technique called liquid chromatography-mass spectrometry on KCC2 protein isolated from mouse brains . This revealed that there are 181 proteins in the KCC2 interactome . Of these proteins , the most abundant was a protein called PACSIN1 , which helps to pull proteins out of the membrane that surrounds each neuron . To investigate how the interaction between PACSIN1 and KCC2 regulates the activity of this transporter , Mahadevan et al . performed fluorescence imaging of neurons and recorded their electrical activity . This revealed that PACSIN1 restricts the expression of KCC2 , meaning that the more PACSIN there is in the neuron , the less KCC2 will be present . The KCC2 interactome provides a database of proteins that can be targeted to increase the activity of KCC2 . This could allow new treatments to be developed for brain disorders in which the inhibition of neurons is reduced . | [
"Abstract",
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"neuroscience"
] | 2017 | Native KCC2 interactome reveals PACSIN1 as a critical regulator of synaptic inhibition |
Recent studies posit a role for non-coding RNAs in epithelial ovarian cancer ( EOC ) . Combining small RNA sequencing from 179 human serum samples with a neural network analysis produced a miRNA algorithm for diagnosis of EOC ( AUC 0 . 90; 95% CI: 0 . 81–0 . 99 ) . The model significantly outperformed CA125 and functioned well regardless of patient age , histology , or stage . Among 454 patients with various diagnoses , the miRNA neural network had 100% specificity for ovarian cancer . After using 325 samples to adapt the neural network to qPCR measurements , the model was validated using 51 independent clinical samples , with a positive predictive value of 91 . 3% ( 95% CI: 73 . 3–97 . 6% ) and negative predictive value of 78 . 6% ( 95% CI: 64 . 2–88 . 2% ) . Finally , biologic relevance was tested using in situ hybridization on 30 pre-metastatic lesions , showing intratumoral concentration of relevant miRNAs . These data suggest circulating miRNAs have potential to develop a non-invasive diagnostic test for ovarian cancer .
Invasive epithelial ovarian cancer ( EOC ) is the leading cause of death from gynecologic cancer among women in developed countries ( Siegel et al . , 2016 ) . Most women with EOC present with advanced stage disease , where 5 year survival rates average 25–30% , highlighting the need for an effective screening strategy . Unfortunately , two large-scale randomized clinical trials involving ultrasound and CA125 , including the Prostate , Lung , Colorectal , and Ovarian Cancer ( PLCO ) trial and the United Kingdom Collaborative Trial of Ovarian Cancer Screening ( UKCTOCS ) trial did not demonstrate a meaningful impact on overall survival from EOC ( Zhu et al . , 2011; Jacobs et al . , 2016 ) . These and other non-experimental longitudinal studies reaffirm CA125 can detect advanced disease but with poorer sensitivity for early stage and non-serous cancers . In addition , CA125 has limited specificity , with the majority of abnormal CA125 values being the result of non-gynecologic malignancies or benign gynecologic conditions ( Moss et al . , 2005 ) . The hope that adding more biomarkers to CA125 would improve screening was not realized in a re-analysis of the PLCO data as well as a recent longitudinal study from the European Prospective Investigation of Nutrition and Cancer ( Zhu et al . , 2011; Terry et al . , 2016 ) . In a separate strategy to improve EOC outcome , several panels ( which have CA125 as part of them ) have received FDA approval to be used in the differential diagnosis of EOC to encourage referral of EOC cases to centers with greater expertise in cancer surgery and chemotherapeutic treatment ( Karst and Drapkin , 2010 ) . However , these have not been effective for early diagnosis . Among the alternatives to serum proteins for the diagnosis or early detection of EOC , circulating microRNAs ( miRNAs ) have shown great potential ( Nakamura et al . , 2016 ) . miRNAs are short ( 18–24 nucleotide ) non-coding RNAs that regulate gene expression through post-transcriptional modification of mRNA transcripts . miRNAs have several advantages over protein measures: ( 1 ) PCR amplifies detection of rare transcripts in blood; ( 2 ) all miRNAs use the same units of measure , easing incorporation into multiplexed panels; and ( 3 ) miRNAs play a critical role in ovarian cancer biology , whereas the function of CA125 is unknown ( Deb et al . , 2017; Katz et al . , 2015 ) . Moreover , non-invasive sampling of circulating miRNAs has a clear advantage over analytes obtained through biopsy ( Wang et al . , 2016 ) . Preliminary studies have suggested that circulating miRNAs profiles are altered in women with ovarian cancer ( Nakamura et al . , 2016; Chung et al . , 2013; Langhe et al . , 2015; Resnick et al . , 2009; Zuberi et al . , 2015; Samuel and Carter , 2016 ) . In addition , miRNAs have prognostic significance for EOC survival ( Merritt et al . , 2008; Bagnoli et al . , 2016; Cramer and Elias , 2016 ) . However , efforts to develop a diagnostic signature based on circulating miRNAs have been hampered by issues regarding the best statistical approach to develop a model , reproducibility of miRNA measurement across technology platforms ( e . g . qPCR , next generation sequencing , microarray ) , and the biologic heterogeneity of EOC ( Nakamura et al . , 2016 ) . In this study , our objective was to develop a serum-based miRNA model for the diagnosis of ovarian cancer that could address these concerns and demonstrate the biologic and clinical relevance of this diagnostic tool .
To produce our diagnostic circulating miRNA signature from human sera , we constructed a study population of pre-treatment ( prior to either surgery or chemotherapy ) subjects comprising 179 women selected from three independent prospective studies ( ERASMOS , PMP , and NECC ) ( Table 1 ) . ERASMOS contributed consecutive cases presenting for evaluation of an adnexal mass , while PMP allowed enrichment of the population for specific histopathologic diagnoses . NECC added healthy controls age-matched to PMP . After completing small RNA sequencing on the sera , subjects were randomly assigned into model training and testing sets ( Figure 1 ) . After the randomization , the training and testing sets were demographically similar , and there were no differences in the distribution of histopathological diagnoses between the sets ( Table 2 ) . We then deployed a series of statistical tools , including machine-learning approaches to analyze the miRNA-seq data to create an algorithm with the best performance for discriminating cases of ovarian cancer from either benign tumors , non-invasive ( ‘borderline’ ) tumors , or healthy controls . This began by using three different potential strategies for selecting miRNA variable inputs to build the models: significance-based ( by t-test ) , correlation-based feature subset , or expression fold change ( Table 3 ) . Each miRNA variable list method was entered into one of 11 different models , which were compared both by AUC ( Table 4 ) as well as sensitivity and specificity ( Figure 2 ) . Although many of the models performed well , the neural network model employing miRNA expression fold changes was the only model to meet our pre-specified statistical objective with an AUC of 0 . 90 ( 95% CI: 0 . 81–0 . 99; p=0 . 03 over a theoretical AUC of 0 . 75 ) . The network consisted of 14 individual miRNAs with seven neurons in the hidden layer ( Source code 1 ) . As the network relied on complex interactions between miRNA levels we tested whether its performance was not biased by batch adjustment performed at the initial step of the analysis . The neural network worked equally well on the adjusted and unadjusted raw datasets with an AUC of 0 . 93 ( 95%CI: 0 . 89–0 . 98 ) on the training and 0 . 90 ( 95%CI 0 . 80–0 . 99 ) on the testing set ( Figure 3; Supplementary file 1A [by model] and 1B [by sample] ) . In post-hoc secondary analyses , the neural network worked equally well for older and younger patients , serous and non-serous histologies , and early and advanced stage disease ( Supplementary file 2A-C ) . Serum CA125 data were available for 120 subjects ( Supplementary file 1B and 3A ) . Among these , the neural network ( AUC 0 . 93; 95% CI 0 . 88–0 . 97 ) significantly outperformed CA125 ( AUC 0 . 74; 95% CI 0 . 65–0 . 83; p=0 . 001; Figure 4 ) . The primary advantage of the neural network over CA125 was avoiding false positives ( 8/43 for the neural network versus 23/43 for CA125; p=0 . 002 ) ( Supplementary file 2A ) . Notably , the neural network and CA125 levels were independent of one another ( Figure 4—figure supplement 1; Supplementary file 3B ) . We tested using the neural network and CA125 in a tiered testing strategy , subjecting all negative neural network algorithm results to a second review with CA125 , but found this would increase the probability of a false positive test result from 4 . 2% ( 5/120 ) to 19 . 2% ( 23/120 ) and a false negative rate from 5 . 8% ( 7/120 ) to 13 . 3% ( 16/120 ) ( Figure 4—figure supplement 2 ) . The alternative of initial screening with CA125 followed by neural network yielded only three additional correctly diagnosed cases of invasive cancer at the expense of 19 additional false positive results . The specificity of the neural network algorithm for the diagnosis of ovarian cancer was tested using an external , independent , dataset previously published by Keller , et al ( Keller et al . , 2011 ) . These data were generated via a third technology platform , probe-based microarray , which fortunately contained all 14 miRNAs from our original signature , allowing for 1:1 mapping without exclusions ( Supplementary file 4A and Supplementary file 6 ) . The neural network perfectly classified patients in the training set ( AUC 1 . 00 , 95% CI 1 . 00–1 . 00 ) and provided very good discriminatory power on the testing set ( AUC 0 . 93 , 95% CI 0 . 81–1 . 00 ) , with an overall sensitivity of 75% and specificity of 100% . The signature was specific to ovarian cancer compared to all other diagnoses , as it did not show any clinically-efficient diagnostic capabilities for any of the 12 other morbidities analysed in the set and showed good performance in distinguishing ovarian cancer samples against all other diagnoses combined ( AUC 0 . 92 , 95% CI 0 . 82–1 . 00 ) ( Figure 5 ) . Having established our miRNAs of interest using next generation sequencing , we next sought to validate the sequencing data across technology platforms by measuring the miRNAs from the neural network using qPCR . While small RNA sequencing is a more robust technology for miRNA discovery , qPCR is a more time efficient and cost-effective diagnostic tool . For this we used 120 samples from PMP and NECC for which we had excess RNA . We internally validated the 14 miRNAs in the neural network ( plus an additional nine potential reference miRNAs derived from the sequencing data ) by qPCR and recalibrated the algorithm to accept qPCR inputs ( Supplementary file 6 ) . We then performed a global sensitivity analysis on the best neural network for qPCR data and iteratively removed the variables which did the least in terms of improving the classifier’s performance . This reduced the neural network to only seven miRNAs ( miR-29a-3p , miR-92a-3p , miR-200c-3p , miR-320c , miR-335–5 p , miR-450b-5p , and miR-1307–5 p ) plus four normalizers ( miR-423–3 p , miR-191–5 p , miR-221–3 p , and miR-103a-3p ) . To increase the statistical power of this qPCR-based classifier and create a fully locked-down model for clinical application , we added 205 more samples from PMP and NECC , including more than 100 additional healthy controls , to create a 325 subject population for qPCR model development ( Table 5 ) . These samples were randomized 3:1 into training and testing sets to create a neural network . The resulting network performed well with an AUC 0 . 89 on the training set and AUC 0 . 80 on the testing set . We then tested the clinical performance of the final , locked-down diagnostic test on a completely independent external sample set collected from 51 preoperative patients treated in Lodz , Poland ( Table 6 ) . In this population , the neural network had a positive predictive value of 91 . 3% ( 95% CI: 73 . 3–97 . 6% ) and a negative predictive value of 78 . 6% ( 95% CI: 64 . 2–88 . 2% ) with an AUC of 0 . 85 ( Figure 6 ) . Ideally , a serum biomarker should have biologic relevance to the clinical disease . To this end , we returned to the ERASMOS patient set to examine if the expression levels of the miRNAs changed in the cancer patients after surgical cytoreduction . Among the patients with ovarian cancer in the study , 27 had both preoperative and postoperative serum miRNAs profiled . These included 4/7 target miRNAs in the qPCR neural network model . Circulating levels of all three miRNAs decreased within 72 hr of tumor removal , with significant changes for miR-200a-3p and miR-200c-3p ( Figure 7A–D ) . We also wanted to test if the miRNAs were in fact coming from the earliest lesions of this disease . For this , we assembled paraffin-embedded tissue sections from independent sets of 15 cases of serous tubal intraepithelial carcinomas and 15 Stage I high grade ( serous or Grade three endometrioid ) epithelial ovarian cancers . Immunohistochemistry was performed on sequential sections for TP53 and Ki67 to highlight the lesions . We then performed in situ hybridization for three of the miRNAs in our neural network; mir-200c-3p , mir-335–5 p , and mir-92a-3p ( Figure 8 ) . In 100% of the samples , there was complete overlap between lesional cells and the miRNAs crucial for neural network performance , suggesting that the miRNAs detected in the serum are present even in early lesions in the fallopian tube epithelium and raising the possibility of detection of pre-metastatic disease . Finally , we have constructed a web calculator ( http://biostat . umed . pl/ovaries ) to demonstrate how to use these models . The calculator accepts various inputs describing on the method of circulating miRNA quantification ( sequencing , qPCR , or microarray ) and returns the estimated probability of ovarian cancer for a given patient .
We have described the development of a diagnostic model for ovarian cancer using sequencing of circulating miRNA . This is the first study in ovarian cancer to combine next generation sequencing technology for serum miRNA with machine learning techniques . Not only does sequencing provide greater sensitivity for miRNA detection than other methods , but expression levels of various miRNAs are not linearly related and relationships among miRNAs tend to be obscured by more basic statistical approaches . The neural network as presented has several advantages over a traditional biomarker like CA125 . The neural network recognized more Stage I/II ovarian cancers and had significantly fewer false positives . This likely reflects an ability to discriminate relevant biology more than to quantify tumor burden . For example , the neural network correctly classified 35/43 ( 81% ) borderline tumors as being non-invasive neoplasms , compared to just 20/43 ( 47%; p=0 . 002 ) for CA125 . An additional strength of our study is the incorporation of multiple independent datasets . The ERASMOS specimens were obtained from cases enrolled sequentially , reflecting the natural frequency of different ovarian tumor subtypes in the clinical population , including the fact that most women with invasive ovarian cancer present with advanced stage disease . The Pelvic Mass Protocol samples allowed us to enrich the study population for less common clinical cases that would be expected to confound a conventional screening algorithm , including benign complex ovarian masses , borderline tumors , early stage cancers , and non-serous histologic subtypes . NECC provided age-matched healthy controls . The specificity of our model was tested using a publicly available dataset from Keller , et al where we showed that the neural network performed well across disease stages , histologic subtypes , and diagnostic platforms . This ability to specifically identify ovarian cancers and discriminate ovarian cancer from other diagnoses sets the current work apart from prior miRNA studies ( Nakamura et al . , 2016; Chung et al . , 2013; Resnick et al . , 2009; Zuberi et al . , 2015; Häusler et al . , 2010; Zheng et al . , 2013 ) . Finally , we tested our signature using a completely external , independent set of samples from Poland , showing that in a clinical sample set the test performed well without additional modifications . There appears to be biologic relevance to the serum miRNAs in the neural network . The rapid change in circulating levels after surgical cytoreduction for mir-200a and mir-200c suggests these are being produced actively by tumors . Although other miRNAs did not have as great of a decrease , this may reflect differing half-lives for different miRNA species . In future work , it would be interesting to measure changes over a longer time frame than 72 hr , but that was the endpoint for ERASMOS , which is an anesthesia-focused study . We also demonstrated expression of several miRNAs from the neural network in pre-metastatic lesions . This both confirms prior work suggesting that these miRNAs are detectable in advanced ovarian cancers specimens and adds the new finding that these miRNAs are expressed in very early stage and even pre-invasive lesions ( Bagnoli et al . , 2016 ) . Future work will examine the kinetics of these miRNA changes in tumor pathogenesis . The phase II specimens used in this study are like those used to support development of assay panels subsequently approved for the differential diagnosis of ovarian cancer vs . a benign pelvic mass . The first panel , named OVA1 , was approved by the FDA in 2009 and consisted of 5 analytes including CA125 ( Zhang et al . , 2004; Ueland et al . , 2011 ) . While those authors emphasized the assay’s negative predictive value of 95% ( when combined with physician assessment ) , the assay had an AUC of only 0 . 80 ( 95% CI: 0 . 73–0 . 88 ) for pre-menopausal women and 0 . 82 ( 95% CI: 0 . 77–0 . 87 ) for post-menopausal women . The second panel was approved in 2011 and consisted of just two markers , CA125 and HE4 , combined with menopausal status ( Moore et al . , 2010 ) . While the ROMA algorithm had an overall AUC for discriminating cancer from benign tumors of 0 . 91 ( 95% CI: 0 . 88–0 . 94 ) , this was in the setting of including borderline tumors as malignancies . Moreover , the positive predictive value of the test for distinguishing benign masses from Stage I/II EOC was only 0 . 27 . In 2016 , the FDA approved an updated version of the OVA1 test which retained CA125 but replaced 2 of the markers with HE4 and FSH ( Coleman et al . , 2016 ) . This improved the overall AUC to 0 . 92 ( 95% CI: 0 . 89–0 . 96 ) for the assay alone and 0 . 94 ( 95% CI: 0 . 91–0 . 97 ) when combined with physician assessment , although 80% of the tumors in this study were benign . Although the above panels included some clinical information and therefore are not equivalent to our panel , we point out that the AUC of our panel to distinguish a malignant from benign pelvic mass was similar , while not including borderline tumors as positive results and agnostic to clinical or imaging information . As timely referral to a gynecologic oncologist is a strong predictor of ovarian cancer survival , we believe that there is a role for a test based on blood markers alone ( Earle et al . , 2006 ) . FDA approval of the various panels for use in the differential diagnosis of pelvic masses did not extend to their use in the general population . Based upon the results of the PLCO and UKCTOCS randomized clinical trials ( or so called ‘phase 4’ ) the US Preventive Services Task Force and the Society of Gynecologic Oncology ( SGO ) have not recommended routine screening for ovarian cancer ( Zhu et al . , 2011; Skates et al . , 2001 ) . However , screening with CA125 and transvaginal ultrasound is recommended by the National Comprehensive Cancer Network guidelines and the SGO for women with known hereditary syndromes of ovarian cancer ( such as women with germline BRCA1/2 mutations ) , even though there is currently no evidence that this screening strategy improves survival in elevated risk populations ( Schorge et al . , 2010 ) . Recent studies ( Chung et al . , 2013; Langhe et al . , 2015; Resnick et al . , 2009; Zuberi et al . , 2015 ) have identified circulating ( serum/plasma ) miRNAs that are altered in ovarian carcinomas , and there is limited overlap with miRNAs that emerged from our analysis . One possible cause of this difference is the limited number of samples examined in these studies . For example , in Langhe et al , a training set of 5 serous ovarian carcinomas and five benign serous cystadenomas were selected for the initial experiments . The validation set was 20 serous ovarian carcinomas and 20 benign serous cystadenomas . In Resnick et al , 28 ovarian carcinoma patients and 15 healthy controls were used to identify to differential expression of circulating miRNAs . Such limited numbers diminish the statistical robustness of the results . Another possible cause for the differences is the miRNA expression profiling platform . Recently a study ( Mestdagh et al . , 2014 ) systematically compared 12 different miRNA expression platforms . Specifically , for serum miRNAs there was a 12-fold difference between the highest and lowest number of detected miRNA when identical samples were profiled by different platforms . According to this report the LNA-based platform from Exiqon has the highest specificity but maybe limited for sensitivity thereby for detection . To circumvent both these concerns we started with next-generation sequencing of 179 samples which captures all small RNAs and addresses any issues of detection or specificity due to limitations of platforms . Next , we did validation using the Exiqon qRT-PCR platform on 325 local samples , and a further validation using an additional cohort of 51 samples from Poland . The large number of samples along with the methodologies used for identification and validation of the circulating miRNAs in our study provides strong support for our conclusions and distinguishes our work from prior reports . Our study does have several limitations . Whether our miRNA panel will prove useful in the differential diagnosis of early detection will require further study in the following areas . First , additional study is necessary to determine whether integrating clinical risk factors could further improve its performance . Second , confirmation in other phase II data sets are necessary to validate our study results and demonstrate its generalizability . Third , specimens collected and stored months or years prior to a clinical diagnosis ( so called phase III specimens ) are necessary to demonstrate the model’s potential in the early detection of EOC in a general population or elevated risk setting . For the former , we have access to PLCO specimens; and for the latter , we plan to apply for specimens to the National Clinical Trials Network . Fourth , a logical extension of our work is to determine whether our current miRNA panel ( or a new one ) would be useful in predicting survival after EOC . A tissue-based MiROvaR signature involving 35 miRNAs for predicting EOC prognosis has recently been described ( Bagnoli et al . , 2016 ) . Although several miRNAs appear in the tissue signature and our model ( Supplementary file 4B ) , full concordance is unlikely since the tissue model was built to predict prognosis whereas our model was built to predict diagnosis . In addition , about two-thirds of the miRNAs in the tissue signature are not reliably detectable in circulation , which can be attributed to the fact that relatively few miRNAs circulate in serum ( Mestdagh et al . , 2014; Dinh et al . , 2016 ) . Our serum panel is reliant on a smaller number of miRNAs simply because the neural model prioritizes ones that provide novel information . If miRNAs are correlated ( for example within the same chromosomal cluster ) , they will be invariant and knowing one will convey sufficient information about the rest for them to be excluded from model building . Finally , more is to be learned about the basic biology of serum miRNA . Are they all coming from cancer cells or also other cells in the tumor microenvironment ? ( Likely , both are included in the signature ) . It is noteworthy that two of the miRNAs are members of the mir-200 family , confirming prior reports identifying these miRNAs as overexpressed in ovarian cancer ( Zuberi et al . , 2015; Pecot et al . , 2013 ) . Some of the miRNAs incorporated into the neural network algorithm have connections to other disease types . For example , miR-1246 has been identified in the serum of ovarian cancer , lung cancer , prostate cancer , and stroke patients ( Todeschini et al . , 2017; Zhang et al . , 2016; Alhasan et al . , 2016; Li et al . , 2015 ) . However , as noted in Figure 5 , the network as a whole was specific to ovarian cancer , again emphasizing the importance of multimarker panels . In conclusion , serum miRNA adds to the toolbox of options to diagnose ovarian cancer . We plan several future studies to characterize the miRNA neural network . Whether serum miRNA offers a lead time advantage over other putative biomarkers remains to be proven . We need to study the performance characteristics of the miRNA neural network in high risk and low risk populations . Finally , we are performing laboratory investigations to elucidate the biologic function of these miRNAs and to understand the kinetics of miRNA expression in ovarian cancer pathogenesis . With our improved understanding of miRNA analytic approaches , we can develop better models for this and other diseases .
Results have been reported according to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis ( TRIPOD ) guidelines ( Reporting Standards Document ) ( Collins et al . , 2015 ) . The checklist appears in the Supplement . Our model was developed from two ‘phase II’ specimen sets ( i . e . samples collected from women prior to surgery or chemotherapy ) - Effects of Regional Analgesia on Serum microRNAs after Oncology Surgery ( ERASMOS ) and the Pelvic Mass Protocol ( Cramer et al . , 2010; Elias et al . , 2015 ) . To these , healthy controls were selected from subjects who participated in the New England Case-Control ( NECC ) study , a large epidemiologic study matching cases of ovarian cancer to geographically situated controls ( Rice et al . , 2013 ) . These studies were approved by the Dana-Farber Cancer Institute Institutional Review Board Protocol 05–060 ( NECC study ) , Brigham and Women’s Hospital Institutional Review Board Protocol 2000-P-001678 ( Pelvic Mass Protocol ) , and Dana-Farber/Harvard Cancer Center Institutional Review Board Protocol 12–532 ( ERASMOS ) . All subjects were enrolled after signing informed consent , and samples were collected fresh in 13 × 75 mm BD Vacutainer Plus Plastic Serum tubes ( BD Life Sciences , Franklin Lakes , NJ ) with spray-coated silica . Samples were allowed to clot 1 hr at room temperature before processing , then spun down by centrifugation at 1300 x g x 10 min , aliquoted into 1 . 5 ml vials and stored at – 80 C . Samples from the other studies were thawed and aliquoted for the current study and then refrozen . ERASMOS enrolled 60 patients from 03/2013 – 05/2015 from the Gynecologic Oncology service at DFCI and BWH . Patients were approached consecutively for enrollment . Eligible patients were scheduled to undergo exploratory laparotomy for a pelvic mass suspicious for invasive epithelial ovarian cancer . Serum blood samples were collected preoperatively and postoperatively for each patient and then stratified for analysis by anesthetic and analgesic exposure . The primary endpoint of the study is overall survival; study results have not been published to date as the final data are not mature . The Pelvic Mass Protocol ( PMP ) enrolled women referred to the DFCI/BWH Gynecologic Oncology service over the period 1992 to 2013 ( Williams et al . , 2014 ) . Of some 455 women with a pelvic mass enrolled , we selected a total of 120 samples from the following categories: serous cystadenoma ( Samuel and Carter , 2016 ) , serous borderline tumor ( Samuel and Carter , 2016 ) , Stage I/II invasive serous adenocarcinoma ( Häusler et al . , 2010 ) , and Stage III/IV invasive serous adenocarcinoma ( Wang et al . , 2016 ) , endometrioma ( Samuel and Carter , 2016 ) , Stage I/II invasive clear cell or endometrioid adenocarcinoma ( Häusler et al . , 2010 ) , or Stage III/IV invasive clear cell or endometrioid adenocarcinoma ( Wang et al . , 2016 ) . Overall , 37% of the subjects had benign disease , 12 . 6% had borderline tumors , 10 . 1% had low grade carcinomas , and 40 . 4% had high grade carcinomas . One sample of serous cystadenoma was excluded as an outlier due to a recent cardiovascular event as evidenced by extreme elevation of myocardial ischemia-associated miRNAs . From the most recent phase ( 2004–2008 ) of the NECC study , we selected fifteen age and race matched healthy controls matched to the demographics of the EOC cases and benign disease controls from the PMP study . There was no overlap of subjects between the two studies . The samples sizes were based on a plan for a 2:1 ratio of early stage ( Stage I/II ) cancer cases to advanced stage ( Stage III/IV ) cases , a 1:1 ratio of invasive cancer cases: benign/borderline/control subjects , and for balanced numbers of healthy control: benign serous: benign endometrioid: borderline serous subjects . Borderline endometrioid or clear cell tumors were exceedingly rare and thus not included . For the qPCR model , we added 113 epithelial ovarian cancer cases and 113 healthy controls , matched for age and collection year . 20 failed quality control , leaving 206 additional samples to add to the 119 samples originally profiled from PMP and creating a 325 sample set for qPCR-based model building and cut-off calibration . Serum samples were collected from consecutive women undergoing surgical evaluation at the Medical University of Lodz , Poland , for a pelvic mass in association with an IRB-approved tumor collection protocol . All subjects were enrolled after signing informed consent , and samples were collected fresh in 13 × 75 mm BD Vacutainer Plus Plastic Serum tubes ( BD Life Sciences , Franklin Lakes , NJ ) with spray-coated silica . Samples were allowed to clot 1 hr at room temperature before processing , then spun down by centrifugation at 1300 x g x 10 min , aliquoted into 1 . 5 ml vials and stored at – 80 C . Samples were thawed only for the present study . Samples were classified as either invasive cancer or benign/borderline/controls . Although borderline tumors are not strictly benign , they are clinically indolent and seldom fatal , thus we grouped them with benign lesions as our goal was to diagnose the tumors most contributing to mortality . For each patient , an estimated probability of >0 . 5 was classified as predicting invasive ovarian cancer . For next generation sequencing ( NGS ) , sample preparation , library construction , and miRNA sequencing were performed by Exiqon , Inc . ( Vedbæk , Denmark ) . 500 μl of human serum from each sample were analyzed in duplicate . RNA from each serum sample was isolated using the miRCURYTM RNA isolation kit ( Exiqon , Vedbæk , Denmark ) per the manufacturer’s protocol optimized for serum . The quality of the isolated RNA was checked using qPCR . Total RNA was converted into microRNA NGS libraries using the NEBNEXT library generation kit ( New England Biolabs Inc . , Ipswich , MA ) per the manufacturer’s instructions . Each individual RNA sample had adaptors ligated to its 3’ and 5’ ends and converted into cDNA . Then the cDNA was pre-amplified with specific primers containing sample-specific indices . After 18 cycles of pre-PCR the libraries were purified on QiaQuick columns and the insert efficiency evaluated by a Bioanalyzer 2100 instrument on a high sensitivity DNA chip ( Agilent Inc . , Lexington , MA ) . The microRNA cDNA libraries were size fractionated on a LabChip XT ( PerkinElmer Waltham , MA ) and a band representing adaptors and 15–40 bp insert excised using the manufacturer’s instructions . Samples were then quantified using qPCR and concentration standards . Based on the quality of the inserts and the concentration measurements , the libraries were pooled in equimolar concentrations ( all concentrations of libraries to be pooled were of the same concentration ) . The library pools were finally quantified again with qPCR and the optimal concentration of the library pool used to generate the clusters on the surface of a flowcell before sequencing using v3 sequencing methodology according to the manufacturer instructions ( Illumina Inc . , Dedham , MA ) . Samples were sequenced on the Illumina NextSeq 500 system ( Illumina Inc . , Dedham , MA ) using a single-end read length of 50 nucleotides at an average of 10 million reads per sample . Sequence tags were mapped to miRbase 20 ( http://www . mirbase . org/ ) . After sequencing adapters were trimmed off as part of the base calling , trimming of adapters from the dataset revealed distinct peaks representing microRNA ( ~18–22 nt ) . Putative microRNAs not in standard miRBase or Rfam classification were identified based on the prediction algorithm miRPara and are included with the sequencing data in the GEO file ( Wu et al . , 2011 ) . Expression levels were quantified in tags per million ( TPM ) ( unadjusted data and batch-adjusted data available in Supplementary file 6 ) . TPM is a unit used to measure expression in NGS experiments . The number of reads for a particular miRNA is divided by the total number of mapped reads and multiplied by 1 million . Raw sequencing data are accessible as . fastq files through the Gene Expression Omnibus ( GEO ) database , www . ncbi . nlm . nih . gov/geo Accession GSE94533 . The most stable miRNAs from the sequencing data were selected as normalizers using the NormFinder algorithm ( Andersen et al . , 2004 ) . miRNAs incorporated into the final neural network model were confirmed using qPCR with Exiqon ( Vedbæk , Denmark ) LNA-containing miRNA-specific probes . We selected nine potential reference miRNAs ( hsa-miR-423–3 p , hsa-miR-103a-3p , hsa-miR-222–3 p , hsa-miR-221–3 p , hsa-miR-191–5 p , hsa-miR-181a-5p , hsa-miR-148b-3p , hsa-miR-146b-5p , and hsa-let-7c-5p ) from the miRNA sequencing data using the NormFinder algorithm ( Andersen et al . , 2004 ) . Both the 14 miRNAs from the test set and nine potential reference miRNAs were profiled using Exiqon’s pick-and-mix array with LNA-containing miRNA-specific probes . Small RNA from each serum sample was isolated using the miRCURY RNA isolation kit ( Exiqon , Vedbæk , Denmark ) per the manufacturer’s protocol optimized for serum . The quality of the isolated RNA was checked using qPCR . All miRNAs were polyadenylated and reverse transcribed into cDNA in a single reaction step . cDNA and ExiLENT SYBR Green master mix were transferred to qPCR panels pre-loaded with primers using a pipetting robot . Amplification was performed using a Roche Lightcycler 480 ( Roche , Basel , Switzerland ) . Amplification quality was determined by generating melting curves . Raw Cq values and melting points , detected by the Lightcycler software , were exported . Assays with several melting points or with melting points deviating from assay specifications were flagged and removed from the dataset . Reactions with amplification efficiency below 1 . 6 were also removed . Assays giving Cq values within 5 Cq values of the negative control sample were also removed from the dataset . Spike-in positive controls and no template negative controls were included . Minimum detection values for qPCR were established at 37 cycles; miRNAs with no amplification before that number of qPCR cycles were assumed to have their expression undetectable , and a quantification cycle ( Cq ) value of 37 was imputed as a substitute value . Raw , background filtered , and normalized data appear in the supplement ( Supplementary file 6 ) in accordance with Minimum Information for Publication of Quantitative Real-Time PCR Experiments ( MIQE ) Guidelines ( Bustin et al . , 2009 ) . Data were normalized to the average of the assays detected in all samples ( n = 120 samples ) . The nine selected reference miRNAs were reevaluated after profiling for their stability across the arrays and the average Cq of the two best ones ( miR-423–3 p and miR-103a-3p ) was selected as the reference for dCq calculations of the 14-miRNA and the 7-miRNA diagnostic sets using the NormFinder method . Individual miRNAs measurements from preoperative and postoperative serum samples from the ERASMOS study had been measured previously using multiplexed miRNA hydrogel probes ( FirePlex , Abcam , Cambridge , MA ) on a flow cytometer . Samples were profiled in duplicate , then replicates were merged . Fluorescence intensity values across all samples were normalized with Firefly Analysis Workbench ( Abcam , Cambridge , MA ) using the geNorm algorithm to identify appropriate normalizers ( Vandesompele et al . , 2002 ) . We sought a testing set showing a superiority of 0 . 1 in the area under the receiver operating characteristic curve ( AUC ) against a value of 0 . 75 ( assumed as a null hypothesis for a clinically useful biomarker ) with a statistical power of 80% and a type 1 error probability <0 . 05 ( Hanley and McNeil , 1982 ) . For statistical power estimation purposes we assumed that the model predictions would be moderately correlated with CA-125 levels ( r > 0 . 3 ) . The calculation yielded a required testing set of 44 patients ( 22 with invasive cancer and 22 without invasive cancer ) . To train the classifiers , we assumed the training set would require 3-fold more patients ( N = 132 ) bringing the total number of required patient samples to 176 samples . We increased the sample size to 180 to account for potential clinical or technical outliers . All three sets of variables were analyzed using 11 different classification models for a total of 33 different algorithms . Six models ( linear discriminant analysis , logistic regression , multivariate adaptive regression splines , naive Bayes , neural network , and support vector machine ) were developed using STATISTICA Data Miner 12 . 5 ( StatSoft , Tulsa , OK , USA ) . The remaining five models ( functional tree , LAD tree , Bayesian network , elastic net regression , and random forest ) were created using Weka 3 . 9 . 0 ( University of Waikato , New Zealand ) . Detailed descriptions of the classification models appear below . Interestingly , relationships among individual miRNA species were non-linear , so these relationships would likely have been obscured as evidenced by a simple hierarchical clustering of the statistically significant miRNAs from univariate analysis ( Figure 10 ) . All three sets of variables were analyzed using 11 different classification models for a total of 33 different algorithms . Six models ( linear discriminant analysis , logistic regression , multivariate adaptive regression splines , naive Bayes , neural network , and support vector machine ) were developed using STATISTICA Data Miner 12 . 5 ( StatSoft , Tulsa , OK , USA ) . The remaining five models ( functional tree , LAD tree , Bayesian network , elastic net regression , and random forest ) were created using Weka 3 . 9 . 0 ( University of Waikato , New Zealand ) . Interestingly , relationships among individual miRNA species were non-linear , so these relationships would likely have been obscured as evidenced by a simple hierarchical clustering of the statistically significant miRNAs from univariate analysis ( Figure 7 ) . For the neural network , we built 5000 neural networks for each variable selection method ( 15000 networks in total ) and retained the best one in terms of performance in properly assigning cases to classes in the test set . The networks were built in a semi-automated way . Their structure was of a multilayer perceptron with a number of neurons in the hidden layer iteratively optimized from ( n variables ) /3 to ( n variables ) *1 . 5 to avoid overfitting . Admissible linking functions between the neuron layers were linear , logistic , hyperbolic tangential , and exponential . Neuron weights were calculated using the BFGS ( Broyden-Fletcher-Goldfarb-Shanno ) algorithm and the network was trained in each epoch using an error back-propagation algorithm to optimize weights in each pass ( Broyden , 1970; Fletcher , 1970; Goldfarb , 1970; Shanno , 1970; Shanno and Kettler , 1970 ) . The method creates a new set of spatial coordinates that allow for linear separation of the groups . The most discriminative features were extracted on the basis of their correlations and the model used a backward stepwise variable selection algorithm only retaining in the model variables that showed final F values > 5 . This two-step filtering ( variable selection after one of the three initial variable filtering algorithms ) of the variables used in sample classification was aimed at the reduction of the number of miRNAs required for the model to work . Depending on the number of variables selected by the filters , the discriminatory function of the LDA was based on a reduced set of miRNAs that passed the F value threshold and were retained in the model . For the subset of miRNAs filtered by statistical significance , the model used three miRNAs: miR-30d-5p , miR-200c-3p and miR-320d . For CFS variable selection the model used three miRNAs: miR-320d , miR-200a-3p and miR-16-2-3p . The variable selection method based on stratified fold change used a yet another different set of miRNAs: miR-200c-3p , miR-320d and miR-150–5 p . As above , the logistic regression model was built using a backward stepwise variable selection procedure , with variables showing p<0 . 15 being retained in the final model . The procedure allowed for second order interactions between the variables to detect potential subgroup-specific effects . A standard quasi-Newton estimation procedure was performed in model development . After exclusion of variables with p values > 0 . 15 in the multivariate model , the miRNAs remaining in the classifier were miR-30d-5p , miR-320d , miR-200c-3p , miR-1246 , and an interaction of miR-200c-30p*miR-1246 . A logistic regression model based on miRNAs selected by the CFS variable algorithm required only two miRNAs to work: miR-200c-3p and miR-320d . A logistic regression classifier built on the fold change filter-selected miRNAs used three miRNAs: miR-150–5 p , miR-320d , miR-1246 , and an interaction between miR-200c-30p*miR-1246 . Results of all three models were convergent and the crucial role of miR-200c/miR-320d was confirmed by all models . The logistic regression model was very similar in terms of performance to the neural net in the CFS-selected variable subset . This was a logical consequence of a strong variable filtering leaving too few input variables for the network to identify subtle patterns . An alternative approach to modeling of the classification function was the MARS model – a modification of a multivariate joint-point regression which estimates a number of basal function most appropriate for data from specific fragments of the multidimensional dataset . The method is used in complex function modeling of non-monotonous or non-linear associations . Within our analysis we used a MARS model that allowed for up to third degree interactions between the variables , allowing for up to 1 . 5* ( n variables ) basal function in each model and penalizing the introduction of additional basal functions by a factor of 2 . Interactions between variables were tested for improvement of model performance up to the degree of three . During the model building procedure we iteratively removed variables absent in any of the basal functions until only miRNAs used in at least one basal function remained in the MARS model . Using 11 miRNAs filtered on the basis of significance we created a MARS model composed of 14 basal functions . All functions were transformation of five , single miRNAs: miR-30d-5p , miR-200c-3p , miR-450b-5p , miR-200a-3p , and miR-1307–3 p . The MARS model built on CFS-filtered variables consisted of 7 basal functions based on four miRNAs: miR-200c-3p , miR-320d , miR-16-2-3p , and miR-320b . The final MARS model built on 14 miRNAs filtered by the stratified fold change threshold was optimized at 10 basal functions based on 5 miRNAs: miR-200c-3p , miR-150–5 p , miR-200a-3p , miR-92–3 p , miR-203a , and miR-320c . All MARS models showed relatively poor performance hinting at issues with model overfitting and low specificity ( for example , the ROC AUC for the significance-based and CFS variable selection inputs did not meet statistical significance ) . An elastic-net regularized generalized linear model is a linear regression using coordinate descent . In order to train this model we have used Java implementation of a component of the R package ‘glmnet’ in WEKA software . As we wanted to use a regression method for classification , class was binarized and one regression model was built for each class value ( i . e . meta-scheme classification via regression ) . The alpha elastic-net mixing parameter was chosen to be 0 . 001 while the epsilon value for generating the lambda sequence was set to 10−4 . Additionally , a covariance update method was used . This resulted in the following formula: weka . classifiers . meta . ClassificationViaRegression -W weka . classifiers . functions . ElasticNet -- -m2 y -alpha 0 . 001 -lambda_seq -thr 1 . 0E-7 -mxit 10000000 -numModels 100 -infolds 10 -eps 1 . 0E-4 -sparse n -stderr_rule n -addStats n . Please note that reproduction of model induction may require installing additional packages from WEKA package manager . Elastic net is a type of linear modeling . As so , application of classification via regression resulted in construction of 2 linear functions equations and as the class was binary – those equations had equally opposite coefficients . For example , classifier for class cancer in CFS-based dataset was based on the equation:P ( 17 ) =−0 . 110hsa−miR−16−2−3p+0 . 050hsa−miR−200a−3p+0 . 275hsa−miR−200c−3p+0 . 043hsa−miR−320b+0 . 261hsa−miR−320d−0 . 031 Model files can be loaded in WEKA for further evaluation . This classifier was built with a set of different entry parameters: kernel function types , function parameters , and hinge loss function . Admissible kernel functions were linear , polynomial ( 2nd and 3rd order ) and radial basis function ( gamma from 0 . 1 to 1 tested in 0 . 1 increments ) . All possible combinations were tested and the resulting best model was selected on the basis of classifier performance in the test set . All SVM models codes for significance , CFS and stratified fold change-based variable selection algorithms are available as pmml files . The models performed worse than simpler classification tools ( logistic regression/linear discriminant analysis ) , possibly due to a small number of cases available for testing . A priori class probabilities were estimated empirically on the basis of class frequencies in the dataset , normal distribution was assumed for all log-10 transformed miRNA expression values quantified as transcripts per million . The exact probability estimator of the naïve Bayes classifier showed similar performance on all three variable subsets , achieving accuracy comparable to that of the SVM model Multi-class alternating decision tree using the LogitBoost strategy ( LAD Tree [http://www . cs . waikato . ac . nz/~bernhard/papers/ecml2002 . pdf] ) . The number of boosting iterations to use , which determined the size of the trees , was set to be 10 . Formula: weka . classifiers . trees . LADTree -B 10 . Please note that reproduction of model induction may require installing additional packages from WEKA package manager . LADTree is a completely deterministic tree that allows decision making by counting respective probabilities on the pathway though the tree . Those trees and probabilities are available as buffer text files and WEKA model files . It is notable , that our configuration allowed the algorithm to consider a maximum of 10 miRNAs in the final schema . Functional trees are logistic classification decision trees that have logistic regression functions at the inner nodes or leaves . Training of models was performed again by WEKA software . As in default settings , the minimum number of instances at which a node is considered for splitting was 15 , number of iterations for LogitBoost was also 15 and no weight trimming was applied . Formula: weka . classifiers . trees . FT -I 15 F 0 -M 15 -W 0 . 0 . Please note that reproduction of model induction may require installing additional packages from WEKA package manager . All functional trees were models with one node . In order to infer how this model works , evaluation of values for linear combination function at each node for every class has to be done . For example , for cancer in the CFS-processed dataset the formula is:F1=−1 . 75+[hsa−miR−16−2−3p]∗−0 . 29+[hsa−miR−200a−3p]∗0 . 08+[hsa−miR−200c−3p]∗1 . 07+[hsa−miR−320b]∗−0 . 21+[hsa−miR−320d]∗1 . 29 F1 = −1 . 75 + [hsa-miR-320d] * 1 . 29 As our classifiers are binary , the result for the second class ( F2 ) should be an opposite number ( F1 = -F2 ) . In the next step the value of the following formula should be calculated and compared to threshold of the node:eF1eF1+eF2 Model files can be loaded in WEKA for further evaluation . A Bayes Network was trained using a K2 search algorithm , which is a hill climbing algorithm restricted by an order on the variables . The initial network used for structure learning was a Naive Bayes Network and there could be only one parent a node . Conditional probability tables of a Bayes network were driven directly from data once the structure has been learned ( with alpha value equal to 0 . 5 ) . Formula: weka . classifiers . bayes . BayesNet -D -Q weka . classifiers . bayes . net . search . local . K2 -- -P 1 -S BAYES -E weka . classifiers . bayes . net . estimate . SimpleEstimator -- -A 0 . 5 . Please note that reproduction of model induction may require installing additional packages from WEKA package manager . Structures of networks as well as LogScores are available as buffer text files . Model files can be loaded in WEKA for further evaluation . Random forest is a technique of random decision forests that considers K randomly chosen attributes at each node . K was calculated as integer of 1 plus binary logarithm of number of predictors . Minimum proportion of the variance needed at a node in order for splitting to be performed was set to 0 . 001 . No backfitting was performed . Formula: weka . classifiers . trees . RandomForest -P 100 -I 100 -num-slots 1 -K 0 -M 1 . 0 -V 0 . 001 -S 1 . Please note that reproduction of model induction may require installing additional packages from WEKA package manager . Random forest is a form of bagging with 100 iterations and base learner . Model files can be loaded in WEKA for further evaluation . Differences in the distribution of histopathologic diagnoses , grade , and stage between the study populations were calculated using chi-square tests . Differences in false-positive and false-negative assignment were compared using Fisher’s exact test . Differences in age and CA125 levels between the study populations were calculated using a Mann-Whitney U test . For all tests , a two-tailed p-value<0 . 05 was considered significant . For the ROC curves , cut-off values for prediction with the best diagnostic performance were established using the Youden index ( sensitivityc + specificityc – 1 ) ( Youden , 1950 ) . Preoperative and postoperative serum samples from patients enrolled in ERASMOS were compared using a Wilcoxon matched pairs sign rank test . Computer codes are available as raw pmml files in the supplement . The neural network approach was applied to an independent , publicly available published dataset by Keller , et al . GEO Accession GSE31568 ( Keller et al . , 2011 ) In that study , the authors collected blood samples from 454 individuals , including 15 women with ovarian cancer and 70 healthy controls . Further clinical annotation of the samples was not provided . The samples include a variety of other diagnoses ( stomach cancer , sarcoidosis , prostate cancer , periodontitis , pancreatitis , pancreatic cancer , multiple sclerosis , melanoma , lung cancer , chronic obstructive pulmonary disease , Wilms tumor , and acute myocardial infarction ) . Circulating miRNAs were quantified using a highly specific primer extension–based microarray that shows a very small degree of cross-hybridization ( Supplementary file 6 ) ( Vorwerk et al . , 2008 ) . Paraffin blocks were selected from the surgical pathology files of the Brigham and Women’s Hospital per BWH IRB Protocol #2016P002742 . Hematoxylin and eosin sections of the cases were reviewed by a gynecologic pathologist ( CC ) . The tissues had been routinely fixed in 10% neutral formalin and embedded in paraffin . Immunohistochemistry for TP53 and Ki-67 were performed using commercially available antibodies as previously described ( Perets et al . , 2013 ) . Appropriate positive and negative ( without primary antibodies ) controls were used simultaneously for each antibody . In situ hybridization was performed using commercially available RNA probes from Exiqon ( Vedbæk , Denmark ) according to the manufacturer’s instructions . All probe concentrations were 1 nM . A probe for the small nuclear RNA U6 served as a positive control while a non-targeting scramble RNA probe served as negative control . | Ovarian cancer is a major cause of cancer death among women . A woman’s survival often hinges on doctors detecting the tumor before it has spread beyond the ovary . Unfortunately , most women with ovarian cancer are not diagnosed until they have symptoms – such as pelvic pain , bloating , swelling of the abdomen or appetite loss . By then , the disease has usually spread and is difficult to treat . There is currently no reliable test to diagnose ovarian cancer before symptoms emerge . Some tests measure proteins in the blood or use ultrasound images to identify ovary tumors . These tests usually still identify the disease too late . Sometimes they produce “false positive” results , which may cause women without cancer to undergo unnecessary surgery . Many ovarian cancers have defects in small pieces of genetic information called microRNAs . These microRNAs impact the tumor in multiple ways , and cells release microRNAs into the blood . Testing a seemingly healthy women’s blood for the same pattern of altered microRNAs found in women with ovarian cancer might be one way to detect the disease earlier . Now , Elias et al . have identified a pattern of seven microRNAs in the blood that appears to predict ovarian cancer . In the experiments , a computer program searched for microRNA patterns in women with ovarian cancer . The program sifted through the microRNAs in blood from women with and without ovarian cancer . Over time , the computer program “learned” to identify a pattern of microRNAs found only in women with ovarian cancer . It then created a formula for identifying ovarian cancer based on seven of the microRNAs . Elias et al . then verified that the formula accurately detected ovarian cancer by testing it on blood samples from more women with and without cancer . They also found the seven microRNAs in tiny ovarian cancer tumors collected from women . This suggests the formula might be able to detect even the smallest tumors . More studies are needed to determine when this cancer-linked pattern first emerges and confirm that this ovarian cancer-detection formula works . If the test is validated , it might be used to screen women who are at high risk for ovarian cancer because of mutations in the BRCA1 and BRCA2 genes . | [
"Abstract",
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] | [
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] | 2017 | Diagnostic potential for a serum miRNA neural network for detection of ovarian cancer |
Mitochondria play important roles in cellular processes and disease , yet little is known about how the transcriptional regime of the mitochondrial genome varies across individuals and tissues . By analyzing >11 , 000 RNA-sequencing libraries across 36 tissue/cell types , we find considerable variation in mitochondrial-encoded gene expression along the mitochondrial transcriptome , across tissues and between individuals , highlighting the importance of cell-type specific and post-transcriptional processes in shaping mitochondrial-encoded RNA levels . Using whole-genome genetic data we identify 64 nuclear loci associated with expression levels of 14 genes encoded in the mitochondrial genome , including missense variants within genes involved in mitochondrial function ( TBRG4 , MTPAP and LONP1 ) , implicating genetic mechanisms that act in trans across the two genomes . We replicate ~21% of associations with independent tissue-matched datasets and find genetic variants linked to these nuclear loci that are associated with cardio-metabolic phenotypes and Vitiligo , supporting a potential role for variable mitochondrial-encoded gene expression in complex disease .
Mitochondria are involved in a wide range of fundamental cellular processes , including cellular energy production , thermogenesis , lipid biosynthesis and cell death , and mutations in both nuclear and mitochondrial DNA ( mtDNA ) encoded genes have been linked to an array of different diseases ( Taylor and Turnbull , 2005; He et al . , 2010; Nunnari and Suomalainen , 2012; Hudson et al . , 2014; Idaghdour and Hodgkinson , 2017 ) . Most of the genes encoded in the mitochondrial genome are transcribed as one strand of RNA , and post-transcriptional processes are therefore particularly important for gene regulation . After transcription , poly-cistronic mitochondrial RNA is processed under the ‘punctuation model’ whereby transfer RNAs ( tRNAs ) that intersperse protein-coding regions are recognized for cleavage and the release of gene products ( Ojala et al . , 1981; Sanchez et al . , 2011 ) . Various processes including RNA modifications ( Helm et al . , 1998; Helm et al . , 1999; Agris et al . , 2007 ) , further cleavage events ( Mercer et al . , 2011; Rackham et al . , 2012 ) , RNA degradation ( Sasarman et al . , 2010; Rackham et al . , 2011 ) and translation rates then ultimately determine the levels of mitochondrial proteins available for utilization in the electron transport chain . Across tissues , different cell types have specific physiological requirements and thus variable energy demands . In mammals it has been shown that mitochondrial DNA replication ( Herbers et al . , 2019 ) and segregation ( Jokinen et al . , 2010 ) , mitochondrial DNA copy number ( Wachsmuth et al . , 2016 ) and the abundance of nuclear-encoded mitochondrial proteins ( Mootha et al . , 2003 ) vary across cell types , perhaps as a way to match local energy requirements , however it is unclear whether regulation of the mitochondrial transcriptome varies across tissues . Understanding these processes is important , since many mitochondrial disorders are thought to be tissue specific ( Koppen et al . , 2007; Hämäläinen et al . , 2013 ) . Although the mitochondrial genome is transcribed , processed and translated within the mitochondria , almost all of the proteins required for these processes are coded for in the nuclear genome . Previous work has shown that the expression of a large number nuclear genes correlates with mitochondrial encoded gene expression ( Mercer et al . , 2011; Barshad et al . , 2018 ) , pointing to strong links between the two genomes , yet there is still not a complete understanding of which nuclear genes are directly involved in regulating the mitochondrial genome and how this might vary in different tissues , as well as whether nuclear genetic variation drives variation in these processes across individuals . Despite the wide-ranging impact of mitochondrial dysfunction on health and disease , to our knowledge only a single mitochondria-focussed study has been carried out comparing nuclear genome-wide genetic variation with mitochondrial encoded gene expression , which analysed two sets of ~70 samples and was underpowered to detect genetic variation acting across two genomes ( Wang et al . , 2014 ) . More recently , studies have shown links between mitochondrial genome mutations and nuclear gene expression , identifying 11 significant associations ( Kassam et al . , 2016 ) , as well as associations between single nucleotide polymorphisms ( SNPs ) in mitochondrial RNA-binding proteins and haplogroup-specific mtDNA encoded gene expression patterns in LCLs ( Cohen et al . , 2016 ) , providing good evidence for regulatory links between the two genomes . In general , genetic variation associated with the expression of distal genes ( trans expression quantitative trait loci ( eQTLs ) ) has been more difficult to find due to the large statistical burden when comparing large numbers of variants and genes , and very few significant associations have been replicated in independent datasets ( Innocenti et al . , 2011; Kirsten et al . , 2015; GTEx Consortium et al . , 2017 ) . Here we aim to characterize variation in mitochondrial encoded gene expression across >11 , 000 RNA sequencing libraries for 36 different tissue/cell types . We also aim to identify genetic links between the mitochondrial and nuclear genomes through the detection of trans-genome eQTLs , not only to evidence occasions where genetic mechanisms act at long range across different genetic regions , but also to identify novel genes and genetic variation in the nuclear genome that are associated with fundamental processes taking place in human mitochondria .
Overall , despite their polycistronic origins , there is significant variation between mean expression levels of the 15 mitochondria-encoded genes within each dataset ( one-way ANOVA , p<2e-16 in all cases ) , highlighting the influence of post-transcriptional events in generating variation in transcript abundance along the mitochondrial transcriptome in all tissues . On average across samples and datasets , MTCO3 and MTCO2 show the highest median expression levels and MTRNR1 the lowest . Hierarchical clustering of log median expression values per dataset shows the consistency of the data , as the same tissue types from independent sequencing datasets generally tend to cluster together ( Figure 1A ) . Whole blood , LCL and skin datasets group by tissue type , however subcutaneous adipose data do not; this may be a consequence of the large heterogeneity in cell type composition observed across these datasets ( Glastonbury et al . , 2018 ) . High-energy tissues ( for example heart and brain tissues ) also tend to cluster together and appear to show similar patterns of mitochondrial encoded gene expression . In general , the rank order of mitochondrial-encoded gene expression levels between tissues is broadly similar ( spearman rank rho >0 . 5 for 894/903 pairwise comparisons of independent datasets ) with genes that show high relative expression levels in one tissue tending to show high relative expression levels in others tissues , however there are gene specific patterns . Standardized median MTRNR2 expression levels are highly variable , showing higher relative expression in whole blood and sub regions of the brain compared to other tissue types , whereas MTND4L , MTND5 and MTATP8 have low variance across tissue types and show relatively low standardized expression ( Figure 1B ) . Across individuals within each tissue , mitochondria-encoded genes show similar variance to comparable nuclear genes; on average across genes and datasets , the coefficient of variation of mitochondrial encoded TPM values is higher than 443 of the top 1000 most highly expressed nuclear genes and distributions of coefficients of variation overlap ( Figure 1C ) . However , there are differences across tissues; mitochondrial encoded genes in sub-regions of the brain generally show low variation in gene expression across individuals , and expression variance in whole blood is generally high . Collectively these results point to significant variation in the expression of genes along the mitochondrial genome , across tissues and across individuals . To identify nuclear genetic variation associated with mitochondrial encoded transcript abundance , we obtained genotyping data for the same samples for which we had RNA sequencing data and then performed per tissue and dataset association analyses between nuclear genetic variants ( with MAF >5% ) and the expression levels of fifteen mitochondrial encoded genes within a linear model , controlling for ancestry , sex , batch ( where applicable ) and probabilistic estimation of expression residuals ( PEER factors ) ( Stegle et al . , 2010 ) obtained from RNA sequencing data . For whole blood , subcutaneous adipose , non-sun exposed skin and LCLs where we had multiple independent datasets , we defined discovery and replication datasets . Across all tissues , we identify a total of 64 trans-genome eQTLs ( unique peak genetic variant-gene expression pairs ) for mitochondrial encoded gene expression at FDR 5% ( range of FDR corrected p-values: 0 . 046 – 8 × 10−26 , Supplementary file 1 , example association shown in Figure 2A and C ) . For each significant association , we also calculate point-wise empirical P-values ( as well as gene-level and tissue-level family-wise error rates ) via permutation analysis , and find that these closely match raw P-values ( see Materials and methods and supplementary file 1 ) . In total , fourteen out of the fifteen mitochondrial encoded genes have at least one nuclear genetic variant associated with its expression; MTATP8 shows no significant associations , MTND1 has the most with seven independent associations . We also observe five instances where a peak nuclear variant is associated with the expression of multiple mitochondrial-encoded genes within a tissue , perhaps indicating a shared influence on mitochondria RNA processing . However , mitochondrial encoded genes associated with the same genetic variant are no more likely to be located closer to each other along the mitochondrial genome than random ( p=0 . 29 , bootstrapping versus same number of random chosen genes ) . For the 49 unique peak genetic variants remaining after removing duplicate variants with multiple associations , four are missense mutations , 32 intronic , 12 intergenic and one falls in a 3’ UTR region . To ensure that trans-genome eQTLs are not driven by alignment errors that are a consequence of sequence similarity between the nuclear and the mitochondrial genomes , we tested for the presence of nuclear mitochondrial DNA segments ( NUMTs ) in the regions surrounding each peak nuclear genetic variant . NUMTs are mitochondrial DNA sequences that have transposed into the nuclear genome over evolutionary time scales , and as such often retain moderate to high sequence similarity with the mitochondrial genome . For the 64 trans-genome eQTLs , we find only two occurrences where at least 50 bp ( the smallest read length in our analysis ) of the mitochondrial encoded gene is present within a NUMT that is within 1 MB of the corresponding peak nuclear genetic variant , and we observe ~4 and~15 mismatches per 100 bp in these sequences compared to the corresponding mitochondrial encoded sequence . Additionally , for each peak nuclear genetic variant that is associated with the expression of a mitochondrial-encoded gene , we also tested whether any 50 bp segment of the mitochondrial-encoded gene also mapped to a nuclear gene ( following the approach defined in Saha and Battle , 2018 ) that has its transcription start site within 1 MB of the corresponding peak nuclear variant; we find no such occurrences . As such , alignment errors are unlikely to be driving the detection of trans-genome eQTLs for mitochondrial encoded gene expression . RNA levels of mitochondrial-encoded genes are likely driven by a number of features including mitochondrial copy number , polycistronic transcription rates and post-transcriptional events . Although all of these processes are important in a biological context , after detecting initial associations we focussed on the effects of post-transcriptional processing in driving variation in mitochondrial encoded gene expression . To do this , we controlled for variable mitochondrial copy number and polycistronic transcription rate by recalculating TPM values for each mitochondrial gene and sample using the number of mitochondrial reads rather than the total RNA sequencing library size . Repeating association analyses as before , 63/64 associations remain significant at FDR 5% ( Supplementary file 2 ) . Since mitochondrial encoded gene expression values are represented as a proportion of the total reads mapping to the mitochondrial genome in this analysis , this suggests that post-transcriptional processes play a significant role in these associations . To identify whether genetic associations are tissue specific , for the 64 significant associations we tested whether the same peak variant-gene pair was significant with the same direction of effect in each of the other tissue types ( at p<0 . 05 , corrected for the number of variants and the number of tissues , we used the nearest variant in LD ( r2 >0 . 8 ) if the same variant was not present , or the nearest variant with r2 >0 . 5 otherwise ) . In total , 22 of the 64 associations are significant in more than one tissue , with 8 of the associations being observed in at least three other tissue types ( Supplementary file 3 , Supplementary file 6 ) . Lowering the p-value threshold to 5% with the same direction of effect , only 12 associations are not replicated outside of the tissue they were originally detected in , and 19 associations are significant across 10 or more tissue types . Although sample sizes and detection criteria may influence our ability to detect all associations , these results indicate that a large number of associations between the nuclear and mitochondrial genomes may be operating via general mechanisms that occur across multiple tissue types . In order to elucidate the potential biological mechanisms influencing mitochondrial processes , we attempted to identify the nuclear gene of action through which each nuclear genetic variant is associated with mitochondrial encoded gene expression . For missense variants , we assume a direct influence on the gene in which they are located and thus identify three nuclear genes associated with mitochondrial encoded gene expression ( Table 1 ) , all of which have a known role in mitochondrial processes . TBRG4 localizes to the mitochondria to modulate energy balance ( particularly under stress ) and plays a role in processing mitochondrial RNA ( Boehm et al . , 2017 ) , MTPAP synthesizes the 3' poly ( A ) tail of mitochondrial transcripts , and LONP1 mediates the degradation of mis-folded or damaged polypeptides in the mitochondrial matrix . There is evidence that all three proteins are targeted to the mitochondria , and mass spectrometry experiments have identified the presence of these proteins in mitochondria ( Smith and Robinson , 2016 ) . For genetic variants in non-coding regions ( from 49 unique associations ) , we first annotated variants using chromatin state predictions obtained from 128 cell types within the Roadmap Epigenetic project ( Kundaje et al . , 2015 ) . Using tissue matched data ( information available for 44 of the 49 non-coding variants ) , we find that none of the nuclear genetic variants associated with mitochondrial encoded gene expression fall in enhancer regions , which is not different to that expected by chance ( p=0 . 676 using randomly selected variants matched for MAF , distance to nearest transcription start site and annotation ) . Under the assumption that associations between nuclear genetic variants and mitochondrial encoded gene expression occur ubiquitously across the body , we tested for the presence of peak variants in enhancer regions in any cell type . In total , 24 variants fall in enhancer regions , which again is not significantly different from that expected by chance ( p=0 . 691 , using randomly selected variants as before ) . To test more directly if each nuclear non-coding genetic variant potentially acts upon mitochondrial-encoded gene expression through a nearby nuclear gene , we perform mediation analysis ( requiring an association between the peak nuclear genetic variant and the expression of a nearby nuclear-encoded gene , and then significant mediation of the initial association via bootstrapping , requiring an average causal mediation effect with p<0 . 05 after FDR correction ) . Considering only nuclear genes known to play a role in mitochondrial processes first , we identify seven genes whose expression accounts for a significant component of the relationship between the nearby nuclear genetic variant and the expression of the associated mitochondrial-encoded gene ( Table 1 ) . These include TBRG4 and MTPAP ( described above ) , as well as MRPP3 , which is known to form part of a complex that cleaves and processes the 5’ end of mitochondrial transfer RNAs ( Holzmann et al . , 2008 ) ; LRPPRC , which is thought to play a role in the stability and transcriptional regulation of mitochondrial RNA ( Xu et al . , 2004 ) ; MRPS35 , which is a mitochondrial ribosomal protein; PNPT1 , which is an RNA binding protein that plays a role in numerous RNA metabolic processes and the import of RNA into the mitochondria; and FASTKD1 , which is an RNA binding protein that regulates the energy balance of mitochondria under stress . Five out of the seven proteins ( PNPT1 , TBRG4 , MTPAP , MRPP3 and LRPPRC ) contain RNA binding domains ( Wolf and Mootha , 2014 ) , and as such it is possible that they bind directly to mitochondrial RNA . For the remaining non-coding peak genetic variants ( from 36 unique associations ) , we tested whether any nearby nuclear genes ( not yet implicated in mitochondrial processes ) significantly mediated the expression of a mitochondria-encoded gene ( as above ) . Using this approach , we identify eleven candidate genes that may play a previously unknown role in influencing mitochondrial gene expression ( Table 1 ) . In general , these genes are not predicted to contain mitochondria targeting sequences , although SLC7A6OS and TGM3 show partial evidence of being targeted to mitochondria in some databases ( SLC7A6OS prediction score of 1 in IPSort and both genes have a score >0 . 6 in TargetP ( Smith and Robinson , 2016 ) ) . Finally , to test whether peak genetic variants may be acting on mitochondrial encoded gene expression via distal associations with genes in the nuclear genome , we performed association analyses between each peak genetic variant and all other nuclear genes not in cis ( genes > 1 MB away or on different chromosomes ) . After correcting for multiple tests , we observe no significant associations ( p>0 . 05 in all cases , Bonferroni correction ) . Collectively these results suggest that the common mechanisms by which nuclear genetic variation influences mitochondrial encoded gene expression could be either through functional mutations within nuclear genes themselves , or via their effects on the expression of nearby nuclear genes . There is also some evidence that the protein products of some of these genes then enter the mitochondria and bind directly to mitochondrial RNA . Genes identified via these approaches therefore represent the most promising candidates for causal nuclear genes that influence fundamental biological processes taking place in human mitochondria . In order to test the robustness of associations between common nuclear genetic variants and mitochondrial gene expression , we tested whether trans-genome eQTLs detected in multi-dataset tissues were significant in independent tissue-matched samples ( see Materials and methods ) . In total , 61 eQTLs were found in multi-dataset tissues; to consider the signal replicated we required the association to be between the same variant ( or nearest variant in LD ( r2 >0 . 8 ) if the same variant was not present , or the nearest variant with r2 >0 . 5 otherwise ) and mitochondrial gene in the same tissue type , with the same direction of effect and passing a significance threshold corrected for the number of tests ( 0 . 05/61 = 0 . 00082 in this case ) . In total we replicate 13/61 ( ~21 . 3% ) of the mitochondrial trans-genome eQTLs ( Figure 3A , example association shown in Figure 2B and C , Table 2 ) , and for ten of these we find a link to a potential casual gene through mediation by a nearby nuclear gene or via functional mutations as outlined above ( Table 1 ) . We also find that an additional 12 associations replicate at the 5% level , and in total 43/61 of the associations show the same direction of effect in replication datasets; larger sample sizes may increase replication rates in these cases . In order to uncover potential reasons for a lack of replication for some associations , we performed power analysis using the variance explained by each genetic variant on the associated mitochondrial encoded gene expression level in the discovery dataset , together with the replication sample size , and find that ~40 . 5 associations would be expected to replicate ( at p=0 . 00082 ) . Beyond this , we find significant differences between discovery and replication datasets for the proportion of mapped reads aligning to the mitochondrial genome in whole blood and subcutaneous adipose ( Wilcoxon tests , p<0 . 05 after correcting for multiple tests ) . It is unclear whether this would influence our ability to replicate associations in these cases , although we note that PEER factors ( which we include as covariates in our association analyses ) have been shown to correlate with known technical and biological features of RNA sequencing data ( Stegle et al . , 2010; GTEx Consortium et al . , 2017; Glastonbury et al . , 2018 ) and as such should control for some systematic variation across individuals . Even so , given the unexplained lack of replication in some cases , it is possible that false positives may contribute to our results . To validate our results for one association ( rs2304694-MTND4 in LCLs ) using an alternative RNA quantification method , we obtained LCLs with homozygous reference and non-reference genotypes at rs2304694 , matched for sex and ethnicity between the two groups , and measured expression levels of MTND4 using quantitative PCR . We find significant differences in the expression levels of MTND4 between samples that are homozygous for the reference allele at rs2304694 versus samples that are homozygous for the non-reference allele at rs2304694 ( p=0 . 0325 , one-way ANOVA , Figure 3B ) , thus validating the original association with the same direction of effect . Finally , since genetic variation modulating gene expression may underlie a large proportion of genetic associations with disease ( Nicolae et al . , 2010 ) , we intersected peak mitochondrial trans-genome eQTL SNPs , as well as those in strong linkage disequilibrium ( LD , r2 >0 . 8 , calculated within our data ) , with significant associations documented in the NHGRI genome wide association study ( GWAS ) catalogue and find overlapping variants for two diseases/disease risk traits . First , the peak nuclear genetic variant associated with the expression of MTCYB in whole blood ( rs782633 ) is in strong LD with rs782590 , a variant that has been linked to systolic blood pressure ( a known risk factor for heart disease and stroke ) in a study of individuals with metabolic syndrome and controls ( Kristiansson et al . , 2012 ) . We also note that the same peak nuclear genetic variant associated with the expression of MTCYB is also in LD with rs1975487 ( r2 = 0 . 84 for Europeans in 1000 Genomes data ) , a variant that is associated with diastolic blood pressure in a larger GWAS for blood pressure ( Ehret et al . , 2016 ) ( p=2×10−9 ) . Rs1975487 was not present in our original analysis due to a missingness rate that was above our threshold for filtering ( 3% , 2% and 1 . 7% missing genotype rate in CARTaGENE , TwinsUK and GTEx data respectively ) . Mitochondrial processes have previously been associated with blood pressure ( Dikalov and Dikalova , 2016 ) , and given the association here , this may at least partially be modulated though changes in mitochondrial encoded gene expression . The genetic variant associated with mitochondrial encoded gene expression falls within the intron of PNPT1 , suggesting that this may be the gene of action influencing blood pressure , although further fine mapping and functional work would be required to establish a causal link . Second , two peak genetic variants associated with the expression of MTND5 and MTND6 in whole blood ( rs10172506 and rs7558127 respectively ) are in strong LD with rs10200159 , which has been associated with Vitiligo ( Jin et al . , 2016 ) , a disease that is driven by the functional loss of melanocytes in the skin which leads to a loss of pigmentation . High reactive oxygen species generation and a deficit of the antioxidant network are key processes in Vitiligo , and thus altered mitochondrial function is thought to play a role ( Dell'Anna et al . , 2017 ) . Although we detect significant associations in whole blood , there is suggestive evidence of the same relationships in sun-exposed skin data , with both associations occurring with p<0 . 05 ( p=0 . 016 and p=0 . 0027 , GTEx data ) and the same direction of effect . For both peak nuclear genetic variants the gene of action appears to be PNPT1 , where we find evidence of significant mediation on the expression of MTND5 and MTND6 ( Table 1 ) . Genome-wide association studies considering blood pressure were conducted in individuals of Finnish ( Kristiansson et al . , 2012 ) and European descent ( Ehret et al . , 2016 ) , and the study of Vitiligo was also conducted using individuals of European descent ( Jin et al . , 2016 ) . Since our eQTL analysis included individuals from diverse ancestries ( although largely of European descent ) , we attempted to match LD structure more closely to populations used in the above GWAS associations by re-running mitochondrial encoded eQTL analyses using only samples from individuals of European descent ( see Materials and methods ) . Using the same approach as before , in whole blood data we find that rs782633 remains significantly associated with the expression of MTCYB in Europeans ( p=6 . 33×10−11 in Europeans , p=8 . 58×10−11 in all samples ) , rs10172506 is significantly as associated with the expression of MTND5 in Europeans ( p=4 . 01×10−25 in Europeans , p=5 . 26×10−32 in all samples ) and rs7558127 is significantly as associated with the expression of MTND6 in Europeans ( p=1 . 94×10−31 in Europeans , p=4 . 40×10−40 in all samples ) . Furthermore , we find that overlapping mitochondrial-encoded eQTL and GWAS variants are in strong LD in combined European populations surveyed by the 1000 Genomes project ( r2 >0 . 8 in all cases ) . These results imply that genetic variants associated with mitochondrial encoded gene expression are genuinely in LD with GWAS signals , however some caution should still be applied if populations within Europe are likely to generate further substructure in the data , which we have limited power to disentangle here .
Despite key roles for mitochondria in a range of fundamental biological processes , as well as a wide array of human diseases , knowledge of how the mitochondrial transcriptome is processed across different individuals and tissues on a population scale is incomplete . Using RNA sequencing data for a large number of individuals and across a wide range of tissues , we find considerable variation in mitochondrial gene expression along the mitochondrial genome , across tissues and between individuals . Variation in mitochondrial encoded gene expression profiles is likely important for the cells ability to respond to changing energy demands in specific cell types and environments , and may also play a role in tissue specific disease processes across individuals . Through integrated analysis of genetic and RNA data , we identify a large number of common nuclear genetic variants associated with mitochondrial encoded gene expression and replicate a substantial fraction of these ( ~21% after correcting for multiple testing , ~41% at nominal 5% with the same direction of effect ) in independent tissue-matched datasets . Through mediation analysis and functional genetic variants we identify the potential causal nuclear gene influencing mitochondrial encoded gene expression in 36 cases . A large number of these genes are already known to play a role in mitochondrial processes , and thus validate our findings in a biological context , but also implicate functional mechanisms by which common nuclear genetic variation can act between chromosomes ( and indeed , genomes ) to influence gene expression . Such trans-eQTLs have been notoriously difficult to replicate in humans , and thus the 13 replicated associations identified in this study provide candidates to test the mechanisms associated with genetic variation that acts over large genetic distances . For some of the potential causal nuclear genes that we identify as being linked to variation in the expression mitochondrial-encoded genes , it is not difficult to speculate on potential mechanisms through which they might act . For example , MTPAP ( within which we identify a missense mutation associated with the expression of MTND3 in LCLs ) synthesizes the poly ( A ) tail of mitochondrial transcripts . Since polyadenylation of mitochondrial transcripts is required in many cases to complete the termination codon and is thought to influence RNA stability ( Rackham et al . , 2012 ) , a functional mutation in this enzyme may lead to variable accumulation of unprocessed mitochondrial transcripts and ultimately influence mitochondrial encoded gene expression levels . Similarly , TBGR4 ( within which we identify a missense mutation associated with the expression of multiple mitochondrial genes in multiple tissues ) is known to process mitochondrial precursor transcripts and stabilize some mature mitochondrial messenger RNAs ( Boehm et al . , 2017 ) , thus having obvious links to changes in mitochondrial gene expression . These findings lay the foundation for future work to functionally validate the causal role of these genetic variants . Beyond this , we also identify nuclear genes through mediation analysis that have not previously been linked with mitochondrial gene expression . These results potentially point to novel roles for these proteins and thus may be important new targets in the context of mitochondrial disease in cases where it has thus far been difficult to identify causal mutations in patients . Examples that may be interesting for further study include ZFP90 , a zinc finger protein that modulates nuclear gene expression . ZFP90 transgenic mice show altered expression of genes involved in oxidative phosphorylation and fatty acid elongation in mitochondria compared to wild type littermates ( Yang et al . , 2009 ) , pointing to a potential role in mitochondrial processes . Similarly , CCM2 is involved in the stress-activated p38 mitogen-activated protein kinase ( MAPK ) signalling cascade and is thought to localize to the mitochondria . CCM proteins are implicated in Cerebral Cavernous Malformation and accumulating evidence points to a role for these proteins in processes related to mitochondrial function , including cellular responses to oxidative stress and autophagy ( Retta and Glading , 2016 ) . Finally , the common genetic variants we identify here as associated with mitochondrial encoded gene expression profiles across individuals potentially have downstream functional consequences that influence disease processes and risk . We find some evidence for this , as nuclear genetic variation associated with variable mitochondrial encoded gene expression is linked to mutations that have been implicated in blood pressure and Vitiligo , yet further study of these genes is required to identify the causal mechanisms that influence how mitochondrial RNA is processed in the cell and how dysregulation of these mechanisms may cause disease . Combined , these data now serve as a frame of reference for mitochondrial disease researchers who wish to consider how patient samples may vary in mitochondrial gene expression versus a healthy cohort in the relevant tissue type , and for the community as whole interested in the genes and genetics of fundamental processes taking place in mitochondria and the genetic architecture of gene expression .
Raw human RNA sequencing and genotyping data were obtained through application to five independent sequencing projects: CARTaGENE: CARTaGENE is a healthy cohort of individuals aged between 40 and 69 from Quebec , Canada . Whole blood , 100 bp paired-end RNA sequencing and genotyping data ( Illumina Omni 2 . 5M arrays ) for 911 individuals were obtained from the CARTaGENE project ( Awadalla et al . , 2013; Hodgkinson et al . , 2014 ) through application to the data access committee ( instructions are available at www . cartagene . qc . ca ) . Samples with multiple sequencing runs were merged prior to alignment . TwinsUK: 50 bp paired-end RNA sequencing data from 391 whole blood samples , 685 subcutaneous adipose samples , 672 non-sun exposed skin samples and 765 LCL samples ( Buil et al . , 2015 ) , as well as accompanying genotyping information ( obtained from either Illumina HumanHap300 and HumanHap610Q arrays ) , were derived from a mix of unrelated samples and monozygotic and dizygotic twin pairs through application to the TwinsUK data access committee and then downloaded from the European Genome-Phenome archive ( https://ega-archive . org ) through study ID EGAS00001000805 . GTEx ( Genotype-Tissue Expression ) Project: 75 bp paired-end RNA sequencing data from 44 tissue/cell types from up to 572 individuals ( GTEx Consortium et al . , 2017 ) , along with accompanying genotyping data ( obtained from either Illumina Omni5M and Omni2 . 5M arrays ) were obtained by application to dbGaP through accession number phs000424 . v6 . p1 . Tissues were selected if the organ they were obtained from had at least 100 samples . In cases where samples had multiple sequencing experiments for a given individual and tissue , we selected the dataset containing the highest number of raw sequencing reads . NIMH ( National Institute of Mental Health ) Genomics Resource: 50 bp single end RNA sequencing data and matched genotyping data ( Illumina HumanOmni1-Quad BeadChip ) from 937 whole blood samples ( Battle et al . , 2014; Mostafavi et al . , 2014 ) from the Depression Genes and Networks study were obtained via transfer from external hard drives after application to the data access committee ( through www . nimhgenetics . org ) . Geuvadis Project: 75 bp paired end RNA sequencing data from 462 LCL samples ( Lappalainen et al . , 2013 ) were downloaded from the European Nucleotide Archive under submission number ERA169774 . Accompanying genetic variants from whole genome sequencing data ( which were generated as part of the 1000 Genomes Project ( Abecasis et al . , 2012 ) ) were downloaded from the 1000 genomes FTP site . We used phase three data that was phased and imputed ( v5a . 20130502 ) . All RNA sequencing data derived from different projects were processed in the same way to ensure comparability across analyses . Raw RNA sequencing reads ( fastq format ) from 13 , 261 individual samples were trimmed for adaptor sequences , terminal bases with nucleotide quality below 20 and poly ( A ) tails > 4 bp in length , before being aligned to a reference genome ( 1000G GRCh37 reference , which contains the mitochondrial rCRS NC_012920 . 1 ) with STAR 2 . 51a ( Dobin et al . , 2013 ) , using two-pass mapping , version 19 of the Gencode gene annotation and allowing for 1/18*read_length mismatches , rounded down to the nearest integer . Following this , in order to minimize the likelihood of incorrectly placed reads ( particularly those associated with NUMT sequences ) , we used a stringent filtering pipeline , focusing only on reads that were properly paired and uniquely mapped . After mapping we removed low quality samples that had either <10 thousand reads mapping to the mitochondrial genome , <5 million total mapped reads , >30% of reads mapping to intergenic regions , >1% total mismatches or >30% reads mapping to ribosomal RNA using in house scripts and RNAseQC ( DeLuca et al . , 2012 ) . To calculate transcript abundances , we used HTseq ( Anders et al . , 2015 ) with the ‘intersect non-empty’ model and version 19 of the Gencode gene annotation , before converting raw counts to transcripts per million ( TPM ) . We plotted the log10 transformed distributions of all genes with mean TPM >2 per sample and removed visual outlier samples . We also calculated principle components using the same data and removed outlier samples . Finally , samples were only included in analyses if they had accompanying high quality genotyping information ( see below ) and there were at least 70 samples available for analysis within each tissue/dataset; in total after matching samples to genotyping data and quality control filtering we were left with 11 , 371 RNA sequencing datasets for analysis . We focused on mitochondrial encoded protein coding and ribosomal RNA genes only , since transfer RNAs showed lower sequencing coverage overall and were not expressed highly in all tissues and datasets . For analysis of mitochondrial encoded gene expression variation across genes and datasets , for TwinsUK data we used only unrelated samples ( which involved picking one of each twin pair at random and combining these with unrelated samples ) . For NIMH samples , which were derived from 454 depression cases and 454 controls , we tested whether disease status may affect our results by comparing TPM values for mitochondrial-encoded genes between the two groups; in all cases we find no significant differences ( Wilcoxon test , p>0 . 05 in all cases after correction for multiple testing ) . Genotyping data from different arrays and sequencing studies were processed separately . For TwinsUK data , only one twin from each twin pair was genotyped and thus processed , with data duplicated to represent the missing twin pair after quality control and filtering . Genotyping quality control and calculation of genetic principle components for Twins data was thus performed only on unrelated samples . Within each dataset , samples with high relatedness ( >0 . 125 ) , high SNP heterozygosity ( visual outliers ) , non-matching sex , ambiguous X-chromosome homozygosity estimates or high SNP missingness ( >5% ) were removed . Autosomal SNPs were flipped to the positive strand and those with minor allele frequency ( MAF ) >1% , in Hardy Weinberg equilibrium ( p>0 . 001 ) and not missing in more than 1% of individuals were then phased with shapeit2 ( Delaneau et al . , 2013 ) using no reference panel and default settings . Problematic sites were removed and remaining SNPs were used for imputation in 2 MB intervals using impute2 ( Howie et al . , 2009 ) with default settings , incorporating the 1000 Genomes phase three reference panel . Imputed data were then hard-called to produce genotypes at each site with a threshold of 0 . 9 and SNPs with information score lower than 0 . 8 were removed . Data from different arrays within each study were then merged and filtered to keep bi-allelic variants with minor allele frequency ( MAF ) >5% , in Hardy Weinberg equilibrium ( p>0 . 001 ) and not missing in more than 1% of individuals for downstream analysis . After processing , we calculated genetic principal components and removed outlier samples by visual inspection . For Geuvadis data we used whole genome sequencing variant calls from the 1000 Genomes project ( Abecasis et al . , 2012 ) . As such , these samples did not undergo phasing and imputation within our pipeline , but were filtered in the same way as genotyping data after this stage of the analysis . Expression QTL mapping was performed within each tissue and sequencing dataset . In each case , TPM values for thirteen mitochondrial encoded protein coding genes and two mitochondrial encoded ribosomal RNA genes were extracted before being log10 transformed ( Supplementary file 4 ) . Mitochondrial encoded gene expression distributions were median normalized , before outlier values were removed per gene ( defined as three interquartile ranges above or below the upper and lower quartile respectively ) . To control for unidentified confounding factors in RNA sequencing data , we calculated PEER factors ( Stegle et al . , 2010 ) per dataset using all genes ( nuclear and mitochondrial ) that had a mean TPM >2 . For genotyping data , we restricted the data to only those samples that had corresponding mitochondrial encoded gene expression values for the given dataset and calculated genetic principle components on this reduced set in each case . We then performed association analyses on each tissue and dataset using a linear model within PLINK ( Purcell et al . , 2007 ) for unrelated samples . For twin data , we calculated the relatedness matrix of samples before conducting association analyses with GEMMA ( Zhou and Stephens , 2012 ) . In each case we included sex , five genetic principle components , 5 or 10 PEER factors ( five for samples sizes < 100 , ten for sample sizes >= 100 ) and sequencing/genotyping batch ( where applicable ) as covariates . For TwinsUK data , the genotyping array was included as the batch covariate and sex was omitted as all samples were derived from females . For CARTaGENE data , which was original sequenced at higher and lower coverage as part of discovery and replication phase data respectively ( Hodgkinson et al . , 2014 ) , the sequencing phase was included as the batch covariate . For GTEx data , where two different genotyping arrays were used , the genotyping array covariate correlated highly with one of the first genetic principle components for all tissues ( |r| > 0 . 8 in all cases ) and was therefore not included in the linear model . After analysis , QQ plots were visually assessed and show no skew . QQ plots for discovery associations that replicate at the nominal 5% level are shown in Figure 2—figure supplement 1 . False discovery correction ( Benjamini-Hochberg ) was applied to raw p-values within each dataset by merging all genes ( 15 ) and genetic variants in each case , following the approach applied by the GTEx consortium ( GTEx Consortium et al . , 2017 ) . To calculate P-values via permutation analysis , for each association that we originally identified as being significant at FDR 5% ( 64 variant-gene pairs ) , we performed 100 , 000 point-wise permutations for the relevant tissue type , mitochondria-encoded gene and nuclear genetic variant by randomly shuffling phenotypes . In each case , we then collected the test statistic across all 100 , 000 permutations to generate a null distribution , and compared our observed test statistic against this to calculate an empirical P-value . For tissue types with multiple datasets ( Whole Blood and LCLs ) we performed permutations per dataset , combined these within a meta-analysis , and then derived the null distribution from the meta-analysis results . In each case , we also then followed the approach outlined in Ongen et al . ( 2016 ) to calculate a more precise P-value by estimating the underlying beta distribution of the null distribution via maximum likelihood ( using the ‘ebeta’ function within the R package ‘EnvStats’ ) . Additionally , we also calculated the family-wise error rate on the gene level for each association originally detected at FDR 5% . To do this , we performed 200 random permutations across all nuclear genetic variants for the relevant mitochondria-encoded gene and tissue type , and then calculated the null distribution by selecting the largest test statistic per permutation across all nuclear genetic variants . To calculate the overall family wise error rate , we repeated this again , this time selecting the largest test statistic across all nuclear genetic variants and all 15 mitochondria-encoded genes per permutation to generate the null distribution in the relevant tissue type . For the calculation of both family-wise error rates , we repeated the approach outlined in Ongen et al . ( 2016 ) to obtain a more precise P-value by extrapolating from the beta-distribution generated from the null . P values generated across all methods are shown in supplementary file 1 . NUMT sequences were obtained from the UCSC genome browser track named ‘numtS’ , and were generated by Simone et al . ( 2011 ) , who used blastN to map nuclear chromosomes to the mitochondrial genome , setting the e-value threshold to 0 . 001 . Sequences in this database range from 31 to 14904 bp in length , with a similarity percentage ranging between 63% and 100% , thus the approach has the potential to tolerate a large number of mismatches between nuclear and mitochondrial sequences . To test whether any 50 bp segments of mitochondrial genes also aligned to nuclear genes , we followed the approach defined in Saha and Battle ( 2018 ) . Specifically , we took all 50 bp k-mers from each mitochondrial encoded gene and then aligned these sequences to the nuclear genome using bowtie v1 . 22 ( Langmead et al . , 2009 ) , allowing for up to two mismatches and reporting all alignments . For each nuclear genetic variant associated with a mitochondrial encoded gene , we then tested whether any of the 50 bp k-mers from the mitochondrial encoded gene aligned within a nuclear gene whose transcription start site fell within 1 MB of the corresponding nuclear genetic variant . For tissue types with multiple independent datasets , we defined discovery and replication datasets . Discovery datasets were chosen as the dataset with the largest starting sample size for each given tissue , with the replication dataset as the second largest . For whole blood , where four independent datasets were available , we performed meta analysis within PLINK using a fixed affects model , combining data from the CARTaGENE project , GTEx and TwinsUK for the discovery phase , and then used NIMH data for replication . For LCLs , where three independent datasets were available , we performed meta analysis combining data from the Twins and GTEx for the discovery phase , and then used Geuvadis data for replication . For Subcutaneous adipose and non-sun exposed skin , we used TwinsUK data for discovery and GTEx data for replication . For all other tissues , only a single dataset was available , and so no replication analysis was performed . In all association analyses we defined the peak SNP as the genetic variant with the lowest p-value within a block of 1 MB , and tested for replication using the exact same SNP where available ( using the nearest SNP in LD ( r2 >0 . 8 ) if the exact match was not present , followed by the nearest SNP with r2 >0 . 5 otherwise ) . We used the same approach when comparing association signals across tissues . To perform power calculations , we obtained the correlation coefficient ( r2 ) between the genetic variant and the expression of the associated mitochondrial encoded gene in the relevant discovery dataset ( or largest dataset where the genetic variant is present , if multiple datasets are available for the tissue ) . We then used a power calculator ( Purcell et al . , 2003 ) , specifying our estimate for the variance explained by the genetic variant ( r2 ) , the minor allele frequency , replication sample size and the significance threshold ( 0 . 05/61 ) in each case . Following this , we summed power values across all 61 associations . We also repeated all association analyses after using mitochondrial library size ( all reads mapping to the mitochondrial ) to calculate TPM for mitochondrial genes , rather than total library size . We tested this approach as a way to remove the effects of variable mitochondrial copy number and poly-cistronic transcription rate , however in all cases we obtained very similar results to those obtained using the method outlined above . Additionally , we also repeated all analyses shown in Figure 1 using mitochondrial reads to normalize gene expression values; again we find very similar results . It has recently been shown that the post-mortem interval ( PMI ) appears to influence gene expression patterns in GTEx data ( Ferreira et al . , 2018 ) . As such , to test for an effect in our data , we repeated association analyses for significant associations discovered in GTEx data and including PMI as a covariate ( where PMI data were available ) . In both cases , we find that the P-values do not change dramatically ( Atrial appendage ( heart ) , rs11811165-MTND4L , original raw P value: 5 . 09 × 10−10 , P value including PMI as a covariate: 3 . 50 × 10−9; Tibial nerve , rs932345-MTND4L , original raw P value: 6 . 47 × 10−10 , P value including PMI as a covariate: 7 . 57 × 10−10 ) . In order to identify the potential causal nuclear gene associated with mitochondrial encoded gene expression , we identified genes associated with the peak eQTL variant in the following ways . First , if the peak variant was a missense mutation , we assumed that its mode of action was via functional changes in the gene it was located in . Second , for non-coding mutations , we tested whether non-coding peak variants fell in enhancer regions using chromatin state predictions obtained from 128 cell types within the Roadmap Epigenetic project ( Kundaje et al . , 2015 ) , using matched tissue data as outlined in the GTEx project ( GTEx Consortium et al . , 2017 ) , and compared this against a set of random genetic variants matched for minor allele frequency , distance from transcription start site and genome annotation ( using 1000 random sets to generate a P-value ) . Third , for non-coding peak variants , we tested for mediation via the expression of nuclear genes located near to the peak SNP . To do this , for each tissue we used the largest dataset available and restricted our analysis to unrelated samples ( for TwinsUK data , this involved picking one of each twin pair at random and combining these with unrelated samples ) . Within each dataset we then again tested for a significant correlation between the peak SNP and the expression of the mitochondrial gene in question ( p<0 . 05 , linear model , t-test of regression coefficient ) , as well as a significant correlation between the peak SNP and the expression of any nuclear gene within 1 MB of the variant ( p<0 . 05 , linear model , t-test of regression coefficient ) . For genes/variants passing these criteria , we then tested whether the expression of the nuclear gene significantly mediated the relationship between the peak nuclear variant and the mitochondrial encoded gene expression using the module ‘mediation’ ( testing significant mediation of the initial association via bootstrapping , requiring an average causal mediation effect with p<0 . 05 after FDR correction ) within R . To prioritize potential causal genes within this framework , we first selected nuclear genes with a known role in mitochondrial processes ( any gene listed in the Mitocarta database ( Calvo et al . , 2016 ) , shown to influence mitochondrial RNA processing ( Wolf and Mootha , 2014 ) or listed as being involved in mitochondrial disorders in the Genomics England PanelApp - https://panelapp . genomicsengland . co . uk ) , before moving on to any other nuclear gene . Finally , we tested whether non-coding peak variants were associated with the expression of more distal genes ( those whose transcription start site was >1 MB away , or on another chromosome ) within a linear model ( and meta-analysis where relevant ) including the same datasets , methods and covariates as the original discovery analysis . In order to identify whether genetic variants associated with mitochondrial encoded gene expression may play a role in complex disease , we first identified any SNP in linkage disequilibrium ( r2 >0 . 8 , calculated using datasets and samples used in this study ) with peak eQTL SNPs in any of the datasets used for the tissue type in which the association was identified . We then tested whether any of these variants overlapped with significant associations documented in the NHGRI GWAS catalogue ( for association where p<5e-8 ) . To test whether associations between nuclear genetic variants and mitochondrial encoded gene expression that overlap GWAS signals are significant in individuals of European descent , we plotted the first two genetic principal components against those derived from 1000 genomes samples with known ancestry for any dataset that had associated RNA sequencing data from whole blood . We then selected samples that clustered with Europeans in 1000 genomes data by visual inspection and re-ran association analyses as before for whole blood data from CARTaGENE , TwinsUK and GTEx , before performing meta-analysis to calculate P-values . In order to validate the association between rs2304694 and expression levels of MTND4 in LCLs , we obtained ten LCL samples carrying the homozygous reference genotype and ten LCL samples carrying the homozygous non-reference genotype for rs2304694 from the Coriell Institute for Medical Research , matched between the two genotype groups for sex and ethnicity ( Supplementary file 5 ) . The following cell lines were obtained from the NIGMS Human Genetic Cell Repository at the Coriell Institute for Medical Research: GM11919 , GM11932 , GM12003 , GM12414 , GM12717 , GM12842 . The following cell lines were obtained from the NHGRI Sample Repository for Human Genetic Research at the Coriell Institute for Medical Research: GM20582 , GM20822 , HG00118 , HG00254 , HG00284 , HG00290 , HG01524 , HG01625 , HG01631 , HG01777 , HG01800 , HG01804 , HG01812 , HG01815 . Cultures were tested as standard by Coriell Cell Repositories before shipping and found free of mycoplasma , and microsatellite profiling was used to confirm identity ( see ‘Quality Control’ at www . coriell . org ) . Cells were handled as per supplier’s instructions . Total RNA was extracted using the RNeasy kit ( Qiagen ) according to the manufacturer's instructions . 1 ug total RNA was pre-treated with 2 units of Turbo DNase ( Fisher Scientific ) and subsequently reverse-transcribed using the ProtoScript First Strand cDNA synthesis kit ( New England BioLabs ) with random primers . The first strand reaction was diluted five fold with deionised water and 1% ( vol/vol ) was used as template for each real-time PCR ( RT-PCR ) reaction . RT-PCR was carried out using QuantiNova SYBR Green ( Qiagen ) and a StepOnePlus RT-PCR System ( Applied Biosystems ) . Primers used were as follows: GAPDH ( F: TCTGCTCCTCCTGTTCGACA , R: AAAAGCAGCCCTGGTGACC ) , MTND4 ( F: CACTAAACATTCTACTACTCACTCTC , R: GGAGTCATAAGTGGAGTCCGTA ) . Expression levels of MTND4 were determined after normalization to GAPDH ( theoretical quantities ) , and two technical qPCR replicates were performed per sample before being averaged . Outlier values were removed ( defined as three interquartile ranges above or below the upper and lower quartile respectively ) within each genotypic category , leaving 19 samples for analysis . This association was chosen for replication analysis since it is associated with mitochondrial encoded gene expression across multiple tissue types and is significantly associated with MTND4 in a dataset and tissue type for which we had access to the relevant biological material ( Geuvadis dataset , LCLs ) . | Mitochondria are like the batteries of our cells; they perform the essential task of turning nutrients into chemical energy . A cell relies on its mitochondria for its survival , but they are not completely under the cell’s control . Mitochondria have their own DNA , separate from the cell’s DNA which is stored in the nucleus . It contains a handful of genes , which carry the code for some of the important proteins needed for energy production . These proteins are made in the mitochondria themselves , and their levels are tweaked to meet the cell's current energy needs . To do this , mitochondria make copies of their genes and feed these copies into their own protein-production machinery . By controlling the number of gene copies they make , mitochondria can control the amount of protein they produce . But the process has several steps . The copies come in the form of a DNA-like molecule called RNA and , at first , they contain several genes connected one after the other . To access each gene , the mitochondria need to cut them up . They then process the fragments , fine-tuning the number of copies of each gene . This process – called gene expression – happens in the mitochondria , but they cannot do it on their own; they need proteins that are coded within the DNA in the cell nucleus . Genes in the cell nucleus can affect gene expression in the mitochondria , changing the cell's energy supply . Scientists do not yet know all of the genes involved , or how this might differ between different tissues or among different individuals . To find out , Ali et al . examined more than 11 , 000 records of RNA sequences from 36 different human cells and tissues , including blood , fat and skin . This revealed a large amount of variation in the expression of mitochondrial genes . The way the mitochondria processed their genes changed in different cells and in different people . To find out which genes in the nucleus were responsible for the differences in the mitochondria , the next step was to compare RNA levels from the mitochondria to the DNA sequences in the nucleus . This is because changes in the DNA sequence between different people – called genetic variants – can also affect how genes work , and how genes are expressed . This comparison revealed 64 genetic variants from DNA in the cell nucleus that are associated with the expression of genes in the mitochondria . Some of these had a known link to genetic variants involved in diseases like the skin condition vitiligo or high blood pressure . So , although mitochondria contain their own DNA , they rely on genes from the cell nucleus to function . Changes to the genes in the nucleus can alter the way that the mitochondria process their own genetic code . Understanding how these two sets of genes interact could reveal how and why mitochondria go wrong . This could aid in future research into illnesses like heart disease and cancer . | [
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] | 2019 | Nuclear genetic regulation of the human mitochondrial transcriptome |
The six-subunit Origin Recognition Complex ( ORC ) is believed to be an essential eukaryotic ATPase that binds to origins of replication as a ring-shaped heterohexamer to load MCM2-7 and initiate DNA replication . We have discovered that human cell lines in culture proliferate with intact chromosomal origins of replication after disruption of both alleles of ORC2 or of the ATPase subunit , ORC1 . The ORC1 or ORC2-depleted cells replicate with decreased chromatin loading of MCM2-7 and become critically dependent on another ATPase , CDC6 , for survival and DNA replication . Thus , either the ORC ring lacking a subunit , even its ATPase subunit , can load enough MCM2-7 in partnership with CDC6 to initiate DNA replication , or cells have an ORC-independent , CDC6-dependent mechanism to load MCM2-7 on origins of replication DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 001
The discovery of the six-subunit ORC ( Bell and Stillman , 1992 ) identified the long sought initiator protein that binds to replicator sequences to initiate DNA replication in eukaryotes . ORC is an essential six-subunit , ring-shaped ATPase complex that recruits and co-operates with the CDC6 protein to promote the loading of CDT1 and then the MCM2-7 subunits of the replicative helicase during the ‘licensing’ of origins of replication ( Bleichert et al . , 2015; Yeeles et al . , 2015; Bell and Stillman , 1992; Blow and Tada , 2000; Masai et al . , 2010 ) . Of the six subunits of human ORC , ORC2-5 form a tight core complex ( Dhar et al . , 2001 ) . ORC1 is the only subunit responsible for the ATPase activity of ORC ( Chesnokov et al . , 2001; Giordano-Coltart et al . , 2005; Klemm et al . , 1997 ) All six ORC subunit genes are essential for the viability of S . cerevisiae and S . pombe ( Bell et al . , 1993; Foss et al . , 1993; Herskowitz , 1993; Loo et al . , 1995; Micklem et al . , 1993 ) . It is therefore expected that eukaryotic cells will not be viable , and will not replicate , if any of the ORC subunit genes are deleted . However , we have now deleted both alleles of human ORC2 or of ORC1 , to discover that cells can still survive and replicate in the complete absence of either of these two critical subunits of Origin Recognition Complex .
CRISPR/Cas9 was used to insert a ~600 bp blasticidin gene and poly A site into exon 4 ( amino acid 40 ) of ORC2 in HCT116 p53-/- ( Bunz et al . , 1998 ) colon cancer cells ( WT: HCT116 p53-/- , ORC2+/+ ) ( Figure 1A and B ) . All clones were viable and proliferated for months despite the disruption of the ORC2 gene . Immunoblotting and immunoprecipitation-immunoblotting of cell extracts showed that the ORC2-/- B2 and BP8 clones had no detectable ORC2 protein ( Figure 1D–H ) . An anti-ORC2C antibody recognizing the C terminal half of ORC2 ( Figure 1C ) ensured that a C-terminal fragment of ORC2 was not being expressed from an alternatively spliced transcript using an internal methionine ( Figure 1E ) . Quantitative immunoblotting showed that if any ORC2 was expressed , it must be at a level <1% of wild type levels ( Figure 1D ) . Quantitative immunoblotting of cell lysates and carefully measured amounts of recombinant bacterially produced ORC2 fragment showed that wild type cells express ~153 , 000 ORC2 molecules per cell ( Figure 1I , J ) . Thus , if the B2 or BP8 clones contain any ORC2 molecules below the level of detection , they can have no more than 1530 molecules/cell . ORC2 was also deleted in 293T human embryonic kidney cells or HBEC human bronchial epithelial cells immortalized with CDK4 and hTERT , and these cells too continued proliferating in the absence of ORC2 protein ( Figure 1K ) . 10 . 7554/eLife . 19084 . 003Figure 1 . Knockout of ORC2 in HCT116 p53-/- cells . ( A ) Strategy for insertion of a blasticidin gene and poly A site in the fourth exon of ORC2 at aa 40 of ORC2 . ( B ) PCR on genomic DNA of indicated clones . WT: HCT116 p53-/- and ORC2+/+ . ORC2 Knockout clones , B2 and BP8 have an insert on both alleles of ORC2 as indicated by the absence of 0 . 6 kb PCR product . ( C ) Verification of antibodies recognizing N-terminal or C-terminal parts of ORC2 . Recombinant ORC2 protein halves with Flag epitope tags were expressed and blotted with indicated antibodies . Ponceau S staining of total protein shows equal loading of lanes . * indicates full length endogenous ORC2 protein . Arrow indicates recombinant protein . ( D ) Quantitative Western blot for ORC2 with an antibody recognizing the N-terminal half of ORC2 . Indicated amount of lysate loaded in each lane . ( E ) Western blot with antibody recognizing C-terminal half of ORC2 . * Non specific band ( F ) Input cell lysate and immunoprecipitates of ORC2 immunoblotted for ORC2 . Darker exposure of the top blots is shown in the middle . HSP90 in the cell lysate or the IgG band in the immunoprecipitate serves as loading control . ( G ) Western blot for indicated proteins in clones indicated on the top . Darker exposure of the ORC2 blots is shown at the bottom . Ponceau S stains all proteins on the blot and also indicates equal loading of lanes . ( H ) Immunoblot of soluble and chromatin-associated proteins in the clones indicated at the top . Ponceau S staining of histones serves as loading control for chromatin fractions . For each panel , all the lanes are from the same blot and exposure . ( I ) Comparison of Coomassie Brilliant Blue signal of pure BSA and recombinant purified GST-ORC2 to show that the top-most band in the ORC2 lane is at 170 ng/ 10 μl . ( J ) Immunoblot with different amounts of cell lysate with the GST-ORC2 to show that 1×10e5 cells give an ORC2 signal equal to 2 . 54 ng ( 1 . 4 fold of 1 . 67 ng ) of GST-ORC2 , which corresponds to 153×10e8 molecules of GST-ORC2 . ( K ) Western blot of ORC2 in HBEC and 293T cell lines . Ponceau S staining of total protein or immunoblot of Chk1 show equal loading of the pairs of lanes . DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 003 The ORC2-/- cells suffer a decrease of ORC3 , ORC4 and ORC5 , which are destabilized when not complexed with ORC2 ( Figure 1G ) . ORC1 was also decreased , but ORC6 , CDC6 and CDT1 were unchanged . There was no activation of the DNA damage checkpoint , measured by the phosphorylation of Chk1 or H2AX , as would have occurred with impaired DNA replication . Chromatin association of ORC2-5 and of ORC1 was decreased in ORC2-/- cells ( as expected from the decrease of these proteins in cell lysates ) ( Figure 1H ) . The chromatin association of MCM3 , 5 and 7 of the MCM2-7 was reduced , but not completely eliminated . Surprisingly , chromatin association of ORC6 or CDT1 was relatively unchanged , while CDC6 association was slightly increased . The proliferation rate of the ORC2-/- cells was >50% that of WT cells ( Figure 2A ) and ORC2 did not re-appear even after passage of the cells for over a year . The ORC2-/- cells did not accumulate in S phase ( Figure 2B ) and completed DNA replication after release from an early S-phase block in the same time as WT cells ( Figure 2C ) . The percentage of cells synthesizing DNA during a 30 min pulse was not significantly decreased ( Figure 2D ) . By molecular combing the median distance between bi-directional origins of replication in ORC2-/- cells was marginally increased to 113–118 kb from 96 kb and the fork progression rate was slightly increased to 1 . 3–1 . 5 kb/min from 1 . 2 kb/min ( Figure 2E ) . Given the total DNA content of six billion bp in these cells , this measurement suggests that the ORC2-/- cells fire about 52 , 000 origins of replication . 10 . 7554/eLife . 19084 . 004Figure 2 . Cell proliferation and DNA replication in the ORC2-/- cell lines . ( A ) Growth curves of indicated clones of cells over five days , expressed as MTT absorbance relative to the level at day 1 . ( Mean ± S . D . ; n = 4 biological replicates . Cells after passage for three months ) . ( B ) FACS profile of propidium-iodide stained cell-cycle asynchronous cells from indicated clones . ( C ) Cells arrested in double-thymidine block released into nocodazole containing medium and harvested at indicated times after release to measure rate of progression through S phase . AS: asynchronous cells . The red dotted lines indicate cells with G1 and G2 DNA content . ( D ) Cell-cycle asynchronous cells labeled with BrdU for 30 min . % of BrdU labeled cells evaluated by two color FACS . ( Two-sided t-test for two samples , Mean ± S . D . ; n = 4 biological replicates ) . ( E ) Molecular combing of chromosomal DNA after a pulse of CldU for 30 min chased with a pulse of IdU for 30 min . Top: Schematic shows distances that were measured to estimate fork progression rate and inter-origin distance . Middle: Representative image of the combed DNA stained for CldU ( green ) and IdU ( red ) shown below the schematic . Bottom: Box and whiskers plot for fork progression rate and inter-origin distance of indicated clones of cells . ( P value < 0 . 01 , two-sided Wilcoxon rank sum test for two samples; N = number of tracks counted . P: Statistical significance of any difference between WT and ORC2-/- cells . ( F ) Overlap of the BrIP-seq peaks between WT and ORC2-/- cells . ( G ) Box and whiskers plot for inter-origin distances ( measured by BrIP-seq ) for each chromosome in WT and ORC2-/- cells . The median inter-origin distance for all chromosomes together indicated at bottom right . ( H ) Circos plot of Origins mapped by BrIP-seq for chromosome 1 . Outer circle: the chromosome with the karytotyping bands . Inner two circles: the locations of BrIPseq peaks in the WT and ORC2-/- cell lines . ( I ) Distribution of BrIP-seq mapped origins relative to distance from Transcription Start Sites ( TSS ) . In WT and ORC2-/- cells . DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 004 We mapped replication initiation sites by a second method: enriching for BrdU-labeled , origin-centered nascent strands by immunoprecipitation and sequencing ( BrIPseq ) ( Karnani et al . , 2010 ) . ~13 , 000 BrIPseq origins were mapped to unique DNA sequence in the ORC2-/- cells compared to ~20 , 000 in WT cells ( Figure 2F ) . Taking into consideration repetitive DNA and the diploid nature of the human genome , this method also suggests that about 52 , 000 origins are fired in the ORC2-/- cells . 40% of the BrIP-seq peaks in ORC2-/- cells overlapped with that in WT cells , comparable to the overlap reported among origins mapped by different groups ( Karnani et al . , 2010 ) . The <100% of overlap is explained by plasticity of origin usage ( Cadoret et al . , 2008 ) . The inter-origin distances for the BrIPseq origins in ORC2-/- cells ( 26 kb ) were slightly longer than in the WT ( 25 kb ) cells ( P value ~4 . 3e-07 , Wilcoxon rank sum test ) and the chromosome-by-chromosome distribution of inter-origin distances was similar to that in WT cells ( Figure 2G ) . The 4-fold shorter inter-origin distance in a population-based approach of origin mapping ( BrIPseq ) compared to molecular combing is also due to the plasticity of origin usage: only one out of four possible origins in a single DNA segment fire in one S phase , while all four origins are used in the entire population of cells ( see discussion in Karnani et al . , 2010 ) . Origins are enriched in gene-rich domains and near transcription start sites ( Figure 2H and I ) as reported in previous studies ( Karnani et al . , 2010; Cadoret et al . , 2008; Danis et al . , 2004; MacAlpine et al . , 2004; Mesner et al . , 2011; Sequeira-Mendes et al . , 2009 ) . We next disrupted the only subunit of ORC demonstrated to have ATPase activity , ORC1 ( Chesnokov et al . , 2001; Giordano-Coltart et al . , 2005; Klemm et al . , 1997 ) . A blasticidin or a plasmid-derived DNA fragment was inserted in exon 1 of ORC1 at the initiator methionine ( Figure 3A , B ) . Western blotting and immunoprecipitation-western blotting of ORC1 protein showed no ORC1 protein in the ORC1-/- clones ( Figure 3C–E ) . ORC1 is loosely associated with the other subunits of ORC , and unlike ORC2 , ORC1 depletion did not decrease ORC2 , ORC3 , ORC4 , ORC5 , ORC6 , CDT1 , and CDC6 ( Figure 3C ) . Although ORC1 was absent , the remaining five subunits of ORC along with CDC6 and CDT1 ( at least in one clone ) could associate with chromatin . However , the chromatin loading of MCM3 , 5 , and 7 subunits of MCM2-7 is significantly decreased ( Figure 3D ) . 10 . 7554/eLife . 19084 . 005Figure 3 . Knockout of ORC1 in HCT116 p53-/- cells . ( A ) Strategy for insertion of a blasticidin gene and poly A site after first methionine of ORC1 in the second exon . ( B ) PCR on genomic DNA of indicated clones . WT: HCT116 p53-/- and ORC1+/+ . ORC1 Knockout clones , B14 , B48 and BP32 have an insert on both alleles of ORC1 as indicated by the absence of 0 . 6 kb PCR product . ( C ) Western blot for indicated proteins in clones indicated on the top . Darker exposure of the ORC1 blots is shown at the bottom . Ponceau S stains all proteins on the blot and also indicates equal loading of lanes . ( D ) Immunoblot of soluble and chromatin-associated proteins in the clones indicated at the top . For each panel , all the lanes are from the same blot and exposure . ( E ) Input cell lysate and immunoprecipitates of ORC1 immunoblotted for ORC1 . Darker exposure of the top blots is shown in the middle . Tubulin in the cell lysate or the IgG band in the immunoprecipitate serves as loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 005 At early passage ( at one month ) , the ORC1-/- clones proliferated at 10–20% of the rate of WT cells but by 6 months of passage , their proliferation rates at about 50% of WT cells without reappearance of ORC1 protein ( Figures 4A and 3E ) . The cells did not accumulate in S phase ( Figure 4B ) and passage through S phase was not slowed ( Figure 4C ) . By molecular combing , inter origin distance in ORC1-/- cells was unchanged or slightly decreased ( Figure 4D ) . Fork progression was decreased to 0 . 8–1 . 0 kb/min from 1 . 2 kb /min . BrIPseq analysis showed that the ORC1-/- cells fire ~13 , 000 unique origins , with 43% of them overlapping with origins in WT cells ( Figure 4E ) . The distribution of inter-origin distances between chromosomes , enrichment of origins in gene-rich segments and near transcription start sites was similar to that of WT cells or ORC2-/- cells ( Figure 4F–H ) . 10 . 7554/eLife . 19084 . 006Figure 4 . Cell proliferation and DNA replication changes in the ORC1-/- cell lines . ( A ) Growth curves of indicated clones of cells over four days , expressed as MTT absorbance relative to the level at day 1 . ( Mean ± S . D . ; n = 4 biological replicates ) Cells after passage for 1 month or six months . ( B ) FACS profile of propidium-iodide stained cell-cycle asynchronous cells from indicated clones . ( C ) Cells arrested in double-thymidine block released into nocodazole containing medium and harvested at indicated times after release to measure rate of progression through S phase . AS: asynchronous cells . The red dotted lines indicate cells with G1 and G2 DNA content . ( D ) Molecular combing of chromosomal DNA after a pulse of CldU for 30 min chased with a pulse of IdU for 30 min . Box and whiskers plot for fork progression rate and inter-origin distance of indicated clones of cells . ( P value < 4 . 6e-06 , two-sided Wilcoxon rank sum test for two samples N = number of tracks counted ) ( Inter origin disntance N = 91 ( WT ) , 131 ( ORC1B14 ) , 174 ( ORC1BP32 ) p: Statistical significance of any difference between WT and ORC1-/- cells . ( E ) Overlap of the BrIP-seq peaks between WT and ORC1-/- cells . ( F ) Box and whiskers plot for inter-origin distances ( measured by BrIP-seq ) for each chromosome in WT and ORC1-/- cells . The median inter-origin distance for all chromosomes together indicated at bottom right . ( G ) Circos plot of Origins mapped by BrIP-seq for chromosome 1 . Outer circle: the chromosome with the karytotyping bands . Inner two circles: the locations of BrIPseq peaks in the WT and ORC1-/- cell lines . ( H ) Distribution of BrIP-seq mapped origins in ORC1-/- cells relative to distance from Transcription Start Sites ( TSS ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 006 The increase in chromatin association of CDC6 in the ORC2-/- cells ( Figure 1H ) led us to test whether CDC6 acts inefficiently to load enough MCM2-7 in the absence of the six-subunit ORC and the ATPase subunit of ORC ( in the ORC1-/- cells ) . Knocking down CDC6 in WT cells ( Figure 5A ) did not decrease either the % of cells in active S phase ( Figure 5B ) or colony formation ( Figure 5C ) . In contrast , knockdown of CDC6 in the ORC1-/- or ORC2-/- cells increased phosphoChk2 , suggesting the activation of DNA damage checkpoints indicating problems in S phase ( Figure 5A ) , decreased actively replicating cells , and decreased colony formation ( Figure 5B–C ) , suggesting that CDC6 becomes more important for DNA replication and cell proliferation in the absence of ORC2 or ORC1 . 10 . 7554/eLife . 19084 . 007Figure 5 . CDC6 is more essential for replication and colony formation in the ORC mutant cells . ( A ) Immunoblots of extracts from indicated cell lines following transfection of siGL2 ( negative control siRNA against luciferase ) or siCDC6 . ( B ) % of BrdU+ cells after transfection of indicated siRNAs . Data from two color FACS . ( P value < 0 . 01 , two-sided t-test for two samples , Mean ± S . D . n = 4 or 3 biological replicates ) ( C ) Top: 72 hr after transfection of indicated siRNAs , 2000 cells were plated per plate for colony formation detected by Crystal violet staining after seven days . Bottom: Crystal violet stained colony density were measured . Data presented for each cell line normalized to the density of the siGL2 transfected cells . ( P value < 0 . 001 , two-sided t-test for two samples , Mean ± S . D . n = 3 biological replicates ) . ( D ) Immuno blots of ORC5 ( E ) FACS profile of propidium-iodide stained cells for cell-cycle determination at three days after transfection of indicated siRNA . ( F ) % of BrdU+ cells after transfection of indicated siRNAs . Data from two color FACS . ( Two-sided t-test for two samples , Mean ± S . D . n = 3 biological replicates ) . DOI: http://dx . doi . org/10 . 7554/eLife . 19084 . 007 To test whether any of the other subunits of the six-subunit ORC remain important for replication in the ORC1-/- or ORC2-/- cells , we depleted ORC5 by siRNA ( Figure 5D ) . Knockdown of ORC5 did not change the cell cycle profile in ORC-/- mutant cells ( Figure 5E ) , and in fact more severely repressed the % of actively replicating cells in WT cells than in the ORC1-/- or ORC2-/- cells ( Figure 5F ) . However knockdown of ORC5 still inhibited BrdU incorporation in the ORC1-/- and ORC2-/- cells . We have not yet succeeded in knocking out the ORC4 and ORC5 genes , though of course such clones may emerge in future attempts . At the present state , our results suggest that the other subunits of ORC may support DNA replication independent of ORC1 or ORC2 .
The surprising results from this genetic investigation of ORC in human cell lines suggest that two subunits of ORC are dispensable for DNA replication . Although we believe that no ORC2 protein is synthesized in the ORC2-/- cells , we have to entertain the caveat that up to 1530 ORC2 molecules can escape the limits of detection with our current antibodies . This number is still too few to license the 52 , 000 origins of replication mapped by two independent methods . It is unlikely that a single ORC complex can catalytically load thirty-five MCM2-7 hexamers distributed 100 kb apart over 3 . 5 Mb of chromosomal DNA . Since knockdown of ORC5 still affects DNA synthesis in the ORC1-/- or ORC2-/- cells , we cannot yet conclude that replication initiation can occur in the absence of all subunits of ORC . To reach that conclusion we have to successfully delete both alleles of the other four subunits , ORC3-6 . However , even if the remaining ORC subunits are functional for loading MCM2-7 our result requires reconsideration of existing models of ORC function in replication initiation . ( 1 ) The crystal structure suggests that ORC2-3-5-4-1 are arranged in a gapped ring ( with a central channel that is wide enough to surround a DNA double-helix ) , and that later in licensing , CDC6 slips into the gap between ORC2 and ORC1 to close the gap . The ORC-CDC6 ring is proposed to interact with the MCM2-7 ring end-on-end during the loading of MCM2-7 ( Bleichert et al . , 2015 ) . Loss of one subunit in the five-membered ORC ring makes it difficult for the remaining subunits to form a ring large enough ( i ) to surround a DNA double-helix in the same manner as wild type ORC or ( ii ) to interact with the MCM2-7 ring end-on-end . ( 2 ) Human ORC1 and ORC4 are the only subunits that have intact Walker A and B motifs . Multiple groups have shown that the ATPase activity of ORC ( in S . cerevisiae , in D . melanogaster and in H . sapiens ) depends exclusively on the Walker A and B motifs of the ORC1 subunit , and that this ATP binding and hydrolysis activity is essential for ORC function ( Chesnokov et al . , 2001; Giordano-Coltart et al . , 2005; Klemm et al . , 1997 ) . Even if an altered or partial ORC is initiating replication , we have to conclude that any ATPase activity necessary can be provided by ORC4 or CDC6 . There is a report arguing that ORC1 is not essential for endoreplication in Drosophila , because ORC1-/- larvae still allowed endoreplication in salivary gland cells , with only a twofold reduction of ploidy ( Park and Asano , 2008 ) . However the paper did not show the sensitivity of the western-blot to measure the level of residual ORC , so that it is theoretically possible that there was enough residual maternal ORC in the endoreplicating cells . The classic model of replication initiation where ORC first associates with the DNA , helps load CDC6 , which then helps load CDT1 and MCM2-7 may still be important for efficient MCM2-7 loading . The surprise is that inefficient MCM2-7 loading , perhaps with the help of CDC6 , is sufficient for replication initiation and cell survival in the absence of six-subunit ORC . The other surprise is that the six-subunit ORC does not appear to associate with chromatin as a holocomplex . Clearly ORC6 associates with chromatin normally despite a decrease in ORC1 ( in the ORC2-/- or ORC1-/- cells ) or ORC2-5 loading ( in the ORC2-/- cells ) . Similarly ORC2-5 loading is independent of ORC1 loading ( in the ORC1-/- cells ) . The origin plasticity of eukaryotes is attributed to the loading of excess subunits of MCM2-7 on chromatin . It is thus also surprising that the plasticity persists despite a significant reduction in the association of MCM2-7 on chromatin . In summary , we suggest that the absolute requirement for six-subunit ORC for licensing bi-directional origins of replication can be bypassed in some cell lines .
HCT116 p53−/− cells ( Bunz et al . , 1998 ) ( RRID:CVCL_S744 , a generous gift from Fred Bunz , Johns Hopkins ) were maintained in McCoy's 5A-modified medium supplemented with 10% fetal bovine serum . HBEC3 ( RRID:CVCL_X491 ) -p53KD-K-RasV12 cells ( a generous gift from , David R . Jones , Memorial Sloan-Kettering Cancer Center ) were originally engineered to express sh-p53 RNA and K-Rasv12 protein by John D . Minna’s group in the University of Texas Southwestern Medical Center ( Sato et al . , 2006 ) and were maintained in Keratinocyte-SFM . HBEC3-p53KD-K-RasV12 cells we used also express shGL2 and GFP ( Hall et al . , 2014 ) . Lipofectamine 2000 ( Thermo Fisher Scientific , Waltham , MA ) was used to transfect plasmids and RNAiMAX ( Thermo Fisher Scientific ) was used to transfect siRNAs according to the manufacturer’s protocol . CDC6 siRNA ( GAUCGACUUAAUCAGGUAU ) , ORC5 siRNA ( CCCUGGUUGGCCAUGACGA ) was synthesized by Thermo Fisher Scientific , Waltham , MA . HEK293T cells ( RRID:CVCL_0063 ) were from ATCC ( CRL-3216 ) . No mycoplasma contamination was found . 293T cells and HCT116 p53-/- cells were authenticated by STR profiling . gRNA was cloned into pCR-Blunt II-TOPO vector backbone ( Addgene 41820 , Cambridge , MA ) using PCR and In-Fusion cloning ( Clontech , Mountain View , CA ) . gRNA target sequence was as follows . GAAGGAGCGAGCGCAGCTTTTGG . Human codon optimized Cas9 nuclease ( hCas9 ) expression vector was obtained from Addgene ( 41815 ) . Blasticidin or Hygromycin resistance genes terminated by a polyA sequence and flanked by two homology arms ( 0 . 9 kb–1 . 6 kb in length ) were cloned into pDONR 221 ( Thermo Fisher Scientific ) using PCR and In-Fusion cloning ( Clontech ) . Cells were pulse labeled with 100 µM CldU for 30 min , following by 250 µM IdU for 30 min before embedding into agarose plug . DNA was combed on silanized coverslips ( Genomic Vision , Bagneux , France ) , dehydrated at 65°C for 4 hr and denatured in 0 . 5 M NaOH and NaCl for 8 min . CldU or IdU were immune-detected with either anti-BrdU antibody that recognizes CldU ( MA 182088 , Thermo Fisher Scientific , Waltham , MA , RRID:AB_927214 ) or anti-BrdU antibody that recognizes IdU ( 347580 , BD Biosciences , Franklin Lakes , NJ , RRID:AB_400326 ) . Image acquisition was performed with Zeiss AxioObserver Z1 , 63 X objective . DNA lengths were measured using Image J software . BrdU incorporation was conducted as previously described ( Machida et al . , 2005 ) with the following modifications . Cells were labeled with 10 μM BrdU for 30 min and fixed in 70% Ethanol . DNA was denatured in 2 M hydrochloric acid and stained with FITC-conjugated BrdU antibody ( 556028 , BD Biosciences , RRID:AB_396304 ) and propidium iodide ( Sigma-Aldrich , St . Louis ) according to the manufacturer's instruction . To determine the effects of CDC6 knock down , cells were transfected with siRNA twice . 48 hr after the first siRNA transfection , 2000 cells were plated in six well plates . Colonies were fixed , stained with crystal violet , and counted 1 week later . All experiments were conducted in triplicate . 1000 cells were plated in 96 well plates . The absorbance of cells were measured every 24 hr using CellTiter 96 Non-Radioactive Cell Proliferation Assay ( Promega , Fitchburg , WI ) according to the manufacturer's instructions . All experiments were conducted in triplicate and absorbance relative to that on day one was expressed . Cells were lysed in lysis buffer 50 mM Tris-HCl ( pH 8 . 0 ) , 150 mM NaCl , 5 mM EDTA , 0 . 5 % NP-40 , 1 mM DTT , 20 mM NaF , and protease inhibitor cocktail . Lysate was cleared by centrifugation and incubated with ORC1 ( Machida et al . , 2005 ) or ORC2 ( Dhar et al . , 2001 ) antibody for 4 hr . Immunoprecipitate was collected on Dynabeads Protein G ( Thermo Fisher Scientific ) and eluted with 2 x SDS sample buffer . Antibodies used were as follows . ORC2 ( Dhar et al . , 2001 ) ( Figures 1F , G , H , and and K ) , ORC2N ( Figures 1C , D , and and J ) ( sc-32734 , Santa Cruz Biotechnology , Dallas , TX , RRID:AB_2157726 ) , ORC2C ( Figure 1C and E ) ( sc-13238 , Santa Cruz Biotechnology , RRID:AB_2157715 ) , MCM3 ( sc-9850 , Santa Cruz Biotechnology , RRID:AB_2142269 ) , HSP90 ( sc-13119 , Santa Cruz Biotechnology , RRID:AB_675659 ) , Cdt1 ( Senga et al . , 2006 ) , ORC3 , ORC4 , ORC5 , and ORC6 ( Machida et al . , 2005 ) , ORC1 ( 4731 , Cell Signaling Technology , Danvers , MA , RRID:AB_2157583 ) , CDC6 ( 3387 , Cell Signaling Technology , RRID:AB_2078525 ) , p-Chk1 ( 2341 , Cell Signaling Technology , RRID:AB_330023 ) , p-Chk2 ( 2661 , Cell Signaling Technology , RRID:AB_331479 ) , Chk2 ( 3440 , Cell Signaling Technology , RRID:AB_2229490 ) , p-H2AX ( 2577 , Cell Signaling Technology , RRID:AB_2118011 ) , and H2AX ( 2595 , Cell Signaling Technology , RRID:AB_10694556 ) , MCM5 ( A300-195A , Bethyl Laboratories , Inc Montgomery , TX , RRID:AB_185552 ) , MCM7 ( A300-128A , Bethyl Laboratories , Inc . , RRID:AB_2142821 ) , FLAG ( F1804 , Sigma , RRID:AB_262044 ) , and Chk1 ( NB100-464 , Novus Biologicals , LLC , Littleton , CO , RRID:AB_10002158 ) . GST tagged ORC2 recombinant protein was purchased ( H00004999-P01 , Abnova , Taipei City , Taiwan ) The cells were labelled with 100 µM BrdU ( Sigma ) in exponential phase of their growth ( 50–60% of confluency ) for 1 hr . The cells were lysed and genomic DNA was isolated , denatured and nascent strands were separated on neutral sucrose gradient . The fragments of 0 . 5 to 3 . 0 kb were selected . After dialysis against TE , the DNA was sheared , denatured and immune precipitated with anti-BrdU antibody ( B8434 , Sigma , RRID:AB_476811 ) . The single stranded BrdU immunoprecipitate ( 10 ng ) was then used to prepare next generation sequencing libraries using Takara Chip-Seq kit . The library from the control genomic DNA was prepared the same way as for the BrdU sample but the only difference was that sucrose gradient centrifugation and size selection was not done for the genomic control . Also the BrdU incorporation was for a longer time ( 36 hr compared to 1 hr for Br-IP sample ) . Single-end 75 bp reads were obtained for wildtype ( WT ) and ORC2 or ORC1 knockout cells . BrdU incorporated genomic strands was also sequenced and used as control ( CNTL ) . Perl script was written and used to trim T’s present at the 3’ end of reads . The trimmed reads were aligned to hg38 using Bowtie2 with the default parameters . Alignment with Bowtie2 resulted in 12597288 ( 81% ) and 10204177 ( 77% ) and 13171609 ( 93% ) mapped reads in WT , KO and CNTL respectively . To define peaks , the genome was divided into 1 kb windows . Any 1 kb window in WT or KO cells with two fold more reads than CNTL were considered to calculate mean and SD . 1 kb windows with reads ≥ mean + 3 SD number of reads were defined as peaks in WT or KO cells . Dataset for transcription start sites ( TSSs ) was downloaded from the UCSC genome browser . Circos v0 . 67 ( Krzywinski et al . , 2009 ) was used to construct circular genome visualizations . Peaks coordinates of WT and KO chromosome one was parsed to create files appropriately formatted for input to Circos . Chromatin fractionation was performed as previously described ( Méndez and Stillman , 2000 ) . Cells were resuspended in buffer A ( 10 mM HEPES [pH7 . 9] , 10% glycerol , 1 mM DTT , protease inhibitor cocktail [Thermo Fisher] ) . After adding 0 . 1% Triton X-100 , cells were incubated for 5 min on ice and centrifuge at 1300 g , 4°C . Supernatants were further clarified by centrifugation at 20000 g , 4°C ( S2 ) . Pellets ( Nuclei ) were washed in buffer A and lysed in buffer B ( 3 mM EDTA , 0 . 2 mM , EGTA , 1 mM DTT , protease inhibitor cockatil ) . After incubation for 30 min on ice , lysate was centrifuged at 1700 g , 4°C . Pellets ( chromatin ) were washed in buffer B and lysed in 2 x SDS sample buffer and sonicated ( P3 ) . We have used the pwr package in R for choosing sample size for all the experiments . Owing to the nature of the performed experiments , no randomization and no blinding were used . All test statistics were calculated with R ( http://www . r-project . org/ ) . t-test and Wilcoxon–rank sum test was performed to test the difference in mean for the normal and skewed data respectively . Source data including BrIP seq data were deposited in Dryad ( Shibata et al . , 2016 ) . | Most of the DNA in human cells is packaged into structures called chromosomes . Before a cell divides , the DNA in each chromosome is carefully copied . This process begins at multiple sites ( known as origins ) on each chromosome . A group of six proteins collectively known as the Origin Recognition Complex ( or ORC for short ) binds to an origin and then recruits several additional proteins . When the cell is ready , the assembled proteins are activated and DNA copying begins . It is thought that all of the ORC proteins are essential for cells to survive and copy their DNA . Here , Shibata et al . reveal that human cells can survive without ORC1 or ORC2 , two of the six proteins in the ORC complex . Disrupting the genes that encode the ORC1 and ORC2 proteins in human cancer cell lines had little effect on the ability of the cells to copy their DNA and survive . Furthermore , these cells spend the same amount of time copying their DNA and use a similar set of origins as normal cells . However , the experiments also reveal that cells without ORC1 or ORC2 are more dependent on the presence of one particular protein recruited to the origin after the ORC assembles . Reducing the availability of this protein , CDC6 , decreased the ability of these cells to survive and divide . Future efforts will aim to identify the mechanism by which cells bring together the proteins required to copy DNA in the absence of a complete ORC . | [
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The poor efficacy of seasonal influenza virus vaccines is often attributed to pre-existing immunity interfering with the persistence and maturation of vaccine-induced B cell responses . We previously showed that a subset of vaccine-induced B cell lineages are recruited into germinal centers ( GCs ) following vaccination , suggesting that affinity maturation of these lineages against vaccine antigens can occur . However , it remains to be determined whether seasonal influenza vaccination stimulates additional evolution of vaccine-specific lineages , and previous work has found no significant increase in somatic hypermutation among influenza-binding lineages sampled from the blood following seasonal vaccination in humans . Here , we investigate this issue using a phylogenetic test of measurable immunoglobulin sequence evolution . We first validate this test through simulations and survey measurable evolution across multiple conditions . We find significant heterogeneity in measurable B cell evolution across conditions , with enrichment in primary response conditions such as HIV infection and early childhood development . We then show that measurable evolution following influenza vaccination is highly compartmentalized: while lineages in the blood are rarely measurably evolving following influenza vaccination , lineages containing GC B cells are frequently measurably evolving . Many of these lineages appear to derive from memory B cells . We conclude from these findings that seasonal influenza virus vaccination can stimulate additional evolution of responding B cell lineages , and imply that the poor efficacy of seasonal influenza vaccination is not due to a complete inhibition of vaccine-specific B cell evolution .
Measurably evolving populations are systems that undergo evolution rapidly enough for significant genetic differences to be detected in longitudinally sampled timepoints ( Drummond et al . , 2003 ) . While this concept is frequently applied to viruses such as HIV ( Rambaut et al . , 2004 ) and SARS-CoV-2 ( e . g . , du Plessis et al . , 2021 ) , B cells experience similarly rapid evolution during affinity maturation . B cell affinity maturation is critical for developing high-affinity antibodies in response to infection and vaccination ( Shlomchik and Weisel , 2012; Victora and Nussenzweig , 2012 ) . During affinity maturation , somatic hypermutation ( SHM ) introduces mutations into the B cell receptor ( BCR ) loci at a rate orders of magnitude higher than the background rate of somatic mutations ( McKean et al . , 1984; Murphy et al . , 2008 ) . These modified BCRs are selected based on their binding affinity , and the process repeats cyclically within germinal centers ( GCs Teng and Papavasiliou , 2007; Victora and Nussenzweig , 2012 ) . Infection or vaccination can also stimulate pre-existing memory B cells that rapidly differentiate into antibody secreting plasmablasts or possibly re-enter GCs to undergo additional affinity maturation ( Ellebedy , 2018; Mesin et al . , 2020 ) . A lack of vaccine-specific affinity maturation is thought to underlie the poor efficacy of seasonal influenza virus vaccination ( Arevalo et al . , 2020; Ellebedy , 2018 ) . While recent work has shown that antigen-specific B cell lineages can be recruited into GCs following influenza vaccination ( Turner et al . , 2020 ) , other work has been unable to detect significant increases in SHM frequency among circulating influenza-binding antibody lineages following vaccination ( Ellebedy et al . , 2016 ) . Whether seasonal influenza vaccination stimulates an increase in SHM frequency can be answered by determining whether influenza-binding B cell lineages found in GCs are measurably evolving following vaccination . This is distinct from simply quantifying SHM frequency . While influenza vaccination stimulates memory B cell lineages with high SHM frequency ( Laserson et al . , 2014; Wrammert et al . , 2008 ) , these lineages are only measurably evolving if their level of SHM detectably increases during the sampling interval surrounding vaccination . In this study , we show how a phylogenetic test of measurable evolution can be a powerful tool to detect increasing SHM frequency in longitudinally sampled BCR sequence datasets ( Duchêne et al . , 2015; Murray et al . , 2016 ) . We validate this approach through simulations and a survey of measurable evolution in B cell repertoires across a wide range of infections and vaccinations . We document significant heterogeneity among conditions , with some like HIV infection and primary hepatitis B vaccination enriched for measurably evolving lineages in the blood . We further show that while most circulating lineages following influenza virus vaccination are not measurably evolving , a subset of memory B cell lineages re-enter GCs and increase in SHM frequency .
We develop a framework to test for measurable evolution in B cells based on longitudinally sampled sequence data from the BCR variable region . After preprocessing the sequencing data , we first identify clonal lineages – B cells that descend from a common V ( D ) J rearrangement – using clustering based on nucleotide sequence similarity , which we have previously shown detects clonal relationships with high confidence ( Gupta et al . , 2017; Zhou and Kleinstein , 2019 ) . The pattern of shared SHM among BCR sequences within a lineage is then used to build a B cell lineage tree , which represents a lineage’s history of SHM . Branch lengths within these trees represent SHM per site . The divergence of each tip is the sum of branch lengths leading back to the lineage’s most recent common ancestor . In evolving lineages , sequences sampled at later timepoints are expected to have higher divergence than those from earlier timepoints ( Figure 1A ) . To estimate the rate of evolution over time , we calculate the slope of the regression line between timepoint ( weeks ) and divergence ( SHM/site ) for each tip ( Figure 1B , E; Rambaut et al . , 2016 ) . Because tips are not independent , standard linear regression p values are improper . We instead quantify significance using a modified phylogenetic date randomization test ( Duchêne et al . , 2015; Murray et al . , 2016 ) . This tests whether the Pearson’s correlation between divergence and time is significantly greater than that observed in the same tree with timepoints randomized among tips ( Figure 1C , F ) . To account for population structure and sequencing error , we permute timepoints among single-timepoint monophyletic clusters of tips rather than individual tips ( Figure 1—figure supplements 1 and 2; Duchêne et al . , 2015; Murray et al . , 2016 ) . Further , it is possible that the combined effects of PCR and sequencing error will generate tree structures with multiple spurious tips radiating from a single node . This could increase the error rate of the date randomization test . Because trees are strictly binary , this would produce clusters of zero-length branches ( soft polytomies ) that could increase the error rate . To limit potential effects of this source of error , we resolve polytomies into the fewest number of single-timepoint monophyletic clades possible ( Figure 1—figure supplements 1 and 2 ) . We refer to lineages with a date randomization test p < 0 . 05 as ‘measurably evolving’ . To limit our analyses to lineages with adequate statistical power , we include only lineages with ≥15 total sequences sampled over at least 3 weeks , and have a minimum possible p value <0 . 05 based on the number of distinct permutations . Because we use a p value cutoff of 0 . 05 , we expect a false positive rate of approximately 5% if no measurable evolution is occurring . We therefore refer to datasets with >5% measurably evolving lineages as ‘enriched’ for measurable evolution . This test is implemented within the Immcantation . org framework in the R package dowser ( Hoehn et al . , 2020 ) . To determine the necessary sampling interval to detect B cell evolution , we benchmarked the date randomization test using affinity maturation simulations performed with the package bcr-phylo ( Davidsen and Matsen , 2018; Ralph and Matsen , 2020 ) . This simulates alternating GC cycles of B cell proliferation , SHM , and selection based on amino acid similarity to a target sequence . Within these simulations , each lineage was first sampled after 10 simulated GC cycles , and then sampled a second time after a variable number of additional cycles . Using this framework in which all lineages are evolving , the date randomization test detected measurable evolution in 47% of lineages after 10 additional GC cycles , and 77% after 15 additional cycles ( Figure 1G ) . Given a GC cycle time of 6–24 hr , 15 cycles corresponds to 4–15 days , within the timeframe of many longitudinal B cell repertoire studies ( Ellebedy et al . , 2016; Laserson et al . , 2014 ) . Interestingly , the date randomization test had higher power to detect measurable evolution in simulations of neutral evolution than those that included selection ( Figure 1—figure supplements 3–4 ) . This is likely because selection can reduce the rate of divergence within lineages compared to neutral evolution ( Figure 1—figure supplement 5 ) . To quantify the false positive rate , we repeated these calculations on the same simulations but with randomized sample time associations . Here , the date randomization test found measurable evolution in <4% in each case , indicating a low false positive rate ( Figure 1—figure supplements 3–4 ) . These analyses demonstrate that the date randomization test has sufficient sensitivity and specificity to detect ongoing B cell evolution from longitudinally sampled BCR data . To further validate our approach , we tested for measurable evolution in cases of known or suspected affinity maturation in humans . We hypothesized that primary immune responses would be enriched for measurably evolving lineages . To test this , we used publicly available data primarily from the Observed Antibody Space ( OAS ) database ( Kovaltsuk et al . , 2018 ) to survey measurable evolution in BCR datasets from 99 human subjects in 21 studies spanning 10 conditions including HIV infection , Ebola virus infection , and healthy controls ( Table 1 ) . We observed considerable heterogeneity in measurable evolution among conditions . Confirming our hypothesis , we observed an enrichment of measurably evolving lineages ( >5% of tested lineages ) in primary immune responses including HIV infection , meningococcus vaccination , primary but not secondary hepatitis B vaccination , and early childhood development ( Table 1 , Figure 2A , and Figure 2—figure supplement 1 ) . Chronic HIV infection stimulates ongoing affinity maturation as B cells evolve to contain viral escape mutants ( Liao et al . , 2013; Wendel et al . , 2020 ) . Consistent with this arms race , HIV infection was more enriched for measurably evolving lineages than other conditions surveyed , with each study having between 5 . 9% and 53% of lineages measurably evolving ( Figure 2A ) . Lineages from subjects with broadly neutralizing anti-HIV lineages sampled over multiple years ( Doria-Rose et al . , 2014; Landais et al . , 2017; Liao et al . , 2013; Wu et al . , 2015 ) were particularly enriched ( 26–53% measurably evolving ) . Importantly , the HIV studies included were sampled over longer time periods than studies of other conditions ( mean = 225 vs 45 weeks , Table 1 ) . To determine whether these results were simply due to longer sampling intervals , we repeated our analysis of subjects with HIV using only samples within the first 60 weeks of the study . These truncated datasets were still highly enriched for measurably evolving lineages ( 6 . 9–64% ) compared to other non-HIV datasets with similar sampling intervals ( 0–7 . 2% , Figure 2A ) . This indicates that the observed high frequency of measurably evolving lineages is not simply due to long sampling intervals . Other primary immune responses were also enriched for measurably evolving lineages ( Table 1 , Figure 2A ) . B cell lineages from healthy children sampled during the first 3 years of life were enriched for measurable evolution ( 14% ) , possibly reflecting continual exposure to novel antigens ( Nielsen et al . , 2019 ) . We also observed an enrichment of measurably evolving lineages following primary meningococcus vaccination ( 10%; Galson et al . , 2015a ) and primary but not secondary hepatitis B vaccination ( 7 . 2% vs 2 . 9% , respectively; Galson et al . , 2016; Galson et al . , 2015b ) . Primary hepatitis B vaccinees were sampled over a longer time period than secondary vaccines , so this difference may also be due to different sampling intervals ( Figure 2—figure supplement 2 ) . Further , allergen-specific immunotherapy , which stimulates tolerance of allergy-causing antigens through exposure , was also enriched for measurable evolution ( 6 . 5%; Levin et al . , 2016 ) . Interestingly , Ebola virus infection showed a borderline ( 5% ) percentage of measurably evolving lineages ( Table 1 ) despite likely being a primary infection . Overall , however , these results confirm that the date randomization test can detect ongoing SHM in empirical datasets where it is expected to be occurring . We next investigated whether measurable evolution was associated with antigen-binding lineages . While antigen-binding information was not available for most B cell lineages surveyed , some studies included experimentally validated monoclonal antibody sequences ( mAbs ) . Lineages containing these sequences thus contain B cells that bind to the antigen under study . Experimentally validated mAbs were included from six studies: four in HIV ( Doria-Rose et al . , 2014; Landais et al . , 2017; Liao et al . , 2013; Wu et al . , 2015 ) , one in Ebola virus infection ( Davis et al . , 2019 ) , and one in influenza vaccine response ( Turner et al . , 2020 ) . We found that across these studies measurably evolving lineages were more likely to contain mAbs than nonmeasurably evolving lineages ( p = 0 . 031 , Wilcoxon test , Figure 2—figure supplement 3 ) . This is consistent with the hypothesis that measurably evolving lineages are actively responding to antigens relevant to the condition being studied . Seasonal influenza vaccination is believed to trigger a memory B cell response in adults . If memory B cells rarely re-enter GCs to undergo additional affinity maturation ( Mesin et al . , 2020 ) , and there is little evolution of naive B cell lineages , we expect little measurable evolution in the blood following vaccination . To test this , we applied the date randomization test to three longitudinally sampled adult influenza vaccine datasets . The first comprised three adults sampled seven times between 1 hr and 28 days postvaccination ( Gupta et al . , 2017; Laserson et al . , 2014 ) ; the second contained eight adults sampled five times between 0 and 90 days postvaccination ( Ellebedy et al . , 2016 ) the third used blood samples from a single individual sampled five times between 0 and 60 days postvaccination ( Turner et al . , 2020 ) . Across subjects in each study , between only 2 . 9% and 5 . 2% of lineages were measurably evolving ( Table 1 ) . These values are approximately as expected under the null hypothesis of no measurable evolution , and histograms of p values from these datasets are roughly uniform , suggesting the measurably evolving lineages identified are mostly false positives from multiple testing ( Figure 2—figure supplement 1 ) . Distributions of p values for all datasets are also available in Figure 2—figure supplement 1 . To verify the 4- to 13-week sampling range of these studies was sufficient to detect measurable evolution , we performed simulation analyses replicating the sampling strategy of the influenza dataset with the shortest sampling range ( Figure 1—figure supplement 4; Laserson et al . , 2014 ) . These simulations show this timescale was sufficiently long to detect ongoing affinity maturation with high sensitivity ( >90% , Figure 1H ) . Overall , these results indicate B cell lineages present in blood infrequently undergo additional evolution within 13 weeks following influenza vaccination , consistent with a primarily GC-independent memory B cell response and/or rarity of antigen-specific lineages in the peripheral blood ( Wrammert et al . , 2008 ) . While measurably evolving lineages do not occur at high frequency in the blood following influenza vaccination , we checked if any could be identified after adjustment for multiple testing . To adjust for multiple hypothesis tests , we pooled lineages across all studies and adjusted their p values using the Benjamini–Hochberg procedure ( Benjamini and Hochberg , 1995 ) . We identified 15 lineages in influenza datasets , and 354 lineages in other conditions , with adjusted date randomization p values < 0 . 1 . We investigated if these ‘adjusted’ measurably evolving lineages were derived from naive or pre-existing memory B cells . Because memory B cell lineages have already undergone affinity maturation , they are expected to have higher initial SHM levels compared to naive B cell lineages . To test this , we compared germline sequence divergence in adjusted measurably evolving lineages from influenza vaccination to other conditions . Consistent with memory B cell reactivation , lineages from influenza vaccination had significantly higher initial divergence ( median = 8 . 6% ) than those from primary responses such as early HIV infection ( median = 5% , p = 0 . 0012 ) and primary hepatitis B vaccination ( median = 2 . 8% , p = 0 . 0019 ) ( Figure 2B ) . Further , these influenza lineages had initial divergence levels similar to lineages from subjects with HIV first sampled >5 years after infection ( Huang et al . , 2016; Wu et al . , 2015 ) , and hepatitis B booster vaccination subjects ( Figure 2B; Galson et al . , 2015b ) . Ebola virus infection , meningococcus vaccination , and early childhood development had median initial divergence levels of 0 . 4% , 6 . 6% , and 2 . 0% , respectively , but contained less than three adjusted measurably evolving lineages each . To understand the effect of multiple hypothesis correction on these results , we repeated the comparisons in Figure 2B using all measurably evolving lineages ( unadjusted p < 0 . 05 ) from the same datasets . Considering this larger set of lineages , initial divergence of lineages from influenza vaccination studies was significantly higher than those in all other conditions except late HIV infection ( Figure 2—figure supplement 4 ) . The same pattern from Figure 2B was also found when repeating these comparisons with a more strict cutoff ( adjusted p < 0 . 05 , Figure 2—figure supplement 4 ) . Overall , these results are consistent with measurably evolving lineages from influenza vaccination arising mainly from pre-existing memory B cells . We next investigated the type and degree of selection operating on measurably evolving B cell lineages . One way to detect natural selection in DNA sequences is to estimate the ratio of nonsynonymous ( amino acid replacement ) to synonymous ( silent ) mutation rates . This ratio is often called ω ( Nielsen and Yang , 1998 ) . Neutral evolution , where amino acid replacements are not selected for or against , should result in ω = 1 . Purifying selection , where amino acid replacements are disfavored , should result in ω < 1 . Diversifying selection , where amino acid replacements are favored , should result in ω > 1 . In B cell lineages , ω is often estimated separately for complementarity-determining regions ( CDRs ) involved in antigen binding , and framework regions ( FWRs ) , which are more structural . Further , it is important to estimate ω or similar metrics using models that account for intrinsic hot- and cold-spot biases of SHM ( Hoehn et al . , 2017; Uduman et al . , 2011; Yaari et al . , 2012 ) . To understand what kind of selection operated on measurably evolving lineages , we estimated separate ω values for CDR and FWR regions ( ωCDR and ωFWR ) of the adjusted measurably evolving lineages ( Figure 2B ) using the HLP19 model in IgPhyML ( Hoehn et al . , 2019 ) . Model parameters were shared among lineages within the same subject , and only subjects with at least two adjusted measurably evolving lineage were included to reduce noise . Across all conditions surveyed , we found evidence of purifying selection operating on adjusted measurably evolving lineages ( mean ωCDR = 0 . 58 , mean ωFWR = 0 . 48 , Table 2 ) . We estimated the significance of these results using a phylogenetic likelihood ratio test ( Huelsenbeck and Rannala , 1997 ) . We found that ωCDR was significantly <1 in 10/13 subjects ( significantly >1 in none ) and ωFWR was significantly <1 in 13/13 subjects ( Table 2 ) . This signal of purifying selection was particularly strong in both early and late HIV . Influenza vaccination showed higher ω values , comparable to primary hepatitis B vaccination . While we found little measurable evolution in the blood following seasonal influenza vaccination , influenza vaccination has been shown to stimulate both naive and memory B cells to enter GCs ( Turner et al . , 2020 ) . This raises the possibility that additional affinity maturation could be occurring in GCs , but its products are not enriched in the blood . Data from Turner et al . , 2020 provided both blood samples and fine-needle aspirations of lymph nodes ( including GCs ) from the same subject . By combining these samples , we identified 53 powered B cell lineages containing at least one GC B cell following influenza vaccination , and 100 powered lineages that contained none . We refer to lineages containing one or more GC B cells as ‘GC-associated’ . To determine whether GC-associated lineages were undergoing additional SHM , we tested whether they were enriched for measurable evolution . We found that 7 . 5% of lineages containing sequences from GC B cells were measurably evolving , compared to only 3 . 0% of lineages with no identified GC sequences . This signal of measurable evolution increased with the fraction of GC sequences . For instance , while 10% of lineages containing ≥10% GC sequences were measurably evolving , 38% ( 3/8 ) of those with ≥25% GC sequences were measurably evolving ( Figure 3A ) . Lineages with higher proportions of GC sequences also had a higher correlation between divergence and time ( linear regression slope = 1 . 1 , p = 8 . 9 × 10−13 , Figure 3—figure supplement 1 ) . We further estimated the significance of this positive relationship by bootstrapping our data using 10 , 000 resampling repetitions with replacement . We found that in all 10 , 000 resampling repetitions , the slope of the linear regression between GC sequence proportion and the correlation between divergence and time was positive , with 95% of repetitions having a slope between 0 . 81 and 1 . 3 ( Figure 3—figure supplement 1 ) . Measurably evolving lineages in this dataset did not contain significantly more sequences than other lineages , indicating these results were not significantly confounded by lineage size ( Figure 3—figure supplement 2 ) . Finally , the measurably evolving lineages with the highest proportion of GC sequences contained mAbs that bound to vaccine antigens ( Figure 3B , C ) . These lineages show signs of origin from memory B cells , such as clonal relatedness to blood plasmablasts sampled 5 days postvaccination , and high mean germline divergence at their first sampled timepoint ( 6 . 3% , 7 . 2% , Figure 3B , C , respectively ) . To test whether GC-associated lineages accumulated new amino acid replacement mutations rather than just silent mutations , we repeated the date randomization test but calculated the divergence of each tip as the number of amino acid differences between that tip’s sequence and the unmutated germline ancestor . This amino acid-based correlation analysis also showed a strong positive relationship between the proportion of lineages that were measurably evolving and the percentage of sequences derived from GC B cells ( Figure 3A ) . This indicates that these GC-associated lineages accumulated new amino acid mutations as well as nucleotide mutations over the study interval . Overall , these analyses demonstrate that influenza-binding , GC-associated B cell lineages undergo additional , measurable evolution following vaccination . A possible alternative explanation for measurable evolution following influenza vaccination is that SHM is not occurring over the sampled time interval , but that highly mutated B cells were preferentially recalled due to their higher binding affinity . Preferential recall of highly mutated B cells would likely result in a positive correlation between divergence and sample time . While difficult to directly test , we believe this explanation is unlikely to be the sole source of measurable evolution in our data . First , blood samples taken 5 days postvaccination represent the breadth of the pool of memory B cells . If measurable evolution was simply due to expansion of mutated memory B cells , we would expect divergence of later-sampled B cells to be within the range of day 5 plasmablasts . Instead , many later-sampled GC sequences are clearly more diverged than earlier-sampled sequences within the measurably evolving , influenza-binding lineages we observed ( Figure 3B , C ) . Second , if measurable evolution were due simply to preferential expansion of more mutated B cells , we would expect to observe measurable evolution within influenza-binding lineages in both the blood and GC . This is not tested in Figure 3A because that analysis includes all lineages , not just those that bind to influenza . To adjust for this , we repeated the analysis in Figure 3A while only including lineages that contained influenza-binding mAbs . Even among influenza-binding lineages , we still observed an association between GC cells and measurable evolution . While 1/10 of influenza-binding lineages found only in the blood were measurably evolving , 2/5 lineages with >25% GC sequences were measurably evolving ( Figure 3—figure supplement 3 ) . Overall , while we cannot rule out preferential expansion of highly mutated memory B cells , these results are more easily interpretable as the result of ongoing SHM in GCs .
The extent to which seasonal influenza vaccination stimulates affinity maturation against vaccine antigens is unclear , and poor efficacy of seasonal influenza virus vaccination is often attributed to stimulation of pre-existing memory B cells interfering with novel responses to vaccine antigens ( Ellebedy , 2018 ) . While a prior study has shown that influenza-binding B cell lineages are found in GCs following seasonal influenza vaccination ( Turner et al . , 2020 ) , other work has suggested that circulating influenza-binding B cell lineages do not accumulate additional SHM following vaccination ( Ellebedy et al . , 2016 ) . To determine whether seasonal influenza vaccination stimulates additional evolution in B cell lineages , we developed and validated a framework to detect measurable evolution using longitudinally sampled BCR sequencing data . This phylogenetic test can be a powerful tool to detect ongoing B cell evolution using longitudinally sampled BCR datasets across a wide array of immunological conditions , including influenza virus vaccine responses . Our results confirm prior findings that there is little evidence of B cell evolution among lineages sampled in the peripheral blood following seasonal influenza vaccination ( Ellebedy et al . , 2016 ) . However , we also show that seasonal influenza vaccination is capable of stimulating measurable evolution in influenza-binding , GC-associated B cell lineages . To place our analyses of the influenza vaccination response in a broader context , we surveyed measurable evolution across a broad range of infections and vaccinations . Prior work has shown that chronic HIV infection induces long-term affinity maturation of broadly neutralizing antibody lineages in response to viral escape mutants ( Liao et al . , 2013; Vieira et al . , 2018; Wu et al . , 2015 ) . Our results show that HIV infection is associated with an exceptionally strong signature of B cell evolution over time . This signature is not limited to single lineages . Rather , a substantial fraction of longitudinally sampled B cell lineages within the repertoires of subjects with HIV are measurably evolving , consistent with clonal competition among B cell lineages during HIV infection ( Nourmohammad et al . , 2019 ) . Early childhood development during the first 3 years of life ( Nielsen et al . , 2019 ) showed the second-highest enrichment of measurably evolving lineages among surveyed conditions . This possibly reflects continual exposure to novel antigens during childhood . Further , primary vaccinations ( meningococcus , primary hepatitis B ) ( Galson et al . , 2016; Galson et al . , 2015a ) showed stronger signatures of measurable evolution than secondary vaccinations ( adult seasonal influenza , hepatitis B booster ) ( Ellebedy et al . , 2016; Galson et al . , 2015b; Laserson et al . , 2014; Turner et al . , 2020 ) . Overall , our results are consistent with the hypothesis that GC responses are stronger in response to novel antigens . In addition to detecting measurable evolution , we also characterized selection operating on measurably evolving B cell lineages . We found that measurably evolving lineages showed evidence of purifying selection ( ωCDR < 1 ) ( Table 2 ) . Though perhaps counterintuitive , a strong signal of purifying selection is a straightforward prediction of evolution toward an adaptive peak ( Hoehn et al . , 2019 ) . Similar evidence of purifying selection during affinity maturation has been observed in other studies including influenza vaccination , HIV infection , and healthy controls ( Cizmeci et al . , 2021; Hoehn et al . , 2019; Sheng et al . , 2016; Yaari et al . , 2015 ) . Importantly , ω estimates are an average across all codons within CDRs or FWRs . It is possible that positive selection operated on a small number of codon sites , but that this signal was outweighed by the larger number of sites under purifying selection . Codon-specific models may be useful in future analyses to identify these sites under positive selection ( e . g . , Yang et al . , 2000 ) . When interpreting these results , it is also important to note that parameters were estimated using all mutations represented by each lineage tree , including those that potentially occurred before the first sampled timepoint . In all , these results are consistent with typical forces of selection having operated on measurably evolving lineages . There are several limitations to this study . Data from different studies were sampled according to different schedules and time intervals . Because the power to detect measurable evolution should increase over time ( Figure 1G ) , this could confound comparisons among datasets . We note however that multiple influenza and HIV datasets were surveyed , and enrichment of measurable evolution within these conditions was not strongly related to the sampling range ( Figure 2A ) . This suggests immunological condition , rather than sample range , was the primary determinant of observed differences . By including monoclonal antibody ( mAb ) sequences with experimentally validated binding in several datasets , we were able to show that measurably evolving lineages are more likely than nonmeasurably evolving lineages to contain mAb sequences . This is consistent with the idea that measurably evolving lineages are actively responding to antigen . These results should be interpreted cautiously , however . With the exception of Turner et al . , 2020 only a small number mAb sequences were found in the lineages analyzed ( mean = 3 . 6 per study ) . Further , lineages containing GC sequences were preferentially selected for mAb generation in Turner et al . , 2020 , which may artificially increase the likelihood that measurably evolving lineages contain mAbs . While intriguing and biologically plausible , conclusively determining whether measurable evolution predicts antigen binding is beyond the scope of this study . Another limitation is that maximum parsimony was used to estimate lineage tree topologies and branch lengths . While more sophisticated methods are available for inferring B cell lineage trees ( e . g . , Hoehn et al . , 2019 ) , maximum parsimony often has competitive performance for topology estimation ( Davidsen and Matsen , 2018 ) and is faster than more complex maximum likelihood models designed for B cell lineages . Computational efficiency was particularly important as our analyses required constructing more than 20 , 000 lineages trees spanning approximately 1 , 100 , 000 BCR sequences . Finally , our analysis of GC-associated B cell lineages was limited to data sampled from a single subject . Thus , while the results here demonstrate that influenza vaccination is capable of inducing measurable evolution , it remains unclear whether this is a general feature of influenza vaccination . Our analyses of measurable evolution involved a series of hypothesis tests , and the definition for ‘enrichment’ of measurable evolution ( >5% of lineages ) was chosen based on the expected false positive rate under the null hypothesis . This enrichment measure was chosen to compare the relative frequency of measurably evolving lineages among datasets . A lack of enrichment does not indicate a complete lack of measurably evolving lineages . Conversely , slight enrichment of measurably evolving lineages ( ~5% ) should not be interpreted as proof of ongoing affinity maturation in a set of lineages . The analysis in Figure 2A was not intended to test the null hypothesis that no lineages are measurably evolving in a particular dataset . Because multiple studies were surveyed ( Table 1 ) , it is possible our results contain false positives . Indeed , while some conditions such as HIV have a strong signal of measurable evolution across multiple studies , others such as naive hepatitis B vaccination and allergen-specific immunotherapy are just above the significance threshold ( ≤7 . 2% lineages with p < 0 . 05 ) and are each represented by a single study ( Figure 2A , Table 1 ) . These latter datasets should be interpreted cautiously and with the understanding that the vast majority of their lineages were not measurably evolving . To limit the influence of false positives , a multiple testing correction ( false discovery rate < 0 . 1 ) was performed in analyses investigating the properties of measurably evolving lineages ( Figure 2B , Table 2 ) . We note , however , that repeating our analyses of initial germline divergence using all measurably evolving lineages ( unadjusted p < 0 . 05 ) or a more strict p value cutoff ( adjusted p < 0 . 05 ) yielded similar results ( Figure 2—figure supplement 4 ) , indicating our results are robust to the thresholds used . Finally , we validated the specificity of the date randomization test in empirical data ( Figure 1—figure supplement 2 ) . While a significant decrease in divergence over time is biologically unlikely , false positives due to multiple testing or sequencing error should produce a similar number of lineages with a significant correlation in either direction . We quantified correlation in either direction using a two-tailed version of the clustered , resolved date randomization test with a critical value of 0 . 025 ( see Methods ) . Under a null hypothesis of no ongoing evolution , 2 . 5% of lineages were expected to have a significant negative correlation between divergence and time ( all false positives ) . However , we found such ‘negatively evolving’ lineages at a mean frequency of only 1 . 2% ( median = 1 . 3% , range = 0–2 . 8% ) across all datasets when using clustered permutations and resolved polytomies ( Figure 1—figure supplement 2 ) . This indicates the date randomization test we used is conservative , and that our chosen thresholds are likely more strict than necessary . Beyond multiple testing , there are some biological scenarios that could plausibly give rise to a signal of measurable evolution without additional SHM occurring during the sampling interval . For instance , it is possible that all SHM within a lineage occurred before the first sampled timepoint in a study , but that more mutated , higher-affinity BCRs were preferentially stimulated and sampled in later timepoints . Such a scenario would likely result in a positive correlation between divergence and sample time . However , our results are easier to explain if measurable B cell evolution results at least in part from ongoing SHM . For instance , our analysis of measurably evolving lineages following influenza vaccination showed ( 1 ) many later-sampled GC B cells had higher divergence than any sampled day 5 plasmablast sequence in the same lineage and ( 2 ) continued association between GC cells ( rather than the blood ) and measurable evolution even among influenza-binding lineages . Nonetheless , it is still theoretically possible that more mutated B cells were not sampled at early timepoints , and were preferentially expanded in GCs compared to the peripheral blood . Thus , while our interpretation is that measurable B cell evolution most likely represents an ongoing SHM process , we cannot conclusively rule out biased selection of more mutated sequences that were generated before the sampling interval . Affinity maturation is a rapid evolutionary process . It is perhaps surprising that , while we identified conditions enriched for measurably evolving lineages , most lineages in circulation were not measurably evolving ( Table 1 ) . One explanation is that our analyses did not use sufficiently long sampling intervals to detect affinity maturation , though we believe this is unlikely . Studies in mice have estimated that SHM occurs at ~10−3 SHM/bp/division ( Kleinstein et al . , 2003; McKean et al . , 1984 ) , and that GC B cells cycle every 6–12 hr ( Allen et al . , 2007; Hauser et al . , 2007; Victora and Nussenzweig , 2012 ) . Simulations using conservative assumptions ( strong selection , 24-hr cell cycle ) and replicating the sample structure of our shortest-term influenza dataset ( 4 weeks ) , showed high power with >90% true positive rate ( Figure 1H ) . Further , we found an enrichment of blood-derived measurably evolving lineages after only 4 weeks in one study ( Galson et al . , 2015a ) , and after 8 weeks corresponding to a known context of affinity maturation ( GC entry , Figure 3 ) . Overall , these results show that the sample times of our surveyed datasets should be sufficient to detect ongoing B cell evolution if it were occurring . However , it is possible that lineages may not remain in GCs continuously , which would slow the rate of evolution compared to our simulations . A more plausible explanation for the lack of measurable evolution is that most lineages in the blood are either nonspecific to the condition being studied , or derive from a GC-independent response ( Mesin et al . , 2020; Takemori et al . , 2014; Taylor et al . , 2012 ) . It is also possible that lineages relevant to the condition being studied are inefficiently stimulated . We find that seasonal influenza virus vaccination in young adults induces a GC reaction where maturation of vaccine-specific B cell lineages occurs , including those likely recruited from the pre-existing memory B cell compartment . These results imply that poor efficacy of seasonal influenza vaccination does not result from a complete lack of vaccine-induced B cell evolution . While we showed that B cells in these evolving lineages increased in amino acid replacement mutation frequency , it remains possible that this evolution is less able to select affinity-increasing mutations ( Hoehn et al . , 2019 ) , that the overall number of evolving lineages is reduced , or that the products of this vaccine-induced evolution are not efficiently translated into memory and long-lived plasma cells . These latter two explanations are consistent with the results of our survey of longitudinally sampled peripheral blood datasets , which found an enrichment of measurably evolving lineages in some primary immune response conditions , but not influenza vaccination . Future studies will be needed to fully test these hypotheses about the causes of poor efficacy of seasonal influenza vaccination .
The goal of this study was to determine whether B cell lineages found in GCs following influenza vaccination evolved over a given sample interval . This necessitated describing and validating a test for measurable evolution from longitudinally sampled BCR sequencing data . Simulation-based power analyses determined that this date randomization test has sufficient sensitivity to detect evolving B cell populations over a sampling interval of approximately 2 weeks . To determine whether the date randomization test also worked on known examples of affinity maturation , all longitudinally sampled datasets hosted on OAS ( as of 6/2020 ) were downloaded and tested . To cover as wide a variety of conditions as possible , these datasets were supplemented with processed , publicly available datasets from other prior studies . To ensure datasets were appropriately powered , datasets were only included if they contained at least 10 B cell lineages with at least 15 sequences sampled over 3 weeks and a minimum possible date randomization test p value <0 . 05 . BCR data from blood and fine-needle aspirations following influenza vaccination were obtained from Turner et al . , 2020 . All longitudinally sampled BCR repertoire datasets were publicly available and obtained both from primary publications and through the OAS database ( antibodymap . org , accessed 6/2020; Kovaltsuk et al . , 2018 ) . Both assembled nucleotide sequences and deduplicated amino acid sequences were obtained from OAS . To reduce the effect of sequencing error in OAS datasets , only nucleotide sequences corresponding to an amino acid sequence with a multiplicity of at least two were included . Datasets obtained from OAS are labeled in Table 1 . Raw sequence data obtained from Nielsen et al . , 2019 were preprocessed with pRESTO v0 . 5 . 13 ( Vander Heiden et al . , 2014 ) . Quality control was performed by first removing all sequences with a Phred quality score <20 , length <300 bp , or any missing ( ‘N’ ) nucleotides . The 3′ and 5′ ends of each read were matched to forward and constant region primers with a maximum error rate of 0 . 1 . The region adjacent to the constant region primer was exactly matched to subisotype-specific internal constant region sequences . Only sequences with the same isotype predicted by their constant region primer and internal constant region sequence were retained . Identical reads within the same isotype were collapsed and sequences observed only once were discarded . All other datasets used processed BCR sequence data provided by the authors of their respective publications . Data from Wang et al . , 2014 were processed in Hoehn et al . , 2019 . Data from Jiang et al . , 2020b used only blood samples . Datasets were processed using the Immcantation framework ( immcantation . org ) . V ( D ) J gene assignment on data obtained from Nielsen et al . , 2019 was performed using IgBLAST v1 . 13 ( Ye et al . , 2013 ) against the IMGT human germline reference database ( Giudicelli et al . , 2005 ) ( IMGT/GENE-DB v3 . 1 . 24; retrieved August 3 , 2019 ) . V ( D ) J gene assignments and clonal cluster assignments were already available in all other non-OAS datasets and were retained . Nonproductively rearranged sequences were excluded . Using Change-O v1 . 0 . 0 ( Gupta et al . , 2015 ) , the V and J genes of unmutated germline ancestors for each sequence were constructed with D segment and N/P regions masked by ‘N’ nucleotides . Sequence chimeras were filtered by removing any sequence with more than six mutations in any 10 nucleotide window . Individual immunoglobulin genotypes were computationally inferred using TIgGER v1 . 0 . 0 and used to finalize V ( D ) J annotations ( Gadala-Maria et al . , 2015 ) . To infer clonal clusters , sequences were first partitioned based on common V and J gene annotations , and junction region length . Within these groups , sequences differing from one another by a specified Hamming distance threshold within the junction region were clustered into clones using single linkage hierarchical clustering ( Gupta et al . , 2017 ) . The Hamming distance threshold was determined by finding the local minimum of a bimodal distance to nearest sequence neighbor plot using SHazaM v1 . 0 . 2 . 999 ( Yaari et al . , 2013 ) . In cases where automated threshold detection failed , usually because the distance to nearest neighbor distribution was not bimodal , the threshold was set to 0 . 1 and verified by manual inspection to ensure that a threshold of 0 . 1 was near a local minimum . Finally , the V and J genes of unmutated germline ancestors for each clone were constructed . Within these unmutated ancestral sequences , D segments and N/P regions were masked using ambiguous ‘N’ nucleotides . Testing for measurable evolution begins with building B cell lineage trees . Within each B cell clone , identical sequences or those differing only by ambiguous nucleotides were collapsed unless they were sampled at different timepoints . To reduce computational complexity , lineages were randomly down-sampled to at most 500 sequences each . B cell lineage tree topologies and branch lengths were estimated using maximum parsimony using the pratchet function of the R package phangorn v2 . 5 . 5 ( Schliep , 2011 ) . R packages dowser v0 . 0 . 3 ( Hoehn et al . , 2020 ) , alakazam v1 . 0 . 2 . 999 ( Gupta et al . , 2015 ) , and ape v5 . 4-1 ( Paradis et al . , 2004 ) were used for phylogenetic analysis . Trees were visualized using ggtree v2 . 4 . 2 ( Yu et al . , 2016 ) , and other figures were generated using ggplot2 v3 . 3 . 5 ( Wickham , 2016 ) and ggpubr v0 . 4 . 0 ( Kassambara , 2020 ) . R v3 . 6 . 1 ( R Development Core Team , 2017 ) was used for analysis of measurable evolution except for data from Davis et al . , 2019 . Due to technical upgrades , figure generation and selection analysis were performed using R v4 . 0 . 3 , as well as ape v5 . 5 , phangorn v2 . 7 . 1 , shazam v1 . 1 . 0 , alakazam v1 . 1 . 0 , and dowser v0 . 1 . 0 . Data from Davis et al . , 2019 were also analyzed using these updated packages . To test for measurable evolution over time , we use a modified version of the previously described phylogenetic date randomization test ( Duchêne et al . , 2015; Murray et al . , 2016 ) implemented in dowser v0 . 0 . 3 ( Hoehn et al . , 2020 ) . Briefly , for a given tree the divergence of each tip was calculated as the sum of branch lengths leading to the tree’s most recent common ancestor ( MRCA ) . Only branches directly between a tip and the tree’s most recent common ancestor were used to calculate divergence . We next calculated the Pearson’s correlation between the divergence and sampling time of each tip . Measurably evolving lineages should show a positive correlation between divergence and time ( Figure 1A ) . Divergence from the lineage’s predicted unmutated ancestral sequence rather than the MRCA could also be used . Because all sequences relate to the unmutated ancestral sequence through the MRCA node , this would add a constant additional divergence to all sequences , resulting in the same correlation as when the MRCA is used . We next identified monophyletic clades containing only sequences from a single timepoint ( here referred to as ‘clusters’ ) . We then randomly permuted sampling times among clusters , such that all sequences within each cluster had the same , randomly chosen timepoint . We next measured the correlation between divergence and time in this randomized tree , and repeated the process 100 , 000 times . We then estimated the p value that the observed correlation between divergence and time was no greater than expected from random distribution of times among clusters . This p value was calculated as the proportion of permutation replicates that had an equal or higher correlation than in the observed tree . We used a pseudocount of one for this calculation . The minimum possible p value for a lineage was calculated as one divided by possible number of distinct cluster permutations . We modified the date randomization test to account for the high degree of topological uncertainty of many B cell lineage trees . More specifically , B cell lineage trees often contain large clusters of zero-length branches ( soft polytomies ) that represent high uncertainty in branching order ( e . g . , Figure 1—figure supplement 1 ) . In bulk BCR data , these polytomies may be due to PCR error or sequencing error . If polytomies are resolved randomly into bifurcations , this can produce more single-timepoint monophyletic clades than necessary and lead to a high false positive rate of the date randomization test ( Figure 1—figure supplements 1 and 2 ) . To ensure this source of uncertainty did not increase the false positive rate of our analyses , we resolved bifurcations within each polytomy such that sequences from the same timepoint were grouped into the fewest possible number of single-timepoint monophyletic clades before performing permutations . While we do not have direct evidence that polytomies in B cell lineages trees are produced from PCR error , the fact that resolving them reduces the rate of lineages with a significant negative correlation between divergence and time ( a biologically implausible result , Figure 1—figure supplement 2 ) suggests they are at least in part due to technical artifacts . The clustered date randomization approach is more conservative than tests that permute tips uniformly ( e . g . , Unterman et al . , 2020 ) , but has been shown to be less biased if different subpopulations are sampled at each timepoint ( Murray et al . , 2016 ) . To explore the effect of this modeling choice , we repeated the analyses in Table 1 using two-tailed clustered and uniform date randomization tests ( Figure 1—figure supplement 2 ) . Two-tailed tests can identify lineages with a significant positive or negative correlation between divergence and time . This is useful because a significant negative correlation between divergence and time is biologically implausible and represents a likely false positive result . Due to multiple testing under an alpha value of 0 . 025 , we expect no more than 2 . 5% of lineages to have a significant negative correlation from these two-tailed tests . We found the uniform permutation test had a high rate of negatively evolving lineages ( mean = 8 . 3% ) , indicating a high false positive rate . By contrast , the clustered permutation test without resolved polytomies had a mean rate of only 2 . 2% negatively evolving lineages , approximately as expected given an alpha value of 0 . 025 . Resolving polytomies and then performing the clustered permutation test improved performance even more , with a mean rate of 1 . 2% negatively evolving lineages and no dataset having more than 2 . 8% of lineages negatively evolving . This analysis shows the uniform date randomization test is prone to false positives in empirical B cell data , while the clustered date randomization test with resolved polytomies corrects this issue . All other tests performed in this study used a one-tailed , clustered date randomization test with resolved polytomies and an alpha value of 0 . 05 . To identify and characterize measurably evolving lineages while adjusting for multiple testing , all lineages tested were pooled together and p values were adjusted using the Benjamini–Hochberg procedure ( Benjamini and Hochberg , 1995 ) implemented in the function p . adjust ( R Development Core Team , 2017 ) . Lineages with adjusted p values <0 . 1 were referred to as adjusted measurably evolving lineages ( Figure 2B ) . To determine whether lineages were measurably increasing in amino acid divergence ( Figure 3A ) , we repeated the clustered date randomization test for each tree . However , instead of calculating divergence as the sum of phylogenetic branch lengths leading from each tip to the most recent common ancestor of the lineage , we calculated divergence as the number of nonambiguous amino acid differences between each tip and the lineage’s clonal germline . The clustered permutation test then proceeded as before , using the same cluster assignments as in the nucleotide-based test . This tested whether sequences at later timepoints had more amino acid substitutions compared to the germline than sequences at earlier timepoints . It is possible that the results reported are affected by the size ( number of sequences ) of lineages in each dataset . A large number of lineages without adequate power could result in a spurious lack of measurable evolution . To ensure the lineages included in each study were adequately powered , we included only lineages with at least 15 sequences , that were sampled over at least 3 weeks , and had a minimum possible p value <0 . 05 based on the number of distinct permutations of timepoints among clusters . If measurable evolution were still strongly confounded by lineage size even after these filtering steps , we would expect measurably evolving lineages to be larger on average than nonmeasurably evolving lineages . By contrast , measurably evolving lineages were significantly larger than nonmeasurably evolving lineages in only 5/21 datasets surveyed ( Figure 3—figure supplement 2 ) , indicating our results are not strongly confounded by lineage size . To identify B cell lineages that likely bind to the antigen under study , we included experimentally validated monoclonal antibody ( mAb ) heavy chain sequences provided from multiple studies . This included multiple anti-HIV mAbs: 11 from Liao et al . , 2013 , 12 from Doria-Rose et al . , 2014 , 7 from Johnson et al . , 2018 , 42 from Landais et al . , 2017 , 31 from Wu et al . , 2015 , and 4 from Huang et al . , 2016 . Doria-Rose et al . , 2014 and Wu et al . , 2015 also provided 680 and 1033 bulk BCR sequences , respectively , identified as clonally related to the provided anti-HIV broadly neutralizing mAbs . These sequences were also included in processing and clonal clustering but were not labeled as experimentally validated mAbs . Davis et al . , 2019 provided 885 mAb heavy chain sequences , some of which were tested for binding against Ebola virus proteins . All of these sequences were included in processing and clonal clustering , but only 368 validated by ELISA to bind to Ebola virus were labeled as EBV-binding mAbs . All of the above sequences were processed in the same manner as bulk sequences from OAS , except they were not filtered as potential PCR chimeras . Clonal lineages containing experimentally validated mAbs were labeled as antigen-binding; however , because sample timepoints were not always apparent , mAb sequences themselves were removed before lineage tree inference for the abovementioned studies . Processed data from Turner et al . , 2020 also included 196 anti-influenza mAbs . These sequences were retained during tree inference because they were explicitly labeled by timepoint and usually cloned from previously identified sequences within the data . Of all mAbs included , only the 58 clonally clustered within powered lineages ( at least 15 sequences sampled over 3 weeks , and minimum p value <0 . 05 ) were included in tests of mAb enrichment ( Figure 2—figure supplement 3 ) . We used simulations to determine whether the clustered date randomization test was sufficiently powered to detect ongoing B cell evolution . These analyses used the bcr-phylo package accessed 9/21/2020 ( Davidsen and Matsen , 2018; Ralph and Matsen , 2020 ) , which simulates clonal lineages of B cells undergoing affinity maturation against a target sequence . For all simulations , a random naive heavy chain sequence was chosen from those provided in bcr-phylo and the rate of SHM was set to the default of λ = 0 . 356 , which corresponds to an SHM rate of ~0 . 001 SHM/site/division ( Teng and Papavasiliou , 2007 ) . Mutations were introduced according to the S5F model ( Yaari et al . , 2013 ) . Selection strength was chosen to be either 0 ( neutral ) or 1 ( entirely affinity driven ) . A single target sequence was chosen for affinity maturation . All other parameters were set to their default . We performed two sets of simulations . In the first , we simulated single B cell lineages from which 50 cells were sampled at generation 10 , and 50 more cells were sampled after a specified number of additional generations ( Figure 1G , Figure 1—figure supplement 3 ) . In the second type of simulation , we replicated the sampling strategy of Laserson et al . , 2014 . Briefly , for each clone in subject hu420143 from Laserson et al . , 2014 , we simulated one lineage with the same number of cells ( if enough cells had been generated ) sampled after the number of generations corresponding to 1 , 3 , 7 , 14 , 21 , and 28 days ( Figure 1—figure supplement 4 ) . The number of generations corresponding to each sample day was calculated using a strict generation time of either 12 or 24 hr , which are conservative given previous GC cycle estimates of 6–12 hr ( Allen et al . , 2007; Hauser et al . , 2007; Victora and Nussenzweig , 2012 ) . These simulations used a selection strength of 1 , which gave more conservative results in previous simulations ( Figure 1—figure supplement 3 ) . To account for possible issues with clonal clustering , we did not preserve clonal identities among simulated sequences in either simulation type . Instead , we pooled sequences from all simulation repetitions under a particular parameter set and used the same clonal clustering method used for empirical data analyses to group them into clonal clusters . We did not repeat the genotyping or chimera filtering steps done on empirical data analyses as genotyped individuals and sequence chimeras were not part of the simulations . We performed the clustered date randomization test with resolved polytomies on each lineage with a minimum possible p value <0 . 05 . Because all sequences were simulated under affinity maturation , the proportion of lineages with p < 0 . 05 indicated the true positive rate of the test . To determine the false positive rate , we randomized sample times among tips within each tree and repeated the date randomization test ( Figure 1—figure supplements 3 and 4 ) . Here , the proportion with p < 0 . 05 indicated the false positive rate . To understand the force of selection operating on B cell lineages , we first separated all adjusted measurably evolving lineages into their respective subjects within each study . We then excluded all subjects with only one measurably evolving lineage . While all sequences included were labeled as productive by IgBlast , three contained premature stop codons in their IMGT-aligned sequences , likely due to insertions that were removed during alignment . These sequences were removed . For computational efficiency , all lineages were down-sampled to a maximum size of 100 sequences . Due to uncertainty in germline D-region assignment , only V-gene ( IMGT positions 1–312 ) nucleotides were included for analyses of selection , similar to Hoehn et al . , 2017 . We then estimated lineage tree topologies , branch lengths , and subject-wide substitution model parameters under the GY94 model ( Hoehn et al . , 2019; Nielsen and Yang , 1998 ) . Using fixed tree topologies estimated from the GY94 model , we then estimated branch lengths , subject-wide ω values for CDR and FWR partitions ( ωCDR and ωFWR ) , and all six canonical SHM hot- and cold-spot motif parameters under the HLP19 model in IgPhyML v1 . 1 . 3 ( Hoehn et al . , 2019 ) for all adjusted measurably evolving lineages . Significance of ω estimates was determined using two phylogenetic likelihood ratio tests , similar to Hoehn et al . , 2017 . To determine the significance of ωCDR estimates , we compared the maximum log-likelihood obtained when both ωCDR and ωFWR were estimated by maximum likelihood ( L ) to that obtained when ωFWR was estimated by maximum likelihood but ωCDR was fixed at 1 ( LCDR=1 ) . The likelihood ratio statistic ( LRS ) for this test was calculated as 2× ( L – LCDR=1 ) . Because these models differ by one freely estimated parameter , the LRS will be approximately chi-squared distributed with one degree of freedom under the null hypothesis that ωCDR = 1 , which allows for p value calculation ( Huelsenbeck and Rannala , 1997 ) . To determine significance of ωFWR estimates , the process is the same except LRS = 2× ( L – LFWR=1 ) , where LFWR=1 is the maximum log-likelihood obtained when ωCDR was estimated by maximum likelihood but ωFWR was fixed at 1 ( LFWR=1 ) . All of the above statistics are reported in Table 2 . All data are publically available from prior publications . Script to reproduce all analyses performed are available at https://bitbucket . org/kleinstein/projects . git ( Kleinstein Lab , 2021; copy archived at swh:1:rev:1ca83cda5d1baac880c71c314b0adc359314f6fa ) . | When the immune system encounters a disease-causing pathogen , it releases antibodies that can bind to specific regions of the bacterium or virus and help to clear the infection . These proteins are generated by B cells which , upon detecting the pathogen , can begin to mutate and alter the structure of the antibody they produce: the better the antibody is at binding to the pathogen , the more likely the B cell is to survive . This process of evolution produces B cells that make more effective antibodies . After the infection , some of these cells become ‘memory B cells’ which can be stimulated in to action when the pathogen invades again . Many vaccines also depend on this process to trigger the production of memory B cells that can fight off a specific disease-causing agent . However , it is unclear to what extent memory B cells that already exist are able to continue to evolve and modify their antibodies . This is particularly important for the flu vaccine , as the virus that causes influenza rapidly mutates . To provide high levels of protection , the memory B cells formed following the vaccine may therefore need to evolve to make different antibodies that recognize mutated forms of the virus . It is thought that the low effectiveness of the flu vaccine is partially because the response it triggers does not stimulate additional evolution of memory B cells . To test this theory , Hoehn et al . developed a computational method that can detect the evolution of B cells over time . The tool was applied to samples collected from the blood and lymph nodes ( organ where immune cells reside ) of people who recently received the flu vaccine . The results were then compared to B cells taken from people after different infections , vaccinations , and other conditions . Hoehn et al . found the degree to which B cells evolve varies significantly between conditions . For example , B cells produced during chronic HIV infections frequently evolved over time , while such evolution was rarely observed during the autoimmune disease myasthenia gravis . The analysis also showed that memory B cells produced by the flu vaccine were able to evolve if recruited to the lymph nodes , but this was rarely detected in B cells in the blood . These findings suggest the low efficacy of the flu vaccine is not due to a complete lack of B cell evolution , but likely due to other factors . For instance , it is possible the evolutionary process it stimulates is not as robust as in other conditions , or is less likely to produce long-lived B cells that release antibodies . More research is needed to explore these ideas and could lead to the development of more effective flu vaccines . | [
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] | 2021 | Human B cell lineages associated with germinal centers following influenza vaccination are measurably evolving |
Alterations in global mRNA decay broadly impact multiple stages of gene expression , although signals that connect these processes are incompletely defined . Here , we used tandem mass tag labeling coupled with mass spectrometry to reveal that changing the mRNA decay landscape , as frequently occurs during viral infection , results in subcellular redistribution of RNA binding proteins ( RBPs ) in human cells . Accelerating Xrn1-dependent mRNA decay through expression of a gammaherpesviral endonuclease drove nuclear translocation of many RBPs , including poly ( A ) tail-associated proteins . Conversely , cells lacking Xrn1 exhibited changes in the localization or abundance of numerous factors linked to mRNA turnover . Using these data , we uncovered a new role for relocalized cytoplasmic poly ( A ) binding protein in repressing recruitment of TATA binding protein and RNA polymerase II to promoters . Collectively , our results show that changes in cytoplasmic mRNA decay can directly impact protein localization , providing a mechanism to connect seemingly distal stages of gene expression .
mRNA decay is a critical stage of gene expression that regulates the abundance and lifespan of cellular mRNAs . Many viruses including alpha and gammaherpesviruses , influenza A virus , and SARS coronavirus accelerate host mRNA degradation through the use of viral proteins that trigger endonucleolytic cleavage of mRNAs in the cytoplasm . Each of these viral proteins bypasses the rate-limiting deadenylation step of the basal decay pathway , resulting in cleaved mRNAs that are rapidly degraded by the major cellular 5’−3’ exonuclease Xrn1 ( Covarrubias et al . , 2011; Gaglia et al . , 2012 ) . This process , termed ‘host shutoff’ , allows viruses to rapidly restrict cellular gene expression in order to blunt immune responses and liberate resources for viral replication ( Abernathy and Glaunsinger , 2015; Burgess and Mohr , 2015; Gaglia and Glaunsinger , 2010; Rivas et al . , 2016 ) . Viral endonucleases have also served as tools for deciphering how cells sense and respond to large changes in mRNA abundance . While mRNA decay is often considered the terminal stage of gene expression , the rate of mRNA decay has recently been shown to influence transcription by RNA polymerase II ( RNAPII ) in both yeast and mammalian cells ( Abernathy et al . , 2015; Braun and Young , 2014; Haimovich et al . , 2013; Sun et al . , 2013 ) . In yeast , a buffering system exists in which Xrn1 plays a major role in connecting mRNA synthesis and decay , presumably allowing cells to maintain an appropriate overall mRNA abundance . Mammalian cells also have a mechanism to sense mRNA levels , though the pathway appears to operate differently than in yeast . Here , accelerated cytoplasmic mRNA degradation does not lead to a compensatory increase in mRNA synthesis , as might be predicted by the homeostatic model , but instead decreases cellular RNAPII promoter recruitment , thereby amplifying the restrictive gene expression environment ( Abernathy et al . , 2015 ) . Significant transcriptional repression as measured by nascent mRNA production was reported to occur at approximately 9% of host genes , although validation experiments suggested this number is likely to be an underestimate ( Abernathy et al . , 2015 ) . The mRNA decay-transcription feedback pathway is activated in mammalian cells infected with gammaherpesviruses like Kaposi’s sarcoma-associated herpesvirus ( KSHV ) and murine gammaherpesvirus 68 ( MHV68 ) , as well as upon expression of virally encoded mRNA endonucleases in uninfected cells . Herpesviral endonucleases , including the muSOX protein of MHV68 , cleave mRNA but do not impact the abundance of noncoding RNAs transcribed by RNA polymerase I ( RNAPI ) or III ( RNAPIII ) ( Covarrubias et al . , 2011 ) . Correspondingly , muSOX-induced mRNA decay elicits a significant decrease in RNA polymerase II ( RNAPII ) recruitment to cellular promoters ( Abernathy et al . , 2015 ) . Notably , depletion of Xrn1 from muSOX-expressing cells prevents the ensuing RNAPII transcriptional repression . This suggests that the initial mRNA cleavage and translational inactivation are insufficient to restrict RNAPII recruitment , and that subsequent exonucleolytic degradation of the cleaved mRNA fragments is a critical signaling step . Little is currently known about this pathway linking cytoplasmic mRNA decay to RNAPII activity in mammalian cells , including the nature of the signal that is transmitted between the two cellular compartments . An attractive hypothesis is that one or more RNA binding proteins ( RBPs ) differentially traffics between the cytoplasm and the nucleus when basal rates of mRNA decay are perturbed , thereby conveying global mRNA abundance information . Recent analyses indicate that mammalian cells contain hundreds of RBPs that bind polyadenylated mature mRNAs , and proteins within this group have been shown to regulate all stages of gene expression ( Gerstberger et al . , 2014; Mitchell and Parker , 2014; Müller-McNicoll and Neugebauer , 2013; Singh et al . , 2015 ) . Furthermore , RBPs frequently display nucleocytoplasmic shuttling behavior . Here , we charted global alterations in protein localization that occur specifically in response to increased or decreased Xrn1 activity . This revealed a set of mammalian RBPs that preferentially move from the cytoplasm to the nucleus during accelerated mRNA decay , as well as components of the 5’−3’ decay machinery and other RBPs whose subcellular distribution is altered in cells lacking Xrn1 . Poly ( A ) tail associated proteins are overrepresented among the RBPs that accumulate in the nucleus under conditions of global mRNA decay , offering an explanation for how RNAPII could be selectively sensitive to mRNA abundance . Indeed , we uncovered a new role for cytoplasmic poly ( A ) binding protein ( PABPC ) in mediating mRNA decay-driven repression of RNAPII promoter recruitment . Furthermore , we show that the recruitment of TATA binding protein ( TBP ) to promoters is also impaired in response to PABPC nuclear translocation , indicating that cytoplasmic mRNA decay impacts early events in preinitiation complex assembly . Our results reveal how mRNA levels exert significant influence on RBP localization and suggest that select RBPs transmit mRNA abundance information from the cytoplasm to the nucleus to broadly influence gene expression , particularly under conditions of cellular stress .
To chart mRNA decay-driven movement of proteins between the cytoplasm and the nucleus , we used a quantitative liquid chromatography/tandem mass spectrometry ( LC/MS-MS ) -based approach . Specifically , following subcellular fractionation , proteins from nuclear and cytoplasmic fractions were labeled with isobaric tandem mass tags ( TMT ) . TMT labeling enables multiplexing of up to 11 samples per run and was proven to improve the analytical power for quantitation during viral infections ( McAlister et al . , 2012; Jean Beltran et al . , 2016 ) . We used HEK293T cells expressing the MHV68 muSOX endonuclease to create a condition of accelerated , Xrn1-dependent cytoplasmic mRNA decay . We previously demonstrated that muSOX expression in these cells activates the mRNA decay-RNAPII transcription feedback pathway similar to virally infected fibroblasts ( Abernathy et al . , 2015 ) . Pure populations of cells expressing either WT muSOX or the catalytically dead D219A muSOX point mutant were generated using Thy1 . 1-based cell sorting . Here , muSOX was fused to the cell surface glycoprotein Thy1 . 1 with an intervening self-cleaving 2A protease , causing release of Thy1 . 1 from muSOX for cell surface expression and selection . Three biological replicates of control , WT , and D219A muSOX expressing cells were then separated into nuclear and cytoplasmic fractions , and trypsin-digested proteins from each fraction were differentially TMT labeled prior to LC/MS-MS ( Figure 1A ) . Among the 5994 total quantifiable nuclear proteins ( detected in all replicates ) , 123 displayed significant nuclear enrichment ( adjusted P value of < 0 . 05 ) in WT muSOX expressing cells relative to the D219A mutant ( Figure 1B , Table S1-2 in Supplementary file 1 ) . We then removed from further analysis proteins that were simultaneously increased in the cytoplasm in muSOX expressing cells to remove proteins that increase in overall abundance , as well as proteins displaying significant differences between the D219A catalytic mutant and the empty vector control . These filtering steps yielded a final list of 67 proteins that were differentially enriched in the nucleus under conditions of accelerated mRNA decay ( Figure 1B , Table S3 in Supplementary file 1 ) . Notably , 22 of the 67 proteins ( 33% ) are annotated as RBPs ( Pantherdb ) in line with the expectation that mRNA-bound proteins in particular should be impacted during widespread mRNA degradation . In addition , 31 of the 67 proteins ( 46% ) are listed as localized both to the cytoplasm and nucleus according to the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) , supporting the idea that they are shuttling factors . As an independent validation of these results , we evaluated 12 of the top hits by western blotting of fractionated cell lysates in control or muSOX-expressing cells , 10 of which recapitulated the MS results ( Figure 1—figure supplement 1A ) . The poly ( A ) tail is a defining mRNA feature and during basal mRNA decay , deadenylation is the initiating step that licenses subsequent decapping and exonucleolytic degradation of an mRNA ( Schoenberg and Maquat , 2012 ) . Thus , the binding state of poly ( A ) tail associated proteins could theoretically serve as a readout to distinguish the overall abundance of mRNA over other forms of RNA in the cytoplasm . Notably , nuclear relocalization of PABPC has been observed during infection with multiple viruses that promote mRNA decay , supporting the idea that poly ( A ) tail associated proteins may be particularly sensitive to mRNA abundance ( Harb et al . , 2008; Lee and Glaunsinger , 2009; Park et al . , 2014; Piron et al . , 1998; Salaun et al . , 2010 ) . Indeed , an overrepresentation analysis using Pantherdb revealed that poly ( A ) binding proteins , poly ( U ) binding proteins , and mRNA 3’UTR binding proteins were significantly overrepresented among the 67 differentially expressed proteins ( Figure 1C , Figure 1—figure supplement 1B–C ) . Proteins linked to the poly ( A ) tail consistently arose as robust hits in our MS dataset , including PABPC proteins 1 and 4 ( PABPC1 , PABPC4 ) , LA-related protein 4 ( LARP4 ) , and heterogeneous nuclear ribonucleoprotein Q ( HNRNPQ ) ( Table S3 in Supplementary file 1 , Figure 1D ) . We confirmed that PABPC1 and LARP4 also translocate to the nucleus in NIH3T3 cells infected with WT MHV68 , but not in cells infected with an MHV68 muSOX mutant virus ( R443I ) with impaired mRNA cleavage activity ( Adler et al . , 2000; Richner et al . , 2011 ) ( Figure 1E ) . Thus , poly ( A ) associated proteins preferentially move from the cytoplasm to the nucleus in response to muSOX-activated mRNA decay in both transiently transfected and virally infected cells . Xrn1 is the major 5’−3’ exonuclease in mammalian cells and is responsible for the degradation of 3’ RNA fragments generated upon cleavage by muSOX ( Gaglia et al . , 2012 ) . In the absence of Xrn1 , muSOX-induced repression of RNAPII promoter occupancy does not occur , suggesting that Xrn1 activity should be required for release and subsequent nuclear translocation of RBPs involved in this phenotype ( Abernathy et al . , 2015 ) . We therefore used Cas9-based genome editing to generate Xrn1 knockout clones in HEK293T cells and confirmed that muSOX expression in these cells failed to reduce RNAPII promoter occupancy ( Figure 2—figure supplement 1A–B ) . The Xrn1 knockout cells exhibited a ~ 2 fold reduction in growth compared to control Cas9-expressing WT cells ( Figure 2—figure supplement 1C ) , in line with observations in yeast ( Larimer and Stevens , 1990 ) . Importantly , this did not lead to broad changes in gene expression ( see below ) . Given that Xrn1 is a central component of the mammalian mRNA decay machinery , only low passage versions of these cells were used to decrease the likelihood of compensatory changes occurring in other decay components . Using the same TMT-LC/MS-MS strategy described above , we analyzed nuclear and cytoplasmic fractions from three biological replicates of Xrn1 knockout cells expressing muSOX or an empty vector control ( Figure 1A ) . Comparison of these data to the list of proteins from Table S3 in Supplementary file 1 indicated that 45 of the 67 hits failed to shuttle in muSOX-expressing Xrn1 knockout cells , confirming that our workflow identified factors that differentially shuttle in response to mRNA degradation ( Figure 2A , B ) . Poly ( A ) tail degradation is normally carried out by deadenylases prior to activation of Xrn1-mediated decay from the 5’ end , but we previously demonstrated that SOX-cleaved mRNAs are not deadenylated prior to their targeting by Xrn1 ( Covarrubias et al . , 2011 ) . Indeed , analysis of endogenous PABPC1 and LARP4 localization by confocal microscopy and western blot analysis of fractionated cells confirmed that both proteins translocated from the cytoplasm to the nucleus upon muSOX expression in WT but not Xrn1 knockout cells ( Figure 2C–D , Figure 2—figure supplement 1D ) . Given that increased Xrn1 activity caused nuclear translocation of mRNA-associated RBPs , we hypothesized that RBPs linked to Xrn1 function might also exhibit altered subcellular distribution in cells lacking Xrn1 . We first looked broadly for proteins with reproducibly altered abundance in the nucleus or the cytoplasm of Xrn1 knockout cells relative to the vector control cells . There were 149 and 158 proteins differentially expressed in the absence of Xrn1 in the nucleus or cytoplasm , respectively ( adjusted P value< 0 . 05 ) ( Figure 3A , Table S4 in Supplementary file 1 ) . Both the oligosaccharyltransferase ( OST ) complex and RBPs were significantly overrepresented among the set of differentially expressed proteins in each compartment ( Figure 3A , Figure 3—figure supplement 1A ) . The significance of the OST enrichment is currently unknown , although the OST complex has been shown to be critical for infection with flaviviruses , which depend on Xrn1 for the production of a subgenomic viral noncoding RNA ( Chapman et al . , 2014; Moon et al . , 2012 ) . However , the RBP enrichment is in line with Xrn1 function , and it is notable that among the proteins significantly enriched in the nucleus of Xrn1 KO cells were factors that encompass the first steps of 5’−3’ mRNA decay . These included all members of the decapping complex ( DCP1A , DCP1B and DCP2 ) , factors that promote decapping complex formation ( EDC3 and EDC4 ) , and a protein that connects the decapping complex to the deadenylation machinery ( PATL1 ) ( Figure 3B ) . We next examined whether the absence of Xrn1 also impacted the relative abundance of its known interaction partners ( as listed in the BioGRID database ) in the two compartments ( Figure 3C ) . This did not appear to be the case in the TMT data , as the majority of known Xrn1 protein partners were expressed at normal levels in the absence of Xrn1 . However , there was a significant increase in the cytoplasmic levels of UPF1 , a mediator of nonsense mediated mRNA decay ( NMD ) . Similarly , DNASE2 , a nuclease which contributes to the degradation of DNA in dying cells , had increased cytoplasmic abundance . Secernin-2 ( SCRN2 ) , a protein involved in exocytosis , translocated to the nucleus in the absence of Xrn1 . Conversely , PABPC4 levels were decreased in both compartments , and two centrosomal proteins CEP152 and CEP128 were reduced in the nucleus . Finally , we considered the possibility that upon loss of Xrn1 , cells might upregulate other components of the mRNA decay machinery . Perhaps surprisingly , out of all 5994 detected proteins , only three were significantly upregulated in both the nucleus and the cytoplasm of Xrn1 knockout cells: GW182 , Galectin-3 , and BAG1 . Among these , GW182 stands out because it is a member of the miRNA-induced silencing complex ( miRISC ) involved in recruitment of deadenylases to initiate degradation of target mRNAs ( Figure 3D ) . This increase in GW182 abundance , along with the changes to DCP2 , DDX6 , and PABPC4 , were independently validated by western blot analysis ( Figure 3E ) . To determine whether the increases in the whole cell protein abundance of GW182 and in the nuclear protein abundance in DDX6 and PATL1 occurred at the mRNA level or were a result of translational regulation , we measured steady-state mRNA expression for each of these factors by RT-qPCR . In each case , the mRNA abundance was increased in Xrn1 knockout cells compared to WT cells ( Figure 3—figure supplement 1B ) . Importantly , the increases appeared specific to these transcripts and not due to generalized mRNA abundance changes in the absence of Xrn1 , as there was no significant difference in gapdh or actB mRNA levels ( Figure 3—figure supplement 1B ) . Collectively , these data suggest that there are not broad increases in cellular proteins in response to inhibition of 5’−3’ mRNA decay . However , there appear to be selective increases in the whole cell or compartment-specific abundance of select factors associated with mRNA decay , which likely arises from increases in their mRNA levels in Xrn1 knockout cells . Protein relocalization in response to altered cytoplasmic mRNA decay could occur as a consequence of direct interactions with the nuclear transport machinery that are antagonized by mRNA , as has been documented for the PABPC nuclear localization signal ( NLS ) ( Kumar et al . , 2011 ) . Alternatively , translocation could occur indirectly via interactions with other proteins that contain nuclear transport signals . To test for this latter possibility , we first plotted the network of known interactions among the list of proteins that relocalized in cells undergoing accelerated mRNA decay using the STRING database ( Figure 4A ) . There were significantly more interactions among this set of proteins than would be predicted for a random group of proteins of similar size ( p=0 . 0496 ) , with many of the interactions involving PABPC . This enrichment suggests that these proteins are biologically related , confirming what was seen in the GO term analysis . We examined the relocalization mechanism for one of the PABPC interacting proteins , LARP4 ( Yang et al . , 2011 ) . We reasoned that if LARP4 relocalization involved direct interactions with the nuclear import machinery , then it should relocalize in muSOX-expressing cells in a PABPC independent manner . Conversely , if it was ‘escorted’ into the nucleus via its interaction with PABPC , then its relocalization should be blocked by PABPC depletion . Depletion of PABPC1 has been shown to lead to compensatory induction of PABPC4 , which can function in a redundant manner ( Kumar and Glaunsinger , 2010 ) . Therefore , we co-depleted both PABPC1 and PABPC4 using siRNAs . Upon co-depletion of the PABPC proteins , LARP4 no longer accumulated in the nucleus of muSOX-expressing cells ( Figure 4B ) . In contrast , siRNA-mediated depletion of LARP4 had no effect on PABPC1 shuttling in these cells ( Figure 4C ) . These results support a model in which LARP4 is brought into the nucleus in cells undergoing accelerated mRNA decay through its interaction with PABPC . Given the nuclear enrichment of many poly ( A ) and poly ( U ) associated proteins , we considered these factors to be strong candidates for involvement in the signaling pathway linking accelerated mRNA decay to RNAPII transcriptional repression . To determine if they were required for the mRNA decay-transcription feedback loop , we tested whether depletion of several of these factors individually altered RNAPII occupancy using chromatin immunoprecipitation assays ( ChIP ) . To test the role of PABPC we co-depleted both PABPC1 and PABPC4 using siRNAs , then monitored RNAPII occupancy at two cellular promoters ( gapdh , rplp0 ) previously shown to be responsive to mRNA decay-induced transcriptional repression ( Abernathy et al . , 2015 ) . In cells depleted of PABPC1 and PABPC4 , there was no longer a reduction in RNAPII occupancy at the gapdh and rplp0 promoters in muSOX expressing cells relative to vector control cells ( Figure 5A ) . In contrast , RNAPII promoter occupancy remained repressed in muSOX expressing cells upon depletion of LARP4 ( Figure 5B ) . In addition to poly ( A ) tail associated proteins , we tested the effects of depleting three additional factors that translocated to the nucleus in an mRNA-decay dependent manner: the poly ( U ) binding protein MSI1 , the CHD3 transcriptional regulator , and one of the top scoring hits from the MS data , TRIM32 ( Figure 5—figure supplement 1A–C ) . RNAPII occupancy remained reduced in muSOX-expressing cells relative to vector control cells upon depletion of MSI1 , CHD3 , and TRIM32 ( Figure 5—figure supplement 1A–C ) . It should be noted that when we measured the effect of depleting the above factors on RNAPII occupancy in the absence of muSOX , we unexpectedly observed that their knockdown alone reduced the RNAPII ChIP signal ( Figure 5—figure supplement 2A–E ) . However , unlike the case for PABPC , RNAPII levels were further reduced in muSOX expressing cells after depletion of LARP4 , MSI1 , CHD3 , and TRIM32 ( Figure 5B , Figure 5—figure supplement 1A–C ) . We hypothesize that knockdown of these factors may lead to broad impacts on cellular function , in ways that directly or indirectly influence transcription . Therefore , although PABPC appeared to be selectively involved in suppressing RNAPII occupancy during enhanced mRNA decay , we sought an alternative strategy to evaluate its connection to this process . Endogenous PABPC is subject to translational autoregulation , and our previous data suggested that the abundance of PABPC in uninfected cells is fine-tuned to match poly ( A ) tail availability ( Kumar and Glaunsinger , 2010; Kumar et al . , 2011 ) . In this regard , even modest over-expression of PABPC1 leads to nuclear accumulation of the ‘excess’ ( presumably non-poly ( A ) bound ) protein in cells lacking muSOX ( Figure 5C ) . This feature enabled us to test whether nuclear accumulation of PABPC1 was sufficient to cause a reduction in RNAPII promoter recruitment in the absence of muSOX-induced mRNA decay . Indeed , FLAG-PABPC1 transfected cells displayed a significant decrease in RNAPII occupancy at the gapdh and rplp0 promoters ( Figure 5D ) . These observations suggested that the failure of muSOX to trigger transcriptional repression in Xrn1 knockout cells might be overcome by driving PABPC into the nucleus via overexpression . In agreement with this prediction , muSOX-induced transcriptional repression was restored in Xrn1 knockout cells upon transfection of FLAG-PABPC1 , confirming that nuclear translocation of this RBP plays a central role in connecting cytoplasmic mRNA decay to RNAPII promoter recruitment ( Figure 5E ) . To more precisely define the stage ( s ) of transcription impacted by mRNA decay-induced translocation of PABPC , we began by measuring RNAPII occupancy at both the promoter and the gene body ( exon ) of the genes gapdh , actB , and tlcd1 . In each of the experiments below , we evaluated cells transfected with empty vector control , muSOX ( to activate cytoplasmic mRNA decay ) , or FLAG-PABPC1 ( to selectively increase nuclear PABPC levels in the absence of widespread mRNA decay ) . Cells expressing muSOX or FLAG-PABPC1 exhibited parallel phenotypes , in which RNAPII occupancy was reduced at promoters as well as within the gene body compared to control cells ( Figure 6A ) . Western blotting confirmed that the reduced ChIP signals were not due to a decrease in the overall levels of RNAPII in these cells ( Figure 6—figure supplement 1 ) . The C-terminal domain ( CTD ) of the RNAPII Rpb1 subunit has unique phosphorylation patterns associated with each phase of transcription; it initially binds DNA in an unphosphorylated state , but undergoes progressive serine 5-phosphorylation ( Ser5P ) during initiation , then serine 2-phosphorylation ( Ser2P ) during elongation ( Heidemann et al . , 2013 ) . To determine whether mRNA decay-induced PABPC1 translocation impacted RNAPII initiation or elongation in addition to promoter recruitment , we measured the ratio of total RNAPII to either Ser5P or Ser2P RNAPII ( Figure 6B and C ) . In both muSOX and FLAG-PABPC expressing cells , these ratios were unchanged relative to control cells , suggesting that the primary defect is in promoter recruitment , and that there are not independent impacts on downstream events . These data are consistent with previous observations in MHV68-infected cells ( Abernathy et al . , 2015 ) . RNAPII promoter recruitment occurs during assembly of the transcription preinitiation complex ( PIC ) , a multi-step event involving numerous general transcription factors and transcription associated factors ( Roeder , 1996 ) . The initial promoter-defining event in PIC assembly that occurs prior to RNAPII recruitment is binding of TATA-binding protein ( TBP ) as part of the transcription factor TFIID complex , whose recruitment is essential for initiating transcription ( Darzacq et al . , 2007; Louder et al . , 2016 ) . Notably , TBP ChIP revealed that its occupancy at the gapdh , actB , tlcd1 , and rplp0 promoters was significantly reduced in cells expressing either muSOX or FLAG-PABPC1 compared to control cells ( Figure 6D ) . Similar to RNAPII , western blotting confirmed that this reduction in promoter binding was not due to altered expression of TBP in these cells ( Figure 6—figure supplement 1 ) . Given that TBP is a transcription factor required by cellular polymerases other than just RNAPII , we considered the possibility that all TBP-dependent transcription might be impaired as a consequence of cytoplasmic mRNA decay . This was not the case however , as 7SK and U6 promoter occupancy by RNA polymerase III ( RNAPIII ) , which also requires TBP , was unaltered in cells expressing muSOX or FLAG-PABPC1 compared to control cells ( Figure 6E ) . We therefore conclude that mRNA decay-driven nuclear accumulation of PABPC1 reduces PIC assembly selectively at the promoters of RNAPII transcribed genes .
Cellular mRNA abundance can be dramatically altered in response to a variety of pathogenic and nonpathogenic stresses including both viral and bacterial infections , and early apoptosis ( Abernathy and Glaunsinger , 2015; Barry et al . , 2017; Thomas et al . , 2015 ) . In many of these cases , accelerated cytoplasmic mRNA decay initiates a widespread reduction in transcript levels , often through the engagement of the major mammalian 5’−3’ exonuclease Xrn1 ( Covarrubias et al . , 2011; Gaglia et al . , 2012 ) . In addition to altering the translational landscape , depletion of cytoplasmic mRNA elicits changes in upstream components of the mammalian gene expression pathway , including RNAPII transcription , largely by unknown mechanisms ( Abernathy et al . , 2015 ) . Here , we tested the hypothesis that cellular RNA binding proteins may shift their subcellular localization in response to altered mRNA decay , thus conveying mRNA abundance information between the cytoplasm and the nucleus ( Figure 6F ) . Quantitative proteomics was previously reported to allow the discovery of viral infection-induced protein translocations ( Jean Beltran et al . , 2017 ) . Indeed , our unbiased TMT-based proteomics approach revealed that among the total cellular protein pool , an RBP-enriched protein subset concentrates in the nucleus specifically in response to increased mRNA decay in an Xrn1 dependent manner . RBPs have critical roles in all stages of gene expression ( Müller-McNicoll and Neugebauer , 2013 ) , and our data further emphasize their multifunctional capacity . We also found that RBPs are enriched in the set of proteins with altered nuclear or cytoplasmic localization in Xrn1 knockout cells . Interestingly , factors involved in decapping , the event that directly precedes Xrn1 attack during basal mRNA decay , were selectively increased in the nuclei of cells lacking Xrn1 . Furthermore , we detected increased levels of the NMD factor UPF1 in the cytoplasm and overall elevated levels of GW182 in these cells . One speculative possibility is that these changes occur in response to cellular ‘reprogramming’ of the mRNA decay network , for example shifting emphasis towards 3’ end targeting mechanisms to compensate for the absence of the primary 5’ end decay mechanism . This scenario might explain the increase in GW182 levels , as it recruits the cellular deadenylase complexes PAN2-PAN3 and CCR4-NOT to mRNA targets to promote mRNA decay by Xrn1 ( Braun et al . , 2011; Chekulaeva et al . , 2011; Fabian et al . , 2011; Huntzinger and Izaurralde , 2011 ) . In its absence , the increased GW182 levels may accelerate deadenylation to instead promote 3’−5’ decay . Alternatively , the RBP nuclear and/or cytoplasmic enrichment in Xrn1 knockout cells may reflect changes that occur when the cytoplasmic mRNA decay rate is reduced . The fact that we did not observe significant redistribution of the majority of Xrn1 interacting proteins argues against a model in which physical association with Xrn1 helps control decay factor protein localization . Aside from RBPs , there was a clear overrepresentation of the OST complex , which catalyzes co-translational N-glycosylation , among the set of differentially expressed proteins in Xrn1 knockout cells . Although OST does not have established links to RNA decay or Xrn1 , it has been shown to be a critical component of the replication cycle of flaviviruses such as Dengue and Zika ( Marceau et al . , 2016; Puschnik et al . , 2017 ) . Furthermore , these arthropod-borne flaviviruses inhibit Xrn1 activity through a subgenomic viral noncoding RNA that contains an Xrn1 blocking sequence ( Chapman et al . , 2014; Moon et al . , 2012 ) . In this context , it will be exciting to explore possible links between these two processes , especially given that small molecule OST inhibitors are now being tested for their pan-flaviviral inhibition ( Puschnik et al . , 2017 ) . Among the set of proteins that translocated in cells undergoing accelerated Xrn1-dependent mRNA decay , there was a striking enrichment in factors that bind the 3’end of mRNAs . This supports the hypothesis that this class of RBPs would be significantly impacted by the mRNA abundance and availability . PABPC nuclear translocation in particular has been well documented in the context of infection with viruses that drive mRNA decay ( Bablanian et al . , 1991; Borah et al . , 2012; Harb et al . , 2008; Lee and Glaunsinger , 2009; Park et al . , 2014; Piron et al . , 1998; Salaun et al . , 2010 ) , and our unbiased proteomics approach establishes it as one of the most robustly relocalized RBPs under these conditions . Several features of PABPC render it an ideal indicator of mRNA abundance . First , its association with poly ( A ) tails implies that depletion of mRNAs but no other type of abundant non-polyadenylated RNAs should selectively alter the level of PABPC in the RNA bound versus unbound state . Second , nuclear import of PABPC is antagonized by cytoplasmic mRNA abundance . We previously reported that PABPC harbors noncanonical NLSs within its RNA recognition motifs ( RRMs ) ; upon poly ( A ) binding , these elements are masked and the protein is thus retained in the cytosol ( Kumar et al . , 2011 ) . However , release of PABPC from mRNA exposes the NLSs , enabling its interaction with importin α and its subsequent nuclear import . The observation that PABPC localization is directly influenced by mRNA abundance suggest that cells must carefully calibrate the ratio of PABPC to mRNA . Indeed , PABPC protein binds an autoregulatory A-rich sequence in the 5’UTR of its own mRNA to disrupt 40S ribosomal scanning and reduce its translation ( de Melo Neto et al . , 1995; Wu and Bag , 1998 ) . When bound to poly ( A ) tails in the cytoplasm , PABPC contributes to mRNA stability and facilitates protein-protein interactions for efficient translation by the ribosome ( Burgess and Gray , 2010; Fatscher et al . , 2014 ) . However , when concentrated in the nucleus , PABPC functions instead to restrict gene expression . One previously established mechanism by which gene expression is inhibited involves disruption of mRNA processing , where PABPC drives hyperadenylation of nascent mRNAs ( Kumar and Glaunsinger , 2010 ) . In this study , we reveal that nuclear accumulation of PABPC phenotypically mimics muSOX-dependent repression of RNAPII promoter binding; it appears necessary and sufficient to repress RNAPII promoter recruitment as a consequence of accelerated mRNA decay . Both muSOX and FLAG-PABPC1 expression target early stages of PIC assembly , as TBP and RNAPII occupancy are reduced at promoters . Interestingly , the S . cerevisiae nuclear poly ( A ) binding protein Nab2 has been shown to potentiate RNAPIII activity by directly binding RNAPIII and stabilizing TFIIIB with promoter DNA ( Reuter et al . , 2015 ) , providing a precedent for PABPs influencing transcription . However , although TBP is required for the activity of other polymerases including RNAPIII , we found that the impact of mRNA decay-induced PABPC translocation appears specific to RNAPII responsive promoters . Furthermore , while RNAPII levels are reduced at both promoters and in the gene body , the residual promoter-bound population of RNAPII does not appear to have additional defects in promoter escape or elongation , as measured by polymerase CTD phosphorylation patterns . Collectively , these observations suggest that altered PABPC trafficking primarily impacts the very earliest stages of PIC assembly . Determining which factors govern the specificity for RNAPII responsive promoters during accelerated mRNA decay and their connection to nuclear PABPC remain important challenges for the future . Although we did not detect a role for LARP4 , MSI1 , CHD3 , or TRIM32 in muSOX-induced transcriptional repression , these findings are complicated by the observation that their depletion alone impaired RNAPII recruitment . In our hands , this phenotype is common to the depletion of a number of different RBPs ( though not all ) , suggesting that their absence may cause secondary effects on gene expression . This also underscores the importance of using alternative assays to evaluate their contributions , as we did for PABPC . Interestingly , some of these proteins are likely to engage in distinct gene regulatory functions in the nucleus that could also be impacted by their altered nuclear-cytoplasmic trafficking . For example , a nuclear role for MSI1 has recently been uncovered during mouse spermatogenesis , when it translocates from the cytoplasm to the nucleus ( Sutherland et al . , 2015 ) . In the cytoplasm , MSI1 negatively regulates the translation of its target RNAs by competing with eukaryotic initiation factor eIF4G for binding to PABPC ( Kawahara et al . , 2008 ) . However , upon spermatocyte differentiation , MSI1 relocalizes to the nucleus through direct interaction with importin-5 ( IPO5 ) , where it concentrates at the silent XY chromatin domain . This not only releases its repression on translation , but also alters its repertoire of RNA targets in the nucleus . LARP4 also binds PABPC , but unlike MSI1 , this interaction promotes mRNA poly ( A ) tail lengthening and stabilization in the cytoplasm ( Yang et al . , 2011 ) . Our findings suggest that nuclear accumulation of LARP4 is also dependent on its interaction with PABPC . LARP4 protein levels are controlled post-transcriptionally via an instability determinant within its coding sequence , suggesting that akin to PABPC , its protein abundance is tightly regulated ( Mattijssen et al . , 2017 ) . The functions of LARP4 in the nucleus , as well as other RBPs identified in this work , are currently unknown . Exploring these roles and how they become manipulated during times of cellular stress are areas ripe for future studies . Finally , it is notable that connections between Xrn1-driven mRNA decay and RNAPII transcription have also been made in yeast , providing further evidence that these seemingly divergent stages of the gene expression cascade are intimately linked ( Haimovich et al . , 2013; Sun et al . , 2013 ) . However , one key difference between this pathway in yeast and mammalian cells is that in yeast it appears to operate as a compensatory mechanism to maintain optimal mRNA abundance: reduced mRNA decay results in reduced transcription , and vice versa ( Haimovich et al . , 2013; Sun et al . , 2013 ) . This potentially represents an evolutionary divergence in which a unicellular eukaryote ‘buffers’ its overall gene expression for continued maintenance of the organism . In multicellular eukaryotes like mammals , global mRNA decay ( which is induced by numerous pathogens ) may instead serve as a stress signal , and the ensuing response is thus geared towards shutdown of major cellular programs .
Primers used for cloning are listed in Table S5 in Supplementary file 1 . MHV68 muSOX was cloned into the Gateway entry vector pDON207 ( Invitrogen ) , and then transferred into the Gateway-compatible peGFP-C1 destination vector to generate GFP-muSOX . Thy1 . 1-muSOX was generated by Infusion cloning ( Clontech ) of Thy1 . 1 ( CD90 . 1 ) followed by a self-cleaving 2A peptide from foot-and-mouth disease virus in place of GFP into the Nhe1 and SacII restriction enzyme sites of GFP-muSOX . The D219A muSOX mutant was made using Quikchange site-directed mutagenesis ( Agilent ) . Thy1 . 1-GFP was created with Infusion cloning to insert GFP back into the vector with the BamHI and EcoRI restriction enzyme sites to replace muSOX with GFP . pCDEF3-Flag-PABPC1 was described previously ( Kumar and Glaunsinger , 2010 ) . The Cas9 ( lentiCas9-Blast ) and sgRNA ( lentiGuide-Puro ) viral vectors were made as previously described ( Sanjana et al . , 2014; Shalem et al . , 2014 ) . The Xrn1 sgRNA was chosen using the Broad sgRNA design website ( Doench et al . , 2014 ) . NIH3T3 cells and HEK293T cells , both from ATCC and obtained through the UC Berkeley Tissue Culture Facility , were maintained in DMEM ( Invitrogen ) supplemented with 10% fetal bovine serum . Cell lines were authenticated by STR analysis , and determined to be free of mycoplasma by PCR screening . DNA transfections were carried out in HEK293T cells at 70% confluency in 15 cm plates with 25 μg DNA using PolyJet ( SignaGen ) for 24 hr . For small interfering RNA ( siRNA ) transfections , HEK293T cells were transfected twice over 48 hr with 100 μM siRNA using Lipofectomine RNAiMAX ( Thermo Fisher ) , whereupon the cells were transfected with the indicated DNA plasmid for an additional 24 hr . Non-targeting scramble siRNAs , LARP4 , MSI1 , CHD3 , and TRIM32 siRNAs were obtained from Dharmacon ( Scramble: D-001206-13-50 , LARP4: M-016523-00-0020 , MSI1: M-011338-01-0010 , CHD3: M-023015-01-0020 , TRIM32: M-006950-01-0010 ) . PABPC1 and PABPC4 siRNAs have been previously described and are listed in Table S5 in Supplementary file 1 ( Kumar and Glaunsinger , 2010; Lee and Glaunsinger , 2009 ) . The Xrn1 knockout clone and control Cas9-expressing cells were made by transducing HEK293T cells as previously described ( Sanjana et al . , 2014; Shalem et al . , 2014 ) . Briefly , lenti-Cas9-blast lentivirus was spinfected onto a monolayer of HEK293T cells , which were then incubated with 20 μg/ml blasticidin to remove non-transduced cells . These Cas9-expressing HEK293T cells were then spinfected with lentivirus made from lentiGuide-Puro containing the Xrn1 sgRNA sequence and selected with 1 μg/ml puromycin . The pool of Xrn1 knockout cells was then single-cell cloned in 96-well plates and individual clones were screened by western blot to determine knockout efficiency . Pure populations of cells expressing muSOX were generated using the Miltenyi Biotec MACS cell separation system . HEK293T cells were transfected with either Thy1 . 1-GFP , Thy1 . 1-muSOX , or Thy1 . 1-muSOX D219A for 24 hr , whereupon cells were washed twice with PBS and cell pellets were resuspended in 95 μl auto-MACS rinsing buffer supplemented with 0 . 5% FBS and incubated with 3 μl anti-CD90 . 1 microbeads on ice for 10 – 15 min , and mixed by flicking the tube every 5 min . Cells were then magnetically separated according to the manufacturer’s instructions . Thy1 . 1 positive cells were used in all downstream experiments unless otherwise stated . The MHV68 bacterial artificial chromosome ( BAC ) , and the construction of the R443I muSOX mutant were previously described ( Adler et al . , 2000; Richner et al . , 2011 ) . MHV68 was produced by transfecting NIH3T3 cells in 6-well plates with 2 . 5 μg BAC DNA using Mirus TransIT-X2 ( Mirus Bio ) for 24 hr , whereupon the cells were split into a 10 cm dish and harvested after 5–7 days , once all the cells were green and dead . Virus was amplified in NIH 3T12 cells and titered by plaque assay . Cells were infected with MHV68 at an MOI of 5 for 24 hr . Cells were plated on coverslips coated with 100 ug/mL poly-L-lysine and transfected at 70% confluency with either GFP or GFP-muSOX for 24 hr . Transfected cells were fixed in 4% formaldehyde , permeabilized with ice-cold methanol , and incubated with blocking buffer [1% Triton X-100 , 0 . 5% Tween-20 , 3% Bovine Serum Albumin] prior to incubation with mouse monoclonal PABPC diluted 1:25 ( Santa Cruz Biotechnologies , 10E10 ) or rabbit polyclonal LARP4 diluted 1:200 ( Thermo Fisher ) in blocking buffer at 4°C overnight , followed by incubation with Alexa Fluor 594-conjugated goat anti-mouse , or anti-rabbit secondary antibody ( Thermo Fisher , 1:1000 ) and DAPI ( Pierce , 1:1000 ) . Coverslips were mounted on slides using Vectashield hard-set mounting medium ( VectorLabs ) and imaged by confocal microscopy on a Zeiss LSM 710 AxioObserver microscope . HEK293T cells were fractionated using the REAP method ( Nabbi and Riabowol , 2015 ) . Briefly , cells were washed twice with ice-cold PBS and the cell pellet was lysed in 0 . 1% NP-40 PBS lysis buffer . The nuclei were then isolated by differential centrifugation at 10 , 000 x g for 10 s and the supernatant retained as the cytoplasmic fraction . For western blotting , the nuclei were sonicated in 0 . 1% NP-40 PBS lysis buffer . WT Cas9-HEK293T cells were transfected with Thy1 . 1-GFP , Thy1 . 1-muSOX , or Thy1 . 1-muSOX D219A . Xrn1 knockout HEK293T cells were transfected with Thy1 . 1-GFP or Thy1 . 1-muSOX for 24 hr , followed by Thy1 . 1 separation . Separated cells were then fractionated as described above , and nuclear pellets were snap-frozen in liquid nitrogen . Cytoplasmic fractions were concentrated using an Amicon ultra filtration unit with a molecular weight cutoff of 3 kDa ( Millipore ) and exchanged into a 50 mM NH4HCO3 , 2% Deoxycholate buffer and then snap frozen in liquid nitrogen . The nuclear pellets were lysed in 200 µL of 100 mM Tris-HCl , pH 8 . 0 , 4% SDS , 1 mM EDTA preheated to 70°C . Cytoplasmic fractions were thawed and adjusted to 1% SDS with a 10% SDS solution . Complete lysis of samples was achieved via five successive rounds of heating at 95°C for 3 min followed by sonication for 10 s in a cup horn sonicator set on 1 s pulses at medium output . Protein amounts were assessed by BCA protein assay ( Pierce ) and 50 µg of protein from each sample was simultaneously reduced and alkylated with 20 mM tris ( 2-carboxyethyl ) phosphine ( Pierce ) and chloroacetamide respectively for 20 min at 70°C . Protein samples were then cleaned up by methanol-chloroform precipitation ( Wessel and Flügge , 1984; Federspiel and Cristea , 2018 , In press ) . LC-MS grade methanol , chloroform , and water ( at a 4:1:3 ratio ) were added to the sample with vortexing following each addition . The samples were spun at 2 , 000 × g for 5 min at room temperature and the top phase was removed . Three volumes of cold methanol were then added and the samples were spun at 9 , 000 × g for 2 min at 4°C . All liquid was removed and the protein pellets were washed with five volumes of cold methanol and then spun at 9 , 000 × g for 2 min at 4°C . All liquid was removed again and the dried protein pellets were resuspended in 50 mM HEPES pH 8 . 5 at a 0 . 5 µg/µL concentration . Trypsin ( Pierce ) was added at a 1:50 trypsin:protein ratio and the samples were incubated at 37°C overnight . Digested samples were concentrated by speed vac to one half the original volume prior to labeling and adjusted to 20% acetonitrile ( ACN ) . All three biological replicates were labeled concurrently with a 10-plex TMT kit ( Thermo Fisher Scientific ) as in ( Sauls et al . , 2018 ) . The TMT reagents ( 0 . 8 mg per channel ) were dissolved in 42 µL of anhydrous ACN and 14 µL of this was added to each sample following the scheme in Figure 1A and allowed to react at RT for 1 hr . The labeling was quenched by the addition of hydroxylamine to a final 0 . 5% ( v/v ) concentration followed by incubation at RT for 15 min . Labeled peptides were pooled at equal peptide amounts thereby generating three 10-plex experiments , each of which was an individual biological replicate . An initial test mix for each replicate was analyzed , and the apparent peptide ratios were determined . Mixing ratios were adjusted using the information from the test mix to correct for sample losses and generate mixes with equal peptide amounts per channel . Pooled peptides were acidified and fractionated by 2D StageTip ( Sauls et al . , 2018 ) . Peptides were first desalted via C18 StageTips to remove unreacted TMT reagent by washing the bound peptides with 5% ACN , 0 . 5% formic acid ( FA ) and then eluting the peptides in 70% ACN , 0 . 5% FA . The eluted peptides were then bound to SCX StageTips and eluted in four fractions with sequential elution ( 100 µL ) as follows: ( 1 ) 0 . 05 M ammonium formate/20% ACN , ( 2 ) 0 . 05 M ammonium acetate/20% ACN , ( 3 ) 0 . 05 M ammonium bicarbonate/20% ACN , and ( 4 ) 0 . 1% ammonium hydroxide/20% ACN . Each of these fractions were diluted 1:1 with 1% trifluoroacetic acid and further fractionated by SDB-RPS StageTips with sequential elution ( 50 µL ) into three fractions as follows: ( 1 ) 0 . 2 M ammonium formate/0 . 5% FA/60% ACN , ( 2 ) 0 . 2 M ammonium acetate/0 . 5% FA/60% ACN , ( 3 ) 5% ammonium hydroxide/80% ACN . The resulting 12 fractions for each 10-plex experiment were dried in vacuo and resuspended in 5 µL of 1% FA , 1% ACN in water . Peptides ( 2 µL ) were analyzed by LC-MS/MS using a Dionex Ultimate 3000 UPLC coupled online to an EASYSpray ion source and Q Exactive HF . Peptides were separated on an EASYSpray C18 column ( 75 µm x 50 cm ) heated to 50°C using a linear gradient of 5% ACN to 42% ACN in 0 . 1% FA over 150 min at a flow rate of 250 nL/min and ionized at 1 . 7kv . MS/MS analysis was performed as follows: an MS1 scan was performed from 400 to 1800 m/z at 120 , 000 resolution with an automatic gain control ( AGC ) setting of 3e6 and a maximum injection time ( MIT ) of 30 ms recorded in profile . The top 18 precursors were then selected for fragmentation and MS2 scans were acquired at a resolution of 60 , 000 with an AGC setting of 2e5 , a MIT of 105 ms , an isolation window of 0 . 8 m/z , a fixed first mass of 100 m/z , normalized collision energy of 34 , intensity threshold of 1e5 , peptide match set to preferred , and a dynamic exclusion of 45 s recorded in profile . MS/MS data were analyzed by Proteome Discoverer ( Thermo Fisher Scientific , v2 . 2 . 0 . 388 ) . The nuclear channels ( 126 – 128C ) and cytoplasmic channels ( 129 N-131 ) were analyzed in separate Proteome Discoverer studies to not bias the quantitation due to the expected protein expression differences between these two compartments . The Spectrum Files RC node was utilized to perform post-acquisition mass recalibration and the recalibrated spectra were passed to Sequest HT where two successive rounds of searching were employed against a Uniprot human database appended with common contaminants ( 2016 – 04 , 22 , 349 sequences ) . Both search rounds required 5ppm accuracy on the precursor and 0 . 02 Da accuracy on the fragments and included static carbamidomethyl modifications to cysteine , static TMT additions to peptide N-termini and lysine residues , dynamic oxidation of methionine , dynamic deamidation of asparagine , and dynamic methionine loss and acetylation of protein n-termini . The first Sequest HT search was for fully tryptic peptides only and any unmatched spectra were sent to a second Sequest HT search , which allowed semi-tryptic peptide matches . All matched spectra were scored by Percolator and reporter ion signal-to-noise ( S/N ) values were extracted ( The et al . , 2016 ) . The resulting peptide spectrum matches were parsimoniously assembled into a set of identified peptide and protein identifications with a false discovery rate of less than 1% for both the peptide and protein level and at least two unique peptides identified per protein . TMT reporter ion quantification was performed for unique and razor peptides with an average S/N of at least 10 and a precursor co-isolation threshold of less than 30% which did not contain a variable modification . Reporter ion values were normalized to the total detected signal in each channel and protein abundances were calculated as the sum of all normalized reporter ion values for each channel in each protein . Missing values were input using the low abundance resampling algorithm . The reporter ion values for the empty vector WT samples ( channels 126 and 129N ) were set as 100 and the other channels were scaled to this value . Statistically differential proteins were assessed via a background based ANOVA analysis implemented in Proteome Discoverer . Proteins and associated TMT reporter ion abundances and adjusted p-values from the ANOVA analysis were exported to Excel for further analysis . The mass spectrometry proteomics data reported in this paper have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository ( Vizcaíno et al . , 2014 ) . The PRIDE accession number is PXD009487 . Differential proteins ( adjusted p-value≤0 . 05 ) were analyzed via over representation analysis ( www . pantherdb . org ) for associated gene ontology enrichments ( Mi et al . , 2016 ) . Example proteins of different classes , along with all heatmaps , were graphed in GraphPad Prism v7 . Nuclear , cytoplasmic , and whole cell lysates were quantified by Bradford assay and resolved by SDS-PAGE and western blotted with antibodies against PABPC ( Cell Signaling , 1:1000 ) , PABPC4 ( Bethyl , 1:1000 ) , LARP4 ( Thermo Fisher , 1:1000 ) , Gapdh ( Abcam , 1:3000 ) , Histone H3 ( Cell Signaling , 1:2000 ) , LYRIC ( Abcam , 1:1000 ) , RRBP1 ( Bethyl , 1:1000 ) , MSI1 ( Abcam , 1:1000 ) , Lin28b ( Abcam , 1:1000 ) , CHD3 ( Cell Signaling , 1:1000 ) , RPP20 ( Novus , 1:1000 ) , THOC6 ( Life Technologies , 1:1000 ) , PNN ( Life Technologies , 1:1000 ) , EXO4 ( rabbit polyclonal produced using recombinant EXO4 with an MBP tag , 1:1000 ) , NPM ( Abcam , 1:1000 ) , GW182 ( Abcam , 1:1000 ) , DDX6 ( Bethyl , 1:1000 ) , DCP2 ( Bethyl , 1:1000 ) , TRIM32 ( Abcam , 1:1000 ) , RNAPII Rpb1 ( BioLegend , 1:2000 ) , TBP ( Abcam , 1:2000 ) . ChIP was performed on 15 cm plates of HEK293T cells transfected twice 4 hr apart with the indicated plasmid DNA . 24 hr after the first transfection , cells were crosslinked in 1% formaldehyde in PBS for 10 min at room temperature , quenched in 0 . 125 M glycine , and washed twice with ice-cold PBS . Crosslinked cell pellets were mixed with 1 ml ice-cold ChIP lysis buffer ( 5 mM PIPES pH 8 . 0 , 85 mM KCl , 0 . 5% NP-40 ) and incubated on ice for 10 min , whereupon the lysate was dounce homogenized to release nuclei and spun at 1 . 5 x g for 5 min at 4°C . Nuclei were then resuspended in 500 μl of nuclei lysis buffer ( 50 mM Tris-HCl pH 8 . 0 , 0 . 3% SDS , 10 mM EDTA ) and rotated for 10 min at 4°C followed by sonication using a QSonica Ultrasonicator with a cup horn set to 75 amps for 20 min total ( 5 min on , 5 min off ) . Chromatin was spun at 16 , 000 x g for 10 min at 4°C and the pellet was discarded . 100 μl of chromatin was diluted 1:5 in ChIP dilution buffer ( 16 . 7 mM Tris-HCl pH 8 . 0 , 1 . 1% Triton X-100 , 1 . 2 mM EDTA , 167 mM NaCl ) and incubated with 10 μg mouse monoclonal anti-RNAPII ( BioLegend , 8WG16 ) , rabbit IgG ( Fisher Scientific ) , rabbit polyclonal anti-RNAPII phospho S5 ( Abcam ab5131 ) , rabbit polyclonal anti-RNAPII phospho S2 ( Abcam ab5095 ) , rabbit polyclonal anti-TBP ( Abcam ab28175 ) , or rabbit polyclonal anti-POLR3A ( Abcam ab96328 ) overnight , whereupon samples were rotated with 20 μl protein G dynabeads ( with mouse antibodies ) , or 20 μl mixed protein G and A dynabeads ( with rabbit antibodies ) ( Thermofisher ) for 2 hr at 4°C . Beads were washed with low salt immune complex ( 20 mM Tris pH 8 . 0 , 1% Triton-x-100 , 2 mM EDTA , 150 mM NaCl , 0 . 1% SDS ) , high salt immune complex ( 20 mM Tris pH 8 . 0 , 1% Triton-x-100 , 2 mM EDTA , 500 mM NaCl , 0 . 1% SDS ) , lithium chloride immune complex ( 10 mM Tris pH 8 . 0 , 0 . 25 M LiCl , 1% NP-40 , 1% Deoxycholic acid , 1 mM EDTA ) , and Tris-EDTA for 5 min each at 4°C with rotation . DNA was eluted from the beads using 100 μl of elution buffer ( 150 mM NaCl , 50 μg/ml proteinase K ) and incubated at 50°C for 2 hr , then 65°C overnight . DNA was purified using a Zymo Oligo Clean and Concentrator kit . Purified DNA was quantified by qPCR using iTaq Universal SYBR Mastermix ( BioRad ) with the indicated primers ( Table S5 in Supplementary file 1 ) . Each sample was normalized to its own input . In this study , individual biological replicates are experiments performed separately on biologically distinct samples representing identical conditions and/or time points . For cell culture-based assays , this means that the cells are maintained in different flasks . Technical replicates are experiments performed on the same biological sample multiple times . See Figure Legends for the number of experimental replicates performed for each experiment . No outliers were encountered in this study . Criteria for the inclusion of data was based on the performance of positive and negative controls within each experiment . | The nucleus of a cell harbors DNA , which contains all information needed to build an organism . The instructions are stored as a genetic code that serves as a blueprint for making proteins – molecules that are important for almost every process in the body – and to assemble cells . But first , the code on the DNA needs to be translated with the help of a ‘middle man’ , known as messenger RNA . These molecules carry information to other parts of the cell , wherever it is needed . Messenger RNA is produced in the nucleus of a cell , and then exported into the material within a cell , called the cytoplasm , as a template to produce proteins . Once this process has finished , the template is destroyed . The rate at which the messenger RNA is made affects the flow of genetic information . However , recent evidence suggests that the speed at which messenger RNA is destroyed in the cytoplasm can influence how much of it is made in the nucleus , i . e . , if high levels of RNA are destroyed , the production is stopped . For example , it has been shown that certain viruses possess proteins that speed up the destruction of messenger RNA to gain control over the host cell . Here , Gilbertson et al . wanted to find out more about how the breakdown of RNA can signal the nucleus to stop producing these molecules . Messenger RNAs are coated with proteins , which are released when the RNA is destroyed . To test if some of those proteins travel back to the nucleus to influence the production of messenger RNA , proteins in human cells grown in the laboratory were labeled with specific trackers . RNA destruction was induced , in a way that is similar to what happens during a virus attack . The experiments revealed that many RNA-binding proteins indeed return to the nucleus when RNA is destroyed . One of these proteins , named cytoplasmic poly ( A ) -binding protein , played a key role in transmitting the signal between the cytoplasm and the nucleus to control the production messenger RNA . The amount of messenger RNA can change in many ways throughout the life of a cell . For example , viral infections can lower it and limit the growth and health of cells . A drop in these molecules could act as an early warning of ill health in cells and trigger responses in the nucleus . This new link between messenger RNA destruction and production may help to shed new light on how cells use different signals to control the production of their own genes while restricting pathogens from taking over . A next step will be to determine how these signals communicate with the RNA production machinery in the nucleus and how certain viruses can subvert this process to activate their own genes . | [
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] | 2018 | Changes in mRNA abundance drive shuttling of RNA binding proteins, linking cytoplasmic RNA degradation to transcription |
The voltage-gated potassium channel KV7 . 1 and the auxiliary subunit KCNE1 together form the cardiac IKs channel , which is a proposed target for future anti-arrhythmic drugs . We previously showed that polyunsaturated fatty acids ( PUFAs ) activate KV7 . 1 via an electrostatic mechanism . The activating effect was abolished when KV7 . 1 was co-expressed with KCNE1 , as KCNE1 renders PUFAs ineffective by promoting PUFA protonation . PUFA protonation reduces the potential of PUFAs as anti-arrhythmic compounds . It is unknown how KCNE1 promotes PUFA protonation . Here , we found that neutralization of negatively charged residues in the S5-P-helix loop of KV7 . 1 restored PUFA effects on KV7 . 1 co-expressed with KCNE1 in Xenopus oocytes . We propose that KCNE1 moves the S5-P-helix loop of KV7 . 1 towards the PUFA-binding site , which indirectly causes PUFA protonation , thereby reducing the effect of PUFAs on KV7 . 1 . This mechanistic understanding of how KCNE1 alters KV7 . 1 pharmacology is essential for development of drugs targeting the IKs channel .
The voltage-gated potassium channel KV7 . 1 and the auxiliary subunit KCNE1 together form the slowly activating and voltage-gated IKs potassium channel , an important channel for cardiomyocyte repolarization ( Nerbonne and Kass , 2005 ) . More than 300 mutations in the genes encoding for KV7 . 1 and KCNE1 have been found in patients with cardiac arrhythmias ( Hedley et al . , 2009 ) . Mutations that reduce IKs currents delay repolarization of the ventricular cardiac action potential and prolong the QT interval in the electrocardiogram , referred to as Long QT syndrome ( Hedley et al . , 2009 ) . Long QT syndrome is a known risk factor for ventricular fibrillation and sudden cardiac death ( Nerbonne and Kass , 2005 ) . Up to 30% of patients with inherited Long QT syndrome are not protected against severe cardiac events using current anti-arrhythmic treatments ( Goldenberg et al . , 2006; Priori et al . , 2004 ) . Therefore , several studies have promoted the need for novel pharmacological drugs that increase or even restore the function of mutated potassium channels critical for cardiomyocyte repolarization; as these drugs could potentially be used to treat Long QT syndrome in carriers with loss-of-function potassium channel mutations ( Anderson et al . , 2014; Perry et al . , 2016 ) . Several promising compounds have been found to activate the KV7 . 1 channel ( Busch et al . , 1994; Gao et al . , 2008; Mattmann et al . , 2012; Salata et al . , 1998 ) . Unfortunately , the effects of several KV7 . 1 channel activators are dramatically impaired by KCNE1 ( Busch et al . , 1997; Gao et al . , 2008; Salata et al . , 1998; Yu et al . , 2013 ) . For example , we have previously described that the activating effect of polyunsaturated fatty acids ( PUFAs ) on the human KV7 . 1 channel , expressed in Xenopus oocytes , is impaired by KCNE1 ( Liin et al . , 2015b ) . Because KV7 . 1 is co-assembled with KCNE1 in the native IKs channel complex in the heart ( Barhanin et al . , 1996; Sanguinetti et al . , 1996 ) , KV7 . 1 channel activators must affect the KV7 . 1+KCNE1 complex ( referred to as KV7 . 1+E1 ) to prevent cardiac arrhythmias , such as in Long QT syndrome . Although KCNE1 is important for the pharmacology of the IKs channel , little is known about the molecular mechanisms underlying how KCNE1 changes the sensitivity of KV7 . 1 to various compounds . This lack of mechanistic understanding limits the clinical utility and further rational design of several KV7 . 1 channel activators that potentially could be used to improve treatment of patients with conditions due to compromised KV7 . 1+E1 channels . KV7 . 1 , the alpha subunit of the IKs channel , is a potassium channel protein composed of six membrane-spanning segments , S1-S6: Helices S1 to S4 form the peripheral voltage-sensing domains and helices S5 and S6 form the central pore domain ( Figure 1A–B ) ( Liin et al . , 2015a ) . KCNE1 , a single-transmembrane protein , is proposed to interact with KV7 . 1 in the lipid-filled space between two voltage-sensing domains ( Figure 1B ) ( Chung et al . , 2009; Nakajo and Kubo , 2015; Xu et al . , 2013 ) . We have previously proposed that PUFAs incorporate into the outer leaflet of the cell membrane in the same lipid-filled space as KCNE1 , but they incorporate closer than KCNE1 does to the transmembrane segments S3 and S4 ( Figure 1B ) ( Liin et al . , 2015b ) . In this position close to S4 , negatively charged PUFAs , such as docosahexaenoic acid ( DHA ) , facilitate KV7 . 1 channel opening by electrostatically promoting the outward movement of the positively charged S4 helix ( Figure 1C ) ( Liin et al . , 2015b ) . As the DHA is negatively charged , DHA shifts the voltage dependence of KV7 . 1 channel opening toward more negative voltages ( Figure 1C ) ( Liin et al . , 2015b ) . However , we previously observed that this activating effect of DHA at physiological pH ( i . e . pH 7 . 4 ) was abolished when KV7 . 1 was co-expressed with KCNE1 to form the IKs channel complex ( Liin et al . , 2015b ) . In addition , we proposed that this reduced effect is the result of KCNE1 decreasing the local pH at the DHA-binding site , inducing protonation of the DHA carboxyl head at pH 7 . 4 ( Figure 1D ) ( Liin et al . , 2015b ) . Therefore , DHA becomes uncharged and ineffective at physiological pH . As a consequence , PUFA analogues with a lower pKa of the head group , which prevents protonation at physiological pH , was able to activate KV7 . 1+E1 at physiological pH ( Liin et al . , 2016 , 2015b ) . Moreover , we showed that the inhibiting effect of the positively charged PUFA analogue arachidonoyl amine ( AA+ ) was potentiated by KCNE1 , as if the decreased local pH at the PUFA-binding site further protonated the amine head of AA+ ( Liin et al . , 2015b ) . This improved protonation improves the electrostatic repulsion on the voltage sensor induced by AA+ ( Liin et al . , 2015b ) . However , it remains unclear how KCNE1 decreases the local pH at the PUFA-binding site . A mechanistic understanding of how KCNE1 tunes the pharmacology of the IKs channel is critical for our ability to predict which PUFAs modulate the IKs channel , knowledge that will guide the development of synthetic PUFA analogues that pharmacologically target the KV7 . 1+E1 channel . In this work , we propose a molecular mechanism that explains the KCNE1-induced protonation of PUFA . To identify structural motifs in the KV7 . 1+E1 channel that are responsible for PUFA protonation , we took advantage of the distinct pH dependence of the DHA effect on KV7 . 1 and KV7 . 1+E1 . We previously described that the ability of DHA to shift the voltage dependence of KV7 . 1 channel opening increases as pH increases , most likely due to deprotonation of DHA at higher pH ( Figure 1E , dashed line ) ( Liin et al . , 2015b ) . We also described that the pH dependence of the DHA effect on KV7 . 1+E1 is shifted by about 1 pH unit compared to KV7 . 1 , making DHA completely protonated and ineffective on KV7 . 1+E1 at physiological pH ( Figure 1E , compare dashed and solid lines ) ( Liin et al . , 2015b ) . In this work , we systematically mutated motifs in KV7 . 1 and KCNE1 that could potentially cause KCNE1-induced protonation of DHA . We then compared the pH dependence of the DHA effect for each KV7 . 1+E1 channel mutant to that of wild-type ( WT ) KV7 . 1 with and without KCNE1 co-expressed . Our findings suggest that negatively charged amino acids in the S5-P-helix loop of KV7 . 1 cause DHA protonation , but only when DHA is bound to the KV7 . 1+E1 channel . We propose a model in which KCNE1 indirectly modulates the pharmacology of KV7 . 1 by inducing structural re-arrangements of the extracellular S5-P-helix loop of KV7 . 1 , moving acidic residues in this loop close to the DHA molecule .
First , we tested the impact that charged amino acids in the extracellular N terminus of KCNE1 have on the pH dependence of the DHA effect on KV7 . 1 . We created three KCNE1 constructs to systematically remove these charged amino acids . The first construct , E1/∆N2-38 , removed most of the N terminus , including the charges E19 , R32 , R33 , and R36 ( Figure 2A ) . The second construct , E1/D39C/E43C , removed the two remaining negative charges in the N-terminal end of KCNE1 , and the third construct , E1/K41C , removed the remaining positive charge ( Figure 2A ) . We assessed the pH dependence of the effect of extracellular application of 70 µM DHA ( relative ΔV50 , see Materials and methods for details ) on these KCNE1 mutants to test whether each mutant had a KV7 . 1+E1 like ( continuous line in Figure 2B ) or KV7 . 1-like ( dashed line in Figure 2B ) pH dependence of the DHA effect . When co-expressed with WT KV7 . 1 , all three KCNE1 mutants generated currents with voltage dependence of channel opening shifted to more positive voltages compared to WT KV7 . 1+E1 ( Supplementary file 1 ) . As shown earlier , interactions of the N-terminal end of KCNE1 with several parts of KV7 . 1 ( e . g . S1 , S4 , S6 , and the S5-P-helix loop ) may underlie the shifts in voltage dependence induced by these mutations ( Barro-Soria et al . , 2017; Chung et al . , 2009; Xu et al . , 2013 ) . We found that the pH dependence of the DHA effect on all three constructs was similar to the pH dependence of the DHA effect on WT KV7 . 1+E1 ( Figure 2B ) . The apparent pKa of the DHA effect on WT KV7 . 1 co-expressed with the KCNE1 mutants were close to the apparent pKa for the DHA effect on WT KV7 . 1+E1 ( Figure 2C ) . Thus , mutations of the extracellular N terminus of KCNE1 did not restore KV7 . 1-like pH dependence of the DHA effect , as if extracellular charged amino acids in KCNE1 are not important for protonation of DHA in KV7 . 1+E1 . Because charged amino acids in the N terminus of KCNE1 are not responsible for the KCNE1-induced change in the pH dependence of the DHA effect , we looked at charged amino acids in the extracellular loops of KV7 . 1 . The S5-P-helix loop in KV7 . 1 is long and contains several negatively charged residues ( Figure 3A ) . Xu et al . previously reported that cysteines introduced into the S5-P-helix loop of KV7 . 1 form disulfide bonds with residues in the N terminus of KCNE1 ( Xu et al . , 2013 ) . In other KV channels , the S5-P-helix loop exerts electrostatic effects on S4 due to its close proximity ( Broomand et al . , 2007; Elinder et al . , 2016 ) . Because the S5-P-helix loop could be in close proximity to the PUFA-binding site ( which is proposed to be next to S4 ) , we tested whether charged residues in the S5-P-helix loop influence DHA protonation . To this end , we created mutants in which the negatively charged amino acids E284 , D286 , E290 , E295 , and D301 in the S5-P-helix loop were , one by one , exchanged for cysteines ( Figure 3A ) . When co-expressed with WT KCNE1 , four of the KV7 . 1 mutants ( D301C being the exception ) generated currents with voltage dependence of channel opening that were shifted slightly to more positive voltages compared to WT KV7 . 1+E1 ( Supplementary file 1 ) . By plotting the pH dependence of the DHA effect , we found that these mutants showed a range of pH-response curves in-between the curves of WT KV7 . 1+E1 and KV7 . 1 alone ( Figure 3B ) . The pH-response curve for the DHA effect on KV7 . 1/D301C + E1 most closely resembled the pH-response curve for the DHA effect on WT KV7 . 1+E1 ( Figure 3B , blue curve ) . In contrast , the pH-response curve for the DHA effect on KV7 . 1/E290C + E1 overlapped with the pH-response curve for the DHA effect on KV7 . 1 alone ( Figure 3B , red curve ) . The apparent pKa of the DHA effect on KV7 . 1/E290C + E1 was close to the apparent pKa for the DHA effect on WT KV7 . 1 ( Figure 3C ) . The apparent pKa of the DHA effect on KV7 . 1/E290A + E1 or KV7 . 1/E290C + E1 with DTT ( 1 , 4-Dithiothreitol ) in the extracellular solution , to prevent formation of any potential disulfide bonds by E290C , were also similar to the apparent pKa for the DHA effect on WT KV7 . 1 ( Figure 3—figure supplement 1A ) . In addition , the apparent pKa of the DHA effect on KV7 . 1/E290R + E1 was lower than for KV7 . 1/E290A + E1 ( Figure 3—figure supplement 1B ) . Altogether , these data suggest that negatively charged residues in the S5-P-helix loop ( especially E290 ) promote the protonation of DHA in KV7 . 1+E1 . This protonation could be due to the negative charges in the S5-P-helix loop attracting hydrogen ions to the DHA-binding site . Because DHA protonation is promoted by KCNE1 , a requisite for this hypothesis is that these negatively charged residues in the S5-P-helix loop are located close to the binding site for DHA when KCNE1 is present , but not when KCNE1 is absent . To further explore this possibility , we performed experiments using the KV7 . 1/E290C mutation with the largest impact on the pH dependence of the DHA effect . To test the prediction that the E290C mutation does not alter the pH dependence of the DHA effect in the absence of KCNE1 , we tested the effect of DHA on KV7 . 1/E290C without KCNE1 co-expressed . As described previously ( Wang et al . , 2015 ) , KV7 . 1/E290C generated currents with WT KV7 . 1-like voltage dependence for channel opening ( Supplementary file 1 ) . The pH-response curve for the DHA effect on KV7 . 1/E290C alone closely resembled the pH-response curve for the DHA effect on WT KV7 . 1 ( Figure 4A , compare red dashed and black dashed curves ) . The apparent pKa of the DHA effect on KV7 . 1/E290C was close to the apparent pKa for the DHA effect on WT KV7 . 1 ( Figure 4B ) . Extracellular application of 70 µM DHA at pH 8 . 2 shifted the voltage dependence of channel opening of KV7 . 1/E290C similar to WT KV7 . 1 ( Figure 4C ) . This was clearly distinct from the altered pH dependence and increase in the DHA effect by the E290C mutation at pH 8 . 2 in the presence of KCNE1 ( Figure 4A–C ) . These findings suggest that E290 only promotes DHA protonation when KV7 . 1 is co-expressed with KCNE1 . Next , we tested whether we could restore WT KV7 . 1+E1 like response to DHA by restoring the negative charge at position 290 in the KV7 . 1/E290C + E1 mutant . For these experiments , we used the negatively charged cysteine-specific sodium [2-sulfonatoethyl] methanethiosulfonate ( MTSES— ) reagent to covalently attach the negatively-charged SES— group to E290C . To maximize the chance of seeing a difference in the DHA effect , we compared the effect of DHA on KV7 . 1/E290C + E1 with and without MTSES— modification at pH 8 . 2 , the pH at which the difference in the DHA effects was greatest between KV7 . 1 and KV7 . 1+E1 . Modification of KV7 . 1/E290C + E1 by extracellular application of 10 mM MTSES— had no clear effect on the intrinsic properties of KV7 . 1/E290C + E1 ( Figure 4—figure supplement 1 ) . However , modification of KV7 . 1/E290C + E1 with MTSES— dramatically reduced the ability of 70 µM DHA to shift V50 ( Figure 4D–F ) . The DHA-induced shift of V50 in MTSES— modified and unmodified KV7 . 1/E290C + E1 was −11 . 8 ± 1 . 4 mV and −30 . 2 ± 2 . 6 mV , respectively . In addition , DHA induced a similar V50 shift in both WT KV7 . 1+E1 and MTSES— modified KV7 . 1/E290C + E1 at pH 8 . 2 ( Figure 4D ) . This data further supports the notion that the negative charge at position 290 is important for tuning DHA protonation . As a final test of whether E290 changes the local pH at the binding site of PUFAs , we tested the effect of arachidonoyl amine ( AA+ ) on KV7 . 1/E290C + E1 . AA+ is a PUFA analogue in which the negatively charged carboxyl head has been exchanged for a positively charged amine head ( structure in Figure 4G ) . We previously showed that AA+ shifts the V50 of KV7 . 1 and KV7 . 1+E1 by approximately +10 and +23 mV , respectively ( Liin et al . , 2015b ) , as if KCNE1-induced protonation of the amine head improves the electrostatic repulsion on the voltage sensor induced by AA+ and further prevents channel opening ( Figure 4G , cartoon ) . In the presence of KCNE1 , here we found that mutation of E290 caused a significant reduction in the AA+-induced V50 shift ( Figure 4G , compare black and red bar ) . The AA+ effect on KV7 . 1/E290C + E1 was similar to that on WT KV7 . 1 alone ( Figure 4G , compare red and striped bar ) , as if primarily E290 is responsible for the improved effect of AA+ in the presence of KCNE1 . These experiments using the positively charged PUFA analogue AA+ provide further support for the hypothesis that E290 is important for the KCNE1-induced protonation of the PUFAs .
In this study , we examined how KCNE1 changes the pharmacology of KV7 . 1 by inducing PUFA protonation . Our results show that negatively charged residues in the loop connecting S5 to the pore helix , but not charged residues in the extracellular part of KCNE1 , are important for KCNE1-induced DHA protonation . Neutralization of residue E290 at the top of the turret in the S5-P-helix loop had the largest impact on DHA protonation . Neutralization of E290 fully restored KV7 . 1-like pharmacological sensitivity of KV7 . 1+KCNE1 to DHA . That is , DHA induced a shift in V50 of KV7 . 1/E290C + KCNE1 at pH 7 . 4 and the pH dependence of the DHA effect was similar as for KV7 . 1 expressed without KCNE1 . We further show that neutralization of E290 only improved the DHA effect on KV7 . 1 when KV7 . 1 was co-expressed with KCNE1 and that it was the negative charge at position E290 that was important for the change in PUFA effect . Figure 5 shows our proposed model of how KCNE1 changes the pharmacology of KV7 . 1 to PUFAs . We propose that KCNE1 indirectly promotes PUFA protonation by inducing conformational re-arrangements in the KV7 . 1 channel , which moves the S5-P-helix loop closer to the PUFA-binding site . This hypothesis fits with the location of each tested amino acid and the size of the effect of each tested amino acid when neutralized: E290 , the residue with the largest effect , is located in the middle of the long S5-P-helix loop and may easily reach over to the putative DHA binding site , whereas D301 , the residue with the least effect , is in the P-helix , located far from the putative DHA-binding site ( Figure 5D ) . Although the structural details and extent of the KCNE1-induced re-arrangements in KV7 . 1 will need more study , our proposed model agrees with previous findings . In a recently published cryo electron-microscopy structure of Xenopus KV7 . 1 , the S5-P-helix loop forms a negatively charged cap above the pore domain ( Sun and MacKinnon , 2017 ) . Especially S280 ( which corresponds to E290 in human KV7 . 1 ) reaches all the way to the ion-conducting pore ( Sun and MacKinnon , 2017 ) . This finding agrees with our proposed model in which the S5-P-helix loop is fairly far from the PUFA binding site in KV7 . 1 expressed without KCNE1 ( schematically illustrated in Figure 5A ) . When KV7 . 1 was co-expressed with KCNE1 , Xu et al . reported that cysteines introduced in the S5-P-helix loop of KV7 . 1 ( at positions 284 , 286 , 290 , or 295 ) form disulfide bonds with cysteines introduced at positions 32 and 33 in the N terminus of KCNE1 ( Xu et al . , 2013 ) . Chung et al . reported that cysteines introduced in the S5-P-helix loop of KV7 . 1 ( at positions 284 , 285 , or 286 ) may form disulfide bonds also with cysteines introduced at positions 40–43 in the very end of the N terminus of KCNE1 connecting to the transmembrane segment of KCNE1 , especially for KV7 . 1/E284C – E1/E43C and KV7 . 1/D286C – E1/G40C ( Chung et al . , 2009 ) . In addition , Y46 in the outermost end of the transmembrane segment of KCNE1 was found in molecular dynamics simulations to dynamically interact with residues G297-D301 in the S5-P-helix loop of KV7 . 1 ( Xu et al . , 2013 ) . These observations suggest that the S5-P-helix loop can reach all the way to KCNE1 in the lipid-filled space between neighboring voltage-sensing domains , a finding that agrees with our proposed model ( schematically illustrated in Figure 5B ) . The charge distribution in the S5-P-helix loop will then determine to what extent protonatable compounds , such as PUFAs , are negatively charged and thereby able to electrostatically interact with the voltage sensor S4 ( schematically illustrated in Figure 5B–C ) . It is , however , insufficient to remove the N-terminal KCNE1 residues , as in our KCNE1/∆N2-38 construct , or to neutralize charged residues in the N-terminus of KCNE1 to restore KV7 . 1-like pH dependence of the DHA effect . We therefore find it unlikely that the S5-P-helix loop is attracted to KCNE1 by the N terminus of KCNE1 . Instead , we propose that the binding of the KCNE1 transmembrane segment to the transmembrane segments of KV7 . 1 induces a re-arrangement of KV7 . 1 , which moves the top of the turret ( the S5-P-helix loop ) and its acidic residues closer to the PUFA binding site; therefore these acidic residues in the S5-P-helix loop promote PUFA protonation . During the last two decades , several KV7 . 1 and IKs channel activators have been identified ( e . g . Busch et al . , 1994; Gao et al . , 2008; Mattmann et al . , 2012; Salata et al . , 1998 ) . KCNE1 has a major impact , either positive or negative , on the effect of some of these activators . For example , ML277 , ZnPy , and R-L3 activate KV7 . 1 , but the effect is reduced by KCNE1 co-expression ( Gao et al . , 2008; Salata et al . , 1998; Yu et al . , 2013 ) . The proposed mechanism for the reduced sensitivity in KV7 . 1+E1 channels to these compounds is that KCNE1 and the compound compete for the same overall binding site ( Gao et al . , 2008; Seebohm et al . , 2003 ) or that KCNE1 blocks the access to the compound binding site ( Xu et al . , 2015 ) . In contrast , mefenamic acid and DIDS ( 4 , 4´-diisothiocyanatostilbene-2 , 2´-disulfonic acid ) have effects on KV7 . 1 expressed alone smaller than on KV7 . 1 co-expressed with KCNE1 ( Busch et al . , 1997 ) . For DIDS and mefenamic acid , amino acids at the top of the KCNE1 transmembrane segment ( KCNE1 amino acid 39–43 ) are important for the effect , but there is no clear mechanism for how KCNE1 increases the effect of mefenamic acid and DIDS ( Abitbol et al . , 1999 ) . Altogether , it is clear that KCNE1 can impair or promote the effect of KV7 . 1 channel activators through diverse mechanisms . Our novel model explains how KCNE1 impairs the effect of negatively charged PUFAs on KV7 . 1 by indirectly promoting PUFA protonation . A detailed mechanistic understanding of how KCNE1 impairs the sensitivity of KV7 . 1 to activators will enable rational drug design of compounds that circumvent KCNE1-induced impairment . For example , charged PUFA analogues and related compounds may be chemically optimized to preserve their charge or designed to bind to a slightly different site to evade protonation promoted by KCNE1 . A mechanistic framework for the design of IKs channel activators may open up new avenues for treating cardiac arrhythmias , such as Long QT syndrome .
KV7 . 1 ( GenBank Acc . No . NM_000218 ) in expression plasmid pXOOM and KCNE1 ( NM_000219 ) in pGEM have been previously described ( Jespersen et al . , 2002; Schmitt et al . , 2007 ) . Mutations were introduced using site-directed mutagenesis ( QuikChange II XL with 10 XL Gold cells , Agilent , CA ) . Newly mutated constructs were sequenced at the core facility at Linköping University to ensure correct sequence . cRNA was prepared using T7 mMessage mMachine transcription kit ( Ambion/Invitrogen , CA ) . RNA concentration was quantified using spectrophotometry ( NanoDrop 2000c , Thermo scientific , MA ) . Xenopus oocytes were surgically isolated at Linköping University or purchased from EcoCyte Bioscience ( Castrop-Rauxel , Germany ) . Animal experiments were uppriven by the local ethics committee . Isolated Xenopus oocytes were injected with 50 nl RNA ( each oocyte injected with 50 ng KV7 . 1 RNA for expression of KV7 . 1 alone or 25 ng KV7 . 1 RNA and 8 ng KCNE1 RNA for co-expression of KV7 . 1+E1 ) . The oocytes were incubated at 16°C for 2 to 3 days before performing two-electrode voltage clamp experiments . The two-electrode voltage clamp recordings were performed using a Dagan CA-1B Amplifier ( Dagan , MN ) . Currents were filtered at 500 Hz and sampled at 5 kHz . The holding voltage was generally set to −80 mV . Activation curves were generally generated in steps between −80 and +80 mV in increments of 10 mV ( 3 s duration for KV7 . 1 alone and 5 s duration for KV7 . 1+E1 ) . The tail voltage was generally set to −20 mV . In experiments using arachidonoyl amine , a brief hyperpolarizing pulse ( 50 ms at −120 mV ) was introduced between the activation step and tail step to relief channels from inactivation , as previously described ( Liin et al . , 2015b ) . The control solution contained 88 mM NaCl , 1 mM KCl , 15 mM HEPES , 0 . 4 mM CaCl2 , and 0 . 8 mM MgCl2 . pH was set to 7 . 4 using NaOH . When performing experiments at higher pH , pH was set the same day as the experiment using NaOH . 4 , 7 , 10 , 13 , 16 , 19-all-cis-Docosahexaenoic acid was bought from Sigma-Aldrich ( Stockholm , Sweden ) . Arachidonoyl amine was synthesized in house , as previously described ( Liin et al . , 2015b ) . Stock solutions of the compounds were prepared in 99 . 5% ethanol . Final test solution was prepared shortly before experiments . Previously , the effective concentration of PUFA has been shown to be 70% of the nominal concentration due to PUFA binding to the chamber walls ( Börjesson et al . , 2008 ) . Here , the PUFA concentrations are the estimated effective concentration ( i . e . 70% of the nominal concentration ) . Control solution was applied using a gravity driven perfusion system . Test compounds were added manually using a syringe , as previously described ( Börjesson et al . , 2008 ) . The chamber was cleaned in-between each oocyte using albumin-supplemented control solution . For MTS experiments , fresh MTSES— ( sodium [2-sulfonatoethyl] methanethiosulfonate , Toronto Research Chemicals Inc . , North York , Ontario , Canada ) stock solution of 1 M was prepared on the day of recording . The stock solution was kept on ice . Final MTSES— solution ( 10 mM ) was diluted immediately before application to each oocyte and applied using a pump ( Harvard Apparatus MP II , CMA Microdialysis , Sweden ) with a speed of 0 . 5 ml/min for 6 min . For DTT experiments , 0 . 5 mM DTT ( 1 , 4-Dithiothreitol , Sigma-Aldrich , Stockholm , Sweden ) was added to the incubation solution and the control solution to prevent disulphide-bond formation during incubation and experiment . Electrophysiological analysis was performed in GraphPad Prism 6 and 7 ( GraphPad Software Inc . , CA ) . To quantify the voltage dependence for channel opening , tail currents were measured shortly after stepping to the tail voltage and plotted against the preceding activation voltage . A Boltzmann function was fitted to the data to generate the conductance versus voltage ( G ( V ) ) curve: ( 1 ) G ( V ) =A1+ ( A2−A1 ) / ( 1+exp ( v50−vs ) ) , where A1 is the minimal conductance , A2 the maximal conductance , V50 the midpoint ( i . e . the voltage at which the conductance is half the maximal conductance determined from the fit ) and s the slope of the curve . The slope of the curve ( s ) was constrained to be equal for control and PUFA in each oocyte . The difference in V50 induced by DHA in each oocyte ( i . e . ∆V50 ) was calculated to quantify the shift in the voltage dependence for channel opening . In the figures , G ( V ) curves have been normalized between 0 and 1 based on the fitted maximum conductance for clarity . For representative current traces , the current generated by a voltage step 20 mV more negative than V50 was selected . To plot the pH dependence of the DHA-induced shift in V50 as a function of the H+ concentration , the following concentration-response curve was fitted to the data: ( 2 ) ΔV50=ΔV50 , max/ ( 1+ ( [H+]50[H+] ) −1 ) , where ∆V50 , max is the maximal shift in V50 and [H+]50 the H+ concentration needed to cause 50% of the maximal shift in V50 . ∆V50 was then normalized between 0 and −1 for each mutant ( referred to as relative ∆V50 ) . The normalization is based on the fitted maximal value of ∆V50 from Equation ( 2 ) , set as −1 . Massive cell leakage at pH 10 prevented us from quantifying the DHA effect at pH 10 for KCNE1 and KV7 . 1 mutants . Therefore , the Hill coefficient of the concentration-response curves was constrained to −1 ( as found for the DHA concentration-response curve for WT KV7 . 1 ) to make the fits more robust . [H+]50 values were determined with asymmetrical 95% confidence interval in GraphPad Prism 7 . [H+]50 and confidence interval were log-transformed to achieve apparent pKa values . Average values are expressed as mean ± SEM or mean ± 95% confidence interval ( indicated in each figure legend ) . Statistical analyses were done using one-way ANOVA followed by a multiple comparison test . Dunnett’s multiple comparisons test was used when comparing to defined reference data . Tukey’s multiple comparisons test was used when testing all data against each other . p<0 . 05 was considered statistically significant . | The muscle cells in the heart must contract and relax in a coordinated way for the heart to pump blood efficiently around the body . Different ions flow in and out of these cells , which are known as cardiomyocytes , to control when they contract and relax . The ions enter and leave by passing through channel proteins in each cell’s membrane . People with mutations in the genes that encode these channel proteins are often prone to irregular heartbeats and may suddenly die because their heart stops pumping blood effectively . Drugs that can restore the function of mutated ion channels are considered an attractive new avenue for treating the irregular heartbeats and preventing the sudden deaths . Yet a poor understanding of how the other proteins that interact with these channels may reduce the effect of these drugs has hampered their development . In 2015 , researchers showed that some polyunsaturated fatty acids could help restore a normal heartbeat via an effect on the IKs channel , one of the ion channels that regulate the electric activity in cardiomyocytes . But , a protein called KCNE1 – which forms part of the IKs channel – reduced the effect of these fatty molecules via an unknown mechanism . Now , Larsson et al . – who are three of the researchers involved in the 2015 study – report how KCNE1 reduces the effect of polyunsaturated fatty acids on the IKs channel . The experiments involved mutated human IKs channels produced in the egg cells of African clawed frogs – a popular model system for a wide variety of biological studies . Larsson et al . found that flexible loop-like part of the IKs channel has an overall negative charge that attracts positively charged hydrogen ions to the polyunsaturated fatty acid . This masks the electrical change of the fatty acid so that it no longer has any effect on the IKs channel . Yet , this phenomenon only occurs when KCNE1 is present , suggesting that KCNE1 moves specific parts of the loop close to the polyunsaturated fatty acid . Several ion channels in cardiomyocytes are made from multiple subunits . Understanding how some of these subunits alter the effect of drugs will help scientists to develop drugs that efficiently act on these ion channels . Such drugs may offer new treatments for irregular heartbeats and prevent the sudden deaths . But as with all new drugs , extensive testing and clinical trials will be needed before anything reaches the clinic . | [
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] | 2018 | KCNE1 tunes the sensitivity of KV7.1 to polyunsaturated fatty acids by moving turret residues close to the binding site |
HLA-restricted T cell responses can induce antitumor effects in cancer patients . Previous human T cell research has largely focused on the few HLA alleles prevalent in a subset of ethnic groups . Here , using a panel of newly developed peptide-exchangeable peptide/HLA multimers and artificial antigen-presenting cells for 25 different class I alleles and greater than 800 peptides , we systematically and comprehensively mapped shared antigenic epitopes recognized by tumor-infiltrating T lymphocytes ( TILs ) from eight melanoma patients for all their class I alleles . We were able to determine the specificity , on average , of 12 . 2% of the TILs recognizing a mean of 3 . 1 shared antigen-derived epitopes across HLA-A , B , and C . Furthermore , we isolated a number of cognate T cell receptor genes with tumor reactivity . Our novel strategy allows for a more complete examination of the immune response and development of novel cancer immunotherapy not limited by HLA allele prevalence or tumor mutation burden .
Cancer immunotherapy is a cancer treatment that is designed to harness the power of the body's immune system to fight cancer ( Finn , 2018a ) . The magnitude and breadth of T cell responses can serve as a critical biomarker in cancer immunotherapy and conventional cancer treatments , such as chemotherapy and radiation ( Schumacher et al . , 2015 ) . Immunotherapy is now widely considered a game-changer , as it is rapidly becoming the 4th pillar of cancer treatment , following surgery , radiotherapy , and chemotherapy . The adoptive transfer of antitumor T cells can induce clinical responses in cancer patients ( Feldman et al . , 2015; Hu et al . , 2018; Karpanen and Olweus , 2015; Rapoport et al . , 2015; Robbins et al . , 2015; Sahin and Türeci , 2018; Tran et al . , 2016; Tran et al . , 2017 ) . However , comprehensive analysis of the specificity of antitumor T cell responses against mutated or non-mutated tumor antigens is lacking . This deficit is exacerbated by the fact that non-mutated antigens are greater in number than mutated antigens by multiple orders of magnitude ( Finn and Rammensee , 2018 ) and the high polymorphism of HLA genes ( Shao et al . , 2018 ) . The clinical importance of identifying antitumor T cell responses is highlighted by the results of recent clinical trials which attest that a shared antigen , such as NY-ESO-1 , can be targeted with potent on-target efficacy and minimal off-target toxicity in T cell receptor ( TCR ) gene therapy ( Rapoport et al . , 2015; Robbins et al . , 2015; Thomas et al . , 2018 ) . Unlike shared antigens , the vast majority of neoantigens are unique to each patient ( Coulie et al . , 2014; Finn , 2018b ) . The elucidation of T cell epitopes derived from shared antigens may facilitate the robust development of an efficacious and safe adoptive T cell therapy that is readily available to a larger cohort of cancer patients . Analysis of antigen-specific T cells using peptide/HLA ( pHLA ) multimers has been utilized as a standard technique in immunology over the past two decades ( Bentzen and Hadrup , 2017; Dey et al . , 2019; Dolton et al . , 2015 ) . Recent progress in multimer technology has enabled the high-throughput analysis of antigen-specific T cells during immune responses ( Bentzen and Hadrup , 2017 ) . However , the production of each multimer is still cumbersome and expensive because in vitro peptide exchange of generated complete pHLA proteins requires multiple complicated steps ( Toebes et al . , 2006 ) and some class I alleles are not easily produced in bacteria ( Migueles et al . , 2000 ) . Therefore , further advancements in multimer technology are needed to investigate the multitude of potential tumor-associated antigen ( TAA ) peptides presented by any given class I allele . The combination of pHLA multimer-based analysis and functional assays are utilized to measure antigen-specific T cell responses . We have developed a novel peptide-exchangeable pHLA class I multimer technology that can be applied for any given class I allele and bound peptide . Unlike comparable methods ( Andersen et al . , 2012; Migueles et al . , 2000 ) , our novel pHLA multimer technology enables a one-step peptide exchange in one tube and is more effective in class I alleles that are difficult to produce in bacteria . These technical advantages allow for a rapid , versatile , and less labor-intensive examination of the immune response . In addition , to functionally detect HLA-restricted antigen-specific T cell responses , we previously demonstrated the versatility of gene-engineered artificial antigen-presenting cells ( APCs ) individually expressing various class I alleles as a single HLA allele ( Butler and Hirano , 2014 ) . Our strategy using a paired library of the novel pHLA multimer and human cell-based artificial APC system enables identification of novel class I epitopes and detection of cognate T cells in a comprehensive and systematic way .
The adoptive transfer of TILs can induce sustained antitumor responses in patients with metastatic melanoma in combination with lymphodepletion and IL-2 administration ( Yang and Rosenberg , 2016 ) . It is well established that melanoma TILs contain antitumor T cells that are specific for both non-mutated and mutated antigens ( Andersen et al . , 2012; Bentzen et al . , 2016; Duhen et al . , 2018; Ye et al . , 2014 ) . Here , TILs were isolated from eight metastatic melanoma patients , polyclonally expanded in vitro ( Nguyen et al . , 2019 ) , and then examined for their shared antigen specificities recognizing epitopes in the context of all 25 different class I alleles present . The combination of structure-based analysis using pHLA multimers and functional analysis has been widely used to measure antigen-specific T cell responses ( Bentzen and Hadrup , 2017; Bentzen et al . , 2016; Newell et al . , 2013 ) . We initially stained the T cells using our novel peptide-exchangeable pHLA multimers with bound peptides that were previously known or predicted by publicly available algorithms ( Andreatta and Nielsen , 2016 ) . The previously known peptides were chosen from the Cancer Antigenic Peptide Database ( https://caped . icp . ucl . ac . be ) for all the class I alleles of eight melanoma patients , and we also synthesized several high-affinity binding peptides derived from TAAs based on prediction algorithms for the class I alleles that were positive in multiple patients ( Figure 1 , Figure 1—figure supplement 1 , Supplementary file 1 ) . As shown in Table 1 , all eight samples showed positivity for at least one of the chosen multimers . In addition to the previously known epitopes , we found that the newly predicted epitopes A*02:01/ABCB5700-708 and C*05:01/tyrosinase460-468 were immunogenic ( Figure 1A , B , Figure 2A–C ) . All the multimer-positive T cells secreted detectable IFN-γ in an HLA-restricted peptide-specific manner according to ELISPOT analysis , with the exception of A*02:01/gp100154-162 and A*02:01/tyrosinase369-377 T cells among the M87 TILs , only 0 . 14% and 0 . 23% of which were stained with multimer , respectively ( Figure 2A , B , Figure 2—figure supplement 1A , B , D ) . However , stimulation of M87 TILs with A*02:01-artificial APCs engineered to express the A*02:01 gene as a single HLA allele and pulsed with the gp100154-162 or tyrosinase369-377 peptide resulted in expansion and IFN-γ production in an A*02:01/gp100154-162- or A*02:01/tyrosinase369-377-specific manner , respectively , validating the low percentage of staining ( Figure 2D , E ) . Notably , only a single nominal stimulation was performed which was sufficiently weak to induce the expansion of in vivo-primed peptide-specific T cells , but avoid the in vitro priming and deletion of high-avidity T cells ( Hirano et al . , 2006 ) . The low percentage of staining of M37 and M87 TILs with A*24:02/gp100-intron4 ( 0 . 19% ) and C*05:01/tyrosinase460-468 ( 0 . 54% ) multimers , respectively , was similarly validated; one nominal peptide-specific stimulation of M37 and M87 TILs with the HLA class I-matched artificial APCs led to their expansions in an A*24:02/gp100-intron4- and C*05:01/tyrosinese460-468-specific manner , respectively ( Figure 2C , Figure 2—figure supplement 1C ) . The melanoma TILs that were studied had been polyclonally expanded in the absence of peptide-specific stimulation using an established protocol ( Nguyen et al . , 2019 ) . Conceivably , the expanded TILs contained a low ( below the detection limit ) frequency of T cells targeting shared antigens ( Bentzen et al . , 2016 ) . To explore this possibility , TILs were weakly stimulated once with class I-matched artificial APCs pulsed with the peptides listed in Supplementary file 1 ( Hirano et al . , 2006 ) . The B*18:01/MAGE-A3167-176 multimer positivity of polyclonally expanded M40 TILs was only 0 . 04% prior to peptide-specific stimulation . However , following one stimulation with B*18:01-artificial APCs pulsed with the MAGE-A3167-176 peptide , 5 . 5% of the TILs were stained with the cognate multimer and secreted IFN-γ in a B*18:01/MAGE-A3167-176-specific manner ( Figure 3A , B ) . Since pHLA multimer production requires the use of a peptide with a known exact sequence , it is not straightforward or practical to conduct high-throughput screening for new epitope peptides using a pHLA multimer-based strategy . To identify new epitope peptides , we conducted functional assays using artificial APCs , which can take up and process longer peptides and present epitope peptides via class I molecules , as stimulator cells ( Butler and Hirano , 2014 ) . The class I-matched artificial APCs were pulsed with overlapping peptides ( 20-mers with an overlap of 15 amino acids ) to cover the whole proteins of six shared antigens ( MART1 , NY-ESO-1 , SSX2 , gp100 , MAGE-A1 , and survivin ) that are frequently expressed by melanomas ( Finn , 2018b; Supplementary file 2 ) and used as stimulators in cytokine ELISPOT assays . When stimulated with B*18:01-artificial APCs pulsed with MART1-derived overlapping peptides , B*18:01+ M87 TILs showed positive responses to two adjacent peptides with the shared sequence 21YTTAEEAAGIGILTV35 ( Figure 4A , Supplementary file 2a ) . Using a series of deletion mutant peptides , we determined the minimally required epitope peptide , 25EEAAGIGIL33 presented by B*18:01 molecules . Notably , this epitope partially overlaps with but is distinct from one of the most immunogenic epitopes , A*02:01/MART127-35 , suggesting that this region of the MART1 protein is an immunological hotspot ( Cole et al . , 2010; Kawakami et al . , 1994 ) . Importantly , the B*18:01/MART125-33 multimer successfully stained up to 9 . 2% of the polyclonally expanded M87 TILs , suggesting that the B*18:01/MART125-33 T cells were a dominant population of TILs ( Figure 4B , C ) . Similarly , we detected C*03:04/NY-ESO-192-100 T cells , the frequency of which was 18 . 2% of polyclonally expanded M31 TILs , and they were also a dominant population of TILs ( Figure 4—figure supplement 1D–F , Supplementary file 2b ) . Additionally , following peptide-specific stimulation with B*40:01-artificial APCs pulsed with NY-ESO-1-derived overlapping peptides , a novel B*40:01-restricted NY-ESO-1 epitope , 125EFTVSGNIL133 , was determined ( Figure 4D–F , Supplementary file 2b ) . The B*40:01/NY-ESO-1125-133 multimer positivity was only 0 . 16% in polyclonally expanded M31 TILs . However , following one peptide-specific stimulation of the TILs with B*40:01-artificial APCs pulsed with the NY-ESO-1125-133 peptide , the frequency of B*40:01/NY-ESO-1125-133 T cells increased to 6 . 1% ( Figure 4E ) . Using a similar strategy , we identified HLA-B*40:01/gp100448-458 , C*06:02/gp100190-198 , and C*07:01/gp100479-487 T cells , which accounted for 0 . 11% , 1 . 2% , and 0 . 14% of CD8+ T cells among the polyclonally expanded TILs , respectively ( Figure 4—figure supplement 1A–C , G–L , Supplementary file 2d ) . Following peptide-specific stimulation with class I-matched artificial APCs , the frequency of B*40:01/gp100448-458 and C*07:01/gp100479-487 T cells increased to 1 . 8% and 1 . 2% , respectively , excluding the possibility that the low percentages of staining represented false positives ( Figure 4—figure supplement 1B , K ) . Although we performed similar experiments using overlapping peptides for SSX2 , MAGE-A1 , and survivin , no T cell response was observed . It should be noted that in this study , we did not investigate the expression of shared antigens in the patient’s own tumor tissues . Therefore , it is possible that the absence of a T cell response toward some shared antigens is due to the lack of their expression . The results of TCR gene therapy clinical trials demonstrate that the adoptive transfer of T cells transduced with high-affinity TCR genes can induce sustained clinical responses in cancer patients ( Feldman et al . , 2015; Fesnak et al . , 2016; Karpanen and Olweus , 2015; Morgan et al . , 2006; Rapoport et al . , 2015; Robbins et al . , 2015 ) . Several tumor-reactive TCR genes have been cloned from melanoma TILs ( Feldman et al . , 2015; Scheper et al . , 2019 ) . Indeed , some of these TCRs have been tested in TCR gene therapy clinical trials and shown to induce clinically relevant responses ( Feldman et al . , 2015; Karpanen and Olweus , 2015 ) . For the majority of tumor reactivities that we identified , multimer-positive antitumor T cells were collected and their TCR genes were molecularly cloned . All the HLA-B- and HLA-C-restricted T cell populations contained one pair of TCR genes each . These results suggested that all the HLA-B- and HLA-C-restricted antigen-specific T cells found in this study were monoclonal ( Table 1 , Figure 5—figure supplements 1 and 2 , Supplementary file 3 ) . The antigen specificity and functional reactivity of the cloned TCRs were verified by multimer staining and ELISPOT assays of TCR-reconstituted T cells . For example , when reconstituted on primary T cells , B*18:01/MART125-33 TCR-transduced T cells were successfully stained with the cognate multimer ( Figure 5A ) and strongly reacted with the MART125-33 peptide presented by surface B*18:01 molecules ( Figure 5B ) . Importantly , these cells were able to recognize B*18:01-matched and peptide-unpulsed tumor cells naturally expressing the MART1 gene ( Figure 5C ) . Although both the Malme-3M and SK-MEL-28 melanoma cell lines are negative for B*18:01 , they express the MART1 gene endogenously . When B*18:01 molecules were ectopically expressed , both melanoma cell lines were successfully recognized by B*18:01/MART125-33 TCR-transduced T cells . Moreover , A375 melanoma cells , which lack endogenous expression of both B*18:01 and MART1 , became reactive to B*18:01/MART125-33 TCR-transduced T cells only when both the B*18:01 and full-length MART1 genes ( but not either of the single genes ) were transduced ( Figure 5C , Figure 5—figure supplements 6A and 7B ) . Additionally , after transduction of the B*40:01/NY-ESO-1125-133 TCR genes , primary T cells were successfully stained with a cognate multimer ( Figure 5D ) and strongly reacted with the NY-ESO-1125-133 peptide presented by B*40:01 molecules on the cell surface ( Figure 5E ) . Furthermore , these cells were able to recognize B*40:01-matched and peptide-unpulsed tumor cells endogenously expressing NY-ESO-1 ( Figure 5F , Figure 5—figure supplements 6A and 7B ) . These results clearly demonstrate that the B*18:01/MART125-33 and B*40:01/NY-ESO-1125-133 TCR-transduced T cells were sufficiently avid to recognize tumor cells and that the cloned B*18:01/MART125-33 and B*40:01/NY-ESO-1125-133 TCRs were both tumor-reactive . Using a similar strategy , we molecularly cloned the TCR genes of A*02:01/SSX241-49 , A*24:02/gp100-intron4 , B*07:02/NY-ESO-160-72 , B*07:02/MAGE-A1289-297 , B*18:01/MAGE-A3167-176 , B*40:01/gp100448-458 , C*03:04/NY-ESO-192-100 , C*05:01/tyrosinase460-468 , C*06:02/gp100190-198 , and C*07:01/gp100479-487 and confirmed their tumor reactivities ( Table 1 , Figure 5—figure supplements 2–7 , Supplementary file 3 ) . Using a library of pHLA class I multimers and artificial APCs , we were able to determine the specificity of 12 . 2 ± 7 . 3% ( mean ± SD , max 25 . 9% , min 4 . 6% ) of the CD8+ TILs from eight melanoma patients toward 3 . 1 ± 2 . 0 ( mean ± SD , max 7 , min 1 ) previously known and novel peptides derived from shared antigens across HLA-A , B , and C . Notably , the maximum accumulative total percentage of multimer-positive CD8+ T cells was 25 . 9% ( M31 TILs ) ( Figure 6 ) . At least one shared antigen-derived epitope was identified in all the TILs that were studied . One TIL sample ( M87 ) possessed reactivity to as many as seven different antigens in the context of four different class I alleles . Interestingly , we observed that the M66 TIL sample reacted with three different A*02:01-restricted gp100-derived epitopes . Furthermore , M31 TILs exhibited reactivity to three different NY-ESO-1 peptides via three different class I alleles , and M87 TILs possessed reactivity to two different HLA-restricted epitopes each from gp100 , tyrosinase , and MART1 ( Table 1 ) . These results underscore the established strong immunogenicity of these shared antigens .
HLA-restricted T cell responses toward immunogenic peptides , mutated or non-mutated , can induce antitumor effects in cancer patients ( Feldman et al . , 2015; Rapoport et al . , 2015; Robbins et al . , 2015; Tran et al . , 2016 ) . Since the HLA gene is the most polymorphic gene in the human genome ( Shao et al . , 2018 ) , previous human T cell research has largely been limited to the few HLA alleles that are prevalent in a subset of ethnic groups . Therefore , a precise and comprehensive understanding of the antigen specificity for antitumor T cell responses remains lacking , including against non-mutated tumor antigens and infrequent HLA alleles . The sheer number of potential non-mutated antigens and the high polymorphism of HLA genes may have hampered comprehensive analysis of the specificity of antitumor T cell responses . The adoptive transfer of tumor-reactive T cells , such as TILs or T cells transduced with high-affinity TCR genes , can induce sustained tumor regression in some cancer patients ( Feldman et al . , 2015; Rapoport et al . , 2015; Robbins et al . , 2015; Tran et al . , 2016 ) . In this study , using a library of the paired pHLA multimers and artificial APCs for 25 different class I alleles and greater than 800 peptides , we systematically and comprehensively mapped shared antigenic epitopes recognized by TILs from eight melanoma patients for all their class I alleles . Furthermore , we isolated multiple TCR genes highly tumor-reactive to shared antigens from the TILs . In particular , NY-ESO-1 is one of the shared antigens that have been most promising and extensively studied , and many clinical trials using A*02:01/NY-ESO-1157-165 TCR genes are ongoing . The use of these newly cloned tumor-reactive B*07:02 , B*40:01 , and C*03:04-restricted NY-ESO-1 TCR genes may widen the applicability of anti-NY-ESO-1 TCR gene therapy and immune mobilizing monoclonal TCRs against cancer ( ImmTAC ) therapy beyond HLA-A*02:01-positive cancer patients . ImmTAC therapy trials targeting A*02:01/gp100 are also ongoing in patients with metastatic uveal melanoma ( Liddy et al . , 2012 ) . The newly cloned tumor-reactive gp100 TCRs restricted by four different class I alleles ( A*24:02 , B*40:01 , C*06:02 , and C*07:01 ) could substantially extend the applicability of ImmTAC therapy targeting gp100 . The strategy employed in this study has enabled us to decipher the antigen specificity of tumor-specific T cells for any given HLA class I allele , regardless of allele frequency ( Supplementary file 4 ) , and for a very large number of peptides . Although we studied T cell responses to greater than 800 peptides derived from more than 90 proteins across all the 25 class I alleles expressed by eight melanoma patients , our study is still limited . Additional comprehensive studies are required to elucidate the full spectrum of antitumor T cell response in TILs . Since in vitro expanded TILs were studied in the current study , further assessment of TIL directly ex vivo is needed in future studies . Our strategy has also allowed us to build a large database of class I-restricted peptides and cognate tumor-reactive TCR genes at an unprecedented scale . This database will facilitate individual examination of the personal immune response with precision as well as the identification and validation of biomarkers to aid in patient-based selection of a cancer immunotherapy regimen . Furthermore , this database will help the robust development of novel cancer vaccines , TCR gene therapies , and ImmTAC therapies for patients without being limited by HLA allele prevalence or tumor mutation burden .
Peripheral blood samples were obtained from healthy donors after Institutional Review Board approval . Mononuclear cells were obtained via density gradient centrifugation ( Ficoll-Paque PLUS; GE Healthcare ) . K562 is an erythroleukemic cell line with defective HLA expression . T2 is an HLA-A*02:01+ T cell leukemia/B-LCL hybrid cell line . Jurkat 76 is a T cell leukemic cell line lacking TCR and CD8 expression ( a gift from Dr . M . Heemskerk , Leiden University Medical Center , Leiden , the Netherlands ) ( Heemskerk et al . , 2003 ) . A375 , Malme-3M , SK-MEL-21 , SK-MEL-28 , SK-MEL-37 , Me275 , and LM-MEL-53 are melanoma cell lines . The HEK293T , MCF7 and melanoma cell lines , except for Malme-3M and LM-MEL-53 , were grown in DMEM supplemented with 10% FBS and 50 μg/ml gentamicin ( Thermo Fisher Scientific ) . Malme-3M was cultured in IMDM supplemented with 20% FBS and 50 μg/ml gentamicin . The K562 , T2 , Jurkat 76 , and LM-MEL-53 cell lines were grown in RPMI 1640 supplemented with 10% FBS and 50 μg/ml gentamicin . ACHN was cultured in EMEM supplemented with 10% FBS and 50 μg/ml gentamicin . The K562 , T2 , A375 , Malme-3M , SK-MEL-28 , LM-MEL-53 , HEK293T , MCF7 , and ACHN cells were obtained from the American Type Culture Collection ( ATCC , Manassas , VA ) . The SK-MEL-21 and SK-MEL-37 cells were obtained from Memorial Sloan Kettering Cancer Center ( New York , NY ) . The Me275 cells were obtained from Ludwig Institute for Cancer Research ( New York , NY ) . All cell lines were routinely checked for the presence of mycoplasma contamination using PCR-based technology . TILs isolated from eight metastatic melanoma patients were grown in vitro as reported previously ( Nguyen et al . , 2019 ) . High-resolution HLA DNA typing ( American Red Cross ) was performed for all TIL samples . Melanoma specimens were obtained from UHN Biospecimen Program . This study was conducted in accordance with the Helsinki Declaration and approved by the Research Ethics Board of the University Health Network , Toronto , Canada . Written informed consent was obtained from all healthy donors who provided peripheral blood samples . Synthetic peptides were purchased from Genscript ( Piscataway , NJ ) and dissolved at 50 mg/ml in DMSO . The purity of the vast majority of the peptides exceeded 85% . The peptide sequences are shown in Supplementary files 1 and 2 . All the HLA class I genes except for HLA-A*02:01 were fused with a truncated version of the human nerve growth factor receptor ( ΔNGFR ) via the internal ribosome entry site ( Renaud-Gabardos et al . , 2015 ) . ΔNGFR-transduced cells were isolated using anti-NGFR mAb . The full-length MART1 , NY-ESO-1 , SSX2 , and tyrosinase genes were cloned from Malme-3M , Me275 , SK-MEL-37 , and SK-MEL-28 cells via RT-PCR according to their published sequences , respectively . The full-length gp100 , MAGE-A1 , and MAGE-A3 genes were purchased from Dharmacon ( Lafayette , CO ) . Genomic DNA of gp100 was isolated using PureLink Genomic DNA Mini Kit ( Thermo Fisher Scientific ) . TCR genes were cloned via 5’-rapid amplification of cDNA ends ( RACE ) PCR as previously described ( Nakatsugawa et al . , 2016 ) . The 5’-RACE PCR products were cloned into a retrovirus vector and sequenced . All genes were cloned into the pMX retrovirus vector and transduced using the 293GPG cell-based retrovirus system ( Ory et al . , 1996 ) . Jurkat 76/CD8 cells were transduced with individual TCRα and TCRβ genes as reported previously ( Ochi et al . , 2015 ) . The Jurkat 76/CD8-derived TCR transfectants were purified ( >95% purity ) using CD3 Microbeads ( Miltenyi Biotec ) . The K562-based artificial APCs individually expressing various HLA class I genes as a single HLA allele in conjunction with CD80 and CD83 have been reported previously ( Butler and Hirano , 2014 ) . PG13-derived retrovirus supernatants were used to transduce TCR genes into human primary T cells . TransIT293 ( Mirus Bio ) was used to transfect TCR genes into the 293GPG cell line . SSX2- SK-MEL-21 and SK-MEL-28 cells were retrovirally transduced with the full-length SSX2 gene to generate SK-MEL-21/SSX2 and SK-MEL-28/SSX2 cells , respectively . Gp100- A375 cells were retrovirally transduced with exons 1 , 2 , and 3 and intron 4 of the gp100 gene to generate A375/gp100-intron4 cells as reported previously ( Robbins et al . , 1997 ) . Gp100- SK-MEL-37 , ACHN , and A375 cells were retrovirally transduced with the full-length gp100 gene to generate SK-MEL-37/gp100 , ACHN/gp100 , and A375/gp100 cells , respectively . MART1- A375 cells were retrovirally transduced with the full-length MART1 gene to generate A375/MART1 cells . NY-ESO-1- SK-MEL-21 and SK-MEL-28 cells were retrovirally transduced with the full-length NY-ESO-1 gene to generate SK-MEL-21/NY-ESO-1 and SK-MEL-28/NY-ESO-1 cells , respectively . MAGE-A1- SK-MEL-21 cells were retrovirally transduced with the full-length MAGE-A1 gene to generate SK-MEL-21/MAGE-A1 cells . MAGE-A3- HEK293T cells were retrovirally transduced with the full-length MAGE-A3 gene to generate HEK293T/MAGE-A3 cells . Tyrosinase- MCF7 cells were retrovirally transduced with the full-length tyrosinase gene to generate MCF7/tyrosinase cells . The expression of transduced MART1 , NY-ESO-1 , gp100 , MAGE-A1 , and tyrosinase was evaluated by flow cytometry after staining with an anti-MART1 mAb ( clone A103; Santa Cruz Biotechnology ) , anti-NY-ESO-1 mAb ( clone D1Q2U; Cell Signaling Technology ) , anti-gp100 mAb ( clone 7E3; LifeSpan Biosciences ) , anti-MAGE-A1 mAb ( clone MA454; LifeSpan Biosciences ) , and anti-tyrosinase mAb ( clone ERP10141; Abcam ) , respectively . The expression of SSX2 and MAGE-A3 in the transduced cells was evaluated by Western blot analysis with an anti-SSX2 pAb ( Thermo Fisher Scientific ) and anti-MAGE-A3 pAb ( LifeSpan Biosciences ) , respectively . HLA-A*02:01- SK-MEL-28 cells were retrovirally transduced with the HLA-A*02:01 gene to generate SK-MEL-28/A*02:01 . HLA-A*24:02- Malme-3M , SK-MEL-28 , and A375 cells were retrovirally transduced with HLA-A*24:02 to generate Malme-3M/A*24:02 , SK-MEL-28/A*24:02 , and A375/A*24:02 cells , respectively . Similarly , A375/B*07:02 , SK-MEL-37/B*07:02 , Me275/B*07:02 cells , Malme-3M/B*18:01 , SK-MEL-28/B*18:01 , A375/B*18:01 , HEK293T/B*18:01 , A375/B*40:01 , SK-MEL-37/B*40:01 , A375/C*03:04 , SK-MEL-37/C*03:04 , Malme-3M/C*05:01 , Me275/C*05:01 , Malme-3M/C*06:02 , SK-MEL28/C*06:02 , and A375/C*07:01 cells were generated using a retrovirus system . All the class I genes except for A*02:01 , were tagged with the ΔNGFR gene as described above , and the ΔNGFR+ cells were purified ( >95% purity ) and used in subsequent experiments . The ΔNGFR gene alone was retrovirally transduced as a control . Cell surface molecules were stained with a PC5- or Pacific Blue-conjugated anti-CD8 mAb ( clone B9 . 11; Beckman Coulter ) , FITC-conjugated anti-NGFR mAb ( clone ME20 . 4; Biolegend ) , APC/Cy7-conjugated anti-CD3 mAb ( clone UCHT1; Biolegend ) , and FITC-conjugated anti-HLA-A2 mAb ( clone BB7 . 2; Biolegend ) . Dead cells were discriminated with the LIVE/DEAD Fixable Aqua Dead Cell Stain kit ( Thermo Fisher Scientific ) . For intracellular staining , cells were fixed and permeabilized by using a Cytofix/Cytoperm kit ( BD Biosciences ) . Stained cells were analyzed with flow cytometry ( BD Biosciences ) , and data analysis was performed using FlowJo ( Tree Star ) . Cell sorting was conducted using a FACS Aria II ( BD Biosciences ) . IFN-γ ELISPOT analysis was conducted as described previously ( Kagoya et al . , 2018 ) . PVDF plates ( Millipore , Bedford , MA ) were coated with the capture mAb ( clone 1-D1K; MABTECH , Mariemont , OH ) , and T cells were incubated with 2 × 104 target cells per well in the presence or absence of a peptide for 20–24 hr at 37°C . The plates were subsequently washed and incubated with a biotin-conjugated detection mAb ( clone 7-B6-1; MABTECH ) . HRP-conjugated SA ( Jackson ImmunoResearch ) was then added , and IFN-γ spots were developed . The reaction was stopped by rinsing thoroughly with cold tap water . ELISPOT plates were scanned and counted using an ImmunoSpot plate reader and ImmunoSpot version 5 . 0 software ( Cellular Technology Limited , Shaker Heights , OH ) . Equal amounts of proteins were separated on 8% gels by SDS-PAGE and transferred to Immobilon-P PVDF membranes ( Millipore ) . The membranes were probed with the primary antibodies at 4°C overnight . The membranes were then washed and incubated with HRP-conjugated anti-mouse IgG ( Promega ) or anti-rabbit IgG ( Santa Cruz Biotechnology ) secondary antibody at room temperature for 1 hr . The following antibodies were used: anti-SSX2 pAb ( Thermo Fisher Scientific ) , anti-MAGE-A3 pAb ( LifeSpan Biosciences ) , and anti–β-actin antibody ( Santa Cruz Biotechnology ) . The signal was detected by Amersham ECL Prime Western Blotting Detection Reagent ( GE Healthcare ) . The TIL expansion procedure was performed as previously published ( Nguyen et al . , 2019 ) . Briefly , melanoma tissue was processed by cutting into ~1 mm3 fragments . Tissue fragments were either plated directly into 24-well plates or enzymatically dissociated in IMDM containing collagenase ( Sigma ) and Pulmozyme ( Roche ) and then plated in 24-well plates . Cells were cultured in complete medium ( as previously described ) and 6 , 000 IU/mL IL-2 and expanded for approximately 4 weeks prior to cryopreservation . For the REP , TILs were thawed , rested , and seeded in T175 flasks with 30 ng/mL OKT3 ( Miltenyi Biotec ) , irradiated ( 50 Gy ) allogeneic PBMC feeder cells ( 1:200 TIL:PBMC ) , and 600 IU/mL IL-2 in ‘50/50’ media containing 50% complete medium prepared using human serum AB+ ( Gemini Bio Products ) and 50% AIM V media ( Gibco ) . TILs were harvested on day 14 of the REP and cryopreserved before analysis . CD8+ TILs were purified through negative magnetic selection using a CD8+ T Cell Isolation Kit ( Miltenyi Biotec ) . HLA class I-matched artificial APCs were pulsed with 10 μg/ml class I-restricted peptides of interest for 6 hr . The artificial APCs were then irradiated at 200 Gy , washed , and added to the TILs at an effector to target ( E:T ) ratio of 20:1 . After forty-eight hours , 10 IU/ml IL-2 ( Novartis ) , 10 ng/ml IL-15 ( Peprotech ) , and 30 ng/ml IL-21 ( Peprotech ) were added to the cultures every three days . CD3+ T cells were purified through negative magnetic selection using a Pan T Cell Isolation Kit ( Miltenyi Biotec ) . Purified T cells were stimulated with artificial APC/mOKT3 irradiated with 200 Gy at an E:T ratio of 20:1 . Starting on the next day , activated T cells were retrovirally transduced with the cloned TCR genes via centrifugation for 1 hr at 1 , 000 g at 32°C for three consecutive days . On the following day , 100 IU/ml IL-2 and 10 ng/ml IL-15 were added to the TCR-transduced T cells . The culture medium was replenished every 2–3 days . The affinity-matured HLA class I gene was engineered to carry a Glu ( E ) residue in lieu of the Gln ( Q ) residue at position 115 of the α2 domain and a mouse Kb gene-derived α3 domain instead of the HLA class I α3 domain ( Wooldridge et al . , 2007 ) . By fusing the extracellular domain of the affinity-matured HLA class I gene with a Gly-Ser ( GS ) flexible linker followed by a 6x His tag , we generated the soluble HLA class IQ115E-Kb gene . HEK293T cells were individually transduced with various soluble HLA class IQ115E-Kb genes using the 293GPG cell-based retrovirus system ( Ory et al . , 1996 ) . Stable HEK293T cells expressing soluble affinity-matured class IQ115E-Kb gene were grown until confluent , and the medium was then changed . Forty-eight hours later , the conditioned medium was harvested and immediately used or frozen until use . The soluble HLA class IQ115E-Kb-containing supernatant produced by the HEK293T transfectants was mixed with 100–1000 μg/ml of class I-restricted peptide of interest overnight at 37°C for in vitro peptide exchange . Soluble monomeric class IQ115E-Kb loaded with the peptide was multimerized using an anti-His mAb ( clone AD1 . 1 . 10; Abcam ) conjugated to a fluorochrome such as phycoerythrin ( PE ) or allophycocyanin ( APC ) at a 2:1 molar ratio for 2 hr at room temperature or overnight at 4°C . The concentration of functional soluble HLA class IQ115E-Kb molecules was measured by specific enzyme-linked immunosorbent assay ( ELISA ) using an anti-pan class I mAb ( clone W6/32; in-house ) and an anti-His tag biotinylated mAb ( clone AD1 . 1 . 10; R and D systems ) as capture and detection Abs , respectively . The efficiency of peptide exchange in soluble class IQ115E-Kb monomers was assessed using a competition binding assay and ELISA . The monomer was loaded with 100 μg/ml biotinylated peptide and incubated overnight at 37°C . Biotinylated peptide-monomer was purified and exchanged with PBS using Amicon Ultra filters ( molecular weight cut-off ( MWCO ) 10 kDa ) ( Millipore Sigma , Burlington , MA ) and mixed with 1 mg/ml HLA-restricted peptide of interest or an equivalent volume of DMSO followed by overnight incubation at 37°C . ELISA plates were coated with anti-pan class I mAb ( clone W6/32 ) at 10 μg/mL in PBS overnight at 4°C . The plates were washed and blocked with 10% nonfat dry milk in PBS for 30 min at room temperature . Peptide-monomer was added and incubated for 2 hr at room temperature . After washing , the plates were incubated with streptavidin-conjugated alkaline phosphatase for 30 min at room temperature . Finally , the plates were washed and incubated with p-nitrophenyl phosphate ( PNPP ) substrate ( Pierce , Rockford , IL ) at room temperature . The reaction was terminated by adding 1 mol/L NaOH . The optic density ( OD ) ( 405 nm ) was read ( Spectramax 190 Microplate Reader; Molecular Devices , Sunnyvale , CA ) . The OD values from the control wells containing nonbiotinylated peptide were subtracted from the OD values in test wells containing biotinylated peptide . The efficiency of peptide exchange for each monomer was calculated as follows: Peptide exchange efficiency = [1 - ( OD value with peptide/OD value with DMSO ) ] x 100 . Every sample was assayed in triplicate wells . The biotinylated peptides used were HLA-A*02:01-restricted telomerase540-548 ILAK ( -biotin ) FLHWL , B*07:02-restricted MiHAg SMCY1041-1051 SPSVDK ( -biotin ) ARAEL , and C*07:02-restricted adenovirus B585-593 FRK ( -biotin ) DVNMVL . The efficiencies of peptide exchange in the A*02:01 , B*07:02 , and C*07:02 monomers were shown in Figure 6—figure supplement 1 . T cells ( 1 × 105 ) were incubated for 30 min at 37°C in the presence of 50 nM dasatinib ( LC laboratories ) . The cells were then washed and incubated with 5–10 μg/ml of PE-conjugated multimer for 30 min at room temperature , and R-phycoerythrin-conjugated AffiniPure Fab fragment goat anti-mouse IgG1 antibody ( Jackson ImmunoResearch Laboratories ) was added for 15 min at 4°C . Next , the cells were washed three times and costained with an anti-CD8 mAb for 15 min at 4°C . Dead cells were finally discriminated using the LIVE/DEAD Fixable Dead Cell Stain kit . For multiplex staining , after incubation with PE-conjugated multimer , the cells were washed three times and then incubated with 5–10 μg/ml of APC-conjugated multimer for 30 min at room temperature followed by costaining with an anti-CD8 mAb . The stability and multiplexing ability of the reagents were confirmed ( Figure 6—figure supplements 2 and 3 ) . Statistical analysis was performed using GraphPad Prism 5 . 0 . To determine whether two groups were significantly different for a given variable , we conducted an analysis using Welch’s t test ( two-sided ) . P values < 0 . 05 were considered significant . | The immune system is the body’s way of defending itself , offering protection against diseases such as cancer . But to remove the cancer cells , the immune system must be able to identify them as different from the rest of the body . All cells break down proteins into shorter fragments , known as peptides , that are displayed on the cell surface by a protein called human leukocyte antigen , HLA for short . Cancer cells display distinctive peptides on their surface as they generate different proteins to those of healthy cells . Immune cells called T cells use these abnormal peptides to identify the cancer so that it can be destroyed . Sometimes T cells can lack the right equipment to detect abnormal peptides , allowing cancer cells to hide from the immune system . However , T cells can be trained through a treatment called immunotherapy , which provides T cells with new tools so that they can spot the peptides displayed by HLA on the previously ‘hidden’ cancer cells . There are many different forms of HLA , each of which can display different peptides . Current research in immunotherapy commonly targets only a subset of HLA forms , and not all cancer patients have these types . This means that immunotherapy research is only likely to be of most benefit to a limited number of patients . Immunotherapy could be made effective for more people if new cancer peptides that are displayed by the other ‘under-represented’ forms of HLA were identified . Murata , Nakatsugawa et al . have now used T cells that were taken from tumors in eight patients with melanoma , which is a type of skin cancer . A library of fluorescent HLA-peptides was generated – using a new , simplified methodology – with 25 forms of HLA that displayed over 800 peptides . T cells were then mixed with the library to identify which HLA-peptides they can target . As a result , Murata , Nakatsugawa et al . found the cancer targets of around 12% of the tumor-infiltrating T cells tested , including those from under-represented forms of HLA . Consequently , these findings could be used to develop new immunotherapies that can treat more patients . | [
"Abstract",
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"Results",
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"immunology",
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] | 2020 | Landscape mapping of shared antigenic epitopes and their cognate TCRs of tumor-infiltrating T lymphocytes in melanoma |
After endocytosis , membrane proteins can recycle to the cell membrane or be degraded in lysosomes . Cargo ubiquitylation favors their lysosomal targeting and can be regulated by external signals , but the mechanism is ill-defined . Here , we studied the post-endocytic trafficking of Jen1 , a yeast monocarboxylate transporter , using microfluidics-assisted live-cell imaging . We show that the ubiquitin ligase Rsp5 and the glucose-regulated arrestin-related trafficking adaptors ( ART ) protein Rod1 , involved in the glucose-induced internalization of Jen1 , are also required for the post-endocytic sorting of Jen1 to the yeast lysosome . This new step takes place at the trans-Golgi network ( TGN ) , where Rod1 localizes dynamically upon triggering endocytosis . Indeed , transporter trafficking to the TGN after internalization is required for their degradation . Glucose removal promotes Rod1 relocalization to the cytosol and Jen1 deubiquitylation , allowing transporter recycling when the signal is only transient . Therefore , nutrient availability regulates transporter fate through the localization of the ART/Rsp5 ubiquitylation complex at the TGN .
Cells remodel the protein composition of the plasma membrane , such as receptors , transporters or channels , in response to external cues . Endocytosis is a major component of this adaptation . The conjugation of ubiquitin to membrane proteins ( ‘cargoes’ ) acts as a major determinant of their intracellular sorting in the endocytic pathway . Ubiquitin can notably act as a signal to promote cargo endocytosis from the plasma membrane ( reviewed in Dupré et al . , 2004 ) . This is particularly true in yeast , and studies have shown the critical involvement of Rsp5 , a conserved ubiquitin ligase of the Nedd4 family , in cargo ubiquitylation at the plasma membrane upon triggering endocytosis ( reviewed in MacGurn et al . , 2012 ) . The regulation of yeast nutrient transporters by nutritional signals in the environment has been used as a paradigm to dissect the mechanisms of signal-induced ubiquitylation and endocytosis ( Lauwers et al . , 2010 ) . Indeed , the nutritional status of the medium , such as the absence/excess of a given substrate , regulates the dynamics of the cognate transporters at the plasma membrane through their regulated ubiquitylation and endocytosis . Recent work has notably contributed to our understanding of the mechanism responsible for the signal-induced ubiquitylation of transporters . Indeed , at the plasma membrane , the ubiquitin ligase Rsp5 ubiquitylates endocytic cargoes and is assisted in its function by adaptor proteins of the ART family ( Arrestin-Related Trafficking adaptors ) , that confer to Rsp5 the ability to ubiquitylate numerous transporters in response to various external inputs ( Lin et al . , 2008; Nikko et al . , 2008; Nikko and Pelham , 2009; Hatakeyama et al . , 2010; O'Donnell et al . , 2010 , 2013; Karachaliou et al . , 2013 ) . ART proteins are both targets of nutrient signaling pathways and actors of transporter ubiquitylation , and as such act as relay molecules allowing to coordinate transporter endocytosis with the nutrient status of the cell ( MacGurn et al . , 2011; Merhi and André , 2012; Becuwe et al . , 2012b ) . Interestingly , the human arrestin-related protein TXNIP was recently reported to regulate glucose influx in cells by promoting the endocytosis of the glucose transporter GLUT1 in an AMP-activated protein kinase ( AMPK ) -dependent manner , illustrating the conservation of arrestin-related proteins function in endocytosis ( Wu et al . , 2013 ) . In mammalian cells , cargo ubiquitylation often occurs at the plasma membrane , and in some cases , this is essential for internalization , as demonstrated for the sodium channel ENaC ( Zhou et al . , 2007 ) or the Major Histocompatibility Complex ( MHC ) class I ( Hewitt et al . , 2002 ) . However , this is not a general rule , because affecting the ubiquitylation of the G-protein-coupled receptors β2-AR and CXCR4 ( Shenoy et al . , 2001; Marchese and Benovic , 2001 #885 ) , or the receptor tyrosine kinases EGFR and FGFR-1 ( Huang et al . , 2006; Haugsten et al . , 2008 ) does not abrogate their internalization . Instead , cargo ubiquitylation becomes critical later in the endocytic pathway , to promote cargo sorting towards late endosomes and their subsequent degradation ( see for instance Miranda et al . , 2007; Kabra et al . , 2008 ) . At endosomes , ubiquitinated cargoes are captured and sorted by ESCRT proteins ( Endosomal Sorting Complex Required For Transport ) into the lumen of the endosome through a process known as multivesicular body ( MVB ) biogenesis ( MacGurn et al . , 2012 ) . Loss of cargo ubiquitylation reportedly leads to their recycling to the cell surface ( Shenoy et al . , 2001; Huang et al . , 2006; Haugsten et al . , 2008; Eden et al . , 2012 ) . Therefore , a major function of ubiquitin conjugation to endocytic cargoes is to regulate their post-endocytic fate . Because ubiquitylation is a reversible modification , the competition between ubiquitylation and deubiquitylation at endosomes can decide the fate of many of the endocytosed cargoes , as only proteins that are stably ubiquitylated will be ultimately degraded . In mammalian cells , several deubiquitylating enzymes were indeed shown to control the recycling of endosomal cargoes such as EGFR ( McCullough et al . , 2004; Mizuno et al . , 2005; Row et al . , 2006 ) , the Cystic Fibrosis Transmembrane Regulator ( CFTR ) ( Bomberger et al . , 2009 ) , ENaC ( Butterworth et al . , 2007 ) or the β2-adrenergic receptor ( β2-AR ) ( Berthouze et al . , 2009 ) ( reviewed in Clague et al . , 2012 ) . However , the mechanisms by which external signals are integrated and transduced to the ubiquitylation machinery in order to regulate cargo trafficking are unknown . We recently reported that glucose , which triggers the endocytosis of various yeast carbon sources transporters ( reviewed in Horak , 2013 ) , promotes the successive dephosphorylation and ubiquitylation of the ART protein Rod1 ( also known as Art4 ) , leading to its activation ( Becuwe et al . , 2012b ) . In turn , activated Rod1 promotes the glucose-induced ubiquitylation and degradation of Jen1 , a monocarboxylate transporter . However , the place where Rod1 regulates endocytosis is unknown . Whereas ART proteins are considered to promote transporter internalization ( Becuwe et al . , 2012a ) , there are conflicting reports regarding the step at which they control intracellular trafficking ( Helliwell et al . , 2001; Soetens et al . , 2001; Merhi and André , 2012 ) . Moreover , ART proteins were observed at various subcellular locations , both in yeast ( Lin et al . , 2008; O'Donnell et al . , 2010 ) and mammalian cells ( Vina-Vilaseca et al . , 2011; Han et al . , 2013 ) suggesting other roles beyond the regulation of cargo internalization . Furthermore , it was reported that akin to the situation in mammalian cells , the yeast iron transporter complex Fet3/Ftr1 is internalized independently of its ubiquitylation , although the latter is required for its vacuolar targeting . This suggests a control of transporter degradation through a post-endocytic ubiquitylation event , for which the molecular actors are not known ( Strochlic et al . , 2008 ) . Here , through the use of microfluidics-assisted live-cell imaging , we studied the place of action of Rod1 during endocytosis and report a dual function of Rod1 in transporter sorting at two successive locations in the cell , that is , at the plasma membrane to control transporter internalization , and at a post-endocytic compartment to promote vacuolar sorting and degradation .
As a first approach to identify the place of action of Rod1 in endocytosis , we set out to precisely document the endocytosis defect displayed by the rod1Δ mutant . For this purpose , we monitored transporter trafficking in wild type ( WT ) and rod1Δ cells using two different cargoes , namely the glycerol/proton symporter Stl1 , and the monocarboxylate transporter Jen1 . Stl1 is expressed when cells are grown in glycerol/lactate medium , and endocytosed in response to glucose ( Ferreira et al . , 2005 ) . The glucose-induced endocytosis and degradation of Stl1 required Rod1 , establishing Stl1 as a new Rod1-regulated cargo ( Figure 1A , B ) , in addition to the previously described hexose transporter Hxt6 ( Nikko and Pelham , 2009 ) and Jen1 ( Becuwe et al . , 2012b ) . We then performed time-lapse microscopy using a microfluidics device to precisely monitor Stl1-GFP localization immediately after glucose addition in WT and rod1Δ cells . Both strains were co-injected into the microfluidics chamber and observed simultaneously . Their identification was made possible by a pre-staining of the rod1Δ cells with the vital dye CMAC . Whereas Stl1-GFP was internalized within 5 min after glucose addition in WT cells , it remained stably associated to the plasma membrane in the rod1Δ mutant and was not internalized even 30 min after glucose treatment ( Figure 1C , Video 1 ) . This is in agreement with a canonical role of Rod1 in transporter internalization at the plasma membrane . 10 . 7554/eLife . 03307 . 003Figure 1 . Dual function of Rod1 in transporter internalization and post-endocytic sorting . ( A ) Rod1 is required for the glucose-induced endocytosis of Stl1 , the glycerol/proton symporter , from the plasma membrane to the vacuole . WT ( ySL1146 ) and rod1Δ ( ySL1153 ) cells were grown in lactate/glycerol medium to induce Stl1-GFP expression and targeting to the plasma membrane . Cells were treated with glucose for the indicated times and imaged for Stl1-GFP localization . Scale bar = 2 . 5 µm . ( B ) Stl1-GFP degradation in response to glucose requires Rod1 . WT ( ySL1146 ) and rod1Δ ( ySL1153 ) cells expressing Stl1-GFP were grown as in A . Crude extracts were prepared at the indicated times and were immunoblotted with anti-GFP antibodies . ( C ) Rod1 is required for Stl1 internalization in response to glucose . WT ( ySL1146 ) and rod1Δ ( ySL1153 ) cells were grown in lactate/glycerol medium to induce Stl1-GFP expression and targeting to the plasma membrane . rod1Δ cells were then labeled with CMAC and were co-injected with WT cells into the microfluidics device in lactate/glycerol medium , before glucose was added . Images taken at 10 and 20 min after glucose addition are shown . Scale bar = 2 . 5 µm . See also Video 1 . ( D ) Jen1-GFP is internalized upon glucose treatment even in the absence of Rod1 . Lactate-grown WT ( ySL1150 ) and rod1Δ ( ySL743 ) cells expressing Jen1-GFP were injected into a microfluidics device in lactate medium . Cells were imaged over time after glucose addition . Scale bar = 2 . 5 µm . ( E ) The appearance of Jen1-GFP-positive puncta in the rod1Δ mutant is inhibited by latrunculin A ( LatA ) . Left panel , rod1Δ ( ySL743 ) cells expressing Jen1-GFP were grown on lactate medium and injected into the microfluidics device in lactate medium , before glucose was added . Right panel , glucose and LatA were simultaneously added . After 5 min , LatA was removed and cells were fueled only with glucose medium . Scale bar = 2 . 5 µm . ( F ) rod1Δ cells display a kinetic delay in Jen1 internalization . Top , WT ( ySL1150 ) and rod1Δ ( ySL743 ) cells expressing Jen1-GFP were grown on lactate medium . The rod1Δ cells were then labeled with CMAC and were co-injected with WT cells into the microfluidics device in lactate medium , before glucose was added . Images taken at 5 and 13 min after glucose addition are shown . Bottom , images representative of WT and rod1Δ cells are shown at various times and are shown in false colors to visualize Jen1 fluorescence intensity . Arrowheads indicate strongly fluorescent vesicles , presumably late endosomes , which do not appear in the rod1Δ mutant . Scale bar = 2 . 5 µm . See also Video 3 . ( G ) Quantification of the experiment shown in F . The mean number ( ±SEM ) of vesicles in a focal plane for each strain ( 30 cells/strain , n = 3 ) was plotted as a function of time . ( H ) Graphical representation of the phenotype observed in rod1Δ cells . A fraction of Jen1 is internalized but recycles to the cell membrane . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 00310 . 7554/eLife . 03307 . 004Video 1 . Rod1 is required for the glucose-induced internalization of the glycerol/proton symporter Stl1 . WT and rod1Δ ( CMAC-positive ) cells expressing Stl1-GFP were grown in lactate/glycerol medium and simultaneously observed for 20 min after glucose addition . See also Figure 1C . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 004 Then , we monitored the trafficking of the monocarboxylate transporter Jen1-GFP in rod1Δ cells after glucose addition . We observed that , in sharp contrast with the result obtained for Stl1 ( see Figure 1C ) , glucose triggered the transient localization of Jen1 to cytoplasmic puncta ( Figure 1D , Video 2 ) . The appearance of these puncta was strongly affected by latrunculin A treatment , which disrupts the actin cytoskeleton and abolishes endocytosis , indicative of their endocytic origin ( Figure 1E ) . This showed that Jen1 was still internalized in the rod1Δ mutant . To evaluate the contribution of Rod1 in Jen1 internalization , we then quantitatively compared Jen1 trafficking in both WT and rod1Δ cells using microfluidics ( Figure 1F , Video 3 ) . First , we observed that the appearance of Jen1-positive vesicles was delayed in the rod1Δ mutant as compared to the wild type ( Figure 1G ) . This clearly showed that in the absence of Rod1 , Jen1 internalization still occurred but was less efficient , which was also supported by the persistence of a Jen1-GFP pool at the plasma membrane in the rod1Δ strain . A second observation was that whereas Jen1-GFP was targeted into larger and brighter structures ( likely to be late endosomes ) at later time points in the WT , it did not reach this compartment in the rod1Δ mutant ( Figure 1F , Video 3 ) but rather re-localized to the plasma membrane , as described previously ( Becuwe et al . , 2012b ) ( see also Figure 1D and Video 2 ) . Because JEN1 expression is repressed by glucose ( Bojunga and Entian , 1999 ) , this plasma membrane-localized pool did not originate from de novo Jen1 synthesis , but rather from the recycling of internalized Jen1 back to the cell surface . This result strongly suggested a role for Rod1 in the post-endocytic targeting of Jen1 to the vacuole , in addition to its function at the plasma membrane ( Figure 1H ) . 10 . 7554/eLife . 03307 . 005Video 2 . Jen1-GFP is internalized upon glucose treatment even in the absence of Rod1 . WT cells ( left ) and in rod1Δ cells ( right ) expressing Jen1-GFP were grown in lactate medium and observed for 45 min after glucose addition . See also Figure 1D . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 00510 . 7554/eLife . 03307 . 006Video 3 . rod1Δ cells display a kinetic delay in Jen1 internalization . WT and rod1Δ ( CMAC positive ) cells expressing Jen1-GFP were visualized simultaneously during 20 min after glucose addition ( left ) . Images of the same video were treated in ImageJ using the ‘Fire’ lookup table ( LUT ) to visualize pixel intensity ( right ) . See also Figure 1F . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 006 Because we previously showed that Rod1 is required for an efficient glucose-induced ubiquitylation of Jen1 ( Becuwe et al . , 2012b ) , the observation that Jen1 can be partially internalized in the absence of Rod1 led us to investigate whether this endocytosis still relies on Jen1 ubiquitylation . Indeed , the remnant internalization of Jen1 observed in the rod1Δ mutant still required its ubiquitylation , as demonstrated through the use of a non-ubiquitylatable Jen1 mutant in which all cytosolic lysine residues have been mutated into arginine residues ( Jen1-KR-GFP , Figure 2A ) . As expected , these mutations abolished Jen1 ubiquitylation in response to glucose ( Figure 2B ) , but this construct was still functional , as judged by its ability to transport selenite , that can be used as a readout for Jen1 activity ( Figure 2—figure supplement 1 ) ( McDermott et al . , 2010 ) . The visualization of the subcellular localization of Jen1-KR-GFP showed that it was indeed targeted to the plasma membrane , although it displayed a small delay in secretion as observed by its transient accumulation at the ER ( Figure 2C ) . More importantly , the endocytosis of Jen1-KR-GFP in response to glucose was abolished ( Figure 2C ) . Since Jen1 was partially endocytosed in the absence of Rod1 ( see Figure 1D , F , G ) , we conclude that Jen1 can be ubiquitylated at the plasma membrane independently of Rod1 , at least when Rod1 is absent . Consequently , it is likely that there are additional ubiquitylation systems besides Rod1 at the plasma membrane , which can promote Jen1 internalization and that remain to be identified . Of note , Jen1 was still internalized in the multiple 9-arrestin mutant ( Figure 2D ) , which lacks Rod1 and eight other arrestin-related proteins ( Nikko and Pelham , 2009 ) . Altogether , these results show that the function of Rod1 at the plasma membrane is either compensated or is redundant with other adaptors , but that this is not the case regarding its post-endocytic function in Jen1 trafficking . 10 . 7554/eLife . 03307 . 007Figure 2 . Jen1 ubiquitylation is required for its glucose-induced endocytosis . ( A ) Schematic of the lysine-to-arginine mutations introduced in the cytosolic loops of Jen1 to generate the Jen1-KR construct . ( B ) Jen1-KR-GFP is not ubiquitylated in response to glucose . WT cells carrying a plasmid-encoded Jen1-GFP ( pSL161 ) or the same plasmid bearing the KR mutations ( pSL163 ) were grown on lactate medium and glucose was added for the indicated times . Crude extracts were immunoblotted with the indicated antibodies . Yeast PGK ( phosphoglycerate kinase ) is used as a loading control . Appearance of the glucose-induced higher molecular weight species of Jen1 , which were previously shown to correspond to Jen1 ubiquitylated adducts ( Paiva et al . , 2009; Becuwe et al . , 2012b ) , do not appear upon mutations of Jen1 lysines . ( C ) Jen1-KR-GFP is not internalized in response to glucose . WT cells carrying a plasmid-encoded Jen1-GFP ( pSL161 ) or the same plasmid bearing the KR mutations ( pSL163 ) were grown on lactate medium , then glucose was added and cells were imaged at the indicated times . Upon glucose addition , Jen1-GFP is internalized into vesicles and then is targeted to the vacuole , but Jen1-KR-GFP remains stable at the plasma membrane . Note the partial endoplasmic reticulum ( ER ) labeling of Jen1-KR-GFP , showing that this construct displays a mild defect in ER exit to the secretory pathway . Scale bar = 5 µm . ( D ) Deletion of nine genes encoding arrestin-related proteins is not sufficient to abolish Jen1 internalization . The 9-arrestin strain ( strain EN60 , a kind gift from Hugh Pelham: art1Δ ecm21Δ aly2Δ rod1Δ art5Δ aly1Δ rog3Δ csr2Δ art10Δ ) ( Nikko and Pelham , 2009 ) expressing Jen1-GFP tagged at its endogenous genomic locus ( ySL1318 ) was grown in lactate medium . Glucose was then added to the medium and Jen1-GFP localization was monitored at the indicated times . Vesicles resulting from Jen1-GFP internalization can still be observed in this mutant after glucose addition ( white arrows ) . Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 00710 . 7554/eLife . 03307 . 008Figure 2—figure supplement 1 . Jen1-KR-GFP is a functional protein . Jen1 transports selenite ( McDermott et al . , 2010 ) , which is used here as a readout for Jen1 activity . WT and jen1Δ strains carrying either an empty vector ( Ø ) , a plasmid-encoded Jen1-GFP ( pSL161 ) or the same plasmid bearing the KR mutations ( pSL163 ) were grown on galactose medium ( control plate ) or galactose + 300 µM selenite . Note that growth on galactose is required to derepress the expression of the JEN1 gene and to reveal its selenite transport activity at the plasma membrane . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 008 To identify the additional compartment at which Rod1 controls the post-endocytic trafficking of Jen1 , we next investigated the subcellular localization of Rod1 . Rod1-GFP appeared as a diffuse cytosolic protein in lactate-grown cells that transiently localized to punctate structures upon glucose stimulation ( Figure 3A , Video 4 ) . This localization was due to the recruitment of pre-existing Rod1 to these puncta , because inhibition of translation with cycloheximide did not affect this localization ( Figure 3B ) . Also , these puncta did not originate from the plasma membrane by endocytosis because Rod1 behaved similarly when endocytosis was abolished by latrunculin A treatment ( Figure 3C ) . Instead , these puncta co-localized with Sec7-mCh ( Figure 3D , Video 5 ) , a marker of the TGN ( trans-Golgi network ) ( Franzusoff et al . , 1991 ) , indicating that Rod1 transiently localizes to the TGN in response to glucose . Deletion of REG1 , encoding a subunit of protein phosphatase 1 ( PP1 ) required for Rod1 activation ( Becuwe et al . , 2012b ) , abolished Rod1 targeting to the TGN , demonstrating that Rod1 localization to the TGN was a consequence of its glucose-induced activation ( Figure 3E ) . 10 . 7554/eLife . 03307 . 009Figure 3 . Rod1 is dynamically recruited to the trans-Golgi network when endocytosis is triggered . ( A ) Rod1-GFP re-localizes from the cytosol to punctate structures in response to glucose . Lactate-grown cells ( ySL542 ) expressing Rod1-GFP were injected into a microfluidics device in lactate medium , and were then imaged over time after glucose addition . Scale bar = 2 . 5 µm . See also Video 4 . ( B ) Inhibition of translation using cycloheximide ( CHX ) does not alter Rod1-GFP re-localization . Cells ( ySL542 ) expressing Rod1-GFP were grown on lactate medium and injected into a microfluidics device in lactate medium . Cells were then treated with 100 µg/ml CHX for 10 min in lactate medium and imaged over time after addition of glucose in the presence of CHX . Scale bar = 2 . 5 µm . ( C ) Disruption of the actin cytoskeleton using latrunculin A ( LatA ) does not alter Rod1-GFP re-localization . Cells ( ySL542 ) expressing Rod1-GFP were grown on lactate medium and injected into a microfluidics device in lactate medium . Cells were then treated with 0 . 2 mM LatA for 5 min in lactate medium , then imaged over time after addition of glucose in the presence of LatA . Scale bar = 2 . 5 µm . ( D ) Rod1 co-localizes with the trans-Golgi network marker , Sec7-mCherry , in response to glucose . Cells ( ySL638 ) expressing both Rod1-GFP and Sec7-mCh were grown on lactate medium and injected into a microfluidics device in lactate medium . Cells were then imaged over time after glucose addition . Scale bar = 2 . 5 µm . See also Video 5 . ( E ) Rod1 does not localize to the TGN in response to glucose in the mutant for the regulatory subunit of protein phosphatase 1 , reg1Δ . WT ( ySL542 ) and reg1Δ ( ySL600 ) cells expressing Rod1-GFP were grown on lactate medium . The reg1Δ cells were then labeled with CMAC and were co-injected with WT cells into the microfluidics device in lactate medium , before glucose was added . Images taken before and after 5 min of glucose treatment are shown . Scale bar = 2 . 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 00910 . 7554/eLife . 03307 . 010Video 4 . Rod1-GFP relocalizes from the cytosol to punctate structures in response to glucose . WT cells expressing Rod1-GFP were grown in lactate medium and visualized for 60 min after glucose addition . See also Figure 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01010 . 7554/eLife . 03307 . 011Video 5 . Rod1 co-localizes with the trans-Golgi network marker , Sec7-mCherry , in response to glucose . WT cells expressing both Rod1-GFP and Sec7-mCh were grown during 4 hr in lactate medium and visualized during 45 min after glucose addition . Merge of Rod1-GFP fluorescence ( left panel ) and Sec7-mCh fluorescence ( middle panel ) is observed on the right panel . See also Figure 3D . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 011 Our data showing a role for Rod1 in the post-endocytic sorting of Jen1 , as well as the glucose-induced localization of Rod1 to the TGN , prompted us to delineate the compartments with which Jen1 associates during its endocytosis . We tested Jen1 co-localization with either the endosomal marker Vps17-mCh , a component of the recycling complex Retromer ( Seaman , 2004; Strochlic et al . , 2008 ) or the TGN marker Sec7-mCh , two proteins that did not co-localize with each other in wild-type cells as determined in a control experiment ( Figure 4—figure supplement 1 ) . Early after glucose treatment , the Jen1-GFP puncta co-localized nearly exclusively with Vps17-mCh , but this was followed by a partial co-localization with Sec7-mCh ( Figure 4A , C , Figure 4—figure supplement 2 , Video 6 ) , showing that Jen1-GFP associates with the TGN after internalization . Although Jen1-GFP and Sec7-mCh sometimes appeared to be in adjacent structures , rather than completely overlapping , this was likely an artifact due to the fast mobility of TGN structures in yeast ( Kojima et al . , 2012 ) , which led to a small shift between the image acquisition in each channel . Indeed , the simultaneous observation of Sec7-mCh and Jen1-GFP using a microscope equipped with two cameras showed a complete overlap of these signals ( Figure 4—figure supplement 3 ) . The same co-localization analysis was carried out in the rod1Δ mutant , and revealed that Jen1-GFP followed essentially the same route as in WT cells , although Jen1-GFP displayed a prolonged co-localization with Sec7-mCh ( Figure 4B , C , Figure 4—figure supplements 4 , 5 , Video 7 ) . This was not due to an indirect effect of ROD1 deletion on organelle identity , because Vps17 and Sec7 were still observed in distinct compartments in the rod1Δ strain ( Figure 4—figure supplement 6 ) . Therefore , Jen1 not only traffics to the TGN after internalization before it reaches the vacuole in wild-type cells , but also transiently accumulates at this compartment in the absence of Rod1 . 10 . 7554/eLife . 03307 . 012Figure 4 . Jen1 transits through the TGN during its endocytosis and requires Rod1 for exit from the TGN to the vacuole . ( A ) Jen1 co-localizes with the TGN marker , Sec7-mCh , during its trafficking to the vacuole in wild-type cells . WT cells expressing Jen1-GFP and either Vps17-mCh ( ySL1168 ) , a marker of the early endosomal compartment , or Sec7-mCh ( ySL1165 ) were grown on lactate medium . Of note , little or no co-localization was observed between Sec7-mCh and Vps17-GFP ( Figure 4—figure supplement 1 ) . The Sec7-mCh-expressing cells were labeled with CMAC and were co-injected with the Vps17-mCh-expressing cells into the microfluidics device in lactate medium , before glucose was added . Cells have been cropped to show only one Vps17-mCh expressing cell ( left ) or one Sec7-mCh expressing cell ( right ) . The uncropped picture is displayed in Figure 4—figure supplement 2 , and the full video in Video 6 . Co-localization events between Jen1-GFP and either Vps17-mCh or Sec7-mCh are indicated with white arrows or pink arrows , respectively . Scale bar = 2 . 5 µm . Of note , the co-localization of another transporter , Dip5 , with Sec7-mCh after its endocytosis was also observed ( see Figure 4—figure supplement 3 ) . ( B ) Jen1 also co-localizes with the TGN marker , Sec7-mCh , after internalization in the rod1Δ mutant . rod1Δ cells expressing Jen1-GFP and either Vps17-mCh ( ySL1177 ) , an endosomal marker , or Sec7-mCh ( ySL1176 ) were grown on lactate medium . As in panel A , the Sec7-mCh-expressing cells were labeled with CMAC and were co-injected with Vps17-mCh-expressing cells into the microfluidics device in lactate medium , before glucose was added . Cells have been cropped to show only one Vps17-mCh expressing cell ( left ) or one Sec7-mCh expressing cell ( right ) . The uncropped picture is displayed in Figure 4—figure supplement 4 , and the full video in Video 7 . Co-localization events between Jen1-GFP and either Vps17-mCh or Sec7-mCh are indicated with white arrows or pink arrows , respectively . Scale bar = 2 . 5 µm . ( C ) Quantification of the co-localization of Jen1-GFP puncta with either Vps17-mCh or Sec7-mCh puncta in WT ( top ) or rod1Δ ( bottom ) cells ( 20 cells , n = 3 ) . Jen1 co-localizes successively with Vps17 and Sec7 . Furthermore , Jen1 co-localizes more robustly with Sec7-mCh in the rod1Δ mutant . See also Figure 4—figure supplement 5 . ( D ) Schematic showing the place of action of the VFT/GARP complex , Ypt6 and the GGA proteins in endosome-to-Golgi trafficking . ( E ) Deletions of VPS52 ( VFT/GARP complex ) , YPT6 or genes encoding the Ypt6 GEF complex ( RGP1 and RIC1 ) abolish Jen1 trafficking to the vacuole . Lactate-grown WT ( ySL1150 ) , vps52Δ ( ySL1369 ) , ypt6Δ ( ySL1175 ) , ric1Δ ( ySL1630 ) and rgp1Δ ( ySL1631 ) cells expressing Jen1-GFP were imaged before or after the addition of glucose at the indicated time . At t = 30 min Glc treatment , CMAC staining was used to visualize the localization of the vacuole . Scale bar = 5 µm . ( F ) Deletions of VPS52 or YPT6 prevent the vacuolar degradation of Jen1-GFP . Crude extracts from lactate-grown WT ( ySL1150 ) , vps52Δ ( ySL1369 ) and ypt6Δ ( ySL1175 ) cells expressing Jen1-GFP were prepared at the indicated times before and after glucose addition , and were immunoblotted with anti-GFP antibodies to reveal the full-length Jen1-GFP and its degradation product ( free GFP ) . ( G ) Deletion of YPT6 abrogates Jen1 co-localization with Sec7-mCh . Lactate-grown WT cells ( ySL1165 ) or ypt6Δ cells ( ySL1526 ) expressing Jen1-GFP and Sec7-mCh were injected in a microfluidics device and imaged before ( Lactate ) and 15 min after glucose addition . Co-localization between Jen1-GFP and Sec7-mCh is indicated with arrowheads . ( H ) Deletions of GGA1 and GGA2 , encoding redundant Golgi-localized clathrin adaptor proteins , alter Jen1 trafficking to the vacuole . Strains gga1Δgga2Δ ( ySL1307 ) or gga1Δ gga2Δ expressing Gga2-HA ( ySL1308 ) , used as a positive control , both expressing Jen1-GFP were grown on lactate medium , and imaged before and after the addition of glucose at the indicated times . The gga1Δ gga2Δ cells also express Sec7-mCherry , which allows evaluating of Jen1-GFP co-localization with the TGN ( arrowheads ) . Scale bar = 5 µm . ( I ) Quantification of the co-localization of Jen1-GFP puncta with Sec7-mCh puncta in gga1Δgga2Δ ( ySL1307 ) cells or gga1Δ gga2Δ cells expressing Gga2-HA ( ySL1619 ) ( 20 cells , n = 3 ) over time after glucose addition . Deletion of the GGA genes leads to a transient accumulation of Jen1 at the TGN . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01210 . 7554/eLife . 03307 . 013Figure 4—figure supplement 1 . Sec7 and Vps17 localize to distinct compartments . WT cells expressing Sec7-GFP and Vps17-mCh ( ySL1531 ) were injected in the microfluidic device and observed every minute during 9 min . Sec7-GFP-positive vesicles never co-localize with Vps17-mCh-positive vesicles , showing that they represent two different compartments within the cell . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01310 . 7554/eLife . 03307 . 014Figure 4—figure supplement 2 . Jen1 traffics through the TGN in the course of its endocytosis in wild-type cells . Uncropped pictures corresponding to the panel presented in Figure 3A . See corresponding legend for details . Cells expressing Sec7-mCh were marked with an asterisk . Co-localization events between Jen1-GFP and either Vps17-mCh or Sec7-mCh are indicated with white arrows or pink arrows , respectively . Scale bar = 2 . 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01410 . 7554/eLife . 03307 . 015Figure 4—figure supplement 3 . Jen1-GFP and Sec7-mCh co-localize to the same compartment when observed simultaneously . WT cells expressing Jen1-GFP and Sec7-mCh ( ySL1165 ) were grown on lactate medium , and were injected in the microfluidics device in lactate medium , before glucose was added . Cells were imaged at the indicated time with a Revolution xD TuCam system ( Andor ) allowing the simultaneous acquisitions of the red and green channels . Arrows illustrate examples of co-localizations . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01510 . 7554/eLife . 03307 . 016Figure 4—figure supplement 4 . Jen1 also co-localizes to the TGN in rod1Δ mutant cells . Uncropped pictures corresponding to the panel presented in Figure 3B . See corresponding legend for details . Cells expressing Sec7-mCh were marked with an asterisk . Co-localization events between Jen1-GFP and either Vps17-mCh or Sec7-mCh are indicated with white arrows or pink arrows , respectively . Scale bar = 2 . 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01610 . 7554/eLife . 03307 . 017Figure 4—figure supplement 5 . Quantification of Sec7-mCh puncta that are also Jen1-GFP positive in WT and rod1Δ cells . This quantification was based on the data presented in Figure 4A , B . Jen1 co-localizes more robustly with Sec7-mCh in the rod1Δ mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01710 . 7554/eLife . 03307 . 018Figure 4—figure supplement 6 . Sec7 and Vps17 localize to distinct compartments in rod1Δ cells . rod1Δ cells expressing Sec7-GFP and Vps17-mCh ( ySL1602 ) were injected in the microfluidic device and observed every minute during 9 min . Sec7-GFP-positive vesicles never co-localize with Vps17-mCh-positive vesicles , showing that they represent two different compartments within the cell . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01810 . 7554/eLife . 03307 . 019Figure 4—figure supplement 7 . Sec7 and Vps17 localize to distinct compartments in gga1Δgga2Δ cells . gga1Δgga2Δ cells expressing Vps17-GFP and Sec7-mCh ( ySL1615 ) were injected in the microfluidic device and observed every minute during 9 min . Vps17-GFP-positive vesicles never co-localize with Sec7-mCh-positive vesicles , showing that they represent two different compartments within the cell . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 01910 . 7554/eLife . 03307 . 020Figure 4—figure supplement 8 . Dip5-GFP traffics through the TGN in the course of its endocytosis in WT cells . WT cells expressing Dip5-GFP and Sec7-mCh ( ySL956 ) were grown on aspartate-free medium , and injected into the microfluidics device in the same medium . Aspartic acid was then added to the medium ( 200 µg/ml ) and cells were imaged at the indicated times . Arrowheads also indicate examples of co-localizations between the Sec7-mCh and Dip5-GFP . Scale bar = 2 . 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02010 . 7554/eLife . 03307 . 021Figure 4—figure supplement 9 . Deletions of GGA1 and GGA2 , encoding redundant Golgi-localized clathrin adaptor proteins , alter Dip5 trafficking to the vacuole after endocytosis . Strains gga1Δgga2Δ ( ySL1323 ) and gga1Δgga2Δ expressing Gga2-HA ( ySL1322 ) , used as a positive control , both expressing Dip5-GFP genomically tagged at its endogenous locus were grown on aspartate-free medium , and imaged before and after the addition of aspartic acid ( 200 µg/ml ) at the indicated times . The gga1Δ gga2Δ cells also express Sec7-mCherry , allowing to evaluate the localization of Dip5-GFP to the TGN during its endocytosis . Contrary to what is observed in the control , Dip5-GFP fails to reach the vacuolar lumen in gga1Δ gga2Δ cells . Arrowheads also indicate examples of co-localizations between the Sec7-mCh and Dip5-GFP Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02110 . 7554/eLife . 03307 . 022Video 6 . Uncropped video corresponding to Figure 4A . Jen1-GFP co-localizes with the endosomal marker Vps17-mCh and the TGN marker Sec7-mCh during its trafficking to the vacuole in wild-type cells . WT cells expressing either both Jen1-GFP and Sec7-mCh ( indicated with a white asterisk on the first image ) , or both Jen1-GFP and Vps17-mCh , were grown in lactate medium and visualized simultaneously for 45 min after glucose addition . A merge of Jen1-GFP fluorescence ( left panel ) and Vps17-mCh/Sec7-mCh fluorescence ( middle panel ) is shown on the right panel . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02210 . 7554/eLife . 03307 . 023Video 7 . Uncropped video corresponding to Figure 4B . Jen1-GFP co-localizes with the endosomal marker Vps17-mCh and the TGN marker Sec7-mCh during its trafficking to the vacuole in rod1Δ cells . rod1Δ cells expressing either both Jen1-GFP and Sec7-mCh ( indicated with a white asterisk on the first image ) , or both Jen1-GFP and Vps17-mCh , were grown in lactate medium and visualized simultaneously for 45 min after glucose addition . A merge of Jen1-GFP fluorescence ( left panel ) and Vps17-mCh/Sec7-mCh fluorescence ( middle panel ) is shown on the right panel . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 023 To address whether the observed localization of Jen1 to the TGN is required for its vacuolar degradation , we probed Jen1 trafficking in mutants affected in retrograde , endosomes-to-TGN trafficking . We notably used a mutant ( vps52Δ ) of the Golgi-localized VFT ( Vps Fifty-Three ) /GARP ( Golgi-associated retrograde protein ) complex , involved in the fusion of vesicles of endosomal origin with the TGN ( Conibear and Stevens , 2000; Siniossoglou et al . , 2000; Siniossoglou and Pelham , 2001 ) ( Figure 4D ) . In this strain , Jen1 was internalized but accumulated into cytoplasmic puncta and failed to reach the vacuole ( Figure 4E ) . Similar results were obtained in other mutants affected in retrograde trafficking , such as mutants for the yeast Rab6 homologue , ypt6Δ , or for components of its GEF ( guanine nucleotide exchange factor ) , rgp1Δ and ric1Δ ( Siniossoglou et al . , 2000 ) ( Figure 4E ) . Moreover , Jen1 degradation was strongly affected in these retrograde mutants upon glucose treatment ( Figure 4F ) . As expected , in the ypt6Δ mutant , Jen1 no longer co-localized with the TGN ( Figure 4G ) . Therefore , these data reveal that endosome-to-TGN trafficking is critical for the vacuolar sorting of Jen1 after its internalization , suggesting that a step in the post-endocytic of Jen1 occurs at the TGN , prior to its delivery to the vacuole . In order to identify the components involved in the vacuolar sorting of TGN-localized Jen1 , we studied Jen1 trafficking in mutants of genes encoding the Golgi-localized clathrin adaptor proteins Gga1 and Gga2 , involved in protein sorting from the TGN to endosomes ( Scott et al . , 2004 ) ( see Figure 4D ) . As shown in Figure 4H , the absence of the Gga1/Gga2 proteins did not reduce Jen1 internalization , but partially impaired its vacuolar targeting , leading to Jen1 recycling at the plasma membrane . Of note , Jen1-GFP co-localized more strongly with the TGN marker , Sec7-mCh , after endocytosis in the gga1Δ gga2Δ mutant than in WT cells ( Figure 4H , I ) , thus mimicking the defects observed in the rod1Δ mutant ( Figure 1 and Figure 4B , C ) . Again , this was not due to a defect in organelle identity caused by the deletion of the GGA1 and GGA2 genes because Sec7 and Vps17 appeared in distinct compartments in this strain ( Figure 4—figure supplement 7 ) . These results showed that TGN-to-endosome sorting is also critical for the post-endocytic targeting of Jen1 to the vacuole . Of note , we also obtained evidence that another transporter , the dicarboxylic amino acid transporter Dip5 , also transiently co-localizes with the TGN during its substrate-induced endocytosis ( Figure 4—figure supplement 8 ) , and that its vacuolar sorting also depends on the Gga1/Gga2 proteins ( Figure 4—figure supplement 9 ) . Therefore , the post-endocytic control of transporter fate at the TGN is not restricted to Jen1 but is likely to be a more general mechanism . We showed that Jen1 ubiquitylation is required for its internalization at the plasma membrane ( see Figure 2 ) . Since we identified an additional role for Rod1 , which we previously showed to be critical for Jen1 ubiquitylation , in Jen1 sorting at the TGN , we hypothesized that transporter ubiquitylation may be remodeled between these two locations . This was further suggested by the observation that in retrograde mutants , in which Jen1 is internalized but not degraded , Jen1 ubiquitylation disappeared over time after glucose addition ( see Figure 4F ) . Indeed , in the vps52Δ mutant , in which Jen1 departs from the plasma membrane in response to glucose but cannot reach the TGN , we observed a sharp decrease in Jen1 ubiquitylation after 20 min of glucose treatment ( Figure 5A; see also Figure 4F ) . Because Jen1 expression is repressed by glucose ( Bojunga and Entian , 1999 ) , the progressive loss of Jen1 ubiquitylated species was likely due to a deubiquitylation event occurring during the progression of Jen1 from the plasma membrane to the TGN . We then performed the same experiment in a vrp1Δ ( end5Δ ) mutant , in which endocytosis is blocked due to the deletion of the gene encoding the yeast WASP-interacting protein ( WIP ) homologue , verprolin ( Munn et al . , 1995 ) . In this mutant , however , Jen1 displayed a robust ubiquitylation pattern , indicating that the disappearance of Jen1 ubiquitylated species was not due to an intrinsic lability of ubiquitin-conjugated Jen1 , and that this remodeling occurred post-internalization ( Figure 5A ) . 10 . 7554/eLife . 03307 . 024Figure 5 . The prolonged presence of glucose is required for the full endocytosis of Jen1 . ( A ) Jen1 is deubiquitylated after endocytosis . WT ( ySL1150 ) , vps52Δ ( ySL1369 ) and vrp1Δ ( ySL1610 ) cells expressing Jen1-GFP were grown in lactate medium and treated with glucose . Crude extracts were prepared at the indicated times and were immunoblotted with the indicated antibodies . In contrast to the situation in the vps52Δ mutant , in which Jen1 ubiquitylation vanishes over time , Jen1 ubiquitylation remains stable in the endocytic mutant vrp1Δ . ( B ) Jen1 ubiquitylation at the TGN requires retrograde sorting from endosomes to the TGN . WT ( ySL1636 ) , gga1Δgga2Δ ( ySL1638 ) and ypt6Δ gga1Δ gga2Δ ( ySL1639 ) cells expressing Jen1-GFP were grown in lactate medium and treated with glucose for the indicated time . Crude extracts were prepared and immunoblotted with antibodies against GFP . An increased ubiquitylation of Jen1-GFP is observed in the gga1Δgga2Δ mutant , that is abolished upon the additional deletion of YPT6 . Note that Jen1-GFP is expressed at a lower level in the triple ypt6Δ gga1Δ gga2Δ mutant , therefore samples were loaded so that a comparable signal is observed in each strain . ( C ) A fraction of the ubiquitin ligase Rsp5 re-localizes to the TGN upon glucose addition . Cells ( ySL1011 ) expressing both Sec7-mCh and GFP-Rsp5 were imaged after growth on lactate medium , and 10 min after glucose addition . Scale bar = 5 µm . ( D ) Rsp5 co-localizes with Jen1 at the TGN . Cells ( ySL1622 ) expressing Sec7-mCh , Jen1-GFP and BFP-Rsp5 were grown in lactate medium , and imaged 15 min after glucose addition . Scale bar = 5 µm . ( E ) Rsp5 co-localizes with Rod1 at the TGN . Cells ( ySL1625 ) expressing Sec7-mCh , Rod1-GFP and BFP-Rsp5 were grown in lactate medium , and imaged 15 min after glucose addition . Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02410 . 7554/eLife . 03307 . 025Figure 5—figure supplement 1 . Jen1 accumulates in an ubiquitylated form in the gga1Δ gga2Δ mutant . Strains gga1Δgga2Δ ( ySL1307 ) or gga1Δ gga2Δ expressing Gga2-HA ( ySL1308 ) , used as a positive control , both expressing Jen1-GFP were grown on lactate medium , and crude extracts were prepared before and after the addition of glucose at the indicated times . Crude extracts were immunoblotted with the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 025 Because we showed that Jen1 trafficking to the vacuole requires its prior targeting at the TGN , where Rod1 is localized , we hypothesized that Jen1 might be re-ubiquitylated at this compartment after endocytosis . In agreement with this hypothesis , we observed that in the gga1Δgga2Δ strain , in which Jen1 transiently accumulates at the TGN ( see Figure 4H , I ) , Jen1 also accumulated in an ubiquitylated state , suggesting that retaining Jen1 at the TGN leads to its massive ubiquitylation ( Figure 5B , Figure 5—figure supplement 1 ) . To confirm that this was due to the ubiquitylation of Jen1 molecules originating from endosomes , we used a triple ypt6Δ gga1Δ gga2Δ mutant in which retrograde trafficking is abolished . In this mutant , Jen1 ubiquitylation was similar to that observed in a single ypt6Δ mutant , showing that the accumulation of ubiquitylated species of Jen1 at the TGN observed in the gga1Δ gga2Δ mutants indeed required retrograde trafficking ( Figure 5B ) . In further support of an ubiquitylation event occurring at the TGN , we also observed a glucose-induced recruitment of the ubiquitin ligase Rsp5 to this compartment in response to glucose ( Figure 5C ) . Using three-color imaging , we also showed a co-localization of Rsp5 with Jen1 and Rod1 at the TGN ( Figure 5D , E ) . Altogether , these data strongly suggest that Jen1 is ubiquitylated at the plasma membrane and is deubiquitylated after its internalization , and lead us to propose that a second , glucose-regulated ubiquitylation step at the TGN involving Rsp5 and Rod1 may control Jen1 progression from the TGN to the vacuole . The yeast TGN is a well-established compartment from which cargoes can recycle to the plasma membrane ( Holthuis et al . , 1998; Conibear et al . , 2003; Hettema et al . , 2003; Reggiori et al . , 2003 ) . We thus reasoned that a control of the post-endocytic sorting of transporters at the TGN might allow transporter recycling to the cell surface upon the sudden disappearance of the signal triggering endocytosis . To test this prediction , we treated lactate-grown cells expressing Jen1-GFP with a short pulse of glucose ( 10 min ) , which was sufficient to drive Jen1-GFP internalization , and then followed Jen1-GFP trafficking upon glucose removal . Using this protocol , Jen1 redistributed back to the plasma membrane within 20 min ( Figure 6A ) , showing that Jen1 endocytosis was reversible upon glucose removal . This change in localization coincided with a loss of Jen1 ubiquitylation , suggesting that a continuous presence of glucose is required for the persistent ubiquitylation of Jen1 and its degradation ( Figure 6B ) . The same experiment performed in the vrp1Δ mutant , in which endocytosis is blocked , also led to a loss of Jen1 ubiquitylation , showing it is not due to Jen1 degradation but rather to a rapid deubiquitylation event following glucose removal , regardless of Jen1 localization ( Figure 6B ) . Of note , Jen1 no longer recycled to the plasma membrane in the vps52Δ strain in these conditions ( Figure 6C and Video 8 ) , indicating that Jen1 recycling requires a functional endosome-to-TGN retrograde pathway . This also confirmed that the signal recovered at the plasma membrane upon glucose removal in WT cells ( see Figure 6A ) did not originate from Jen1 neosynthesis . Therefore , a second , glucose-based sorting event occurs after internalization and takes place at the TGN . Accordingly , Jen1 recycling led to its polarized redistribution to the cell buds ( Figure 6C and Video 8 ) , similarly to the exocytic SNARE Snc1 and the chitin synthase Chs3 , which localize to the cell membrane in a polarized fashion after endocytosis and recycling through the TGN ( Holthuis et al . , 1998; Lewis et al . , 2000 ) . Interestingly , Jen1 recycling to the cell membrane upon glucose removal also correlated with the loss of Rod1 localization at the TGN ( Figure 6D ) . Rod1 localization was indeed extremely responsive to glucose treatments , and applying repeated glucose/lactate cycles revealed a robust sensor-like response of Rod1 regarding its localization ( Figure 6D and Video 9 ) and post-translational modifications ( Figure 6E , F ) . Therefore , Jen1 ubiquitylation and endocytosis can be re-evaluated at the TGN after internalization upon disappearance of the endocytic stimulus , and this correlates with a rapid glucose-induced remodeling of Rod1 post-translational modifications and its redistribution from the TGN to the cytosol . 10 . 7554/eLife . 03307 . 026Figure 6 . The control of Jen1 trafficking at the TGN allows the recycling of internalized transporters back to the cell membrane . ( A ) Jen1 endocytosis is reversible upon glucose removal . Lactate-grown WT cells expressing Jen1-GFP ( ySL1150 ) were injected into a microfluidics device in lactate medium . Cells were then imaged over time , before and after 10 min glucose addition , and then 20 min after glucose removal . Scale bar = 2 . 5 µm . ( B ) The persistence of the glucose signal is required to maintain Jen1 ubiquitylation . Lactate-grown WT cells expressing Jen1-GFP ( ySL1150 ) were treated with glucose for 5 min . Cells were then briefly centrifuged and incubated back into lactate medium for 5 min . Crude extracts were prepared at each step and were immunoblotted with the indicated antibodies . The high-molecular weight species of Jen1 appearing upon glucose addition correspond to ubiquitylated Jen1 ( see Becuwe et al . , 2012b ) ( see also Figure 2B ) . The same extracts prepared from vrp1Δ cells show that Jen1 deubiquitylation observed in WT cells is not due to Jen1 degradation but to an active deubiquitylation process . ( C ) Lactate-grown WT ( ySL1165 ) cells expressing both Jen1-GFP and Sec7-mCh , and vps52Δ ( ySL1369 ) cells expressing only Jen1-GFP were co-injected into the microfluidics device in lactate medium , before glucose was added for 10 min and then removed . Cells were imaged at the indicated times . Arrowheads indicate examples of co-localization of Jen1-GFP and Sec7-mCh in WT cells , and arrows show the plasma membrane localization of Jen1-GFP after glucose removal in WT cells . Note the polarized distribution of Jen1-GFP after recycling in WT cells , but not in vps52Δ cells ( inset ) . PM , plasma membrane; end: endosomes . Scale bar = 2 . 5 µm . See also Video 8 . ( D ) Jen1 recycling correlates with the loss of Rod1 localization to the TGN . Lactate-grown cells ( ySL542 ) expressing Rod1-GFP were injected into a microfluidics device in lactate medium . Cells were then subjected to 5-min pulses of glucose addition/removal and imaged simultaneously . Only the first three cycles are shown here . Scale bar = 2 . 5 µm . See also Video 9 . ( E–F ) Dynamics of Rod1 post-translational modifications upon glucose/lactate cycles . Lactate-grown WT cells expressing a plasmid-borne Rod1-GFP were treated with glucose for 15′ , or subjected to glucose addition/removal as indicated . Crude extracts were prepared at each step and were immunoblotted with the indicated antibodies . A non-specific ( NS ) cross-reacting band is used as a loading control . The high-molecular weight species of Rod1-GFP observed in the lactate samples correspond to phosphorylation ( panel E , lanes 1 , 5 and 7 ) , because phosphatase treatment ( CIP ) abolishes the migration shift ( panel F , lanes 7 and 9 ) . The glucose-induced doublet ( panel E , lanes 2 , 3 , 4 , 6 and 8 ) corresponds to ubiquitylated ( higher band ) and non-ubiquitylated ( lower band ) forms of Rod1 , because mutation of its ubiquitylation sites ( using a plasmid encoding Rod1-KR ) abolishes the migration shift ( panel F , lane 6 ) , as described previously ( Becuwe et al . , 2012b ) . PGK is used as a loading control in panel F . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02610 . 7554/eLife . 03307 . 027Video 8 . Jen1 recycles back to the plasma membrane via the TGN . WT cells expressing both Jen1-GFP and Sec7-mCh , and vps52Δ cells expressing only Jen1-GFP were grown 4 hr in lactate medium and observed simultaneously for 10 min of glucose addition and 20 min after glucose removal . Co-localization between Jen1-GFP and Sec7-mCh in WT cells is indicated by white arrows in the merge panel ( second on the right ) . See also Figure 6C . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02710 . 7554/eLife . 03307 . 028Video 9 . Jen1 recycling correlates with the loss of Rod1-localization to the TGN . WT cells expressing Rod1-GFP were grown for 4 hr in lactate medium and observed during 3 cycles of glucose addition/removal ( 5-min pulses ) . See also Figure 6D . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 028 Neosynthesized transporters traffic through the TGN en route to the plasma membrane in the secretory pathway . However , many transporters can escape this pathway and traffic from the TGN to the vacuole when they are expressed in conditions that would normally trigger their endocytosis at the plasma membrane ( for review , see Haguenauer-Tsapis and André , 2004 ) . We used a galactose-inducible Jen1-GFP construct , which allows Jen1-GFP targeting to the cell surface in galactose medium ( Figure 7A ) ( Paiva et al . , 2009 ) , and followed the effect of glucose during Jen1-GFP targeting to the membrane ( see Figure 7B , top ) . We observed that the addition of glucose perturbed Jen1 sorting in the secretory pathway , as Jen1 was targeted to the vacuole instead of the plasma membrane in WT cells ( Figure 7B , Video 10 ) . This localization did not involve Jen1 targeting to the plasma membrane and its endocytosis , because Jen1 behaved similarly in the endocytic mutant vrp1Δ ( Figure 7—figure supplement 1 ) . In the rod1Δ mutant , however , Jen1 was no longer targeted to the vacuole , but instead reached the cell surface ( Figure 7B , Video 10 ) , showing that Rod1 is required to divert Jen1 trafficking from the secretory pathway to the vacuole . Noteworthy , Rod1 was again recruited to the TGN in these conditions upon glucose addition ( Figure 7C ) . 10 . 7554/eLife . 03307 . 029Figure 7 . Rod1 promotes Jen1 exit from the secretory pathway to the vacuole . ( A ) A galactose-inducible Jen1-GFP is targeted to the plasma membrane in galactose medium . WT cells ( ySL1083 ) expressing a galactose-inducible Jen1-GFP were grown in raffinose medium overnight , and transferred to galactose medium for 45 min to allow Jen1-GFP expression and targeting to the plasma membrane . Scale bar = 5 µm . ( B ) Rod1 is required for the glucose-induced retargeting of Jen1 from the secretory pathway to the vacuole . Top , outline of the experiment . A 15-min pulse of galactose allows the synthesis of Jen1-GFP , and the effect of glucose on the sorting of neosynthesized Jen1-GFP is then monitored . Bottom , WT ( ySL1083 ) and rod1Δ ( ySL781 ) cells both expressing a galactose-inducible Jen1-GFP were grown overnight on raffinose medium . After 15 min of galactose induction , glucose was then added to the cells and Jen1-GFP fluorescence was followed over time . Scale bar = 2 . 5 µm . See also Video 10 . Scale bar = 2 . 5 µm . Note that the sorting of neosynthesized Jen1 to the vacuole in response to glucose does not require targeting to the plasma membrane and endocytosis , see Figure 7—figure supplement 1 . ( C ) Rod1 localizes to the TGN when transferred from galactose to glucose medium . Cells ( ySL638 ) co-expressing Rod1-GFP and Sec7-mCh were grown overnight on raffinose medium , and observed 15 min after galactose addition and then and 5 min after glucose addition . Arrowheads indicate co-localization events between Rod1-GFP and Sec7-mCH . Scale bar = 5 µm . ( D ) The non-ubiquitylatable Jen1-KR-GFP construct fails to be targeted from the secretory pathway to the vacuole in response to glucose . WT cells expressing either a plasmid-encoded , galactose inducible Jen1-GFP ( ySL1083 ) or the mutant Jen1-KR-GFP ( ySL1339 ) construct were grown as in Figure 7B , and imaged at the indicated times . Note that the Jen1-KR-GFP presents defects in ER exit upon synthesis ( ER labeling observed throughout the experiment ) but still manages to reach the plasma membrane , and is not associated with the vacuole . Scale bar = 5 µm . ( E ) The sorting of neosynthesized Jen1 to the vacuole in response to glucose requires Rsp5 . WT ( ySL1083 ) and npi1 ( a hypomorphic rsp5 mutant; ySL1556 ) cells both expressing a galactose-inducible Jen1-GFP were grown overnight on raffinose medium . After 15 min of galactose induction , glucose was then added to the cells and Jen1-GFP fluorescence was followed over time . Scale bar = 5 µm . ( F ) The Golgi-localized clathrin adaptor proteins , Gga1 and Gga2 , are required for the glucose-induced retargeting of Jen1 from the secretory pathway to the vacuole . Strains gga1Δgga2Δ ( ySL1311 ) or gga1Δgga2Δ expressing Gga2-HA ( ySL1310 ) , used as a positive control , both expressing Jen1-GFP from a plasmid-encoded , galactose-inducible construct were induced as in Figure 7B , and imaged at the indicated times . Scale bar = 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 02910 . 7554/eLife . 03307 . 030Figure 7—figure supplement 1 . The sorting of neosynthesized Jen1 to the vacuole in response to glucose does not require targeting to the plasma membrane and endocytosis . WT ( ySL1083 ) , rod1Δ ( ySL781 ) and vrp1Δ ( ySL1650 ) cells both expressing a galactose-inducible Jen1-GFP were grown overnight on raffinose medium . After 15 min of galactose induction , glucose was then added to the cells and Jen1-GFP fluorescence was followed over time . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 03010 . 7554/eLife . 03307 . 031Video 10 . Rod1 is required for the glucose-induced retargeting of Jen1 from the secretory pathway to the vacuole . WT cells ( left panel ) and rod1Δ cells ( right panel ) expressing Jen1-GFP under a galactose-inducible promoter were grown in raffinose medium overnight and simultaneously observed for 15 min during galactose induction and then 45 min after glucose addition . See also Figure 7B . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 031 Because Rod1 contributes to Jen1 ubiquitylation in response to glucose ( Becuwe et al . , 2012b ) , we tested whether the inability of Jen1 to be sorted from the TGN to the vacuole in the rod1Δ mutant was due to a lack of Jen1 ubiquitylation . In agreement with this hypothesis , a non-ubiquitylatable Jen1-KR-GFP construct ( see Figure 2 ) was targeted to the plasma membrane despite the presence of glucose ( Figure 7D ) , showing that Jen1 ubiquitylation is critical for its rerouting from the secretory pathway to the vacuolar pathway . A similar result was obtained in a hypomorphic mutant of RSP5 , npi1 , showing that the vacuolar sorting of neosynthesized Jen1 from the TGN to the vacuole also involved Rsp5 ( Figure 7E ) . Furthermore , deletion of the genes encoding the ubiquitin-binding , clathrin adaptor proteins Gga1 and Gga2 proteins also led to a defect in the vacuolar targeting of neosynthesized Jen1 in the presence of glucose ( Figure 7F ) . Altogether , these data indicate that the observed glucose-induced recruitment of Rod1 and Rsp5 to the TGN promotes Jen1 ubiquitylation and its exit from the TGN to the vacuole in a Gga1-Gga2 dependent manner .
Arrestin-related proteins have emerged as key players in the regulation of transporter endocytosis and degradation by nutrient signaling pathways in yeast and mammalian cells ( O'Donnell , 2012; Wu et al . , 2013 ) . In the present study , the use of microfluidics-assisted live cell imaging allowed us to follow , for the first time , the dynamics of cargo trafficking in response to variations in the presence of the endocytic stimulus , as well as the localization of the ART protein Rod1 in these conditions . This revealed a dual function of the ART protein Rod1 in the control of transporter endocytosis and degradation by glucose availability ( Figure 8 , left and middle panel ) . First , Rod1 exerts a function at the plasma membrane , as it is essential for the endocytosis of the glycerol/proton symporter Stl1 , and required for the efficient internalization of Jen1 upon glucose treatment ( Figure 1 ) . Second , Rod1 also controls transporter sorting after endocytosis , a step which takes place at the trans-Golgi network ( Figure 8 , middle panel ) . Indeed , Jen1 localizes to the TGN after endocytosis , and both ( i ) the endosome-to-Golgi retrograde pathway and ( ii ) the Gga1/2-dependent , Golgi-to-vacuole pathway are required for the vacuolar delivery of Jen1 , indicating a critical role for the TGN in transporter degradation ( Figure 4 ) . Moreover , we showed that the endocytosis of the unrelated amino acid transporter , Dip5 , in response to its substrate also involves a step at the TGN as well as the GGA proteins . These results shed a new light on previous studies showing the involvement of GGA proteins in the endocytic trafficking of Gap1 , the general amino-acid permease ( Scott et al . , 2004; Lauwers et al . , 2009 ) , which could be explained by a similar control of Gap1 fate at the TGN . The yeast TGN therefore appears as a critical organelle for the post-endocytic sorting of transporters . This new level of regulation provides an opportunity to re-evaluate , after internalization , the commitment of transporters to the degradation pathway . Indeed , upon glucose removal , we show that internalized Jen1 recycles to the cell surface after it has reached the TGN ( Figure 6; Figure 8 , right panel ) . 10 . 7554/eLife . 03307 . 032Figure 8 . Working model for the dual function of Rod1 in the regulation of transporter endocytosis and recycling . Left , in lactate medium , Jen1 is synthesized and targeted to the plasma membrane . Although Rod1 interacts with Rsp5 ( Becuwe et al . , 2012b ) , it is inactive ( phosphorylated ) and cytosolic . Middle , In the presence of glucose , Rod1 is activated by a protein phosphatase 1 ( PP1 ) -dependent dephosphorylation and its subsequent Rsp5-mediated ubiquitylation ( Becuwe et al . , 2012b ) . Rod1 promotes transporter internalization at the plasma membrane ( 1 ) , but also localizes to the TGN ( 2 ) . There , Rod1 controls the fate of internalized transporters that have been retrograde-trafficked by a VFT- and Ypt6-dependent pathway , and also that of transporters coming from the secretory pathway . Transporter sorting from the TGN to the vacuole requires Rod1 and the clathrin adaptors Gga1 or Gga2 ( GGAs ) . Right , Upon glucose removal , shortly after endocytosis is initiated , Rod1 is rephosphorylated ( likely by the kinase Snf1 ) ( Becuwe et al . , 2012b ) , leading to Rod1 dissociation from the TGN and Jen1 recycling to the cell membrane . DOI: http://dx . doi . org/10 . 7554/eLife . 03307 . 032 Whereas the involvement of the TGN in cargo recycling to the cell membrane has already been documented ( Holthuis et al . , 1998; Conibear et al . , 2003; Hettema et al . , 2003; Reggiori et al . , 2003 ) , little information was available concerning the regulation of cargo recycling in response to external signals ( Strochlic et al . , 2008 ) . Here , we propose that external signals can remodel transporter ubiquitylation at the TGN through the regulation of ART proteins , and therefore control transporter targeting towards the recycling or degradation pathways . Indeed , Jen1 ubiquitylation is highly dynamic and can be re-evaluated after internalization . Through the use of trafficking mutants , we observed a progressive loss of Jen1 ubiquitylation between the plasma membrane and the TGN , even in the presence of glucose ( Figure 5A ) suggesting that Jen1 ubiquitylation status is reset between these compartments , likely through the action of deubiquitylating enzymes that are yet to be identified . This may set the stage for a second Jen1 ubiquitylation event at the TGN , that would allow cells to control transporter progression to the vacuole in a glucose- and Rod1-dependent manner ( Figure 8 , right panel ) . This is further suggested by the fact that Jen recycling upon glucose removal coincides with the loss of its ubiquitylation ( Figure 6B ) . Therefore , the continuous presence of glucose is not only required to trigger Jen1 internalization , but also to maintain Jen1 in the endocytic pathway and prevent its recycling . Altogether , our data shed light on the integrated nature of the endocytic decision , and the multiple places where signaling can impact on transporter sorting through the ART/Rsp5 network . This regulation recalls that of the β2-adrenergic receptor , for which the multifunctional adaptor protein , β-arrestin2 , promotes receptor ubiquitylation after its endocytosis through the recruitment of the ubiquitin ligase Nedd4 ( Shenoy et al . , 2008 ) but also regulates receptor deubiquitylation by the deubiquitylating enzymes USP33 and USP20 , hence its recycling , depending on the duration of the stimulation ( Berthouze et al . , 2009 ) ( reviewed in Kommaddi and Shenoy , 2013 ) . However , in this specific case , the way by which agonist exposure is perceived and conveyed to edit the ubiquitin signal on the cargo is not fully understood . Here , we propose that a glucose-induced recruitment of the arrestin-related protein Rod1 to the TGN curtails Jen1 recycling and promotes its vacuolar sorting , leading to its degradation . This re-localization relies on rapid glucose-induced changes in Rod1 post-translational modifications through a mechanism that we previously established ( Becuwe et al . , 2012b ) , and both are reversible upon glucose removal ( Figure 6D–F ) . The latter situation coincides with a loss of Jen1 ubiquitylation and its recycling , which may be caused by its inability to be re-ubiquitylated at the TGN because Rod1 is no longer at this compartment . The mechanism of the dynamic recruitment of Rod1 to the TGN will need to be further explored . Rod1 is so far the only arrestin-related protein shown to display a signal-induced localization to the TGN , although other arrestin-related proteins also localize to internal compartments at steady-state , such as the TGN or endosomes , both in yeast ( Lin et al . , 2008; O'Donnell et al . , 2010 ) and mammalian cells ( Vina-Vilaseca et al . , 2011; Han et al . , 2013 ) . We also showed a dynamic recruitment of Rsp5 to the TGN in response to glucose , which is line with our model of a re-ubiquitylation of Jen1 at the TGN after endocytosis . The previously reported interaction between Rsp5 and Sec7 ( Dehring et al . , 2008 ) may provide insights into the molecular basis of Rsp5 recruitment to this organelle . Noteworthy , the use of the same machinery to regulate transporter trafficking at the plasma membrane and the TGN also allows an integrated control of transporter sorting both during secretion and after endocytosis at the same location ( Figure 7; Figure 8 , middle panel ) . This may be critical to prevent neosynthesized cargoes transiting in the secretory pathway from reaching the cell surface in conditions that induce endocytosis . Intriguingly , the ART-related proteins Bul1 and Bul2 were shown to control the sorting of neosynthesized Gap1 between the Golgi and the vacuole ( Helliwell et al . , 2001; Soetens et al . , 2001; Risinger and Kaiser , 2008 ) , and then later found to be involved in Gap1 down-regulation , suggestive of a role at the plasma membrane ( Risinger and Kaiser , 2008; Merhi and André , 2012 ) . In the light of our data , it appears likely that the Bul1/Bul2 proteins may in fact act at the TGN to control both of these sorting events . The study of the subcellular localization of the Bul1/Bul2 proteins may provide important information in this regard . Interestingly , recent studies in the evolutionary distant yeast Schizosaccharomyces pombe also point to a role of the arrestin-related protein Any1 ( also named Arn1 ) in transporter trafficking at multiple places in the cell ( Nakase et al . , 2013; Nakashima et al . , 2014 ) . Finally , data on the ART protein Aly2 suggest both a role in endocytosis and endosomal recycling depending on the transporter or the physiological condition considered ( Hatakeyama et al . , 2010; O'Donnell et al . , 2010; Crapeau et al . , 2014 ) . In metazoans , recycling endosomes are specific organelles dedicated to the recycling of internalized proteins to the plasma membrane ( Sheff et al . , 1999 ) . They notably contribute to epithelial cell polarity ( Golachowska et al . , 2010 ) and membrane remodeling in neurons ( Schmidt and Haucke , 2007 ) but the regulation of cargo recycling is poorly understood at the molecular level ( Maxfield and McGraw , 2004 ) . So far , a role of the TGN in cargo recycling has been only marginally studied ( Csaba et al . , 2007; Escola et al . , 2010; Cheng and Filardo , 2012 ) . The finding that Ypt32 , a yeast TGN-localized Rab protein involved in recycling from the TGN to the cell surface , localizes to recycling endosomes in mammalian cells and affects transferrin receptor recycling ( Kail et al . , 2005 ) suggests that recycling from endosomes and the TGN may be evolutionary related . Our work in yeast further indicates that this may be an ancestral mode of regulation , and provides insights into the molecular mechanism by which it is achieved . This study also illustrates an underappreciated role of the TGN in the integrated regulation of recycling and secretion that should be considered for future studies .
Strains are listed and detailed in Supplementary file 1 . All strains are derivatives of the BY4741/2 strains , except for the gga1Δ GGA2-HA and gga1Δ gga2Δ strains , which were kindly provided by Prof . R Piper , University of Iowa , Iowa City , USA ( Scott et al . , 2004 ) and the gga1Δgga2Δ and gga1Δgga2Δ ypt6Δ strains ( 23344c background ) that were kindly provided by Prof . B André , Université Libre de Bruxelles , Belgium ( Lauwers et al . , 2009 ) . The 9-arrestin mutant was kindly provided by Dr H Pelham ( MRC Laboratory of Molecular Biology , Cambridge , UK ) ( Nikko and Pelham , 2009 ) . Yeast was transformed by standard lithium acetate/polyethylene glycol procedure . Cells were grown in yeast extract/peptone/glucose ( YPD ) rich medium , or in synthetic complete ( SC ) medium containing 2% ( wt/vol ) Glc , or 0 . 5% ( vol/vol ) Na-lactate ( pH 5 . 0 ) ( Sigma-Aldrich , Lyon , France ) . For lactate inductions , cells were grown overnight in SC-Glc , harvested in early exponential phase ( A600 = 0 . 3 ) , resuspended in the same volume of SC-lactate and grown for 4 hr ( A600 = 0 . 5 ) , before the addition of glucose ( 2% wt/vol , final concentration ) . For the observation of Stl1-GFP , cells were grown for 2 hr in lactate medium , and glycerol was then added ( 3% vol/vol ) for 2 hr to induce Stl1-GFP expression and targeting to the plasma membrane . For galactose induction , cells were precultured in SC-Glc medium , and grown overnight to early exponential phase ( A600 = 0 . 3 ) in SC medium containing 2% raffinose ( wt/vol ) and 0 . 02% Glc ( wt/vol ) to initiate growth . Galactose was then added at a final concentration of 2% ( wt/vol ) and cells were grown for the indicated times . Chase/endocytosis was started by adding glucose to a final concentration of 2% ( wt/vol ) and incubating for the indicated times . Latrunculin A ( Sigma ) was used a final concentration of 0 . 2 mM . For the Jen1-KR-GFP mutagenesis , the JEN1 ORF and its promoter were first amplified by PCR from BY4741 genomic DNA ( using oligonucleotides oSL337/oSL338 ) , the fragment was digested with SacI/SpeI , cloned at SacI/SpeI sites into a pRS416-based vector containing GFP ( pRHT140 , lab collection ) , and sequenced ( pSL161 ) . A synthetic gene encoding the Jen1-KR mutant was generated ( Eurofins MWG Operon , Ebersberg , Germany ) , amplified by PCR ( oSL371/oSL394 ) , cloned by gap-repair in yeast into pSL161 digested with HindIII/SpeI , and sequenced ( pSL163 ) . The galactose-inducible version was cloned similarly: the synthetic gene was amplified by PCR ( oSL476/oSL477 ) , and cloned by gap-repair in yeast into pRHT373 ( Becuwe et al . , 2012b ) ( pSL184 ) . The plasmid encoding Rod1-GFP ( Figures 2D , 4G ) was described previously ( pSL93 ) ( Becuwe et al . , 2012b ) . The plasmid encoding Rod1-KR-GFP ( Figure 6F ) was generated by cloning a SacI/XmaI fragment from pSL143 ( Becuwe et al . , 2012b ) into pSL93 . The plasmid encoding mTag-BFP2-Rsp5 ( pSL303 , Figure 5D , E ) was constructed by PCR amplification of the mTagBFP2 sequence ( TagBFP2-N , Evrogen JSC , Moscow , Russia ) ( oSL652/oSL653 ) , digestion and cloning at XbaI/NotI sites in place of GFP in pSL19 ( p415-pADH-GFP-Rsp5 , Leon et al . , 2008 ) . For total protein extracts , trichloroacetic acid ( TCA; Sigma-Aldrich ) was added directly in the culture to a final concentration of 10% ( vol/vol ) , and cells were precipitated on ice for 10 min . Cells were then harvested by centrifugation for 1 min at room temperature at 16 , 000×g , then lysed with glass beads in a 100 µl of TCA ( 10% , vol/vol ) for 10 min at 4°C . Beads were removed , the lysate was centrifuged for 1 min at room temperature at 16 , 000×g , and the resulting pellet was resuspended in TCA-sample buffer ( Tris–HCl 50 mM pH 6 . 8 , dithiothreitol 100 mM , SDS 2% , bromphenol blue 0 . 1% , glycerol 10% , containing 200 mM of unbuffered Tris solution ) at a concentration of 50 µl/initial OD unit , before being denatured at 37°C for 10 min . Phosphatase treatment was performed as previously described ( Becuwe et al . , 2012b ) . We used monoclonal antibodies raised against GFP ( clones 7 . 1 and 13 . 1; Roche Diagnostics , Meylan , France ) , HA ( clone F7; Santa Cruz Biotechnology , Dallas , TX ) , anti-ubiquitin antibody coupled to horseradish peroxidase ( clone P4D1; Santa Cruz Biotechnology ) , and polyclonal antibodies against 3-phosphoglycerate kinase ( PGK ) ( clone 22CS; Life Technologies , Saint Aubin , France ) . Immunoblots were acquired with the LAS-4000 imaging system ( Fuji , Tokyo , Japan ) . Quantification was performed using ImageJ ( NIH ) on non-saturated blots . Cells were mounted in synthetic complete medium with the appropriate carbon source and observed with a motorized Olympus BX-61 fluorescence microscope equipped with an Olympus PlanApo 100× oil-immersion objective ( 1 . 40 NA ) , a Spot 4 . 05 charge-coupled device camera and the MetaVue acquisition software ( Molecular Devices; Sunnyvale , CA ) . Cells were mounted in SD medium and imaged at room temperature . GFP-tagged proteins were visualized using a Chroma GFP II filter ( excitation wavelength 440–470 nm ) . mCh-tagged proteins were visualized using an HcRed I filter ( excitation wavelength 525–575 nm ) . Images were processed in ImageJ ( NIH ) and Photoshop ( Adobe , San Jose , CA ) for levels . Vacuolar staining was obtained by incubating cells with 100 µM CMAC ( Life Technologies ) for 10 min under agitation at 30°C , then cells were then washed twice with water before observations with a confocal microscope ( see references below ) equipped with a DAPI filter ( 450QM60 ) . For the microfluidics experiments , cells growing in exponential phase ( DO = 0 . 3–0 . 6 ) were injected in a CellASIC microfluidics chamber ( ref . YO4C , Merck-Millipore , Darmstadt , Germany ) , using the Microfluidic Perfusion Platform ( ONIX ) , driven with the interface software ONIX-FG-SW ( Merck-Millipore ) . Cells were trapped and maintained in a uniform plane . Normal growth conditions were reproduced by adjusting the ambient temperature at 30°C with a thermostated chamber , and by flowing cells with the indicated culture medium at 3 psi . The microfluidics device was coupled to a DMI6000 ( Leica , Buffalo Grove , IL ) microscope , equipped with an oil immersion plan apochromat 100× objective NA 1 , 4 , a QuantEM cooled EMCCD camera ( Photometrics , Tucson , AZ ) , and a spinning-disk confocal system CSU22 ( Yokogawa , Tokyo , Japan ) . Image resolution was 1 pixel = 149 nm . GFP-tagged proteins , mCh-tagged proteins and CMAC staining were visualized with a GFP Filter 535AF45 , RFP Filter 590DF35 , and DAPI Filter 450QM60 respectively . Images were acquired with MetaMorph 7 software ( Molecular Devices , Sunnyvale , CA ) , and denoised ( Figure 1A , C , D–F; Figure 3; Figure 3A , B , F; Figure 6A , C , D; Figure 7B; and all videos ) , with the Image J plugin Safir Filter ( Kervrann and Boulanger , 2006 ) . For Figure 4—figure supplement 3 , images were acquired with a Revolution xD TuCam system ( Andor ) equipped with a confocal scanner unit CSU-X1 ( Yokogawa ) and a Ti microscope with a 100x/1 . 4 NA objective ( Nikon , Tokyo , Japan ) and piloted by MetaMorph software ( Molecular Devices ) . Simultaneous acquisitions of two channels were done using the TuCam device ( Andor , Belfast , UK ) equipped with 580 dicroic filter and 525/50 ( green ) , 616/73 ( red ) emission filters . Detectors on the TuCam are iXon3 EMCCD camera ( Andor ) with 8 µm well size . Image stacks are acquired with a Pifoc ( Physik Intrumente , Karlsruhe , Germany ) with a z-step of 0 . 2 µm . Quantifications to evaluate the delayed internalization of Jen1-GFP in rod1Δ vs WT cells were performed manually ( Figure 1G ) . The number of Jen1-containing vesicles was quantified over time in each strain by three manual counting on 30 cells , taken from three independent experiments . The total number of Jen1-GFP-labeled structures were counted for the 30 cells and divided by 30 . STDEV was also calculated for each time point . Manual quantifications were also performed for co-localization event between Jen1-GFP with markers of internal compartments ( Sec7-mCh and Vps17-mCh ) ( Figure 4C , I ) . Counting was performed three times on 20 cells , taken from three independent experiments . Over time , the percentage of Jen1-GFP-containing vesicles co-localizing with the mCherry marker ( Sec7 or Vps17 ) was calculated for each cell and divided by the mean number of vesicles per cell . STDEV was also calculated for each time point . | The plasma membrane that surrounds cells contains many different proteins that perform tasks such as detecting signals sent to the cell , and transporting molecules into or out of the cell . To adapt to changing conditions , cells remodel their membrane to change how much of each type of protein is present . A process called endocytosis—where part of the plasma membrane and the proteins it contains buds off into the cell—plays an important role in this remodeling . The fate of a membrane protein after endocytosis can depend on whether a protein ‘tag’ called ubiquitin has been added to it . Ubiquitin-marked proteins bud off into the cell and are then sent to cell structures called lysosomes to be degraded , whereas unmarked proteins are recycled back to the plasma membrane . Yeast cell membranes contain a protein called Jen1 that transports certain molecules , including one called lactate that can be used as fuel for growth . However , glucose is a preferred nutrient for yeast , so when glucose is available , another protein called Rod1 becomes activated and promotes the addition of ubiquitin to Jen1 , and hence its degradation . This means that the cells can no longer use lactate as a source of energy . However , it was not known where in the cell the Rod1 protein does this . Becuwe and Léon labeled proteins involved in endocytosis with fluorescent tags and used microscopy to observe their fate in live yeast cells exposed to glucose . This revealed two roles for Rod1 . At the plasma membrane , Rod1 helps Jen1 to be taken into the cell in the early stages of endocytosis . But unexpectedly , Rod1 is also found at a cellular structure called the trans-Golgi network , small membrane sacs that are typically responsible for packaging proteins so they can be transported to a new destination , in particular the plasma membrane . This suggests that Rod1 can also act at this location in the cell . When the proteins responsible for maintaining transport to the trans-Golgi network are inhibited , Jen1 is no longer degraded , even when glucose is present; instead , Jen1 is recycled back to the plasma membrane . Becuwe and Léon therefore propose that a second level of control of the degradation of plasma membrane proteins occurs in the trans-Golgi network , and so this compartment has an essential role in sorting proteins for degradation or recycling . The group of proteins that Rod1 belongs to , named arrestins , has been suggested to play important roles in several diseases , including diabetes and cancer . As many of the features of the endocytic pathway are conserved in a broad range of species , arrestins may also be important for controlling the fate of membrane proteins at multiple places in mammalian cells . However , further work is required to confirm this . | [
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] | 2014 | Integrated control of transporter endocytosis and recycling by the arrestin-related protein Rod1 and the ubiquitin ligase Rsp5 |
We have identified a replication-independent histone variant , Hist2h2be ( referred to herein as H2be ) , which is expressed exclusively by olfactory chemosensory neurons . Levels of H2BE are heterogeneous among olfactory neurons , but stereotyped according to the identity of the co-expressed olfactory receptor ( OR ) . Gain- and loss-of-function experiments demonstrate that changes in H2be expression affect olfactory function and OR representation in the adult olfactory epithelium . We show that H2BE expression is reduced by sensory activity and that it promotes neuronal cell death , such that inactive olfactory neurons display higher levels of the variant and shorter life spans . Post-translational modifications ( PTMs ) of H2BE differ from those of the canonical H2B , consistent with a role for H2BE in altering transcription . We propose a physiological function for H2be in modulating olfactory neuron population dynamics to adapt the OR repertoire to the environment .
The cellular composition and connectivity of vertebrate sensory systems are shaped by signals from the external environment . These activity-dependent changes occur during critical windows of neuronal development as well as in the adult brain , enabling the animal to best perform in a given environment . Pioneering experiments in the visual system showed that patterns of light stimuli reaching each of the two eyes are essential for the activity-dependent refinement of ocular dominance columns in the visual cortex during perinatal development ( Hubel and Wiesel , 1977 ) . Similarly , experience-dependent plasticity has been shown to participate in the functional maturation of other sensory systems , including the auditory , somatosensory , and olfactory systems ( Hensch , 2004 ) . In addition to their important role in shaping sensory circuits during development , environmental stimuli can also significantly affect adult brain structures , leading to adaptive as well as maladaptive changes in sensory responses ( Buonomano and Merzenich , 1998; Ramachandran and Hirstein , 1998; Moseley and Flor , 2012 ) . Experience-dependent changes in sensory systems alter the cellular composition of sensory relays , as well as the excitability and synaptic connections of neurons involved in processing sensory information . Although a molecular-level understanding of these changes is far from complete , synaptic refinement and activity-dependent transcriptional changes appear to play prominent roles ( Holtmaat and Svoboda , 2009; Dulac , 2010; Riccio , 2010; West and Greenberg , 2011 ) . To date , activity-dependent structural remodeling of sensory systems has primarily been demonstrated in the central nervous system rather than in peripheral organs . The mouse main olfactory epithelium ( MOE ) offers a unique opportunity to investigate the range and mechanisms of experience-dependent plasticity within a peripheral sensory tissue , where the primary sensory detection occurs . The MOE detects large arrays of chemical cues through the expression of a large family of olfactory receptor ( OR ) genes ( Buck and Axel , 1991 ) . Individual olfactory neurons express a single OR allele chosen through a largely stochastic process ( Chess et al . , 1994 ) . The olfactory epithelium of mammals displays continuous neurogenesis throughout adulthood , such that a slow dividing olfactory neural stem cell population continuously replaces mature olfactory neurons that have variable , though finite , life spans ( Kondo et al . , 2010 ) . We identified a histone H2B variant , H2be , which is exclusively expressed by olfactory sensory neurons , and we hypothesized may participate in olfactory neuron gene regulation . We show that H2be displays activity-dependent expression , and that it regulates the transcriptional program and life span of olfactory sensory neurons . Our data suggest that H2be participates in a pathway that shapes the cellular and molecular composition of the olfactory epithelium based on signals from the external environment , and thus uncover a novel chromatin-based mechanism for activity-dependent neuronal plasticity .
A molecular and bioinformatics search for genes expressed differentially in neuronal subpopulations of the mouse MOE and VNO led to the identification of an uncharacterized H2B histone variant ( symbol: Hist2h2be; named for its position ( e ) among H2B-encoding genes within mouse histone cluster 2 ( Marzluff et al . , 2002 ) and referred to herein as H2be ) . Microarray and in situ hybridization ( ISH ) analyses revealed high expression of H2be in the MOE and apical VNO neuroepithelium ( Figure 1A , B ) , but no expression in the olfactory bulb ( OB ) or brain . The Genepaint and Genomics Institute of the Novartis Research Foundation ( GNF ) GeneAtlas ( Su et al . , 2004 ) databases , which contain transcriptional information for embryo sections ( Figure 1C ) and 61 mouse tissues ( Figure 1D ) , respectively , confirmed the remarkable specificity of H2be expression in the MOE and VNO . 10 . 7554/eLife . 00070 . 003Figure 1 . Mouse H2BE is detected exclusively in chemosensory neurons . ( A ) Analysis of H2be mRNA in the MOE of an 8-week old mouse , showing expression limited to sensory neurons . Boxed region is magnified ( right ) . ( B ) Analysis of H2be mRNA in the VNO of an 8-week old mouse , showing expression limited to sensory neurons , especially in the apical zone . ( C ) Analysis of H2be mRNA in a sagittal section of an E14 . 5 mouse embryo ( image from genepaint . org; ID: ES2590 ) . ( D ) Profile of H2be mRNA levels in 61 mouse tissues ( listed , right ) , showing exclusive expression in MOE and VNO . Data from GNF , now maintained by BioGPS ( http://biogps . org/ ) . ( E ) Alignment of H2BE and canonical H2B sequences . H2BE variant positions are highlighted in red; H2B PTM sites supported by >5 or ≤5 reports are highlighted in dark and light gray , respectively ( http://www . phosphosite . org ) . Scale bars for ( A ) , 500 µm; ( B ) , 100 µm; ( C ) , 1000 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 003 The H2be mRNA possesses a long 3′-untranslated region ( UTR ) and a poly-A tail . This contrasts with typical histone transcripts that lack poly-A tails but contain short 3′-stem loop UTRs , which facilitate coordination of histone expression with the cell cycle . These features , together with the observed presence of its mRNA in post-mitotic neurons , suggest that H2be encodes a replication-independent replacement histone . H2BE displays only five amino-acid differences with the canonical mouse H2B protein ( Figure 1E ) . Potential human ( Collart et al . , 1992 ) , rat , and bovine orthologs to H2be exist , although their expression is uncharacterized . To facilitate the characterization and unambiguous identification of H2BE from canonical H2B , we fused a FLAG tag to the N-terminus of H2BE , an approach used successfully for several other H2B proteins ( Kao and Osley , 2003 ) , and constructed a bacterial artificial chromosome ( BAC ) transgenic mouse line expressing the tagged protein ( Figure 2A ) . Control experiments confirmed insertion of the transgene into the genome as a single copy and recapitulation of endogenous H2be expression ( Figure 2B–E ) . We refer to the H2be:Flag-H2be mouse line as Flag-H2be . 10 . 7554/eLife . 00070 . 004Figure 2 . Generation of an H2be:Flag-H2be transgenic mouse ( referred to as Flag-H2be ) reveals that H2BE levels are stereotyped according to OR identity . ( A ) Flag-H2be transgenic construct , generated through modification of a BAC containing the H2be genomic region by insertion of a FLAG-encoding sequence immediately upstream of the H2be CDS . ( B , C ) Representative images of FLAG-H2BE in MOE ( B ) and VNO ( C ) from 10-week old Flag-H2be transgenic mice . ( D ) Quantitative PCR ( qPCR ) analysis of Flag-H2be transgene mRNA levels in whole MOE tissue from three-week old Flag-H2be ( +/− ) transgenic mice . Signals were normalized to a value of three , corresponding to a primer pair recognizing all three H2be mRNAs ( two endogenous and one transgenic ) . A primer pair specific for the Flag-tagged transgenic allele produces a normalized signal of approximately 1 , indicating similar per-allele expression levels for the transgenic and endogenous H2be alleles . Negative control samples ( −RT ) were prepared by omitting reverse transcriptase during cDNA synthesis . ( E ) Colocalization analysis of FLAG-H2BE protein and H2be mRNA in the MOE of a 3-week old Flag-H2be ( +/− ) transgenic mouse . Observation of occasional basally-located neurons that are H2be-mRNA-positive and FLAG-H2BE-negative is likely due to an expected lag in protein production and accumulation following H2be transcription onset during neuronal development . ( F ) Colocalization analysis of Olfr867 or Olfr1463 ( arrowheads ) and FLAG-H2BE , showing representative ORs associated with high or low levels of H2BE , respectively . Mouse age: 10 weeks . ( G ) Quantification of average H2BE levels in neurons expressing specific ORs ( n = from 4 to 36 neurons per OR examined; mean , 18 ) . Gene symbols are from the Mouse Genome Informatics database ( MGI; http://www . informatics . jax . org/ ) . Scale bars for ( B , left ) , 500 µm; ( B , right ) and ( F ) , 20 µm; ( C ) and ( E ) , 100 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 004 Due to the abundance of available molecular and genetic tools , we focused our study on the prospective role of H2be in the MOE . Initial expression analyses revealed that H2be mRNA and protein levels are not uniform among olfactory neurons . Rather , neurons with variable expression levels appear intermingled , and apically-located neurons generally display the highest levels of expression ( Figures 1A and 2B ) . The heterogeneous expression of H2be among MOE neurons could potentially reflect differences in the identity of the co-expressed OR , in neuronal maturity , or in some unknown biological feature . To examine the relationship between H2be and OR expression , we used fluorescent ISH ( FISH ) to identify mature neurons expressing each of 42 specific OR genes . We then used quantitative fluorescence microscopy ( Waters , 2009 ) ( see ‘Materials and methods’ ) to assess the level of FLAG-H2BE within nuclei of the identified neurons relative to their surrounding neuronal field , imaged in tissue sections spanning the length of the MOE , and across multiple animals ( n ≥ 2 per OR ) . Strikingly , each OR tested appears consistently and reproducibly associated with a stereotyped level of H2BE in mature neurons ( Figure 2F , G ) . Indeed , for 73% of the tested ORs , neurons expressing the same OR display significantly less variation with each other in their H2BE level than is observed within the entire MOE neuronal population ( p<0 . 05 after false-discovery rate [FDR] correction; 1-tailed F-test ) . These results indicate that H2BE expression in a given neuron is tightly correlated with the identity of the co-expressed OR , and not with a specific stage of neuronal development or maturity . To further investigate the function of H2be , we generated an H2be-null line by targeted replacement of the H2be coding sequence with a membrane-localized mCherry reporter ( Gap43-mCherry; Figure 3A ) . We refer to this line as H2be-KO . Analysis of GAP43-mCherry fluorescence in the MOE of H2be-KO mice revealed a variable intensity of the fluorescent reporter similar to that observed for the endogenous gene ( Figure 3B ) . Analysis of GAP43-mCherry in the olfactory bulb revealed fluorescence limited to sensory axon termini in the glomerular layer . Fluorescence intensities appeared to vary widely among glomeruli , which are each innervated by neurons expressing a given OR , a result consistent with our observations of variable but stereotyped H2BE levels in neurons expressing specific ORs ( Figure 3C ) . 10 . 7554/eLife . 00070 . 005Figure 3 . Generation of an H2be-KO/GAP43-mCherry-KI mouse line ( referred to as H2be-KO ) reveals that loss of H2be causes defects in olfaction . ( A ) H2be-KO allele , constructed through replacement of the endogenous H2be CDS with a membrane-targeted mCherry-encoding sequence ( Gap43-mCherry ) . ( B , C ) Intrinsic GAP43-mCherry fluorescence in the MOE ( B ) and OB ( C ) of H2be-KO mice , showing GAP43-mCherry localization to the cell membranes and processes of olfactory neurons . Mouse ages: ( B ) , 6 months; ( C ) , 2 months . Scale bar for ( B ) , 20 µm; ( C ) , 200 µm . ( D ) Performance of approximately 3-month old water-restricted H2be-KO and control littermates in discriminating between hexanol/hexanoic acid ( left ) or ( + ) / ( − ) -carvone ( right ) odor pairs to obtain water ( n = 5 per genotype ) . *p<0 . 05 . ( E ) Effects of H2be loss-of-function on odor-evoked electrical responses in the MOE . Electro-olfactogram traces ( black and red ) represent average responses to a 0 . 5-s stream of air from the head space of a 1% solution of isoamyl-acetate in mineral oil . Gray traces show timing of switching between the delivery of clean , de-odorized air ( low ) , and odor-containing air ( high ) . Results shown are representative of multiple trials , odorants , and concentrations; experimental procedures were adapted from those described previously ( Waggener and Coppola , 2007 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 005 H2be-KO mice appear healthy and fertile . Training of H2be-KO mice and littermate controls to discriminate between odors for a water reward revealed that H2be-KO mice learn the task more slowly and perform less efficiently than the controls , suggesting a defect in olfactory function ( Figure 3D ) . To investigate a potential defect in olfactory signal transduction , we performed electro-olfactograms on MOEs from H2be-KO and heterozygous littermates ( Figure 3E ) but found no obvious anomalies , indicating that the observed phenotype is not due to gross defects in odor-evoked signaling . Because histones are central components of chromatin , we suspected that loss of H2be might affect olfactory gene expression . Microarray analysis of whole MOE tissue in 6-month old H2be-KO and WT mice revealed that approximately 6% of transcripts are differentially expressed ( p<0 . 05; Supplementary file 1A; Figure 4—source data 1 ) , a value that may underestimate the effects of H2be loss in the fraction of MOE cells that express H2be . Qualitatively , the effects of H2be loss appear to depend on the normal expression level of a given gene: highly transcribed genes tend to be up-regulated , while moderately transcribed genes appear up- or down-regulated in H2be-KO mice ( not shown ) . Although the differentially-expressed genes do not pass the p<0 . 05 threshold after FDR adjustment for the 28 , 064 probe sets tested , likely due to their moderate fold changes , they do show statistically significant enrichment in several gene ontology categories ( FDR-adjusted p<0 . 05; Figure 4A ) . The categories enriched most strongly among up-regulated genes include ‘olfactory detection’ ( consisting of OR genes ) and ‘RNA processing’ , and among down-regulated genes include ‘developmental process’ and ‘positive regulation of transcription’ . 10 . 7554/eLife . 00070 . 006Figure 4 . Loss of H2be causes defects in gene expression and OR expression frequencies . ( A ) Enriched gene ontology categories among genes up- ( top ) or down-regulated ( bottom ) in 6-month old H2be-KO vs WT MOEs , based on microarray analyses of whole MOE tissue ( n = 6 samples/genotype , 2 animals/sample ) . ( B ) Multiplex qPCR analysis of OR mRNA levels in MOE tissue of six-month old H2be-KO and WT mice . Signals represent normalized ratios of specific OR mRNAs to Cnga2 , which is unaltered in H2be-KO mice and used as an internal control ( n = 6 ) . ( C ) Expression differences in H2be-KO and WT MOEs ( based on microarray analysis ) , plotted as a function of age . For simplicity , only ORs with differences >20% at age 6 months are shown , with up- and down-regulated ORs shown in red and black , respectively; statistical analysis included all ORs interrogated . ( D , E ) Representative images ( left ) and quantification ( right ) of Olfr867 ( D ) and Olfr1463 ( E ) expression frequencies in 6-month old H2be-KO and WT littermates ( n = 3 mice , 10 sections per mouse ) . Scale bars , 200 µm . ( F ) Relationship between OR gene expression defects in 6-month old male H2be-KO mice ( based on microarray analysis ) and stereotypical H2BE levels as measured in male Flag-H2be mice . Red line , best fit . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , ****p<10−30 . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 00610 . 7554/eLife . 00070 . 007Figure 4—source data 1 . Effects of H2be loss of function on gene expression in the main olfactory epithelium of 6-month old mice . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 007 The highly significant enrichment of the ‘olfactory detection’ gene category ( FDR-adjusted p=2 × 10−17 ) reveals that a large number of ORs may be up-regulated in the olfactory epithelia of H2be-KO mice , which we indeed confirmed by quantitative PCR on a subset of genes ( Figure 4B ) . Interestingly , a comparison of gene expression at 6 months with an earlier age of 5 weeks revealed that OR expression differences between WT and H2be-KO mice increase dramatically with age ( Figure 4C ) , and display a significant bias towards up-regulation in the KO ( p<10−30 , 1-tailed paired t-test ) . To determine if defects in OR mRNA levels originate from differences in the frequency of OR expression in the MOE , in the cellular level of OR transcripts in individual neurons , or in both , we analyzed the expression of specific ORs by RNA FISH . For each of four ORs quantified , expression differences in H2be-KO and WT mice strongly correlate with differences in OR frequencies ( Figure 4D , E ) and not cellular levels ( not shown ) . Remarkably , expression differences observed for various ORs in H2be-KO and WT mice were also found to correlate with the level of H2BE with which they are normally co-expressed: ORs with increased expression in H2be-KO mice are associated with high levels of H2BE , while ORs with decreased or unchanged expression appear associated with low H2BE levels ( Figure 4F ) . Changes in the representation of neurons expressing specific ORs upon loss of H2be led us to predict that ectopic over-expression of H2be might also affect OR frequencies . We generated an Omp:Flag-H2be transgenic mouse line ( referred to as H2be-GF ) in which Flag-H2be is expressed under the control of the promoter for olfactory marker protein ( Omp; Figure 5A ) , a gene highly expressed in mature olfactory neurons ( Danciger et al . , 1989 ) . An H2be-GF founder line was identified by quantitative PCR as expressing the transgene at a approximately 10-fold higher level than the endogenous H2be gene ( not shown ) . This line displayed a high level of FLAG-H2BE in all mature olfactory neurons except for a subset in zone 2 ( Figure 5B ) . We reasoned that this serendipitous mosaic expression provided an internal control for the effects of H2be over-expression . Comparison of overall gene expression in MOE tissue from H2be-GF and WT mice revealed widespread differences ( Supplementary file 1B; Figure 5—source data 1 ) . Strikingly , both up- and down-regulated genes are dominated by ORs , exhibiting FDR-adjusted p-values of 7 × 10−29 and 3 × 10−18 , respectively , for enrichment in the ‘olfactory detection’ category ( Figure 5C ) . Using an FDR-adjusted p-value cutoff of 0 . 05 , we found that approximately 7% of ORs display significant expression differences in H2be-GF mice ( Figure 5D ) . As observed for H2be-KO mice , OR expression changes in H2be-GF mice are strongly correlated with H2BE expression ( Figure 5E , F , H ) and reflect changes in OR expression frequency , such that ORs that are consistently co-expressed with the transgene are reduced in frequency , while ORs that escape co-expression with the transgene within zone 2 are increased ( Figure 5F–I ) . 10 . 7554/eLife . 00070 . 008Figure 5 . Generation of an Omp:Flag-H2be transgenic mouse ( referred to as H2be-GF ) reveals that ectopic overexpression of H2be in olfactory neurons alters gene expression and OR expression frequencies . ( A ) H2be-GF transgenic construct , generated through replacement of the Omp CDS with a FLAG-H2BE-encoding sequence in a vector containing the Omp genomic region . ( B ) Analysis of FLAG-H2BE in the MOE of a 5-week old H2be-GF mouse , showing high transgene expression in all mature neurons , except for a band near zone 2 ( Sullivan et al . , 1996 ) . Boxed region is magnified ( right ) . ( C ) Gene ontology categories enriched among genes up- ( top ) or down-regulated ( bottom ) in 5-week old H2be-GF vs WT MOEs , based on microarray analyses of whole MOE tissue ( n = 4 samples , 3 animals per sample ) and WT ( n = 6 samples , 2 animals per sample ) mice . ( D ) Percentage of OR genes with significantly differential expression ( FDR-adjusted p<0 . 05 ) in 5-week old H2be-GF and WT mice . ( E ) Relationship between OR gene expression defects in H2be-GF mice and transgene co-expression penetrance . Red line , best fit . ( F , H ) Colocalization of Olfr1277 or Olfr293 ( arrowheads ) and FLAG-H2BE in 5-week old H2be-GF mice , showing representative ORs associated with high- or low transgene penetrance , respectively . ( G , I ) Representative images ( left ) and quantification ( right ) of Olfr1277 ( G ) and Olfr293 ( I ) expression frequencies in 5-week old H2be-GF and WT littermates ( n = 3 mice; 10 sections per mouse ) . *p<0 . 05 , **p<0 . 01 . Scale bars for ( B ) , 500 µm; ( F ) and ( H ) , 20 µm; ( G ) and ( I ) , 200 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 00810 . 7554/eLife . 00070 . 009Figure 5—source data 1 . Effects of the ectopic over-expression of H2be ( expressed from an Omp-promoter-driven transgene and tagged with a FLAG epitope ) on gene expression in the main olfactory epithelium of 5-week old mice . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 009 Altered OR expression frequencies in H2be-KO and H2be-GF mice could result from changes in OR gene choice or in the life span of neurons expressing a given receptor . To investigate the temporal onset of H2BE expression relative to OR gene choice , we took advantage of the basal to apical gradient of neuronal development in the MOE . Analysis of the onset of H2BE relative to ORs normally associated with high levels of H2BE revealed that H2BE expression is initiated well after OR expression ( Figure 6A ) . We next compared the onset of H2be expression to that of Neurod1 , Gap43 , and Omp , which are expressed prior to , concurrently with , and subsequent to OR gene choice , respectively ( Cau et al . , 2002; Iwema and Schwob , 2003; Kolterud et al . , 2004 ) . We found that the onset of H2be expression follows the window of Neurod1 expression , as observed at both the protein and RNA levels in MOE tissue from Flag-H2be and WT mice , respectively ( Figure 6B , C ) . Moreover , H2BE expression begins after GAP43 ( Figure 6D ) and before OMP ( Figure 6E ) , but overlaps extensively with both . Together , these results confirm that H2BE is present prior to full neuronal maturity , but subsequent to OR choice . Thus H2BE is unlikely to participate in the process of OR choice . 10 . 7554/eLife . 00070 . 010Figure 6 . The expression onset of H2be follows OR choice during neuronal development . ( A ) H2BE is undetectable in an Olfr827+ neuron that is newly-differentiated ( note its basal position in the epithelium; boxed and magnified , right ) , but expressed at uniformly high levels in mature Olfr827+ neurons . ( B , D , E ) MOE expression patterns in adult ( left ) or embryo ( right ) of H2be relative to Neurod1 , Gap43 , and Omp , which have expression onsets prior to , concurrently with , and following OR choice , respectively . All NEUROD1+ cells ( B ) and a fraction of basal GAP43+ ( newly-differentiated ) neurons ( C ) are H2BE− ( arrowheads ) , whereas a fraction of H2BE+ neurons are OMP− ( E; arrowheads ) . ( C ) Colocalization of endogenous H2be and Neurod1 mRNAs in the MOE of a 3-week old WT mouse . Mouse ages: ( A ) , 3 months; ( B , left ) , 4 months; ( C ) , 3 weeks; ( D , left ) and ( E , left ) , 10 months . Scale bars for ( A ) to ( E ) , 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 010 Next , we further analyzed H2be-KO mice for evidence of defects in choice , or in stabilization of OR gene choice . We found no defects in OR co-expression frequencies ( Figure 7A ) or in the position or number of glomeruli ( Figure 7B ) in H2be-KO mice , which are phenotypes expected to be associated with the aberrant expression of multiple OR genes or the abnormal switching between distinct ORs ( Shykind et al . , 2004 ) , suggesting that H2BE is not likely involved in the maintenance of OR gene choice . Finally , as described previously , gene expression analyses indicate that OR expression defects in H2be-KO mice are markedly more severe at 6 months of age than at 5 weeks ( Figure 4C ) . Since disruption in OR gene choice would be expected to cause defects in OR expression that are independent of age , the delayed appearance of OR expression defects in H2be-KO mice is inconsistent with a role for H2BE in this process . 10 . 7554/eLife . 00070 . 011Figure 7 . H2BE affects olfactory neuronal longevity , not OR choice . ( A ) Representative images ( left ) and quantification ( right ) of Olfr1507 and Olfr727 co-expression frequencies in H2be-KO and control littermates ( n = 10 sections ) . ( B ) Labeled axons of Olfr17-expressing neurons form indistinguishable glomeruli in the OBs of H2be-KO and control littermates . ( C , E ) Representative images ( C , left; arrowheads ) and quantification ( C , right; E ) of apoptosis in mature neurons of H2be-KO ( C ) or H2be-GF ( E ) mice compared to controls ( C: n = 3 mice , 12 sections per mouse; E: n = 10 sections ) . ( D , F ) Schematic of experimental analysis timeline ( left ) and quantification ( right ) of relative BrdU+ neuron frequencies in H2be-KO ( D ) and H2be-GF ( F ) mice compared to controls , respectively ( D: n = 3 mice per timepoint , 12 sections per mouse; F: n = 10 sections per timepoint ) . F-H: Flag-H2be . ( G ) Representative images ( left ) and quantification ( right ) of neurogenesis in H2be-GF ( GF ) mice and Flag-H2be ( F-H ) controls by analysis of frequencies of BrdU+ neurons ( arrowheads ) 15 days post-injection of BrdU ( T = 0 timepoint; n = 10 sections ) . *p<0 . 05 , ***p<0 . 001 . Mouse ages: ( A ) and ( E ) , 4 months; ( B ) and ( G ) , 2 months; ( C ) , 15 months . Scale bars for ( A ) and ( C ) , 20 µm; ( B ) , 200 µm; ( G ) , 100 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 011 We next investigated the alternative hypothesis that altered OR frequencies in H2be-KO and H2be-GF mice may result from changes in neuronal longevity . Analysis of active-CASP3-labeled mature olfactory neurons in H2be-KO and littermate controls revealed a 45% lower frequency of apoptosis in the mutant epithelium ( Figure 7C ) . Moreover , analysis of the life span of 5-bromo-2′-deoxyuridine ( BrdU ) -labeled neurons revealed a significantly higher survival rate in H2be-KO mice compared to controls ( Figure 7D ) . These results indicate that the loss of H2be increases the life span of mature neurons . Accordingly , similar analyses in H2be-GF mice revealed a twofold higher rate of apoptosis among mature neurons ( Figure 7E ) and a significantly reduced survival rate compared to Flag-H2be mice ( Figure 7F ) . Remarkably , the initial BrdU labeling frequency of H2be-GF neurons is approximately 2 . 9-fold higher than that observed in control neurons , reflecting an elevated rate of neurogenesis , presumably a consequence of the increased rate of apoptosis ( Figure 7G ) . To ensure that the observed increase in apoptosis in transgenic epithelia is not due to a non-specific toxic effect of the overexpressed histone , we expressed H2be or Flag-H2be at high levels in cultured fibroblast ( NIH-3T3 ) or embryonic kidney ( HEK-293T ) cells . Transgene-expressing cells showed no increase in cell death compared to control cells overexpressing the canonical H2b or containing no transgene ( not shown ) , suggesting that H2BE's action in affecting cellular longevity requires an olfactory-specific cellular context . Taken together , these data indicate that the expression of H2be in mature olfactory neurons directly affects neuronal longevity , such that neurons with low expression of H2BE tend to have longer life spans , while neurons with elevated H2BE tend to be relatively short-lived . We next sought to identify the source of heterogeneity in H2BE level among neurons expressing different ORs in WT mice . To investigate a possible link with neuronal activity , we performed unilateral naris occlusion ( UNO ) by surgically blocking airflow into one nostril of Flag-H2be mice for 10 days . Upon analysis of H2BE in the MOE of these mice , we observed dramatically higher H2BE expression on the closed side of the epithelium relative to the open side . This increase can be observed at the cellular level in neurons expressing a specific OR ( Figure 8A , B ) , and in the GAP43-mCherry fluorescence within the ipsilateral and contralateral olfactory bulbs of H2be-KO ( +/− ) mice ( Figure 8C ) . Together , these results indicate that olfactory deprivation causes an up-regulation of H2BE . 10 . 7554/eLife . 00070 . 012Figure 8 . H2be is regulated by activity . ( A , B ) Effects of unilateral naris occlusion ( UNO ) on H2BE level in the MOE . ( A ) Representative images of FLAG-H2BE and Olfr733 colocalization ( boxed regions magnified , right; Olfr733+ neurons , arrowheads ) . ( B ) Distributions ( main ) and averages ( inset ) of H2BE level within nuclei of randomly-sampled ( main; inset , left; n = 200 ) or Olfr733+ ( inset , right; n = 10 ) neurons on the two sides of the MOE . ( C ) Effects of UNO on intrinsic GAP43-mCherry fluorescence in glomeruli ( arrowheads ) within the OB of an H2be-KO heterozygous mouse . Reduced tyrosine hydroxylase ( TH; a marker of olfactory activity ) staining on the closed side indicates completeness of naris closure . ( D–F ) Effects of odor exposure on H2BE level in olfactory neurons . Representative images ( D ) and quantification ( E , left ) of FLAG-H2BE in Olfr2+ neurons ( D , arrowheads ) exposed to odors or mineral oil ( no odor ) . ( E , right ) Quantification of FLAG-H2BE in Olfr653+ neurons ( chosen randomly as a negative control ) exposed to odors or mineral oil ( no odor ) . ( F ) Average H2BE level within neurons expressing odor-stimulated or control ORs ( n = 20–60 ) . ( G , H ) Representative image ( G; boxed region magnified , right ) and quantification ( H ) of the relationship between GAP43-mCherry and tyrosine hydroxylase intensities within glomeruli of an H2be-KO heterozygous mouse . Red line , best fit . *p<0 . 05; **p<0 . 01; ****p<0 . 0001 . Mouse ages: ( A ) and ( C ) , 4 weeks; ( D ) , 8 weeks; ( G ) , 10 weeks . Scale bars for ( A ) and ( D ) , 20 µm; ( C ) and ( G ) , 100 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 01210 . 7554/eLife . 00070 . 013Figure 8—figure supplement 1 . Distribution of relative H2BE levels within neurons expressing Olfr73 , Olfr958 , Olfr16 , and Olfr167 for odor-exposed and control littermates . Stimulating odors are indicated , except for Olfr167 , which serves as a negative control . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 013 To test whether neuronal stimulation results in reduced H2BE levels , we exposed mice to a mixture of odorants or , as a negative control , to mineral oil . The odorant mixture consisted of four ligands corresponding to known ORs: heptanal/octanal for Olfr2 ( Bozza et al . , 2002 ) , lyral for Olfr16 ( Touhara et al . , 1999 ) , and eugenol for Olfr73 ( Oka et al . , 2004 ) and Olfr958 ( Oka et al . , 2006 ) . After 3 weeks of exposure to the odor mixture or mineral oil , the four stimulated neuronal subtypes showed significantly reduced levels of H2BE compared to the same subtypes in control mice ( Figure 8D–F; Figure 8—figure supplement 1 ) . In contrast , neurons expressing two randomly picked orphan ORs , Olfr167 and Olfr653 , showed no statistically significant differences between odor-exposed and control mice . Thus , olfactory stimulation of specific neurons results in a relative reduction in their nuclear H2BE levels . To further corroborate the activity-dependence of H2BE , we used GAP43-mCherry fluorescence as a reporter of H2be gene expression in H2be-KO ( +/− ) mice and measured its intensity relative to that of tyrosine hydroxylase , a marker of neuronal activity in the glomerular layer of the olfactory bulb ( Cho et al . , 1996 ) . These analyses revealed a significant negative correlation between the two markers , further supporting the view that H2be gene expression is inversely related to neuronal activity ( Figure 8G , H ) . To investigate the possibility that H2BE expression is regulated by cAMP or Ca2+ , two important signaling components and modulators of gene expression in the MOE ( Mori and Sakano , 2011 ) , we looked for evidence of changes in H2BE levels in Adcy3-null and Cnga2-null mice , respectively . We found that olfactory neurons that contain undetectable levels of H2BE in WT mice frequently show extremely high levels in their Adcy3 ( −/− ) counterparts ( Figure 9A ) . However , using GAP43-mCherry as a reporter of H2be expression in Cnga2 ( +/− ) mosaic females , we observed no elevation in GAP43-mCherry fluorescence in Cnga2− glomeruli relative to Cnga2+ glomeruli ( Figure 9B ) . These results suggest that the activity-dependent down-regulation of H2BE is cAMP- but not Ca2+-mediated . The precise mechanism of H2BE regulation by cAMP is unclear . One possibility is that H2be expression is suppressed by a factor such as ICER , a cAMP-dependent repressor form of the cyclic-AMP responsive element binding protein ( CREB ) family member CREM ( Lyons and West , 2011 ) . Interestingly , we identified two CRE half-sites within the H2be coding region , although their function , if any , is unknown . 10 . 7554/eLife . 00070 . 014Figure 9 . H2BE levels are cAMP- , but not Ca2+-dependent . ( A ) Representative example of the effects of Adcy3 loss-of-function on H2BE levels in newborn ( P0 ) mice . In Adcy3 ( +/+ ) mice , Olfr1508+ neurons contain extremely low H2BE levels ( left , arrowheads ) , while in Adcy3 ( −/− ) mice , Olfr1508+ neurons frequently contain extremely high levels ( right , arrowhead ) , indicating that cAMP participates in the negative regulation of H2BE . Note: age P0 was chosen due to the low postnatal survival rate of Adcy3 ( −/− ) mice . ( B ) Effects of Cnga2 loss-of-function on H2be expression . Mice in which Cnga2 ( an X-chromosomal gene necessary for odor-evoked Ca2+ signaling ) was replaced with the Tau-LacZ gene ( Zhao and Reed , 2001 ) were crossed with H2be-KO mice to generate three-week old Cnga2-KO-Tau-LacZ ( +/− ) /H2be-KO-Gap43-mCherry ( +/− ) compound heterozygous females . In these mice , one half of new olfactory neurons express TAU-LACZ instead of CNGA2 and project to glomeruli distinct from neurons expressing Cnga2 ( Zheng et al . , 2000 ) . Analysis of β-GAL+ ( TAU-LACZ ) and GAP43-mCherry intensities within glomeruli revealed that Cnga2− neurons ( β-GAL+; arrowheads ) do not have higher levels of GAP43-mCherry than Cnga2+ neurons ( β-GAL− ) , indicating that H2be expression is not negatively regulated by Ca2+ signaling . Scale bars for ( A ) , 40 µm; ( B ) , 100 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 014 To investigate a potential role for H2be in mediating activity-dependent changes in gene expression , we performed UNO on H2be-KO and WT mice and compared gene expression differences for each genotype on the open and closed sides of the MOE after 3 weeks of nostril closure . Consistent with a recent study ( Coppola and Waggener , 2011 ) , we identified widespread differences in gene expression after activity deprivation in WT mice , with ‘olfactory detection’ ( ORs ) identified as the most highly enriched gene ontology category among both up- and down-regulated genes ( Figure 10A; Figure 10—source data 1 ) . Using an FDR-adjusted p-value cutoff of 0 . 05 , approximately 11 . 5% of ORs display significant expression differences after olfactory deprivation in WT mice ( Figure 10B ) . FISH analyses revealed that these differences reflect altered OR expression frequencies within the MOE ( Figure 10C , D ) . 10 . 7554/eLife . 00070 . 015Figure 10 . Unilateral naris occlusion ( UNO ) alters gene expression and OR expression frequencies . ( A ) Gene ontology ( biological process ) terms enriched at the top of a gene list ranked descendingly according to differential expression on the two MOE halves from 5-week old WT mice subjected to UNO ( 21 days ) , based on microarray analysis ( n = 3 samples per MOE side , four animals per sample ) . ( B ) Percentage of OR genes with significantly differential ( FDR-adjusted p<0 . 05 ) expression on the two sides of the MOE of WT mice after UNO ( 21 days; values from microarray data ) . ( C , D ) Representative images of Olfr1325 ( C ) and Olfr1336 ( D ) expression in the MOE after UNO . ( E ) Representative images of normal FLAG-H2BE levels in neurons associated with ORs that are down- ( left ) or up-regulated ( right ) in frequency after olfactory deprivation . ( F ) Relationship between UNO-mediated OR gene expression differences on the two sides of the MOE ( values from microarray data for WT mice subjected to UNO for 21 days ) and associated FLAG-H2BE levels measured in intact mice . Red line , best fit; ****p<0 . 0001 . Mouse ages: ( C ) and ( D ) , 5 weeks; ( E ) , 12 weeks . Scale bars for ( C ) and ( D ) , 200 µm; ( E ) , 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 01510 . 7554/eLife . 00070 . 016Figure 10—source data 1 . Effects of H2be loss of function on gene expression changes in the main olfactory epithelium ( MOE ) as a result of activity deprivation through unilateral naris occlusion ( UNO ) in 5-week old mice . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 016 We then investigated a possible relationship between H2BE levels and changes in OR expression after UNO in WT mice . Remarkably , this analysis revealed that ORs that are down-regulated following UNO are normally co-expressed with high levels of H2BE , while up-regulated ORs are normally co-expressed with low H2BE levels ( Figure 10E , F ) . Thus , if H2BE level is taken as a measure of neuronal inactivity , these data suggest that olfactory deprivation causes normally inactive neurons to decrease in relative abundance , while leading to a relative increase in neurons that are highly active . Comparison of MOE expression data obtained for WT and H2be-KO mice revealed that for a subset of UNO-altered genes ( Figure 11A; Figure 10—source data 1 ) , including a large fraction of ORs ( Figure 11B , C ) , UNO-mediated changes are significantly attenuated in the MOE of the KO compared to the WT . These results suggest that H2BE participates in activity-dependent modulation of olfactory gene expression , though it is clearly not the only mediator of the observed changes . 10 . 7554/eLife . 00070 . 017Figure 11 . H2be affects activity-dependent gene expression . ( A ) Gene ontology ( biological process ) terms enriched among genes with UNO-mediated expression differences in WT mice ( log2 fold-change > 0 . 3; unadjusted p<0 . 02 ) , but at least 20% less altered expression in H2be-KO compared to WT mice after UNO , based on microarray analysis of MOE halves from 5-week old WT and H2be-KO mice subjected to UNO ( 21 days; n = 3 samples per MOE side , four animals per sample ) . ( B ) Histograms of UNO-mediated OR expression differences on the closed and open sides of the MOE of H2be-KO mice as a percentage of the corresponding WT differences ( normalized to 100% or −100%; red lines ) for ORs significantly up- ( left ) or down-regulated ( right ) in WT mice after olfactory deprivation ( FDR-adjusted p<0 . 05; values from microarray data ) . ( C ) Comparison of UNO-altered Olfr1336 and Olfr1313 frequencies in 5-week old WT and H2be-KO mice subjected to UNO ( 21 days ) . Values correspond to relative OR expression frequency differences on the two sides of the MOE according to the anterior ( Ant ) / posterior ( Post ) position ( n = 3 mice , five sections per region per mouse ) . Note: UNO appears to affect OR frequencies differently in the anterior and posterior regions of the MOE . *p<0 . 05; **p<0 . 01***p<0 . 001; ****p<0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 017 Our findings support a model in which the level of olfactory activity of a neuron determines its level of H2BE , which in turn affects its life span ( Figure 12 ) . According to this model , inactive neurons express high levels of H2BE , which leads to reduced longevity , while active neurons maintain low levels of H2BE and are relatively long-lived . This model predicts that OR expression frequencies should change with age and experience and lead to the gradual enrichment of active olfactory neurons at the expense of inactive neurons in the MOE . Consistent with this prediction and previous reports of age-dependent changes in OR expression ( Lee et al . , 2009; Rimbault et al . , 2009; Rodriguez-Gil et al . , 2010 ) , we found that 65% of all ORs are significantly altered in their expression from 5 weeks to 6 months of age in WT mice ( FDR-adjusted p<0 . 05; not shown ) . Future experiments will determine the physiological significance of the ORs displaying age-dependent altered expression . 10 . 7554/eLife . 00070 . 018Figure 12 . Model for the effects of neuronal activity on H2BE expression level , life span and resulting neuronal representation . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 018 To further investigate H2BE's function at the molecular level , we first sought confirmation that transgenic FLAG-H2BE protein , a tool critical for these studies , is normally integrated into chromatin . Fractionation of unfixed MOE cell nuclei from Flag-H2be and H2be-GF transgenic mice showed that FLAG-H2BE is present at barely detectable levels in soluble nucleoplasm and is instead almost entirely chromatin-bound ( Figure 13A ) . Moreover , mass spectrometric analysis of proteins associated with FLAG-H2BE mononucleosomes from Flag-H2be MOE tissue identified numerous chromatin-associated proteins , including several canonical and variant histones ( Figure 13B ) . Finally , chromatin immunoprecipitation ( ChIP ) using anti-FLAG antibodies yielded large quantities of DNA from Flag-H2be MOE tissue ( Figure 13C ) . Together , these results provide strong evidence that FLAG-H2BE is readily integrated into the chromatin of olfactory neurons and support the use of Flag-H2be and H2be-GF mice for investigating the molecular function of H2BE . 10 . 7554/eLife . 00070 . 019Figure 13 . Chromatin incorporation and localization of FLAG-H2BE . ( A ) Two-color western analysis of FLAG-H2BE and H3 in soluble nucleoplasm ( sol ) and chromatin ( chr ) fractions of unfixed MOE cell nuclei from 16-week old Flag-H2be , H2be-GF , and WT mice . ( B ) SDS-PAGE analysis ( left ) , mass spectrometric identification ( listed , middle ) , and western analysis ( right ) of proteins associated with immunoprecipitated FLAG-H2BE-containing native mononucleosomes from MOE tissue of Flag-H2be transgenic mice . Proteins identified by mass spectrometry are listed according to their approximate electrophoretic mobility . ( C ) Quantification of DNA immunoprecipitated from crosslinked and fragmented chromatin derived from MOE tissue of Flag-H2be transgenic mice . ( D ) Representative image of FLAG-H2BE localization within olfactory neurons of a 10-week old Flag-H2be mouse . Scale bar , 5 µm . ( E ) Genome-wide ChIP analysis of relative FLAG-H2BE levels with respect to distance from the transcript start sites ( TSS ) for all mouse genes ( grey ) , and genes expressed at high ( red ) and low ( black ) levels in olfactory neurons . ( F ) Genome-wide ChIP analysis of relative FLAG-H2BE levels with respect to distance from the CDS start sites for mouse histone ( left ) , OR ( middle ) , and vomeronasal type 1 receptor ( V1R; right ) genes in comparison to all genes . ( G ) Quantitative PCR analysis of relative FLAG-H2BE levels in the protein-coding regions of representative histone and OR genes . Analyses were performed on ligation-mediated-PCR-amplified Flag-H2BE and H3 ChIP DNA samples . ( H ) Genome-wide ChIP analysis shows depleted FLAG-H2BE levels within representative OR CDS regions . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 019 Using FLAG-H2BE as a molecular surrogate for H2BE , we examined the histone variant's localization on genomic DNA . High-resolution confocal imaging of neuronal nuclei in the MOE of Flag-H2be mice revealed that H2BE is not confined to nuclear puncta , but rather is widely distributed throughout the nucleus ( Figure 13D ) . Analysis of H2BE localization by genome-wide chromatin immunoprecipitation ( ChIP ) in MOE neurons confirmed its wide distribution and revealed a slight enrichment near gene promoters , especially in genes highly expressed in mature olfactory neurons ( Figure 13E ) . Interestingly , these analyses and subsequent qPCR experiments revealed that the protein-coding regions of histone genes are particularly enriched for H2BE , while those of OR and vomeronasal receptor ( VR ) genes are relatively devoid of the variant ( Figure 13F–H ) . Because the ChIP input was obtained from a heterogeneous mixture of MOE nuclei and approximately 99 . 9% of OR alleles and all VR alleles are silent in a given olfactory neuron , the latter results may reflect the lower accessibility of silent OR and VR loci for replacement of canonical H2B by H2BE , perhaps due to the highly compacted nature of these loci ( Magklara et al . , 2011 ) . Together , our results support a model in which H2BE replacement of canonical H2B within olfactory chromatin is widespread , but most extensive within transcriptionally active loci . Four of the five amino acid variant positions within H2BE are located near post-translational modification ( PTM ) sites of canonical H2B ( Figure 1E ) , raising the question of whether PTMs may differ between the two proteins . We analyzed three relatively well-characterized H2B PTMs: mono-methylation and acetylation of lysine 5 ( Lys5-Me and Lys5-Ac ) , which lies close to the H2BE variant residue P3L , and ubiquitination of lysine 120 ( Lys120-Ub ) , near the variant residue S124A . Both H2B-Lys5 PTMs have been shown to be positively correlated with transcriptional activity , with Lys5-Me and Lys5-Ac enriched within transcribed and promoter regions , respectively ( Barski et al . , 2007; Wang et al . , 2008 ) . H2B-Lys120 ubiquitination , which has also been linked with transcription , appears to play a complex regulatory role ( Chandrasekharan et al . , 2010 ) . Analysis of H2BE and H2B-Lys5-Me levels in Flag-H2be MOEs revealed a striking inverse correlation between the two staining patterns , such that neurons expressing a high level of H2BE display conspicuously low H2B-Lys5-Me immunoreactivity , and vice versa ( Figure 14A , B ) . Using an ELISA assay , we ruled out the possibility that the polyclonal antibody against H2B-Lys5-Me is incompatible with the H2BE sequence ( Figure 14C ) . To determine if there is a causal link between the seemingly mutually exclusive expression of H2BE and H2B-Lys5-Me , we examined the prevalence of H2B-Lys5-Me in H2be-KO and H2be-GF mice . Remarkably , loss of H2be leads to widespread H2B-Lys5-Me immunoreactivity in H2be-KO MOEs ( Figure 14D ) , while over-expression of H2BE further reduces H2B-Lys5-Me staining in H2BE-positive nuclei ( Figure 14E ) . Western analysis of MOE lysates from Flag-H2be mice provided a biochemical confirmation that the Lys5-Me modification is absent from the tagged H2BE ( Figure 14F ) . Together , our results suggest that , unlike canonical H2B , H2BE does not undergo detectable mono-methylation at Lys5 and that , consequently , replacement of H2B by H2BE causes a direct reduction of the Lys5-Me modification in H2BE expressing cells . Interestingly , we observe an increase in the number of H2BE expressing neurons , and in H2BE level in individual cells in the MOEs of older animals ( Figure 14G–I ) . This is supported by western analysis , which revealed that the overall level of H2BE in the MOE doubles from 7 weeks to 45 weeks ( approximately 10 months ) of age ( Figure 14J ) . In addition , we observe that the mutual exclusion between H2BE and H2B-Lys5-Me dramatically increases with age ( Figure 14G–I ) , indicating the gradual replacement of canonical H2B by H2BE , which proceeds to near-completion in a fraction of neurons by 36 weeks ( approximately 8 . 5 months ) of age . Further , it suggests that the accumulation of H2BE through replacement of canonical H2B proceeds at a faster pace than it's loss through neuronal turnover . 10 . 7554/eLife . 00070 . 020Figure 14 . H2BE's post-translational modifications ( PTMs ) differ from those of canonical H2B . ( A , B , D , E ) Representative images of H2B-Lys5-Me ( A , B , D , and E ) and FLAG-H2BE ( A , B , and E ) staining in the MOE of Flag-H2be ( A and B ) , H2be-KO ( D ) or H2be-GF ( E ) mice . ( B ) High-magnification image of FLAG-H2BE and H2B-Lys5-Me colocalization shows that H2BE is depleted in nuclear regions enriched for H2B-Lys5-Me ( arrowheads ) . Mouse ages: ( A ) , ( B ) , and ( E ) , 10 weeks; ( D ) , 34 weeks . ( C ) Confirmation of reactivity of the anti-H2B-Lys5-Me1 polyclonal antibody with the Lys5-Me PTM in the context of the H2BE protein sequence . Image ( top ) and quantification ( bottom ) of an ELISA assay for peptides corresponding to canonical H2B or H2BE and containing the Lys5-Me PTM . ( F ) Two-color fluorescent western analysis of Lys5-Me modification of FLAG-H2BE in MOE lysates from WT and Flag-H2be ( F-H ) mice . No detectable H2B-Lys5-Me staining of the FLAG-H2BE bands ( red; arrowheads ) is observed . Approximate molecular weights ( kDa ) are indicated ( left ) . The bands observed at approximately 30–35 kDa likely correspond to histone dimers . ( G–I ) Age-dependence of H2BE accumulation . ( G and H ) Images ( left ) and quantification ( right ) of FLAG-H2BE and H2B-Lys5-Me co-localization in 3- ( G ) and 34-week old ( H ) mice . Red lines , best fits . ( I ) Quantification of H2B-Lys5-Me levels in high-H2BE neurons ( relative to apical sustentacular cells; n = 20 nuclei from two images per timepoint ) . ( J ) Two-color fluorescent western analysis ( left ) and quantification ( right ) of FLAG-H2BE relative to total H2B as a function of age in MOE lysates from WT and Flag-H2be ( F-H ) mice . Approximate molecular weights ( kDa ) are indicated ( left ) . The bands observed at approximately 30–35 kDa likely correspond to histone dimers . **p<0 . 01; ****p<0 . 0001; n . s . , not significant . Scale bar for ( A ) , ( D ) , ( E ) , ( G ) , and ( H ) , 20 µm; ( B ) , 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 00070 . 020 Further analyses revealed that the presence of H2BE causes a similar reduction in the level of acetylation at Lys5 , indicating that the variant also receives less of this modification compared to canonical H2B ( not shown ) . In contrast , H2BE appears to undergo higher levels of ubiquitination at Lys120 , though analyses of the modification in H2be-KO and H2be-GF mice indicate that the association is correlative but not causative ( not shown ) .
We have shown here that the activity-dependent replacement of canonical H2B with H2BE , an olfactory-specific histone variant , has a direct impact on the gene expression and life span of olfactory sensory neurons . These findings uncover a novel mechanism by which the sensory experience of a neuron is recorded within its chromatin to affect its transcriptional program and longevity . The mammalian olfactory epithelium has the unusual property of persistent neuronal self-renewal throughout adult life . Thus , the repertoire of expressed ORs in the MOE is determined by the combined probabilities associated with the choice of a specific OR by olfactory neuron precursors and the subsequent longevity of those neurons . OR gene choice has been shown to obey a largely stochastic process influenced by the genomic context of local enhancers ( Mori and Sakano , 2011 ) . In contrast , olfactory life span appears variable and may be influenced by environmental factors such as pathogens and odorant stimulation ( Watt et al . , 2004; Kondo et al . , 2010 ) . Our data uncover a chromatin-based pathway in which the absence of odor-evoked activity of specific MOE neuronal populations leads to increased H2BE expression , and in turn changes in transcription and reduced neuronal life span . In addition to a pro-apoptotic role of high levels of H2BE , we cannot exclude other roles for this olfactory-specific histone variant at low or moderate levels . Indeed our data indicate that neurons that are normally highly active and therefore express low levels of H2BE increase in abundance following olfactory deprivation , an effect that is diminished in H2be-KO mice . This result may be due to a compensatory increase of the low-H2BE expressing cells following olfactory deprivation , but could also indicate that a modest level of H2BE is optimal for neuronal longevity . In addition , our findings that changes in transcription and OR frequency following unilateral naris occlusion are significantly reduced but not eliminated in H2be-KO mice indicate that H2BE plays a key role in modulating activity dependent changes , but is likely part of a larger pathway . The transcription factor CREB has been shown to play a major role in orchestrating transcriptional changes associated with activity-dependent neuronal plasticity and survival ( Lyons and West , 2011; West and Greenberg , 2011 ) . Interestingly , a previous study showing enhanced longevity of odor-stimulated adenovirus-infected olfactory neurons implicated CREB as the mediator of this effect ( Watt et al . , 2004 ) . The absence of full CRE sites in the H2be gene does not permit the establishment of a direct functional link between the activity of a CREB family member such as ICER and H2BE levels at this point , but other indirect signaling pathways may exist . An alternative scenario would postulate the existence of a cAMP-regulated chaperone that exchanges canonical H2B and H2BE . In addition to odor-stimulated neuronal activity , olfactory neurons have been shown to display heterogeneous levels of ligand-independent , OR-derived basal activity that vary according to OR identity and are critical for regulating cAMP signals involved in axon guidance ( Imai et al . , 2006; Mori and Sakano , 2011 ) . Such studies suggest the possibility that ligand-independent , OR-derived basal signaling may also contribute to the overall activity level in mature olfactory neurons , especially under laboratory housing conditions where the odor repertoire is minimal . This scenario would help explain the observation that loss of Adcy3 , which eliminates both basal and odor-stimulated activity , appears to affect H2be levels more dramatically than olfactory deprivation through UNO , which is expected to only eliminate odor-evoked activity . Thus , along with odor-evoked activity , OR-derived basal activity may be a significant contributor to the control of H2BE levels and , in turn , of olfactory neuronal longevity . H2BE joins a list of molecules with known activity-dependent expression in olfactory neurons , many of which have roles in axon guidance and refinement . These include NRP1 , KIRREL2 , and EPHA5 , which are up-regulated , and SEMA3A , KIRREL3 , and EFNA5 , which are down-regulated by neuronal activity ( Imai et al . , 2006; Serizawa et al . , 2006 ) . Like SEMA3A , H2BE levels are reduced via cAMP-dependent/ Ca2+-independent signaling , but unlike the other known activity dependent molecules , which are generally expressed in either immature or mature neurons , H2BE is expressed in both . The unusual regulation of H2BE expression likely reflects the variant's unique function in olfactory neurons . How do the five amino acid differences between H2BE and H2B convey such distinct functional attributes ? Studies of H3 . 3 , a histone variant that differs with canonical H3 by merely four amino acids and that plays a critical role in embryonic development and gene expression in adulthood , illustrate how small sequence variations in histones can generate distinct functions ( Elsaesser et al . , 2010 ) . Our analyses of relative PTM levels for H2BE and canonical H2B at a handful of known PTM sites suggest that incorporation of the variant could affect cellular transcription at least in part via differential post-translational modifiability . It must be acknowledged , however , that as is true for most histone PTMs , evidence that the PTMs affected by H2BE replacement play a role in transcription is merely correlative and therefore insufficient to support a strong functional prediction . Nevertheless , our results are consistent with the idea that H2BE shortens neuronal life span via changes in cellular transcription and metabolism , likely over the time course of several days or weeks , although the precise mechanism for this process remains to be determined . Notably , although phosphorylation of H2B Ser14 has been associated with short trigger of apoptosis in mammalian cells ( Cheung et al . , 2003 ) , we have not found evidence for involvement of H2BE in this pathway ( not shown ) . The extensive activity-dependent shifts in the OR repertoire that we observe complement previous studies reporting experience-dependent sensitivity enhancements ( Hudson , 1999 ) and changes in the sensory neuron representation within the MOE ( Jones et al . , 2008 ) . A scenario thus emerges according to which neurons expressing ORs associated with environmentally salient odors are frequently active and may increase in relative abundance over time due to enhanced longevity , while neurons expressing infrequently activated ORs have a shortened life span , mediated in part by H2BE , and become less abundant ( Figure 12 ) . Differential longevity among olfactory sensory may provide an effective mechanism by which individuals with similar genomes adapt to diverse olfactory environments , facilitating enhanced sensitivity to odors important for survival . Accordingly , we observed significant impairment of olfactory learning behavior in H2be-KO mice , although it remains to be determined whether these defects are due to aberrant OR expression frequencies resulting from the lack of H2BE expression or , alternatively , to the altered expression of other genes involved in olfactory neuron signaling . Among the more than 30 histone variants encoded in the mouse genome ( Marzluff et al . , 2002 ) only a handful have so far been characterized in terms of expression and function . Known functions of histone variants include the modulation of transcription , DNA repair , meiotic recombination , chromosome segregation , sex chromosome condensation and sperm chromatin packaging ( Banaszynski et al . , 2010; Talbert and Henikoff , 2010 ) . Further characterization of histone variants in the brain and in developing and self-renewing tissues represents an exciting area of future investigation .
The Flag-H2be transgenic mouse line ( Figure 2A ) , which expresses FLAG-H2BE under control of the H2be promoter , was generated based on a described protocol ( Yang et al . , 1997 ) . Briefly , a FLAG-encoding DNA sequence was inserted through homologous-recombination immediately upstream of the H2be CDS within BAC RP23-16G3 , which contains a 200-kb region of mouse genomic sequence surrounding the H2be gene . The modified BAC was amplified in E . coli , confirmed by sequencing , and injected ( Harvard Genome Modification Facility ) into fertilized mouse zygotes . Transgenic founders were crossed to C57Bl/6 mice to establish the Flag-H2be line . Heterozygous Flag-H2be mice contain a single genomic copy of the transgene that is expressed in a pattern and at a level indistinguishable from that of the endogenous gene ( see Figure 2B–E ) . The H2be-KO mouse line ( Figure 3A ) , in which the endogenous H2be CDS is replaced with a sequence encoding GAP43-mCherry ( an N-terminal fusion of the first 20 amino acids of GAP43 to mCherry ) , was generated through homologous recombination of the endogenous H2be locus in mouse embryonic stem cells ( ESCs ) using standard methods . Following selection , ESCs were screened for the desired recombination events , confirmed by sequencing , and injected ( Harvard Genome Modification Facility ) into mouse blastocysts . Founders were crossed to C57Bl/6 mice to establish the H2be-KO line , in which Gap43-mCherry is expressed in a pattern indistinguishable from that of H2be ( see Figure 3B ) . The H2be-GF transgenic mouse line ( Figure 5A ) , which expresses Flag-H2be under control of the olfactory marker protein ( Omp ) promoter , was generated by complete replacement of the Omp CDS in plasmid pJOMP ( Danciger et al . , 1989 ) with a sequence encoding FLAG-H2BE , followed by pronuclear injection ( Harvard Genome Modification Facility ) of the linearized construct into fertilized mouse zygotes . Transgenic founders were crossed to C57Bl/6 mice to establish the H2be-GF line . Heterozygous mice contain approximately 12 genomic copies of the transgene , which are expressed in mature olfactory neurons throughout the MOE with the exception of a band of neurons near zone 2 ( see Figure 5B ) , a pattern that was reproducibly observed in all individuals examined ( n = 8 ) . The P2-IRES-Tau-LacZ mouse line ( Mombaerts et al . , 1996 ) , which was used to identify possible axon guidance defects in H2be-KO mice ( Figure 7B ) , and the Cnga2-null/Tau-LacZ ( Zhao and Reed , 2001 ) and Adcy3-null ( Wong et al . , 2000 ) mouse lines , which were used to identify second messengers affecting H2be expression ( Figure 9 ) were described previously . All histological OR gene expression analyses were performed using fluorescent in situ hybridization ( ISH ) . With the exception of H2be mRNA analyses ( Figure 1A , B ) , which were performed using chromogenic ISH , all other histological analyses were performed using immunofluorescence ( IF ) or immunohistochemistry ( IHC; for Active-CASP3 , Figure 7C , E ) . Unless noted , all images are of coronal tissue sections . ISH target sequences were amplified by PCR and inserted into the pCRII-TOPO vector ( Life Technologies , Grand Island , NY , USA ) . OR antisense probes were designed to span 500–1000 base pairs , to target CDS or UTR gene regions , and to have <70% identity to any other sequence in the mouse genome . Probes were generated from 1 μg of linearized plasmid template using T7 or Sp6 polymerases ( Promega ) and digoxigenin or fluorescein RNA labeling mixes ( Roche Applied Science , Indianapolis , IN , USA ) , treated with DNaseI ( Promega ) and ethanol precipitation , and dissolved in a 30-μL volume of water . Whole tissues were carefully dissected from surrounding bones , frozen immediately in OCT compound ( Sakura Finetek USA , Torrance , CA , USA ) on dry ice , and stored at −80°C . Tissue blocks were cut into 12-μm thick cryo-sections , placed onto slides , and stored at −80°C . Chromogenic ISH experiments were performed essentially as described ( Schaeren-Wiemers and Gerfin-Moser , 1993 ) . Fluorescent ISH experiments were performed using a modified version of the chromogenic ISH method . Briefly , slide-mounted sections were warmed ( 37°C , 10 min ) , equilibrated in phosphate-buffered saline ( PBS; pH 7 . 2; 5 min , room temperature [RT] ) , fixed in paraformaldehyde ( PFA; 4% in PBS; 10 min , RT ) , washed in PBS ( 3 min , RT ) , permeabilized with Triton-X-100 ( 0 . 5% in PBS; 10 min , RT ) followed by sodium dodecyl sulfate ( 1% in PBS; 5 min , RT ) , washed in PBS ( 3 × 3 min , RT ) , incubated in acetylation solution ( triethanolamine [0 . 1 M; pH 7 . 5] , acetic anhydride [0 . 25%]; 10 min , RT ) , washed in PBS ( 3 × 3 min , RT ) , incubated in hybridization solution ( formamide [50%] , SSC [2×] , Denhardts [5×] , yeast tRNA [250 μg/mL] , herring sperm DNA [200 μg/mL] , EDTA [1 mM] , sodium phosphate [0 . 05 M; pH 7]; 30 min , RT ) , hybridized with a digoxigenin-labeled antisense RNA probe ( 1:1000 in hybridization solution; 16 hr , 42°C ) , washed with SSC ( 2×; 5 min , 42°C ) , washed with SSC ( 0 . 2×; 3 × 30 min , 42°C ) , incubated in H2O2 ( 3% in TN [Tris–HCl ( 0 . 1 M; pH 7 . 5 ) , 0 . 15 M NaCl]; 30 min , RT ) , washed in TNT ( Tween-20 [0 . 05%] in TN; 3 × 3 min , RT ) , incubated in TNB ( Blocking Reagent [Perkin Elmer , Waltham , MA , USA; 0 . 05% in TN]; 30 min , RT ) , incubated with anti-digoxigenin-POD antibody ( Roche; 1:1000 in TNB; 12 hr , 4°C ) , and washed in TNT ( 3 × 20 min , RT ) . Fluorescent signals were generated using the Tyramide Signal Amplification ( TSA ) Plus Fluorescein Kit ( Perkin Elmer ) according to the manufacturer's instructions . Slides were mounted using Vectashield ( Vector Laboratories , Burlingame , CA , USA ) containing DAPI ( 5 μg/mL ) . Two-color ISH was performed as described for one-color ISH , with the following modifications: Tissue sections were simultaneously hybridized with both digoxigenin- and fluorescein- or dinitrophenyl-labeled antisense RNA probes ( 1:1000 each in hybridization solution ) . Following incubation in TNB ( 30 min , RT ) , sections were incubated with anti-fluorescein-POD antibody ( Roche; 1:1000 in TNB; 12 hr at 4°C ) or anti-dinitrophenyl-HRP antibody ( Perkin Elmer; 1:350 in TNB; 3 hr at 25°C ) and washed in TNT ( 3 × 20 min , RT ) . Fluorescent signals corresponding to the fluorescein- or dinitrophenyl-labeled probes were generated using the TSA Plus Fluorescein Kit , after which sections were washed in TNT ( 2 × 3 min , RT ) , incubated in H2O2 ( 3% in TN; 1 hr , RT ) , washed in TNT ( 3 × 3 min , RT ) , incubated with anti-digoxigenin-POD antibody ( 1:1000 in TNB; 12 hr , 4°C ) , and washed in TNT ( 3 × 20 min , RT ) . Fluorescent signals corresponding to the digoxigenin-labeled probe were generated using the TSA Plus Cyanine5 Kit ( Perkin Elmer ) according to the manufacturer's instructions . Slides were mounted using Vectashield containing DAPI ( 5 μg/mL ) . Combined ISH and IF experiments were performed as described for one-color ISH , with the following modifications: Acetylation , which dramatically reduces detection of the FLAG epitope , was omitted . Following incubation in TNB ( 30 min , RT ) , sections were incubated with a mixture of anti-digoxigenin-POD and mouse anti-FLAG antibodies ( each 1:1000 in TNB; 12 hr , 4°C ) and washed in TNT ( 3 × 20 min; RT ) . Fluorescent signals corresponding to the digoxigenin-labeled RNA probe were generated using the TSA Plus Fluorescein Kit , after which sections were washed in TNT ( 2 × 3 min , RT ) , incubated with anti-mouse-Alexa647 antibody ( Invitrogen; 1:1000 in TNB; 12 hr , 4°C ) , and washed in TNT ( 3 × 20 min , RT ) . Slides were mounted using Vectashield containing DAPI ( 5 μg/mL ) . Animals were anesthetized with ketamine and perfused transcardially on ice with ice-cold PBS ( 25 mL ) followed by ice cold PFA ( 4% in PBS; 25 mL ) . Whole tissues were carefully dissected from surrounding bones and immersed in ice-cold PFA ( 4% in PBS; 1 hr [OB] or overnight [MOE] ) . MOE tissue was decalcified in EDTA ( 250 mM in PBS , pH 8 . 5; 2 days , 4°C ) , and all tissues were cryoprotected in sucrose ( 10 , 20 , and 30% in PBS; 2 hr , 2 hr , and overnight , respectively ) . Tissues were frozen in OCT on dry ice and stored at −80°C . Tissue blocks were cut into 12-μm thick cryo-sections , placed onto slides , and stored at −80°C . IF experiments were performed as follows: briefly , slide-mounted sections were warmed ( 37°C , 10 min ) , equilibrated in PBS ( 5 min , RT ) , fixed in PFA ( 4% in PBS; 10 min , RT ) , washed in PBS ( 3 min , RT ) , permeabilized with Triton X-100 ( 0 . 5% in PBS; 10 min , RT ) followed by SDS ( 1% in PBS; 5 min , RT; omitted if preservation of intrinsic mCherry was necessary ) , washed in TNT ( 3 × 5 min , RT ) , blocked in fetal bovine serum ( FBS; 10% in TN; 30 min , RT ) , incubated with primary antibodies ( typically diluted 1:500–1:1000 in 10% FBS; 12 hr , 4°C ) , washed in TNT ( 3 × 5 min , RT ) , incubated with secondary antibodies ( typically , Alexa488-labeled [or Alexa488- and Alexa647-labeled , for two-color IF]; Invitrogen; 1:1000 in 10% FBS; 12 hr , 4°C ) , and washed in TNT ( 3 × 15 min , RT ) . Slides were mounted using Vectashield containing DAPI ( 5 μg/mL ) . Mice were perfused and the MOE tissue processed as described for IF with the following modifications: After fixation in PFA and PBS washes , slide-mounted sections were permeabilized with Triton X-100 ( 0 . 5% in PBS; 30 min , RT ) , washed with PBS ( 3 × 3 min , RT ) , incubated in H2O2 ( 3% in TN buffer; 30 min , RT ) , washed with TNT ( 3 × 3 min , RT ) , blocked in TNB ( 30 min , RT ) , incubated with a mixture of anti-active-CASP3 and anti-OMP antibodies ( each 1:300 in TNB; 12 hr , 4°C ) , washed with TNT ( 3 × 3 min , RT ) , and incubated with anti-rabbit-HRP ( Jackson; 1:500 in TNB ) for 12 hr , 4°C . Fluorescent signals corresponding to active-CASP3 were generated using the TSA Plus Fluorescein Kit , after which sections were washed in TNT ( 3 × 5 min , RT ) , incubated with anti-goat-Cy5 ( Jackson ImmunoResearch Laboratories , West Grove , PA , USA; 1:500 in TNB; 12 hr , 4°C ) , and washed in TNT ( 3 × 15 min , RT ) . Slides were mounted using Vectashield containing DAPI ( 5 μg/mL ) . Mice were injected intraperitoneally with BrdU ( 3 × 50 mg/kg in PBS; injections spaced 30 min apart ) and sacrificed at the indicated timepoints ( Figure 7D , F ) . The T = 0 timepoint , defined as 15 days post-injection , was chosen to avoid analysis of immature neurons , a large fraction of which are known to die prior to maturity ( Kondo et al . , 2010 ) . Mice were perfused and the MOE tissue processed as described for IF with the following modifications: After permeabilization with Triton X-100 and SDS , slide-mounted sections were washed with PBS ( 3 × 3 min , RT ) and water ( 3 min at RT ) , incubated in HCl ( 2 N; 1 hr , 37°C ) , and washed with TNT ( 3 × 3 min , RT ) . Sections were blocked and further processed as described . Primary antibodies were used at concentrations of 1:50 ( BrdU ) and 1:300 ( OMP ) . Images were obtained using LSM710 and AxioImager Z2 ( Carl Zeiss , Oberkochen , Germany ) microscopes . Confocal images ( 1–5-μm thick optical sections ) were used to quantify fluorescence signals , with care taken to ensure that exposures not exceed the instrument's dynamic range . Intensities were quantified using Zen software ( Zeiss ) . For quantification of nuclear fluorescence in the MOE , circular regions encompassing individual nuclei were defined by DAPI fluorescence . Within each quantified image , a region surrounding the neuronal population ( excluding immature and sustentacular cells ) was defined to allow normalization to average neuronal nuclear fluorescence . For quantification of fluorescence in the OB , circular regions encompassing individual glomeruli were defined based on surrounding periglomerular cells , which were identified by morphology and DAPI fluorescence . p-Values corresponding to relative H2BE expression variances associated with specific ORs were calculated using a one-tailed F-test with FDR correction for multiple comparisons ( Benjamini and Hochberg , 1995 ) . Fluorescent olfactory neuron counts corresponding to MOE tissue from an individual mouse were determined from a series of 10–12 stained coronal sections located approximately 400 μm apart and spanning the anterior–posterior length of the organ . Fluorescent cell counting was performed using Velocity software ( Perkin Elmer ) or , when necessary due to difficulties in resolving individual olfactory neurons , manually . Epithelial volumes were calculated from areas determined using Velocity software , based on OMP and DAPI signals . Coding sequences for H2BE , FLAG-H2BE , and consensus H2B were inserted into the pLNCX2 vector ( Clontech Laboratories , Mountain View , CA , USA ) . Retroviruses carrying the resulting clones were generated and used according to the Retroviral Gene Transfer and Expression User Manual ( Clontech ) . NIH-3T3 and HEK-293 cells were transduced by retroviral infection and cell lines stably expressing high levels of each transgene were selected using Geneticin ( 500 µg/mL; Invitrogen ) . Expression of transgenes in selected lines was verified by quantitative RT-PCR . Transgenic and non-transgenic cell lines were analyzed for cell viability using a Vi-Cell XR Cell Viability Analyzer ( Beckman Coulter , Brea , CA , USA ) . For experiments leading to the initial identification of H2be , RNA was obtained by laser-capture micro-dissection ( LCM ) of apical and basal neurons in the VNO . Briefly , whole VNOs from 8-week old CD1 male mice were carefully dissected from surrounding bones and immediately frozen in OCT . Tissue blocks were cut into 12-μm thick cryo-sections , placed alternately onto Superfrost and Superfrost plus slides ( VWR , Radnor , PA , USA ) . Sections on Superfrost plus slides were stained by ISH for the Gnai2 and Gnao genes , which mark the apical and basal zones , respectively , and used as guides for LCM . Sections on Superfrost slides were stained with toluidine blue and used for LCM of approximately 100 apical and 100 basal neurons per section using a PixCell II LCM system ( Arcturus , now Life Technologies , Grand Island , NY , USA ) . Samples were immediately frozen and pooled into groups of 10 ( approximately 1000 cells per group ) , from which the RNA was extracted using the Arcturus Picopure Kit ( Life Technologies , Grand Island , NY , USA ) . LCM RNA samples were amplified in parallel with whole VNO RNA samples using the Arcturus RiboAmp OA RNA Amplification Kit ( Applied Biosystems ) and analyzed using Mouse Genome 430 2 . 0 Arrays ( Affymetrix , Santa Clara , CA , USA ) according to the manufacturer's instructions . For all other data reported in this study , RNA was prepared from whole MOE tissue or , in the case of UNO expression analyses , from MOE halves that had been carefully removed from the medial bone . RNA was isolated using Trizol Reagent ( Invitrogen ) and purified using an RNeasy Miniprep Kit ( Qiagen , Valencia , CA , USA ) . Experiments were performed using 3–6 biological replicates per condition or genotype and MOE tissue from 2–4 individual mice per replicate . Samples were processed and applied to Affymetrix Mouse Gene 1 . 0 ST Arrays according to the manufacturer's instructions . Probe cell intensity files ( CEL ) were analyzed for potential outliers using the Bioconductor software package arrayQualityMetrics ( Kauffmann et al . , 2009 ) . CEL files were processed with the Affymetrix Expression Console software to generate probe level summarization files ( CHP ) using the iterative Probe Logarithmic Intensity Error Estimation ( IterPLIER ) and sketch-quantile normalization algorithms . Statistical analyses of differential expression between groups were carried out using the Bioconductor limma package ( Smyth , 2004 ) implemented through the Bioconductor affylmGUI software ( Wettenhall et al . , 2006 ) to generate unadjusted p-values and false discovery rate ( FDR ) corrections for multiple comparisons . For analyses of gene expression defects in H2be-KO MOE tissue , FDR corrections were made based on all represented genes with log2 expression levels above five . For analyses of OR expression defects in H2be-GF mice and expression differences following UNO , FDR corrections were made based on all represented OR genes with log2 expression levels above seven or five , respectively . Gene ontology analyses were performed using the GOrilla software ( http://cbl-gorilla . cs . technion . ac . il/ ) ( Eden et al . , 2007; Eden et al . , 2009 ) . All analyses were carried out using the ‘single ranked list of genes’ mode , with the exception of the analysis summarized in Figure 11A , which was performed using the ‘two lists of genes’ mode . Reported gene ontology summaries represent a subset of biological process terms selected based on redundancy using the REViGO software ( http://revigo . irb . hr/; Supek et al . , 2011 ) . All reported enrichment p-values are FDR-adjusted using the Benjamini–Hochberg method ( Benjamini and Hochberg , 1995 ) . cDNA samples for qPCR analysis were prepared using the QuantiTect Reverse Transcription Kit ( Qiagen ) starting from whole MOE RNA prepared using Trizol Reagent and purified using an RNeasy Miniprep Kit . Single-plex experiments ( Figure 2D ) were performed using the QuantiTect SYBR Green PCR Kit ( Qiagen ) with an MJ Opticon 2 instrument ( Bio-Rad Laboratories , Hercules , CA , USA ) . Multiplex experiments ( Figure 4B ) were performed using the QuantiTect Multiplex PCR Kit ( Qiagen ) with an ABI 7900HT instrument ( Applied Biosystems ) . Primer pairs ( Integrated DNA Technologies , Coralville , IA , USA ) and fluorophore/quencher-labeled probes ( Eurofins MWG Operon , Huntsville , AL , USA; Integrated DNA Technologies ) were designed using the Primer-BLAST tool ( NCBI ) . Primer efficiencies were assessed using standard curves and pairs exhibiting efficiencies of >99% were used for analysis . 14-day old mice were administered Buprenorphine ( 0 . 05 mg/kg ) , anesthetized using isoflurane ( confirmed through a tail pinch ) , and subjected immediately to electrocautery for approximately 5 s on the right nostril under a dissecting microscope ( with care taken to avoid contact of the electrocautery unit with any non-superficial tissues ) . Mice were administered additional doses of Buprenorphine 12 and 24 hr after the procedure and examined on a daily basis to ensure complete blockage of the right nostril through scar formation ( typically approximately 3–5 days after the procedure ) and normal mouse development and activity . Odors were presented as mixtures in mineral oil ( octanal [0 . 31%] , heptanal [0 . 068%] , eugenol [2 . 65%] ) or propylene glycol ( lyral [10%] ) continuously for 21 days at concentrations designed to achieve a vapor pressure of approximately 1 Pa per odorant . Odorants were presented in 100-µL volumes of each solution applied to a cotton pad and inserted into a metal mesh tea ball , which was suspended in the middle of the mouse cage by a metal chain . Odorants were exchanged every 24 hr . Preparation of soluble nuclear proteins , chromatin , and mononucleosomes from MOE tissue and immunoprecipitation of FLAG-H2BE-containing mononucleosomes were performed as described ( Okada and Fukagawa , 2006; Okada et al . , 2006 ) , with modifications . Briefly , MOE tissue from four 8-week old Flag-H2be and four 8-week old WT mice were dissected and immediately minced and mechanically homogenized . Nuclei were filtered through a cell strainer , pelleted , washed , and disrupted by sonication . Chromatin was separated from soluble nucleoplasm by centrifugation , washed , and digested with micrococcal nuclease to mononucleosomes . Mononucleosomes were solubilized in 350 mM KCl buffer and affinity-purified using Anti-FLAG M2 affinity gel ( Sigma-Aldrich ) . Proteins were eluted with FLAG peptide ( Sigma-Aldrich ) , TCA-precipitated , run on a 15% SDS-PAGE gel , and separated into the following molecular weight fractions: >100 , 35–100 , 18–35 , and <18 kDa . Gel fractions were submitted for MS/MS analysis and protein identification within the Harvard University Microchemistry and Proteomics Analysis Facility . Chromatin immunoprecipitation ( ChIP ) and ligation-mediated PCR amplification of the purified DNA were performed essentially as described ( Lee et al . , 2006 ) . Input chromatin was prepared from whole MOE tissue dissected from 5-week old male Flag-H2be mice using mouse anti-FLAG and rabbit anti-histone H3 antibodies . Amplified DNA samples from the FLAG and H3 ChIPs were separately fragmented and analyzed in duplicate ( 2 mice per replicate ) using GeneChip Mouse Promoter 1 . 0R Arrays ( Affymetrix ) , which tile 10 kb of DNA surrounding the transcript start site of approximately 25 , 500 mouse genes . CEL files were processed using Tiling Analysis Software ( Affymetrix ) to generate BAR files . ChIP signals were analyzed at each position in the genome as a ratio of FLAG to H3 and assigned to a specific gene promoter region using Galaxy ( http://main . g2 . bx . psu . edu/ ) . Transcripts were grouped based on their expression level in OMP+ olfactory neurons , using expression values downloaded from ( Sammeta et al . , 2007 ) , or based on their gene family . Within each group , signals at each position relative to the transcript or CDS start site were averaged and plotted to obtain signal profiles for each gene group . Quantitative PCR analyses of relative FLAG-H2BE levels in the protein-coding ( CDS ) regions of histone and OR genes were performed on ligation-mediated PCR-amplified DNA from FLAG or H3 ChIP samples . Experiments were performed in triplicate for each input ( FLAG or H3 ) and primer pair combination starting from 0 . 2 ng of input DNA per reaction . Reactions were performed using the QuantiTect SYBR Green PCR Kit ( Qiagen ) and an MJ Opticon 2 instrument ( Bio-Rad ) . Primer pairs ( Integrated DNA Technologies ) were designed using the Primer-BLAST tool ( NCBI ) . Primer efficiencies were assessed using standard curves and pairs exhibiting efficiencies of >99% were used for analysis . Protein samples were separated by electrophoresis on a TRIS-glycine-SDS polyacrylamide gel ( 10–20% gradient; Bio-Rad ) , and transferred to nitrocellulose membranes . Membranes were washed in TN ( 5 min , RT ) , blocked in Blotto ( Santa Cruz Biotechnology; 5% in TN; 1 hr , RT ) , incubated with primary antibodies ( each diluted 1:2000 in 5% Blotto [in TNT]; 12 hr , 4°C ) , washed in TNT ( 3 × 5 min , RT ) , incubated with a mixture of Alexa488-conjugated anti-rabbit and Alexa647-conjugated anti-mouse secondary antibodies ( Invitrogen; 1:2000 in 5% Blotto [in TNT]; 1–2 hr , RT ) , and washed in TNT ( 3 × 10 min , RT ) . Blots were scanned using a Typhoon Trio Imager and analyzed using ImageQuant software ( GE Healthcare Biosciences , Pittsburgh , PA , USA ) . Eight-well Streptavidin High Binding Capacity strips ( Thermo-Fisher Scientific , Waltham , MA , USA ) were washed ( Tris [25 mM , pH 7 . 5] , NaCl [150 mM] , BSA [1%] , and Tween-20 [0 . 05%] ) . Custom-synthesized C-terminally-biotinylated peptides ( Abgent , San Diego , CA , USA ) corresponding to the N-terminal 13 amino acids of H2BE or the mouse consensus H2B and containing a methyl-modified lysine residue at position five were immobilized within the well strips ( 1 μg per well in block buffer [0 . 2 µg mouse IgG in wash buffer]; 3 hr , RT ) , washed , incubated with anti-H2B-Lys5-Me antibody ( 1:5000–1:5 , 000 , 000 in block buffer; 12 hr; 4°C ) , washed , incubated with anti-rabbit-HRP ( 1:2000 in block buffer; 1 hr , RT ) , and washed . ELISA signals were developed using the 1-Step Ultra TMB-ELISA reagent ( Thermo Scientific ) according to the manufacturer's instructions . Ten 4-month old H2be-KO and heterozygous littermates were subjected to odor discrimination training using water restriction for motivation , under a behavioral paradigm similar to that described ( Uchida and Mainen , 2003 ) , but adapted for mice . In an initial experiment , mice were challenged to discriminate between hexanol and hexanoic acid , and in a second experiment , were challenged with the two stereoisomers of carvone . Prior to the initial experiment , mice were trained to obtain water from two ports unconditionally ( day 1 ) , after first poking an odor port ( days 2–5 ) , after poking an odor port presenting isoamyl-acetate odor ( days 6–13 ) , and after only a single poke of an odor port presenting isoamyl-acetate odor ( days 14–20 ) . Odor discrimination training began on day 21 . Odors streams , controlled by an olfactometer , were generated from air passed through a filter containing 10% of the concentrated odorant in mineral oil and diluted 1:20 into a stream of clean air . | A hallmark of the nervous systems of all mammals is their capacity to undergo changes in function that are shaped by experience . This phenomenon underlies the ability of our brains to develop properly and to learn , and also enables various sensory systems—including the visual , auditory and olfactory systems—to perform optimally in diverse environments . In most mammals , a high-functioning olfactory system is essential for carrying out tasks that are crucial for survival , such as finding food , avoiding predators and mating . In general , sensory systems have to decipher only a limited collection of stimuli , but the olfactory system must be able to process information from thousands of distinct odors that are found in a given environment and which may vary dramatically from one environment to the next . Each odor-sensing neuron in the nose of a mammal contains just one kind of odorant receptor protein , although mammalian genomes typically encode 1000 or so different kinds of receptor proteins . This suggests that it might be possible to ‘tune’ the olfactory system to a particular environment by changing the relative numbers of the different types of neurons . Indeed , it is known that the relative abundance of each type of odor-sensing neuron changes with age and experience , and that these changes might be caused by variations in the lifespans of the neurons . Although our understanding of how these experience-dependent changes are orchestrated at the molecular level is far from complete , it is clear that adjustments in the levels of specific gene products is necessary . But how do experiences alter the levels of gene products to give rise to lasting changes in the brain ? One hypothesis is that changes to a structure called chromatin are key to this process: chromatin is an assembly of DNA molecules , which are quite long , and organizing proteins , mostly proteins known as histones , that together form a compact structure that can fit inside the nucleus of a cell . Santoro and Dulac have now discovered a previously uncharacterized protein called H2BE that is found only in the odor-sensing neurons of mice . H2BE is a variant of a protein called H2B , which is a well-known histone . They found that in odor-sensing neurons , H2BE replaces H2B to an extent that depends on the amount of activity experienced by the neuron: H2BE is nearly undetectable in highly active neurons , but almost completely replaces H2B in neurons that are inactive . Moreover , genetic manipulation showed that the deletion of H2BE significantly extended the lifespan of neurons , whereas elevated levels of H2BE shortened their lifespan . These findings reveal an extraordinary process that involves inactive odor-sensing neurons being depleted relative to active ones over time . How does H2BE , which differs from H2B by just five amino acids , cause such dramatic changes in neuronal composition ? One hint comes from evidence that these amino acids disrupt interactions between chromatin and ‘effector’ proteins , which modulate gene activity . Consistent with this , Santoro and Dulac have found that the replacement of H2B by H2BE strongly alters gene activity , although the precise mechanism by which these alterations regulate neuronal lifespans remains to be determined . Understanding this process in detail , and exploring if similar phenomena are involved in experience-dependent changes elsewhere in the nervous system , are fascinating areas of future research . | [
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"neuroscience"
] | 2012 | The activity-dependent histone variant H2BE modulates the life span of olfactory neurons |
Many transcription factors co-express with their homologs to regulate identical target genes , however the advantages of such redundancies remain elusive . Using single-cell imaging and microfluidics , we study the yeast general stress response transcription factor Msn2 and its seemingly redundant homolog Msn4 . We find that gene regulation by these two factors is analogous to logic gate systems . Target genes with fast activation kinetics can be fully induced by either factor , behaving as an 'OR' gate . In contrast , target genes with slow activation kinetics behave as an 'AND' gate , requiring distinct contributions from both factors , upon transient stimulation . Furthermore , such genes become an 'OR' gate when the input duration is prolonged , suggesting that the logic gate scheme is not static but rather dependent on the input dynamics . Therefore , Msn2 and Msn4 enable a time-based mode of combinatorial gene regulation that might be applicable to homologous transcription factors in other organisms .
Homologous transcription factors ( TFs ) often co-exist in eukaryotic cells , resulting in seemingly redundant regulation of their target genes . Although a large number of TF homologs have diversified over time to obtain distinct target genes from their partners , others have remained relatively conserved and share the same DNA binding motif , which limits their downstream interactions to identical target genes . Recent studies suggest that some closely related TF homologs or isoforms , which regulate a shared set of target genes , might have diverged expression patterns , dynamic responses or gene regulatory functions . For example , the yeast transcriptional regulator Dig1 inhibits the expression of mating response genes to pheromone stimulation , whereas its homolog Dig2 exhibits both negative and positive regulation depending on the conditions ( Chou et al . , 2008; Houser et al . , 2012 ) . In mammalian cells , two TF isoforms NFAT1 and NFAT4 display distinct nuclear translocation dynamics in response to stimuli . It has been suggested that this dynamic diversity of isoforms might enhance the temporal signal processing function of the cell ( Yissachar et al . , 2013 ) . In addition , a very recent study showed that the TF homologs STAT5A and STAT5B differentially contribute to the immune transcriptional response due to their different expression levels ( Villarino et al . , 2016 ) . Here we use the yeast homologous stress responsive TFs Msn2 and Msn4 as a model to quantitatively study the functional relevance of closely related TFs in the same single cells . Msn2 and Msn4 are C2H2 zinc-finger TFs that regulate cellular responses to a wide range of environmental stresses ( Schmitt and McEntee , 1996 ) . Upon stress stimulation , both TFs rapidly translocate from the cytoplasm to the nucleus where they bind to the same DNA recognition sequence and induce the expression of a common set of stress responsive genes ( Martinez-Pastor et al . , 1996 ) . Their nucleocytoplasmic translocation is controlled by phosphorylation and is directly regulated by protein kinase A ( PKA ) and phosphatases ( Gorner et al . , 1998 ) ( Figure 1A , left ) . Therefore , it has been long believed that Msn2 and Msn4 are functionally redundant in regulating gene expression response . In fact , since Msn2 is assumed to play a more pronounced role in gene regulation , many previous studies focused only on Msn2 , deleting the MSN4 gene to simplify analysis ( Hansen and O'Shea , 2013 , 2015b , 2016; Hao and O'Shea , 2012; Lin et al . , 2015; Petrenko et al . , 2013 ) . A microarray analysis , however , suggested that Msn2 and Msn4 might have different contributions to gene induction at individual promoters ( Berry and Gasch , 2008 ) , but the mechanism underlying these differences remains unknown . 10 . 7554/eLife . 18458 . 003Figure 1 . Msn4 is required for the induction of target genes with slow promoter kinetics . ( A ) Homologous TFs Msn2 and Msn4 are regulated by the same upstream PKA signals in response to natural stresses or chemical inhibitors and control a common set of target genes with stress response elements ( STREs ) in their promoters . In the same strain , Msn2 and Msn4 are fused with RFP and YFP respectively , at their native loci; a CFP reporter under the Msn2/4 specific promoter is introduced to monitor gene expression responses . Middle: Translocation of Msn2-RFP and Msn4-YFP and reporter gene expression can be monitored in the same single cells over time . Right: In response to stimulation , time traces of Msn2 and Msn4 translocation and reporter gene expression can be quantified for each single cell . For each condition , single-cell data are collected from at least three independent experiments . ( B ) Violin plots showing the distributions of reporter expression under ( left ) the fast kinetics promoter PDCS2 or ( right ) the slow kinetics promoter PSIP18 in single cells in response to 3 μM inhibitor inputs with 30-min pulse duration ( illustrated by the top inset ) in wild-type , msn2Δ , and msn4Δ strains , respectively ( n: ~300 cells per condition per strain ) . The mean value of single cell responses was labeled using the black bar for each condition . The expression of the reporter gene was tracked in single cells over a 3-hr period in which the reporter fluorescence in most cells has already reached the plateau . The last point of each single-cell time trace was used in the plots ( a . u . : arbitrary units ) . ( C ) Violin plots showing the distributions of reporter expression under the slow kinetics promoter PSIP18 in response to a 60 min pulse of inhibitor input . ( D ) Violin plots showing the distributions of reporter expression under the faster mutant promoter PSIP18-A4 in wild-type and msn4Δ strains , respectively , in response to 30-min inhibitor input . ( E ) Violin plots showing the distributions of reporter expression under ( left ) the fast kinetics promoter PDCS2 or ( right ) the slow kinetics promoter PSIP18 in response to 0 . 5 M KCl in wild-type and msn4Δ strains , respectively . The sustained KCl stimulation leads to a transient pulse of TF activation , as illustrated in the top cartoon panel . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 00310 . 7554/eLife . 18458 . 004Figure 1—figure supplement 1 . Dynamic profiles of reporter gene expression . Averaged single-cell time traces of reporter gene expression under ( A ) the fast kinetics promoter PDCS2 and ( B ) the slow kinetics promoter PSIP18 , in response to 30-min inhibitor inputs , ( C ) the slow kinetics promoter PSIP18 , in response to 60-min inhibitor inputs , and ( D ) under no inhibitor condition . The single-cell data are the same data used to generate Figure 1B and C . The solid curves represent the averaged single-cell time traces; the shaded regions represent the standard deviations of single cell responses . For each condition , single-cell responses have been measured over a 3-hr period , which is sufficient for the fluorescent gene expression reporter to reach the plateau in most cells . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 00410 . 7554/eLife . 18458 . 005Figure 1—figure supplement 2 . The dependence on Msn4 might expand generally to slow kinetics promoters . Violin plots showing the distributions of reporter expression under ( A ) the fast kinetics promoter PDDR2 and ( B ) the slow kinetics promoter PTKL2 , respectively . In ( B ) , the inset shows the distributions of PTKL2 reporter expression in response to 60 min pulse of inhibitor input . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 005 Here , we combine quantitative single-cell imaging and high-throughput microfluidics to monitor and compare the dynamic responses and gene regulatory functions of Msn2 and Msn4 in single cells . We find that Msn2 and Msn4 have non-redundant and distinct functions in the combinatorial gene regulation . We have previously demonstrated that Msn2/4 target genes differ significantly in their promoter activation kinetics , which dramatically influences their responses to dynamic inputs ( such as transient versus sustained inputs ) ( Hao and O'Shea , 2012 ) . In this work , we show that , in response to a transient input , either Msn2 or Msn4 alone is sufficient to induce the expression of target genes with fast kinetics promoters , constituting what is essentially a biological 'OR' logic gate . In contrast , the induction of target genes with slow kinetics promoters requires activation of both factors , forming an 'AND' gate . At the single-cell level , even though Msn2 and Msn4 show similar nuclear translocation dynamics , they exhibit different levels of heterogeneity in nuclear localization and distinct gene regulatory functions . Msn2 is activated in a relatively homogeneous manner and functions as a low threshold 'switch' essential for turning on slow kinetics promoters . In contrast , Msn4 activation is highly heterogeneous and it serves as a 'rheostat' to effectively tune the induction level of target genes with slow kinetics promoters in individual cells . Therefore , while target genes with fast kinetics promoters are uniformly expressed in most cells , those with slow promoters are more likely to be fully induced in only a fraction of cells with high Msn4 activity . Our work reveals that the seemingly redundant TF Msn4 has a distinct gene regulatory role from its homolog Msn2 and enables diversified gene expression responses within a cell population , which might be beneficial for survival under rapidly changing environments .
To investigate gene regulation by Msn2 and Msn4 in single cells , we fused Msn4 with a yellow fluorescent protein ( YFP ) and Msn2 with a red fluorescent protein ( RFP ) at their native loci . A cyan fluorescent protein ( CFP ) reporter under Msn2/4 specific target promoters was introduced into the same strain to monitor downstream gene expression . To understand gene responses to dynamic TF activation , we have previously developed a chemical genetics method for controlling the Msn2/4 nuclear localization using a small molecule 1-NM-PP1 that specifically inhibits protein kinase A ( PKA ) activity ( Hao et al . , 2013; Hao and O'Shea , 2012 ) . Here , we combine this method with quantitative time-lapse microscopy and microfluidics ( Hansen et al . , 2015; Hao et al . , 2013; Hao and O'Shea , 2012 ) to simultaneously track Msn2 and Msn4 localization and target gene expression in a large number of individual cells in response to dynamic inputs ( Figure 1A ) . In each experiment , we measure single-cell responses over a 3-hr period , which is sufficient for the fluorescent gene expression reporter to reach the plateau in most cells ( Figure 1A , right ) . Our previous studies revealed that Msn2/4 target promoters can be characterized as having fast or slow activation kinetics relative to one another based on the time needed for their activation ( Hansen and O'Shea , 2013; Hao and O'Shea , 2012 ) . While target genes with fast kinetics promoters respond strongly to transient TF inputs , slow kinetics promoters , due to their long activation delay , filter out inputs with short durations . The activation kinetics of target promoters depends on their promoter architectures , in particular , the organization of TF binding sites and nucleosomes ( Hansen and O'Shea , 2015a; Hao and O'Shea , 2012 ) . To analyze dynamic gene regulation by Msn2 and Msn4 , we focus here on two well-characterized promoters – PDCS2 and PSIP18 , which are Msn2/4 specific ( not induced in a msn2Δ msn4Δ strain ) ( Hansen and O'Shea , 2013 ) , and have been routinely used to represent Msn2/4 target promoters with fast ( PDCS2 ) or slow ( PSIP18 ) activation kinetics , respectively ( Hansen and O'Shea , 2013 , 2015a , 2015b , 2016 ) . The DCS2 promoter can be activated 5 times faster than the SIP18 promoter for a given TF input ( Hansen and O'Shea , 2013 ) . To first determine the dependence of target gene expression on Msn2 and Msn4 , we measure the induction of Msn2/4 target promoters in response to TF inputs with various durations in wild-type cells and cells lacking MSN2 or MSN4 and plotted the distributions of single-cell expression responses ( Figure 1; The dynamic profiles of reporter gene expression are shown in Figure 1—figure supplement 1 ) . We find that , in response to a transient inhibitor input ( 30 min ) , activation of either Msn2 or Msn4 is sufficient to fully induce the fast kinetics promoter PDCS2 ( Figure 1B , left ) . In contrast , the induction of slow kinetics promoter PSIP18 requires activation of both Msn2 and Msn4: the absence of either factor abolishes the expression of reporter gene ( Figure 1B , right ) . Interestingly , in response to a prolonged input pulse ( 60 min ) , while Msn2 is still needed for the induction of the slow gene promoter , Msn4 is no longer required ( Figure 1C ) . These results suggest that Msn4 functions to shift the activation time-scales of slow kinetics promoters and thereby enables the induction of such promoters by transient inputs . To determine whether the requirement of Msn4 for gene induction is specific to slow promoter kinetics , we employed a mutant of the PSIP18 promoter ( PSIP18-A4 ) , which has been converted to a fast kinetics promoter by moving the Msn2/4 binding sites more adjacent to the TATA box ( Hansen and O'Shea , 2015a ) . In accordance with the fast kinetics promoter PDCS2 , Msn4 is not needed for the expression of reporter gene under the PSIP18 mutant promoter ( Figure 1D ) . To further examine whether other target gene promoters have similar dependence to Msn4 , we analyzed the responses of fast kinetics promoter PDDR2 and slow kinetics promoter PTKL2 . Similar to the fast kinetics promoter PDCS2 , the expression of the PDDR2 reporter gene does not require Msn4 . In contrast , Msn4 is needed for the full induction of PTKL2 in response to a transient input ( Figure 1—figure supplement 2 ) . These results suggest that the dependence on Msn4 might be a general feature of slow kinetics promoters . Finally , to determine whether target promoters would respond similarly when cells are faced with natural stressors , we monitored the reporter expression of PDCS2 and PSIP18 , respectively , in response to osmotic stress , which leads to a transient pulse of TF activation ( Hao et al . , 2013; Hao and O'Shea , 2012 ) . Consistent with the inhibitor experiments , while Msn4 is not critical for the expression of fast kinetics promoter PDCS2 , it is required for the full induction of slow kinetics promoter PSIP18 ( Figure 1E ) . Therefore , our work reveals that , contrary to what has been previously believed , Msn4 is not redundant to its homolog Msn2 in regulating gene expression . In particular , while activation of either Msn2 or Msn4 is sufficient to trigger the expression of target genes with fast promoter kinetics , target genes with slow promoter kinetics depend on both Msn2 and Msn4 for their full induction in response to biologically relevant transient inputs . Having established that Msn4 is not redundant to its homolog Msn2 , we next investigate the dynamic and functional differences between the two factors at the single cell level that can account for their specific contributions to gene regulation . We first focus on the dynamics of Msn2 and Msn4 nuclear translocation . We observe that Msn2 and Msn4 show similar temporal dynamics of translocation in the same single cells in response to a transient inhibitor input ( Figure 2A ) . However , we find that the level of Msn4 nuclear translocation is highly heterogeneous across single cells: some cells show high level of nuclear translocation , while other cells have very low localization levels . In contrast , the translocation levels of Msn2 are relatively homogeneous amongst individual cells . To illustrate the noise levels of Msn2 and Msn4 translocation , we plotted the standard deviation of their single-cell time traces scaled by the mean and reported the coefficient of variation ( CV: standard deviation scaled by the mean ) for the peak point of time traces . As shown in Figure 2A ( ii ) , Msn4 nuclear translocation exhibits a higher level of cell-cell variability than that of Msn2 . To investigate the dynamics of Msn2 and Msn4 translocation in response to natural stresses , we subject yeast cells to osmotic stress and ethanol stress treatments . As shown in Figure 2B and C , osmotic stress elicits a transient pulse of Msn2 and Msn4 translocation , while ethanol stress induces sustained nuclear localization of Msn2 and Msn4 . In response to either stress , Msn4 exhibits similar temporal dynamics of nuclear translocation to Msn2 in single cells , consistent with the inhibitor treatments . In addition , the level of Msn4 nuclear localization shows a higher degree of cell-cell heterogeneity than that of Msn2 under natural stress conditions , in accordance with the inhibitor treatments . 10 . 7554/eLife . 18458 . 006Figure 2 . Msn2 and Msn4 show different levels of heterogeneity in single cells . Time traces of Msn2 and Msn4 nuclear translocation in the same single cells in response to ( A ) 20-min 1 μM inhibitor pulse , ( B ) 0 . 5M KCl , or ( C ) 3% ethanol . In each panel , ( i ) representative single-cell time traces of Msn2 and Msn4 nuclear translocation in the same single cells; ( ii ) standard deviation of single-cell time traces . For each condition , the single-cell time traces and standard deviations of single cell responses are scaled by the peak value of the averaged time traces ( % max of mean ) . In ( ii ) , the solid curve represents the averaged time trace; the shaded region represents the scaled standard deviation of single cell responses . The coefficient of variation ( CV; the standard deviation divided by the mean ) is calculated for the peak time point of time traces for each condition and displayed above each time trace . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 00610 . 7554/eLife . 18458 . 007Figure 2—figure supplement 1 . A direct comparison of the levels of Msn2 and Msn4 nuclear localization in the same cells . ( A ) To directly compare the nuclear level of Msn2-RFP relative to that of Msn4-YFP in the same single cells ( Figure 1A ) , a scaling factor between RFP and YFP is needed to account for unique microscope settings used in each channel as well as inherent emission differences between each fluorophore . This was determined by creating two yeast strains in which Msn2 was C-terminally tagged with either florescent protein RFP or YFP , respectively ( illustrated in the top panel ) . Left: Sustained nuclear translocation of Msn2 was induced in both stains with an identical stimulus and the averaged single-cell time traces of Msn2 translocation were generated for both strains ( n: ~100 cells per strain ) . Right: The scaling factor ( 1 . 52 ) was determined by taking the ratio between the maximal fluorescence intensity of each averaged trace . The time trace of Msn2-RFP , when times the scaling factor ( dashed curve ) , overlaps with the time trace of Msn2-YFP . Therefore , this factor normalizes the fluorescence arbitrary unit of RFP with the fluorescence arbitrary unit of YFP and enables the direct comparison of the nuclear level of Msn2-RFP with that of Msn4-YFP in the same single cells ( in the unit of 'normalized a . u . ' ) . ( B ) Averaged time traces of Msn2 and Msn4 nuclear translocation in the same single cells in response to 20-min 1 μM inhibitor pulse , 0 . 5M KCl , or 3% ethanol , as indicated . The top left panel illustrates that Msn2-YFP and Msn4-RFP are expressed in the same strain . The averaged traces were normalized by the scaling factor to allow a direct comparison of Msn2 and Msn4 in the same cells . ( C ) Averaged time traces of Msn2 and Msn4 nuclear translocation from the same cells were normalized as% of max . The traces were plotted together and zoomed in to the early time period of the response to demonstrate the small time delay of Msn4 translocation . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 00710 . 7554/eLife . 18458 . 008Figure 2—figure supplement 2 . Single-cell time traces of Msn2 and Msn4 after normalization of YFP and RFP fluorescence . After the YFP and RFP normalization as shown in Figure 2—figure supplement 1 , time traces of Msn2 and Msn4 nuclear translocation are plotted in the same single cells in response to ( A ) 20-min 1 μM inhibitor pulse , ( B ) 0 . 5M KCl , ( C ) 3% ethanol , or ( D ) no stress . Each panel shows ( i ) representative single-cell time traces of Msn2 and Msn4 nuclear translocation in the same single cells; ( ii ) standard deviation of single-cell time traces . For each condition , the single-cell time traces and standard deviations of single cell responses are normalized so that the levels of Msn2 and Msn4 can be compared directly . In ( ii ) , the solid curve represents the averaged time trace; the shaded region represents the standard deviation of single cell responses . The standard deviation is calculated for the peak time point of time traces for each condition and displayed above each time trace . For the condition without stress , the standard deviation is calculated for the time point used in the inhibitor condition . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 008 To determine the relative nuclear concentrations of Msn2-RFP and Msn4-YFP molecules at the single cell level , we performed a control experiment to obtain a scaling factor that normalizes the nuclear fluorescence intensities of YFP and RFP into 'normalized a . u . ' ( Figure 2—figure supplement 1A ) . We observe that the nuclear localization level of Msn4 is generally lower ( ~3-fold lower ) than that of Msn2 in the same cells under inhibitor or natural stress conditions ( Figure 2—figure supplement 1B , yellow curves versus red curves ) . In addition , although Msn4 has a higher coefficient of variation , the standard deviation of Msn4 nuclear localization in single cells ( without being scaled by the mean ) is lower than that of Msn2 ( Figure 2—figure supplement 2 ) . These results suggest that the high degree of cell–cell variability of Msn4 might be largely due to its relatively low nuclear levels compared to that of Msn2 . Thus , our single-cell imaging analysis shows that , in response to various stimuli , nuclear translocation of Msn4 temporally correlates with that of Msn2 in the same cells; however , the level of Msn4 nuclear localization in individual cells is more heterogeneous than that of its homolog . Given that Msn2 and Msn4 show different levels of cell-cell variability in nuclear translocation , we speculate that they may play different regulatory roles in controlling heterogeneous gene expression at the single cell level . Using the deletion strains , we have revealed that target genes with different promoter activation kinetics exhibit different dependence to Msn2 and Msn4 ( Figure 1 ) . To further determine the dependence of gene regulation specifically on Msn2 or Msn4 in single cells when both factors are present , we simultaneously monitored nuclear localization of Msn2 and Msn4 and reporter gene expression under the fast kinetics promoter PDCS2 or the slow kinetics promoter PSIP18 in the same wild-type cells . We then quantified and plotted the maximal level of reporter expression versus the peak nuclear localization level of Msn2 and/or Msn4 of each single cell to analyze the relationship between gene expression and the activity of Msn2 and Msn4 , respectively . To cover a full range of TF translocation levels , we combined the single cell responses to 30-min inhibitor inputs with various doses . We find that , for the fast kinetics promoter PDCS2 , gene expression can be induced in the majority of cells in which either Msn2 or Msn4 is adequately activated ( Figure 3A , i ) . The level of reporter expression shows a similar graded relationship with both Msn2 and Msn4 , reaching the saturation when either factor is activated over a low threshold level ( Figure 3Aii; Single-cell distributions of gene expression versus Msn2 or Msn4 are shown in Figure 3—figure supplement 1A; The probabilities of gene induction versus Msn2 or Msn4 are shown in Figure 3—figure supplement 2A ) . These results are consistent with our observation by deletion analysis that Msn2 and Msn4 play largely redundant roles in regulating target genes with fast kinetics promoters ( Figure 1 ) . 10 . 7554/eLife . 18458 . 009Figure 3 . Msn2 and Msn4 exhibit distinct gene regulatory functions in single cells in response to 30-min inhibitor inputs . ( A ) ( i ) A scatter plot showing the relationship of the fast kinetics promoter PDCS2 reporter expression with Msn2 and Msn4 activation at the single cell level . Each dot represents a single cell . Single-cell time traces were tracked over a 3-hr period in which the reporter fluorescence in most cells has already reached the plateau . The x and y axes represent the peak values of Msn4 and Msn2 nuclear translocation ( the maximal values in the first 30 min of translocation time traces ) , respectively; and the dot color represents the maximal level of gene expression as indicated in the color bar . To cover the full dynamic range of TF translocation , the data from the experiments using 30 min inhibitor pulses with 0 . 1 , 0 . 25 , 0 . 5 , 0 . 75 and 1 μM doses have been combined ( n: 444 cells ) . ( ii ) Plots show the relationships between PDCS2 reporter expression and ( left ) Msn2 or ( right ) Msn4 , respectively . Single cells are binned based on their Msn2 or Msn4 nuclear level as indicated in the x-axis and the average of reporter expression is calculated for each binned groups of single cells and shown in the bar graphs . ( B ) Scatter plots and bar graphs showing the relationship of the slow kinetics promoter PSIP18 reporter expression with Msn2 and Msn4 activation at the single cell level . The data analysis and presentation schemes are consistent with those in ( A ) ( n: 595 cells ) . Single-cell data used in these plots are provided in the source data files . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 00910 . 7554/eLife . 18458 . 010Figure 3—source data 1 . Source data for Figure 3A . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01010 . 7554/eLife . 18458 . 011Figure 3—source data 2 . Source data for Figure 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01110 . 7554/eLife . 18458 . 012Figure 3—figure supplement 1 . Single-cell distributions of reporter gene expression versus nuclear TF levels in response to 30-min inhibitor inputs . ( A ) Single-cell scatter plots showing the relationships between PDCS2 reporter expression with ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively . Single-cell data are from Figure 3A . ( B ) Single-cell scatter plots showing the relationships between PSIP18 reporter expression with ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively . Single-cell data are from Figure 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01210 . 7554/eLife . 18458 . 013Figure 3—figure supplement 2 . The relationship between the probability of reporter gene expression and nuclear TF levels in response to 30-min inhibitor inputs . ( A ) Bar graphs showing the relationships between the probability of PDCS2 reporter expression with ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively . Single-cell data are from Figure 3A . Single cells are binned based on their Msn2 or Msn4 nuclear level as indicated in the x-axis and the proportion of 'responder' cells ( green and red cells in Figure 3 ) , instead of the average of reporter expression , is calculated for each binned groups of single cells and shown in the bar graphs . ( B ) Bar graphs showing the relationships between the probability of PSIP18 reporter expression with ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively . Single-cell data are from Figure 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01310 . 7554/eLife . 18458 . 014Figure 3—figure supplement 3 . Relationship between PSIP18 reporter gene expression and the ratio of nuclear Msn2 versus Msn4 in response to 30-min inhibitor inputs . ( A ) Scatter plot showing the single-cell distribution of PSIP18 reporter expression with the ratio of nuclear Msn2 versus Msn4 . Single-cell data are from Figure 3B . ( B ) Bar graph shows the relationship between PSIP18 reporter expression and the ratio of nuclear Msn2 versus Msn4 . Single cells are binned based on their ratio of nuclear Msn2 versus Msn4 as indicated in the x-axis and the average of reporter expression is calculated for each binned groups of single cells and shown in the bar graphs . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01410 . 7554/eLife . 18458 . 015Figure 3—figure supplement 4 . Msn2 and Msn4 exhibit similar gene regulatory functions in single cells in response to 60-min inhibitor inputs . ( A ) ( i ) A scatter plot showing the relationship of the slow kinetics promoter PSIP18 reporter expression with Msn2 and Msn4 activation at the single cell level . To cover the full dynamic range of TF translocation , the data from the experiments using 60 min inhibitor pulses with 0 . 1 , 0 . 25 , 0 . 5 , 0 . 75 and 1 μM doses have been combined ( n: 702 cells ) . ( ii ) Single-cell scatter plots showing the relationships between PSIP18 reporter expression with ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively . Single-cell data are from ( i ) . ( B ) Plots show the relationships between PSIP18 reporter expression and ( left ) Msn2 or ( right ) Msn4 , respectively . Single cells are binned based on their Msn2 or Msn4 nuclear level as indicated in the x-axis and the average of reporter expression is calculated for each binned groups of single cells and shown in the bar graphs . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01510 . 7554/eLife . 18458 . 016Figure 3—figure supplement 5 . Relationship between reporter gene expression and the area-under-the-curve ( AUC ) of nuclear TF levels in response to 30-min inhibitor inputs . Single-cell data from Figure 3 were analyzed to show the relationship of ( A ) the fast kinetics promoter PDCS2 or ( B ) the slow kinetics promoter PSIP18 reporter expression with the AUC of Msn2 and Msn4 nuclear translocation . The AUC is calculated as the sum of TF nuclear levels for each single-cell time trace ( data points taken every two minutes ) . ( i ) Scatter plots showing single-cell distributions; ( ii ) Bar graphs showing the average level of gene expression in single cells binned based on their TF AUCs . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01610 . 7554/eLife . 18458 . 017Figure 3—figure supplement 6 . Single-cell distributions of reporter gene expression versus the speed of TF nuclear import or export . ( A ) Scatter plots showing single-cell distributions of PDCS2 reporter expression versus ( i ) the speed of nuclear import or ( ii ) the speed of nuclear export of Msn2 and Msn4 . The speed of nuclear translocation is quantified by the time needed to reach half maximum of nuclear translocation of Msn2 or Msn4 in response to stimulation ( nuclear import speed ) or upon the removal of stimulation ( nuclear export speed ) . Single-cell data are from Figure 3A . The means of reporter expression are calculated for grouped single cells and shown using black solid lines . The mean and standard deviation of nuclear translocation times in single cells are calculated and shown above each plot . ( B ) Scatter plots showing single-cell distributions of PSIP18 reporter expression versus ( i ) the speed of nuclear import or ( ii ) the speed of nuclear export of Msn2 and Msn4 . Single-cell data are from Figure 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 017 In contrast , for the slow kinetics promoter PSIP18 , gene expression is highly heterogeneous among single cells , consistent with previous results ( Hansen and O'Shea , 2013 ) . Furthermore , the reporter gene is fully induced predominantly in the fraction of cells in which Msn4 is highly activated ( Figure 3B , i , red solid circles ) . In individual cells with a fixed level of Msn2 activity , a higher level of Msn4 activation results in an increase in both the probability and the level of gene induction ( Figure 3B , i , with a fixed y-axis value and an increasing x-axis value ) . However , in single cells with a fixed level of Msn4 activity , higher Msn2 activation does not necessarily lead to higher gene induction; in fact , too much Msn2 activation will suppress gene induction in the same cell , suggesting a competing role of Msn2 for binding to the promoter ( Figure 3B , i , with a fixed x-axis value and an increasing y-axis value ) . To quantitatively demonstrate this competing role of Msn2 against Msn4 , we plotted the relationship between gene expression and the ratio of Msn2 versus Msn4 in single cells . As shown in Figure 3—figure supplement 3 , gene expression decreases dramatically when the ratio of Msn2 versus Msn4 increases . We further analyzed the relationship between reporter gene expression with Msn2 or Msn4 activity , individually . Gene expression shows a switch-like relationship to Msn2 activation with a low threshold ( ~10 normalized a . u . : ~25% of maximal Msn2 localization ) : while a low level of Msn2 activity is required for turning the gene on , the induction level is independent of Msn2 activity ( Figure 3B , ii , left , averaged response of single cells binned based on their TF levels ) . In contrast , gene expression exhibits a linear relationship with Msn4 activity in which both the probability and the level of gene induction increase with the level of Msn4 activity in single cells ( Figure 3B , ii , right; Single-cell distributions of gene expression versus Msn2 or Msn4 are shown in Figure 3—figure supplement 1B; The probabilities of gene induction versus Msn2 or Msn4 are shown in Figure 3—figure supplement 2B ) . In accordance with the deletion analysis in Figure 1C , in response to prolonged ( 60-min ) input pulses , the reporter expression of PSIP18 no longer specifically depends on Msn4 and the level of reporter expression shows graded relationships with both Msn2 and Msn4 ( Figure 3—figure supplement 4 ) . These results demonstrate that Msn2 and Msn4 play distinct and cooperative regulatory roles in controlling target genes with slow promoter kinetics . In response to transient inputs , consistent with the required role of Msn2 for slow promoter induction shown in Figure 1 , Msn2 in single cells serves as a low threshold 'switch' for gene induction: it is required to be activated above a certain threshold ( ~25% of its maximal level ) to turn on transcription in the cell; once its activity is above that threshold , a further increase in Msn2 activity cannot positively contribute to the extent of gene induction . In contrast , despite of its low expression level , Msn4 is a highly potent activator of slow target promoters and thus is also required the full induction of slow promoters ( Figure 1 ) . Once Msn2 turns on gene transcription in a cell , Msn4 functions as a 'rheostat' in the same cell to effectively fine-tune the probability and level of gene induction . Furthermore , high levels of Msn2 in the nucleus may compete with Msn4 for binding to the same target promoters and can thus suppress gene induction . This possible inhibitory role of Msn2 against Msn4 counteracts its modest positive contribution to gene expression . Therefore , the expression level of target genes with slow promoter kinetics depends specifically on the Msn4 activity in individual cells . Furthermore , the homolog-specific gene regulation depends on the transient dynamics of TF inputs . In response to sustained inputs , the slow kinetics promoters no longer exhibit the specific dependence on Msn4 and behave like fast kinetics promoters upon transient inputs . Finally , to evaluate the influence of other dynamic characteristics ( such as nuclear import rate or export rate ) of Msn2 or Msn4 translocation , we also plotted the level of gene expression versus the area under the curve ( AUC ) of Msn2 or Msn4 nuclear localization . As shown in Figure 3—figure supplement 5 , the relationship between gene expression and AUC are similar to those shown in Figure 3 . In accordance , because the time differences in nuclear import or export for Msn2 or Msn4 among single cells ( ~1–2 min ) are relatively small comparing to the total duration of inputs ( 30 min ) , we observed modest influence on gene expression from the variations in these characteristics under our experimental conditions ( Figure 3—figure supplement 6 ) . To determine whether Msn4 functions differently from Msn2 under natural stress conditions , we treated yeast cells with osmotic stress ( 0 . 5 M KCl ) or ethanol stress ( 4% ethanol ) , respectively , and measured nuclear localization of Msn2 and Msn4 and reporter gene expression under the slow kinetics promoter PSIP18 in the same cells . In response to 0 . 5 M KCl treatment , we find that the reporter gene is fully induced specifically in the cells with high Msn4 activity ( Figure 4A , i and ii ) , in accordance with the inhibitor experiments . In addition , the probability and the level of gene induction are independent of Msn2 activity , but increase linearly with Msn4 activity in single cells ( Single-cell distributions of gene expression versus Msn2 or Msn4 are shown in Figure 4—figure supplement 1A; the relationship between gene expression and AUC are shown in Figure 4—figure supplement 2A ) . Because the osmotic stress treatment elicits Msn2 nuclear translocation above the threshold required for gene induction ( ~10 normalized a . u . : ~25% of maximal Msn2 localization; determined from the inhibitor experiments in Figure 3 ) in almost all of the cells , we could not observe the basal 'off' state of gene expression when we binned the cells with their Msn2 levels ( Figure 4A , ii , left ) . This result indicates that , in response to the stress condition , Msn2 is 'switched on' in all of the cells and the induction level of slow kinetics promoters in individual cells is primarily controlled by Msn4 activity . 10 . 7554/eLife . 18458 . 018Figure 4 . Msn2 and Msn4 exhibit distinct gene regulatory functions in single cells in response to natural stresses . ( A ) ( i ) A scatter plot showing the relationship of the slow kinetics promoter PSIP18 reporter expression with Msn2 and Msn4 activation at the single cell level in response to 0 . 5M KCl . Each dot represents a single cell . The x and y axes represent the peak values of Msn4 and Msn2 nuclear translocation ( the maximal values in the first 30 min of translocation time traces ) , respectively; and the dot color represents the peak level of gene expression as indicated in the color bar ( n: 182 cells ) . ( ii ) Plots show the relationships between PSIP18 reporter expression and ( left ) Msn2 or ( right ) Msn4 , respectively . Single cells are binned based on their Msn2 or Msn4 nuclear level as indicated in the x-axis and the average of reporter expression is calculated for each binned groups of single cells and shown in the bar graphs . ( B ) Scatter plots and bar graphs showing the relationship of the slow kinetics promoter PSIP18 reporter expression with Msn2 and Msn4 activation at the single cell level in response to 4% ethanol . The data analysis and presentation schemes are consistent with those in ( A ) . Because the majority of cells are not able to express the reporter gene , the proportion of 'responder' cells ( green and red cells ) is quantified and shown in the bar graphs , instead of the average of reporter expression . Data from a large number of single cells are collected to obtain enough responders ( n: 924 cells ) . Single-cell data used in these plots are provided in the source data files . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01810 . 7554/eLife . 18458 . 019Figure 4—source data 1 . Source data for Figure 4A . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 01910 . 7554/eLife . 18458 . 020Figure 4—source data 2 . Source data for Figure 4B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02010 . 7554/eLife . 18458 . 021Figure 4—figure supplement 1 . Single-cell distributions of reporter gene expression versus nuclear TF levels in response to natural stresses . ( A ) Single-cell scatter plots showing the relationships between PSIP18 reporter expression and ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively , in response to 0 . 5 M KCl . Single-cell data are from Figure 4A . ( B ) Single-cell scatter plots showing the relationships between PSIP18 reporter expression and ( left ) Msn2 or ( right ) Msn4 nuclear level , respectively , in response to 4% ethanol . Single-cell data are from Figure 4B . Because the majority of cells are not able to express the reporter gene , scatter plots , instead of boxplots , are used here to show the distributions of responder cells ( green and red ) and non-responder cells ( blue ) with different TF levels . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02110 . 7554/eLife . 18458 . 022Figure 4—figure supplement 2 . Relationship between reporter gene expression and the area-under-the-curve ( AUC ) of nuclear TF levels in response to natural stresses . Single-cell data in Figure 4 were analyzed show the relationship of the slow kinetics promoter PSIP18 reporter expression with the AUC of Msn2 and Msn4 nuclear translocation in response to ( A ) 0 . 5 M KCl and ( B ) 4% ethanol . For the ethanol treatment , the AUC is calculated as the sum of TF nuclear levels for the first 30 min of stress treatment for each single-cell time trace ( data points taken every two minutes ) . Due to the translational arrest induced by ethanol stress , TF nuclear localization at the later time points would not be able to contribute significantly to gene expression . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 022 Similarly , in response to 4% ethanol treatment , the reporter gene is also induced specifically in cells with high Msn4 activity . The treatment of ethanol stress is a relatively harsh stress condition that leads to a global translational arrest ( Ding et al . , 2009; Stanley et al . , 2010 ) . As a result , the majority of cells are not able to express the reporter gene under the slow kinetics promoter; however , the cells that do express the reporter gene are those with high Msn4 activity ( Figure 4B , i ) . Similar to the osmotic stress condition , Msn2 is activated above the threshold required for gene induction in most cells; therefore , the probability of gene expression does not show any dependence on Msn2 activity in single cells ( Figure 4B , ii , left ) . In contrast , the probability of gene expression shows a linear relationship with Msn4 activity in single cells ( Figure 4B , ii , right; Single-cell distributions of gene expression versus Msn2 or Msn4 are shown in Figure 4—figure supplement 1B; the relationship between gene expression and AUC are shown in Figure 4—figure supplement 2B ) , consistent with the inhibitor and osmotic stress experiments . Taken together , these results show that , under natural stress conditions , Msn4 plays a distinct regulatory role from Msn2 in controlling target genes with slow kinetics promoters . Similar to inhibitor treatments , expression of these genes depends specifically on the Msn4 activity in a linear fashion at the single-cell level . Given its heterogeneous activity in single cells and its critical role in gene regulation , Msn4 , working in parallel with its homolog Msn2 , can diversify the expression of a specific group of target genes within a cell population in response to natural stresses . To determine whether Msn2 and Msn4 exhibit distinct regulatory functions on promoters other than PDCS2 and PSIP18 , we measured the nuclear localization of Msn2 and Msn4 and reporter gene expression under fast kinetics promoter PDDR2 and slow kinetics promoter PTKL2 . We find that , similar to the fast kinetics promoter PDCS2 , expression of the PDDR2 reporter gene can be induced in most cells in which either Msn2 or Msn4 is activated over a low threshold level and the level of reporter expression shows a similar graded relationship with both Msn2 and Msn4 ( Figure 5A ) . In contrast , the slow kinetics promoter PTKL2 shows a similar response to that of PSIP18 , in which reporter expression is specifically induced in the fraction of cells with high Msn4 activity . The level of reporter expression shows a switch-like relationship to Msn2 activity , but a linear relationship with Msn4 activity ( Figure 5B ) . These results suggest that the distinct functions of Msn2 and Msn4 in combinatorial gene regulation might be applicable to other target genes . 10 . 7554/eLife . 18458 . 023Figure 5 . Gene regulatory functions of Msn2 and Msn4 on other fast or slow kinetics promoters . ( A ) ( i ) A scatter plot showing the relationship of the fast kinetics promoter PDDR2 reporter expression with Msn2 and Msn4 activation at the single cell level . Each dot represents a single cell . Single-cell time traces were tracked over a 3-hr period in which the reporter fluorescence in most cells has already reached the plateau . The x and y axes represent the peak values of Msn4 and Msn2 nuclear translocation ( the maximal values in the first 30 min of translocation time traces ) , respectively; and the dot color represents the maximal level of gene expression as indicated in the color bar . To cover the full dynamic range of TF translocation , the data from the experiments using 30 min inhibitor pulses with 0 . 1 , 0 . 25 , 0 . 5 , 0 . 75 and 1 μM doses have been combined ( n: 407 cells ) . ( ii ) Plots show the relationships between PDDR2 reporter expression and ( left ) Msn2 or ( right ) Msn4 , respectively . Single cells are binned based on their Msn2 or Msn4 nuclear level as indicated in the x-axis and the average of reporter expression is calculated for each binned groups of single cells and shown in the bar graphs . Scatter plots and bar graphs showing the relationship between gene expression and Msn2 and Msn4 activation for ( B ) the slow kinetics promoter PTKL2 ( n: 476 cells ) , ( C ) the promoter mutant PSIP18-A4 ( n: 553 cells ) , and ( D ) the promoter PDCS2 in snf6Δ ( n: 352 cells ) . The data analysis and presentation schemes are consistent with those in ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02310 . 7554/eLife . 18458 . 024Figure 5—source data 1 . Source data for Figure 5A . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02410 . 7554/eLife . 18458 . 025Figure 5—source data 2 . Source data for Figure 5B . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02510 . 7554/eLife . 18458 . 026Figure 5—source data 3 . Source data for Figure 5C . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02610 . 7554/eLife . 18458 . 027Figure 5—source data 4 . Source data for Figure 5D . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 027 To further examine the generality of distinct regulatory functions of Msn2 and Msn4 , we analyzed the gene expression response under the mutated PSIP18 promoter ( PSIP18-A4 ) , which , by incorporating a few mutations , has been converted from a slow kinetics promoter to a fast kinetics promoter ( Hansen and O'Shea , 2015a ) . As shown in Figure 5C , in accordance with other fast kinetics promoters , gene expression under this mutant promoter is induced in most cells and displays a similar graded relationship with both Msn2 and Msn4 . Therefore , Msn2 and Msn4 no longer show distinct regulatory functions when the PSIP18 promoter , with the majority of the promoter sequence intact , is mutated to obtain fast activation kinetics . We next tested the opposite situation in which we slow down a fast kinetics promoter . To this end , we monitored the gene expression response under the PDCS2 promoter in cells lacking the SWI/SNF chromatin remodeling complex ( snf6Δ ) . It has been shown previously that this mutant significantly slows down the activation kinetics of fast promoters ( Hansen and O'Shea , 2013 ) . Consequently , we observe that gene expression under the PDCS2 promoter in this mutant exhibits switch-like versus linear relationships with Msn2 and Msn4 , respectively ( Figure 5D ) , consistent with the responses of other slow kinetics promoters . In summary , these results suggest that the distinct regulatory functions of Msn2 and Msn4 might depend more generally on the kinetics of promoter activation , but not on specific target promoters . In this study , we have focused on a few representative promoters because our single-cell analysis requires live-cell time-lapse experiments , the throughput of which hinders the examination of gene regulation at a more global level . However , we anticipate that , in near future , the technological advances will allow us to track single-cell gene regulation at the whole genome level . Such technologies will undoubtedly provide further insights into combinatorial gene regulation by Msn2 and Msn4 . For example , we previously grouped target genes with fast versus slow promoter kinetics based on a population-level assay using cells with the msn4Δ background ( Hao and O'Shea , 2012 ) . Given the newly identified role of Msn4 in shifting the promoter activation timescales in a subpopulation of cells , we suspect that its presence might alter the classification of some target genes with intermediate promoter kinetics . A genome-wide single-cell analysis will enable a more accurate classification of target genes and , more importantly , will lead to a comprehensive understanding about dynamic regulation of global transcriptional responses to environmental stimuli .
Here we show that the homologous TFs Msn2 and Msn4 , which have long been assumed to be functionally redundant , play distinct roles in coordinating differential expression of target genes depending on their promoter kinetics . For target genes with fast promoter kinetics , both factors can contribute to gene expression in a graded manner . In contrast , for target genes with slow promoter kinetics , Msn2 and Msn4 play distinct and cooperative roles , in which Msn2 functions as a low threshold 'switch' governing the 'ON' and 'OFF' state of promoter activation , while Msn4 serves as a 'rheostat' to effectively tune the induction level of gene expression ( Figure 6A ) . Further biochemical analysis is needed to elucidate the mechanistic details underlying these distinct regulatory functions of Msn2 and Msn4 at slow target promoters . One possible mechanism could involve the recruitment of distinct chromatin remodeling factors by Msn2 and Msn4 to target gene promoters . For example , to function as a low threshold 'switch' , Msn2 might first recruit some initiation factors critical for opening up the tightly packed nucleosomes , characteristic of slow kinetics promoters ( Hansen and O'Shea , 2013 , 2015a; Hao and O'Shea , 2012 ) . This could be followed by the subsequent promoter binding of Msn4 , leading to the recruitment of Msn4-specific chromatin remodelers or modifiers to effectively promote and stabilize chromatin disassembly . In accordance with this mechanism , we observe that Msn4 always follows Msn2 in nuclear translocation with a short delay ( ~2–3 min ) under the inhibitor or natural stress conditions ( Figure 2—figure supplement 1C and Figure 3—figure supplement 6 ) . 10 . 7554/eLife . 18458 . 028Figure 6 . Schematics of the gene regulatory logic by Msn2 and Msn4 . ( A ) Diagrams illustrating the gene regulatory schemes of Msn2 and Msn4 in controlling ( left ) fast or ( right ) slow kinetics promoters . Left: Either Msn2 or Msn4 is sufficient for the induction of fast promoters , constituting an 'OR' logic gate . At the single cell level , gene expression shows a similar graded dependence on both Msn2 and Msn4 and reaches saturation upon a low TF activity . Right: Msn2 and Msn4 are both required for the induction of slow promoters , constituting an 'AND' logic gate . At the single cell level , Msn2 serves as a low threshold 'switch' turning transcription ON or OFF depending on its activity . In contrast , Msn4 functions as a 'rheostat' , tuning the gene induction level in a linear fashion . ( B ) Diagrams illustrating how the gene regulatory schemes of Msn2 and Msn4 contribute to the heterogeneity in gene expression at the population level . Left: an 'OR' logic gate will lead to homogeneous gene expression in a cell population . Right: an 'AND' logic gate with the 'rheostat' TF Msn4 produces a heterogeneous response in a population of cells . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 02810 . 7554/eLife . 18458 . 029Figure 6—figure supplement 1 . Biological functions of target genes with fast or slow kinetics promoters . Pie charts are used to illustrate the functional enrichments for target genes with ( A ) fast and ( B ) slow kinetics promoters . Detailed functional classification for each gene in the two gene groups are shown below the pie charts . Only genes with known functions are included in the pie charts . 'Early stress response' includes the genes that are important for early transcriptional response during stresses . The groups of target genes are from Hao and O’Shea ( 2012 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 029 Target genes with fast and slow promoter activation kinetics are regulated differently and hence might have distinct physiological functions . In support of this , we find a close correlation between gene functions and promoter kinetics for previously identified target gene groups with fast or slow kinetics promoters ( Figure 6—figure supplement 1; target gene groups are from Hao and O’Shea , 2012 ) . Target genes with fast kinetics promoters are primarily involved in metabolic and cellular adaptation to glucose starvation ( carbohydrate metabolism and autophagy ) , whereas the majority of target genes with slow kinetics promoters are involved in cellular protection against chronic stresses . These results suggest that , in response to carbon source changes that might require an immediate adaptive response , cells could quickly modulate their metabolism via activating those genes with fast kinetics promoters . In contrast , the induction of genes with slow kinetics promoters is more tightly controlled . These genes are important for preparing cells to survive under chronic stress conditions , the response to which might be less time-sensitive and the occurrence of which might be less frequent in natural habitats . Cells would only activate these genes upon a sustained presence of stresses . This temporal separation of target genes with different functions could avoid initiating resource-intensive cell protection processes in response to minor environmental fluctuations and thereby optimize resource allocation under rapidly changing environments . Furthermore , cells may use Msn4 to control the level of heterogeneity at the population level ( Figure 6B ) , as part of a bet hedging strategy against unpredictable environmental conditions . During the first onset of stresses , a subpopulation of cells with high Msn4 activity induce the expression of stress resistance genes with slow kinetics promoters , preparing for upcoming severe or chronic stresses; meanwhile , other cells with low Msn4 activity cannot induce these genes and therefore may consequently obtain a better fitness advantage if the subsequent stress is minor or short term . In this way , cells can be divided into two subpopulations , each of which is specialized at coping with one of the possible environmental scenarios . Cells lacking Msn4 , however , are not able to induce the slow target genes within the whole population , and hence might be more likely to go extinct in the face of extreme stresses . Therefore , the Msn4-dependent gene regulation may represent a strategy that enables a homolog-'controlled' form of heterogeneity within a cell population . The coupling of the homologous factors Msn2 and Msn4 in combinatorial gene regulation is analogous to logic gate systems commonly found in digital circuits: fast kinetics promoters behave as an 'OR' gate , becoming fully induced with adequate amount of either factor , while slow kinetics promoters behave as an 'AND' gate , requiring the activation of both Msn2 and Msn4 ( Figure 6 ) . Interestingly , this logic gate scheme is not fixed , but rather dependent on the upstream dynamics of TF input – an 'AND' gate upon a transient input can become an 'OR' gate when the input duration is prolonged . As shown in Figure 1B and C , in response to a 30-min input pulse , the slow kinetics promoter PSIP18 is an 'AND' gate , requiring both Msn2 and Msn4 for gene induction; But it becomes an 'OR' gate in response to a 60-min input pulse , in which Msn4 is no longer required . Therefore , given enough amount of time in the nucleus , Msn2 can also function as a 'rheostat' to compensate the absence of Msn4 and tune the induction level of slow target promoters , consistent with a previous study showing that increasing the steady-state expression level of Msn2 leads to a graded induction of its target genes ( Stewart-Ornstein et al . , 2013 ) . Our results suggest that the architectures of gene regulatory networks are not static and could be rewired by various upstream dynamics of TF inputs . In yeast , a recent proteomic analysis found that most proteins that exhibit transient pulsatile dynamics to environmental changes are members of paralogous or closely related TFs ( Dalal et al . , 2014 ) . For example , Msn2 and a related transcriptional repressor Mig1 , both having pulsatile dynamics , regulate their common target genes by modulating their relative pulse timing ( Lin et al . , 2015 ) . Moreover , in mammalian systems , an increasing number of TFs , including some closely related TF pairs such as NFAT1 and NFAT4 ( Yissachar et al . , 2013 ) , have been identified to possess highly diverse activation dynamics that contribute to gene expression responses ( Behar and Hoffmann , 2010; Purvis et al . , 2012; Purvis and Lahav , 2013; Tay et al . , 2010; Werner et al . , 2005 ) . Given the prevalence of seemingly redundant TFs in eukaryotes , we anticipate that the time-dependent combinatorial gene regulation revealed here for Msn2 and Msn4 will be widely applicable to homologous or closely related TFs that are controlled dynamically in other organisms including mammals .
Standard methods for the growth , maintenance and transformation of yeast and bacteria and for manipulation of DNA were used throughout . All Saccharomyces cerevisiae strains used in this study are derived from the W303 background ( ADE+ MATa trp1 leu2 ura3 his3 can1 GAL+ psi+ ) . A list of strains is provided in Table 1 . 10 . 7554/eLife . 18458 . 030Table 1 . Yeast strains used in this work . DOI: http://dx . doi . org/10 . 7554/eLife . 18458 . 030Strain NameDescriptionNH0084NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0094MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0095msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0108MSN4-mCitrineV163A , msn2Δ::natMX , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0096msn4Δ::TRP1 , msn2Δ::natMX , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0116PSIP18-mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0117PSIP18- mTurqouise2-HIS , msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0119PSIP18- mTurqouise2-HIS , MSN4-mCitrineV163A , msn2Δ::natMX , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0120PDCS2- mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0121PDCS2- mTurqouise2-HIS , msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0110PDCS2- mTurqouise2-HIS , MSN4-mCitrineV163A , msn2Δ::natMX , , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0333PSIP18--A4- mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0334PSIP18--A4- mTurqouise2-HIS , msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0335PSIP18-A4- mTurqouise2-HIS , MSN4-mCitrineV163A , msn2Δ::natMX , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0425PTKL2- mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0426PTKL2- mTurqouise2-HIS , msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0427PDDR2- mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0428PDDR2- mTurqouise2-HIS , msn4Δ::TRP1 , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165GNH0459PDCS2- mTurqouise2-HIS , MSN4-mCitrineV163A , MSN2-mCherry , NHP6a-IRFP:kanMX , TPK1M164G , TPK2M147G , TPK3M165G , snf6::cgURA3NH0237MSN2- mCitrineV163A -HIS , NHP6a-IRFP:kanMXNH0267MSN2- mCherry –TRP1 , NHP6a-IRFP:kanMX Msn2 was C-terminally tagged with a yeast codon-optimized mCherry by replacing the endogenous stop codon of the MSN2 locus with URA3 and then replacing the URA3 with a linker-mCherry PCR fragment from a pKT vector using 5-FOA . Msn4 was C-terminally tagged with a linker-mCitrineV163A PCR fragment generated from a pKT vector containing yeast codon-optimized mCitrine with the V163A mutation to allow for fast maturation . The endogenous MSN2 and MSN4 terminators were left unchanged . MSN2 and MSN4 deletion strains ( msn2Δ and msn4Δ , respectively ) were made by replacing the endogenous MSN2 or MSN4 ORF with TRP1 . The introduction of gene expression reporters into yeast was performed as described previously ( Hansen and O'Shea , 2013 ) . The fluorescence reporter gene used is a yeast codon-optimized mTurqouise2 . The microfluidics device used in this study is modified from a previously reported device ( Hersen et al . , 2008 ) . The mask was designed to allow for bonding of two antiparallel Y-shaped devices on a single microfluidics chip using standard methods of soft lithography and replica molding . The device fabrication and the setup of microfluidic experiments were performed as described previously ( Hansen et al . , 2015; Hao et al . , 2013; Hao and O'Shea , 2012 ) . All time-lapse microscopy experiments were performed using a Nikon Ti-E inverted fluorescence microscope with Perfect Focus , coupled with an EMCCD camera ( Andor iXon X3 DU897 ) . The light source is a Spectra X LED system . Images were taken using a CFI Plan Apochromat Lambda DM 60X Oil Immersion Objective ( NA 1 . 40 WD 0 . 13MM ) . During experiments , the microfluidic device was taped to a customized device holder inserted onto the motorized stage ( with encoders ) of the microscope . For the experiments only tracking TF dynamics , three positions were chosen for each channel and the microscope was programmed to acquire Phase , YFP , mCherry , and iRFP images every two minutes . For the experiments measuring reporter gene expression , six positions were chosen for each experiment and the microscope was programmed to take iRFP and YFP or mCherry images every two minutes and both Phase and CFP images every 14 min for a total of three hours . In all experiments , cells in the device were first exposed to SD media for at least 30 min . When the image acquisition started , cells were maintained in SD media for the first five minutes to obtain a baseline for each fluorescence channel prior to the introduction of any stressor or 1-NM-PP1 . The exposure and intensity settings for each channel were set as follows: CFP 300 ms at 9% lamp intensity , YFP 400 ms at 20% lamp intensity , mCherry 300 ms at 10% lamp intensity , and iRFP 200 ms at 15% lamp intensity . The camera was set to an EM Gain of 300 ( within the linear range ) for all four fluorescence channels . Fluorescence microscopy image stacks were pre-processed using ImageJ for background subtraction . The images were then processed using a custom MATLAB code for single-cell tracking and fluorescence quantification as described previously ( Hao et al . , 2013; Hao and O'Shea , 2012 ) . We determine the sample size of our single-cell data based on similar studies published previously ( Hansen and O'Shea , 2013; Hao et al . , 2013; Hao and O'Shea , 2012 ) . | Cells respond to environmental signals by activating proteins called transcription factors . These bind to the DNA that is stored in the cell nucleus and turn on specific genes to make gene products . Many of these transcription factors move in and out of the nucleus once activated . Different environmental signals affect the amount of transcription factor that appears in the nucleus in different ways , and this is important in determining which genes should be turned on and how many copies of gene products should be made . Many transcription factors co-exist with a similar version of themselves in the same cell . These closely related proteins , called homologous transcription factors , respond to the same signals and bind to the same place on the DNA to turn on the same genes . It was not clear what advantages the cells gain from having two molecules that perform the same roles . Two homologous transcription factors called Msn2 and Msn4 are found in baker's yeast . These transcription factors respond to a wide variety of environmental stresses by moving rapidly into the nucleus , where they remain for a short time to turn on hundreds of target genes that are needed for the cell to survive . AkhavanAghdam , Sinha , Tabbaa et al . investigated the roles of Msn2 and Msn4 by tracking where the proteins localized to and which genes they switched on inside the same single cell . Genes that can be turned on quickly could be activated by either Msn2 or Msn4 , and both factors activated the genes to a similar extent . By contrast , both Msn2 and Msn4 were required to activate those genes that take a long time to be turned on . In these cases , Msn2 served as a 'switch' that governed the 'on' and 'off' state of the genes , while Msn4 behaved as a 'rheostat' to tune how much gene product was made . This cooperation between the two transcription factors is equivalent to a design commonly found in electrical circuits and may help the cell to survive in rapidly changing environments . Further studies are now needed to investigate the mechanisms that provide Msn2 and Msn4 with distinct roles in gene regulation . Technological advances that allow the full genetic material of a single cell to be analyzed could also determine whether other homologous transcription factors regulate their target genes in similar ways . | [
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